Markiyan Sloboda

David Swayne
email: dswayne@uoguelph.ca

Autocalibration of Environmental Process Models using a PAC Learning Hypothesis

Keywords: SWAT, autocalibration, high performance computing, distributed computing

Using the probably approximately correct (PAC) learning hypothesis, we have conducted experiments using clustered computers, high-performance workstations and ad-hoc grids of personal computers, to develop an analytical model for, and demonstrate asymptotic convergence of simple parallel search in the parameter space of complex environmental models such as the Soil and Water Assessment Tool (SWAT). SWAT calibration for hydrological flow, N and P is, for our test cases, superior to current genetic algorithms, as well as to SWAT-CUP, a multi-paradigm calibration solver and to its components. With more complex models, there is no current alternative to our approach in a realizable wall-clock time.

Introduction

Environmental models are widely used for analyzing and predicting physical systems. Typically, models require many variables to simulate real world scenarios, thus leading to increasingly complex computations and as the result to dramatic increase in running time.

In order for the model to predict correct results, it has to be adjusted to the specific region of interest. The most common way to calibrate environmental models is a manual approach. The process is monotonous, slow and requires a lot of expertise in the modeled region as well as considerable expertise. Recently, some automatic calibration tools have been developed, such as genetic algorithms (GA) and stochastic algorithms. Most of these approaches, however, require a complex initial set up, and they typically run in sequential mode, which leads to long running times. This paper examines some aspects of autocalibration adapted to high-performance computing (HPC), using machine learning, specifically the notion of Probably Approximately Correct (PAC) learning [START_REF] Russell | Artificial Intelligence: a Modern Approach[END_REF]. Our HPC resource is available from the Shared Hierarchical Academic Research Computing NETwork, located in Ontario, Canada. Our experiments with the Soil and Water Assessment Tool (SWAT) [6] for rainfall / runoff estimation in watersheds, and with complex interconnected hydrological and pollutant transport models such as PolTra and OneLay [START_REF] Simons | Documentation of a Two-Dimensional X-Y Model Package for Computing Lake Circulations and Pollutant Transports[END_REF] have led us to the conclusion that an embarrassingly parallel search strategy is an effective way to harness the power of HPC in fitting models to existing observations. Furthermore, a naïve multiobjective fitting strategy for the combination of runoff and water chemistry in SWAT gives acceptable results so long as all of the components (runoff and chemistry) are fitted simulataneously.

"Goodness Of Fit" Measure For Hydrological Models

We use both the Coefficient of Determination (CoD or r 2 ) and the Nash-Sutcliffe Coefficient of Efficiency (NSE) given respectively by:

∑ ∑ ∑ , 1 and
1.0 ∑ ∑ , 2
where O i and P i are observed and model simulated data at time stamp i respectively and the overbar denotes the observed mean of the entire time period of the evaluation. The CoD ranges from 0 to 1 and NSE ranges from minus infinity to 1 (from poor to perfect). NSE represents an improvement over CoD since it is responsive to differences in observed and model-simulated means and variances [START_REF] Legates | Evaluating the use of Goodness of Fit measures in hydrological and hydroclimatic model validation[END_REF].

where H is the space of all possible hypotheses, and if a learning algorithm returns a hypothesis that is consistent with N examples, then with probability at least 1-δ, it has error at most ε. We typically run the PAC learning approach with δ and ε set at 5% or 1%..

With this in mind, we grid the set of tuneable parameters, and select from the set of all possible configurations a number of parameter sets at least as large as the estimate of N in equation 3. We run those simulations and store in a database for possible future use, and choose on the basis of equation 1 or 2 the best candidate or set of candidates.

The process is embarrassingly parallel and, with the aid of a high performance workstation or a cluster of computers it is faster and (in our experience) more accurate than other methods we have tried, including Shuffled Complex Evolution, and the calibration tools known as GLUE [START_REF] Beven | Equifinality, data assimilation, and uncertainty estimation in mechanistic modelling of complex environmental systems using the GLUE methodology[END_REF] and SUFI-2 [START_REF] Abbaspour | Estimation of uncertain flow and transport parameters by a sequential uncertainty fitting procedure: SUFI-2[END_REF].which form a major part of SWAT-CUP [7].

Data Used in Experiments

For calibrating SWAT model data for Raisin River watershed in Southeastern Ontario, Canada was used. Data was available from three Water Survey of Canada (WGC) stream gauging (hydrometric) stations, which are located within the Raisin watershed. Flow values from the station 02MC001 nearest to the outlet from the watershed was used.

Observed data was provided by the Ontario Ministry of Environment, Raisin River Conservation Authority and Water Survey of Canada for research purposes only.

Monthly averages for 1985-1994 were used for calibration and for 1995-2004 for validation

SWAT Manual Calibration

SWAT model actuators that were varied included: SFTMP, SMTMP, SMFMX, SMFMN, TIMP, SNOCOVMX, ESCO and SURLAG. We used monthly average values for calibration and validation. The manual calibration was conducted by colleagues at Environment Canada. The r 2 and NSE for the monthly calibration were 0.86 and 0.84, respectively. These values are greater than 0.5 and confirm reasonable model results. Validation for TN and TP produced slightly lower, but acceptable, values for NSE.

SWAT-CUP

SWAT-CUP is a freely available computer program which calibrates the SWAT model by linking it to several calibration algorithms, such as the Generalized Likelihood Uncertainty Estimation (GLUE), Sequential Uncertainty Fitting (SUFI-2) among others. It provides a user friendly interface for sensitivity analysis, calibration, validation, and uncertainty analysis of SWAT using only one approach at a time. The following results were obtained for GLUE and SUFI-2.

SWAT PAC Learning

In this Section, we show that a so-called gridded calibration, at least for this watershed, is capable of producing an equivalent answer.

The first step in gridded calibration was to create a grid of actuators, define range and step values for them. Next, for each separate calculation of the SWAT model, actuators are randomly selected from the grid. Actuators are independent of each other and their values are selected only from the grid points using the uniform distribution. After a certain number of simulations dictated by the PAC learning hypothesis, calibration sensors are sorted in increasing value of the objective function defined, and the set of actuators which satisfy the objective function the best are calibrated values.

Since, calibration evaluations for flow, TP and TN loads were calculated at the same time, it was necessary to use a rule to know if the database contains the best result, and if no other improvements can be made to it. Therefore, a simple weighting process was used. All comparison was done based on the NSE values. The weighted NSE adopted was 50% flow, 25% TN and 25% TP. Lower values for the flow CoD and NSE are obtained, but all three measures are acceptable, particularly the NSE for TN and TP. Results displayed in the Tables 5 and6 show high NSE values for flow, TP and TN loads. All these values are above 0.5 and therefore the calibration is successful. To run each SWAT simulation without any modifications using Raisin River watershed dataset, it requires about 4.5 minutes on the average desktop computer and about 30 seconds on most clusters on the SHARCNET, with our parallelized (MPI) SWAT version. Even, with such a short run time it would take a long time to generate the 10 possible values. Therefore, the space was scaled down to 2 million distinct actuator sets. The simulations corresponding to this space were generated, when processors on the SHARCNET clusters were available. It took a week, using 200 processors.

Since, the new space is the sub-space of H, there is a need to calculate a shift in the number of required simulations, which we derived as:

1 10 2 * 10 1 * 13.12 4

where N -total number of simulations required, when the whole space 10 is used -total number of simulations, when the sub space 2 * 10 is used ε -the upper error value

For the original 10 simulations, 5% error would require 613 iterations and 1% error would require 3223 simulations. The minimum number of simulations required to satisfy the PAC learning theorem for the subspace space is, for 1% error at least 1912 calls to the SWAT model and for 5% error at least 350 simulations.

Since, calibration evaluations for flow, TP and TN loads were calculated at the same time, a simple weighting process was used. All comparison was done based on the NSE values, since the NSE represents the best measure of the closeness of the simulated results to the observed data. The weighted NSE adopted was 50% flow, 25% TN and 25% TP. The highest from all three components within the 2 million records in the database was equal to 0.77.

To analyze how results are correlated to the number of simulations and to confirm that a PAC learning theorem is acceptable it was decided to run different number of simulations 1000 times each. The number of simulations being tested were from 500 to 5000 with the 500 iterations interval, i.e. 500, 1000, 1500,…, 4500, 5000. The highest from all three components within the 2 million records in the database was equal to 0.77. We approximated this experiment by building a database containing 2 million records, we did not re-run SWAT simulations, but instead actuators and corresponding results were randomly chosen from the database of 2 million simulations, using the best NSE for flow only.

