Louise Arnal
email: louise.arnal@ecmwf.int

Hannah L Cloke

Linus Magnusson

Bastian Klein

Dennis Meissner

Alberto De Tomas

Johannes Hunink

Ilias Pechlivanidis

Louise Crochemore

Sara Suarez

Abel Solera

Joaquin Andreu

Jeff Knight

Felicity Liggins

Albrecht Weerts

Maria- Helena Ramos

Guillaume Thirel

The sensitivity of sub-seasonal to seasonal streamflow forecasts to meteorological forcing quality, modelled hydrology and the initial hydrological conditions

Keywords:

niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Executive summary

Information about streamflow for the coming months (sub-seasonal time-scale) to seasons is needed for decision-making in many sectors of society. Examples are in a reservoir management context, for applications such as hydropower generation, water allocation for drinking water and agriculture, navigation, flood and drought mitigation. Here, sub-seasonal and seasonal forecasts can be a valuable tool.

Compared to short range forecasts, these forecasts allow for an increased operational margin for early warning and maximised benefits. However, the potential skill on the longer time-scales are limited due to a low inherent predictability (of the atmosphere and hydrosphere) and limited quality of models and observations.

In order to meet these needs and tackle those challenges, IMPREX will (1) analyse the current skill of state-of-the-art sub-seasonal to seasonal streamflow forecasts over Europe and [START_REF] Klein | Vulnerability of Inland Waterway Transport and Waterway Management on Hydro-meteorological Extremes[END_REF] improve their capabilities, with a focus on extreme events (i.e., high and low flows) and variables, aggregation periods, seasons and lead times of interest to the users of the forecasts involved in IMPREX (which cover the water sectors mentioned above).

This deliverable consists of three parts. The first part is a technical intercomparison of the performance of five different sub-seasonal to seasonal streamflow forecasting systems, operated by partners of IMPREX: ECMWF, SMHI, FW, BfG, UPV and Deltares. This is be done for key locations in Europe, selected based on the case studies of the project. They include Central European River and Swedish River stations and the Thames, Segura, Tagus, and Jucar River basins. The forecasting systems investigated in this deliverable all use the same meteorological forecasting system, ECMWF's System 4 (with or without applying a bias correction method to the latter) and a variety of hydrological models. This intercomparison therefore enables us to identify the contribution of hydrological model structure and the presence of a bias correction of the meteorological forecasts to the streamflow forecasting skill on sub-seasonal to seasonal time scales and for an array of diverse locations, seasons and extreme events in Europe. This first part has revealed several major differences between the seasonal streamflow forecasting systems and their impacts on the relevant water sectors. Notably, the BfG seasonal streamflow forecasts tend to underestimate the observed streamflow for the Central European River stations, which could be a problem for the navigation sector, dependent on accurate low flow forecasts in the summer. The ECMWF seasonal streamflow forecasts appear to systematically overestimate the spring flow and to underestimate the winter flow for the Central European River stations and the Thames River basin. The latter could be an issue for the flood protection sector as the forecasts would be prone to missing or underestimating the magnitude of flood events. The ECMWF forecasts are however very accurate for summer flow forecasting in the Segura and Tagus River Basins. This could be highly beneficial for the agricultural sector in this region. Both the ECMWF and the SMHI seasonal streamflow forecasts underestimate the May flow for Swedish River stations, which could be a challenge for the hydropower generation sector, relying on accurate spring flow predictions. The SMHI forecasts seem to generally overestimate the winter flow for the Central European River stations and the Thames River basin, which could once again be a problem for the flood protection sector as the forecasts would potentially lead to false alarms. The FW seasonal streamflow forecasts tend to overestimate largely the early spring-spring flow for the Tagus River Basin and to underestimate slightly the flow during all year for the Segura River Basin. Both biases could be challenging for the agricultural sector.

This first technical intercomparison forms a benchmark, to which improved systems from other IMPREX tasks can be compared. The intercomparison can moreover be enriched during the course of IMPREX, with more stations and scores.

The second part of the deliverable consists in a sensitivity analysis, specifically designed to diagnose the relative contributions of errors in the initial hydrological conditions (IHC) and in the meteorological forecasts (MF, sometimes called seasonal climate forecasts [SCF]) on sub-seasonal to seasonal streamflow forecasting uncertainty. This sensitivity analysis was carried out using the ECMWF and the BfG seasonal streamflow forecasts and highlighted several significant results. The analysis indicates that improving the IHC would yield a higher improvement of the seasonal streamflow forecasts for the first month of lead time, after which the SCF become rapidly more influential on the skill of the streamflow forecasts. This signal is however contrasted in space and time, highlighting geographical and seasonal variations of the flow generating mechanisms in Europe. For example for streamflow forecasts made in the summer (May-July) and with one month of lead time, there appears to be a larger number of regions in Europe where the IHC dominate the quality of the streamflow forecasts, compared to forecasts made in the winter with the same lead time. This is probably due to the lower rainfall over Europe during the summer months, leading to groundwater dominated streamflow. For most leeward regions in Scandinavia, the IHC dominate the quality of the forecasts made in the winter, with one to three months of lead time. This is potentially due to precipitation falling as snow in the winter, leading to groundwater dominated streamflow in the winter and snowmelt driven flow in the following spring. For most regions of the Iberian Peninsula, the IHC seem relatively more important for streamflow forecasts made in the summer (June-September), with one to three months of lead time. This is probably due to groundwater dominated streamflow in the summer in those regions and a land surface memory spanning several months.

Over the eastern part of central Europe, streamflow forecasts made in the spring seem to be more sensitive to the IHC, which might translate snowmelt driven spring flow.

The third part of this deliverable is devoted to the identification of the key drivers (beyond IHC and SCF) that control and influence the hydrological forecasting skill.

For this, an alternative sensitivity analysis was designed based on the results from about 35000 European basins, which allows linking the seasonal hydrological forecasting skill (from the SMHI forecasts) to the regional physiographic-hydroclimatic characteristics. This analysis showed that seasonal hydrological forecasting skill is mainly dependent on the basin's hydrological regime. Other factors, such as the elevation and the remaining bias in temperature, were also identified to be important aspects (i.e., dependence of response of mountainous basins to temperature). Another significant result is that seasonal hydrological forecasting skill seems to be limited for relatively flashy basins, experiencing strong flow dynamics over the year (i.e., less memory in the system).

The results of this deliverable will guide future research in IMPREX, indicating where improvements should be made in the forecasting chain (improvements to the IHC, the SCF) in order to improve the seasonal streamflow forecasts over Europe.

Glossary

Bias correction: Process aiming at removing systematic errors in the output of a model. Methods include: linear scaling, distribution-based Scaling, quantile mapping, to cite a few.

Lead time:

The time between the initiation and completion of a forecast.

Discharge: River discharge is the volume of water flowing through a river channel at any given point and is measured in cubic metres per second (m3/s).

Target month or target season: The season or month for which the forecast is made.

Forecast quality: How well a forecast compares against a corresponding observation of what actually occurred, or some good estimate of the true outcome.

Sensitivity analysis:

The study of how the uncertainty in the output of a model or system can be apportioned to different sources of uncertainty in its inputs.

Skill elasticity: A measure of the sensitivity of the seasonal discharge forecasting skill to changes in the skill of its two main predictability sources: the initial hydrological conditions or the seasonal climate forcing.

Initial hydrological conditions (IHC):

The hydrological states (soil moisture, snow cover, water already in the river, among others) at or close to the start of the forecast run.

Seasonal climate forcing (SCF):

The seasonal meteorological forecast used as input to a hydrological model.

Introduction 1

Information about streamflow during the coming month (sub-seasonal time-scale) and season is needed for decision-making in many sectors. Examples are in a reservoir management context, for applications such as hydropower generation, water allocation for drinking water and agriculture, navigation, flood and drought mitigation. Here, sub-seasonal and seasonal forecast can be a valuable tool.

Compared to short range forecasts, these forecasts allow for an increased operational margin for early warning and maximised benefits. However, the potential skill on the longer time-scales are limited due to a low inherent predictability and limited quality of models and observations.

In order to meet these needs, IMPREX will (1) analyse the current skill of state-ofthe-art sub-seasonal to seasonal streamflow forecasts over Europe and [START_REF] Klein | Vulnerability of Inland Waterway Transport and Waterway Management on Hydro-meteorological Extremes[END_REF] improve their capabilities, with a focus on extreme events (i.e., high and low flows) and variables, aggregation periods, seasons and lead times of interest to the users of the forecasts involved in IMPREX (which cover the water sectors mentioned above).

This deliverable consists of three parts. The first part will use the verification scoreboard designed in WP4 (deliverable 4.1) to analyse and compare the skill of multiple sub-seasonal to seasonal streamflow forecasting systems, operated by partners of IMPREX. This will be done for key locations selected based on the case studies of the project. Since the forecasting systems investigated here all use the same meteorological forecasting system (with or without applying a bias correction method) and a variety of hydrological models, this work will enable us to identify the contribution of (hydrological) model structure and the presence of a bias correction of the seasonal meteorological forecasts to the streamflow forecasting skill on sub-seasonal to seasonal time scales and for an array of diverse locations, seasons and extreme events in Europe. This first technical intercomparison will form a benchmark, to which more stations, scores and improved systems from other IMPREX tasks can be added and compared. The main aim of this part is to highlight major differences and similarities between the performance of the seasonal streamflow forecasting systems and the potential impacts of those performances on the sectoral applications at stake in the case study areas. This technical part will also inform later tasks of the IMPREX project, such as the multi-modelling and the data assimilation exercises.

The second part of this deliverable will inform future IMPREX work through a sensitivity analysis, specifically designed to diagnose the relative contributions of initial hydrological conditions (IHC) and errors of the meteorological forecast (MF, sometimes called seasonal climate forecast [SCF]) on sub-seasonal to seasonal streamflow forecasting quality. This will indicate the potential achievable improvements in sub-seasonal to seasonal streamflow forecasting skill through the improvement in either one of the two error (or predictability) sources.

The third part of this deliverable is devoted to the identification of the key drivers (beyond IHC and SCF) that control and influence the hydrological forecasting skill.

For this an alternative sensitivity analysis was designed based on the results from about 35000 European basins, which allows linking the skill to the regional physiographic-hydro-climatic characteristics.

The aim of this work is to produce a 'hydrological sensitivity chart', providing information about the state-of-the-art in terms of sub-seasonal to seasonal streamflow forecasting, as well as about potential targeted improvements on which IMPREX should focus. An overview of sub-seasonal to seasonal streamflow forecasting and the use of sensitivity analyses to diagnose its uncertainties are given in Section 2. Section 3 introduces the methodology, with an overview of the forecasting systems, the data and the methods used for the analyses. The results are subsequently presented in Section 4 and finally discussed in Section 5. Section 5 additionally states the lessons learnt and recommendations for future work.

Sub-seasonal to seasonal streamflow forecasting: background, 2 applications and limitations

An overview of sub-seasonal to seasonal streamflow forecasting 2.1

The first seasonal streamflow forecasting methods were statistical methods, regression-based, using antecedent hydrological conditions (i.e., snowpack measurements, soil moisture, among others) to give an indication of the streamflow for the following months [START_REF] Church | Principles of Snow Surveying as Applied to Forecasting Stream Flow[END_REF][START_REF] Wood | A Test Bed for New Seasonal Hydrologic Forecasting Approaches in the Western United States[END_REF]. With the understanding of hydrological processes and the advances in computer technologies, the first numerical hydrological models were created [START_REF] Helms | The History of Snow Survey and Water Supply Forecasting: Interviews with US Department of Agriculture Pioneers[END_REF]). In the 1970s, one of the first dynamical forecasting system was constructed using a hydrological model, initialising it with observed hydrological conditions (IHC) and forcing it with historical time series of observed precipitation and temperature from all the previous years of recorded meteorological observations. This method was introduced by the National Weather Service (NWS) in the United States and was termed the Extended Streamflow Prediction (ESP) system (Twedt et al., 1974;[START_REF] Day | Extended Streamflow Forecasting Using NWSRFS[END_REF]. The ESP nowadays stands for Ensemble Streamflow Prediction and describes the same forecasting process.

