Manon Besset

Edward J Anthony

Frederic Bouchette

Frédéric Bouchette

Multi-decadal variations in delta shorelines and their relationship to river sediment supply: An assessment and review

Keywords: River deltas, Delta shoreline mobility, River sediment supply, Dams, Delta vulnerability, Delta erosion

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Introduction

River deltas are a major and coveted asset for countries that have them within their borders, and their future is emerging more and more as an important societal concern. Deltas are home to hundreds of millions of people, and, commonly characterized by fertile wetlands, they form rich and bio-diversified are reworked to supply sediment to the shore. Finally, delta shoreline mobility can be influenced by shoreline engineering. We assume, however that, with the exception of sea-level rise, these other sources of shoreline mobility or stabilization play a much less determinant role, at multi-decadal and longer timescales, than fluvial sediment supply.

Data and methods

Choice of deltas

We analyzed 54 of the world's deltas (Fig. 1) with a choice based mainly on a compromise struck between the twin criteria of availability of data from databases and scientific publications and delta size.

The largest deltas are commonly those subject to the most important anthropogenic pressures, although there are noteworthy examples of large deltas still relatively preserved from such pressures. A number of small deltas (see section 3. 1.3) were also included in order to evaluate whether delta size was a criterion in delta vulnerability related to diminishing fluvial sediment supply. A third additional criterion consisted in adjusting the list of selected deltas in order to offer as much a balanced world view as possible (Fig. 1).

We did not take into account the subaqueous parts of the selected deltas. Subaqueous delta area is rather poorly documented in many of the sites retained in this review. Nevertheless, there are quite a number of large deltas, notably muddy deltas, the subaqueous parts of which are much more voluminous than the subaerial parts, as in the cases of the Ayeyarwady and the Amazon. The review is based on the confrontation of delta shoreline changes with changes in river sediment supply.

Shoreline change

Data on shoreline change for the 54 deltas were derived from two metrics: (1) shoreline mobility and induced coastal area change over time, and (2) conversion of coastal land into water, or conversion of adjacent coastal water into land.

Shoreline mobility and area change

Quantification of the historical evolution of the delta shorelines was based on a prior compilation for each delta culled from the literature (Table 1). Some of these studies were devoted to a delta in particular, while others covered several deltas. The closure years for the analysis are 2013, 2014, or 2015. Where necessary, we completed the coverage up to 2015 (Table 1).

Complementary analysis of shoreline mobility and area change was carried out from a heterogeneous spatial database consisting of orthorectified satellite images of medium to high resolution: mostly Landsat ® (U.S. Geological Survey (USGS), 79 -30 m pixel size), SPOT ® 5 (European Space Agency (ESA) /ISIS, 2.5 m pixel size), and SPOT ® 6 (ESA/GEOSUD, 1.5 m pixel size), depending on availability over the period 1972 -2015 (see Supplementary Material 1). For deltas impacted by seasonal monsoon and monsoon-type changes, all analyzed images covered the dry season. Images were also selected following a scrutiny of hourly tidal data to ensure homogeneity of the water level on all the images of the same site, favouring, wherever possible, high-tide images that are the most appropriate for delimiting the shoreline. End-of-year images were lumped with those of the following year. The spatial dataset was selected to cover the entire delta coastline for each year analyzed, with a minimum cloud cover of less than 10% (sorted in the USGS database for Landsat ® imagery).

Shoreline delimitation

In view of the heterogeneous resolutions of the spatial data, the delta shoreline was delimited by operator photo-interpretation. Although this manual method may appear long and tedious, we preferred it to automatic methods (e.g., [START_REF] Shaw | An image-based method for shoreline mapping on complex coasts[END_REF] because it offers the possibility of involving a much less number of steps than the latter, while good operator judgement can reduce bias in the recognition of the deltaic land-sea interface, a potentially complicated and dynamic zone that can be associated with large fluctuations in tidal range, large volumes of suspended sediments, more or less discernible dunes toes, and seasonal freezing. Manual operator delimitation also has the advantage of enabling identification and referencing of various other shoreline aspects on these deltas (vegetation, land-use) that are not the object of the present study. Since the objective here is to statistically analyze the multi-decadal shoreline evolution trends, we excluded potential shoreline markers subject to instantaneous, daily, or event-related fluctuations, such as swash zones or dune toes. The seaward boundary of mature vegetation cover is considered as a clean and usable limit for mangrove-and other types of forest-bound shorelines [START_REF] Coyne | Mapping Coastal Erosion Hazard Areas in Hawaii: Observations and Errors[END_REF][START_REF] Guy | Erosion Hazard Area Mapping, Lake County, Ohio[END_REF][START_REF] Priest | Coastal shoreline change study: Northern and Central Lincoln County, Oregon[END_REF][START_REF] Trépanier | Suivi de l'évolution du trait de côte à partir d'images HRV (XS) de SPOT: Application au delta du fleuve Rouge, Viêt-Nam[END_REF]. A mature vegetation cover is a reliable indicator of the shoreline, regardless of the resolution of satellite images. In order to distinguish areas of mature, and especially long-established, vegetation from sparse, growing or eroding vegetation, we used a combination of three spectral bands of Landsat satellite imagery. Bands 4 and 5 in the near infrared and band 7 in the mid-infrared provide a clear composition because the concerned wavelengths are attenuated by water, in particular in the shoreline area. With this combination, the vegetation is bright blue on the satellite image when healthy and dense, but dull blue when sparse or stressed. On engineered deltaic shorelines, we selected the seaward limit of protective structures such as dikes and embankments, as well as roads and canals in cultivated areas [START_REF] Morton | Evaluation of Shorelines and Legal Boundaries Controlled by Water Levels on Sandy Beaches[END_REF][START_REF] Coyne | Mapping Coastal Erosion Hazard Areas in Hawaii: Observations and Errors[END_REF][START_REF] Guy | Erosion Hazard Area Mapping, Lake County, Ohio[END_REF]. The shorelines on Landsat ® images were digitized with a magnification ranging from 1:8000 to 1:20,000, whereas this ranged from 1:5000 to 1:8000 for SPOT ® images. Vector points were set over distances of 30 to 100 m depending on the resolution of the image. A plot was made directly on the computer screen with a 0.35 mm thick cursor in ArcGIS © GIS. Shoreline detection is the most delicate step as it combines uncertainties (see below) related to image georeferencing, resolution, the thickness of the cursor, and the appreciation and experience of the operator, in addition to image quality aspects related notably to cloudiness. Figure 2 shows examples of successive delimited shorelines for the Ebro delta.

Surface area change and uncertainties

Using GIS, we determined surface area differentials between shorelines of different dates (about a year interval depending on available data) in terms of annual area gains and losses. The calculated total error 𝐸 𝑝 [m] generated by shoreline digitizing corresponds to the sum of the error relative to image resolution 𝐸 𝑟 [m], the root mean squared georeferencing errors 𝐸 𝑔 [m] of the data, and the precision of the cursor 𝐸 𝑐 [m] used to vectorize the shoreline, expressed by:

𝐸 𝑝 = √𝐸 𝑟 2 + 𝐸 𝑔 2 + 𝐸 𝑐 2 (1)
We georeferenced the satellite images by using a first-order polynomial transformation. The measured error (residue error) is the difference between the final position of the origin point relative to the specified real location (destination point). 𝐸 𝑝 is the sum of the mean squared errors of all the residues. The individual errors are expressed as a ratio relative to the spatial scale used [START_REF] Fletcher | Mapping Shoreline Change Using Digital Orthophotogrammetry on Maui, Hawaii[END_REF]Rooney et al., 2003;[START_REF] Hapke | National Assessment of Shoreline Change Part 3: Historical Shoreline Change and Associated Coastal Land Loss Along Sandy Shorelines of the California Coast National Assessment of Shoreline Change Part 3: Historical Shoreline Change and Associated Coast[END_REF]. 𝐸 𝑐 averaged is 2.8 m, based on the thickness (0.35 mm) of the cursor used to vectorize the shoreline and the mean scale (1:8000) used to visualize the satellite images on the GIS interface. Some parameters taken into account in the uncertainty determined by these authors are not documented for the spatial data used, such as the error related to tidal fluctuations (nevertheless avoided as best as possible during the selection of the satellite images).