The best flow-only result over all 2 million simulations (NSE = .986011) is already stored in the database. This value was taken as a benchmark to which all the other results were compared. Q1 is the lower quartile (25 th percentile), Q3 is the upper quartile (75 th percentile) and IQRange is the interquartile range. The (IQR) interquartile narrows as the sample sizes increase, and the full range of NSE values at and above 99% is significantly higher than from the other methods as calculated

In a further analysis, we derived a tighter approximation to the actual ε and δ in the PAC theorem and we developed a relationship between the δ * for each of the calculated N* values in Table 7, as a ratio of the baseline δ value for N = 315: * * , and

/ * * .
Experimentally, we find that, as N increases, the value of ε stabilizes (Figure 1). This paper has examined some aspects of autocalibration adapted to highperformance computing (HPC), using machine learning, specifically the notion of Probably Approximately Correct (PAC) learning. Our experiments with the Soil and Water Assessment Tool (SWAT) for rainfall / runoff estimation in watersheds, and with complex interconnected hydrological and pollutant transport models have led us to the conclusion that an embarrassingly parallel search strategy is an effective way to harness the power of HPC in fitting models to existing observations. Furthermore, a naïve multiobjective fitting strategy for the combination of runoff and water chemistry in SWAT gives acceptable results so long as all of the components (runoff and chemistry) are fitted simultaneously. Calibration times are reduced from days or weeks, to hours, depending on the availability of high performance computing resource (multi-core server or computer cluster such as SHARCNET), as shown in Table 8.

Figure. 1 .

 1 Figure. 1.Figure 1: Epsilon estimation

Figure 1 :

 1 Figure. 1.Figure 1: Epsilon estimation

Table 1 :

 1 GLUE calibration results for flow, TP and TN Loads.

	r 2 Flow NSE Flow r 2 TP Load NSE TP Load r 2 TN Load NSE TN Load
	0.87	0.85	0.66	-0.06	0.79	0.67

Table 2 :

 2 GLUE validation results for flow, TP and TN Loads.

	r 2 Flow NSE Flow r 2 TP Load NSE TP Load r 2 TN Load NSE TN Load
	0.81	0.81	0.61	-0.13	0.69	0.59

Table 3 :

 3 SUFI-2 calibration results for flow, TP and TN loads.

	r 2 Flow NSE Flow r 2 TP Load NSE TP Load r 2 TN Load NSE TN Load
	0.87	0.85	0.55	0.29	0.79	0.67

Table 4 :

 4 SUFI-2 validation results for flow, TP and TN loads.

	Load

r

2

Flow NSE Flow r 2 TP Load NSE TP Load r 2 TN Load NSE TN Flow results and TN load are acceptable and consistent for both calibration and validation and results are very close to manual or GLUE calibrated results. TP load values for calibration time period can be acceptable, since r 2 is greater than 0.5 but NSE = 0.29. However, validation results for TP load are poor, since NSE is negative, generally considered unacceptable.

Table 5 :

 5 Calibration of flow, TP and TN loads, 1% error (3223 simulations)

	r 2 Flow NSE Flow r 2 TP Load NSE TP Load r 2 TN Load NSE TN Load
	0.86	0.82	0.68	0.62	0.75	0.69

Table 6 :

 6 Validation results for flow, TP and TN loads with 1% error r 2 Flow NSE Flow r 2 TP Load NSE TP Load r 2 TN Load NSE TN Load

	0.87	0.85	0.58	0.27	0.69	0.61

Table 7 :

 7 Box plot statistics of NSE for percent accuracy (flow only).

	Sample size	Q1	Median	Q3	IQRange	Whiskers (from, to)
	315 (95%) 0.866952 0.883709 0.905719 0.0387668 (0.809034, 0.961108)
	500	0.873536 0.891648 0.914876 0.0413396 (0.823719, 0.963922)
	1912 (99%) 0.908964 0.924717 0.938933 0.0299687 (0.865485, 0.982025)
	4000	0.926839 0.938907 0.951072 0.0242329 (0.891142, 0.986011)
	4500	0.928043 0.941754 0.954204 0.0261607 (0.891745, 0.986011)
	5000	0.930305 0.942234 0.953604 0.0232990 (0.897061, 0.986011)

Table 8 :

 8 Comparative run-time for all tested techniques.

	Technique	Computation Run time
	GLUE	Server	15 days, 11 hours, 35 min
	SUFI-2	Server	3days 2 hours, 30 min
	Explicit gridded with 1% error Laptop	4 days 20 hours 26 min
	Explicit gridded with 5% error Laptop	22 hours 11 min
	Explicit gridded with 1% error Server	8 hours 9 min
	Explicit gridded with 5% error Server	1 hour 32 min
	Explicit gridded with 1% error SHARCNET 1 hour 6 min
	Explicit gridded with 5% error SHARCNET 10 min 24 sec

The MethodRussell and Norvig[START_REF] Russell | Artificial Intelligence: a Modern Approach[END_REF] state that the main PAC principle is based on the following: "any hypothesis that is seriously wrong will almost certainly be "found out" with high probability after a small number of examples, because it will make an incorrect prediction. Thus, any hypothesis that is consistent with a sufficiently large set of training examples is unlikely to be seriously wrong: that is, it must be probably approximately correct".The main question answered by a PAC-learning algorithm is the determination of the minimum number of examples required.1 ln ln


Daniele De Rigo
email: daniele.de-rigo@jrc.ec.europa.eu

Claudio Bosco
email: claudio.bosco@jrc.ec.europa.eu

Architecture of a pan-European framework for Integrated Soil Water Erosion Assessment

Keywords: soil erosion, regional scale, environmental modeling, semantic array programming, integrated natural resources management

Soil erosion implications on future food security are gaining global attention because in many areas worldwide there is an imbalance between soil loss and its subsequent deposition. Soil erosion is a complex phenomenon affected by many factors such as climate, topography and land cover (in particular forest resources, natural vegetation and agriculture) while directly influencing water sediment transport, the quality of water resources and water storage loss. A modeling architecture, based on the Revised Universal Soil Loss Equation, is proposed and applied to evaluate and validate at regional scale potential and actual soil water erosion, enabling it to be linked to other involved natural resources. The methodology benefits from the array programming paradigm with semantic constraints (lightweight array behavioural contracts provided by the Mastrave library) to concisely implement models as composition of interoperable modules and to process heterogeneous data.

Introduction

Over the last years, soil erosion is one of the main questions that has attracted considerable attention at a global scale. At geological time-scales there is a balance between erosion and soil formation [START_REF] Tricart | Ecogeography and Rural Management -a Contribution to the International Geosphere-Biosphere Programme[END_REF], but in many areas of the world today there is an imbalance with respect to soil loss and its subsequent deposition, mostly caused by anthropogenic activity (mainly as a result of land use change) and climate change.

The EU Thematic Strategy for Soil Protection [START_REF] Van-Camp | Reports of the Technical Working Groups Established under the Thematic Strategy for Soil Protection, EUR 21319 EN/1-6[END_REF] and the subsequent proposed Soil Framework Directive [START_REF]Proposal for a Directive of the European Parliament and of the Council establishing a framework for the protection of soil and amending Directive[END_REF] recognise soil erosion as a major threat to European soil resources and recommend an indicator-based monitoring approach. Given the increasing of soil erosion in Europe and its implications on future food security (Pimentel [START_REF] Pimentel | World Soil Erosion and Conservation[END_REF] reports that soil is the basis for 97% of all food production) and water quality, it is important that land managers and decision makers are provided with accurate, timely and appropriate information on the areas more susceptible to erosion phenomena.

Setting Soil Erosion in a Wider Context

Soil erosion is a complex phenomenon influenced by very diverse factors such as land cover, climate and topography, and is strictly linked to human practices and activities.

While land cover directly affects soil erosion either positively (i.e. forests cover and good agricultural practices) or negatively (wildfire-degraded cover and bad agricultural practices [START_REF] Foley | Global consequences of land use[END_REF]), climate and climate change affect soil erosion both indirectly by driving land cover changes and directly varying precipitation intensity and duration. Therefore current and long-term analysis frameworks of soil erosion should be suitable to integrate land cover (in particular forest resources and natural vegetation) and wildfire susceptibility as opposite drivers for their influence in mitigating or increasing erosive phenomena.

At the same time, soil erosion influences water sediment transport, water resources quality and water storage loss [START_REF] Hansen | The Value of the Reservoir Services Gained With Soil Conservation[END_REF]. These premises make improvement and integration of soil management, in conjunction with other natural resources -forest, water resources -and land use management a high priority which needs to link many aspects, not least those related to renewable energy, in a multicriteria approach [START_REF] Angelis-Dimakis | Methods and tools to evaluate the availability of renewable energy sources[END_REF].