Despite its strength, the ESP is limited by the fact that it is based on the assumption that the historical weather can give an accurate indication of the future weather. In the 1950s, the use of seasonal meteorological forecasts for seasonal streamflow forecasting for water management was first investigated but its skill was judged too poor for operational purposes [START_REF] Pagano | Integration of Climate Information and Forecasts into Western US Water Supply Forecasts[END_REF]. The 1970s were a milestone for seasonal meteorological forecasting, due to the understanding of atmosphere-ocean-land interactions and the importance of teleconnections forecasting on seasonal time scales (such as the ENSO, NAO, etc; [START_REF] Pagano | Integration of Climate Information and Forecasts into Western US Water Supply Forecasts[END_REF]. It is however not until the late 1990s that seasonal meteorological forecasts were used for operational purposes, as a result of the very strong El-Niño of 1997-98 [START_REF] Pagano | Integration of Climate Information and Forecasts into Western US Water Supply Forecasts[END_REF].

Statistical forecasting techniques are still widely used, sometimes based on complex regression methods, harnessing the teleconnection indicators [START_REF] Wang | Monthly and Seasonal Streamflow Forecasts Using Rainfall-Runoff Modeling and Historical Weather Data[END_REF]. It is only recently that dynamical seasonal streamflow forecasting (based on forcing a hydrological model with meteorological seasonal forecasts to obtain seasonal hydrological forecasts) has become a real potential to surpass statistical seasonal streamflow forecast skill (Easy et al. 2006). Statistical-dynamical hybrid systems also exist, for instance the use of teleconnection indicators to resample the historical observed meteorological years, removing anti-analogues, to force a hydrological model [START_REF] Schaake | The National Weather Service Extended Streamflow Prediction Techniques: Description and Applications during[END_REF][START_REF] Pagano | Integration of Climate Information and Forecasts into Western US Water Supply Forecasts[END_REF][START_REF] Bierkens | Seasonal Predictability of European Discharge: NAO and Hydrological Response Time[END_REF].

Sectoral applications 2.2

Sub-seasonal to seasonal streamflow forecasts are valuable for many applications of the water sector, including reservoir management for hydropower generation and water allocation for drinking water and agriculture, navigation and flood and drought mitigation. These applications are diverse in terms of their needs and operational use of the forecasts. For example, the flood protection sector is more vulnerable to high river flow, while the navigation, agriculture, hydropower and reservoir management sectors are more vulnerable to low flows. Additionally, the flood protection sector requires accuracy in the timing and the intensity of an event, while the hydropower sector requires information on the flow accumulations for the spring. The various sectors and their individual needs and current operational practices are described below.

Flood forecasting -University of Reading

2.2.1

Flood forecasting is currently done successfully at short to medium time scales (up to a month ahead). Beyond this lead time, the capacity of the forecasts to indicate the potential for an extreme event to happen is still limited, let alone the exact day or even week when this event might happen and the exact location of this event. This is the main reason for which the Environment Agency (EA) does not currently use any sub-seasonal to seasonal forecasts for their decision-making. The main need for decision-making in a flood context is the probability of an event happening, an indication of how extreme the event will be and the estimate date of the event. The EA bases their decisions on a very low threshold (i.e., allowing a high false alarm ratio), as the loss for not taking any action is much larger than the cost of taking action. Their strategies could be categorised as risk-averse, as the consequences of a false alarm are lower than for a miss. There is nonetheless the potential to integrate sub-seasonal to seasonal information in their current system.

Information for the longer time scale could give an indication of the trend in discharge for the following months and flag areas to watch for these coming months, following a "ready-set-go" approach [START_REF] Goddard | The International Research Institute for Climate & Society: Why, What and How[END_REF]. The EA has expressed interest in this kind of information.

Navigation -BfG

2.2.2

Monthly to seasonal forecasts are required for Inland Waterway Transport (IWT) for the the medium-to long-term planning and enhancement of the water bound logistic chain (stock management, adjustment of the industrial production chain, modal split planning). Information about the future evolution of flow and water levels in the large rivers is especially required by the stakeholders before and within the typical low flow seasons when transport capacity on rivers is limited. The required forecast lead time depends on the specific waterway user and the decisions to be taken. It ranges from weeks, for example to shift cargo from shipping to another means of transportation, to months, to adapt the fleet / usable transport capacity (see [START_REF] Klein | Vulnerability of Inland Waterway Transport and Waterway Management on Hydro-meteorological Extremes[END_REF]. Despite the great demand and interest of the IWT sector, no operational forecasts with lead times exceeding8 days are available at the moment for the Rivers Rhine (max. lead time 4 days), Elbe (max. published lead time 2-8 days depending on the gauge) and Upper Danube (max. published lead time 2-4 days depending on the gauge), mainly due to the large uncertainties and the limited skill on monthly and seasonal time scales.

In order to provide stakeholders with monthly to seasonal forecast information, a prototype is being developed in the context of IMPREX. To model the water balance and the flow in rivers the hydrological model LARSIM-ME is applied. The hydrological model was set up for the large rivers in Germany including their international parts (model acronym LARSIM-M(iddle)E(urope)) and covers the catchments of the River Rhine, River Elbe, River Weser/Ems, River Odra and River Danube up to gauge Nagymaros in Hungary. The total catchment size simulated by the model is approx. 800 000 km². The spatial resolution is 5km x 5 km. As meteorological forcings resampled observed climatology (ESP) and seasonal forecasts from ECMWF Seasonal Forecast System 4 are used. 2-m temperature of the past 24 hours and daily total precipitation of System4 are interpolated to a common 50km x 50km grid (multiple of the 5km x 5km raster). Both variables were bias corrected on the 50km x 50km grid using linear scaling with the meteorological observation dataset set used for the baseline simulation (also aggregated to the 50km x 50km grid as reference data). As seasonal forecasts tend to drift towards their own model climate with increasing lead time, giving rise to model bias, separate bias correction factors have been estimated for each forecast initialisation date (starting on the first day of each calendar month) and monthly lead times (first month, second month, etc, to sixth month). In total 12 x 6 = 72 scaling factors for precipitation and 72 additive terms for temperature were calculated for each 50km x 50km raster to correct the model drift of ECMWF's System 4. In the next step temperature and precipitation are downscaled to the 5km x 5km model grid. In future versions of the navigation related seasonal forecasting prototype NavSEAS-ME seasonale forecasts from GloSea5 from UK Metoffice will be included in addition.

To analyse the potential skill of ECMWF-System4 for navigation related seasonal forecasting, the reforecast data set 01.01.1981 -01.04.2011 as well as the preoperational and operational forecasts of the period 01.04.2011 -31.12.2015 are applied. In the reforecast, the number of ensemble members is limited to 15 for the initialisation months January, March, June, July, September, October, and December.

The number of ensemble members is extended to 51 for the initialisation months February, May, August and November. From April 2011 onwards, the (operational) ensemble size is 51 for all initialisation months. For verification the ensemble size of the operational forecasts was reduced to the ensemble size of the reforecast (15 members) for the initialisation months January, March, June, July, September, October, and December. The hydrological re-forecasts with LARSIM-ME are evaluated for relevant low-and medium flow indicators.

Agriculture -FutureWater

2.2.3

Irrigated agriculture is the main economic activity of Campo de Cartagena in the Segura River basin, Spain. However, water scarcity compromises such activity, which is mainly dependant on the water input it receives from the connected Tagus River basin. Mitigation measures of droughts in Spain are based on a number of drought indicators that are derived from the available water in the storage reservoirs. In order to anticipate drought episodes, decision-makers need to forecast the corresponding reservoir inflows. In the case of the Segura River, forecasts are currently estimated from simple regressions of river discharges from the preceding 6 months, leading to updated management plans twice a year.

In order to provide stakeholders in the basin with a more robust forecasting system that would allow them to better anticipate drought episodes and put into practice more effective allocation and mitigation practices, a prototype of a hydrological seasonal forecasting system is presented. The prototype uses the Spatial Processes in HYdrology model (SPHY) forced with the ECMWF's System 4 (15 ensembles) seasonal meteorological forecasts to predict monthly river inflows at the reservoirs of the upper basins of the Segura and Tagus Rivers. The model was first calibrated for the 1980-2000 period (using 1979 as a warm-up year) against discharge observations at three stations located at the major storage reservoirs: Entrepeñas and Buendía in the Tagus basin and Fuensanta in the Segura basin (see Figure 1). The Cenajo station in the Segura basin was not included in the calibration due to data availability on water transfers between catchments in the Segura basin, but it was included in the simulation runs.

The system focuses on four major periods (initialisation months) relevant for the regional climatology (January, April, July and October), with a forecasting lead time of three months, aiming at finding the most suitable period(s) to take decisions.

Hydropower -SMHI 2.2.4

The regulated mountainous basins are commonly highly influenced by snowmelt runoff and volumes in hydropower production, particularly when a multi-reservoir system is present. In the case study of the Umeälven River (Sweden), seasonal forecasts of snowmelt runoff volumes, together with ground based and remote sensing snow cover monitoring, are key inputs to the decision models of the hydropower companies when planning the production for the current and next winter seasons. It is very common that the operational seasonal forecasts are based on an ESP. Reservoir operators are interested in accumulated forecasts of inflows over the spring flood period (April to July). Forecasts for the April-July accumulated runoff are issued once a month from January until the start of the melt season in April. An important driver is the reservoir level at the end of summer, where a trade-off between water usage for power production during the spring period and the desire to have high water levels at the end of the summer is present.

Unnecessary release of water that cannot be used for production is recognised as spill and loss of potential production which can be translated into an economical value. Spill of water may happen when the remaining spring flood volumes were underestimated and reservoirs filled up too early. Therefore, score metrics that are based on volume errors are appropriate measures to describe the improvements in forecasting skill.

Reservoir management -UPV 2.2.5

In the Júcar River Basin, an important characteristic is the semi-aridity of the climate that leads to high hydrological variability, resulting in recurrent periods of drought lasting several years (more than 4 years in some instances). In order to decrease the vulnerability of the water resources system, large reservoirs were built and conjunctive use of surface and groundwater is a regular practice, also integrating wastewater reclamation and reuse. Therefore, integrated and improved management of the water resources system is essential. In addition, proactive drought management requires continuous monitoring and assessment of risk in order to anticipate measures. For this purpose, reliable seasonal forecasts of climate variables (i.e., precipitation and temperature) and hydrological forecasts (river flows) are needed for the management of the system, which in this case is based on the risk of failure in the supply for all uses, mainly water allocation for drinking water and agriculture.

The analysis must be performed in an integrated way for all elements of the water resources system of the entire basin. Otherwise, physical connections between elements (rivers, aquifers, returns from irrigation and urban uses, etc.) and implications of any decisions in the rest of the system (even from downstream to upstream), would be ignored and results would not be realistic.

For reservoir management, the key is to be able to use the decision support system (DSS) to estimate the risk of failure in the supply of water to all users, as well the risk of failure in the compliance with the established ecological flows, during the next 12 to 24 months (anticipation period). If the risk is considered to be too high, then measures must be proposed and their efficacy be tested with the DSS. A key result is also the forecast of the volume of water remaining in the reservoirs system at the end of the irrigation season. Deterministic and probabilistic forecasts will allow management measures that optimize farmer yields, maintaining high reliability of supplies to the cities, and with an adequate degree of environmental protection Currently, the analysis is performed using flow forecasts in several places of the basin (we will focus on 5) obtained by multivariate synthetic flow forecasts generation conditioned to the present time state of the system and to past flows.

The objective would be to improve the flow forecasts by incorporating short term and seasonal meteorological forecasts as forcing inputs.