The uncertainty 𝐸 𝐴𝐶 involved in the annual rate of area change (AC) of the coastal fringe (see below for definition of the coastal fringe) of each delta in [km 2 /year] was calculated using the following formula [START_REF] Himmelstoss | DSAS 4.0 Installation Instructions and User Guide[END_REF][START_REF] Hapke | National Assessment of Shoreline Change Part 3: Historical Shoreline Change and Associated Coastal Land Loss Along Sandy Shorelines of the California Coast National Assessment of Shoreline Change Part 3: Historical Shoreline Change and Associated Coast[END_REF]:

𝐸 𝐴𝐶 = √𝑆ℎ𝑎𝐸 𝑡 0 2 +𝑆ℎ𝑎𝐸 𝑡 1 2 𝑡 1 -𝑡 0 (2)
where 𝑆ℎ𝑎𝐸 𝑡 0 and 𝑆ℎ𝑎𝐸 𝑡 1 in [km 2 ] are the averaged estimates of the area uncertainty for successive sets of images and t 0 and t 1 are the two dates studied in decimal years, respectively, the older and the more recent. 𝑆ℎ𝑎𝐸 𝑡 0 and 𝑆ℎ𝑎𝐸 𝑡 1 were obtained from the calculation of the mean squared errors of each 1-km long segment (the shoreline was partitioned into linear 1-km segments using the DSAS v4.4

tool [START_REF] Thieler | The digital shoreline analysis system (DSAS) version 4.0, an ArcGIS extension for calculating shoreline change[END_REF])) (Table 2). The surface area error obtained for each linear km, for each pair of dates studied, is averaged for each delta. Values ranged from 0.0072 to 0.0099 km 2 /year. (Table 2).

The 54 river deltas vary considerably in subaerial size, and it is evident, therefore, that a same shoreline change rate does not have the same significance for two deltas with different dimensions and subjected to varied periods of analysis. In order to compare change rates, we expressed the annual coastal area change of each delta as a percentage of a standard pre-defined coastal band. We used a backshore baseline of 2 km relative to the 2015 shoreline of each delta and calculated the area within this bandwidth of 'active' coastal change (Fig. 2), referred to hereafter as the ACB. We preferred this metric, which incorporates multi-decadal changes pertinent to the shorelines rimming subaerial deltas, to both subaerial delta area, given the disparity in size among deltas and the large uncertainty in calculating the area of the subaerial delta, and a subaerial delta protrusion area (DPA) metric [START_REF] Besset | River delta shoreline reworking and erosion in the Mediterranean and Black Seas: the potential roles of fluvial sediment starvation and other factors[END_REF]. None of the deltas analyzed showed a 30-year shoreline mobility exceeding this 2 km-wide band. The DPA, defined as the area between the current delta shoreline and a straight line behind this shoreline running across the delta plain and linking it to the adjacent non-deltaic shorelines was used to characterize multi-decadal shoreline change in 10 Mediterranean and Black Sea deltas [START_REF] Besset | River delta shoreline reworking and erosion in the Mediterranean and Black Seas: the potential roles of fluvial sediment starvation and other factors[END_REF]. While being convenient for simple or single delta-lobe configurations typical of wave-dominated deltas, its delimitation can be too subjective when applied to deltas with one or more active or inactive interlinked delta distributary mouths, notably bayhead deltas [START_REF] Simms | Bayhead deltas and shorelines: Insights from modern and ancient examples[END_REF]. We acknowledge that calculating the area of the active coastal band might involve a degree of bias in zones of intricate curvilinear shorelines (Fig. 2), but this metric appears more appropriate for comparison than subaerial delta area and the DPA. The ACB area of the 54 deltas is shown in Supplementary Material 2.

The relative error E AC ACB of percentage of area change of the ACB is calculated from the product of the total length of the shoreline L S in [m], and the calculated area error margin for each alongshore kilometre E AC in [km 2 /yr], and then expressed as a ratio relative to the ACB in [km 2 ] over a time span T in

[yr] (here 30 years) (Table 3):

𝐸 𝐴𝐶 𝐴𝐶𝐵 = 𝐿 𝑆 •𝐸 𝐴𝐶 •100 𝐴𝐶𝐵 𝑇 (3)
Change is considered insignificant when the percentage of ACB area gain or loss is below the resulting threshold value of uncertainties thus calculated.

Land/water conversion at the shoreline

Shoreline area changes determined using the method and procedures described in the foregoing sections were complemented with data downloaded from [START_REF] Donchyts | Earth's surface water change over the past 30 years[END_REF] and [START_REF] Pekel | High-resolution mapping of global surface water and its long-term changes[END_REF] for identifying shoreline areas converted from land into water and vice versa (Fig. 3). This database consists of Landsat ® satellite images covering 32 years . The data are derived for each pixel (30 x 30 m) of satellite image over a coastal width of 2 km relative to the 1984 shoreline, for consistency with the shoreline change bandwidth (ACB) of 2 km. The rationale for retaining this width as a common reference is that it corresponds to the bandwidth of change liable to be caused by erosion (delta land conversion into coastal water) or accretion (coastal water conversion into delta land). As in the case of the ACB, we acknowledge, however, a number of limitations with this width, such as size and roughness of the

Changes in river sediment load

The fluvial sediment load corresponding to the drainage basin associated with each delta was obtained from the literature or calculated for two periods: (a) prior to 1970, and (b) between 1970 and 2014. The rationale for this choice of periods is to compare sediment loads over two distinct periods in order to highlight potential variations that may be gauged against shoreline mobility over the last 30 years, and, thus, test the extent to which river delta shorelines can be used as an indicator of delta vulnerability induced by diminishing fluvial sediment supply. Although water discharge data, on which are based calculations of sediment loads (see below), may date back several decades for some rivers, many others do not have data going far back before 1970. This year is close to the 1968 peak in world-wide commissioning of dams that commonly necessitated the acquisition of hydrological data a few years prior to dam projects [START_REF] Beaumont | Man's impact on river systems: A world-wide view[END_REF]IPCC, 2014;[START_REF] Cook | The protection and conservation of water resources[END_REF]. The pre-1970 sediment loads were collated from the literature for the Adra, Arno, Brazos, Ceyhan-Seyhan, Colorado (Mx), Danube, Ebro, Fly, Grijalva, Guadalfeo, Indus, Krishna, Mackenzie, Magdalena, Magra, Mahanadi, Mangoky, Medjerda, Mississippi, Moulouya, Nile, Ombrone, Orange, Parana, Pearl, Po, Rhône, Sao Francisco, Shatt el Arab, Tana, Vistula, Volta, Yellow, Congo, and Zambezi (Table 4), whereas those of the other 18 rivers were estimated from calculations based on the annual water discharge available for the years up to 1970. The post-1970 sediment loads were obtained from the literature for 52 deltas (Table 4), whereas those of the Colville and Mangoky were estimated from calculations based on the annual water discharge, using the same method as for the pre-1970 sediment load calculations. Water discharge data were obtained from the Global Runoff Data Centre (https://www.bafg.de/GRDC/EN/Home/homepage_node.html, see Supplementary Material 3).

To estimate the pre-and post-1970 sediment loads, Q s [kg/s], from the mean annual water discharge Q [m 3 /s], we used the formula of [START_REF] Syvitski | Estimating river-sediment discharge to the ocean: application to the eel margin, northern California[END_REF]:

𝑄 𝑆 = 25.19 𝑄 -2.209 • 𝑄 ([ log( 0.02 𝐻 1.5 𝐴 0.5 25.19 𝑄 -2.209 ) log𝑄 ]-1-0.238)+1 (4)
where 𝐻 is the maximum elevation of the catchment area [m] and 𝐴 the area of the catchment [m 2 ].

