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In semi-arid contexts, the strong spatiotemporal fluctuation of rainfall and the sparsity of the rain gauge (RG) measurement networks are the main limitations for water resources management. Freely available satellite-based rainfall estimates can be a potential source of information to cop data limitations over poorly gauged regions. Thus, the main aim of this work was to investigate how eight Spatial Rainfall Products (SRP, ARC-2, CHIRPSp25, CHIRPSp5, CMORPH-CRT, GPM-IMERG, PERSIANN-CDR, RFE-2, and TRMM-3B42) can be able to reproduce the observed monthly rainfall over a semi-arid context. The SRP estimates were directly evaluated against the RG observations. Then, bias correction techniques were used to account for the bias in the SRPs. The results indicated that the SRPs poorly correlate with the daily rainfall patterns (with Pearson Correlation Coefficients (PCCs) mostly below 0.5) but agreed with the monthly observations. The agreement was stronger over the lowlands than over the mountainous region. Overall, out of all the considered SRPs, IMERG (with a short-term record) and PERSIANN (with a long-term record) performed the best. Still, the monthly SRP estimates were significantly biased as the large rainfall totals were frequently underestimated. However, when the bias correction was applied remarkable improvement in the SRP’s performance was observed. The different adopted correction techniques yielded close results, with a slight prevalence of the Cumulative Distribution Function (CDF) over the Linear Scaling (LS), and Simple Linear Regression (SLR) techniques. Still, to reliably adjust the bias in the SRP estimates, LS and SLR should be preferred over the CDF technique, as they demonstrated more spatially consistent performance after validation.

Daily hydrological modelling is among the most challenging tasks in water resource management, particularly in terms of streamflow prediction in semi-arid areas. Various methods were applied in order to deal with this complex phenomenon, but recently data-driven models have taken a better space, given their ability to solve prediction problems in time series. In this study, we have employed the Long Short-Term Memory (LSTM) network to simulate the daily streamflow over the Ait Ouchene watershed (AIO) in the Oum Er-Rbia river basin in Morocco, based on a temporal sequence of in situ and remotely sensed hydroclimatic data ranging from 2001 to 2010. The analysis adopted in this work is based on three-dimension input required by the LSTM model (1); the input samples used three splitting approaches: 70% of the dataset as training, splitting the data considering the hydrological year and the cross-validation method; (2) the sequence length; (3) and the input features using two different scenarios. The prediction results demonstrate that the LSTM performs poorly using the default data input scenario, whereas the best results during the testing were found in a sequence length of 30 days using approach 3 (R2 = 0.58). In addition, the LSTM fed with the lagged data input scenario using the Forward Feature Selection (FFS) method provides high performance accuracy using approach 2 (R2 = 0.84) in a sequence length of 20 days. Eventually, in applications related to water resources management where data are limited, the use of the deep learning technique is able to create high predictive accuracy, which can be enhanced with the right combination subset of features by using FFS.

Mapping seasonal snow cover dynamics provides essential information to predict snowmelt during spring and early summer. Such information is vital for water supply management and regulation by national stakeholders. Recent advances in remote sensing have made it possible to reliably estimate and quantify the spatial and temporal variability of snow cover at different scales. However, because of technological constraints, there is a compromise between the temporal, spectral, and spatial resolutions of available satellites. In addition, atmospheric conditions and cloud contamination may increase the number of missing satellite observations. Therefore, data from a single satellite is insufficient to accurately capture snow dynamics, especially in semi-arid areas where snowfall is extremely variable in both time and space. Considering these limitations, the combined use of the next generation of multispectral sensor data from the Landsat-8 (L8) and Sentinel-2 (S2), with a spatial resolution ranging from 10 to 30 m, provides unprecedented opportunities to enhance snow cover mapping. Hence, the purpose of this study is to examine the effectiveness of the combined use of optical sensors through image fusion techniques for capturing snow dynamics and producing detailed and dense normalized difference snow index (NDSI) time series within a semi-arid context. Three different models include the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM), the flexible spatio-temporal data fusion model (FSDAF), and the pre-classification flexible spatio-temporal data fusion model (pre-classification FSDAF) were tested and compared to merge L8 and S2 data. The results showed that the pre-classification FSDAF model generates the most accurate precise fused NDSI images and retains spatial detail compared to the other models, with the root mean square error (RMSE = 0.12) and the correlation coefficient (R = 0.96). Our results reveal that, the pre-classification FSDAF model provides a high-resolution merged snow time series and can compensate the lack of ground-based snow cover data.

