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• Performance assessments quantify either abatement (AC) or transaction costs (TC). • We develop tools to quantify both AC and TC. • Socio-hydrology modeling of interconnected human and water systems is used to measure AC. • We develop a cutting-edge longitudinal dataset to assess future directions and magnitude of TC. • Results in the Douro Basin (Spain) show TC can represent 5.7 % − 8.3 % of total costs (AC + TC). • This highlights the need to account for AC and TC in performance assessments. Water reallocations have costs to the users of water, or abatement costs (e.g., charges designed to marginally increase environmental water flows), but also nontrivial institutional transaction costs (e.g., costs incurred to develop institutions and organizations to support and enforce environmental reallocations). However, institutional transaction costs studies are very limited and those available do not integrate abatement costs measurements, which constrains our ability to assess the performance of water reallocation. This paper presents the first integrated analysis of abatement and transaction costs of water reallocation. The analysis is illustrated with an application to the Douro River Basin, an agricultural basin in central Spain that has recently finished its second planning cycle (2015–2021). First, we use a hydroeconomic model that accounts for the two-way feedback responses between human and water systems to estimate the abatement costs of water reallocations, as well as their effectiveness in achieving the good ecological status of water bodies. Second, we measure and monetize realized institutional transaction costs of river basin planning over time and build on this cutting-edge longitudinal dataset to assess future directions and magnitude of transaction costs. We use this information to assess and rank the performance (through cost-effectiveness) of the water reallocations considered in the latest Douro River Basin Plan under alternative climate change scenarios. We find that under the hypothesis of stationary transaction costs, these can represent between 5.7% and 8.3% of the total reallocation costs (abatement plus transaction costs). This non-trivial magnitude highlights the need to account for both abatement and transaction costs when assessing the performance of water reallocations, and environmental policy overall. [ABSTRACT FROM AUTHOR]

[Display omitted] • We utilized GPR surveys to investigate water flow dynamics of a forested hillslope. • We provided empirical evidences of the presence of preferential flow networks. • Stemflow infiltration triggered the activation of these networks. • Network activation caused a rapid response of hillslope subsurface flow. The hydrological response of sloping catchments is strongly conditioned by the connectivity of subsurface preferential flows. The objective of this paper is to investigate the role played by stemflow infiltration in subsurface water flow dynamics, focusing on a forested hillslope located in an Aleppo pine Mediterranean forest (Pinus halepensis , Mill.) located at Sierra Calderona, Valencia province, Spain. We combined stemflow artificial experiments with the ground-penetrating radar (GPR) as a non-invasive technique to investigate stemflow-induced preferential flow paths activated by different trees and the related hydrological connectivity at the hillslope scale. Our observations allowed us to identify different dynamics associated with the initiation of stemflow and then lateral preferential flow, including the activation of connected preferential flow paths that received stemflow water from different trees. These observations provided empirical evidence of the role of stemflow in the formation of lateral preferential flow networks. Our measurements also provided estimations for flow velocities, which provided new insight on the magnitude of stem-induced lateral preferential flow paths. The applied protocol offers a simple, repeatable and non-invasive way to conceptualize hillslope responses to rainstorms. [ABSTRACT FROM AUTHOR]

• We estimate the information transaction costs emerging in a water market between irrigators. • We couple a hydrologic model, a mathematical programming model and an ABM. • Results show that transaction costs can reduce the economic surplus up to 45%. The economic literature has extensively researched water markets, finding that this instrument can deliver superior resource allocations under growing scarcity. However, these assessments typically ignore the information, bargaining, and enforcement costs that occur in trades, known as transaction costs, which can be particularly relevant in emerging markets such as those for water. This paper presents a multi-agent cellular automata model that couples a positive multi-attribute utility programming (PMAUP) model with an agent-based model (ABM) to elicit the information transaction costs of water trading under alternative market and climate scenarios. A first experiment evaluates an ideal water market with no transaction costs, which is then compared to alternative decentralized spot market setups representing different degrees of information availability that are modeled using the coupled PMAUP-ABM. The difference between the economic surplus under the ideal market and that achieved under alternative market setups with information constraints is used as a proxy value of information transaction costs. Methods are illustrated with an application to the Douro River Basin in central Spain. Results show that information transaction costs can reduce the economic surplus between 0.6% and 45% depending on the scenario. [ABSTRACT FROM AUTHOR]