This equation can be expressed in the simplified form:

𝑄 𝑆 = 𝑎𝑄 𝑏+1 (5)
where:

𝑎 = 25.19 𝑄 -2.209 (6) 𝑏 = [log ( 0.02𝐻 1.5 𝐴 0.5 𝑎 )] -1 -𝑐 (7)
The rationale for this choice is that once flows of less than 20 m 3 /s are excluded, the sediment concentration rate [kg/m 3 ] can be estimated from 𝑄, representing 65% of the variance in the course of experiments carried out on a sample of 36 rivers (Syvitski et al., 1998a, b), with 𝑐 = 0.238, where 𝑐 is the organic regression coefficient used to convert the daily sediment load into an average annual sediment load. Using historical data on water discharge (see hydrometric stations in Supplementary Material 3), we calculated the sediment load for each year of analysis.

Estimation of the sediment load of a number of rivers can be affected by sediment trapping behind dam reservoirs. The sediment loads trapped by dams were calculated from the sediment concentrations of the main channels, 𝐶 𝑆 [kg/m 3 ] obtained for each river from the sediment load 𝑄 𝑆 [kg/s] and the average annual flow rate 𝑄 [m 3 /s]:

𝐶 𝑆 = 𝑄 𝑆 𝑄 (8)
By simply converting the mass and time units, the sediment load is expressed in tons per year. The sediment concentration is also used to determine the theoretical mass of sediment that can be stored in one year (𝐶 𝑆 𝑠𝑡𝑜𝑟𝑒𝑑 ) in the volume of existing reservoirs (𝑉 𝑠𝑡𝑜𝑟𝑎𝑔𝑒 ):

𝐶 𝑆 𝑠𝑡𝑜𝑟𝑒𝑑 = 𝐶 𝑆 • 𝑉 𝑠𝑡𝑜𝑟𝑎𝑔𝑒 (9)
Once converted into tons, this mass is subtracted from the annual mass of sediments delivered by the river. Thus, the percentage of sediments retained by the reservoirs in one year, in the case of storage reaching maximum capacity, can be estimated.

Only orders of magnitude and the tendency for loads to increase or decrease are exploitable, because of the lack of hydrometric stations covering the entire drainage basins. Although the flow of each tributary joining the mainstem channel is taken into account, only the potentially transportable solid load to the coast can be calculated from the flow volumes, and assuming that no obstacle, such as water retention by a reservoir dam, alters these volumes downstream. For 16 rivers, this is indeed the case (Fig. 4). Up to 20 dams and reservoirs, constructed after 1970, are located downstream of the flow measurement stations of these rivers. The cumulative storage capacities of these are of the order of ten million m 3 to a hundred km 3 , as in the case of the Orinoco river. Although we know that these dams exist downstream, it is not possible to adjust the flow volumes obtained upstream because there are no reliable and continuous data on what is actually retained by these dams. However, these volumes give an idea of the underestimation of the sediment load reduction for 13 rivers having a negative sediment balance: Brazos, Chao Phraya, Colorado (Tx), Godavari, Indus, Medjerda, Murray, Rhône, Senegal, Shatt el Arab, Mississippi, Volta and Chang Jiang. On the other hand, for the Dnieper, Orinoco and Parana, the increases in sediment load are overestimated, especially as the retention capacities of the dams downstream of the stations on their mainstems are among the most important.

The fact that the data extracted from the literature or calculated from time series of available water discharges (Table 4) are not homogeneous in terms of dates, but only ascribed to a period (pre-or post-1970) constitutes a limitation that should be kept in mind. However, comparison of the pre-and post-1970

values yields the same important information on the order of magnitude and the trend of evolution of fluvial sediment supply.

Results

Delta shoreline mobility

Global situation

The evolution of the 54 deltas has been variable. Seven deltas (13%), the Murray, Po, Cunene, Adra, Arno, Shatt el Arab and Magra, showed percentages of area change within their respective ACB error margins, corresponding to no significant change. The remaining 47 deltas showing significant change are depicted in Fig. 5, of which 29 (53.7%) exhibited retreat and loss of coastal area with a maximum rate of -8.55 km 2 /yr for the Mississippi (Fig. 5a,b). ACB area losses for the most severely affected deltas attained 20.5% for the Colorado (Tx) and 15.2% for the Indus. The Ombrone (-0.18%), Rhône (-0.47%), Guadalfeo (-0.66%), Sao Francisco (-0.68%) and Dnieper (-0.68%) showed the lowest ACB area losses.

Only 18 deltas (33.3%) gained ACB area over the study period (Fig. 5a,b). Among these, the Chang Jiang showed a maximum, with a 49% overall increase, corresponding to +18.5 km 2 /year. The Pearl (35%), Red River (23%), and Parana (12%) also registered significant gains. These five deltas are among the largest. The smallest gains were those of the Danube (0.11%), Tana (0.17%) and Congo (0.35%). The larger the ACB, the larger the change rate tendency in both advancing and eroding deltas (Fig. 6).

Spatial and temporal variations in delta shoreline change

All deltas showed more or less marked spatial and temporal variability in shoreline mobility. This is to be expected given the complexity of delta behavior in space and time, especially where more than one delta lobe is present. Figure 7 depicts coastal area gains and losses for a selection of some of the large deltas. The total area gained by the Ganges-Brahmaputra (GB) delta, i.e. +33.87 km 2 /year, in the present area of the active distributary mouths, was nearly balanced by an overall area loss of 26.47 km 2 /year in the abandoned lobes of the Sundarbans. The overall rate of evolution of the GB was, thus, much higher than that of the other deltas but, compared to its ACB area, this change rate was relatively low. A similar behavior is observed for the Mekong, Ayeyarwady, Mississippi and Indus deltas which have lost and gained, annually, several km 2 depending on the sectors.

The mobility pattern over time has also been quite variable (Fig. 8). Non-parametric Mann-Kendall tests further highlight this variability with some deltas showing a temporal trend and others not.

The Orange, Orinoco, Chang Jiang and Vistula all show an increasing trend in area gain, whereas the Mekong and Paraiba do Sul show a downward trend. Among dominantly eroding deltas, the Mississippi, Volta, Moulouya, Arno, Magdalena, Limpopo, Rhône, and Mackenzie show a trend of decreasing erosion.

The Indus, Medjerda, Godavari, Krishna, Ebro, and Shatt el Arab, show, on the other hand, an aggravation over time of their erosion.

Dichotomy between small and large eroding deltas

We distinguished small from large deltas by setting a limit between the two categories using the first quartile in ACB area ranking. This limit corresponds to 400 km 2 (Fig. 9), with 27 (50%) in the small category. In both categories, shoreline retreat has dominated. Among the 27 large deltas, 11 (40.7%) underwent retreat, whereas 19 of the 27 small deltas (70.4%) suffered the same fate. Losses for the large delta category averaged 8.7%, whereas small deltas lost, on average, 3.7% of their ACB. However, among the 27 large deltas, only 11 (40.7%) underwent retreat, whereas 19 of the 27 small deltas (70.4%) suffered the same fate. Another notable difference between the two categories is the moderately high (18.5%) percentage of small deltas with stable shorelines, compared to large deltas (7.4%).

Land/water conversion of the ACB

Thirty-four (63%) of the 54 deltas were dominated by conversion of land into permanent or seasonal water. 39 (72%) exhibited a larger transformation of coastal land to permanent water than of coastal water into land (Fig. 10). A comparison of these transformations with the 18 deltas showing a gain in ACB area shows that 6 deltas (33%) were dominated by transition towards more conversion of land into permanent water than of conversion of water into permanent land. Among the 29 deltas that have undergone a loss of ACB area, 21 (72%) were dominated by conversion of land into permanent or seasonal water, and 18 (62%) exhibited more conversion of coastal land into permanent water than of coastal water into permanent land. Among the seven deltas showing no significant change in ACB area over the period of analysis, six (86%) were dominated by conversion of more land into permanent water than of conversion of more water into permanent land.

Sediment loads

The changes in sediment load are depicted in Fig. 11 and Fly (Fig. 11).

Discussion

This review shows that 29 out of 54 deltas, of which 11 in the large (ACB area > 400 km 2 ) category, have been undergoing erosion over the last three decades (Figs. 5,8,9). This trend is consistent with a larger conversion of land into water, within the 2 km-wide ACB, than of water into land (Fig. 10).