For this case study, at UPV we have compared river flow data, obtained from the hydrological model E-HYPE, with regional river flow observations. Hydrological model data were provided by SMHI, corresponding to the continentally calibrated E-HYPE model for the period 1980-2010 and for five sub-basins of the Júcar River basin. Regional observations are naturalized river flows (NRF) for these sub-basins. This comparison has the purpose of testing the reliability of the E-HYPE model and to evaluate the need for a bias correction.

Flood & low flow forecasting -Deltares 2.2.6

In the Netherlands, salt intrusion occurs when the river flows of the Rhine and Meuse are low and coincidentally, wind storms push sea water into the river mouth.

As a result, water boards cannot take in water to flush their polders, as these could suffer from saline seepage. This problem is a prerequisite for accurate and reliable forecasts of river flows, water levels, tide and surge, water demand and availability in the polder areas, salt concentrations and intrusion. Rijkswaterstaat (the Ministry of Infrastructure and the Environment) is currently predicting river flows up to 10-15 days for the main rivers Rhine and Meuse.

For drought forecasting in the Netherlands, the National Hydrological Model (LHM) was operationalised to support water management (e.g., lakes, surface water, etc) between April and November [START_REF] Berendrecht | An operational drought forecasting system using coupled models for groundwater, surface water and unsaturated zone[END_REF]. This system is forced with measured and forecasted river flows at the boundary and areal precipitation and potential evaporation (250x250m). Timely information about low flow conditions at the monthly to seasonal scale (1-3 months) can help to take measures such as raising the level of Lake Ijssel. This might also have consequences for the flood risk (e.g., due to windstorms causing surge on Lake IJssel) and flow forecasts for the Rhine and Meuse should therefore be accurate and reliable. Sensitivity analyses as a tool to diagnose seasonal streamflow forecasting 2.3 uncertainties Despite great advances in sub-seasonal to seasonal streamflow forecasting in the last decade, the forecasting skill in Europe is still limited. This is due to a combination of errors, such as the poor seasonal meteorological forecasting skill in the extra-tropics [START_REF] Arribas | The GloSea4 Ensemble Prediction System for Seasonal Forecasting[END_REF], errors in the IHC, hydrological model and downscaling errors.

Sensitivity analyses are a useful tool to diagnose the sensitivity of the model output (here hydrological variables such as discharge) to the model inputs (SCF, IHC and model parameters; [START_REF] Saltelli | Sensitivity Analysis in Practice: A Guide to Assessing Scientific Models[END_REF]2008). They can be used for a variety of motivations, ranging from research prioritisation (improving solely certain aspects of the forecasting chain) to model simplification [START_REF] Saltelli | Global Sensitivity Analysis: The Primer[END_REF]. To this end, forecasting systems intercomparison can help disentangle the sources of uncertainty and/or corroborate skill both in time and space.

Another sensitivity analysis method widely used in seasonal streamflow forecasting is based on the ESP and the reverse-ESP and was first introduced by [START_REF] Wood | Comparing Hydrologic Forecast Uncertainty due to Initial Condition Error versus Climate Forecast Error[END_REF]2008). The reverse-ESP can only be run in hindcast and is produced by forcing the hydrological model with a single meteorological trace (the meteorological observations for that specific time of the year). The hydrological model is initialised with an ensemble of historical IHCs (resampled from that same initialisation month for all the previous years). Contrary, the ESP is started from a single set of current IHC and forced with an ensemble of historical meteorological observations (resampled from the past meteorological observations available for all previous years and for the same time of the year as the one for which the ESP forecast is run). The ESP can be run as a forecast or in hindcast.. Whereas the uncertainty in the ESP is given by the SCF, the uncertainty in the reverse-ESP is given by the IHC. By comparing the ESP and reverse-ESP skill for a catchmentseason-lead time combination, it is possible to tell which component of the forecast mainly leads the uncertainty (i.e., the SCF or the IHC). Recently, this method was extended by [START_REF] Wood | Quantifying Streamflow Forecast Skill Elasticity to Initial Condition and Climate Prediction Skill[END_REF] to a method called VESPA (Variational Ensemble Streamflow Prediction Assessment). The VESPA method aims at assessing intermediate uncertainty points between the climatological and 'perfect' (i.e., current observed meteorological data) skill present in the reverse-ESP and the ESP. This method allows the calculation of a metric called 'skill elasticity', a measure of the potential to increase the seasonal streamflow forecasting skill as a result of increasing the SCF or the IHC skill. In this deliverable we will use an alternative method to the VESPA method, a description of which is given in Section 3.3.

Comparative analysis in large sample hydrology 2.4

Large-scale (i.e. continental) multi-basin modelling can complement the "deep" knowledge from basin-based modelling and enhance process understanding, increase robustness of generalisations, facilitate classification of basin behaviour and prediction, support better understanding of prediction uncertainty, and go beyond sensitivities related to IHC and SCF [START_REF] Pechlivanidis | Large-scale hydrological modelling by using modified PUB recommendations: the India-HYPE case[END_REF]. This type of modelling has the potential to cross regional and international boundaries whilst the analysis over a number of basins allows the consideration of different geophysical and climatic zones [START_REF] Gupta | Large-sample hydrology: a need to balance depth with breadth[END_REF]; hence it can provide a deeper understanding of the underlying sensitivities in the forecasting skill. Such modelling type can also advance hydrological science since it founds a numerical background for comparative hydrology [START_REF] Blöschl | Runoff prediction in ungauged basins. Synthesis across processes, places and scales[END_REF]. The use of a large sample of stations, particularly when analyses are conducted at the continental scale (i.e., as in Europe), can also allow for exploration of emerging patterns and facilitate comparative hydrology, allowing to test sensitivities for many catchments with a wide range of environmental conditions [START_REF] Blöschl | Runoff prediction in ungauged basins. Synthesis across processes, places and scales[END_REF].

However, understanding processes in large systems is challenging, given that physical properties (e.g., vegetation and soil type) generally exhibit high spatial variability, which consequently results in significant differences in system behaviour and predictability. As expected, this spatial heterogeneity introduces further high uncertainty on the categorisation of important drivers that influence the predictive hydrological skill. In addition, large river basins are often strongly influenced by human activities (e.g., irrigation, hydropower production, groundwater use) for which information is rarely available and therefore rarely described in hydrological model processes; hence introducing additional uncertainty regarding process understanding and description. Although such modelling type has limitations which vary in space, in here we make the step forward to gain insights in spatial patterns of hydrological skill at the large scale, and link this to the characteristics of the basin system.

Data and methods 3

The forecasting systems 3.1

All the partners of this deliverable use dynamical ensemble seasonal forecasting systems. These systems all use the same seasonal meteorological forecasts but are diverse in terms of the hydrological models and the presence or not of a bias correction method for the seasonal meteorological forcing. This was done in order to obtain insights into the seasonal discharge forecast sensitivity to the hydrological model type. An overview of the various systems and their characteristics is given in Table 1. For more details on the hydrological models used, see Annex A.

The forecasting systems intercomparison 3.2

The first part of this deliverable compares the performance of the dynamical subseasonal to seasonal hydrological forecasting systems listed above (see Table 1). For the intercomparison, a common set of stations was selected, based on data available to the partners of this deliverable (see Figure 2 and Table 2). Observed discharge data was distributed for the corresponding stations by a few partners to all partners involved in WP4, in order to have a consistent verification across partners. In order to compare the performance of the different ensemble seasonal hydrological forecasting systems, several scores were chosen including both deterministic and probabilistic scores and covering the main attributes of ensemble forecasting relevant for sectoral applications (see Section 3b). These scores include:

• Deterministic scores:

o The Mean Absolute Error (MAE) (cawcr, 2015):

The MAE ranges from 0 to an upper boundary defined by the system's variability, with a perfect score of 0, and indicates the average magnitude of the forecast errors. Where F i is the ensemble mean and O i is the observed discharge for the same time. N is the sample size, it is the total number of forecasts made for the same target month and with the same lead time and temporal aggregation type. This score does not indicate the direction of the forecast deviations, which will be calculated using the Mean Error (ME).

o The Mean Error (ME) (cawcr, 2015):

The ME ranges from -∞ to +∞, with a perfect score of 0, and is a measure of the average forecast error, considering the ensemble mean. It indicates the forecast average additive bias (i.e., its tendency to underestimate or overestimate observed discharge). Note, a good ME score does not guarantee that the forecast is perfect as overestimations and underestimations made by the latter can compensate each other.

o The normalised volumetric term of the Kling-Gupta Efficiency (beta, Gupta et al., 2009):

𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏 = 1 -𝑠𝑠𝑠𝑠𝑠𝑠𝑏𝑏((𝛽𝛽 -1) 2 )
β is defined as the ratio of the monthly mean of the forecasts (the output of the model forced by meteorological forecasts) over the monthly mean of the perfect forecasts (the output of the model forced by the reference forcing dataset); note that the range of the values for each term varies between -∞ and 1 with 1 being the optimum.

• Probabilistic scores:

o The Continuous Ranked Probability Score (CRPS) [START_REF] Hersbach | Decomposition of the Continuous Ranked Probability Score for Ensemble Prediction Systems[END_REF]:

Where P is the ensemble forecast cumulative distribution function (cdf) and P a is the observation cdf and is defined by: For the observed discharge x a , with H(x) the Heaviside function:

The CRPS ranges from 0 to +∞, with a perfect score of 0, and is a measure of the difference between the forecast and the observation cdfs. A perfect score of 0 is achieved in the case of a perfect deterministic forecast. The CRPS is a measure of the forecast accuracy and sharpness. It can be further decomposed into reliability, resolution and uncertainty components, according to:

CRPS reliability CRPSpotential = +
Where the potential CRPS is the CRPS value that a forecast with perfect reliability (reliability=0) would have, expressed as:

CRPSpotential uncertainty resolution = -
The reliability is a measure of the bias and the spread of the system. The uncertainty is the variability of the observations and the resolution is the ability of the forecast to distinguish situations with distinctly different frequencies of occurrence. The components all range from 0 to +∞, with a perfect score of 0. The CRPS and its components were averaged over all the forecasts made for the same forecast initialisation date and with the same lead time and temporal aggregation (monthly averages here).

o The Brier score (BS) (cawcr, 2015):

Where N is the sample size, the total number of forecasts made for the same target season and with the same lead time, temporal aggregation type and for the same event. o i is a binary observation, it is 1 if a predefined event happened and 0 if it did not. p i is the forecast probability of the event happening. The BS ranges from 0 to 1, with a perfect score of 0 and is a measure of the mean squared error of the probability forecasts over the verification sample. The events selected to calculate the Brier score are the upper and the lower terciles of the observed discharge for the specific season for which the score is calculated. These thresholds were chosen in order to have a large enough sample as this score is sensitive to the climatological frequency of the event: the rarer an event is, the easier it will be to obtain a good BS without necessarily having any real skill. The BS can be further decomposed into a (1) reliability, (2) resolution and (3) uncertainty part. The BS and its three parts were averaged over all the forecasts made for the same target season and with the same lead time, temporal aggregation (monthly averages here) and for the same event (upper or lower terciles).

o The skill scores: the forecast skill was also calculated for the CRPS and the BS using the following equation:

1 forecast reference score Skillscore score = -
For the reference, two benchmarks were selected. The first benchmark is the climatology of observed discharge and the corresponding skill scores of the CRPS and the BS are called the CRPSS_CLI and the BSS_CLI, respectively. The climatology covers the same period as is covered by each forecasting system (excluding the year analysed) and is the climatology of a given target month (or season for the Brier Score). The second benchmark is the ESP corresponding to each system, for the same forecast initialisation date, lead time and temporal aggregation (monthly averages here). The corresponding skill scores of the CRPS and the BS are called the CRPSS_ESP and the

BSS_ESP, respectively

The analysis will present scores measured for the discharge forecasted from various forecast starting dates or target seasons (for the BS), lead times, monthly aggregations and several stations in Europe. This intercomparison will provide a spatio-temporal overview of the performance of the seasonal hydrological forecasting systems overall as well as for extreme events (high and low flows).

