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In African countries, the lack of observed rainfall data is a major obstacle for efficient water resources management. The objective of this study is to evaluate satellite rainfall products' ability to estimate river runoff over 12 basins in Morocco using four hydrological models: IHACRES, MISDc, GR4J, and CREST. Satellite products available with a short latency are compared: EUMETSAT H SAF, SM2RAIN-ASCAT, and IMERG. The best results to reproduce river runoff were obtained with SM2RAIN-ASCAT in combination with the IHACRES model, with the highest Kling-Gupta efficiency criterion and probability of detection of extreme runoff. The hydrological model performances differed across catchments and satellite rainfall products, which highlights the need to carefully select hydrological models for a given application. Thus, it is advisable to evaluate satellite rainfall products with different types of hydrological models. This first evaluation over Moroccan basins suggests that SM2RAIN-ASCAT could be a reliable alternative to observed rainfall for hydrological modelling. [ABSTRACT FROM AUTHOR]

In a warming climate, periods with lower than average precipitation will increase in frequency and intensity. During such periods, known as meteorological droughts, the decline in annual runoff may be proportionally larger than the corresponding decline in precipitation. Reasons behind this exacerbation of runoff deficit during dry periods remain largely unknown, and this challenges the predictability of when this exacerbation will occur in the future and how intense it will be. In this work, we tested the hypothesis that runoff deficit exacerbation during droughts is a common feature across climates, driven by evaporation enhancement. We relied on multidecadal records of streamflow and precipitation for more than 200 catchment areas across various European climates, which distinctively show the emergence of similar periods of exacerbated runoff deficit identified in previous studies, i.e. runoff deficit on the order of -20 % to -40 % less than what expected from precipitation deficits. The magnitude of this exacerbation is two to three times larger for basins located in dry regions than for basins in wet regions, and is qualitatively correlated with an increase in annual evaporation during droughts, in the order of +11 % and +33 % over basins characterized by energy-limited and water-limited evaporation regimes, respectively. Thus, enhanced atmospheric and vegetation demand for moisture during dry periods induces a nonlinear precipitation-runoff relationship for low-flow regimes, which results in an unexpectedly large decrease in runoff during periods of already low water availability. Forecasting onset, magnitude, and duration of these drops in runoff have paramount societal and ecological implications, especially in a warming climate, given their supporting role for safeguarding water, food, and energy. The outcome that water basins are prone to this exacerbation of runoff deficit for various climates and evaporation regimes makes further understanding of its patterns of predictability an urgent priority for water-resource planning and management in a warming and drier climate. [ABSTRACT FROM AUTHOR]

Many rainfall-runoff (RR) models are available in the scientific literature. Selecting the best structure and parameterization for a model is not straightforward and depends on a broad number of factors, including climatic conditions, catchment characteristics, temporal/spatial resolution and model objectives. In this study, the RR model 'Modello Idrologico Semi-Distribuito in continuo' (MISDc), mainly developed for flood simulation in Mediterranean basins, was tested on the Seveso basin, which is stressed several times a year by flooding events mainly caused by excessive urbanization. The work summarizes a compendium of the MISDc applications over a wide range of catchments in European countries and then it analyses the performances over the Seveso basin. The results show a good fit behaviour during both the calibration and the validation periods with a Nash-Sutcliffe coefficient index larger than 0.9. Moreover, the median volume and peak discharge errors calculated on several flood events were less than 25%. In conclusion, we can be assured that the reliability and computational speed could make the MISDc model suitable for flood estimation in many catchments of different geographical contexts and land use characteristics. Moreover, MISDc will also be useful for future support of real-time decision-making for flood risk management in the Seveso basin. [ABSTRACT FROM AUTHOR]

