Spatial prediction of water quality variables along a main river channel, in presence of pollution hotspots

In order to treat and evaluate the available data of water quality and fully exploit monitoring results (e.g. characterize regional patterns, optimize monitoring networks, infer conditions at unmonitored locations, etc.), it is crucial to develop improved and efficientmethodologies. Accordingly, estimation ofwater quality along fluvial ecosystems is a frequent task in environment studies. In this work, a particular case of this problem is examined, namely, the estimation of water quality along a main stem of a large basin (wheremost anthropic activity takes place), fromobservational data measured along this river channel. We adapted topological kriging to this case, where eachwatershed contains all the watersheds of the upstreamobserved data ("nested support effect"). Data analysis was additionally extended by taking into account the upstream distance to the closest contamination hotspot as an external drift. We propose choosing the best estimationmethod by cross- validation. Themethodological approach in spatial variability modeling may be used for optimizing the water quality monitoring of a given watercourse. The methodology presented is applied to 28 water quality variables measured along the Santiago River inWestern Mexico. (C) 2017 Elsevier B. V. All rights reserved.