Urban water management via Sustainable Urban Drainage Systems (SuDS) has been successfully applied in cities worldwide. This infrastructure has proven to be a cost efficient solution to manage flood risks whilst also delivering wider benefits. Despite their technical performance, large-scale SuDS uptake in many places has been slow, mostly due to reasons beyond the engineering realm. This is the case of England and Wales, where the implementation of SuDS has not reached its full potential. This paper investigates the strategic role of SuDS retrofit in managing environmental risks to urban infrastructure at a catchment level, through an economic appraisal of all benefits (i.e. flood reduction and wider benefits). The Decoy Brook catchment in London, UK, was used as a case study. Average Annual Benefits were used to monetise the value of SuDS in reducing surface flood risk, whilst a Value Transfer approach was used to appraise wider benefits. It was found that by including the latter, their economic feasibility improves considerably. This paper also shows how to split the investment amongst multiple stakeholders, by highlighting the benefits each one derives. Finally, recommendations regarding incentives and policies to enhance the uptake of SuDS are given. The proposed methodology for SuDS mapping and economic appraisal in the planning phase can be used in cities worldwide, as long as general principles are adapted to local contexts

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Abstract. The accuracy of hydrological assessments in mountain regions is often hindered by the low density of gauges, coupled with complex spatial variations in climate. Increasingly, spatial data sets (i.e. satellite and gridded products) and new computational tools are used to address this problem, by assisting with the spatial interpolation of ground observations. This paper presents a comparison of approaches of different complexity to spatially interpolate precipitation and temperature time-series in the upper Aconcagua catchment in central Chile. A Generalised Linear Mixed Model whose parameters are estimated through approximate Bayesian inference is compared with three simpler alternatives: Inverse Distance Weighting, Lapse Rates and a method based on WorldClim data. The assessment is based on a leave-one-out cross validation, with the Root Mean Squared Error being the primary performance criterion for both climate variables, while Probability of Detection and False Alarm Ratio are also used for precipitation. Results show that for spatial interpolation of the expected values of temperature and precipitation, the WorldClim approach may be recommended as being the more accurate, easy to apply and relatively more robust to tested reductions in the number of estimation gauges, particularly for temperature. The Generalised Linear Mixed Model has comparable performance when all gauges were included, but is more sensitive to the reduction in the number of gauges used for estimation, which is a constraint in sparsely monitored catchments.

The accuracy of hydrological assessments in mountain regions is often hindered by the low density of gauges coupled with complex spatial variations in climate. Increasingly, spatial datasets (i.e. satellite and other products) and new computational tools are merged with ground observations to address this problem. This paper presents a comparison of approaches of different complexities to spatially interpolate monthly precipitation and daily temperature time series in the upper Aconcagua catchment in central Chile. A generalised linear mixed model (GLMM) whose parameters are estimated through approximate Bayesian inference is compared with simpler alternatives: inverse distance weighting (IDW), lapse rates (LRs), and two methods that analyse the residuals between observations and World-Clim (WC) data or Climate Hazards Group Infrared Precipitation with Station data (CHIRPS). The assessment is based on a leave-one-out cross validation (LOOCV), with the rootmean-squared error (RMSE) being the primary performance criterion for both climate variables, while the probability of detection (POD) and false-alarm ratio (FAR) are also used for precipitation. Results show that for spatial interpolation of temperature and precipitation, the approaches based on the WorldClim or CHIRPS residuals may be recommended as being more accurate, easy to apply and relatively robust to tested reductions in the number of estimation gauges. The GLMM has comparable performance when all gauges were included and is better for estimating occurrence of precipitation but is more sensitive to the reduction in the number of gauges used for estimation, which is a constraint in sparsely monitored catchments.

Mountainous regions are a hotspot for water scarcity and anthropogenic pressure on water resources. Substantial uncertainty surrounds projections of future climate and water availability. Furthermore, quantitative and distributed data on water demand are generally scarce, dispersed, and highly heterogeneous. This forms a major bottleneck to studying water resources issues and developing strategies to improve water resource management. Here we present a methodology to produce and evaluate highresolution gridded maps of anthropogenic surface water demand with application to the Andean region. These data are disaggregated according to the major types of water demand: domestic users, irrigated area, and hydropower. This dataset was built by homogenizing, integrating, and interpolating data obtained from various national institutions in charge of water resource management as well as relevant global datasets. The maps can be used to research anthropogenic impacts on water resources, and to guide regional decision-making in regions such as the Andes.