Data modeling and data integration in environmental sciences are actively investigated within ICT [START_REF] Casagrandi | Impact of ICT in Environmental Sciences: A citation analysis 1990-2007[END_REF]. However, this integration is difficult not only from an architectural software perspective but also from a wider scientific point of view: details on how to integrate multiple natural resources "cultures" in the same high level coordination algorithm are quite challenging.

The first step toward a solid and scalable modeling framework to support integrated assessment and management of these resources is the design and development of a reliable modeling architecture which is sufficiently lightweight to be successfully replicated whilst addressing domain-specific peculiarities. An application of the architecture to evaluate and validate at regional scale potential and actual soil erosion is presented. The regional scale allows to properly model the current situation of soil erosion and to link it to other involved resources. Moreover, it enables the possibility to scale the model architecture up to the integrated modeling of natural resources under climate change scenarios.

Methods

The proposed approach benefits from the array programming paradigm [START_REF] Iverson | Notation as a tool of thought[END_REF][START_REF] Quarteroni | Scientific Computing with MATLAB and Octave (Texts in Computational Science and Engineering)[END_REF] to concisely implement models as composition of modules while promoting scalable-parallelism idioms to process massive data. At the same time, the architecture pursues the goal to be really suitable to support real world decision-making complexity, up to the continental scale. This also implies the adopted languages, computational environments and technologies have been chosen considering their diffusion, expected durability 1 and suitability to ease the modeling work for the specialized scientific communities.

The driving epistemological postulate is to attempt boosting scientific collaboration by building reliable operational bridges between related but distant scientific domains.

In order to move from the complexity of modeling natural and man-made resources to the complexity of related decision problems, automatic tools are essential to compare different sub-models and scenarios by also addressing their underlying semantic [START_REF] Villa | Modelling with knowledge: A review of emerging semantic approaches to environmental modelling[END_REF].

Publicly available and improvable free software [START_REF] Stallman | Viewpoint: Why "open source" misses the point of free software[END_REF] for supporting EU decisionmaking can be viewed as a transparency prerequisite to provide involved actors the ability to understand the implications of the technical apparatus on decision-making and to mitigate unwanted technology-driven biases.

Modeling architecture

The modeling architecture can be summarized as a non-intrusive, lightweight set of coherent practices to harmonize and strengthen the way information is processed and exchanged between sub-models.

Each sub-model is provided with the ability to autonomously check at run-time its input and output data against a set of semantic constraints which also link together different input and output variables. While the algorithms implemented within submodels are not constrained to limit their expressiveness, the framework provides a library support to encourage algorithms' implementation to progress in exploiting as much as possible the array programming paradigm.

This lightweight framework has been designed to support regional scale environmental modeling and it has been applied to estimate pan-European soil water erosion by connecting it to climatic and anthropogenic aspects, whose related information is suitable to be provided by existing datasets. On the other hand, updated input information under multiple climatic and anthropogenic scenarios can be in principle estimated using the same modeling approach, within an integrated and multidisciplinary general approach, therefore enabling a future full exploitation of the framework. The framework is mostly based on GRASS GIS [START_REF] Neteler | Open Source GIS: A GRASS GIS Approach[END_REF], MATLAB language [START_REF]The MathWorks: MATLAB[END_REF], GNU Octave [START_REF] Eaton | GNU Octave Manual[END_REF] and GNU R [START_REF]R: A language and environment for statistical computing[END_REF] computing environments and the Mastrave library [START_REF] De Rigo | the Mastrave project[END_REF], which supports array-programming oriented environmental modeling with a wide native set of semantic input requirements for models.

Despite it has been designed to enable a future exposition of its main modules as web services, the whole modeling computation is explicitly constrained not to require such exposition, allowing the complete local reproducibility (e.g. within workstation or cluster architectures). The design modularization which can lead to expose web services is also essential to ease future interactions with third-party sub-models.

Mathematical notation and semantic array programming

Array programming originated to reduce the gap between mathematical notation and algorithm implementations. Ideally, "the advantages of executability and universality found in programming languages can be effectively combined, in a single coherent language, with the advantages offered by mathematical notation" [START_REF] Iverson | Notation as a tool of thought[END_REF]. It would be quite surprising if a promising algorithm could be transposed from the purely theoretical exposition on paper to the application domain, to solve a complex real world problem, without a large set of boundary conditions having a strong influence on the nature of that "naïve" algorithm.

Array programming suggests that an accurate vector-based mathematical description of the model can simplify complex algorithm prototyping while moving mathematical reasoning directly into the source code, where the mathematical description is actually expressed in a completely formalised and reproducible way. To fully benefit from that paradigm in a scientific modeling perspective, we systematically adopted two additional design concepts: modularizing sub-models and autonomous tasks with a strong effort toward their most concise generalization and reusability in other contexts. It also implies consistently self-documenting the code and engineering module interfaces to provide a uniform predictable convention; semantically constraining the information entered in and returned by each module instead of relying on external assumptions (e.g. instead of assuming the correctness of input information structured as an object).

Combining these design recommendations, provided with a supporting library, constitute the essence of the Mastrave project [START_REF] De Rigo | the Mastrave project[END_REF] approach to semantic array programming. The exposed pan-European soil erosion model applies these recommendations.

Semantic constraints may be contextualized in analogy with behavioural subtyping [START_REF] Liskov | Behavioral Subtyping Using Invariants and Constraints[END_REF]. For example, let us consider the way a module interacts with external information provided by the module input arguments. The MATLAB language is dynamically and weakly typed so that the concepts of vector, matrix and multidimensional array can all be represented by the native type double, which can also represent complex numbers. A variable of type double can be an arbitrary array

m m n n n n , L L 1 1 ∀ × × ∈ C C
. A typical modeling task could consist in manipulating time series of data, e.g. composed by a vertical sequence of time intervals, the corresponding sequence of rainfall values measured within each time interval and a third sequence of weightsfrom 0 to 1 -representing the reliability of each value. Passing a time series to a module (implemented as a function) can in principle be done using external data containers (structures, classes, nested arrays of cells…) or directly passing time and data sequences. Irrespective of the way information is passed, if for example the module needs to sort by date the time-series elements whose weight is greater than a certain threshold, then a set of run-time preconditions should hold (::semanticconstraint:: denotes the corresponding Mastrave semantic constraint):

1. the number of time intervals, of values and of weights composing the time series must be the same (::same_rows::); 2. the array of values must be a column vector (::column_vector::) of real non-negative numbers (::nonnegative::); 3. the array of time-intervals must be a two-column matrix whose second-column elements must be not less than to the corresponding elements of the first column (::interval::); 4. the weights must be in [0, 1] (::probability::); 5. the intervals need to be sortable, so that 2 the intersection of any pair of intervals must be empty (::sortable_interval::); 6. the threshold must 3 be a scalar real number (::scalar_real::).

Constraints range from trivial syntactic ones to topological relationships among structured data which can only be tested at run-time.

All constraints (except where explicitly states as in ::scalar_real::) apply to vectors, matrices and in many cases to multi-dimensional arrays, easing their application to complex data, which often require non-trivial networks of semantic constraints used as preconditions, invariants and postconditions. Constraint violations raise exceptions which can be managed enabling model self-healing, or simply can cause a human-readable and self explaining error to abort the computation.

Those errors can be easily exposed in possible future web-interfaces, allowing semantic array-programming filters to wrap pre-existing third-party models and strengthen their robustness in view of their possible integration.

The Soil Water Erosion Model

The model and input datasets selection within the integration framework is crucial as they have to offer the most homogeneous and complete pan-European coverage. It must also allow the produced information to be harmonized and easily validated.

Among the different models for soil erosion estimation, the Universal Soil Loss Equation (USLE) [START_REF] Wischmeier | Predicting Rainfall Erosion Losses -A Guide to Conservation Planning[END_REF] is one of the most widely applied empirical models. The USLE is an erosion model designed to predict the soil loss in runoff and it only predicts soil loss from sheet and rill erosion. Either deposition phenomena or concentrated runoff are not considered within the equation. It is one of the least data driven water erosion models which has been developed and it has been applied at different scales. Another advantage in the USLE architecture is related to its flexibility: it is always possible to set this equation to adapt it to the environment to be analysed.

The Revised Universal Soil Loss Equation (RUSLE) [START_REF] Renard | Predicting Soil Erosion by Water: A Guide to Conservation Planning with the Revised Universal Soil Loss Equation (RUSLE)[END_REF] retains the structure of its predecessor, the USLE. For all the above mentioned reasons we selected the RUSLE, and also for its intrinsic structure which is suitable to enable integrated analysis of different factors (climate, land cover and human practices).

