Citation:
Eilers VHM, Carter RC, Rushton KR. (2007) A single layer soil water balance model for estimating deep drainage (potential recharge): An application to cropped land in semi-arid North-east Nigeria. Geoderma, Volume 140, Issues 1-2, June 2007, pp. 119-131
Abstract:
The understanding and quantification of groundwater recharge in semi-arid areas are fundamental to sound management of water resources in such areas. A soil water balance model, if designed to adequately represent the physical processes involved, and if carried out with a short enough (daily) time step, can provide realistic estimates of deep drainage (potential recharge) over long periods.
We describe a single store (single layer) mass water balance model applicable to semi-arid areas, which recognises the wetting of the near surface during rainfall, with subsequent availability of water for evaporation and transpiration in the days following rainfall. The model allows for the major hydrological processes taking place at or near the soil-vegetation surface including runoff.
Model results are presented for North-east Nigeria, for a continuous period of 36 years during which mean annual rainfall was 431 mm (range 321–650 mm) and mean annual modelled deep drainage was 14 mm (range 0–95 mm, with 23 years having zero potential recharge). The modelling results indicate that annual rainfall totals are not the main predictor of annual recharge. The temporal distribution of daily rainfall and the magnitude of the antecedent (pre-season) soil moisture deficit are the strongest determinants of deep drainage at a particular location, in a particular year. Sensitivity analysis of soil and vegetation parameters suggests that deep drainage is most sensitive to water holding capacity and rooting depth. These are key parameters which determine spatial variability of potential recharge.
The model is shown to be plausible by examination of the concepts which underlie it, by comparison with field soil moisture measurements, and by the model's ability to represent qualitative observations of crop yield variations from year to year.
Future development of the model could include applications to other climatic conditions and the inclusion of other hydrologic processes.
