Drought is an unpredictable hydrological phenomenon, and climate change has made it difficult to predict and analyze droughts. Nyala city airport metrological station rainfall records from 1943 to 2017 (75 years) were investigated. Four statistical drought indices were used; the standardized precipitation index (SPI), the rainfall anomaly index (RAI), the rainfall decile percent index (RDI), and the percent normal precipitation index (PNI). The study analyzes, assesses, compares, and determines the proper drought index. Results show that annual normal drought class (DC4) percentages for PNI, RDI, and RAI are not significantly different at an average of 42% and 65.3% for SPI at a frequency of 49 years. In comparing the average monthly and yearly drought frequency values and considering the historical dry and wet droughts, results showed the indices performance rank as: SPI, RAI, RDI, and PNI. Result reveals that the SPI was superior in all analyses, but it had some defects in detecting monthly dry drought when precipitation is dominated by rare or zero values (start and end of the rainy season). This was concluded and revealed by conducting a zone chart showing the deviations of standard deviation about the mean. Thus, the SPI index outperforms the other three indices.

The United States Natural Resources Conservation Services Curve Number (NRCS-CN) method uses the CN and rainfall to calculate runoff. However, there are still some uncertainties in the method, such as choosing the most appropriate CN value. Therefore, this study attempts to evaluate the effectiveness of using the NRCS-CN method to estimate the runoff of five catchments in Sudan. For each catchment, CN values were obtained from the number of observed rainfall-runoff events using the NRCS table, arithmetic mean, median, and geometric mean methods. For each method, Nash–Sutcliffe efficiency (NSE) was obtained to evaluate the fit between the observed and runoff, and negative NSE values were found for all methods. Negative values of NSE indicate that the observed runoff and estimated runoff are not well fitted, and the NRCS-CN method is not suitable for runoff calculation in the study areas.

Satellite-based rainfall estimates (SREs) represent a promising alternative dataset for climate and hydrological studies, where gauge observations are insufficient. However, these datasets are accompanied by significant uncertainties. Therefore, this study aims to minimize the systematic bias of Artificial Neural Networks–Cloud Classification System (PERSIANN-CCS), Artificial Neural Networks-Climate Data Record (PERSIANN-CDR), Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS), and Global Precipitation Climatology Project (GPCP) rainfall estimates using a quantile mapping (QM) method with climatic zones (CZs). The adjusted rainfall estimates were evaluated for the period from 2003–2017; data from 2003 to 2016 were used for calibration, and data from 2017 were used for validation. The results revealed significant improvements for the adjusted PERSIANN-CCS, PERSIANN-CDR, CHIRPS, and GPCP monthly time series in terms of all statistical measures and evaluation of overall CZs. In terms of Root Mean Square Errors (RMSEs), the adjusted CHIRPS did not show an improvement. This method successfully removed the mean bias of the daily time series for all SREs. The findings suggest that this method can be applied to correct the systematic bias of all SREs in the monthly time series in the future without the need for further gauge measurements over Sudan.