In this study, a regional flood frequency analysis has been carried out, using the index flood L-moments approach. Annual maximum stream flood data observed at 14 gauged sites on the Nile River tributaries (Blue Nile, White Nile, and Atbara River) are investigated. The aim of the study is to investigate and derive hydrologically homogeneous region or regions and to identify and establish the regional statistical distribution. To this end, five distribution functions are used, namely: generalized pareto, generalized extreme-value, generalized logistic, generalized normal, and Pearson type-3 distributions. Analyses have shown that 8 sites form a hydrologically homogeneous region, and this region follows a generalized logistic (GLO) distribution. Furthermore, the other remaining two regions (possibly heterogeneous and definitely heterogeneous) are also defined. Regional dimensionless growth curves for the identified three regions are derived. Results are assessed on the basis of relative RMSE% and relative BIAS% through the use of Monte Carlo simulation.

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.

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.

The main purpose of this paper is to present and apply one of the statistical methodologies-the region of influence (ROI) approach-and form homogenous region/regions for rainfall frequency analysis in order to extract rainfall information guidelines for the Sudan basin. 15 gauging stations are selected, with recorded annual data ranging from 25 to 102 years in length. The aim here is to provide a regional curve with the capacity of taking into account the spatial pattern of variation of hydrologic phenomena across many gauging sites and which can be used for estimating rainfall quantiles at both gauged and ungauged sites within a specified region. This regional curve can also provide the possibility of the transfer of hydrologic behavior of a region to a site of interest in order to improve at-site estimates. The study results are analyses and compared. At first rainfall quantile for selected sites were estimated. In addition, the regression analysis between estimated quantiles (for 100 years) and the geographical features of the basin (drainage area (A), longitude (X dist .) and latidute (Y dist .)) was derived. In an aim to extend the methodologies to the case of ungauged site, a nonlinear regression model is derived and the results are investigated.

Abstract-This paper deals with at-site rainfall frequency estimation in the case when also information on hydrological events from the past with extraordinary magnitude is available. For the joint frequency analysis of systematic observations and historical data, respectively, the Bayesian framework is chosen, which, through adequately defined likelihood functions, allows for incorporation of different sources of hydrological information, e.g., mean annual rainfall, historical events as well as measurement errors. The distribution of the parameters of the fitted distribution function and the confidence intervals of the rain quantiles are derived by means of the Markov chain Monte Carlo simulation (MCMC) technique.The paper presents a sensitivity analysis related to the choice of the most influential parameters of the statistical model, which are the length of the historical period h and the perception threshold X 0 . These are involved in the statistical model under the assumption that except for the events termed as 'historical' ones, none of the (unknown) rains from the historical period h should have exceeded the threshold X 0 . Both higher values of h and lower values of X 0 lead to narrower confidence intervals of the estimated rain quantiles; however, it is emphasized that one should be prudent of selecting those parameters, in order to avoid making inferences with wrong assumptions on the unknown hydrological events having occurred in the past.The Bayesian MCMC methodology is presented on the example of the mean annual rains observed at Sudan in the period 1901-2002.