There are six main factors controlling the soil erosion and considered by the model: the erosivity of the eroding agents (water), the erodibility of the soil, the slope steepness and the slope length of the land, the land cover and the human practices.

RUSLE estimates erosion by means of an empirical equation:

Y c, Y c, Y c, Y c, Y c, Y c, Y c, P C S L K R = A ⋅ ⋅ ⋅ ⋅ ⋅ . ( 1
)
where all factors refer to a given spatial grid cell c and are the average within a certain set of years

} { 1 Y n i y , , y , , y = Y L L
of the corresponding yearly values: [START_REF]Proposal for a Directive of the European Parliament and of the Council establishing a framework for the protection of soil and amending Directive[END_REF] The framework easily allows more flexibility. For example, instead of a scalar an array can describe a composite threshold (e.g. a range of min and max values to exclude measures below the rain gauge resolution or above an outlier threshold) provided a custom comparison function is passed as optional module's input argument (::function_handle::).

A c,Y = (annual) soil loss (t ha -1 yr -1 ). R c,Y = rainfall erosivity factor (MJ mm ha -1 h -1 yr -1 ). K c,Y = soil erodibility factor (t ha h ha -1 MJ -1 mm -1 ). L c,Y = slope length factor (dimensionless). S c,Y = slope steepness factor (dimensionless). C c,Y = cover management factor (dimensionless). P c,Y = human practices aimed at erosion control (dimensionless).

Rainfall erosivity factor. The intensity of precipitations is one of the main factors affecting soil water erosion processes. R is a measure of precipitation's erosivity.

Wischmeier [START_REF] Wischmeier | A rainfall erosion index for a universal Soil-Loss Equation[END_REF] identified a composite parameter, EI 30 , as the best indicator of rain erosivity. It is determined, for the k i -th rain event of the i-th year, by multiplying the kinetic energy of rain by the maximum rainfall intensity occurred within a temporal interval of 30 minutes. The rainfall erosivity factor of the RUSLE model is the average, on a consistent set of data, of n Y sums of EI 30 values, each sum being computed for the whole set of event y i n rainfall events in the i-th year:

. 1

1 1 30 1 1 ∑ ∑ ∑ ∑ ⋅ ⋅ Y event i y i i Y event i y i n = i n = k 30 k c, i k c, n = i n = k i k c, Y Y c, EI = I E n = R (2)
Due to the difficulty to obtain precipitation data with adequate temporal resolution over large areas, the model architecture enables the approximation of R by using one among the many simplified equations available in literature. In the presented application, for the pan-European maps the simplified R equation of [START_REF] Loureiro | A new procedure to estimate the RUSLE EI 30 index, based on monthly rainfall data and applied to the Algarve region, Portugal[END_REF] has been computed using the E-OBS database as data source [START_REF] Haylock | A European daily high-resolution gridded dataset of surface temperature and precipitation[END_REF]. E-OBS is based on the largest available pan-European precipitation data set, and its interpolation methods were chosen after careful evaluation of a number of alternatives. The complete equation has been fully implemented to accurately estimate R where detailed time series of measured precipitation (10 to 15 minutes of time-step) have been made available across Europe.

Soil erodibility factor. The soil erodibility factor "represents the effects of soil properties and soil profile characteristics on soil loss" [START_REF] Renard | Predicting Soil Erosion by Water: A Guide to Conservation Planning with the Revised Universal Soil Loss Equation (RUSLE)[END_REF]. The K factor is affected by many different soil properties and therefore quantifying the natural susceptibility of soils is difficult. For this reasons K is usually estimated using the soil-erodibility nomograph [START_REF] Wischmeier | Predicting Rainfall Erosion Losses -A Guide to Conservation Planning[END_REF]. The European soils database (SGDBE) at 1:1.000.000 scale has been used for the calculation (see also [START_REF] Heineke | Land Information Systems: Developments for planning the sustainable use of land resources[END_REF]).

Cover-Management factor. The cover-management factor represents the influence of terrain cover, cropping and management practices on erosion rate. The calculation of the C factor is very difficult and due to the lack of detailed information in Europe it has been calculated using average values from literature [START_REF] Morgan | Soil Erosion and Conservation[END_REF][START_REF] Šúri | Erosion Assessment of Slovakia at regional scale using GIS[END_REF][START_REF] Cebecauer | The consequences of land-cover changes on soil erosion distribution in Slovakia[END_REF] based on the last version of the Corine Land Cover (CLC 2006) [START_REF] Bossard | CORINE land cover technical guide -Addendum 2000[END_REF] database. The impact of natural vegetation (Fig. 1) suggests further analysis with detailed forest types and tree species distribution maps [START_REF] Casalegno | Modelling and mapping the suitability of European forest formations at 1km resolution[END_REF][START_REF]Forest Type Map[END_REF][START_REF] Kempeneers | Data fusion of different spatial resolution remote sensing images applied to forest type mapping[END_REF] to increase the corresponding C factors accuracy.

Topographic factors (slope length and slope steepness factors).

The effect of topography within the RUSLE model is accounted for by the slope length factor and the slope steepness factor. For the calculation of the LS factors we used the DEM obtained from the Shuttle Radar Topography Mission (SRTM) [START_REF] Farr | The Shuttle Radar Topography Mission[END_REF] that is the most complete high-resolution digital topographic database of Earth.

Human Practices factor. P is the support or land management practice factor. It represents how surface and management practices like terracing, stripcropping or contouring affect erosion phenomenon. For areas where there aren't support practices or without any data the P factor is set equal to 1.0.

Conclusions

A lightweight framework has been presented to support regional scale environmental modeling within the paradigm of semantic array programming. An application to estimate the pan-European potential soil water erosion and soil water erosion using the Revised Universal Soil Loss Equation (RUSLE) has been exposed by showing its intrinsic integrability with related natural resources models and data (i.e. land cover and forest resources). Accurate estimation of rainfall erosivity factor (R-factor) has been implemented to validate simplified R-factor equations.

The proposed architecture is designed to ease the future integration, within the same lightweight framework, of erosion-related natural resources models and data. The framework will be applied to improve the R-factor modeling, and water erosion estimation in mountainous areas will be progressed by modeling their peculiarities.

Fig. 1 .

 1 Fig. 1. Effects of land cover on soil erosion rate. The maps (South of Norway and Denmark; South of Italy) show some details of the pan-European potential soil erosion, soil erosion and land cover map. The land cover map has been created aggregating the Corine Land Cover classes in three different categories: forest, grassland and other natural vegetation areas; transitional, mixed natural and agriculture areas; prevalent agriculture, sparsely vegetated areas, burnt areas. Agriculture practices able to reduce soil erosion have not been modeled.

Stroustrup reports [11] "that on the order of

new languages are developed each year and that about 200 languages become unsupported each year".

Sortability of intervals can also be otherwise defined, depending on the peculiar task for which an ordering is required. However, an obvious and unambiguously sufficient condition holds when all intervals are disjoint. ::sortable_interval:: enforces this condition.

Acknowledgments. We acknowledge the E-OBS dataset from the EU-FP6 project ENSEMBLES (http://ensembles-eu.metoffice.com) and the data providers in the ECA&D project (http://eca.knmi.nl).


Sven Schade
email: sven.schade@jrc.ec.europa.eu

Barbara Fogarty
email: barbara.fogarty@dcu.ie

Michael Kobernus
email: mike.kobernus@nilu.no

Katharina Schleidt
email: katharina.schleidt@umweltbundesamt.at

Paul Gaughan
email: paul.gaughan@marine.ie

Paolo Mazzetti
email: mazzetti@imaa.cnr.it

Arne-Jørgen Berre

Environmental Information Systems on the Internet: A Need for Change

The cost effective delivery of scientific and policy requirements is a key driver for the realization of global sustainability research, integrated assessment and supporting innovative systems. The next generation of geospatial information infrastructures is proposed as a possible solution. Still, questions such as 'what does all this mean to environmental information systems' and 'what is expected to change', have only partially been answered. In this paper, we describe the recent challenges for eEnvironment services in Europe, specify desired capabilities and derive according requirements. We identify affected stakeholder communities and depict their involvement in the overall value chain of environmental knowledge generation. Specific examples illustrate individual needs, while a derived description of the value chain indicates more general outcomes. Developmental requirements of future information systems are discussed. The presented work answers the questions above by bridging the gab between stakeholder needs, Information and Communication Technology (ICT) development and higher level concepts, such as Digital Earth and Future Internet.

