Identifying crop vulnerability to groundwater abstraction: Modelling and expert knowledge in a GIS

Abstract

Water use is expected to increase and climate change scenarios indicate the need for more frequent water abstraction. Abstracting groundwater may have a detrimental effect on soil moisture availability for crop growth and yields. This work presents an elegant and robust method for identifying zones of crop vulnerability to abstraction. Archive groundwater level data sets were used to generate a composite groundwater surface that was subtracted from a digital terrain model. The result was the depth from surface to groundwater and identified areas underlain by shallow groundwater. Knowledge from an expert agronomist was used to define classes of risk defined in terms of their depth in metres below ground level. Combining information on the permeability of geological drift types further refined the assessment of the risk of crop growth vulnerability. The nature of the mapped output is one that is easy to communicate to the intended farming audience because of the general familiarity of mapped information. Such GIS-based products can play a significant role in the characterisation of catchments under the EU Water Framework Directive especially in the process of public liaison that is fundamental to the setting of priorities for management change. The creation of a baseline allows the impact of future increased water abstraction rates to be modelled and the vulnerability maps are a format that can be readily understood by the various stakeholders. This methodology can readily be extended to encompass additional data layers and for a range of groundwater vulnerability issues including water resources, ecological impacts, nitrate and phosphorus.

Bayesian data fusion for water table interpolation: Incorporating a hydrogeological conceptual model in kriging

“…To create a water table map from groundwater level observations, a wide variety of interpolation techniques are available, including radial basis functions, inverse distance weighting (IDW), and different kriging variants. Recent applications of these methods in the context of water table mapping can be found in work by Procter et al [2006], Taany et al [2009], and Sun et al [2009]. Although these methods honor the data at the measurement locations, they suffer from the same drawbacks, namely, an inadequate representation of the flow system and the occurrence of interpolation artifacts.…”