## 4 Discussions  
At local and regional scales, previous studies have been conducted on the connections between hydrological and environmental/climatic variations in arid and semi-arid regions. A study at China's Yarlung Zangbo River (YLZR) basin showed the negatively correlated relationship between the climatic indices, including Pacific decadal oscillation (PDO) and the multivariate El Nino-Southern Oscillation (ENSO) index (MEI)) and precipitation, annual average temperature, and the streamflow of selected hydrological stations [67]. Similarly, this negative relationship between precipitation and ENSO indices was also found for Jana and Karan Islands in the nearshore Saudi Arabia area [36]. The monthly precipitation datasets (the time period 1961-2007) were compared over the arid Balochistan province of Pakistan, showing an increase in drought with global warming, indicated by the time series analysis of gauge-based records and climate models [68]. The study in the Upper Omo-Ghibe river basin of Ethiopia showed the trends in precipitation, temperature, and streamflow in the period from 1981 to 2008. A detailed review of the studies in global-scale monthly hydroclimatic time series was reported [69]. Therefore, our satellite-based approach on the analysis of monthly hydroclimatic time series during the last decade (2010-2019) is an updated continuing study of the dryland hydrology at a global scale. However, one of the fundamental challenges of dryland remote sensing is the decoupling of the ecosystem function (e.g., vegetation photosynthetic ability and stomatal conductance) from optical reflectance [70]. During the drought periods of extreme moisture shortage, some vegetation types, especially drought-tolerant evergreen shrubs, can keep constant greenness even if their vegetation and soil function decrease. This situation can be found in the boxplots in Figures 2-5, where it is noted that the indices (FPAR, LAI and NDVI) do not have strong responses for  
Figure 13: Comparisons of MODIS ET versus ET values obtained from FLDAS, GLDAS and the proposed model using testing data in Egypt. The solid black and red lines represent the one to one line and regression line, respectively. ET as compared to the precipitation pulses. Additionally, high aerosol concentrations, leaves covered by dust, marked adjacency effect, as well as surface anisotropy in sparse and heterogeneous canopies, make high-quality surface reflectance retrievals over drylands still a challenging process [71; 72; 73]. In arid and semi-arid ecosystems, the presence of soil background, standing litter and senesced vegetation, can have a significant impact on the reflectance and vegetation indices (e.g., NDVI), also adding considerable noise to LAI and the retrieval of FPAR [74; 75; 76; 77]. The spurious changes induced by variable soil background reflectance also make it difficult to validate the actual vegetation change [76; 78; 79]. Therefore, the Soil-Adjusted Vegetation Index (SAVI) was introduced with a soil adjustment factor (L), typically set as L = 0.5 [75], to account for differences from the soil background in red and near-infrared reflectance. However, SAVI is limited to differentiate vegetation from soil under sparse vegetation cover conditions [79]. Prior knowledge is also required to select the optimal L value [75]. In addition, one of the major assumptions of global MODIS LAI and FPAR products is that each pixel contains only one biome type with constant within-biome soil and vegetation properties [80]. However, this simplified assumption is unlikely to hold in arid and semi-arid areas due to their heterogeneous characteristics. Sustainable water resources management requires knowledge regarding the effects of land use and land cover change (LULCC) on groundwater recharge and surface runoff [81]. The tropical and sub-tropical semi-arid regions use groundwater to meet improving urban, industrial and agricultural water requirements [82].