Computational uncertainty analysis of groundwater recharge in catchment

Inference of the spatio-temporal variability and storage potential of groundwater in data-deficient regions through groundwater models and inversion of impact factors on groundwater, as exemplified by the Lake Victoria Basin

Groundwater is an important resource for supporting domestic water use for people's livelihoods and for maintaining ecosystems. Borehole observations provide the first-hand data that characterise the fluctuation, depth, and aquifer conditions of the groundwater. Unfortunately, such observations are not available or are insufficient for scientific use in many regions. Taking the Lake Victoria Basin (LVB) as an example of data-deficient regions, this study proposes a simple knowledge-based approach that uses the Global Land Data Assimilation System (GLDAS) Catchment Land Surface Model (CLSM) for the main data, with rainfall, hydrological, topographical and geological datasets as supports, by which to infer the spatio-temporal variability and storage potential of groundwater. The method is based on analysis and inversion of impact factors on groundwater, and the feasibility of such a method is proven by showing that the groundwater results from GLDAS CLSM can correctly indicate the seasonality, as well as the link to topographical and geological features. For example, both results from the water balance equation (WBE) and GLDAS CLSM indicate that there are two groundwater recharge seasons in the basin, e.g., March to May and September to November. Compared to the eastern side of the LVB, the western side has mountains blocking surface runoff, and thus, reasonably, has larger storage potential estimates in GLDAS CLSM. Due to the low degree of weathering of the basement rocks, it is expected that there is only small storage potential and variation of groundwater in the southeastern parts of the LVB. GLDAS CLSM also correctly reflects this behaviour. Additionally, the largest groundwater storage potential over the LVB is found in regions near the Kagera River and the western shoreline, since it associates with unconsolidated rocks and behaviours of large groundwater recharge from GLDAS CSLM during the wet year of 2006. The major limitation of this knowledge-based method is that the uncertainty in terms of magnitude on GLDAS CLSM groundwater changes cannot be assessed, in addition to the fact that the reliability of the results cannot be quantified in terms of specific numbers. Therefore, the results and interpretation of groundwater behaviours using such methods can only be a guide for 'where' and 'when' to find groundwater.

Integrating soil water and tracer balances, numerical modelling and GIS tools to estimate regional groundwater recharge: Application to the Alcadozo Aquifer System (SE Spain)

Groundwater recharge is one of the key variables for aquifer management and also one of the most difficult to be evaluated with acceptable accuracy. This is especially relevant in semiarid areas, where the processes involved in recharge are widely variable. Uncertainty should be estimated to know how reliable recharge estimations are. Groundwater recharge has been calculated in the Alcadozo Aquifer System, under steady state conditions, at regional (aquifer) and sub-regional (spring catchment) scales applying different methods. The regional distribution of long-term average recharge values has been estimated with the chloride mass balance method using data from four rain stations and 40 groundwater samples covering almost the whole aquifer surface. A remarkable spatial variability has been found. Average annual recharge rates ranges from 20 to 243mmyear(-1) across the aquifer, with an estimated coefficient of variation between 0.16 and 0.38. The average recharge/precipitation ratio decreases from 34% in the NW to 6% in the SE, following the topographic slope. At spring-catchment scale, recharge has been estimated by modelling the soil water balance with the code Visual Balan 2.0. The results, calibrated with discharge data of the two main springs Liétor and Ayna, are 35.5 and 50mmyear(-1) respectively, with estimated coefficients of variation of 0.49 and 0.36. A sensitivity analysis showed that soil parameters influence the most the uncertainty of recharge estimations. Recharge values estimated with both methods and at two temporal and spatial scales are consistent, considering the regional variability obtained with the chloride method and the respective confidence intervals. Evaluating the uncertainties of each method eased to compare their relative results and to check their agreement, which provided confidence to the values obtained. Thus, the use of independent methods together with their uncertainties is strongly recommended to constrain the magnitude and to provide reliance to recharge estimations.