Introduction

The International Council for Science (ICSU) identified five scientific priorities, or Grand Challenges, in global sustainability research, including (i) development of observation systems needed to manage global and regional environmental change; (ii) improvement of the usefulness of forecasts of future environmental conditions and their consequences for people; and (iii) investigation of institutional, economic and behavioral responses that can enable effective steps toward global sustainability [START_REF]Scientific Grand Challenges identified to address global sustainability -ISCU prepublication[END_REF]. A next generation of geospatial information infrastructures (Digital Earth) has been proposed as a possible solution, which provides more dynamic systems, new sources of information, and stronger capacities for their integration [START_REF] Craglia | Next-generation digital earth: A position paper from the vespucci initiative for the advancement of geographic information science[END_REF].

Sven Schade, Barbara Fogarty, Michael Kobernus, Katharina Schleidt, Paul Gaughan, Paolo Mazzetti, Arne-Jørgen Berre With the Europe 2020 strategy [3], especially under the umbrella of the Digital Agenda [START_REF]Official Homepage of the European Commission Digital Agenda for Europe[END_REF] and Innovation Union [5] flagship initiatives, the EU provided the required policy context for addressing the above mentioned Grand Challenges and for implementing a Digital Earth. The advent of Digital Science 2030 [START_REF] Accordino | Digital Science and its impact on Scientific Societies[END_REF] indicates a growing importance. Here, among others, ICT-intense domains are challenged to develop innovative applications based on technological enablers, such as manifold sensors, simulation tools and scientific data infrastructures. The evolution of the Internet (Future Internet) [START_REF]European Future Internet Portal[END_REF] shall play a major role.

Yet, many questions remain unanswered. How do these high level concepts reflect reality? What does all this mean to environmental information systems? What is expected to change? Before these topics can be properly addressed, we have to carefully describe the recent challenges for eEnvironment services in Europe [START_REF]Proceedings of the European conference of the Czech Presidency of the Council of the EU TOWARDS eENVIRONMENT. Opportunities of SEIS and SISE: Integrating Environmental Knowledge in Europe[END_REF], specify desired capabilities and derive according requirements. Only if these are met, can we identify affected stakeholder communities, discuss suitable implementation frameworks and suggest sustainable development strategies.

This paper provides such groundwork. We review cases from three environmental spheres (terrain, atmosphere and marine) to illustrate the richness of applications across the borders of EU Member States, and the variety of stakeholders involved; examples range from classical monitoring and reporting, via downscaling of global environmental models to individual needs, to the inclusion of user contributed content into the value chain of environmental knowledge generation. We furthermore introduce some 'science fiction' services, in which we think outside the box of expected environmental applications.

The remainder of this paper is structured as follows. The next section presents viewpoints from three environmental spheres. It also includes a set of (science fiction) applications, which might become reality in the advent of new technologies and user communities. Thereafter, section 3 discuses possible generalization of the requested eEnvironment services and derives a common description of the underlying value chain. A suitable framework for developing required information systems is depicted in section 4. Given future developments in Information and Communication Technology (ICT), we put emphasis on the future of the Internet (or Future Internet). The paper concludes with a summary of our findings and an outlook to required future activities.

eEnvironment Services for Three Spheres

This section presents three concrete cases in which environmental information systems have to change in order to meet arising needs. Each section follows a similar structure. The overall context is defined first; followed by a discussion on the intended goals, steps to achieve them, derived case-specific requirements, and the resulting stakeholders. We specifically include issues of scalability, either in terms of spatial extent (from local to global) or in terms of amount of information to be processed when individualizing information (from global to local).

Environmental Information Systems on the Internet: A Need for Change 3

Terrain: Mapping Terrestrial Biodiversity

Terrestrial biodiversity provides an interesting case considering phenomena on land. The UN Convention on Biodiversity (CBD) and the EU have set a new target of halting the loss to biodiversity by the year 2020. Achieving this goal in the first place requires a solid basis and a new approach to current judging progress. Observational data on biodiversity has to be merged from all available sources while assuring high quality. Outreach groups for data survey can greatly widen the base from which observational data may be gleaned. This data can then be merged with existing data from research organizations as well as historic data available from museums. Scenarios on reporting biodiversity illustrate how humans that are supported by mobile devices, such as smart phones, can act as the main 'sensor' for data provision.

In order to maintain data quality when integrating data from multiple sources, future eEnvironment services will have to include mechanisms for context aware quality assurance of reported data. The initial quality assurance processing should at least account for:

1. Spatial probability, i.e. does this species fit into this bio-geographical region? 2. Temporal probability, i.e. can it be observed at this time of the year? 3. Comparison with common mis-identifications, i.e. providing the user with image of other types often confused with type identified -does the leaf of this species look like this? Such a quality assurance process requires a semantic backbone for:

• Structuring and storage of taxonomic information, including both scientific and common species names. • Storing additional species information as required for context aware quality assurance, i.e. bio-geographical regions of occurrence, types often wrongly identified as. • Semantic mapping between species lists, against common opinion, scientific species names are not completely standardized across Europe. • Semantic annotation of external expert knowledge, where a wide pool of external expert, for correct identification of species sighted, can be leveraged through crowd-sourcing mechanisms.

Atmosphere: Monitoring Individualized Pollen Exposure

On the atmospheric sphere, we concentrate on individual exposure assessment and air quality/meteorological alerts. Today, we have easy access to a great deal of information via television, radio and the World Wide Web. This includes pollution, pollen and meteorological data which are all relatively easily accessed in one or more dissemination channels. All this data contributes to a common sense, but it is not tailored to an individual user's needs. Relevancy of data and interpreting it are key issues for users today, especially with regards to pollen and pollution which directly affect as many as 25% of the population. Gaughan, Paolo Mazzetti, Arne-Jørgen Berre Future eEnvironment services should therefore aid individuals in tailoring information relevant to their specific requirements by providing personalized threshold alerts for air quality, meteorological conditions and pollen, as well as enabling the users to feed data back into the system which will then be used to further enhance the relevancy of the data to the user (Figure 1). This personalization of the information becomes more achievable due to the increasing prevalence of GPS enabled 'smart' phones which will enable the system to assess the individual's exposure to pollution by recording the individual's coordinates and matching them with actual observation data with a higher degree of accuracy than is generally available to the public. In addition, by taking advantage of easier to use interface design we will enable the individual users to become part of the web of sensors by providing mechanisms for Voluntary Geographic Information (VGI) [START_REF] Goodchild | Citizens as sensors: the world of volunteered geography[END_REF]. This data will feed back into the system and provide another layer of information to further enhance the existing infrastructure. An example of VGI could be a user reporting the prevalence of rag weed (to which he might be allergic) in an area where for which no data currently exists, thereby potentially alerting other sufferers of the existence of the allergenic plant.

Marine: Managing Marine Resources

The EU's marine waters support a wide range of economic and social activities. The Integrated Maritime Policy for Europe seeks to realize the significant potential of Europe's vast marine resources through the alignment of research and innovation capacity with relevant policy and market requirements. The Marine Strategy Framework Directive (MFSD) establishes a legally binding framework within which Member States shall take the necessary measures to achieve or maintain good environmental status in the marine environment by the 2020. The Directive constitutes the vital environmental component of the Union's future maritime policy, and is designed to achieve the full economic potential of oceans and seas in harmony Environmental Information Systems on the Internet: A Need for Change 5 with the marine sphere. The scale of the challenge is reflected in range of current monitoring requirements for the European marine environment which includes 70,000 km of coastline and an associated investment required of over €1 billion a year [10].

As new standards and approaches to the collection and management of marine data emerge [START_REF]Official Homepage of the Pan-European infrastructure for Ocean & Marine Data Management[END_REF][START_REF]Official Homepage of the Water Information System for Europe (WISE) project[END_REF][START_REF]Official Homepage of the European Marine Observation and Data Network[END_REF] the next generation of decision based management tools must scale to transcend national borders and facilitate an ecosystem approach to the development of Europe's marine resources and related sectors including offshore energy, environmental monitoring, aquaculture and marine tourism.

Current observations of the marine environment are achieved using a variety of commercially available sensors deployed on static and mobile platforms both above and below water. Measurements can be taken from in-situ, air-borne, water-borne, space-borne or even human-borne sensing methods to measure various meteorological, oceanographic, geophysical and biological processes. The measurement spectroscopy used in these observations can also vary (UV, IR, visible, digital, analog etc.). Mission critical data includes information on meteorological and oceanographic data (e.g. wind, sea level pressure), sea states (namely wave conditions and water movements) and information on changes in the chemical status of the water. As a consequence, a generic approach is required for the description and integration of heterogeneous sensors and sensor data regarding marine observations. Challenges for the integration and interpretation of harvested data also include the ability to deliver large volumes of real time streaming data from advanced sensing platforms such as acoustic (e.g. hydrophones, sonar, ADCP, etc.) and video feeds. The ability to dynamically scale the response of distributed sensor networks in response to unpredictable environmental events is also likely to act as a key enabler in the realization of real time dynamic marine monitoring networks and the development of a range of associated products and services for marine sector activities.