Air temperature is an important meteorological variable in many fields of our life. However, the availability of air temperature measurements over large geographic areas is often limited by the weather stations spatial distribution inadequacy, their low density and difficulties of data quality and collection. In this context, this study consists to develop four simple models to estimate the three components of air temperature (Tmin, Tmax and Tmean) from remotely sensed land surface temperature (Ts) derived from NOAA-AVHRR images, and based on the international Koppen-Geiger climate classification of Morocco. The results confirmed the existence of good relationships between the three components of measured air temperatures and land surface temperature derived from NOAA-AVHRR images for the main four climate classes of Morocco. The coefficient of determination, R2, varied between 0.69 and 0.80 for Tmin versus Ts, between 0.62 and 0.74 for Tmax versus Ts, and between 0.69 and 0.79 for Tmean versus Ts. The root mean square error varied between 3.1 °C and 3.3 °C for Tmin versus Ts, between 3.2 and 4.1 °C for Tmax versus Ts and between 2.7 and 3.4 °C for Tmean versus Ts. K-fold cross validation method was performed to assess the accuracy and the stability of proposed models. The limited number of proposed models is a great advantage to carry further studies requiring air temperature’s components at larger scale.

In arid and semi-arid countries, drought monitoring is a difficult issue to deal with, mainly due to the low density of precipitation measurement stations. The present study aims to evaluate the ability of the Tropical Rainfall Monitoring Mission (TRMM) 3B42 (v7) to monitor annual precipitation in Morocco. The accuracy of TRMM data to estimate annual rainfall was evaluated. Annual precipitations derived from 5113 daily TRMM 3B42 V7 data were compared to the corresponding in situ rainfall measurements from 23 rain gauges, between 1998 and 2012. The results showed a general, good linear relationship between TRMM and rain gauges data. When considering annual records, the Pearson correlation coefficient, R2, was equal to 0.73 and the Root Mean Square Error, RMSE, was equal to 159.8 mm/year. The correlation between rain gauge measurements and TRMM rainfall has been clearly improved when working with long-term annual average precipitation. The R2 increased to 0.79 and the RMSE decreased to 115.2 mm.

Hydrological models, with different levels of complexity, have become inherent tools in water resource management. Conceptual models with low input data requirements are preferred for streamflow modeling, particularly in poorly gauged watersheds. However, the inadequacy of model structures in the hydrologic regime of a given watershed can lead to uncertain parameter estimation. Therefore, an understanding of the model parameters&rsquo, behavior with respect to the dominant hydrologic responses is of high necessity. In this study, we aim to investigate the parameterization of the HBV (Hydrologiska Byr&aring, ns Vattenbalansavedelning) conceptual model and its influence on the model response in a semi-arid context. To this end, the capability of the model to simulate the daily streamflow was evaluated. Then, sensitivity and interdependency analyses were carried out to identify the most influential model parameters and emphasize how these parameters interact to fit the observed streamflow under contrasted hydroclimatic conditions. The results show that the HBV model can fairly reproduce the observed daily streamflow in the watershed of interest. However, the reliability of the model simulations varies from one year to another. The sensitivity analysis showed that each of the model parameters has a certain degree of influence on model behavior. The temperature correction factor (ETF) showed the lowest effect on the model response, while the sensitivity to the degree-day factor (DDF) highly depends on the availability of snow cover. Overall, the changes in hydroclimatic conditions were found to be mostly responsible for the annual variability of the optimal parameter values. Additionally, these changes seem to actuate the interdependency between the parameters of the soil moisture and the response routines, particularly Field Capacity (FC), the recession coefficient K0, the percolation coefficient (KPERC), and the upper reservoir threshold (UZL). The latter combines either to shrink the storage capacity of the model&rsquo, s reservoirs under extremely high peak flows or to enlarge them under overestimated water supply, mainly provoked by abundant snow cover.