At the same time, the fluvial sediment loads associated with 42 deltas have diminished significantly (Fig. 11). The clear dominance of coastal erosion indicates that the shorelines rimming these deltas no longer dispose of the supply of sediment necessary to balance forcing and induced alongshore/offshore sediment redistribution beyond the confines of the subaerial delta. This having been said, however, with the exception of the cases of the Colorado (Tx) and the Indus, losses in delta ACB have been rather low. In the large delta category, the average loss in ACB area diminishes from 8.7% to 2.8% when the Indus (-15.2%) is excluded. This is an important finding in terms of gauging currently perceived delta vulnerability, and would tend to indicate that, over the last three decades, diminishing fluvial sediment supply, the driving force in deltaic equilibrium at a multi-decadal timescale deltas, has not, thus far, had a significant negative impact on delta shoreline mobility. Notwithstanding fluvial sediment load decreases exceeding 20%, ten deltas in fact showed gains in ACB area, some very significantly. Given the diversity of the context of deltas, the strength and quality of the relationship between shoreline mobility and coastal area losses or gains are affected by a whole range of miscellaneous factors that include: (1) direct sediment retention by dams and reservoirs, and, increasingly, channel-bed mining and river engineering;

(2) given the commonality of bedload in constituting delta shorelines, variable lag in the downstream propagation of the effect of bedload trapping by reservoirs, and attendant bedload transfer, downstream of dams, from river channels to delta shorelines, as well as potential upstream-to-downstream changes in the distribution of sediment source-and-sink areas;

(3) the effects of mining and land-use changes that strip catchments of vegetation, resulting in enhanced sediment supply, or the counter-active impact of reforestation that protects catchment slopes from erosion; (4) differences in sensitivity to sediment reduction between the subaerial and the subaqueous parts of a delta, and deltaic self-organization involving, for instance, sediment apportionment among channels, shorelines, the subaqueous delta, and the subaerial delta, in addition to processes such as channel switching and lobe abandonment;

(5) delta shoreline advance due to land reclamation, or the mitigating effects of engineering structures in protecting eroding delta shorelines, or in trapping sediment that would, otherwise, be reworked and evacuated alongshore or offshore; (6) aggravated subsidence due to human activities; and (7) the impact of enhanced storminess and episodic high-energy events where delta resilience is already impaired by dwindling sediment supplies. Land reclamation, for instance, has been particularly important in the two deltas that show the most important gains in ACB, despite a diminishing sediment supply: the Chang Jiang, where 840 km 2 of wetlands were converted to land between 1950 and 1995 [START_REF] Fan | Cross-shore variations in morphodynamic processes of an open-coast mudflat in the Changjiang delta, china: With an emphasis on storm impacts[END_REF], and the P earl delta with a reclaimed area of 356 km 2 between 1988 and 1997 [START_REF] Deng | Morphologic evolution and hydrodynamic variation during the last 30 years in the Lingding bay, south china sea[END_REF][START_REF] Wu | Impact of human activities on subaqueous topographic change in Lingding bay of the Pearl river estuary, china, during 1955 -2013[END_REF]. In both deltas, the 2 km-wide shoreline band for land-water conversion and for defining coastal area change used as a reference in our study was extended seaward (Fig. 10). In other deltas, such as the Rhône, lagoon infill has also occurred locally over the last few decades, expanding the water-to-land conversion in the 2 km-wide coastal band, thus mitigating loss in ACB area.

Our review shows that small deltas (ACB area ≤ 400 km 2 ) tend, proportionately to their number in this study (27/54), to be more prone to erosion compared to the larger (ACB area > 400 km 2 ) deltas (Fig. 9). It is likely that the smaller deltas are more sensitive to the effects of sediment sequestration by dams and reservoirs on their watersheds, whereas these effects are more likely to be more readily dampened or slowed down in catchments with large deltas. These small deltas also generally have small river sediment inputs (Table 4, Fig. 12) which may explain a greater sensitivity to marine reworking and erosion, especially by waves [START_REF] Anthony | Wave influence in the construction, shaping and destruction of river deltas: A review[END_REF][START_REF] Nienhuis | What makes a delta wave-dominated?[END_REF]. The results suggest, thus, that small deltas, which have tended to attract less attention than large deltas, could, in fact, be relatively more vulnerable and less resilient. This is an important point to ponder as many small deltas also support dense populations and valuable activities, as in the Mediterranean [START_REF] Anthony | Human influence and the changing geomorphology of Mediterranean deltas and coasts over the last 6000 years: From progradation to destruction phase?[END_REF]. In contrast, small deltas have a comparatively much higher percentage of stable shorelines (Fig. 9), and this may reflect the potentially relatively lower costs involved in engineered stabilization of the short shorelines rimming these smaller deltas, such as the Magra and the Arno [START_REF] Anfuso | An integrated approach to coastal erosion problems in Northern Tuscany (Italy): Littoral morphological evolution and cell distribution[END_REF], and the Adra [START_REF] Jabaloy-Sánchez | Six thousand years of coastline evolution in the Guadalfeo deltaic system (southern Iberian Peninsula)[END_REF], compared to large deltas with longer shorelines.

Although delta shoreline mobility does not appear, thus far, to have been strongly negatively impacted by diminished fluvial sediment loads, there is, nevertheless a clear relationship between these two parameters (Fig. 12). The linear relation yielded by the regression is poor when all deltas are taken into account. However, exclusion of eight outliers yields a statistically significant relationship that confirms the importance of fluvial sediment supply in determining ACB (and subaerial delta) area. Among the eight outliers, four are North American deltas: Mississippi, Mackenzie, Colville, Grijalva. These deltas, notably the Mississippi and the Mackenzie, have a relatively large ACB area relative to their current sediment supply, thus reflecting not only the effects of prolonged sediment reduction due to dams (the case notably of the Mississippi), but also the relatively sheltered Gulf Coast and Arctic settings of these deltas. Three of these deltas (Mississippi, Mackenzie, Colville) show intricate shorelines that generate a high ACB area (Figs. 5,12). The other four outliers are African deltas (Orange, Cunene, Moulouya, Medjerda) that show a small ACB area relative to their sediment supply, thus possibly indicating either a high-energy wave-dominated context (Cunene, Orange) or prolonged past erosion due to sediment trapping by dams, as in the cases of the Moulouya and Medjerda [START_REF] Besset | River delta shoreline reworking and erosion in the Mediterranean and Black Seas: the potential roles of fluvial sediment starvation and other factors[END_REF].

The synthesis in Fig. 13 highlights the importance of fluvial sediment supply to delta shoreline mobility. Eroding deltas are associated with a reduction in fluvial sediment supply that is twice as important as that of stable or advancing deltas. When grouped together, eroding deltas receive less than a quarter of the fluvial sediment supply of deltas with stable and advancing shorelines. Figure 14 We have synthesized our results in terms of the global distribution of deltas (Fig. 15) within the four vulnerability levels proposed in Fig. 14. The region most affected by the loss of coastal deltaic area is North America, where the seven selected deltas showed significant coastal subaerial retreat, possibly reflecting the long history of dam construction in the USA [START_REF] Ho | The future role of dams in the United States of America[END_REF]. Among the six deltas analyzed in South America, four showed gains in area: Amazon, Orinoco, Paraiba do Sul and Parana, possibly reflecting, in the case of the first three, equatorial-tropical rain-forested catchments and relatively low anthropogenic pressures. In the densely-developed context of Europe and the Mediterranean, only the Vistula, subject to relatively low anthropogenic pressures, significantly gained in ACB in 30 years. From the Persian Gulf to the Gulf of Thailand, eight out of the nine deltas analyzed were eroding, reflecting the impact of anthropogenic pressures, while two large deltas, the Ayeyarwady and the Ganges-Brahmaputra showed gains. In the case of the former, increasing agricultural transformation of the catchment and mining have probably balanced sediment reduction loads due to dams (SOBA, 2017). Among the 10 deltas in sub-Saharan Africa, the Orange, the Congo and the Zambezi have gained coastal area, while six others were eroding and the remaining one (Cunene) did not show any significant evolution over the study period. Among the seven deltas of Pacific Asia and Oceania, five registered significant shoreline advance:

Yellow River, Chang Jiang, Pearl, Red River, and Murray, whereas others showed shoreline retreat. The reasons for this variability are diverse, and include not only the effects of dams, but also those of human pressures and climate change in the river basins, and within individual deltas. This variability also underpins the importance of further analysis of individual deltas in order to implement better future management scenarios.