The EPB sensitivity analysis

3.3

The VESPA method is a sensitivity analysis method in the sense that it measures the response of the model output (discharge in our case) to a known variation in the model input(s) (here the SCF and the IHC). It was designed and tested on 424 catchments in the contiguous United States (CONUS), for which it successfully exposed the relative contributions of the two sources of errors (SCF and IHC) on seasonal streamflow forecasting uncertainty. Moreover, the 'skill elasticity' produced by the VESPA method indicates the potential to improve the seasonal streamflow forecasting skill by improving the SCF and/or the IHC skill. This information is valuable for guiding resources in seasonal forecasting system development towards useful improvements. One drawback of the VESPA method however is that it is computationally expensive to run as it is based on a very large number of simulations. Recently, an alternative and cheaper method called EPB (End Point Blending) was designed and tested on 18 catchments of the CONUS for which it gave almost identical results to the VESPA method [START_REF] Arnal | An Efficient Approach for Estimating Streamflow Forecast Skill Elasticity[END_REF]. Because the EPB sensitivity analysis is a reliable and computationally cheap method which can give insightful results in the context of seasonal streamflow forecasting improvements, it will be used in this deliverable.

The EPB is constructed by combining four sources of data (also called end points): the ESP, the reverse-ESP, the climatology and the 'perfect' forecast. The term 'perfect' refers to current observed meteorological data and the term climatological refers to the whole distribution of historical meteorological observed data. Each end point corresponds to a combination of IHC and SCF weights (w IHC and w SCF respectively; the axes on Figure 3). A weight of 0 is the 'perfect' knowledge (upper right corner on Figure 3) whereas a weight of 1 is the climatological knowledge of either of the two predictability sources (bottom left corner on Figure 3). A 'perfect' forecast (forecast generated by starting a hydrological model with the current IHC and forcing it with the current observed meteorological data) has a w IHC and a w SCF of 0. The climatological forecast ('climo' on Figure 3; forecast generated by starting a hydrological model with all historical IHC and forcing it with all historical observed meteorological data) has a w IHC and a w SCF of 1 by definition. The reverse-ESP is forced with a single meteorological trace, the meteorological observations for that specific time of the year (w SCF of 0) and the model is initialised with a range of historical IHC (w IHC of 1). The ESP is forced with historical observed meteorological data (w SCF of 1) and the current IHC (w IHC of 0).

The EPB combines these four end points for each intermediate SCF and IHC weights (w = 0, 0.05, 0.10, 0.25, 0.50, 0.75, 0.90, 0.95, 1.0), as shown on Figure 3 below. Those intermediate weights were chosen in order to coincide with the VESPA method [START_REF] Wood | Quantifying Streamflow Forecast Skill Elasticity to Initial Condition and Climate Prediction Skill[END_REF]. For each w SCF -w IHC combination (each cross on Figure 3

] ( ) ( ) % 1 1 EP IHC EP SCF EP x w y w = - - × - -
Where x EP and y EP are the w IHC and w SCF values of the end point for which the percentage is calculated, respectively. For example, if the w IHC and w SCF match the end point values, 100 percent of the EPB hindcast members are resampled from that end point (i.e., the end point skill is reproduced). This was done for each forecast initialisation date for a given location. Once the new EPB hindcasts have been generated, their quality can be calculated for each combination point. A plot of the forecast quality as a function of IHC and SCF skill can then be drawn and is called skill surface plot in [START_REF] Wood | Quantifying Streamflow Forecast Skill Elasticity to Initial Condition and Climate Prediction Skill[END_REF]. Finally, for each response surface (i.e., skill elasticity plot) skill elasticities for the IHC and the SCF (E IHC and E SCF respectively) can be measured from the scores from the following equations:

[ ] ( ) [ ] ( ) [ ] ( ) [ ] ( ) [ ] ( ) [ ] ( )
IHC S F S F S F S F S F S F E   - - -   = × + +   - - -     [ ] ( ) [ ] ( ) [ ] ( ) [ ] ( ) [ ] ( ) [ ] ( )
S F S F S F S F S F S F E   - - -   = × + +   - - -    
The numerators, expressed as S(F[-])-S(F[-]), are the streamflow forecast skill gradients between IHC skill (or SCF skill) values of 75% and 19% (the denominator). The values in the square brackets of the numerator are the IHC skill followed by the SCF skill values, indicating a wSCF -wIHC combination point in the example skill surface plot (i.e., Figure 3).

In the denominator, the IHC and SCF skill gradients are gradients in the percentage of the climatological variance explained in the respective predictability source. The skill elasticities (E IHC and E SCF ) are positively oriented; where a skill elasticity of zero is obtained when the predictability source has no influence on the skill of the streamflow forecast, while positive (negative) elasticities mean that an improvement in the predictability source will lead to higher (lower) streamflow forecast skill.

For this deliverable, we calculated the IHC and SCF skill elasticities for the ECMWF seasonal discharge forecasts described in Table 1, for each initialisation date (the first of each month), monthly forecast aggregations from 1 to 7 months of lead time and over 74 geoclimatological regions in Europe. These 74 regions were selected as they are the same regions for which the ECMWF seasonal streamflow forecast is currently operational in EFAS (European Flood Awareness System). The skill elasticities are based on the CRPSS, calculated against the climatological forecast. This analysis assumes that the model is perfect as the CRPS is calculated against the 'perfect' forecast (i.e., discharge simulation) and not actual discharge observations. Additionally, skill elasticities were calculated for the BfG seasonal discharge forecasts described in Table 1, for each initialisation date (the first of each month), monthly forecast aggregations from 1 to 6 months of lead time and for the stations shared by the BfG and presented in Table 2.

Seasonal hydrological forecasts -Clustering of the skill 3.4

To better understand the potential factors influencing the skill of a model and to identify regions of similarity, we apply classification and regression trees (CART). Here, we explored the spatial runoff patterns across the entire subcontinent by analysing the skill in all 35408 catchments modelled by the E-HYPE model. CART is a recursive-partitioning algorithm that classifies the space defined by the input variables/descriptors (i.e. physiographic-hydrologicclimatic characteristics, and remaining climatic biases) based on the output variable (i.e. beta skill for lead month 2 and month March). The tree consists of a series of nodes, where each node is a logical expression based on a similarity metric in the input space (physiographichydro-climatic characteristics etc.). CART also provides information on the probabilities of different output groups at each leaf node. In this case, beta (see section 3.2) is divided into five groupsbad (beta < 0.2), poor (0.2 < beta < 0.4), medium (0.4 < beta < 0.6), good (0.6 < beta < 0.8) and very good (beta > 0.8), which are termed C0, C1, C2, C3 and C4 respectively. A terminal leaf exists at the end of each branch of the tree, where the probability of belonging to any of the three output groups can be inspected. Here we summarised the basin characteristics into climatic, topographic, human impacts, biases in forcing input and hydrologic bias (Table 3). We next calculate the predictors' importance (and rank them) by summing changes in the risk due to splits on every predictor and dividing the sum by the number of branch nodes.

It is important to note that in order to avoid the high dimensionality in the CART analysis, the hydrologic signatures were firstly clustered into 11 groups with each group receiving an ID (named FlowID). We applied a k-means clustering approach within the 12-dimensional space (consisting of the 12 calculated flow signatures in Table 3) to categorise the subbasins based on their combined similarity in flow signatures. Through the mapping of the spatial pattern we gained insight into the similarities of catchment functioning and could identify the dominant flow generating processes for specific regions. A set of scores was added from each forecasting system (from the ECMWF, SMHI, BfG and FW) into the scoreboard for the selected stations shown in Table 2. This allows to have a first view of the similarities as well as differences between the forecasting systems' performances and highlights common forecasts' behaviours across river basins.

The seasonal discharge forecasts' quality depends on the target month, the lead time and the station for which the forecast is made. We will split the results according to the geographical location of the river basins, as there are some noticeable similar characteristics in terms of forecast performance for stations in a given area of Europe. For the Rhine at Lobith, the results are also plotted as monthly values of the mean absolute error for the W3RA model, the lumped HBV96 model and the distributed wflow_hbv model (Figure 7). Note that W3RA was run with EFAS historical forcing data, while the lumped HBV96 and distributed wflow_hbv models were run using HYRAS data (details given in Table 1). From Figure 7, it appears that the W3RA model displays larger errors than the two other models almost all year long, especially in summer. This could be an indication that the EFAS historical forcing data has large uncertainties for this station. It could furthermore be due to a misrepresentation of essential discharge generating mechanisms in this region by the W3RA model. Figures 8 and9 show the bias (i.e., the ME) for all forecast initialisation dates and all lead times for the Rhine at Koeln and the Elbe at Dresden, respectively. For most Central European Rivers stations included in this analysis, the SMHI forecasts overestimate the observed discharge in the winter to spring months. This is both the case for the Rhine at Koeln (Figure 8) and the Elbe at Dresden (Figure 9). This positive bias could be due to a hydrological model error, where the model releases more water as river flow than is observed because it cannot store enough water as groundwater. For the Rhine at Koeln (and a few other stations of the most western Central European Rivers stations, not shown), the SMHI forecasts additionally present a negative bias for the rest of the year.

Central European Rivers

The ECMWF forecasts overall overestimate the observed discharge during the spring months (more largely at longer lead times) while underestimating the winter discharge. This is both true for the Rhine at Koeln and the Elbe at Dresden (see Figures 8 and9). This positive bias extends into the early summer months for some stations. These biases could be due to meteorological forecast error as the input meteorological forecasts used to produce the ECMWF seasonal discharge forecasts was not bias corrected, contrary to the BfG and the SMHI forecasts. It seems that ECMWF generates too much of the precipitation falling as snow in winter, leading to underestimated discharge in those months and a snowmelt compensation in spring.

The BfG forecasts underestimate the observed discharge for winter and early spring months or all target months, depending on the station (see Figures 8 and9). This behaviour could either be due to the bias correction of the meteorological forecasts input to the hydrological model, which produces too dry conditions compared to the observed amount, or to the hydrological model which stores too much incoming water as groundwater.

These are general characteristics of the SMHI, BfG and ECMWF forecasts and the magnitude of the bias depend on the station, target month and lead time for which the forecasts were made. In terms of the reliability of the forecasts (i.e., CRPS reliability), the results are contrasted and vary from station to station. For the most western Central European Rivers stations, the CRPS reliability appears highly influenced by the forecast lead time as well as the event which is being forecasted. Figure 12 is an example of the CRPS reliability for the three systems for the Rhine at Koeln. For this station, the SMHI forecasts are less reliable from February-March and May at 1 month lead time. The ECMWF forecasts are less reliable from May-July and January. The BfG forecasts are less reliable from March-April.

For the most eastern Central European Rivers stations, the CRPS reliability, the ECMWF and the BfG forecasts display a better reliability than the SMHI forecasts, especially for December-April. Figure 13 is an example of the CRPS reliability for the three systems for the Elbe at Dresden. For the upper and the lower terciles of the observed discharge, all forecasts show a very similar accuracy (Brier score of 0.2-0.3 on average over all lead times and all target seasons).

There are however slight differences for single stations, for which the SMHI forecasts have a lower performance than the other forecasts for the summer target season for the lower tercile (BS33) and for the summer and the winter target season for the upper tercile (BS66; not shown).

The Thames River Basin

4.1.2

For stations for the Thames River Basin case study, scores were calculated from the SMHI and the ECMWF forecasting systems. From this set of stations, there are several observable forecast performance behaviours. For several stations, both the SMHI and the ECWMF forecasts appear overall less accurate and sharp (in terms of the CRPS) from October-April. For several other stations, the SMHI forecasts are less accurate and sharp than the ECMWF forecasts throughout the year, especially from November-April. This can be seen on Figure  18). A similar positive bias was described for stations of the Central European Rivers (Section 4.1.1) and could be here again due to a hydrological model error, where the model releases more water as river flow than is observed because it cannot store enough water as groundwater.

