![]() Land-use change showcases the management of the land while revealing what motivated the alteration of the land cover. It is also crucial to determine the biophysical processes in global environmental change. Land cover is an important descriptor of the earth’s terrestrial surface. Future work includes evaluating the developed ANN model with real observations, quantified in situ or remotely sensed. We show that model performance was similar for both the global and fully distributed representation of soil moisture however, both models surpass the single pixel-based models. Moreover, to examine how the spatial distribution of input variables affects forecast accuracy, we compared streamflow forecasts from the ANN using surface soil moisture at three spatial distributions-global, fully distributed, and single pixel-based-for the Androscoggin watershed. We found that the best choice of inputs consists of combining surface soil moisture with observed streamflow for the two watersheds under study. To examine how input variables affect forecast accuracy, we compared streamflow forecasts from the ANN model using four different sets of inputs characterizing the watershed state-surface soil moisture, deep soil moisture, observed streamflow the day before the forecast, and surface soil moisture along with antecedent observed streamflow. A virtual modelling environment is implemented, where data used to train and validate the ANN model were generated using a deterministic distributed model over 16 summers (2000–2015). The current paper focuses on short-term streamflow forecasting, 1 to 7 days ahead, using an ANN model in two northeastern American watersheds, the Androscoggin and Susquehanna. In hydrological modelling, artificial neural network (ANN) models have been popular in the scientific community for at least two decades. Present-day discharge and depth can thus be estimated in near real time with any update of satellite rainfall data and/or water level gained by altimetry missions currently flying in operational mode. These rating curves enable the conversion of water levels, discharge and depth interchangeably. Rating curves are computed by crossing the altimetry information of water height with the simulated discharge outputs. In contrast, the Modelo de Grandes Bacias is calibrated using historical discharge measurements to simulate distributed discharge in the basin. Mostly, satellite data are used, such as water levels from satellite altimetry missions and rainfall from the African Rainfall Climatology 2 product. The methodology was applied to the Tsiribihina watershed in Madagascar. This work presents a practical approach to reconstructing past and present discharge and water depth time series for operational monitoring of small-sized ungauged watersheds using remotely sensed and freely accessible datasets in conjunction with hydrological models. This opens the way to use of satellite radar altimetry for the generation of water height time series on a large scale, and considerably extends the applicability of satellite radar altimetry in hydrology. The results show that the fully automatic method developed herein provides as reliable results as the fully manual one. ![]() Moreover, more than 67% and 92% of time series jointly produced by the methods present root mean square differences lower than 20 and 50 cm, respectively. ![]() The results yielded by these three methods are comparable: maximum absolute magnitudes of water height differences being 0.46, 0.26 and 0.15 m for, respectively, 95, 90 and 80% of the water height values obtained. We propose to investigate qualitative and quantitative differences between three representative virtual station creation methodologies: (a) a fully manual method, (b) a semi-automatic method based on a land cover characterization that allows the water body surface under study to be located and (c) an original fully automatic procedure that exploits a digital elevation model and an estimation of the river width. the definition of "virtual limnimetric stations". Production of water height time series by satellite radar altimetry technology requires first the selection of radar ground target locations corresponding to water body surfaces under study, i.e. Satellite radar altimetry is complementary to in situ limnimetric surveys as a means of estimating the water height of large rivers, lakes and flood plains. ![]()
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