New eEnvironment services will have to consider the need for secure, smart and fast mobile communications, agent-based middleware used to empower the marine stakeholder community to observe and operate in the marine environment. Diverse and heterogeneous data and information sources should be fused and prototyped for delivery on demand using multiple distributed services and might be presented via social networking internet technologies together with sensor web enablement and fusion services technologies (Figure 2).

Beyond Foreseen eEnvironment Services

Beyond the cases outlined above (section 2), which are likely to become reality in the near future, we may expect even more sophisticated applications, such as:

• A fully scalable, real-time environmental and social footprint, which provides near real-time information about individuals or groups of individuals in terms of their waste production, CO2 emission, water consumption, income, connections to other people and organizations etc. • Event tracing trough observation networks, tweets and newspaper articles, in which for example natural disasters (but also political crises) are monitored from the causes, over the happening and direct responses, all the way to long term impacts, using the manifold information channels that are available. • Social networking with sensors and environmental models, which basically enables the discovery of sensor networks, which may potentially be used as inputs to environmental models, but also serve as a communication platform for improving scientific models, and for informing about events, such as a predicted flood. This network might be even augmented with the social networking platforms of today.

Given the rapid development of ICT for environment during the last years and the growing citizen interests and technological capabilities, it is likely that also such or similar applications will become reality within this decade. We live in an era of growing user expectations on the one side and arising technological potential on the other side. Our information systems have to account for these frequent changes.

Generalization and Requirements Analysis

Given the information above and considering similar cases from other domains, we become able to extract generic components for environmental ICT applications and eEnvironment services. In this section, we introduce the roles, which are involved in generating knowledge about our environment and define the overall added-value chain. In a second step, we present common requirements for future eEnvironment services. In doing so, we provide a bridge between practical environmental applications and the wider political framework. The presented findings could equally be applied to other geospatial domains, not only to environment.

The Value Chain of Environmental Knowledge Generation

Analyzing the descriptions of section 2, we can extract a total of six roles, which contribute to the generation of environmental knowledge: 1. Observer, being the initial source of information about the environment. This may reach from sensor to citizen. 2. Publisher, making a resource, such as an observation, discoverable to a wider audience, e.g. by providing required resource descriptions (metadata).

Environmental Information Systems on the Internet: A Need for Change 7

3. Discoverer, being the entity that finds a resource based on all available descriptions. 4. Service Provider, making information or an environmental model accessible to (and usable by) the wider audience. 5. Service Orchestrator, being responsible for combining existing services in a way that they create information for a distinct purpose, i.e. environmental application focusing on a particular sphere topic. 6. Decision Maker, consuming an environmental application in order to retrieve decision supporting material and making a final decision based on the information available.

Consequently, the process workflow can be summarized as in the figure below (Figure 3). We call this workflow the added-value chain of environmental knowledge generation. Notably, following this workflow services may themselves get published in order to serve as building blocks for more complex eEnvironment solutions.

Overview of Stakeholders

The roles identified above (section 3.1) are played by a variety of individuals and organizations. Most of these have been mentioned in the descriptions of desired services in section 2. In a nutshell, those can be defined as:

• Citizens of a particular social, political, or national community;

• Environmental agencies on sub-national, national and European level;

• Public authorities of national and regional and other level;

• Industries from the primary, secondary and service sector;

• Platform providers offering frameworks on which applications may be run;

• Infrastructure providers offering physical components and essential services;

• Sensor network owners holding the sensor and basic communication hardware.

Table 1 provides an overview of the manifold mappings between these stakeholders and the different roles in the value chain of environmental knowledge generation. To highlight only a few aspects: citizens can play all roles, they may even discover available information and provide new services ('mash-ups'). The decisions they may take are on individual level, such as should "I travel through an area with bad air quality?" All decisions that can be taken are strongly dependent on the stakeholder; industries may use the environmental information for logistic or location planning, while the owners of sensor networks may decide about the maintenance of their facilities. We address the first question in the remainder of this section. We outline the cases sketched above and derive functional and non-functional requirements. Available frameworks for future developments are addressed in the next section (section 4). Re-visiting the sections above, we identify at least the following needs:

Sven

• discovery, access, visualization and publication of data sets;

• discovery, access, visualization, and planning of sensor observations;

• access to environmental models and simulations as services;

• transformation of data sets and harmonization of observations;

• composition and invocation of workflows;

• support and enforcement of data and service policies based on identity, licenses, trust chains, etc.; • provision of objective, semi-objective and subjective observations by end users;

• access and use of controlled vocabularies, taxonomies, ontologies and annotations; • integration with the Semantic Web and Web 2.0; and • interoperability with existing and planned infrastructures in the context of: o the most relevant initiatives at international level, such as INSPIRE, GMES, GEOSS; and o relevant well-established communities, including research and egovernment infrastructures.

Specific components (environmental enablers) should support these requirements. They should be designed and developed leveraging existing architectural approaches and technical specifications, and re-using/extending existing tools. Particular attention should be paid to open international standards and communities-of-practice specifications, and to open source components in order to make the resulting system more flexible and scalable (see also [START_REF]Interoperable Solutions for European Public Administrations (isa): European Interoperability Strategy (EIS) for European public services[END_REF]). Programme. The FI-PPP aims to (i) to support an Internet-enabled service economy, (ii) to improve key ICT infrastructures of Europe's economy and society by making them better able to process massive amounts of data originating from multiple sources; (iii) to render the Internet more reliable and secure; and (iv) to allow real time information to be processed into real time services.

Environmental Information Systems on the

There is a clear opportunity to further develop the sustainability-innovation-growth triangle by dedicating a usage area of the FI-PPP to the environment. This provides an excellent frame for addressing the required environmental enablers, which have been identified in section 3. For optimizing the outcomes, the early and strong involvement of application areas from all environmental spheres is essential. Only in this manner, the current push of technological approaches can be balanced with applications' need, which eventually will lead to powerful and useful eEnvironment solutions.

Summary and Outlook

This paper outlined some changes, which are required for improving eEnvironment services. Three concrete cases were presented covering the terrestrial, marine and atmosphere domain; those were complemented by a set of more futuristic applications. Analyzing the various descriptions, we extracted a total of six involved roles and depicted a general workflow for environmental knowledge generation. Stakeholders have been identified for each of these roles and a set of requirement environmental enablers has been presented. The FI-PPP has been identified as a

Fig. 1 .

 1 Fig. 1. Desired eEnvironment Services in the air quality domain.

Fig. 2 .

 2 Fig. 2. Requirements for eEnvironment services for the marine sector.

Fig. 3 .

 3 Fig. 3. The added-value chain of environmental knowledge generation.

Schade, Barbara Fogarty, Michael Kobernus, Katharina Schleidt, Paul Gaughan, Paolo Mazzetti, Arne-Jørgen Berre Tab. 1.

  The added-value chain of environmental knowledge generation.

		observe Provide discover create orchestrate decide
	Citizens	x	X	x	x	x	x
	Environmental agencies	x	X		x		x
	Public authorities		X		x		x
	Industries			x	x	x	x
	Platform providers				x
	Infrastructure providers				x
	Sensor network owners	x	(x)		(x)		x
	3.3

Requirements for a Next Generation of eEnvironment Services Given

  all this, (i) what would be the needs for a next generation of eEnvironment services in Europe and (ii) what might be a suitable framework for establishing requested platforms and infrastructures?

Internet: A Need for Change 9 4 Future Internet: A Framework for Implementing Change

  and territorial cohesion (inclusive growth). Seven flagship initiatives give substance to the strategy. They address innovation, youth and the labor market, digital agenda, resource efficiency, industrial policy in the global context, skills and jobs, and social and territorial cohesion respectively. In particular, the Innovation Union [5] initiative aims to improve conditions and access to funding for research and innovation in Europe, to ensure that innovative ideas can be turned into commercial products and services that create jobs and economic growth, whereas the Digital Agenda for Europe[START_REF]Official Homepage of the European Commission Digital Agenda for Europe[END_REF] outlines policies and actions to maximize the benefit of the Digital Revolution for all. The Digital Agenda foresees the action to "work with the Member States and stakeholders to implement cross-border eEnvironment services, notably advanced sensor networks". Then again, the concept of the Future Internet[START_REF]European Future Internet Portal[END_REF] is part of the Digital Agenda for Europe's efforts to deliver economic benefits from fast to ultrafast Internet and interoperable applications.Considering the latter, the European Commission (EC) provided €90 million for funding Future Internet-related research in 2011, and a further €210 million in 2012-2013 through the Future Internet Public Private Partnership (FI-PPP) FP7

	In response to the world economic crisis in 2008, the European Commission
	formulated its long term vision in Europe 2020 strategy [3], which emphasizes actions
	around three main priorities: (i) developing an economy based on knowledge and
	innovation (smart growth): (ii) promoting a more resource efficient, greener and more
	competitive economy (sustainable growth); and (iii) fostering a high-employment
	economy delivering social

Acknowledgements. We thank the ENVIROFI (FP7 -284898) project consortium for the lively discussions we had. This paper is based on our common findings.