While there is almost no bias for the Pang at Pangbourne for the ECMWF forecasts (Figure 19), they underestimate the discharge from November-January and overestimate it more largely from February-June (increasingly with lead time) for the Thames at Royal Windsor Park (Figure 18). A similar bias pattern was observed for stations of the Central European Rivers and could be here again due to an overestimated percentage of precipitation falling as snow in the winter, leading to underestimated discharge in those months and a snowmelt compensation in spring. In terms of reliability (i.e., CRPS reliability; see Figure 20 for an example at the Thames at Royal Windsor Park), the SMHI forecasts are overall less reliable than the ECMWF forecasts, especially for forecasts for the winter and the spring target months. The ECMWF forecasts are sometimes less reliable than the SMHI forecasts for the late summer or for the winter target months for a few stations. For both systems, the forecasts sometimes become more reliable with lead time, for a forecast made for the winter target months, which is a counterintuitive behaviour. The forecasts' accuracy for the lower and the upper terciles of the observed discharge is variable depending on the station and the target season. In general however, both the SMHI and the ECWMF forecasts show a similar accuracy (Brier score of 0.2 on average over all lead times and all target seasons). For several stations, the SMHI forecasts have a worst BS33 and BS66 for all target seasons and all lead times. However, the ECMWF forecasts are less accurate than the SMHI forecasts for the upper tercile for JJA and MAM target months for a few stations (not shown).

The Segura and Tagus River Basins 4. 1.3 For stations of the Segura and Tagus River Basins, scores were calculated from the SMHI, the ECMWF and FW forecasting systems. From this set of stations, the SMHI forecasts are on average less accurate and sharp than the two other forecasting systems, especially for forecasts made for the winter and the spring. The ECMWF forecasts appear more accurate and sharp for forecasts made for the summer for all stations shared for this river basin. See Figure 22 as an example for the Tagus at Entrepenas. Figures 23 and 24 show the forecast biases (i.e., the ME) for the Tagus at Entrepenas and the Segura at Cenajo, respectively. For these stations, the SMHI forecasts either underestimate or overestimate the observed discharge, more largely for the spring and the winter. The ECMWF forecasts have large biases for the winter and the spring as well, either positive or negative depending on the station. The summer biases are however the closest to zero for the ECMWF forecasts. The FW forecasts display large positive biases from February-April for the two stations on the Tagus River (see Figure 23 as an example for the Tagus at Entrepenas) and small negative biases throughout the whole year for the two stations on the Segura River (see Figure 24 as an example for the Segura at Cenajo). In terms of the seasonal discharge forecasts skill, when compared to the observed discharge climatology (i.e., the CRPSS_CLI), the SMHI, ECMWF and FW forecasts appear less skilful for the summer. The SMHI forecasts are also less skilful than the ECMWF and the FW forecasts.

Both of these results can be seen on Figure 26 of the CRPSS_CLI for the Tagus at Entrepenas. The Jucar River Basin 4. 1.4 The analysis that will be performed in following sectoral work packages (i.e., WP8-Hydroelectricity and WP11-Agriculture), as well as in WP13-Sectoral integration, must be performed using the entire river basin domain, and integrating all the relevant elements of the water resources system. This requires producing forecasts for streamflows in several sites. The reliability of the forecasts and the statistical consistency for time correlations and cross correlation between sites are crucial factors to foster the use of the forecasts in real management of the water resources system. Otherwise, the impacts of droughts would be underestimated.

Since the Jucar River Basin is strongly anthropized, all forecasts and comparisons must be done in terms of natural flows (i.e., flows that would happen if man would not produce changes due to storage and releases from reservoirs, pumping from aquifers, and diversion and return flows from consumptive uses). Natural flows provide a consistent baseline in order to compare the performance of different programmes of measures in planning and management of the basin.

Therefore, we performed a comparison between E-HYPE results and the historical data in five different points or sub-basins of the Jucar River Basin. Four of them are inflows to the main reservoirs (Alarcon, Contreras, Molinar, Tous) and the fifth is located in Sueca, at the lower part of the basin. All of these stations are crucial from the point of view of water management.

In Figure 27, the comparison between the average monthly flows in the five mentioned locations produced by the SMHI-E-HYPE model and the historical re-naturalized flows at the same locations is depicted. As it can be seen, flows produced by E-HYPE in all sites are almost zero in the summer, while the historical values are much higher. This can be explained by the important natural regulation due to aquifers upstream and in the middle section of the basin. It seems that the E-HYPE model is not able to capture this important characteristic of the basin.

On the same figure, the average monthly flows produced by the hydrological model EVALHID, which is currently used by the UPV for the Jucar River Basin, are also depicted. This essential mismatch between the E-HYPE results and observed values cannot be overcome by any bias correction, since in the summer months rainfall is almost negligible. This is only feasible by means of a conceptual modification of the model and recalibration in order to capture the real behaviour of the basin by the model. This will be discussed in the following months of the IMPREX project.

For this deliverable, forecasts produced by the EVALHID hydrological model forced with ECMWF meteorological data as input (i.e., precipitation and mean temperature) is compared to the ECMWF forecasts.

Figure 28 displays the CRPS obtained from the UPV forecasts for all five stations of the Jucar River Basin. From this figure, it appears that the forecast performance varies depending on the selected station, but the forecasts are on average more accurate and sharp in the spring and summer. The largest errors can be found in the winter months. Regarding to the UPV forecasts biases, different behaviours can be seen depending on the selected station (see Figure 30). For the Alarcon and Contreras stations, bias is positive all year except for January when it reaches negative values. This could be due to the fact that those two stations are situated in the mountainous headwaters of the river, where the faster discharge generating processes are likely misrepresented by the model and/or the precipitation is underestimated for this time of the year at those stations. The Molinar and Sueca stations display a slight positive bias all year long, while the Tous presents a negative bias all year.

For the ECMWF forecasts (see Figure 31), the biases are similar to the UPV forecasts biases for the Jucar at Alarcon, which could be due to the same model misrepresentation and/or precipitation underestimation. For the Jucar at Tous, the ECMWF forecasts present slightly different and much larger biases than the UPV forecasts, with negative biases in the summer and positive biases the rest of the year. This hints towards an underestimation of the groundwater discharge by the ECMWF forecasts for this station.

Swedish Rivers

4.1.5

For stations on the Swedish Rivers, scores were calculated from the SMHI and the ECMWF forecasting systems. From this set of stations, the SMHI and the ECMWF forecasts appear equally accurate and sharp, with larger errors from May-September for all four stations shared. This can be seen on Figure 32, which displays the CRPS for the Vindelaelven at Granaker for both systems. These behaviours of the SMHI and the ECMWF forecasts were observed for the other shared stations for Swedish Rivers. For the SMHI forecasts, this could be due to a large underestimation of groundwater storage and recharge in the winter, subsequently leading to underestimated flows in the summer. For the ECMWF forecasts, the biases could be due to a delayed snowmelt process in the model, either due to model errors or to biased seasonal temperature forecasts input into the model. For both systems, the reliability is the worst during from June-August (see Figure 34), approximately when the largest CRPS errors were observed (see Figure 32). In terms of the seasonal discharge forecasts skill, when compared to the observed discharge climatology (i.e., the CRPSS_CLI), the ECMWF forecasts appear less skilful from February-April and in July (see Figure 35 as an example of this general behaviour for the Vindelaelven at Granaker). The skill of the SMHI forecasts depends given the station looked at, but they are generally more skiful than the ECMWF forecasts. The forecasts' accuracy for the lower and the upper terciles of the observed discharge is variable depending on the station and the target season. In general however, both the SMHI and the ECWMF forecasts show a similar accuracy (Brier score of 0.2-0.3 on average over all lead times and all target seasons). For several stations, the ECMWF forecasts have a worst BS33 for DJF for all lead times (not shown). Both systems exhibit a very good accuracy (BS66 close to 0) for the upper tercile for forecasts made for MAM at all lead times (not shown).

The EPB sensitivity analysis 4.2

Figure 36 shows maps of the dominant predictability source (IHC or SCF), the predictability source for which the skill elasticity is highest and which could therefore lead to higher seasonal discharge forecast skill after being improved. The skill elasticities were derived from forecasts produced by the ECMWF seasonal hydrological forecasting system, using the CRPSS calculated against the observed climatology. The results are shown for 74 regions across Europe, as these regions are the same as the ones used for the seasonal outlook in EFAS. The maps were made for each forecast initialisation date (on the first of each month; each row) and for seven months of lead time. However, only the first three months of lead time (each column) are shown here as the impact of initialisation tends to disappear for lead times exceeding 3 months for most regions in Europe.

From the maps one can see that on average, for the first month of lead time, improving the IHC would lead to a higher discharge forecasting skill. As lead time increases, the relative importance of IHC to SCF decreases and improving the SCF becomes more important to improve the discharge forecasting skill. There are however temporal and spatial variabilities.

For the first month of lead time, the density of regions for which the IHC are relatively more important than the SCF is higher for forecasts starting from May to July, with the largest density in June. This is probably because from May to July, rainfall is low in most parts of Europe, leading to groundwater dominated discharges for most European basins.

For most regions in Scandinavia, the IHC appear to dominate the uncertainty for forecasts started in the winter, with a signal that persists until three months of lead time (and further, not shown). This is maybe due to precipitation falling as snow during those months in these regions, leading to a more groundwater based discharge. Furthermore, a good knowledge of the antecedent snow content will lead to a high skill in spring, when discharge is snowmelt driven in those regions. This is however not the case for windward Scandinavia, where the discharge is mostly sensitive to the SCF. This could be due to weather systems raining out on Scandinavia's western part, leading to a rainfall dominated discharge.

Moreover, the ground memory is very low in this part of Scandinavia.

Over the Iberian Peninsula, the IHC dominate the uncertainty for forecast generated in summer (June to September), a signal which persists until three months of lead time. The reason for this pattern is the very dry climate over the Iberian Peninsula during the summer months, leading to a mainly groundwater dominated discharge with long memory over several months.

In central Europe, the eastern side appears to be more IHC dependent for the first month of lead time than the western part. This is probably mostly due to weather systems raining out on central Europe's west coast. The IHC importance in Eastern central Europe could also be due to snowmelt drive discharge in spring. Overall, one can see that the skill elasticities obtained for both the EFAS regions and the BfG stations from the two different forecasts are very similar. There are however slight differences, such as the larger relative importance of the IHC for the forecasts made in the spring for the EFAS region of Figure 37, compared to the corresponding station (The Inn at Passau Ingling). These differences between the skill elasticities for the EFAS regions and the BfG stations could be due to differences between the two systems for which the sensitivity analysis was performed as well as the scale (regional or at a station) at which the analysis was made.

Comparative analysis 4.3

For spatial interpretation of hydrological skill, we investigated potential relationships between predictive skill and physiographic-hydrological-climatic characteristics; hence allowing to identify the key controls of poor/good model skill. First the 15 descriptors (see Table 3) were analysed for inter-dependence, and one of the highly inter-dependent descriptors was omitted to avoid potential artefacts in the CART regression analysis.

Consequently a set of nine significant descriptors was statistically identified for application in the CART analysis, which further allowed us to estimate the descriptors' importance.

Figure 43 shows the ranking of nine descriptors (ranked by importance, with number 1 being the most important descriptor) for all months and lead months. Results show that the dominant descriptors resulting in poor/good model performance are the FlowID (describing the hydrological behaviour of the basin), elevation and remaining bias in temperature (BiasTemp). It is generally expected that remaining biases in temperature will have an impact on the form of precipitation (rainfall or snowfall) during the cold months, and the processes (i.e. changing from (to) snow accumulation to (from) melting. For example, this occurs in northern Europe for April where the mean average temperatures for April is close to 0 o C and hence small deviations in the meteorological forecasts will affect the basin response. Elevation (Elev.) is also an important factor. It is expected that the meteorological forecasts are reliable in predicting the climatological variability in highly elevated basins, which are usually snow dominated. Consequently the hydrological regime can be better described in comparison to a rain-fed basin. The basin's hydrological behaviour (FlowID) seems to be the most important descriptor with basins of similar river flow properties achieving similar skill.