The erosion of deltaic shorelines in the present context of diminishing sediment supply should be a cause for concern in the coming years, given the compounding of impacts expected from climate change and sea-level rise. Mechanisms depriving deltas of sediment, or contributing to high subsidence rates, enhance the effects of sea-level rise in driving delta vulnerability (Ericson et al., 2007;Syvtiski et al., 2009;[START_REF] Tessler | Profiling risk and sustainability in coastal deltas of the world[END_REF][START_REF] Tessler | A model of water and sediment balance as determinants of relative sea level rise in contemporary and future deltas[END_REF]Dunn et al., submitted), and this should include delta shoreline retreat.

Model scenarios by [START_REF] Tessler | A model of water and sediment balance as determinants of relative sea level rise in contemporary and future deltas[END_REF] of the impact of contemporary and future water resource management schemes and hydropower dams in river basins on relative sea-level rise across 46 global deltas suggest that contemporary sediment fluxes, anthropogenic drivers of land subsidence, and climateinduced sea-level rise will result in delta relative sea-level rise rates that average 6.8 mm/year. Assessment of the impacts of dams planned and under construction suggests sea-level increases of the order of 1 mm/y in deltas, whereas reduced sediment retention caused by increased river channelization and management will lead to a relative sea-level rise of nearly 2 mm/year [START_REF] Tessler | A model of water and sediment balance as determinants of relative sea level rise in contemporary and future deltas[END_REF]. Dunn et al. (submitted) analyzed projected changes in fluvial sediment flux over the 21st century to 47 of the world's major deltas under 12 environmental change scenarios constructed using four climate-change pathways, three socioeconomic pathways, and one reservoir construction timeline, and showed that a majority (33)

of the investigated deltas are projected to experience reductions in sediment flux by the end of the century, when considering the average of the scenarios. Projected mean and maximum declines of 38% and 83%, respectively, between 1990-2019 and 2070-2099, will be driven by the effects of anthropogenic activities (changing land management practices and dam construction) overwhelming the effects of future climate change. These results highlight the consequences of direct (e.g. damming) and indirect (e.g. climate change) alteration of fluvial sediment supply to deltas.

To further gauge the impact of dams in affecting fluvial sediment loads in the future, data on the maximum sediment storage capacity behind dams erected across the 54 rivers in our study were obtained from the GRanD database (see also [START_REF] Lehner | Highresolution mapping of the world's reservoirs and dams for sustainable river-flow management[END_REF] developed in the framework of the Global Water System Project © (http://www.gwsp.org/products/grand-database.html). From the calculated fluvial sediment concentrations, the proportion of sediments that can be subtracted from the solid load of each river, in the case of a total filling of reservoirs and dams, has been estimated. In this case, these solid reservoirs would reduce the sediment load of 25 of the 54 rivers studied by more than 50% and 15 of them would be totally deprived of sediments (Fig. 16). The storage capacity by dams would be up to two to five times greater than the solid load available for the Medjerda, Zambezi, Shatt el Arab, Nile and Volta rivers.

This problem will gradually appear in other rivers and become exacerbated in those already affected, given the many projects and plans (GRanD Database) as well as ongoing constructions on some rivers.

These include the project on the largest hydropower dam in Africa on the Congo River, many large dams on the Zambezi River, and more than 400 dams planned along the courses of the Amazon River. Nearly 200 dams are commissioned on the Ayeyarwady River, with a total storage capacity of about 21 km 3 , and another 25 are already planned for the beginning of the 2020s, with more than 29 km 3 of total retention capacity [START_REF] Wle | Dataset on the Dams of the Irrawaddy, Mekong, Red and Salween River Basins[END_REF]. On the Mekong River, no less than 312 dams are commissioned, with a total storage capacity that could reach more than 80 km 3 , and 22 dams planned between 2017 and 2030, and which would store more than 25 km 3 water and solid load [START_REF] Wle | Dataset on the Dams of the Irrawaddy, Mekong, Red and Salween River Basins[END_REF]. The future of the sediment loads of the Ganges-Brahmaputra, Mahanadi, Godavari and Krishna rivers could also be dramatically linked in the coming decades with the inception in 2015 of the largest infrastructure project in the world for irrigation, the Interlinking of Rivers Project (IRP database, http://csdms.colorado.edu/wiki/Data: NRLP India) [START_REF] Bagla | Water. India plans the grandest of canal networks[END_REF]. This project involves linking 37 major rivers of India through thirty connections, 15,000 km of channels designed to derive 170 km 3 of water, and nearly 3,000 dams, including 42 major ones that could be responsible for the displacement of nearly 700,000 people [START_REF] Ciesin | NASA Socioeconomic Data and Applications Center (SEDAC) Documentation for Gridded Population of the World[END_REF]. Beyond the risk of disturbance of the hydro-sedimentary balance of the rivers concerned, the construction of such dams will result in the submergence of more than 2.7 million hectares of cultivated, occupied or preserved land (e.g. the Panna Tiger Reserve) in India, and would force the displacement of nearly 1.5 million people. This non-exhaustive list of future dam construction projects suggests that additional pressures are to be expected on the world's deltas in the coming decades. These impending pressures urgently call for sustainable management strategies for rivers and their deltas.

Conclusions

The vulnerability status of a selection of 54 of the world's deltas was quantified from changes in delta shoreline mobility and coastal area over the last 30 years alongside fluvial sediment loads. The results show a large majority (29) of deltas in overall erosion, in a context of diminished fluvial sediment loads for 42 of the 54 rivers associated with these deltas, by more than 50% for 28 of them. Small deltas (ACB area ≤ 400 km 2 ) are more prone to erosion, but have tended to be stabilized by engineering. Small deltas are probably more sensitive than large deltas to the reduction of fluvial sediment supply, as well as to marine reworking and erosion, especially by waves.

When grouped together, eroding deltas receive less than a quarter of the fluvial sediment supply of deltas with stable and advancing shorelines. With the exception of the Indus, losses in delta ACB area have been rather low among the world's major deltas. These rather mild losses would tend to indicate that delta subaerial area, indexed by shoreline mobility, has, thus far, not been significantly impacted by recent changes in fluvial sediment supply, the driving force in deltaic equilibrium at a multi-decadal timescale.

Delta mobility response has been variable, reflecting the miscellaneous factors that influence fluvial sediment loads, such as mitigation of the effects of dams by deforestation and mining on catchments, sediment trapping by shoreline engineering structures, and long temporal lag between dam construction and sediment reworking and transport to the coast downstream of dams. A clear link exists, nevertheless, between change in the area of the active coastal band (ACB) rimming the subaerial deltas and the reduction in fluvial sediment loads: eroding deltas have been affected by a reduction in fluvial sediment supply that is twice as important as that of stable or advancing deltas. This reduction in river sediment flux will clearly diminish delta resilience to other drivers such as subsidence and climate-induced sea-level rise. The variability of delta shoreline behavior in the face of changing fluvial sediment loads calls for more in-depth studies of individual deltas in order to build up future management plans addressing vulnerability and loss of resilience. Dams currently in place will reduce, in the future, the sediment load to their deltas of 25 of the 54 rivers by more than 50% and 100% for 15 of them. Their potential effect, as well as the anticipated effects of climate change on fluvial sediment supply, need to be further investigated at the scale of individual deltas.

