Projection of Water Availability and Sustainability in Nigeria Due to Climate Change

Abstract

This study projects water availability and sustainability in Nigeria due to climate change. This study used Gravity Recovery and Climate Experiment (GRACE) terrestrial water storage data (TWS), Global Precipitation Climatology Center (GPCC) precipitation data and Climate Research Unit (CRU) temperature data. Four general circulation models (GCMs) of the Coupled Model Intercomparison Project 5 were downscaled using the best of four downscaling methods. Two machine learning (ML) models, RF and SVM, were developed to simulate GRACE TWS data for the period 2002–2016 and were then used for the projection of spatiotemporal changes in TWS. The projected TWS data were used to assess the spatiotemporal changes in water availability and sustainability based on the reliability–resiliency–vulnerability (RRV) concept. This study revealed that linear scaling was the best for downscaling over Nigeria. RF had better performance than SVM in modeling TWS for the study area. This study also revealed there would be decreases in water storage during the wet season (June–September) and increases in the dry season (January–May). Decreases in projected water availability were in the range of 0–12 mm for the periods 2010–2039, 2040–2069, and 2070–2099 under RCP2.6 and in the range of 0–17 mm under RCP8.5 during the wet season. Spatially, annual changes in water storage are expected to increase in the northern part and decrease in the south, particularly in the country’s southeast. Groundwater sustainability was higher during the period 2070–2099 under all RCPs compared to the other periods and this can be attributed to the expected increases in rainfall during this period.

Diagnosing the Terrestrial Water Storage Variation Over the Nigerian Space: The Grace Perspective

“…One clear distinguishable attribute of TWS within Nigeria is the opposite phase phenomenon it displays between the Southern and Northern part of Nigeria in the first dry period. To some extent, this pattern agrees with the projections ofShiru et al (2021) which was explained previously. This stark temporal pattern may be attributed to the lag in the response time between rainfall and TWS.…”

“…In Shiru et al (2021), a projection of water availability and sustainability in Nigeria was conducted. TWS of the region was simulated using GRACE TWS of 2002 -2016.…”

Mathematical and Machine Learning Models for Groundwater Level Changes: A Systematic Review and Bibliographic Analysis

“…Groundwater is a major natural resource for our mother earth, accounting for around 95 percent of all freshwater on the planet [4], making it essential for human existence and economic advancement. The effects of global climate change on groundwater level changes are significant enough to warrant further investigation in order to enhance forecasting and future consequences [5]. Due to poor extraction and overexploitation, the volume of groundwater has declined in recent years, worsening future water shortages [6].…”

A research landscape bibliometric analysis on climate change for last decades: Evidence from applications of machine learning

“…In another study cited 52 times, Shahid and co-workers [ 46 ], proposed and examined a Multiple Criteria Decision Making (MCDM-based) structure for estimating Spatiotemporal rainfall changes in Nigeria using general circulation models. The author's studies have generally been focused on utilising ML techniques to predict water-based issues [ 47 , 48 ] such as droughts [ 49 ], rainfall (precipitation) [ 50 , 51 ], and marine life [ 52 ], along with weather and climatic conditions [ 53 , 54 ] in the tropical regions of the world.…”