International audienceThis paper investigates three categories of models that are derived from the equilibrium temperature concept to estimate water temperatures in the Loire River in France and the sensitivity to changes in hydrology and climate. We test the models' individual performances for simulating water temperatures and assess the variability of the thermal responses under the extreme changing climate scenarios that are projected for 2081-2100. We attempt to identify the most reliable models for studying the impact of climate change on river temperature (Tw). Six models are based on a linear relationship between air temperatures (Ta) and equilibrium temperatures (Te), six depend on a logistic relationship, and six rely on the closure of heat budgets. For each category, three approaches that account for the river's thermal exchange coefficient are tested. In addition to air temperatures, an index of day length is incorporated to compute equilibrium temperatures. Each model is analysed in terms of its ability to simulate the seasonal patterns of river temperatures and heat peaks. We found that including the day length as a covariate in regression-based approaches improves the performance in comparison with classical approaches that use only Ta. Moreover, the regression-based models that rely on the logistic relationship between Te and Ta exhibit root mean square errors comparable (0.90 °C) with those obtained with a classical five-term heat budget model (0.82 °C), despite a small number of required forcing variables. In contrast, the regressive models that are based on a linear relationship Te = f(Ta) fail to simulate the heat peaks and are not advisable for climate change studies. The regression-based approaches that are based on a logistic relationship and the heat balance approaches generate notably similar responses to the projected climate changes scenarios. This similarity suggests that sophisticated thermal models are not preferable to cruder ones, which are less time-consuming and require fewer input data

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The study of the relationship between flow structure and morphodynamic of bars in a channel expansion/contraction is essential to better understand the processes that control the evolution of rivers. Thus, multibeam echosoundings and Acoustic Doppler Profiler (ADP) measurements were performed with a high temporal resolution in an expansion/contraction zone of the Loire River (France) occupied by bars. During the monitoring period, the macroforms presented successively an alternate, a lateral and a transverse configuration. Field data were analyzed to study how the primary and secondary velocities, the flow directions, the bed shear stresses, and the bed roughnesses (associated to dunes) evolve as a function of the water discharge and bars configuration. The bars modify the flow structure imposed by the channel width variations. In fact, the bars induce a topographic forcing which enables the separation and reducing of the mixing of two currents formed in the upstream channel expansion. This forcing is enhanced by the turbulence formed by the large dunes superimposed on the bars. Therefore, the bars promote a nonuniform flow in the channel. In turn, in the channel expansion/contraction, the migrating bars' morphodynamics are affected by the downstream channel narrowing which stops their downstream migration and forced the bars in the system. Then the nonuniformity of the flow encourages the lateral migration of the macroforms until they reach a bank and become nonmigrating. Finally, the nonmigrating bars are eroded by the flow deflected during the migration of a new bar in the channel expansion/contraction.

Daily water temperature was simulated at a regional scale during the summer period using a simplified model based on the equilibrium temperature concept. The factors considered were heat exchanges at the water/atmosphere interface and groundwater inputs. The selected study area was the Loire River basin (110 000 km2), which displays contrasted meteorological, hydrological and geomorphological features. To capture the intra‐basin variability of relevant physical factors driving the hydrological and thermal response of the system, the modelling approach combined a semi‐distributed hydrological model, simulating the daily discharge at the outlet of 68 subwatersheds (drainage area between 100 and 3700 km2), and a thermal model, simulating the average daily water temperature for each Strahler order in each subwatershed. Simulations at 67 measurement stations revealed a median root mean square error (RMSE) of 1.9°C in summer between 2000 and 2006. Water temperature at stations located more than 100 km from their headwater was adequately simulated (median RMSE < 1.5°C; −0.5°C < median biases < 0.5°C). However, performance for rivers closer to their source varied because of the averaging of geomorphological and hydrological features across all the tributaries with the same Strahler order in a subwatershed, which tended to mask the specific features of the tributaries. In particular, this increased the difficulty of simulating the thermal response of groundwater‐fed rivers during the hot spells of 2003. This modelling by coupling subwatershed and Strahler order for temperature simulations is less time‐consuming and has proven to be extremely consistent for large rivers, where the addition of streambed inputs is adequate to describe the effect of groundwater inputs on their thermal regime. Copyright © 2015 John Wiley & Sons, Ltd.

International audienceIn discrete water quality surveys, riverine fluxes are associated with unknown uncertainties (biases and imprecisions). Annual flux errors have been determined from the generation of discrete surveys by Monte Carlo sorting for monthly sampling, from 10 years of daily records (120 records). Eight calculation methods were tested for suspended particulate matter, dissolved solids and dissolved and total nutrients in medium to large basins (10(3) to 10(6) km(2)) covering a wide range of hydrological conditions and riverine biogeochemistry. The performance of each method was analysed first by type of riverine material, which appeared to be much less pertinent than the flux variability matrix. The latter combines the river flow duration in two percent of time (W-2%) and the truncated exponent (b(50sup)) defining the relationship of concentration vs discharge (C-Q) at higher flows (C = aQ(b50sup)). As flux variability increases (high W-2% and/or high b(50sup)), averaging and rating curve methods become less efficient compared to hydrograph separation methods. Flux biases and imprecisions were plotted in the [W-2%, b(50sup)] matrix for discrete monthly surveys

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Sentinel-2 (S2) earth observation satellite mission, launched in 2015, is foreseen to promote within-field decisions in Precision Agriculture (PA) for both: (1) optimizing crop production; and (2) regulating environmental impacts. In this second scope, a set of Leaf Area Index (LAI) derived from S2 type time-series (2006-2010, using Formosat-2 satellite) is used to spatially constrain the within-field crop growth and the related nitrogen contamination of surface water simulated at a small experimental catchment scale with the distributed agro-hydrological model Topography Nitrogen Transfer and Transformation (TNT2). The Soil Water Holding Capacity (SWHC), represented by two parameters, soil depth and retention porosity, is used to fit the yearly maximum of LAI (LAX) at each pixel of the satellite image. Possible combinations of soil parameters, defining 154 realistic SWHC found on the study site are used to force spatially homogeneous SWHC. LAX simulated at the pixel level for the 154 SWHC, for each of the five years of the study period, are recorded and hereafter referred to as synthetic LAX. Optimal SWHC year_I,pixel_j , corresponding to minimal difference between observed and synthetic LAX year_I,pixel_j , is selected for each pixel, independent of the value at neighboring pixels. Each re-estimated soil maps are used to re-simulate LAX year_I . Results show that simulated and synthetic LAX year_I,allpixels obtained from SWHC year_I,allpixels are close and accurately fit the observed LAX year_I,allpixels (RMSE = 0.05 m 2 /m 2 to 0. SWHC can be derived from remote sensing series for one year. Unique SWHC solutions for each pixel that limit the LAX error for the five years to less than 0.2 m 2 /m 2 are found for only 10% of the pixels. Selection of unique soil parameters using multi-year LAX and neighborhood solution is expected to deliver more robust soil parameters solutions and need to be assessed further. The use of optical remote sensing series is then a promising calibration step to represent crop growth within crop field at catchment level. Nevertheless, this study discusses the model and data improvements that are needed to get realistic spatial representation of agro-hydrological processes simulated within catchments.

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