Search

• 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]

Summary Policymakers in arid and semiarid basins face hard choices on water policies needed for adaptation to climate change. Hydro-economic modeling is a state-of-the art approach that can be used to guide the design and implementation of these policies in basins. A major gap in developments of hydro-economic modeling to date has been the weak integration of physically-based representations of water sources and uses such as the interaction between ground and surface water resources, to inform complex basin scale policy choices. This paper presents an integrated hydro-economic modeling framework to address this gap with application to an important and complex river basin in Spain, the Jucar basin, for the assessment of a range of climate change scenarios and policy choices. Results indicate that in absence of adequate policies protecting water resources and natural ecosystems, water users will strategically deplete reservoirs, aquifers and river flows for short-term adaptation to climate change, disregarding the impacts on the environment and future human activities. These impacts can be addressed by implementing sustainable management policies. However, these policies could have disproportionate costs for some stakeholders groups, and their opposition may undermine attempts at sustainable policy. These tradeoffs among water policy choices are important guides to the design of policies aimed at basin-wide adaptation to climate change. [ABSTRACT FROM AUTHOR]

Summary The application of simple empirical equations for estimating reference evapotranspiration (ET o ) is the only alternative in many cases to robust approaches with high input requirements, especially at the local scale. In particular, temperature-based approaches present a high potential applicability, among others, because temperature might explain a high amount of ET o variability, and also because it can be measured easily and is one of the most available climatic inputs. One of the most well-known temperature-based approaches, the Hargreaves (HG) equation, requires a preliminary local calibration that is usually performed through an adjustment of the HG coefficient (AHC). Nevertheless, these calibrations are site-specific, and cannot be extrapolated to other locations. So, they become useless in many situations, because they are derived from already available benchmarks based on more robust methods, which will be applied in practice. Therefore, the development of accurate equations for estimating AHC at local scale becomes a relevant task. This paper analyses the performance of calibrated and non-calibrated HG equations at 30 stations in Eastern Spain at daily, weekly, fortnightly and monthly scales. Moreover, multiple linear regression was applied for estimating AHC based on different inputs, and the resulting equations yielded higher performance accuracy than the non-calibrated HG estimates. The approach relying on the ratio mean temperature to temperature range did not provide suitable AHC estimations, and was highly improved by splitting it into two independent predictors. Temperature-based equations were improved by incorporating geographical inputs. Finally, the model relying on temperature and geographic inputs was further improved by incorporating wind speed, even just with simple qualitative information about wind category (e.g. poorly vs. highly windy). The accuracy of the calibrated and non-calibrated HG estimates increased for longer time steps (daily < weekly < fortnightly < monthly), although with a decreasing accuracy improvement rate. The variability of goodness-of-fit between AHC models was translated into lower variability of accuracy between the corresponding HG calibrated ET o estimates, because a single AHC was applied per station. The AHC fluctuations throughout the year suggest the convenience of using monthly or, at least, seasonal models. [ABSTRACT FROM AUTHOR]

Summary An increase in water demands, exacerbated by climate change and the tightening of environmental requirements, leads to a reduction in available water resources for economic uses. This situation poses challenges for water resource planning and management. Water accounting has emerged as an appropriate tool to improve transparency and control in water management. There are multiple water accounting approaches, but they generally involve a very exhaustive list of accounted concepts. According to our findings in this research, one of the best water accounting methodologies is the Australian Water Accounting Standard. However, its implementation for integrated water resource planning and management purposes calls into questioning the amount of information and level of detail necessary for the users of water accounts. In this paper, we present a different method of applying the Australian Water Accounting Standard in relation to water resource management, which improves its utility. In order to compare the original approach and that proposed here, we present and discuss an application to the Júcar Water Resource System, in eastern Spain. [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]

• Causal reasoning applied to evaluate spatio-temporal dependences in river basins. • Results from Causal reasoning are in line with classic statistical analysis. • Causal reasoning provides more information on dependences than classic methods. • Changes in spatio-temporal dependences for dry years can be assessed with causality. • Causal reasoning improves water management at seasonal scale over classic methods. This paper aims to assess fully the spatio-temporal dependence dimensions of inflow across two adjacent and parallel basins and among different time steps through Causality. This is addressed from the perspective of Causal Reasoning, supported by Bayesian modelling, under a novel framework named Bayesian Causal Modelling (BCM). This is applied, through a "concept-proof", to the Jucar River Basin (the second largest basin of Eastern Spain, characterized by long and severe drought conditions). In this "concept-proof" a double goal is evaluated; first dedicated to a lumped analysis of dependence and second a specific one over dry periods focused on time-horizon of the Jucar basin typical drought (3 years). These challenges comprise the development of two fully connected Bayesian Networks (BNs), one for each challenge populated/trained from historical-inflow records. BNs were designed at a season-scale and consequently, time was upscaled and grouped into Irrigation and Non-Irrigation periods, according to Jucar River Basin Authority operational practices. Results achieved showed that BCM framework satisfactorily captured the spatio-temporal dependencies of systems. Furthermore, BCM is able to answer to some key questions over interdependencies between adjacent and parallel subbasins. Those questions may comprise, the amount of spatial dependences among time series, the temporarily conditionality among subbasins and the spatio-temporal dependence among basins. This provides a relevant insight on the intrinsic spatio-temporal dependence structure of inflow time series in complex basins systems. This approach could be very valuable for water resources planning and management, due to its application power for predicting extreme events (e.g. droughts) as well as improving and optimizing the reservoirs operation rules. [ABSTRACT FROM AUTHOR]

