Application of Earth Observation Data in Assessing Water Resources in Sokoto-Rima Catchment, North-Western Nigeria

Código

I-EBHE0066

Autores

Owolabi Rasaq Rahman

Tema

WG 1.01: REHYDRATE - REtrieve historical HYDRologic dATa & Estimates

Resumo

n semi-arid regions, where reliance on groundwater is paramount, there exists an urgent need for precise quantitative estimation of predominantly non-renewable groundwater systems. Such assessments, particularly concerning alterations in water table levels, are indispensable for devising sustainable water-management strategies and policies. However, the scarcity of data presents formidable obstacles to scientific endeavors aimed at comprehending the distribution, nature, and accessibility of groundwater resources. Over the past two decades, the continuity of long-term, high-quality water datasets has been compromised due to the malfunctioning of monitoring stations in the Sokoto-Rima Hydrological Area. This has led to a substantial 90% reduction in active Monitoring stations. This data deficiency, coupled with limited access to information, is a driving factor behind the scarcity of research on water resources, hydrological modeling, and water balance in the twelve (12) Hydrological Areas (HA) in the Country. In the Sokoto-Rima Hydrological Area (HA-II), over 70% of the population depends on groundwater for subsistence farming. This research aims to estimate various components of the Sokoto- River Catchment with a Remote Sensing and relate with available data and tools as hydro climatological data are scarce due to limited observation Stations. The data obtained from Gravity recovery and climate experiment (GRACE), Moderate Resolution Imaging Spectroradiometer (MODIS) and Climate Hazard Infra-Red Precipitation (CHIRPS) were used for the analysis of components of the catchment water budget of precipitation (P), evapotranspiration (ET) and terrestrial water storage (tws) in the river basin. Time series analysis of dataset was conducted to understand the temporal variations in precipitation and evapotranspiration patterns. Seasonal and annual trends were identified and related to climate and hydrological events. Statistical and Machine learning (ML) models were applied in the correlation of relationship between the components, monitoring data and prediction. The result indicates a slightly varying trend of terrestrial water storage for the study time with water shortages during the dry season and surplus water during the wet season. Precipitation is the most variable components in the water budget that when the precipitation declines to zero, both the soil moisture and change in terrestrial water change decline drastically and rise again with increasing precipitation. These results provide information on groundwater depletion (caused by periods of low rainfall or droughts) and groundwater recovery for effective water management.