<p>Improving streamflow forecasts helps in reducing socio-economical impacts of hydrological-related damages. Among them, improving hydropower production is a challenge, even more so in a context of climate change.<strong> </strong>Deep learning models drew the attention of scientists working on forecasting models based on physical laws, since they got recognition in other domains. Artificial Neural Network (ANN) offer promising performance for streamflow forecasts, including good accuracy and lesser time to run compared to traditional physically-based models.&#160;</p><p>&#160;</p><p>The objective of this study is to compare different spatial discretization schemes of inputs in an ANN model for streamflow forecast. The study focuses on the &#8220;Au Saumon&#8221; watershed in Southern Quebec (Canada) during summer periods, with a forecast window of 7 days at a daily timestep. Parameterization of the ANN was a key preliminary step: the number of neurons in the hidden layer was first optimized, leading to 6 neurons. The model was trained on a 11-year dataset (2000-2005 and 2007-2011) followed by model validation on one dry (2012) and one wet (2006) year to take into account extreme hydrologic regimes.&#160;</p><p>&#160;</p><p>To lead this study, the physically-based hydrological &#8216;Hydrotel&#8217; model is the reference to compare our results. The model defines watershed heterogeneity using hydrological units based on land uses, soil types, and topography, called Relative Homogeneous Hydrological Units (RHHU). The Nash-Sutcliffe Efficiency score (NSE) is the main evaluation criteria calculated. In a preliminary step, we have to ensure the ANN model can satisfactorily mimic Hydrotel. With the same model inputs, that is same variables and same spatial discretizations of variables (total precipitation, daily maximum and minimum temperatures, and soil surface humidity), the ANN forecasts were found to be better than those of Hydrotel for one to 7-day forecasts.&#160;</p><p>&#160;</p><p>Three different watershed spatial discretizations were tested: global, fully distributed, and semi-distributed. For the global model, hydrometeorological data used as inputs to the ANN model were averaged across all RHHUs. The complexity is reduced with loss of spatial details and heterogeneity. For the fully distributed model, a regular grid was defined with six cells of 28x28km2 covering all the watershed. For the semi-distributed model, spatial distribution of the input data was that of the RHHUs. For this discretization, the state variables (soil moisture and outflow) were updated at each forecast timestep, whether on all RHHUs, or only on the RHHU of the outlet.</p><p>&#160;</p><p>Depending on the spatial discretization of inputs used, the accuracy differed. The fully distributed model offered the least performance, with NSE values of 0.85 ,while the global model surprisingly performed better with a 0.93 NSE. Moreover, updating soil moisture on all the RHHUs of the semi-distributed model improved the NSE across the entire window of forecast.</p><p>This research will assess the ANN model performance developed using ERA5-land precipitation and temperature reanalysis and ground observations of soil moisture. Given the promising results obtained with the fully and semi distributed models, our ANN model will be tested with state variables retrieved from satellite data, such as surface soil moisture from SMAP and SMOS missions.</p><p><br><br></p>
<p>Machine learning model approaches for hydrological forecasts are nowadays common in research. Artificial Neural Network (ANN) is one of the most popular due to its good performance on watersheds with different hydrologic regimes and over several timescales. A short-term (1 to 7 days ahead) forecast model was explored to predict streamflow. This study focused on the summer season defined from May to October. Cross-validation was done over a period of 16 years, each time keeping a single year as a validation set.</p><p>The ANN model was parameterized with a single hidden layer of 6 neurons. It was developed in a virtual environment based on datasets generated by the physically based distributed hydrological model Hydrotel (Fortin et al., 2012). In a preliminary analysis, several combinations of inputs were assessed, the best combining precipitation and temperature with surface soil moisture and antecedent streamflow. Different spatial discretizations were compared. A semi-distributed discretization was selected to facilitate transferring the ANN model from a virtual environment to real observations such as remote sensing soil moisture products or ground station time series.</p><p>Four watersheds were under study: the Au Saumon and Magog watersheds located in south Qu&#233;bec (Canada); the Androscoggin watershed in Maine (USA); and the Susquehanna watershed located in New-York and Pennsylvania (USA). All but the Susquehanna watershed are mainly forested, while the latter has a 57% forest cover. To evaluate whether a model with a data-driven structure can mimic a deterministic model, ANN and Hydrotel simulated flows were compared. Results confirm that the ANN model can reproduce streamflow output from Hydrotel with confidence.</p><p>Soil moisture observation stations were deployed in the Au Saumon and Magog watersheds during the summers 2018 to 2021. Meteorological data were extracted from the ERA5-Land reanalysis dataset. As the period of availability of observed data is short, the ANN model was trained in a virtual environment. Two validations were done: one in the virtual environment and one using real soil moisture observations and flows. The number and locations of the soil moisture probes slightly differed during each of the four summers. Therefore, four models were trained depending on the number of probes and their location. Results highlight that location of the soil moisture probes has a large influence on the ANN streamflow outputs and identifies more representative sub-regions of the watershed.</p><p>The use of remote sensing data as inputs of the ANN model is promising. Soil moisture datasets from SMOS and SMAP missions are available for the four watersheds under study, although downscaling approaches should be applied to bring the spatial resolution of those products at the watershed scale. One other future lead could be the development of a semi-distributed ANN model in virtual environment based on a restricted selection of hydrological units based on physiographic characteristics. The future L-band NiSAR product could be relevant for this purpose, having a finer spatial resolution compared to SMAP and SMOS and a better penetration of the signal in forested areas than C-band SAR satellites such as Sentinel-1 and the Radarsat Constellation Mission.</p>
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