• Available rainfall data are characterized by different time resolution, "ta" • A database involving metadata from many geographic areas is presented. • The "ta" history of rainfall data in a variety of rain gauges is reconstructed. • The registration methods of the rainfall data changed over the years. • Currently about 50% of rain gauge stations provide data with any "ta" Collected rainfall records by gauges lead to key forcings in most hydrological studies. Depending on sensor type and recording systems, such data are characterized by different time-resolutions (or temporal aggregations), t a. We present an historical analysis of the time-evolution of t a based on a large database of rain gauge networks operative in many study areas. Globally, t a data were collected for 25,423 rain gauge stations across 32 geographic areas, with larger contributions from Australia, USA, Italy and Spain. For very old networks early recordings were manual with coarse time-resolution, typically daily or sometimes monthly. With a few exceptions, mechanical recordings on paper rolls began in the first half of the 20th century, typically with t a of 1 h or 30 min. Digital registrations started only during the last three decades of the 20th century. This short period limits investigations that require long time-series of sub-daily rainfall data, e.g, analyses of the effects of climate change on short-duration (sub-hourly) heavy rainfall. In addition, in the areas with rainfall data characterized for many years by coarse time-resolutions, annual maximum rainfall depths of short duration can be potentially underestimated and their use would produce errors in the results of successive applications. Currently, only 50% of the stations provide useful data at any time-resolution, that practically means t a = 1 min. However, a significant reduction of these issues can be obtained through the information content of the present database. Finally, we suggest an integration of the database by including additional rain gauge networks to enhance its usefulness particularly in a comparative analysis of the effects of climate change on extreme rainfalls of short duration available in different locations. [ABSTRACT FROM AUTHOR]

• Water entitlements are defined in terms of withdrawal instead of consumption rights. • Water markets reallocate consumption and return flows, impairing downstream uses. • We use a hydro-economic model to assess tradeoffs between trading and return flows. • The model is applied to an inter-basin water market in SE Spain. • Return flow-neutral markets reduce profit by EUR 1.6 per m3 of conserved streamflow. Water markets are water management instruments that can support water reallocation from low to high value agricultural uses. However, unless explicit consideration is given to water uses not directly involved in the trading (e.g. environment), water markets may fail to promote sustainable growth. In inter-basin water markets, the reallocation of water withdrawal entitlements from the selling basin typically involves the reallocation of return flows (the surface and subsurface water that leaves the agricultural system following withdrawal of irrigation water) along with consumptive uses, which may impair environmental flows and affect water uses downstream in the selling basin. This paper couples, using a sequential modular approach, a Positive Multi-Attribute Utility Programming (PMAUP) tool that models the behavior of irrigators and the Soil and Water Assessment Tool (SWAT) eco-hydrologic model to: i) assess the economic and water reallocation potential of a return flow-neutral inter-basin water market; and ii) explore the trading-return flow tradeoffs that stem from relaxing the neutrality principle. Methods are applied to the case of agricultural water markets in the Tagus-Segura interconnected basins in Spain. Results show that enforcing a return flow-neutral water market can reduce returns from trade by up to EUR 1.63 per m3 of conserved streamflow. [ABSTRACT FROM AUTHOR]