Water supply for the arid irrigated plains in Morocco depends largely on the upper mountainous basins where significant amounts of precipitation fall as snow. In the Oum Er-Rbia River Basin (OER), snow covers the highest elevations from November to April. Despite the importance of this component in the hydrological cycle, snowmelt contribution to streamflow is still poorly understood and no monitoring stations exist in this zone. Therefore, studying the spatiotemporal change of snow cover through satellite observations to investigate its influence on the hydrological response of this scarce region is thus required to better manage water resources. This chapter explores basic characteristics of snow cover area (SCA) in the upstream area of the OER River (Tillouguite sub-basin) using MODIS daily snow cover products (MOD10A1). Correspondence between streamflow, accumulated air temperature and SCA changes during the winter and spring periods was examined from 2001 to 2009 at a weekly time step. The result shows an inverse linear relation between the maximum SCA and the mean normalized stream flow values, and a significant relation between the relative streamflow and cumulated temperature, especially during spring melt season depending on the length of the melt period. These primary results could be used to develop simplified predictable models for spring discharge in ungauged watershed using remote sensing and accumulated air temperature.

In arid and semi-arid areas, rainfall is often characterized by a strong spatial and temporal variability. These environmental factors, combined with the sparsity of the measurement networks in developing countries, constitute real constraints for water resources management. In recent years, several spatial rainfall measurement sources have become available, such as TRMM data (Tropical Rainfall Measurement Mission). In this study, the TRMM 3B42 Version 7 product was evaluated using rain gauges measurements from 19 stations in the Oum-Er-Bia (OER) basin located in the center of Morocco. The relevance of the TRMM product was tested by direct comparison with observations at different time scales (daily, monthly, and annual) between 1998 and 2010. Results show that the satellite product provides poor estimations of rainfall at the daily time scale giving an average Pearson correlation coefficient (r) of 0.2 and average Root Mean Square Error (RMSE) of 10 mm. However, the accuracy of TRMM rainfall is improved when temporally averaged to monthly time scale (r of 0.8 and RMSE of 28 mm) or annual time scale (r of 0.71 and RMSE of 157 mm). Moreover, improved correlation with observed data was obtained for data spatially averaged at the watershed scale. Therefore, at the monthly and annual time scales, TRMM data can be a useful source of rainfall data for water resources monitoring and management in ungauged basins in semi-arid regions.

The monitoring of drought statewide is a difficult issue especially when the national network of meteorological stations is sparse or do not cover the entire country. In this paper, rainfall satellite estimates derived from Tropical Rainfall Measuring Mission (TRMM) product have been used to evaluate the ability of remote sensing data to study the trends of annual precipitation in Morocco between 1998 and 2012. The standardized precipitation index, SPI, has been chosen to monitor meteorological drought in Morocco. Firstly, the accuracy of TRMM product to estimate annual rainfall was evaluated. Annual precipitations derived from 5113 daily TRMM data were compared to the corresponding rainfall measurements from 23 rain gauges. The results showed a general good linear relationship between TRMM and rain gauges data. When considering annual record, the Pearson correlation coefficient, R², was equal to 0.73 and the root mean square error, RMSE, was equal to 159.8mm/year. The correlation between rain gauge measurements and TRMM rainfall had been clearly improved when working with long-term annual average precipitation. The R² increased to 0.79 and the RMSE decreased to 115,2mm. Secondly, the Mann-kendall tau coefficient, the Theil Sen slope and the contextual Mann-Kendall significance were used to analyze the SPI trends over Morocco. This analysis showed that mainly two regions appeared to be subject of significant trends during the studied period: The extreme north eastern of Morocco manifests a positive SPI trends and is more and more subject of extreme rainfall while the extreme south of the country is suffering from a decrease of annual precipitation which could represent significant socio-economic risks in these areas.