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A new pre-processing methodology for gridded Satellite Precipitation Products (SPPs) is developed to improve the performance of Machine Learning (ML) algorithms for runoff prediction. The developed approach was applied to capture the rainfall patterns, and to select relevant input data. This approach was tested using the FeedForward Neural Network (FFNN) and the Extreme Learning Machine (ELM) given their flexibility and ability in hydrological modelling. The methodology was tested in a semiarid transboundary watershed located in North Africa (Algeria, Tunisia) with the Integrated MultisatellitE Retrievals for Global Precipitation Measurement (GPMIMERG) and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) products. The results demonstrate the effectiveness of the proposed approach using all employed SPPs. In terms of Nash-Sutcliffe efficiency, the suggested pre-processing technique improved the prediction ability of FFNN by 13%, and of ELM by 15%, which highlights how pre-processing techniques significantly enhance ML models with SPP data. [ABSTRACT FROM AUTHOR]

Climate variability has been identified as one of the major factors affecting water resources. The purpose of this study is to characterize climate variability in the region of Souk Ahras and to evaluate its impact on surface and groundwater resources. Various statistical methods, including Mann-Kendall test, Pettitt test, drought indices, and Fourier filter were used. Pettitt test revealed the occurrence of breaks over the period (1994-1996), showing significant increase in the annual rainfall and temperature data time series. The analysis of drought indices provided insights into the relationship between meteorological and hydrological droughts in the study area. This mainly revealed that rainfall deficit results in an immediate response to runoff regime and a delayed response to groundwater level. Moreover, it was found that both meteorological and hydrological drought conditions significantly decreased after the climatic shift in terms of frequency, intensity and duration. Such information is of great importance for managing water resources and preparing drought mitigation measures. [ABSTRACT FROM AUTHOR]

This study examines the variability of precipitation in the south-east of Tunisia through the analysis of data about annual and monthly precipitation at five stations in theWatershed of Gabes, from 1977 to 2015. Standardized precipitation ratio, wavelet and coherence wavelet analyses were applied to examine the temporal variability of monthly and annual precipitation and to determine the effect of climatic fluctuations on rainfall variability. Results of wavelet analysis showed varied energy bands at the studied stations at annual and inter-annual scales. The depicted bands spread according to intervals of 1-, 2- to 4-, 4- to 8- and 8- to 12-year cycles, obviously influenced by regional factors including altitude, proximity to the Mediterranean Sea and global fluctuations. Eventually, an analysis of wavelet coherence showed a strong correlation between precipitation and Mediterranean Oscillation (MO) in GabesWatershed at different temporal scales. Contribution of the MO ranged between 51% and 93% of fluctuations (8-12 years) in the different examined rainfall stations. [ABSTRACT FROM AUTHOR]

Medjerda is a key transboundary watershed in the Maghreb region, crossing from the Algerian mountains through northern Tunisia. Therefore, the analysis of the rainfall regime in this basin is of paramount importance for water resources management and regional economic development, notably concerning agriculture. This study examines the rainfall trends over the Medjerda watershed on multi-temporal scales (monthly, seasonally and annually) with a database of monthly rainfall observed in 60 stations evenly spread over the watershed. After filling gaps and homogenizing data, the Mann–Kendall test for trend detection was applied to rainfall series and the Sen's slope method was adopted to estimate the trend's magnitude, interpolated over the sub-catchments, to analyze the spatial distribution of rainfall changes within the watershed. Results showed the absence of significant trends at the annual scale for the entire catchment. However, rainfall redistribution was observed throughout the year, with a notable precipitation reduction during spring and increased winter precipitation, which could impact agriculture and ecosystem functioning. This modification of the rainfall regime implies an adaptation of the management of dams and reservoirs, with a reduced filling capacity during spring in anticipation of the summer dry season. [ABSTRACT FROM AUTHOR]

Abstract: Spatial and temporal variability of rainfall characteristics in southern Tunisia are monitored. Performance is evaluated through descriptive and statistical analysis of continuous monthly and annual rainfall data over the period extending from 1930 to 2000. Principal Component Analysis (PCA), representation quality and multiple regression methods are carried out on the rainfall data, recorded in twelve stations, to find out the nature of rainfall distribution and the dominant variables related to its variability. A varimax rotation with Kaiser normalization was used. Applied to the monthly series data, three principal components (PCs) are found to be significant, explaining 69% of the total variance. Rainfall variability is shown to be dependent on seasonal conditions. The longitude presents the best quality of representation, followed closely by winter and fall rainfall. Spatial patterns of shorter rainfall series (annual) are governed mainly by topography and coastal influence. The first three PCs contribute by 90% of the total variance and their loadings indicate that annual rainfall is significant over the south-eastern costal area. Four regions are delineated based on the projection over the factorial plane, showing the influence of topography and seasonality on annual rainfall variability in southern Tunisia. [Copyright &y& Elsevier]

Natural groundwater recharge under semi arid climate, like rainfall, is subjected to large variations in both time and space and is therefore very difficult to predict. Nevertheless, in order to set up any strategy for water resources management in such regions, understanding the groundwater recharge variability is essential. This work is interested in examining the impact of rainfall on the aquifer system recharge in the Northern Gafsa Plain in Tunisia. The study is composed of two main parts. The first is interested in the analysis of rainfall spatial and temporal variability in the study basin while the second is devoted to the simulation of groundwater recharge. Rainfall analysis was performed based on annual precipitation data recorded in 6 rainfall stations over a period of 56 years (1960–2015). Potential evapotranspiration data were also collected from 1960 to 2011 (52 years). The hydrologic distributed model WetSpass was used for the estimation of groundwater recharge. Model calibration was performed based on an assessment of the agreement between the sum of recharge and runoff values estimated by the WetSpass hydrological model and those obtained by the climatic method. This latter is based on the difference calculated between rainfall and potential evapotranspiration recorded at each rainy day. Groundwater recharge estimation, on monthly scale, showed that average annual precipitation (183.3 mm/year) was partitioned to 5, 15.3, 36.8, and 42.8% for interception, runoff, actual evapotranspiration and recharge respectively. [ABSTRACT FROM AUTHOR]