No abstract

Agricultural irrigation represents the main use of global water resources. Irrigation has an impact on the environment, and scientific evidence suggests that it inevitably leads to salinization of both soil and aquifers. The effects are most pronounced under arid and semi-arid conditions. In considering the varied impacts of irrigation practices on groundwater quality, these can be classed as either directthe direct result of applying water and accompanying agrochemicals to croplandor indirectthe effects of irrigation abstractions on groundwater hydrogeochemistry. This paper summarizes and illustrates through paradigmatic case studies the main impacts of irrigation practices on groundwater salinity. Typically, a diverse range of groundwater salinization processes operating concomitantly at different time scales (from days to hundreds of years) is involved in agricultural irrigation. Case studies suggest that the existing paradigm for irrigated agriculture of focusing mainly on crop production increases has contributed to widespread salinization of groundwater resources.

Abstract. A back-propagation artificial neural network (ANN) model is proposed to discriminate zones of high mineral potential in the Rodalquilar gold field, south-east Spain, using remote sensing and mineral exploration data stored in a GIS database. A neural network model with three hidden units was selected by means of the k-fold cross-validation method. The trained network estimated a gold potential map efficiently, indicating that both previously known and unknown potentially mineralized areas can be detected. These initial results suggest that ANN can be an effective tool for mineral exploration spatial data modelling.

The identification and location of groundwater‐dependent ecosystems are the first steps in protecting and managing them. Such identifications are challenging where the surface expressions of groundwater are not obvious. This work presents a remote‐sensing‐based approach to infer the groundwater dependence of semiarid shrubs from their association with fractures that facilitate root access to groundwater. As a case study, we used the Ziziphus lotus matorral in south‐east Spain, a priority conservation habitat in the European Union (Habitat 5220*, Directive 92/43/EEC) that is highly threatened by agricultural and urban sprawl. The approach combines object‐based image analysis of high‐resolution orthoimages to map Ziziphus individuals, geomorphometric analysis of a lidar‐derived terrain model to map bedrock fractures, and spatial statistics to assess the association between Ziziphus and fractures. Electrical resistivity tomography was used to validate the identified fractures, and the seasonal dynamics of the normalized difference vegetation index was used to prove that Z. lotus maintained higher greenness during the summer drought and was less coupled with precipitation than the nearby nonphreatophytic vegetation. A majority (61%) of the Ziziphus patches, particularly the smallest ones, occurred within 50 m of faults. This spatial association between phreatophyte shrubs and fractures contributes to the identification of groundwater‐dependent ecosystems. This approach offers several advantages because it is simple, low cost, and non‐destructive. In addition, the differentiation of shrubs into size classes provided insights into the long‐term environmental controls underlying the establishment of Ziziphus individuals. The evidence of groundwater dependence by Z. lotus in Habitat 5220* indicates the need for its urgent protection under the Water Framework Directive.

a b s t r a c tA software tool is described for the extraction of geomorphometric land surface variables and features from Digital Elevation Models (DEMs). The ArcGeomorphometry Toolbox consists of a series of Python/ Numpy processing functions, presented through an easy-to-use graphical menu for the widely used ArcGIS package. Although many GIS provide some operations for analysing DEMs, the methods are often only partially implemented and can be difficult to find and used effectively. Since the results of automated characterisation of landscapes from DEMs are influenced by the extent being considered, the resolution of the source DEM and the size of the kernel (analysis window) used for processing, we have developed a tool to allow GIS users to flexibly apply several multi-scale analysis methods to parameterise and classify a DEM into discrete land surface units. Users can control the threshold values for land surface classifications. The size of the processing kernel can be used to identify land surface features across a range of landscape scales. The pattern of land surface units from each attempt at classification is displayed immediately and can then be processed in the GIS alongside additional data that can assist with a visual assessment and comparison of a series of results. The functionality of the ArcGeomorphometry toolbox is described using an example DEM.