• High-resolution pluvial flood hazard maps are developed at a municipality scale. • The methodology uses the Safer_RAIN fast-processing DEM-based algorithm. • Empirical probability distributions with 10,000 water depths are obtained in each cell. • Bivariate return-period curves show that several storms can generate the T -year flood. • The stochastic methodology improves the standard approach based on design storms. Climate change and rapid expansion of urban areas are expected to increase pluvial flood hazard and risk in the near future, and particularly so in large developed areas and cities. Therefore, large-scale and high-resolution pluvial flood hazard mapping is required to identify hotspots where mitigation measures may be applied to reduce flood risk. Depressions or low points in urban areas where runoff volumes can be stored are prone to pluvial flooding. The standard approach based on estimating synthetic design hyetographs assumes, in a given depression, that the T -year design storm generates the T -year pluvial flood. In addition, urban areas usually include several depressions even linked or nested that would require distinct design hyetographs instead of using a unique synthetic design storm. In this paper, a stochastic methodology is proposed to address the limitations of this standard approach, developing large-scale ∼ 2 m-resolution pluvial flood hazard maps in urban areas with multiple depressions. The authors present an application of the proposed approach to the city of Pamplona in Spain (68.26 km2). The Safer_RAIN fast-processing algorithm based on digital elevation models (DEMs) is compared with the IBER 2D hydrodynamic model in four real storms by using 10-min precipitation fields. Precipitation recorded at rainfall-gauging stations was merged with continuous fields obtained from a meteorological radar station. Given the hydrostatic limitations of Safer_RAIN, the benchmarking results are adequate in terms of water depths in depressions. A long set of 10 000 synthetic storms that maintain the statistical properties of observations in Pamplona is generated. Safer_RAIN is used to simulate runoff response, and filling and spilling processes, in depressions for the 10 000 synthetic storms, obtaining the probability distribution of water depths in each cell. Maps of pluvial flood hazards are developed in the Pamplona metropolitan area for 10 return periods in the range from two to 500 years from such pixel-based series of simulated water depths. Bivariate return-period curves are estimated in a set of cells, showing that several storms can generate a given T -year pluvial flood with an increasing precipitation with storm duration that depends on the draining catchment soil characteristics. The methodology proposed is useful to develop maps of pluvial flood hazards in large multi-depression urban areas in reasonable computation times, identifying the main pluvial flood hotspots. [ABSTRACT FROM AUTHOR]

• Eco-hydrological distributed models can validate spatial patterns of satellite SSM. • Differences in temporal and spatial dynamics of SSM products were found. • Multi-objective calibration (Q and SSM) increases the robustness of the modelling. • DISPATCH algorithm provided best spatial agreement compared to TETIS results. Reliable distributed hydrological modeling, especially in semi-arid areas, must consider the inclusion of surface soil moisture (SSM) spatial information during the calibration process. This variable plays a key role in the evapotranspiration processes that determine the hydrological cycle. The coarse resolution of the SSM estimates by satellite remote sensing has restricted the application of this approach to only large basins, focusing most of the studies in the consideration of simply the temporal dynamics of this variable. The growing efforts in providing higher spatial resolution through disaggregating methodologies or new sensor estimates facilitates the application of this spatial approach to small basins. This paper explores the applicability of the currently available satellite surface soil moisture estimates for distributed eco-hydrological modelling in Mediterranean forest basins. On one hand, this study contributes to fill the existing research gap on the use of remote sensing SSM spatial patterns within the distributed hydrological modelling framework in small basins. On the other hand, it serves as an indirect validation method for the spatial performance of satellite SSM products. To achieve this goal, we implemented the eco-hydrological model TETIS in three case studies named: Hozgarganta (southern Spain), Ceira (western Portugal) and Carraixet (eastern Spain). The SSM estimates selected for comparison were Sentinel-1 SSM provided by the Copernicus Global Land Services (CGLS), SMAP SSM disaggregated using Sentinel-1 (SPL2SMAP_S) provided by the National Aeronautics and Space Administration (NASA), SMOS SSM provided by the Barcelona Expert Center (BEC), and SMOS and SMAP SSM disaggregated using the DISPATCH algorithm provided by Lobelia Earth. The methodology employed involved a multi-objective and multi-variable calibration in terms of remote sensing SSM spatial patterns and in-situ streamflow, using the Spatial Efficiency Metric (SPAEF) and the Nash-Sutcliffe efficiency index (NSE) respectively. Before model calibration a sensitivity analysis of the most influent variables was performed. The temporal and spatial comparison of the reference SSM products revealed inconsistencies amongst products. The disaggregating methodology determined the spatial agreement to a greater degree than the sensor itself (i.e. SMAP, SMOS). In spite of the differences amongst products, the multi-objective calibration approach proposed increased the robustness of the hydrological modelling. [ABSTRACT FROM AUTHOR]