Acknowledgements

This is a contribution of the Belmont Forum Project BF-Deltas: Catalyzing Action Towards Sustainability of Deltaic Systems with an Integrated Modeling Framework for Risk Assessment. We thank three anonymous reviewers for their salient suggestions for improvement, and Florin Zăinescu for the fruitful discussions and his watchful eye in tracking anomalies in the manuscript. Indus;3. Godavari;4. Colorado (Mx);5. Mississippi;6. Mangoky;7. Fly;8. Magdalena;9. Krishna;10. Medjerda;11. Chao Phraya;12. Brazos;13. Mahanadi;14. Volta;15. Grijalva;16. Burdekin;17. Niger;18. Nile;20. Moulouya;21. Limpopo;22. Colville;23. Mackenzie;24. Ebro;25. Dneiper;26. Sao Francisco;27. Guadalfeo;28. Rhône;29. Ombrone;30. Cunene;31. Arno;32. Shatt el Arab;33. Adra;34. Magra;35. Murray;36. Po;37. Danube;38. Tana;39. Congo;40. Senegal;41. Vistula;42. Paraiba do Sol;43. Mekong;44. Ayeyarwady;45. Orinoco;46. Orange;47. Amazon;48. Zambezi;50. Yellow River;51. Parana;52. Red River;53. Pearl;54. Chang Jiang. 1. Colorado (Tx);2. Indus;3. Godavari;4. Colorado (Mx);5. Mississippi;6. Mangoky;7. Fly;8. Magdalena;9. Krishna;10. Medjerda;11. Chao Phraya;12. Brazos;13. Mahanadi;14. Volta;15. Grijalva;16. Burdekin;17. Niger;18. Nile;20. Moulouya;21. Limpopo;22. Colville;23. Mackenzie;24. Ebro;25. Dneiper;26. Sao Francisco;27. Guadalfeo;28. Rhone;29. Ombrone. 4.

Figure 12. Graphs showing the relationship between ACB area and post-1970 river sediment loads: black dots represent large ACB (> 400 km²), and red dots small ACB (≤ 400 km²). 1. Colorado (Tx);2. Indus;3. Godavari;4. Colorado (Mx);5. Mississippi;6. Mangoky;7. Fly;8. Magdalena;9. Krishna;10. Medjerda;11. Chao Phraya;12. Brazos;13. Mahanadi;14. Volta;15. Grijalva;16. Burdekin;17. Niger;18. Nile;20. Moulouya;21. Limpopo;22. Colville;23. Mackenzie;24. Ebro;25. Dneiper;26. Sao Francisco;27. Guadalfeo;28. Rhône;29. Ombrone;30. Cu nene;31. Arno;32. Shatt el Arab;33. Adra;34. Magra;35. Murray;36. Po;37. Danube;38. Tana;39. Congo;40. Senegal;41. Vistula;42. Paraiba do Sol;43. Mekong;44. Ayeyarwady;45. Orinoco;46. Orange;47. Amazon;48. Zambezi;50. Yellow River;51. Parana;52. Red River;53. Pearl;54. Chang Jiang;

  shoreline, and inclusion of water areas (lagoons) behind narrow shoreline barriers that could have silted up or been reclaimed, or land areas that have been transformed into wetlands. We will highlight examples of these limitations where appropriate. The data also show seasonal patterns of surface conversion between 1984 and 2015 for each pixel. Overall, we retained four conversion categories over the 32-year period of observation (Fig.3): (1) new permanent water surfaces (land into permanent water, [A 2 ]), (2) loss of permanent water (permanent water into land, [A 3 ]), (3) permanent water into seasonal water [A 4 ], and (4) seasonal water into permanent water [A 1 ]. A single category suffices to validate the presence of water. The category representing the year a change is identified is then considered as the first year of change. The category of the last year is always attributed to the last year of observation (October 2014 to October 2015).

  shows a vulnerability classification perspective based on four levels of reduction of fluvial sediment supply. While the yardstick of shoreline change is also used here as an indicator of vulnerability, this metric represents only one of various indicators of delta biophysical sustainability, and, given the variable temporal trends in delta shoreline mobility (Fig.8), cannot be used to project rates of shoreline mobility over the coming decades. The reduction of a river's sediment flux should, however, also clearly diminish a delta's resilience to other drivers. It is important to envisage the supply of sediment less in terms of its direct role in generating accretion, and, therefore, eventual subaerial delta advance seaward, and more in terms of an agent enabling deltas to keep pace with forcing and subsidence, and to maintain their resilience. With few exceptions, deltas are expected to lose resilience and become more vulnerable as a result of decreasing fluvial sediment supply. The levels proposed in Fig.14point out disparities between multi-decadal change in fluvial sediment supply and the expected trend in deltaic shoreline mobility, thus highlighting the need for research on the operation of other factors at play in determining the response and resilience of deltas to a decreasing sediment supply. This variability highlights, in particular, the need, in a management framework, for considering factors influencing the shoreline mobility of deltas individually.

Figure 1 :

 1 Figure captions:Figure 1: Global map showing the 54 river deltas reviewed in this study.

Figure 2 .

 2 Figure 2. Examples of operator-identified shoreline traces of the Ebro delta. Shaded area shows the active coastal band (2 km-wide from the 2015 shoreline).

Figure 3 :

 3 Figure 3: Schematic illustration showing various conversions of a deltaic coastal strip the shoreline to 2 km inland) from water to land, and vice versa. The data retrieved for the 2 km-wide coastal band and calculated for the 54 deltas are from Donchyts et al. (2016) and Pekel et al. (2016).

Figure 4 .

 4 Figure 4. Number of dams with a reservoir retention capacity > 1 km 3 downstream of hydrometric stations (black bars), and the reservoir retention capacity (red dots) that was not taken into account in the study of variations in pre-1970 versus post-1970 sediment loads.

Figure 5 .

 5 Figure 5. Active coastal band (ACB) area for deltas with ACB gains (a) or losses (d), area change per annum for deltas with gains (b) or losses (e), and percentage of area gained (c) or lost (f) over 30 years in relation to the ACB in 2015, together with error margins (green bars). Dashed red line separates large from small deltas (see section 3.1.3).

Figure 6 .

 6 Figure 6. The relationship between area of the active coastal band (ACB) and rate of area change within the ACB: (a) deltas with advancing shorelines; (b) deltas with retreating shorelines. 1. Colorado (Tx); 2.

Figure 7 .

 7 Figure 7. Examples of annual spatial variations in ACB area in individual deltas.

Figure 8 .

 8 Figure 8. Temporal variability of the rate of change [km²/yr] of the ACB for a selection of deltas, the associated linear regressions and their p-values. The p-values concern the raw data, and not the annual anomalies. Using the non-parametric Mann-Kendal test to detect trend in the time series (positive, negative or non-null), we consider that with the null hypothesis H 0 (p-value > α in black), a trend does not exist, whereas H 1 (p-value < α in red) indicates a trend in the time series.

Figure 9 .

 9 Figure9. Graphs showing ranking of loss of delta ACB area with regards to the size of the ACB. The graph on the left shows the percentage of loss in ACB area, over 30 years, of each significantly eroding delta. The graph on the right shows a comparison of small (red dots: ACB area ≤ 400 km 2 ) and large deltas (black dots: ACB area >400 km 2 ) in terms of shorelines in erosion, shorelines with no significant change, and shorelines in advance.1. Colorado (Tx); 2. Indus; 3. Godavari; 4. Colorado (Mx); 5.

Figure 10 .

 10 Figure 10. The four categories of coastal land/water and water/land conversions over a period of 30 years for the 54 deltas. The graph on the right shows change in delta ACB area over the same period.

Figure 11 .

 11 Figure 11. Map and graph showing the percentages of loss of fluvial sediment load between the pre -1970 period and the period 1970-2014. The post-1970 sediment loads are shown in Table4.

Figure 13 .

 13 Figure 13. Graphs summarizing (a) area change within the ACB, (b) fluvial sediment load, (c) change in fluvial sediment load after 1970, and (d) land/water conversion in the ACB.

Figure 14 .

 14 Figure 14. Indicative ranking of delta vulnerability based on reduction of fluvial sediment load and shoreline mobility.

Figure 15 .

 15 Figure 15. Maps showing: (a) the distribution of the 54 river deltas grouped by continent/region and as a function of shoreline mobility, and (b) the individual deltas depicted on the basis of the four vulnerability levels shown in Fig. 14.