It is known that river systems experience processes with high memory in comparison to the natural phenomena occurring in the atmosphere. Hence it is expected that hydrological variables (i.e. discharge, runoff, soil moisture) can have higher predictability than meteorological variables (i.e. precipitation, temperature). However, this cannot be linearly translated since the precipitation-discharge process is also not linear, and therefore different systems are expected to respond differently to the meteorological signal. To get a better understanding of the basin characteristics that are characterised by a good/poor skill, Figure 44 shows the 11 spatially variable clusters, their distribution of flow signatures, and the distribution of skill in each cluster group. Similarity in catchment behaviour for each class was interpreted and dominant flow generating processes could be distinguished.

Results give a clear separation between basins with poor and good skill. Basins in cluster 5 achieve the highest skill. These basins are characterised by high ranges of baseflow (BFI), low monthly variability (intra-annual variability) (DPar), and high values of low and medium flows (q95 and q70). These are properties of basins where short-memory precipitation is aggregated and converted into long-memory discharge. Similar behaviour have the basins in clusters 6, 7 and 9, however not to the distinct level of basins in cluster 5. Basins in cluster 8 and 10 are short-memory rivers characterised by flashy response and high seasonal variability (DPar and CV). These basins are responding quite fast to the precipitation signal and with strong dynamics (RLD) whilst contribution from base flow is small (BFI). Basins that belong to clusters 1, 2 and 3 perform adequately and are generally characterised by the same flow signatures. These basins are mainly located in the Scandinavian region and also in the central Europe at highly elevated regions. They are distinct for their medium to high slope in their flow distribution (mFDC), which is an indicator of a regime driven by snowmelt.

Lessons learnt 5

The intercomparison of seasonal discharge quality from forecasting systems from the ECMWF, the SMHI, the BfG, the FW and the UPV done in a first part of this deliverable is a starting point for this larger task within the IMPREX project. Through this intercomparison, multiple scores of forecast quality were added to the scoreboard developed in WP4 (deliverable 4.1) by the partners of this deliverable for stations within each system's spatial boundaries.

Although the sample of stations for which the intercomparison was made was limited, this task has already revealed several major differences between the seasonal hydrological forecasting systems and their impacts on the relevant water sectors. The BfG forecasting system overall mostly underestimates the observed discharge for stations shared for the Central European Rivers. This could be problematic for the navigation sector, who is most vulnerable to low flows in summer. These forecasts could potentially lead to an underestimation of the expected river flow in summer and consequently to an underestimation of the capacity of the river and a monetary loss. The ECMWF forecasting system appears to almost systematically overestimate the spring flow and underestimate the winter flow for the stations shared for Central European Rivers and for the Thames River Basin. The underestimation of the winter discharge could be a problem for the flood protection sector, as it would not flag regions to watch for potential floods in the coming months. For the Segura and Tagus River Basins, the ECMWF forecasts are very accurate in summer. This could be highly beneficial for the agriculture sector in this region, which relies on accurate drought forecasts for the summer. However, the summer flow is highly biased (as well as the winter flow) in the Jucar River Basin, also in Spain. In Sweden, the ECMWF forecasts underestimate the May flow and overestimate the summer river flow. This could be problematic for the hydropower industry, for which there is a particular interest in forecasting the spring flow accurately. The SMHI forecasting system overestimates the flow in winter and spring for the Central European Rivers and the Thames River Basin. This could be a problem for flood forecasting as it could indicate potential floods in the coming months when none actually occurs. The SMHI is also overall less accurate for the lower tercile of observed discharge for summer for a few Central European River stations. This could be a limitation for the navigation sector as the forecasts would not be able to capture accurately a low extreme event in the summer.

The May and summer flow in Sweden appears underestimated by the SMHI forecasts, while the June discharge is overestimated. This could be an issue for the hydropower sector, which needs accurate forecasts especially in spring. The FW forecasting system overestimates largely the early spring-spring flow for the Tagus River Basin and underestimates slightly the flow during all year for the Segura River Basin.

Both biases could be challenging for the agricultural sector.

The UPV forecasts are overall greatly improved by using the EVALHID hydrological model compared to the E-HYPE model, especially in summer. There are however still some biases which need to be overcome before the forecasts can be used operationally for reservoir management purposes in the Jucar River Basin.

For most stations, after one to two months of lead time, using the observed flow climatology leads to more accurate and sharp forecasts than using seasonal hydrological forecasts. This shows that there are still model and meteorological forecast biases which need to be overcome in order to gain a real valuable additional from using seasonal hydrological forecasts operationally for many applications of the water sector.

These results are a starting point, to which it will be possible to add more results along the course of the project. Indeed, as the project proceeds, anyone will be able to upload additional scores, scores for different stations or from a new or modified system to the scoreboard. The latter will enable us to monitor and visualise progresses made throughout the IMPREX project, such as improvements in the seasonal discharge forecast quality as a result of improving the seasonal meteorological forecast quality. Towards this goal, it is in our plans to expand on the work done in this deliverable by adding seasonal discharge forecasting scores from systems using different seasonal meteorological forecasts, such as GloSea5.

The intercomparison results are useful for the multi-modelling of task 4.3 of WP4. The multimodelling approach could for example use weights for each forecasting system based on the forecasting systems' performances for a certain location, type of event, time of the year.

Beyond this deliverable, the results of the intercomparison are valuable for the risk outlook, a deliverable of WP14 within IMPREX. The risk outlook will provide an overview of the hydrological 'risk' for Europe and it will also showcase examples of making hydrological information relevant at a local scale, focusing on selected IMPREX case studies. It is currently under development, so it is not yet fully known what will be included within the tool, but it is likely to show current hydrological status, climatology and seasonal forecasted anomalies as well as sector-specific indicators. An improved understanding of forecasting systems' strengths and weaknesses will enable IMPREX communicate this information in a comprehensive way, by adding information which will help the users know with what level of confidence each forecast can be used.

The EPB sensitivity analysis enabled to highlight which component of the forecast system should be improved in order to improve the seasonal discharge forecasting skill for all forecast initialisation dates and lead time for regions in Europe and individual Central European River stations. These results should be used as an indication of where to concentrate resources in order to obtain the largest improvements in the seasonal discharge forecasting skill. Where the analysis indicated the IHC to be the largest contributors to the errors in seasonal discharge forecasts, data assimilation methods could be used. Where the SCF were highlighted to be the largest contributors to seasonal discharge errors, the SCF used to force the hydrological models should be improved.

Finally, from the comparative analysis of the hydrological skill we spotted the strengths and weaknesses of ensemble seasonal forecasts from ECMWF System 4. We identified links between forecasting skill and different physiographic and hydro-climatic characteristics.

CART showed that skill is dependent on the basin's hydrological regime. Elevation and remaining bias in temperature were also identified to be important aspects (dependence of response at mountainous basins to temperature). The skill seems to be limited for relatively flashy basins experiencing strong flow dynamics over the year (less memory in the system). A number of evaluations and applications are documented at the website.

6.

List of selected references seasonal function (as it is often the case with hydropower) for water levels above the threshold. A rating curve for the spillways can be used when the reservoir is full.

Irrigation

Irrigation is simulated based on crop water demands calculated either with the FAO-56 crop coefficient method (Allen et al., 1998) or relative to a reference flooding level for sub-merged crops (e.g. rice).

The demands are withdrawn from rivers, lakes, reservoirs, and/or groundwater within and/or external to the sub-basin where the demands originated and are constrained by the water available at these sources. After subtraction of conveyance losses, the withdrawn water is applied as additional infiltration to the irrigated soils. The agriculture and irrigation data sets (see Table 1 Different process descriptions could be selected to model snow processes and evaporation. Here the configuration used in this study is described.

Snow Routine:

Precipitation is divided into rainfall and snowfall using a threshold temperature. On days with temperatures below the threshold, precipitation is supposed to be snow. The consideration of a transition from rain to snow over a temperature interval is possible. Based on a degree-day approach snow melt is computed. Water retention, snow compaction, meltwater outflow is calculated after the snow compaction approach of Bertle. Snow processes could be simulated separately for different elevation zones in the subarea.

Soil Routine:

The

Runoff Generation Routine:

Runoff concentration from direct runoff storage, interflow storage and groundwater storage of the subareas are calculated by a single linear storage model. The combination of the outflows of these storages results in the total outflow of the subarea.

Routing Procedure:

The translation and the retention in the channel are calculated in dependency of the channel geometry and the friction of the channel.

Lake and Reservoir:

Storage effects including operation of dams, lakes and reservoirs can be simulated using different approaches depending on the available data. conditions)

The LISFLOOD model has been developed for European catchments. It was designed to make the best possible use of several existing databases that contain pan-European information on soils (King et al., 1997;Wösten et al., 1999), land cover (CEC, 1993), topography (Hiederer & de Roo, 2003) and meteorology (Rijks et al., 1998). conditions)

Model description

The W3RA model has been applied on global scale The exported time-step depends on the user, i.e. hourly, daily.

The variables (states and fluxes) can be exported for the whole grid or selected gauge locations . The exported time-step depends on the user, i.e. hourly, daily.

The variables (states and fluxes) can be exported for the whole grid or selected gauge locations .

Examples of previous model applications

Catchments, objectives, etc.

Results of existing comparisons with other models

Many (e.g. Rhine, Meuse etc)

List of selected references

D4. 2 :

 2 The sensitivity of subseasonal to seasonal streamflow forecasts to meteorological forcing quality modelled hydrology andTable of Contents Executive summary .....................................................................................................................................Glossary .......................................................................................................................................................... 1 Introduction ........................................................................................................................................ 2 Sub-seasonal to seasonal streamflow forecasting: background, applications and limitations ...................................................................................................................................................... 4.3 Comparative analysis ............................................................................................................. 5 Lessons learnt .................................................................................................................................... 6 References ........................................................................................................................................... 7 Annex A -Tabulated overview on hydrological model features ...............................

Figure 1

 1 Figure 1 Example display of the operational water management system for the Netherlands, showing a daily computed or forecasted water balance for the surface water network for the Netherlands.

  seasonal forecast runs from Deltares were not ready at the time of this deliverable, results of discharge simulations from the two models shown above for Deltares, as well as discharge simulations produced from the HBV96 model will be shown in the results. The W3RA was run with EFAS historical forcing data from 1991-2014, while the lumped HBV96 and distributed wflow_hbv models were

Figure 2

 2 Figure 2 Map of the stations used for the analysis.

  below), a new 100-member hindcast is generated by a weighted averaging of the forecasts carried out for the four end points. The percentage of each end point used, EP [%] (i.e., the number of members randomly selected from each end point), is given for each combination point by the following equation:

[

Figure 3

 3 Figure 3 Resampling surface for the EPB sensitivity analysis method (taken from Arnal et al. 2017).

For

  stations of the Central European Rivers case study, scores were calculated from the SMHI, the BfG and the ECMWF forecasting systems. From this set of stations, there appears to be two types of forecast performance behaviours. For the most western Central European Rivers stations included in this deliverable (the Main at Raunheim, the Rhine at Koeln, Kaub and Maxau and the Mosel at Trier UP), all forecasts show similar CRPS values, with larger errors from November-April. Figure4is an example of the CRPS for the three systems for the Rhine at Koeln.For the most eastern Central European Rivers stations included in this deliverable (the Elbe at Neu Darchau and Dresden and the Saale at Calbe Grizehne), the SMHI forecasts display larger CRPS values than the two other systems, especially from December-April. Figure5is an example of the CRPS for the three systems for the Elbe at Dresden.In general however, the BfG and the ECMWF seasonal discharge forecasts have a lower CRPS than the SMHI forecasts for the first forecast month.