The event runoff coefficient (i.e. the ratio between event runoff and precipitation that originated the runoff) is a key factor for understanding basin response to precipitation events. Runoff coefficient depends on precipitation intensity and duration but also on specific basin geohydrology attributes (including soil type, geology, land cover, topography) and last but not least, antecedent (or pre-storm) conditions (i.e., the amount of water stored in the different hydrological compartments, like the river, groundwater, soil and snowpack). The relation between runoff coefficient and basin pre-storm conditions is critical for flood forecasting, yet, the understanding of where, when and how much basin pre-storm conditions control runoff coefficients is still an open question. Here, we tested the control of basin pre-storm conditions on runoff coefficient for 60620 flood events across 284 basins in Europe. To do so, we derived basin pre-storm conditions from different proxies, namely: antecedent precipitation; surface and root zone soil moisture from hydrological models, reanalyses and land surface models also ingesting satellite observations; pre-storm river discharge, and pre-storm total water storage anomalies. We evaluated the coupling strength between runoff coefficient and pre-storm conditions proxies in relation to five classes of European basins, defined based on land use and soil type (as indexed by the Soil Conservation Service curve number CN), topography, hydrology and long-term climate and tested their ability to explain stormflow volume variability. We found that precipitation explains relatively well the stormflow volumes for both small and large events but not very well the peak discharge, especially for large floods. The runoff coefficient of events shows different distributions for the five different classes and correlates well with deep soil storages (such as root-zone soil moisture and pre-storm total water storage anomalies), pre-storm river discharge, and pre-storm snow water equivalent. Overall, these correlations depend on the class. Poor correlations are found against antecedent precipitation index despite its wide use in the hydrological community. Seasonal and interannual climate variability exert a key role on the coupling strength between runoff coefficient and pre-storm conditions by inducing sharp changes in the correlation with season and climate. These results increase our understanding of the coupling between pre-storm conditions and runoff coefficients. This will aid flood forecasting, hydrological and land surface model calibration, and data assimilation. Furthermore, these findings can help us to better interpret future flood projections in Europe based on expected changes in long and short-term climatic drivers. • Different basin pre-storm proxies explain runoff coefficient variability in Europe • Basin hydrophysioclimatic characteristics control basin pre-storm condition importance • Seasonal and climate variabilities impact basin pre-storm condition importance [ABSTRACT FROM AUTHOR]

In this study, the effects of remotely-sensed soil moisture (SM) data assimilation (DA) on rainfall-runoff model performances have been extensively explored, through a large experiment involving numerous catchments located across Europe (>700), and the use of different observations preprocessing and error characterization approaches. ESA-CCI SM products, that merged several available active and passive observations datasets, are employed, also in order to evaluate the role of the type of sensors on DA performances. Daily discharge time series and basin characteristics are obtained from the Global Runoff Data Centre, while daily rainfall and mean temperature data are collected from the European Climate Assessment & Dataset E-OBS. The MISDc-2L model (Brocca et al., 2012) is used for hydrological simulations.In the preprocessing phase, the exponential filter is adopted to address the depth mismatch between model estimates and observations. Two alternative approaches are considered for satellite data rescaling between reference and rescaled datasets, namely CDF-matching and Triple Collocation analysis (TC), which imply the matching of the total variance and of the signal component, respectively. Then, TC is used for observation error characterization by using different triplet configurations in order to test the impact of different observation weights in DA performances. Finally, the Ensemble Kalman Filter is employed to assimilate the rescaled satellite-based observations into MISDc-2L model.The model performance in open-loop (OL) can be considered generally good, while the effects of remotely-sensed SM assimilation are contrasting. The improvements due to DA are substantially limited to catchments in Mediterranean area, while a degradation of model results is almost systematically observed at northern latitudes. Spatial patterns in DA performances are inversely related with both those of model OL performances and of provided rainfall accuracies; in this sense the assimilation of satellite SM shows skills where model does not work so well and/or higher errors in precipitation data could be expected. No remarkable differences in performances attributable to the different ESA-CCI products or rescaling procedures are observed. However, adopting TC for rescaling appears to be more effective in limiting multiplicative bias (i.e. state-dependent systematic errors) evidences in simulated discharges. The use of lagged model as third variable in error variance characterization, that in this case implies higher uncertainties attributed to satellite-based observations, lead to better DA performances with respect to the integration of two satellite-based datasets in the triplet configuration. In conclusion, this study confirms in some way the contrasting results available in literature on satellite SM data assimilation in hydrological models (e.g., Massari et al., 2015). Here, the integration of remotely-sensed data seems suitable for specific areas, and shows a high potential to correct for uncertainties associated with rainfall estimates. REFERENCESBrocca, L.; Moramarco, T.; Melone, F.; Wagner, W.; Hasenauer, S.; Hahn, S. Assimilation of surface- and root-zone ASCAT soil moisture products into rainfall–runoff modeling. IEEE Trans. Geosci. Remote Sens. 2012, 50, 2542–2555. Massari, C.; Brocca, L.; Tarpanelli, A.; Moramarco, T. Data assimilation of satellite soil moisture into rainfall-runoff modelling: a complex recipe? Remote Sens. 2015, 7, 11403–11433. [ABSTRACT FROM AUTHOR]