• Two problems of the T-V method in determining soil moisture index are proved. • A new long-term T-V scatter plot is proposed. • A new temperature-vegetation index is proposed. • A new soil moisture index is designed which is named SM iSEE. • SM iSEE is studied on two networks and compared with three other well-known indices. The estimation of soil moisture content on the global scale is a key research issue in the field of remote sensing and, to date, a range of methods have been developed to achieve this. The temperature-vegetation (T-V) technique, which is perhaps one of the most common and successful, suffers from being inaccurate in some cases, especially when the daily T-V scatter plots are not able to define the wet and dry edges in a reliable way. A further challenge is the scale inconsistency between the results obtained on different days, which is a serious concern when monitoring soil moisture changes over periods of time. To address these restrictions, this paper introduces a new soil moisture index, called SM iSEE , driven by a new long-term T-V scatter plot that is established from data covering a 1-year period. The normalized NDVI is set as the horizontal axis and three different temperature factors are suggested as the vertical axis in the proposed 1-year scatter plot. From these three temperature factors, the results proved that "LST day -Ta day (10:30 am)" is the most appropriate choice. In contrast to most linear co-distance indexes reported in the literature, the proposed SM iSEE is a novel non-linear soil moisture index that defines the locus of co-moisture points in the scatter plot as unequal distance curves. To investigate the proposed method, two different soil moisture observation networks with different climate and vegetation conditions, namely, SMAPVEX12 in Canada and REMEDHUS in Spain, were applied. The results demonstrated that the efficiency of the proposed 1-year scatter plot, which was even applicable to, and improved upon, other indexes in the literature. In addition, SM iSEE outperformed SEE, iTVDI and φas the most recent similar indexes, achieving correlation coefficients of 0.65 and 0.74 for the SMAPVEX12 and REMEDHUS networks, respectively. These results appear to be promising, especially for the vegetated area in the SMAPVEX12 network. [ABSTRACT FROM AUTHOR]

• Conceptualization and numerical model of two flooded coal mines in Spain. • A methodological approach to incorporate mine information in the numerical models. • Flow is computed using a coupled-continuum pipe-flow approach. • Computational efficiency allows calibration using Monte Carlo method. • Flow regimes, directions and critical velocities in the mine are evaluated. Secure workings in underground coal mines usually require the lowering of water levels via pumping. After mine closure, pump systems are stopped and the water level recovers in a process known as groundwater rebound. In the Asturian Coal Basin, Spain, the closure and flooding of underground coal mines carries associated environmental impacts that ought to be assessed. Among them, those related with groundwater flow and pollutant transport are of main interest. To evaluate the environmental risks during and after mine closure the construction of numerical flow models is suggested, which rely on an appropriate conceptualization of the mine-hydrogeological system. Underground mines of this region are characterized by very steep coal seams due to a folded and faulted geological structure. The resulting geometry translates into a complex network of hundreds of kilometers of mine tunnels and galleries. This is a challenge for the construction of numerical models in issues like geometrical problems, assignation of hydraulic parameters, coupling of physical laws and high computational cost. This paper presents the conceptual and numerical model of two linked mines of the region, Mosquitera and Pumarabule, that recently ended the flooding stage, to be used in assessment of post-closure environmental risks. To implement the geometry of the underground mine, a methodological approach to translate the mining information to the numerical simulator has been developed, that retains the hydraulic behavior of the key underground mine elements. A methodological approach for coupled simulation of mine conduits and porous media flow has been explicitly adapted. The results, in terms of the velocity distribution field inside the mine and possible outflow sites, are useful to provide a basis for later extension of transport models and assessment of hydro chemical impacts on the area. [ABSTRACT FROM AUTHOR]

• The kinetic power per unit volume of rainfall is related to the mean drop diameter. • The kinetic power is accurately estimated by the median volume diameter. • The theoretical kinetic power-median volume diameter equation is not site-specific. • The theoretical equation is independent of measurement technique. • Median volume diameter is estimated by mean diameter and its standard deviation. Empirical relationships linking kinetic energy to rainfall intensity are commonly used to estimate rainfall erosivity. In this paper, using the drop-size distribution (DSD) proposed by Marshall and Palmer, a relationship for calculating the rainfall kinetic power was firstly deduced. This equation requires the simultaneous measurement of both the median volume diameter and the rainfall intensity and its application also reveals the drawback of needing the measurement of the whole DSD by an expensive disdrometric technique. Then, using a fictitious precipitation, which is both constituted by droplets having the same diameter (mean volume diameter) and having the same volume of the actual rainfall, a new expression of the rainfall kinetic power was deduced. This last equation has the advantage of allowing the estimate of the rainfall kinetic power by using the measurements of both rainfall intensity and the total number of droplets, reaching a horizontal unit area in an unit time, which can be acquired by a cheap device using a piezoelectric force transducer. This new relationship for estimating the rainfall kinetic power was tested using disdrometric measurements carried out at two experimental sites in Italy (Palermo) and in Spain (El Teularet). These experimental tests demonstrated that the rainfall kinetic power estimated by the mean volume diameter has an accuracy similar to the literature relationships proposed by Wischmeier and Smith, Brown and Foster and McGregor et al. Finally this investigation also showed that this novel approach can be applied estimating the median volume diameter by the mean volume diameter and the standard deviation of the measured drop diameters, whose measurement can be obtained by a force transducer. [ABSTRACT FROM AUTHOR]