Rainfall depth is a crucial parameter in water resources and hydrological studies. Rain gauges provide the most reliable point-based rainfall estimates. However, they do not have a proper density/distribution to provide sufficient rainfall measurements in many areas, especially in arid regions. To evaluate the adequacy of satellite datasets as an alternative to the rain gauges, the Kingdom of Saudi Arabia (KSA) is selected for the current study as a representative of the arid regions. KSA occupies most of the Arabian Peninsula and is characterized by high variability in topographic and climatic conditions. Five satellite precipitation datasets (SPDSs)—CMORPH, PERSIANN-CDR, CHIRPS V2.0, TMPA 3B42 V7, and GPM IMERG V6—are evaluated versus 324 conventional rain-gauges’ daily precipitation measures. The evaluation is conducted based on nine quantitative and categorical metrics. The evaluation analysis is carried out for daily, monthly, yearly, and maximum yearly records. The daily analysis revealed a low correlation for all SPDSs (0.8). Accordingly, in arid regions, the use of daily SPDSs in rainfall estimation will lead to high uncertainty in the obtained results. The best performance for all statistical metrics was found at 500–750 m altitudes in the central and northern parts of the study area for all satellites except minor anomalies. CMORPH dataset has the lowest centered root mean square error (RMSEc) for all analysis periods with the best results in the monthly analyses.

Drought is a slow-onset phenomenon that evolves over a season or even years. Drought affects people more than any other natural disaster due to its widespread and significant negative impacts. Population growth and associated water demand add further stress on water resources, especially in periods of drought. Drought indices represent a single value resulting from processing a considerable amount of data. These indices provide a short message to stakeholders to adapt water resource management strategies. Since drought results from interconnected phenomena, designing a composite drought index that includes several drought indices can accurately capture drought events. Drought assessment over a large-scale basin (e.g., the Blue Nile) is a challenging objective that has not been deeply tackled before except for small portions of the basin. This paper assessed droughts over the whole basin by evaluating meteorological, agricultural, and hydrological drought indices. The calculated drought indices (Standardized Runoff Index (SRI), Standardized Precipitation Index (SPI), and standardized soil moisture index (SSI)) in addition to the development of a new standardized evapotranspiration index (sETI) are jointly integrated into a novel composite drought index for the Blue Nile (BNI). The optimal weights for SPI, SRI, sETI, and SSI were 0.33, 0.26, 0.2, and 0.19, respectively, in the designed BNI.

Providing an accurate spatiotemporal distribution of rainfall and filling data gaps are pivotal for effective water resource management. This study focuses on the Asir region in the southwest of Saudi Arabia. Given the limited accuracy of satellite data in this arid/mountain-dominated study area, geospatial interpolation has emerged as a viable alternative approach for filling terrestrial records data gaps. Furthermore, the irregularity in rain gauge data and the yearly spatial variation in data gaps hinder the creation of a coherent distribution pattern. To address this, the Centered Root Mean Square Error (CRMSE) is employed as a criterion to select the most appropriate geospatial interpolation technique among 51 evaluated methods for maximum and total yearly precipitation data. This study produced gap-free maps of total and maximum yearly precipitation from 1966 to 2013. Beyond 2013, it is recommended to utilize ordinary Kriging with a J-Bessel semivariogram and simple Kriging with a K-Bessel semivariogram to estimate the spatial distribution of maximum and total yearly rainfall depth, respectively. Additionally, a proposed methodology for allocating additional rain gauges to improve the accuracy of rainfall spatial distribution is introduced based on a cross-validation error (CVE) assessment. Newly proposed gauges in the study area resulted in a significant 21% CVE reduction.

Arid regions are susceptible to flash floods and severe drought periods, therefore there is a need for accurate and gap-free rainfall data for the design of flood mitigation measures and water resource management. Nevertheless, arid regions may suffer from a shortage of precipitation gauge data, whether due to improper gauge coverage or gaps in the recorded data. Several alternatives are available to compensate for deficiencies in terrestrial rain gauge records, such as satellite data or utilizing geostatistical interpolation. However, adequate assessment of these alternatives is mandatory to avoid the dramatic effect of using improper data in the design of flood protection works and water resource management. The current study covers 75% of the Kingdom of Saudi Arabia’s area and spans the period from 1967 to 2014. Seven satellite precipitation datasets with daily, 3-h, and 30-min temporal resolutions, along with 43 geostatistical interpolation techniques, are evaluated as supplementary data to address the gaps in terrestrial gauge records. The Normalized Root Mean Square Error by the mean value of observation (NRMSE) is selected as a ranking criterion for the evaluated datasets. The geostatistical techniques outperformed the satellite datasets with 0.69 and 0.8 NRMSE for the maximum and total annual records, respectively. The best performance was found in the areas with the highest gauge density. PERSIANN-CDR and GPM IMERG V7 satellite datasets performed better than other satellite datasets, with 0.8 and 0.82 NRMSE for the maximum and total annual records, respectively. The spatial distributions of maximum and total annual precipitation for every year from 1967 to 2014 are generated using geostatistical techniques. Eight Probability Density Functions (PDFs) belonging to the Gamma, Normal, and Extreme Value families are assessed to fit the gap-filled datasets. The PDFs are ranked according to the Chi-square test results and Akaike information criterion (AIC). The Gamma, Extreme Value, and Normal distribution families had the best fitting over 56%, 34%, and 10% of the study area gridded data, respectively. Finally, the selected PDF at each grid point is utilized to generate the maximum annual precipitation for 2, 5, 10, 25, 50, and 100-year rasters that can be used directly as a gridded precipitation input for hydrological studies.

In the current study, a three-dimensional CFD model is utilized to investigate the variation of the flow structure and bed shear stress at a single cylindrical pier during scour development. The scour development is presented by seven solidified geometries of the scour hole, collected during previous experimental work at different scour stages. Different turbulence models are evaluated and the (k-ω) model is chosen due to its relative accuracy in capturing the flow oscillation and vortex shedding at the pier downstream side with personal computer computational and storage resources. The numerical results are verified against dimensionless parameters from different previous experimental works. This research describes in detail the flow structure and bed shear stress variations through seven stages of the scour hole development. The dimensionless area-averaged circulation coefficient (Ψ_i) is developed to evaluate the changes in the vortex strength through the scouring process by eliminating the calculation area effect. It was concluded that the circulation in the (Y) direction is the main driving factor in the development of the scour hole more than the circulation in the (X) direction. The ratio between the horseshoe vortex (HV) mean size and the scouring depth (D_V/d_S) in addition to the location of the maximum bed shear stress are investigated during different stages of the scour development.