• Predictive modelling benchmark of Nitrate Vulnerable Zones using Random Forest. • Extrinsic driving forces to groundwater were selected as environmental predictors. • Phenological features derived from remote sensing were included as novel features. • Feature selection methods revealed good performance predicting nitrate pollution. • Phenology and manure production from livestock farms as most important features. Nitrate leaching losses from arable lands into groundwater were a main driver in designating Nitrate Vulnerable Zones (NVZs) according to the Nitrates Directive, with a view to enhancing their water quality. Despite this, developing common strategies for effective water quality control in these areas remains a challenge in the European Union. This paper evaluates the performance of the Random Forest (RF) machine learning algorithm combined with Feature Selection (FS) techniques in predicting nitrate pollution in NVZs groundwater bodies in different periods and using updated environmental features in Andalusia, Spain. A set of forty-four features extrinsic to groundwater bodies were used as environmental predictors, with an aim to make this methodology exportable to other regions. Phenological features obtained through remote-sensing techniques were included to measure the dynamics of agricultural activity. In addition, other dynamic features derived from weather and livestock effluents were included to analyse seasonal and interannual changes in nitrate pollution. Three feature stacks and two nitrate databases were used in the predictive modelling: Period 1 (2009), with 321 nitrate samples for training; Period 2 (2010), with 282 nitrate samples for validation and initial spatial prediction; and Period 3 (2017), to assess the changes in the probability of groundwater nitrate content exceeding 50 mg/L. Random Forest as a wrapper with four sequential search methods was considered: sequential backward selection (SBS), sequential forward selection (SFS), sequential forward floating selection (SFFS) and sequential backward floating selection (SBFS). From among all the Feature Selection methods applied, Random Forest with SFS had the best performance (overall accuracy = 0.891 and six predictor features) and linked the highest probability of nitrate pollution with three dynamic features: the Normalized Difference Vegetation Index (NDVI) base level, NDVI value for the end of the growing season and accumulated manure production of livestock farms; and three static features: slope, sediment depositional areas and valley depth. [ABSTRACT FROM AUTHOR]

• Groundwater-dependent wetlands rank among the world's most endangered ecosystems. • A novel machine learning approach to map groundwater-dependent ecosystems is presented. • Multi-parametric supervised classification rendered test scores over 96%. • Support vector machines, tree-based classifiers and logistic regression proved suitable. • Results could be used to inform wetland management and land use policy. Identifying groundwater-dependent ecosystems is the first step towards their protection. This paper presents a machine learning approach that maps groundwater-dependent ecosystems by extrapolating from the characteristics of a small sample of known wetland and non-wetland areas to find other areas with similar geological, hydrological and biotic markers. Explanatory variables for wetland occurrence include topographic elevation, lithology, vegetation vigor, and slope-related variables, among others. Supervised classification algorithms are trained based on the ground truth sample, and their outcomes are checked against an official inventory of groundwater-dependent ecosystems for calibration. This method is illustrated through its application to a UNESCO Biosphere Reserve in central Spain. Support vector machines, tree-based classifiers, logistic regression and k-neighbors classification predicted the presence of groundwater-dependent ecosystems adequately (>96% test and AUC scores). The ensemble mean of the best five classifiers rendered a 90% success rate when computed per surface area. This method can optimize fieldwork during the characterization stage of groundwater-dependent ecosystems, thus contributing to integrate wetland protection in land use planning. [ABSTRACT FROM AUTHOR]

• We study flow regime changes in 22 dammed rivers representing hydrologic gradients. • We compare instream flows across 3 periods: pre-dam, post-dam and e-flows. • All rivers show significant magnitude and seasonality changes after dam operation. • Different patterns of alteration were detected across north and southern basins. • Current e-flows have a limited ability to reduce post-dam hydrological alteration. Dams cause significant alteration in natural flow patterns of rivers throughout the world. As a response, many countries have established environmental flows (e-flows) to reduce hydrological alteration and mitigate ecosystem degradation. This paper assesses hydrological changes downstream from dams in 22 rivers in Spain, 12 of them located in northern wetter regions (Duero and Ebro basins) and the remaining 10 in southern drier regions (Guadalquivir and Júcar basins). First we compared pre-dam and post-dam instream flow patterns, and then we considered a third period starting with the implementation of e-flows. We quantified changes in mean annual flows, monthly flows, and magnitude and timing of extreme flows (1-day maximum, 95th percentile, 1-day minimum and 5th percentile). The analysis aimed at: i) characterizing hydrological alteration in regulated rivers in four river basins in Spain, with a special focus on differences across regions; and (ii) assessing the capacity of the implemented e-flows to mitigate flow regime alteration downstream from dams. All the studied rivers displayed significant changes in magnitude and timing of flows after the dam construction. A quite homogenous trend was observed in the drier basins, where mean annual flows and annual extreme flows decreased significantly. The wetter basins showed no uniform patterns in mean annual flows, while 1-day maximum and 95th percentile flow values decreased. In contrast to drier basins, 1-day minimum and 5th percentile flow values increased markedly. In most cases, the natural Mediterranean annual hydrograph was inverted, with the high-flow period occurring during the low precipitation months (summer) and the low-flow period during the wet season. After e-flows implementation, these patterns of hydrological alteration remained almost unchanged, pointing to a limited capacity of current e-flows to mitigate the hydrological impacts downstream from dams. [ABSTRACT FROM AUTHOR]