Figure 16 .

 16 Figure 16. Potential loss of river sediments if dams reach their maximum storage capacity.

Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 Figure 14

 1234567891011121314 Figure 1

Figure 15

 15 Figure 15

Figure 16

 16 Figure16

  together with the percentage of load change between the pre-and post-1970 periods. For 38 rivers, the load has diminished by more than 20%. The decrease has been particularly important in Mediterranean deltas: Nile, Ebro, Magra, Moulouya, Ombrone, and Rhône, exceeding 80%, but also in ten other deltas: Colorado (Mx), Grijalva, Orange, Krishna, Yellow River, Red River, Pearl, Mississippi, Volta and Chao Phraya. Several other deltas are, however, associated with catchments that have registered mild increases in sediment loads: Zambezi, Amazon, Dnieper, Ganges-Brahmaputra, Parana, Colville, Mackenzie, Magdalena, Limpopo, Mangoky, Vistula,

Table captions :

 captions Table1. Literature references with data on delta shoreline evolution.

	Godavari 0.0087		Ombrone 0.0090		Grijalva		0.0072
	(b) Kilometric indicator	1	2	3	4	5	6	7	8	9
	Surface error	𝑆ℎ𝑎𝐸 𝑡 0	0.31	0.33	0.28	0.30	0.29	0.37 0.39 0.40 0.40
	(km 2 )	𝑆ℎ𝑎𝐸 𝑡 1	0.01	0.03	0.05	0.03	0.03	0.03 0.03 0.06 0.06
	Quadratic error	δ t	0.31	0.33	0.29	0.30	0.30	0.37 0.39 0.41 0.40
	Dates	1973 2014					01/20/1973 01/01/2014
	Time interval (yr)						40.947
	𝐸 𝐴𝐶 (km 2 /yr)	1973-2014	0.008 0.008 0.007 0.007 0.007 0.009 0.01 0.010 0.01

Table 2 .

 2 Uncertainties in coastal area change rates (EAC) in km²/yr for each delta (a), and an example of error calculations for a 9-km long sector of the Mekong delta shoreline (b).

Table 3 .

 3 Uncertainties in percentage evolution of the area of the active coastal band (EAC ACB ).

Table 4 .

 4 Post-1970 sediment loads of the 54 rivers culled from the literature and from the Global Runoff Data Centre (GRDC) database (https://www.bafg.de/GRDC/EN/Home/homepage_node.html). References for the pre-1970 sediment loads discussed in the text are included here.

Syvitski and Saito, 2007;[START_REF] Milliman | River Discharge to the Coastal Ocean: A Global Synthesis[END_REF][START_REF] Anthony | Deltas[END_REF][START_REF] Goudie | Fluvial Processes and Forms in the Anthropocene[END_REF]Viles, 2016 Sestini, 1991;[START_REF] Milliman | River Discharge to the Coastal Ocean: A Global Synthesis[END_REF][START_REF] Anthony | Human influence and the changing geomorphology of Mediterranean deltas and coasts over the last 6000 years: From progradation to destruction phase?[END_REF][START_REF] Goudie | Fluvial Processes and Forms in the Anthropocene[END_REF] Viles, 2016 X Ombrone 249 [START_REF] Frangipane | Long-term variability of sediment transport in the Ombrone River basin (Italy). Variability in Stream Erosion and Sediment Transport[END_REF][START_REF] Syvitski | Impact of humans on the flux of terrestrial sediment to the global coastal ocean[END_REF][START_REF] Milliman | River Discharge to the Coastal Ocean: A Global Synthesis[END_REF]Anthony et al., 2014 Milliman and[START_REF] Milliman | River Discharge to the Coastal Ocean: A Global Synthesis[END_REF][START_REF] Anthony | Human influence and the changing geomorphology of Mediterranean deltas and coasts over the last 6000 years: From progradation to destruction phase?[END_REF] -Orange 2915 [START_REF] Knighton | Fluvial forms and processes[END_REF][START_REF] Milliman | River Discharge to the Coastal Ocean: A Global Synthesis[END_REF][START_REF] Syvitski | Morphodynamics of deltas under the influence of humans[END_REF][START_REF] Goudie | Fluvial Processes and Forms in the Anthropocene[END_REF]Viles, 2016 Rooseboom and[START_REF] Rooseboom | Changes in the sediment load of the Orange River during the period 1929-1969[END_REF]


Estelle Guerry
email: estelle.guerry@laplace.univ-tlse.fr

Cătălin Daniel Gălățanu

Laurent Canale

Georges Zissis
email: georges.zissis@laplace.univ-tlse.fr

Luminance Contrast Assessment for Elderly Visual Comfort Using Imaging Measurements

Keywords: Contrast of luminance, Elderly visual comfort, Luminance map, Visual pathologies, Camera calibration

The visual perception of the elderly is today question carrying major issues. In a growing context of aging, it's important to pay attention to optimizing their light environments. This is especially true in a situation of visual impairment, impacting their visual comfort. It's therefore necessary to adapt the quantity and the quality of light, but especially the luminance contrast levels. Then using digital imaging, it's possible to measure and identify existing levels, which will subsequently optimize these levels of luminance contrast and therefore the light environment. With the identification of the weaknesses of the luminous environment via digital imaging and a rich geriatric and medical literature, we are able to compensate the effects of visual pathologies inherent to the age and giving daily visual comfort.

Introduction

Today, people over 60 years old are becoming increasingly important in the demographic, social and economic dynamics. For good reason, if at the beginning of the years 2000, they represented 11% of the world population, in 2050 they will represent 22%, going from 605 million to 2 billion during the same period [START_REF]OMS. Aging and quality of life, Highlights on aging. Site of the world health organization[END_REF].

This context of aging is accompanied by new issues, particularly related to their quality of life and their health. They concern especially the evolution of the senses, and more particularly the vision. Raising new questions around the notion of visual comfort, inherent to their visual perception, it's important not to minimize the consequences of this sensory evolution in the elderly [START_REF] Owsley | Aging and Vision[END_REF]. Indeed, they can affect their physical, emotional and social well-being, thus reducing their quality of life [START_REF] Thomas | Les nouvelles approches de la dépression de la personne âgée[END_REF]. But if aging isn't a disease, it can sometimes be accompanied by a development of multiple visual pathologies, resulting for example in a decrease in perceived light, a change in color perception, an increased sensitivity to glare, diminished perception of contrasts or visual acuity disorders [START_REF] Zanlonghi | La vision de la personne âgée, Metrovision. Archives sites pdf of Metrovision[END_REF][5] [START_REF] Owsley | Anging and low-contrast vision : face perception[END_REF]. The most common being cataract, AMD and glaucoma. They affect respectively 20% [7], 30% [START_REF]Macular degeneration. Site of the national institute of health and medical research[END_REF] and 10% [START_REF]Glaucoma. Site of the national institute of health and medical research[END_REF] over 75 years. These are pathologies that today are widely considered in the medical and geriatric literature [START_REF] Mj. Decrion | Les déficiences visuelles de la personne âgée[END_REF][11] [START_REF] Bowers | Older people's vision for long-terme care[END_REF].

But if the demographic characteristics and the direct physical consequences are identified, the observation is different when one looks at the determining factors contributing to compensate the effects of these pathologies, factors often minimized. This is especially true when, as here, we question the visual comfort of these elderly and the constituents of their visual environment. Indeed, today established recommendations for designing a comfortable visual environment are designed to meet the needs of an average observer, not specifically the needs of the elderly [START_REF]Les normes européennes de l'éclairage[END_REF]. If we correlate the results of these recommendations with the geriatric and medical literature, we find that these recommendations aren't adapted to their needs, especially for elderly with visual pathologies. In comparison with the established recommendations, these pathologies cause the need to use more light, better quality, but especially to increase the levels of contrasts so that they become sufficient [START_REF]Lighting your way to better vision[END_REF]. These are the levels of luminance contrast required to discriminate the elements that make up the environment, enabling the elderly to perform their daily activities. But to propose levels of luminance contrasts adapted to these particular needs inherent to the age, one must then be able to measure them.

