Application of Software in Soil and Groundwater Recharge Estimation in Ilorin, Nigeria

Introduction

Groundwater (GW) is an essential natural resource for human life that can be analysed with the help of images provided by Geographical Information Systems (GISs). Excess withdrawal without renewing of this natural resource is tremendously increased along with the growing population (Jumadi, 2015). Management and maintenance efforts to assure the sustainability of this natural water resource are critical. One of the efforts to sustain the existence of GW is to conduct balance management between utilization and potential recharge through an accurate numerical formula which needs adequate information of the physical condition of the environment and socioeconomic condition of the surrounding population. Groundwater Recharge (GWR) includes the drive of water from the unsaturated zone into the saturated region of the soil below the water table. The water runs together with the associated flow pattern away from the water table within the saturated region (Umar et al., 2018). GWR occurs at a point when water containing dissolved minerals flows beyond the Groundwater Level (GWL) and infiltrates into the saturated zone. Modifications in GW storage comprise various recharge and discharge progressions. Most important recharge sources are rainfall, recharge from rivers, recharge from ponds, recharge from irrigation fields, etc. The crucial discharge processes are evapotranspiration, pumping, and baseflow to rivers. Several factors can affect the existence and discrepancy of GWL in a region. The major and significant factors that influence GWRinclude topography, lithology, geological structures, depth of weathering, the extent of fractures, the primary and secondary porosity of the soil, slope and drainage patterns of the region, landform and land cover, environmental factors and climate (Umar et al., 2018). It is well known and established that there is a wide range of direct and indirect methods to estimate GWRprocesses, with a degree of approximation depending on different spatio-temporal scales, have been proposed (Scanlon et al., 2002, 2006, Qinghua et al., 2016; Brian et al., 2016). The methods include lysimeter measurements, soil moisture budgets, and effective infiltration coefficients, as well as water table rise, tracer, and remote sensing methods. At a regional scale, to estimate the endogenous and exogenous variables controlling GWRprocesses, multidisciplinary analyses of hydrological time series, hydrogeological and geomorphological data have been implemented in a GIS environment (Andreo et al., 2008; Dripps and Bradbury, 2010). Moreover, conceptual and physically based models accounting for the spatial variability of parameters which control recharge have been proposed (Hartmann et al., 2012; 2013, Allocca et al., 2014).

These GWRmethods and models employed in the determination of the GWRare characterized into three groups: (i) physical models calculated from the base flow, (ii) chemical models for the measurement of water-soluble substances, and (iii) numerical models using mathematical and computational techniques. The numerical method approaches include the computer programs such as HELP, RORA, PULSE, PART, HY SEP and WellsWater-table fluctuations (Seyed et al., 2013, Umar et al., 2018). Chandra (1979) reported that the methods and techniques that are prevalently applied to estimate the natural are soil water balance, zero flux plane; one-dimensional soil-water flow model, inverse modeling technique, GWL fluctuation, hybrid water fluctuation, GW balance method; isotope and solute profile techniques. Kumar (2000) grouped the methods and the techniques of estimation of GWR into four groups as empirical methods; GW resource estimation; GW balance approach and soil moisture data-based methods (Seyed et al., 2013; Islam et al., 2014).

The empirical methods comprise the following equations modified by Chaturvedi, Kumar, and Seethapathi, UP and Rao formulae. Chaturvedi (1973) derived an empirical equation, which expresses recharge as a function of the annual precipitation as follows (Oke et al., 2017):