Sven Schade, Barbara Fogarty, Michael Kobernus, Katharina Schleidt, Paul Gaughan, Paolo Mazzetti, Arne-Jørgen Berre suitable frame for implementing the requested changes in Europe. The 'application pull' of requirements will be key to success. If we fail to clearly formulate and communicate these requirements we will miss a unique opportunity. We are just starting to investigate these issues and will work to analyze the underlying principles and consequences for the stakeholder communities. We hope to successfully contribute to the future of eEnvironment in this manner.


Simona Littnerová

Jiří Jarkovský

Temporal changes in biological communities affecting models for evaluating of ecological status

Temporal changes in the structure of biological communities are one of the factors affecting water quality assessment systems. The aim of our work was to analyze the temporal variability of benthic macroinvertebrates and its consequences for the established streams typology. The data analyzed come from a biomonitoring of streams of the Czech Republic during 2002-2005. Changes of the structure of macroinvertebrates communities were observed using diversity indices (Shannon index) and biotic indices (saprobic index) and using actual changes in community structure. The time related shift in indices and the community composition was found, but the correlation of the locality typologies and the change was not found.

Introduction

The most important process in communities we can rate is variation in species caused by extinction and colonization [START_REF] Lomolino | Biogeography[END_REF]. Dynamics of extinction and colonization can also affect the response of communities to environmental change, potentially leading to weakly predictable patterns of communities structure [START_REF] Ozinga | Predictability of plant species composition from environmental conditions is constrained by dispersal limitation[END_REF]. Therefore, understanding the influence of temporal variation on community structure and communityenvironment relationships is very important for predicting community distribution along environmental gradients. Current predictive models used in bioassessment mostly rely on single-surveys. We assume that results from single-surveys are representative of local community. The next assumption is that communities were stable in time [START_REF] Bunn | Biological processes in running waters and their implications for the assessment of ecological integrity[END_REF]. However, in reality, the temporal dynamics of local community may swamp any general patterns [START_REF] Olden | Implications of long-term dynamics of fish and zooplankton communities for among-lake comparisons[END_REF].

The stability of habitat and interactions between species in community are the main factor, which have influence on temporal variability in community structure [START_REF] Oberdorff | Is assemblage variability related to environmental variability? An answer for riverine fis[END_REF]. From this viewpoint the stream communities should be more studied, because they are highly variable in response to changes in environments. However, the relatively few studies are interested in temporal variability in streams. Their results are very controversial. Some studies have shown community structure to be relatively stable, especially if environmental conditions do not vary over time [START_REF] Weatherley | The constancy of invertebrate assemblages in soft-water streams: implications for the prediction and detection of environmental change[END_REF], [START_REF] Robinson | Inter-annual patterns in macroinvertebrate communities of wilderness streams in Idaho[END_REF], [START_REF] Scarsbrook | Persistence and stability of lotic invertebrate communities in New Zealand[END_REF], whereas others have reported considerable temporal changes in community structure [START_REF] Townsend | Persistence of stream invertebrate communities in relation to environmental variability[END_REF], [START_REF] Metzeling | Temporal persistence of benthic invertebrate communities in south-eastern Australian streams: taxonomic resolution and implications for the use of predictive models[END_REF], [START_REF] Daufresne | Impacts of global changes and extreme hydroclimatic events on macroinvertebrate community structures in the French Rhone River[END_REF], [START_REF] Mykrä | Concordance of stream macroinvertebrate assemblage classifications: How general are patterns from single-year surveys?[END_REF].

In [START_REF] Weatherley | The constancy of invertebrate assemblages in soft-water streams: implications for the prediction and detection of environmental change[END_REF] were observed stable communities in 19 locations for 5 years. The authors have created for their data classification and ordination for each of the years and found that they are consistent in different years. They also created a model discriminating observations in the first year. This model predicted a correct group of invertebrates. Only for localities with riffles there was evidence of changes of communities that were subject to diminishing vegetation cover of banks.

Community of macroinvertebrates in the reference sites in remote areas of Idaho followed the authors in [START_REF] Robinson | Inter-annual patterns in macroinvertebrate communities of wilderness streams in Idaho[END_REF] for six years (1990)(1991)(1992)(1993)(1994)(1995). Temporal changes of community environmental parameters were observed simultaneously. The authors stated that, despite changes in environmental conditions, caused mainly by climate change, there were only small changes in community structure, and thus could be regarded as stable community.

Also in [START_REF] Scarsbrook | Persistence and stability of lotic invertebrate communities in New Zealand[END_REF] monitored community could be considered stable. The authors had 6year time series for 26 sites. Changes in environmental conditions were indeed statistically significant, but the link between changing environmental conditions and changing communities was very weak. Although communities were very different in different places, the overall change in community structure between years was not statistically significant. The authors also stated that it was followed by inter-season changes in communities.

On the other hand, other studies showed considerable change in communities over time. [START_REF] Townsend | Persistence of stream invertebrate communities in relation to environmental variability[END_REF] watched the 27 rivers in southern England, whether they differ between year 1976 and 1984 in terms of macroinvertebrates and if this change is related to the variation of environmental conditions. The study examined whether the 15 most abundant species in 1976 occurred in 1984. The authors stated that among the surveyed year is great variability in temperature, pH and flow. With these changes also explained the structure of communities. Nevertheless the study reported that despite explanation the change in community structure with environmental parameters, community composition is hardly predictable.

Also in [START_REF] Metzeling | Temporal persistence of benthic invertebrate communities in south-eastern Australian streams: taxonomic resolution and implications for the use of predictive models[END_REF] there were significant community changes. In this study, the authors had available a very long time series (20 years). The 17 sites on two rivers in Australia were monitored. The authors focused on the structure of communities at the family and species level. At the family level the authors did not observed statistically significant changes over time, while at a more detailed level of species the change of community structure was significant. The authors discuss which units of biological classification to use, family or more detailed species. There was found that at the family level the temporal change may not always be detectable. Also, the author argues that it is not clear that the variability of community structure is related to changes of climate. Therefore a change in the community may not directly respond to climate change.

The study [START_REF] Daufresne | Impacts of global changes and extreme hydroclimatic events on macroinvertebrate community structures in the French Rhone River[END_REF] observed influence of changes in environmental conditions on the change of macroinvertebrates in the river Rhone in France. It observed temporal change at the nine sites that were sampled from 1985 to 2004. During this period dry season (2003) occurred in the monitored area, caused by climate change. In this study, the authors used Procrustean analysis. The method discovered to what extent the environmental conditions are associated with the communities. The result was that the environmental conditions and macroinvertebrates communities change over time and changing community is strongly associated with variation of environmental conditions.

A very comprehensive study, which studied the temporal change of community and due to this change in the typology, is [START_REF] Mykrä | Concordance of stream macroinvertebrate assemblage classifications: How general are patterns from single-year surveys?[END_REF]. The study examined how much the general models were based on sampling from one year. In the work were used 34 localities in the north of Finland, which have been studied for three years (2001)(2002)(2003). In this study, three tasks have been solved. First, what was the temporal variability in the community classification? For each year was established typology of communities. By the method Meansim was given the value of the classification strength (CS) for each year. The second task was to determine changes in temporal variability of relationships community-environment. Here the authors used discriminant analysis to determine the significance of environmental parameters for classification. A third task was to detect changes among communities in different years. Here Procrustean analysis was used and the related ProTest, which calculates the statistical significance of the compliance in community. It was found that communities in different years are different. The result of this study was that the classification of the different years is statistically significantly different and that during the monitored period, the composition of the groups formed by the classification changed. In terms of the relationships community-environment the change of community was penalized by vegetation cover of banks.

For conservation and bioassessment of streams is important to know temporal variability in community structure. Predictive models use biological and environmental data to model community-environmental relationships, with the aim predicting of biodiversity patterns. In streams, benthic macroinvertebrates are often used.

The aim of our work was to analyze the temporal variability of macroinvertebrates and its consequences for the established typology of streams.

