Management of groundwater by necessity requires water level measurements collected from monitoring wells to identify areas where water levels are falling rapidly estimate the amount of water left in storage. In turn, groundwater management institutions can use these results to determine the effectiveness of their regulatory practices and develop suitable regulatory instruments that promote sustainable and equitable development of aquifer resources. However, given the heterogeneity of the subsurface matrix, and given the limitations of fiscal and personnel resources, the choice of the wells that should be part of the monitoring network becomes extremely important. A randomization-based sensitivity analysis procedure is developed in this chapter to assess the sensitivity of wells in a large monitoring network. The methodology also provides uncertainty bounds for estimated water levels at unmonitored locations and as such can be used to refine groundwater monitoring networks. The proposed methodology for the identification of sensitive wells within a groundwater network is obtained by integrating the jackknife approach with inverse distance weighting. The methodology is nonparameteric and noted to be robust especially when the observations are skewed and do not meet the assumptions of normality required for parametric analysis. In this chapter, independent verification of the developed sensitivity measures is carried out using terrestrial water storage (TWS) data obtained from GRACE satellite systems. A strong correlation between the sensitivity measures and TWS anomaly was observed, confirming that the critical wells identified for estimating groundwater availability were also the ones showing greatest responses to changes in TWS. This study presents an innovative use of GRACE data to evaluate the adequacy of large-scale regional groundwater monitoring networks.
|Title of host publication||Sensitivity Analysis in Earth Observation Modelling|
|Number of pages||22|
|State||Published - Jan 1 2017|
- GRACE satellite systems
- Inverse distance weighting (IDW)
- Jackknife resampling
- Monitoring well network