The method for quantitative approach for luminance contrast

The most effective way to measure luminance contrast levels is the use of digital imaging. This method makes it possible to simplify the measurement of luminance contrast levels, because in fact, the measurement of luminance itself is nowadays uncommon. Digital imaging makes it possible to correct this problem and thus to propose an effective and objective response. It allows providing imperceptible results by the eye. And this is just as true for the average observer as for the elderly, because of the constant pupillary adaptation.

Digital imagery then makes it possible to develop luminance maps of the visual field. They make it possible to identify the weaknesses and strengths of the visual environment concerned, as well as the levels of luminance contrasts of each element. For an optimal discrimination of these elements by the elderly, the threshold contrast of luminance between two juxtaposed surfaces must be equal to 0.7 (or greater) [6][15].

Considering this threshold of luminance contrast, we expect the number of detectable thresholds to be somewhat limited. This is quickly found, if we accept that maximum luminance is obtained for the gray value (encoded by a byte) of 255, as in Table 1. So, if we consider a total white surface (gray=255), the have a visible adjacency contour, the next gray level must be 178. The maximum detectable thresholds will be 14, but normally could be considered close to 10, due the poor relevance of the values close to zero. An immediate question arises as to the real impact on the usual observer. To answer, it must consider the optoelectronic conversion function (OCF) for camera (Nikon D5300), obtained by the authors experimentally in the laboratory LAPLACE, Paul Sabatier University, Toulouse. For our OCF and an arbitrary exposure parameters (time, F number and ISO) the global conversion function of the camera is available in Fig. 1: In Fig. 1, the lower luminance are converted with a linear function, but for higher luminance (greater than 20 cd/m 2 ) the conversion is obviously nonlinear, giving the possibility to obtain relevant gray levels even for high values of luminance. It results that the number of thresholds of the luminance contrast that can be highlighted is much higher, but the contrast has to be calculated with actual values of the luminance of the scene and not in the values of the gray level. The conclusions are the same even if the image are obtained on two bytes, with increased gray resolution.

Scene analysis

The method of quantitative analysis based on luminance maps requires a progressive approach to investigating the details relevant to the movement of the elderly people. A case study was developed at the elderly nursing home in Iasi, Romania. The building is relatively new, with a high degree of comfort, demonstrating the increased attention the municipality gives to these old people. Paintings are generally white, subordinated to hygienic criteria. This criterion, in addition to its advantages, generates possible disadvantages. The first scene (Fig. 2.a) is a common corridor with access to various functions. Quantitative analysis begins with a first exposure of DSLR camera and image conversion in luminance values, based on OCF as in Fig. 1, affected by exposure time, F number and ISO settings. The luminance map is obtained by plotting lines of equal luminance between minimum values (usually zero) and maximum (but not equal to the maximum luminance of the image, because, as a rule, the maximum value is generated by a non-masked luminaire).

For the average observer, the scene is visually pleasing. This is less important, as quantitative interpretation is sought. The first map of the luminaries (Fig. 2,b) highlights the obvious contrast between the floor and the walls, but it is obvious that many values of the luminance are not displayed: white area on the right part of the map. To correct this, a second luminance map is generated, for values in a lager domain (0 ÷ 20 cd/m 2 ) as in Fig. 3:

The color map has no other significance, opposite to "false colors" representation, as in DIALux. Starting from the image (Fig. 2.a), the contrast between the dark floor and the white wall is positive. Extracting the data from luminance map, particular contrast could be calculated. For the floor-wall contrast we have A-B line, and for the door visibility we have C-D-E-F line. The luminance variance on these zones are illustrated in Fig. 4: A fast evaluation for every detail is possible. Luminance contrast (LC) for the floor -wall transition, with luminance in cd/m 2 , is:

= - = 9 -4 4 = 1.2 > 0.7 1)
So the visibility is certain. The situation for the white wall to the door frame, with luminance in cd/m 2 : = -= 18 -10 10 = 0.8 > 0.7

The visibility is possible, but the threshold is close. Any supplementary depreciation of the elderly's vision will affect the perception of the frame. The next luminance contrast is between the frame and the door itself, D -E -F, Fig. 3.

= -=

18 -13 13 = 0.38 < 0.7

(3) Here, the situation is problematic, because the door is invisible. For a normal observer, it is not obvious, but remember what happened if a clean glass door is close, with no other signaling: in short time, the door will be struck by someone! Here the situation is similar, and even if a serious accident is not possible, the visual discomfort is significant.

This local demonstration is enough to argue the utility of the method, so there is the possibility of examining all other paths. You can even deepen the previous scene if you look at an even broader range of luminance, as in Fig. 5:

The new available data reveal the effect of the closest luminaire on the wall, with a continuous increase of luminance (F to G, Fig. 5.a and b). The effect is difficult to estimate, but the contrast with the door became important, and it is possible to have a total invisible door, due the possible glare from the wall.

Discussion

By employing digital imaging here to analyze and measure luminance contrast levels allows us to provide results not perceptible to the human eye, thus producing objective results, both qualitative and quantitative. Quantitative because one is able to deploy the parameters that will determine the criteria attached to an optimal lighting environment. Using the results defined by these parameters, we can determine the transformations to be performed cd/m 2 cd/m 2 pixels pixels cd/m 2 cd/m 2 pixels in this environment to provide visual comfort, here adapted to the needs of the elderly. That's why it's also a qualitative method. It identifies the determining factors that will allow elderly in this environment to regain the ability to practice their daily activities, in a better quality of life context, through quality lighting.

The major challenge conveyed by this method is the ability to propose a light design that is no longer only the result of the formatting of the established norms, but the result of a consideration of the needs correlated with the weaknesses and strengths of the existing environment. It's thus much more than the result of a calculation of luminous efficiency. Based on this method, it's therefore possible to optimize the necessary interventions to transform a living environment, and thus to design an optimized interior space between relief and spatial landmarks. For the analyzed scene, the immediate and inexpensive intervention must change the paintings for the door frame, and even the total white of the door could be modify, with some decorative picture. The second step, more complex, is to redesign the lighting system, in order to avoid the high values of luminance on the wall.

Conclusion

Aging is often accompanied by visual pathologies, impacting the visual comfort of the elderly. For the usual lighting design the assessment of the visibility on the pathways for elderly is not solved, and for an average observer the lighting environment could be acceptable. But thanks to this method of qualitative and quantitative imaging, combining analysis of luminance contrast levels and visual needs considerations, we are able to identify the weaknesses of an existing environment and then proceed with its optimization. Supplementary sources of contrast could be added, using paintings or decorative objects in connection with light sources. It's then possible to design luminous environments offering reliefs and spatial landmarks.

This method makes it possible to obtain a personalized response, adapted to the individualities of the elderly, to their visual comfort, thus enabling them to keep their daily activities.

Fig. 1 .

 1 Fig. 1. Nikon D5300 optical conversion function (OCF): Luminance (cd/m 2 ) -Gray levels for an arbitrary exposure parameters

  a) Photo of interior scene and b) the first luminance map (cd/m 2 )

Fig. 3 .

 3 Fig. 3. The second luminance map for the scene from Fig.2.a), with extended plotted luminance values (0 ÷ 20 cd/m 2 )

4 .

 4 The luminance variation for luminance map from Fig.3 (cd/m 2 ): a) Luminance contrast (variation) for floor-wall; b) Luminance contrast (variation) for the door frame. a) b) Fig. 5. The third luminance map for the scene from Fig.2.a, with maximum extended luminance values (0 ÷ 40 cd/m 2 ): a) the position of line of vision visibility investigation C-D-E-F-G; b) the luminance variation on horizontal position of vision, for the door frame and wall.

Table 1 .

 1 Numeric values for luminance contrast levels in a grayscale image, to obtain visible threshold

	Level (1 to 7)	0	1	2	3	4	5	6	7
	Luminance (Gray Levels)	255	178	124	86	60	42	29	20
	Level (8 to 14)	8	9	10	11	12	13	14
	Luminance (Gray Levels)	14	9	6	4	2	1	0