Study area

The data analyzed come from a biomonitoring of streams of the Czech Republic during 1996-2007. The data consists of two parts: reference and non-reference localities. On the basis of reference localities classifications were defined, abiotic and biotic. These were used in further analyses. To obtain the time series we selected localities with the complete record of the time period 2002-2005, 261 sites were selected. Changes in the structure of communities were observed between 2002-2003, 2002-2004 and 2002-2005 (Figure 1.). These were non-reference AWMA (Agricultural Water Management Authority) localities, mostly found in middle altitude with the rivers order 3-4. Predominant areas were from central Bohemia and in the nearest surroundings of the selected streams were the agriculture areas.

Statistical methods

Standard descriptive statistics was used for the analysis. Several multivariate statistical methods were used too. Changes were observed in terms of both diversity indices and biotic indices describing the community and in the terms of actual changes in community composition.

The first were created univariate categorizations based on environmental parameters. Next cluster analysis was used to create abiotic and biotic typologies. Both were produced on reference localities. Cluster analysis for abiotic classification was run on ln-transformed data of environmental parameters. The clustering algorithm Complete linkage was used for abiotic classification; biotic classification was computed using association matrix based on Jaccard coefficient of species distribution on sites. Optimal numbers of clusters were assessed by combination of Silhouette validation method, Meansim (Analysis of Similarity; [START_REF] Van Sickle | Classification strengths of ecoregions, catchments, and geographic clusters for aquatic vertebrates in Oregon[END_REF]) and discrimination analysis. Non-reference localities were put into clusters by using discriminant analysis. Changes were observed in terms of both diversity and biotic indices and describing the community in terms of actual changes in community composition. Subsequently was tested whether the typologies of sites has an impact beyond this change; tested was the typology used in various types of assessment models of ecological status. To determine the indices changes over time and within types of sites was used RM ANOVA, for change in community Procrustean analysis [START_REF] Mykrä | Concordance of stream macroinvertebrate assemblage classifications: How general are patterns from single-year surveys?[END_REF], [START_REF] Peres-Neto | How well do multivariate data sets match? Evaluating the association of multivariate biological data sets: comparing the robustness of Mantel test and a Procrustean superimposition approach[END_REF], [START_REF] Jackson | ProTest: a Procrustean randomization test of community environment concordance[END_REF], [START_REF] Legendre | Numerical Ecology[END_REF] followed by detailed analysis of temporal changes of individual taxa.

The Procrustean analysis is prior ordination methods. The aim of Procrustean analysis is measure of association between two ordinations. The method of Procrustean fitting is based on least-square criterion, which minimizes the sum of squared residuals between these two configurations. The results of Procrustean analysis is that every locality have two representations, from every matrix one. The simplified scheme used in this study is in Figure 2.

Computationally, the Procrustean fit can be achieved in two steps: (1) centering and scaling:

        P I X P I X P I X scl     T tr (1)
X is reference configuration, I is (n × n) identical matrix, P is (n × n) matrix with all elements =1/n. Repeated this step for configuration Y we obtain Y scl . Note that the scaling process do not change variance of variables, so that variables with have high variances originally will have more importance in the overall fit. This problem can be eliminated by standardization of variables; (2) Reflection, rotation and the residual sum-of-squares statistics:

  W Tr m   1 2 2 12
(2)

W is obtained by the singular value decomposition

  T T VWU Y X scl scl  (3)
The value of 2 12 m vary between the range of 0 and 1.

The optimal rotation matrix (that providing the best fit) can be calculated as

T UV H  . ( 4
)
The (

) 5
and then finding the sum of the squared distances between X scl and Y rot . Note that where X and Y have only one variable each, W is equal to the Pearson correlation index and therefore.

2 12 1 r m   . ( 6
)
The superimposition process requires that matrices have same dimension, where this is not, the matrix with smaller number of variables can be filled with vectors of zeros until it has the same dimension as the larger matrix.

Statistical significance of Procrustean fit ( 2 12 m ) was assumed by a permutation test - ProTest.

Prior procrustean analysis was used non-metric multidimensional scaling (NMDS) ordination based on Jaccard coefficient to summarize communities structure in each study year.

ProTest provides vector of residuals for every sample. These residuals present difference in the position of individual sample between superimposed ordinations. The length of residuals is lack of fit for an individual sample in between ordinations. It corresponds to change of community structure on each locality between time points. To determinate the influence of categorizations and typologies on residuals was used one-way ANOVA.

Afterwards were observed if vectors of residuals were related to change of chemical parameters. The associations between residuals and change of chemical parameters were assessed by Spearmans correlation coefficient. This analysis was only supplementary, because time series of chemical parameters had incomplete records (190 localities).

Results

The changes of indices were found in years and season (Table 1). Both of indices (Shannon and saprobic) changed in years, under seasons was observed statistically significant difference only in saprobic index, the change in Shannon index not. The influence of the typology on this change was found in years, but in interaction of time and typologies was not found. Afterward was observed change of community structure by Procrustean analysis. It was compared structure of community from year 2002 with years 2003-2005. The ProTest, with 999 permutations, assessed statistical significance of ordination fit, from Table 2. It is obvious that associations for each individual pair of ordination are statistically significant. But the associations were not significant, therefore the changes between ordinations in time were observed. The considerable change was recorded between 2002 and 2003 (Table 2). This change is a possible consequence of high precipitation in 2002. The effect of this change on typology was not found.

The result of Procrustean analysis was positions of localities from ordinations from years 2003-2005 in ordinations from year 2002 (Figure 3). The distances between localities in different years in Figure 3 can be understood as measure of community change.

Thanks to gained values of chemical parameters for several localities was possible to assess relationships between change of chemical parameters and Procrustean residuals. Complementary calculations it was found that the change is linked to the biological oxygen demand.

In conclusion temporal changes of biotic indices and community structure were observed, but relationship between the change and typology was not established.

Changes in community structure could be related to biological oxygen demand and change of nutrients volume. Predictive models of ecological status are based on relationship communityenvironment. Therefore understanding of variability in these relationships is a critical aspect of the utility of these models. Yet models assume that communities are stable over time. First time were monitored indices describing the communities, saprobic and Shannon index. Here it was found that the indices changes over time, but the typology does not follow the changes in these indices, except groups of altitude for saprobic index and type of hydroecoregion for saprobic and Shannon index. Nevertheless, we can say that time changes in indices are consistent in each categorization of localities.

Temporal variability of community structure was also studied by Procrustean analysis and ProTest. It was observed that the communities of the years 2003, 2004 and 2005 differ from the community from the initial 2002. Here it was found that the change from the initial year is considerable. Communities of individual sites in different years were compared using Jaccard index, this confirmed a considerable change among the communities in surveyed years. The Procrustean analysis made a vector of residuals, which represent the distance from the site one year-another year, which means similarity between sites in two time points. The analysis of these residuals was used to determine whether classifications were influenced by the magnitude of the change. The result was that the classification does not affect the levels of residuals. Therefore, the community has changed over time, but in all the categorization by analogy. The complementary calculation, available for several localities, found that the change is linked to the biological oxygen demand.

Figure 1

 1 Figure 1 Observed localities.

2 12 m

 212 can also be calculated by first rotating the matrix Y

Figure 2

 2 Figure 2The scheme of Procrustean analysis computation used in this study

Figure 3

 3 Figure 3 Positions of nine the most changing localities. In ordination from year 2002 there were represented positions of localities from other years.

Table 1

 1 Value of Shannon and saprobic index for spring and fall.

			2002	2003	2004	2005
	Spring	Shannon index Saprobic	2.09/2.13 (0.98;3.06) 1 2.21/2.22	1.70/1.76 (0.71;2.72) 2.32/2.30	1.73/1.73 (0.53;2.76) 2.37/2.42	2.1/2.14 (0.95;3.16) 2.34/2.30
		index	(1.32;3.33)	(1.43;3.34)	(1.34;3.44)	(1.48;3.49)
		Shannon	1.93/1.98	1.90/1.99	1.83/1.93	2.11/2.18
	Fall	index	(0.50;2.90)	(0.74;2.81)	(0.41;2.76)	(1.03;3.00)
		Saprobic	2.25/2.18	2.45/2.32	2.42/2.28	2.38/2.31
		index	(1.50;3.54)	(1.55;3.76)	(1.54;3.70)	(1.60;3.60)
		1)	mean/median (5 th percentile; 95 th percentile)

Table 2

 2 Conclusion of ProTest. Statistical significance of Procrustean fit.

	Comparison	m	m 2	Sig.
	2002 and 2003	0.512	0.262	<0.01*
	2002 and 2004	0.518	0.268	<0.01*
	2002 and 2005	0.447	0.200	<0.01*