Figure 4

 4 Figure 4 Continuous Ranked Probability Score (CRPS) for the Rhine at Koeln for (top) the SMHI forecasts, (middle) the ECMWF forecasts and (bottom) the BfG forecasts. CRPS = 0 denotes a perfect forecast. The CRPS is given for each forecast initialisation date (on the first of each month, different colours) and for 6 months of lead time (for the SMHI and the BfG forecasts) or 7 months of lead time (for the ECMWF forecasts).

Figure 5

 5 Figure 5 Same as Figure 4 but for the Elbe at Dresden.

Figure 6

 6 Figure 6 Mean Absolute Error (MAE) for the Elbe at Dresden (at lead time 0) for the W3RA model for the period 1991-2014.

Figure 7

 7 Figure 7 MAE for the Rhine at Lobith (at lead time 0) for the W3RA (1991-2014), the lumped HBV96 and the distributed wflow_hbv models (both for the period 1991-2006).

Figure 8

 8 Figure 8 Mean Error (ME) for the Rhine at Koeln for (top) the SMHI forecasts, (middle) the ECMWF forecasts and (bottom) the BfG forecasts. ME = 0 denotes no bias, while ME > 0 denotes a positive forecast bias and the ME < 0 a negative forecast bias. The ME is given for each forecast initialisation date (on the first of each month, different

Figure 9

 9 Figure 9 Same as Figure 8 but for the Elbe at Dresden.

Figure 10

 10 Figure10shows the bias (i.e., the ME) for the W3RA model simulation for the Elbe at Dresden. From this figure, it can be seen that the W3RA underestimates the discharge for all months of the year, especially in April.

Figure 10

 10 Figure 10 ME for the Elbe at Dresden (at lead time 0) for the W3RA model for the period 1991-2014.

Figure 11

 11 Figure 11 displays the Mean Error (ME) for the Rhine at Lobith for the W3RA, the lumped HBV96 and the distributed wflow_HBV models. For this station, the W3RA model largely overestimates the discharge for all months. The wflow_hbv model overestimates the observed discharge mostly in the summer, while it underestimates it slightly in November and January-February. The HBV96 model underestimates the observed discharge for October-November and overestimates it for the rest of the year.

Figure 11

 11 Figure 11 ME for the Rhine at Lobith (at lead time 0) for the W3RA (1991-2014), the lumped HBV96 and the distributed wflow_hbv models (both for the period 1991-2006).

Figure 12

 12 Figure 12 CRPS reliability for the Rhine at Koeln for (top) the SMHI forecasts, (middle) the ECMWF forecasts and (bottom) the BfG forecasts. CRPS reliability = 0 denotes a perfect forecast reliability. The CRPS reliability is given for each forecast initialisation date (on the first of each month, different colours) and for 6 months of lead time (for the SMHI and the BfG forecasts) or 7 months of lead time (for the ECMWF forecasts).

Figure 13

 13 Figure 13 Same as Figure 12 but for the Elbe at Dresden.

Figure 14

 14 Figure 14 Continuous Ranked Probability Skill Score (CRPSS) of the seasonal discharge forecast against the observed discharge climatology for the Rhine at Koeln for (top) the SMHI forecasts, (middle) the ECMWF forecasts and (bottom) the BfG forecasts. CRPSS = 1 denotes a perfect forecast skill. The CRPSS is given for each forecast initialisation date (on the first of each month, different colours) and for 6 months of lead time (for the SMHI and the BfG forecasts) or 7 months of lead time (for the ECMWF forecasts).

Figure 15

 15 Figure 15 Same as Figure 14 but for the Elbe at Dresden.

Figure 16

 16 Figure 16 is an example of such a behaviour and shows the CRPS for the Thames at Royal Windsor Park for both systems.

Figure 16

 16 Figure 16 Same as Figure 4 but for the SMHI and the ECMWF forecasts only for the Thames at Royal Windsor Park.

Figure 17

 17 Figure 17 Same as Figure 4 but for the SMHI and the ECMWF forecasts only for the Pang at Panbourne.

Figure 18

 18 Figure 18 Same as Figure 8 but for the SMHI and the ECMWF forecasts only for the Thames at Royal Windsor Park.

Figure 19

 19 Figure 19 Same as Figure 8 but for the SMHI and the ECMWF forecasts only for the Pang at Panbourne.

Figure 20

 20 Figure 20 Same as Figure 12 but for the SMHI and the ECMWF forecasts only for the Thames at Royal Windsor Park.

Figure 21

 21 Figure 21 Same as Figure 14 but for the SMHI and the ECMWF forecasts only for the Thames at Royal Windsor Park.

Figure 22

 22 Figure 22 CRPS for the Tagus at Entrepenas for (top) the SMHI forecasts, (middle) the ECMWF forecasts and (bottom) the FW forecasts. The CRPS is given for each forecast initialisation date (on the first of each month for the SMHI and ECMWF forecasts and on the first of January, April, July and October for the FW forecasts; different colours) and for 6 months of lead time (for the SMHI forecasts), 7 months of lead time (for the ECMWF forecasts) or 3 months of lead time (for the FW forecasts).

Figure 23

 23 Figure 23 ME for the Tagus at Entrepenas for (top) the SMHI forecasts, (middle) the ECMWF forecasts and (bottom) the FW forecasts. The ME is given for each forecast initialisation date (on the first of each month for the SMHI and ECMWF forecasts and on the first of January, April, July and October for the FW forecasts; different colours) and for 6 months of lead time (for the SMHI forecasts), 7 months of lead time (for the ECMWF forecasts) or 3 months of lead time (for the FW forecasts).

Figure 24

 24 Figure 24 Same as Figure 23 but for the Segura at Cenajo.

Figure 25

 25 Figure 25 CRPS reliability for the Tagus at Entrepenas for (top) the SMHI forecasts, (middle) the ECMWF forecasts and (bottom) the FW forecasts. The CRPS reliability is given for each forecast initialisation date (on the first of each month for the SMHI and ECMWF forecasts and on the first of January, April, July and October for the FW forecasts; different colours) and for 6 months of lead time (for the SMHI forecasts), 7 months of lead time (for the ECMWF forecasts) or 3 months of lead time (for the FW forecasts).

Figure 26

 26 Figure 26 CRPSS of the seasonal discharge forecast against the observed discharge climatology for the Tagus at Entrepenas for (top) the SMHI forecasts, (middle) the ECMWF forecasts and (bottom) the FW forecasts. The CRPSS is given for each forecast initialisation date (on the first of each month for the SMHI and ECMWF forecasts and on the first of January, April, July and October for the FW forecasts; different colours) and for 6 months of lead time (for the SMHI forecasts), 7 months of lead time (for the ECMWF forecasts) or 3 months of lead time (for the FW forecasts).

Flows

  in the summer produced by the EVALHID model are much closer to the historical values for all stations.

Figure 27

 27 Figure 27 Comparison between streamflows from SMHI Pan-European data (E-HYPE), historical data as naturalized river flows and data from our hydrological model (EVALHID) in the five main sub-basins of Júcar River Basin: Alarcón, Contreras, Molinar, Tous and Sueca.

Figure 29

 29 Figure 29 displays the CRPS for the ECMWF forecasts for the Jucar at Alarcon and at Tous. Results for these stations show a different behaviour of the ECMWF forecasts. For the Jucar at Alarcon, the largest errors are observed for the winter, similarly to what was observed for the UPV forecasts. For the Jucar at Tous however, the largest errors are situated in the summer.

Figure 28

 28 Figure 28 CRPS for the Jucar River at Alarcon, Contreras, Molinar, Tous and Sueca for the UPV forecasts. The CRPS is given for each forecast initialisation date (on the first of each month, different colours) and for 7 months of lead time.

Figure 29

 29 Figure 29 CRPS for (top) the Jucar at Alarcon and (bottom) the Jucar at Tous for the ECMWF forecasts. The CRPS is given for each forecast initialisation date (on the first of each month, different colours) and for 7 months of lead time.

Figure 30

 30 Figure 30 ME for the Jucar River at Alarcon, Contreras, Molinar, Tous and Sueca for the UPV forecasts. The ME is given for each forecast initialisation date (on the first of each month, different colours) and for 7 months of lead time.

Figure 31

 31 Figure 31 ME for the Jucar River at Alarcon and Tous for the ECMWF forecasts. The ME is given for each forecast initialisation date (on the first of each month, different colours) and for 7 months of lead time.

Figure 32

 32 Figure 32 Same as Figure 16 but for the Vindelaelven at Granaker.

Figure 33

 33 Figure 33 displays the bias (i.e., the ME) for the Vindelaelven at Granaker for both systems. On this figure, the SMHI forecasts appear to underestimate both May and July-September observed discharges, while they overestimate the June observed discharge. The ECMWF forecasts underestimate the May observed discharge and overestimate largely the June-August observed discharge. This last characteristic of the ECMWF forecasts is however only seen for forecasts made in May or earlier. For forecasts made in June or after, the June-August observed discharge is underestimated.

Figure 33

 33 Figure 33 Same as Figure 18 but for the Vindelaelven at Granaker.

Figure 34

 34 Figure 34 Same as Figure 20 but for the Vindelaelven at Granaker.

Figure 35

 35 Figure 35 Same as Figure 21 but for the Vindelaelven at Granaker.

Figure 36

 36 Figure 36 Maps of the dominant predictability source for each forecast initialisation date and the first three months of lead time for the EFAS regions across Europe. Blue [green] colours signify that the SCF [IHC] form the dominant source of predictability.

Figures

  Figures 37 to 42 display the skill elasticities obtained for several EFAS regions (from the ECMWF forecasts) and for the BfG stations falling in each of those regions (from the BfG forecasts). Results are shown for all forecast initialisation dates and for the first and second months of lead time. The seasonal discharge forecasting skill elasticity to SCF (E SCF ; in blue) and the seasonal discharge forecasting skill elasticity to IHC (E IHC ; in green) indicate the potential to improve the seasonal discharge forecasting skill as a result of improving the quality of those respective predictability sources. When one skill elasticity is larger than the other, it implies that this predictability source has the largest potential to improve the seasonal discharge forecasting skill (for that specific station or region, lead time and forecasting initialisation date) once improved. These figures allow a comparison of the sensitivities of the ECMWF and the BfG seasonal discharge forecasts to the IHC and the SCF.

Figure 37

 37 Figure 37 Skill elasticities for (left) the first and (right) the second month of lead time, for (top) the EFAS region and (bottom) the corresponding BfG station for the Inn at Passau Ingling. Skill elasticities are shown for each forecast initialisation month.

Figure 38

 38 Figure 38 Skill elasticities for (left) the first and (right) the second month of lead time, for (top) the EFAS region and (bottom) the corresponding BfG station for the Danube at Hofkirchen. Skill elasticities are shown for each forecast initialisation month.

Figure 39

 39 Figure 39 Skill elasticities for (left) the first and (right) the second month of lead time, for (top) the EFAS region and (bottom) the corresponding BfG stations for the Rhine at Kaub and the Mosel at Trier UP. Skill elasticities are shown for each forecast initialisation month.

Figure 40

 40 Figure 40 Skill elasticities for (left) the first and (right) the second month of lead time, for (top) the EFAS region and (bottom) the corresponding BfG stations for the Rhine at Maxau and the Main at Raunheim. Skill elasticities are shown for each forecast initialisation month.

Figure 41

 41 Figure 41 Skill elasticities for (left) the first and (right) the second month of lead time, for (top) the EFAS region and (bottom) the corresponding BfG station for the Rhine at Koeln. Skill elasticities are shown for each forecast initialisation month.

Figure 42

 42 Figure 42 Skill elasticities for (left) the first and (right) the second month of lead time, for (top) the EFAS region and (bottom) the corresponding BfG stations for the Saale at Calbe Grizehne, the Elbe at Dresden and the Elbe at Neu Darchau. Skill elasticities are shown for each forecast initialisation month.

Figure 43

 43 Figure 43 Importance ranking of key descriptors that influence the hydrological forecasting skill over Europe for all months and in lead month: (a) 0, and (b) 2.

Figure 44

 44 Figure 44 (a) Spatial distribution of hydrologically similar (clusters) basins over Europe, (b) distribution of flow signatures in each cluster group (see also Table 3), and (c) distribution of beta skill in each cluster group

  Scheme of model structure

  often run at a daily time step and simulates the water flow paths in soil for hydrological response units (HRUs), which are defined by gridded soil and land-use classes and can be divided in up to three layers with a fluctuating groundwater table. The HRUs are further aggregated into subbasins based on topography. Elevation is also used to get temperature variations within a sub-basin to influence the snowmelt and storage as well as evapotranspiration. Glaciers have a variable surface and volume, while lakes are defined as classes with specified areas and variable volume. Lakes receive runoff from the local catchment and, if located in the subbasin outlet, also the river flow from upstream subbasins. On glaciers and lakes, precipitation falls directly on the surface and water evaporates at the potential rate. Each lake has a defined depth below an outflow threshold. The outflow from lakes is determined by a general rating curve unless a specific one is given or if the lake is regulated. Regulated lakes and manmade reservoirs are treated equally but a simple regulation rule can be used, in which the outflow is constant or follows a

  Scheme of model structure

  of the model are routines for interception, evapotranspiration, snow accumulation, compaction and melt, soil water retention, storage and lateral water transport, as well as flood-routing in channels and retention in lakes. Spatial units are grid-based subareas or subareas according to hydrologic subcatchments. Hydrological processes are modelled for each single land use category or alternatively for each land use soil type category in a subarea (Hydrological response unit HRU). HRUs can be further subdivided in elevation zones for snow simulation.

  routine mainly controls runoff formation. To simulate the soil storage the Xinanjiang-model is used. Soil water content is calculated by the water balance equation, taking into account as runoff formation. In the configuration applied here three runoff components are considered: runoff formation on saturated areas towards direct runoff storage, water release from soil storage through lateral drainage towards interflow storage and water release through vertical percolation towards groundwater storage. Saturated areas which control the direct runoff are derived from the soil water storage via the soil-moisture-saturated areas function. Actual evapotranspiration is computed from potential evapotranspiration as a function of soil moisture.

•

  considered process descriptions for potential evapotranspiration and modelling of snow processes different input data sets are required. In the configuration used here daily precipitation, temperature and global radiation are required as variables (Ludwig & Bremicker 2006) e.g. total computed outflow, actual evaporation, soil moisture,… Depending on the variable output is available for subareas, HRUs, combination of several connected subareas, and defined output nodes (e.g. gauges) , climate change analysis, water balance, water temperature. Applications and publications are documented at the website www.larsim.info. Mesoscale application for the River Rhine (Ebel et al. 2000). Bremicker, M., M. C. Casper & I. Haag (2011): Extrapolationsfähigkeit des Wasserhaushaltsmodells LARSIM auf extreme Abflüsse am Beispiel der Schwarzen Pockau. KW Korrespondenz Wasserwirtschaft 4(8), 445-451 • Demuth, N. & S. Rademacher (2016): Chapter 5 -Flood Forecasting in Germany -Challenges of a Federal Structure and Transboundary Cooperation A2 -Adams, Thomas E. In: T. C. Pagano (Ed.): Flood Forecasting. Academic Press, Boston, 125-151 • Ebel, M., K. Ludwig & K. G. Richter (2000): Mesoskalige Modellierung des Wasserhaushaltes im Rheineinzugsgebiet mit LARSIM. Hydrologie und Wasserbewirtschaftung 6, 308-312 • Haag, I. & A. Luce (2008): The integrated water balance and water temperature model LARSIM-WT. Hydrological Processes 22(7), 1046-1056 • Ludwig, K. & M. Bremicker (2006): The Water Balance Model LARSIM -Design, Content and Applications. 22. C. Leibundgut, S. Demuth and J. Lange (Eds), Freiburger Schriften zur Hydrologie, Institut für Hydrologie, Universität Freiburg im Breisgau, Freiburg, 141 pp.

  /ec.europa.eu/jrc/en/publication/eur-scientific-andtechnical-research-reports/lisflood-distributed-water-balance-andflood-simulation-model-revised-user-manual-2013 General modelling objectives To produce a tool that can be used in large and trans-national catchments for a variety of applications, including: • Flood forecasting • Assessing the effects of river regulation measures • Assessing the effects of land-use change • Assessing the effects of climate

For•

  Scheme of model structure

  the sub-basin scale. Sub-basin resolution depends on the application. In Europe, this is variables are climate forcing data such as precipitation, temperature and potential evaporation. Other observations can also be used for different purposes.

6 .

 6 ., Dutra, E., Martínez-de la Torre, A., Balsamo, G.,van Dijk, A., Sperna Weiland, F., Minvielle, M., Calvet, J.-C., Decharme, B., Eisner, S., Fink, G., Flörke, M., Peßenteiner, S., van Beek, R., Polcher, J., Beck, H., Orth, R., Calton, B., Burke, S., Dorigo, W., and Weedon, G. P.: A global water resources ensemble of hydrological models: the eartH2Observe Tier-1 dataset, Earth Syst. Sci. Data Discuss., doi:10.5194/essd-2016-55, in review, 2016. List of selected references Van Dijk, Pea-Arancibia, J. L., Wood, E. F., Sheffield, J., & Beck, H. E. (2013). Global analysis of seasonal streamflow predictability using an ensemble prediction system and observations from 6192 small catchments worldwide. Water Resources Research, 49(5), 2729-2746. http://doi.org/10.1002/wrcr.20251 Table 9 Tabulated overview on hydrological model features of wflow_hbv spatially distributed, grid size determined by end user (grid size for Rhine 1.44 km 2 ) in 1972 by the Swedisch Meteological and Hydrological Institute (SMHI). The HBV model is mainly used for runoff simulation and hydrological forecasting. The model is particularly useful for catchments where snow fall and snow melt are dominant factors, but application of the model is by no means restricted to these type of catchments. The wflow_hbv model is based on the HBV-96 model. However, the hydrological routing represent in HBV by a triangular function controlled by the MAXBAS parameter has been removed. Instead, the kinematic wave function is used to route the water downstream. All runoff that is generated in a cell in one of the HBV reservoirs is added to the kinematic wave reservoir at the end of a timestep. There is no connection between the different HBV cells within the model. Wherever possible all functions that describe the distribution of parameters within a subbasin have been removed as this is not needed in a distributed application/ A catchment is divided into a number of grid cells. For each of the cells individually, daily runoff is computed through application of the HBV-96 of the HBV model. The use of the grid cells offers the possibility to turn the HBV modelling concept, which is originally lumped, variables are climate forcing data such as precipitation, temperature and potential evaporation. For the daily model the other observations can also be used for different purposes (for instance DA using OpenDA).

Table 1

 1 Dynamical ensemble seasonal hydrological forecasting systems.

Table 2

 2 Observed discharge data for the selected stations.

	Case study	Data source	Station code	Station name	Station	Drainage area	Elevation	River
					coordinates (lat,	(km2)	(m)	(country)
					lon)
	The Thames	Observed	UOR39088	Rickmansworth	51.64199826, -	105	47.1	Chess (UK)
	River Basin	discharge from			0.461235789
		the NRFA	UOR39072	Royal Windsor Park	51.48562525, -	7046	13.5	Thames (UK)
		(National River			0.589407615
		Flow Archive)
			UOR39068	Castle Mill	51.23842724, -	316	39.2	Mole (UK)
					0.31118153
			UOR39034	Cassington Mill	51.786274, -	430	60.2	Evenlode
					1.351677333			(UK)
			UOR39027	Pangbourne	51.48462376, -	170.9	39.6	Pang (UK)
					1.08759943
			UOR39021	Enslow Mill	51.86135121, -	551.7	65	Cherwell (UK)
					1.301356378
			UOR39016	Theale	51.43274011, -	1033.4	43.4	Kennet (UK)

Table 3

 3 Basin characteristics used in the clustering analysis.

Table 4

 4 Tabulated overview on hydrological model features of SPHY

	1. General Information
	Model name	SPHY (Spatial Processes in HYdrology)
	Version		V2.1
	Author(s) / First	Terink et al. (2015a)
	publication
	Contact person Wilco Terink (w.terink@futurewater.nl)
	Institute		FutureWater
	Website		http://www.sphy.nl/
	General		Calculation of river basins water balance
	modelling
	objectives
	Domain	of	The SPHY model has been applied and tested in various studies
	applicability	ranging from real-time soil moisture predictions in flat lands, to
	(catchment types	operational	reservoir inflow	forecasting applications	in
	and	climate	mountainous catchments, irrigation scenarios in the Nile Basin,
	conditions)	and detailed climate change impact studies in the snow-and
			glacier-melt dominated the Himalayan region.

•

  Terink, W., S. Khanal. 2016. SPHY: Spatial Processes in Hy-

	drology. Advanced training: input data, sensitivity analysis,
	model	calibration, and scenario analyses. Fu-
	tureWater Report 159.
	• Terink, W., A.F. Lutz, G.W.H. Simons, W.W. Immerzeel, P.
	Droogers. 2015a. SPHY v2.0: Spatial Processes in HYdrolo-
	gy. Geoscientific Model Development, 8, 2009-2034,
	doi:10.5194/gmd-8-2009-201
	• Terink, W., A.F. Lutz, W.W. Immerzeel. 2015b. SPHY v2.0:
	Spatial Processes in HYdrology. Model theory, installation,
	and data preparation. FutureWater report 142.
	• Terink, W., A.F. Lutz, W.W. Immerzeel. 2015c. SPHY: Spatial
	Processes in Hydrology. Graphical User-Interfaces (GUIs).
	FutureWater report 144.
	• Terink, W., A.F. Lutz, G.W.H. Simons, W.W. Immerzeel.
	2015d. SPHY: Spatial Processes in Hydrology. Case-studies
	for training. FutureWater report 143.

Table 5

 5 Tabulated overview on hydrological model features of HYPE

Table 6

 6 Tabulated overview on hydrological model features of LARSIM

	1. General Information
	Model name	LARSIM (Large Area Runoff Simulation Model)
	Version		LARSIM Revision 968 (neue Formate)
	Author(s) / First	Ludwig & Bremicker (2006)
	publication
	Contact person LARSIM development community http://www.larsim.info
	Institute		LARSIM development community http://www.larsim.info
	Website		http://www.larsim.info
	General		Continuous simulation of runoff processes in catchments and river
	modelling		networks
	objectives
	Domain	of	Largely applied by forecasting centers in Germany, Austria,
	applicability	Luxembourg, Switzerland and the French regions of Alsace and
	(catchment types	Lorraine, Central Europe
	and	climate
	conditions)

Table 7

 7 Tabulated overview on hydrological model features of LISFLOOD

Table 8

 8 Tabulated overview on hydrological model features of wflow_w3ra

	1. General Information
	Model name		wflow_w3ra (World Wide Water Resources Analysis)
			+ wflow_routing
	Version		v1
	Author(s) / First	Van Dijk et al. (2013)
	publication
	Contact	person Albrecht Weerts (albrecht.weerts@deltares.nl)
			Jaap Schellekens (jaap.schellekens@deltares.nl)
	Institute		Deltares
	Website		https://github.com/openstreams/wflow
	General modelling	Calculation/prediction of hydrological water resources
	objectives
	Domain	of
	applicability
	(catchment	types
	and	climate

Annex A -Tabulated overview on hydrological model features

-For setting-up the model data on streamflows are not necessary.

However, to undertake a proper calibration and validation procedure flow data are required. The model could also be calibrated using actual evapotranspiration, soil moisture contents, or snow coverage. conditions)

Model inputs

/ Model outputs

The HYPE model has been applied and tested in different scales, various domains and hydro-climatic conditions.

See http://hypeweb.smhi.se • Van der Knijff, J. M., Younis, J. and de Roo, A. P. J.:

Model description