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Pressure on freshwater resources has resulted in greater greywater reuse for irrigation. Laundry greywater is one of the largest yet potentially soil damaging streams of domestic greywater. There is limited research on the susceptibility of soils with various properties to degradation by laundry greywater. Thus the aim of this laboratory study was to investigate the effect of typical liquid and powdered laundry detergent greywater application on humus removal and hydraulic conductivity on a range of urban soils from the Cape Town region, South Africa. Powdered detergent (PD) greywater was significantly more detrimental in terms of soil humus removal and decreasing hydraulic conductivity compared to the liquid detergent (LD) greywater attributed to its alkaline pH (pH 9.95) and 30-fold higher Na content. Sandy soils were the most susceptible to humus losses due to PD greywater application (7.5% soil C lost) while Fe oxide-rich chromic soils were the least (1.5% soil C lost). Granite-derived, kaolinitic soils were most susceptible to reduction in hydraulic conductivity (81% reduction) due to PD greywater application, while Fe-rich chromic soils were the least (47% reduction). PD greywater should not be used for irrigating soils due to its aggressive soil degrading qualities while LD greywater should be used cautiously.

Highlights • The effects of lakes and reservoirs on global daily streamflow are evaluated. • Reservoirs affect model performance substantially in the global domain. • Lakes’ effects on model performance are limited to few catchments. • Lakes and reservoirs reduce return levels discharge thresholds globally. • Reservoir parameters contribute to uncertainty of model performance metrics., Lakes and reservoirs affect the timing and magnitude of streamflow, and are therefore essential hydrological model components, especially in the context of global flood forecasting. However, the parameterization of lake and reservoir routines on a global scale is subject to considerable uncertainty due to lack of information on lake hydrographic characteristics and reservoir operating rules. In this study we estimated the effect of lakes and reservoirs on global daily streamflow simulations of a spatially-distributed LISFLOOD hydrological model. We applied state-of-the-art global sensitivity and uncertainty analyses for selected catchments to examine the effect of uncertain lake and reservoir parameterization on model performance. Streamflow observations from 390 catchments around the globe and multiple performance measures were used to assess model performance. Results indicate a considerable geographical variability in the lake and reservoir effects on the streamflow simulation. Nash-Sutcliffe Efficiency (NSE) and Kling-Gupta Efficiency (KGE) metrics improved for 65% and 38% of catchments respectively, with median skill score values of 0.16 and 0.2 while scores deteriorated for 28% and 52% of the catchments, with median values −0.09 and −0.16, respectively. The effect of reservoirs on extreme high flows was substantial and widespread in the global domain, while the effect of lakes was spatially limited to a few catchments. As indicated by global sensitivity analysis, parameter uncertainty substantially affected uncertainty of model performance. Reservoir parameters often contributed to this uncertainty, although the effect varied widely among catchments. The effect of reservoir parameters on model performance diminished with distance downstream of reservoirs in favor of other parameters, notably groundwater-related parameters and channel Manning’s roughness coefficient. This study underscores the importance of accounting for lakes and, especially, reservoirs and using appropriate parameterization in large-scale hydrological simulations.

There has been a series of water resource problems, such as imbalances of the water supply and demand and serious water pollution, in China. It is important for the Chinese government to formulate a sustainable development strategy for water resources and to effectively guarantee the national water resource security. This paper divides the comprehensive carrying capacity of water resources into the following three aspects: the water resource balance capacity, the water resource pressure and driving force, and the water resource development and utilization capacity. Taking 31 provinces, municipalities and autonomous regions of China as research objects, the catastrophe progression method is adopted to evaluate the comprehensive carrying capacity of interprovincial water resources in China from 2010 to 2016. The direct effects and spatial spillover effects of different factors on water resources are discussed by constructing a spatial Durbin model (SDM). The results show that there are obvious spatial differences in the water resource balance ability, pressure and driving force, and development and utilization ability. The spatial distribution follows a decreasing trend from east to west and from south to north. The water supply, water demand, investment in environmental management, economic development and balance of the ecosystem have obvious direct effects on the carrying capacity of water resources. The spatial spillover effect of the water supply and demand, energy consumption, the proportion of protected areas and the newly increased areas of soil erosion control are significant and will have a strong impact on the water resource carrying capacity of surrounding areas. The conclusion of this paper can provide a reference for the government in making water resource management policies.

Evaluation of calculation models for the unfrozen water content of freezing soil is very important for numerical simulations. Based on the published 250 laboratory data and around 5 years field observed data, this paper evaluated the five widely used unfrozen water content models, i.e., the Zhang et al. (2017a) model, the Anderson and Tice (1972) model, the Michalowski (1993) model, the Mckenzie et al. (2007) model, and the Kozlowski (2007) model. The results show that the five models can all be used to calculate the unfrozen water content of laboratory soil samples. The Anderson and Tice (1972) model, the Michalowski (1993) model, and the Kozlowski (2007) model are descripted by gravimetric unfrozen water content. However, it is difficult to directly measure the gravimetric unfrozen water content during the freezing process. The Mckenzie et al. (2007) model and the Zhang et al. (2017a) model are descripted by volumetric unfrozen water content which can be directly measured during the freezing process. Besides, the parameters of the Zhang et al. (2017a) model are relatively simple, although the model slightly overestimates the volumetric unfrozen water content for some laboratory soils. Therefore, the Zhang et al. (2017a) model is more convenient for numerical simulations. Furthermore, all the models have great potential to calculate unfrozen water content of freezing soils in field observations. This paper would provide significant reference for the selection of unfrozen water content models in the description of hydro-thermo coupling process in cold regions.

Balancing the multiple purposes of reservoir operation, including the social, economic, and ecological aspects, has become one of the most complicated problems facing the managers and operators worldwide. This paper formulates a detailed mathematical model to optimize the reservoir operation during the flood season, considering the three major operational purposes, i.e. flood control (social purpose), energy production (economic purpose), and management of sediment transport (ecological purpose). The entire formulations conform to a nonlinear programming (NLP) model, which can be solved effectively and efficiently by the Multi-Start Solver from the LINGO software through equivalent transformation. This model is applied to the Three Gorges Project (TGP) in China, which is the world’s largest and most complex hydraulic engineering in operation. Three typical flood hydrographs (i.e. dry, normal, and wet scenarios) are selected as model inputs under the three strategies (i.e. design operating rules, increasing the flood control water level (FCWL), and impounding in advance). The trade-offs among these three major operational purposes are quantitatively analyzed. Moreover, phosphorus (P), one of the sediment-associated environmental factors, is introduced for exploring its responses to various scenarios and strategies. Results indicate that a higher fore-bay water level leads to a greater energy production, a smaller flood peak but less sediment and P transports to the downstream. Under the design operating rules, the maximum energy production of the TGP can reach 560, 620, and 699 × 108 KWh, respectively, in dry, normal, and wet scenarios, corresponding to a flood peak of 25000–27500 m3/s, a sediment load of 1517–1999 × 104 t, and a P load of 3152–3740 × 104 kg to the downstream. Whereas, the maximum sediment loads are 2796, 4640, and 5377 × 104 t, and the maximum P loads are 4021, 5345, and 5784 × 104 kg, respectively, in dry, normal, and wet scenarios, corresponding to an energy production of 500–588 × 108 KWh, and a flood peak of 38600–52500 m3/s to the downstream. In terms of achieving the comprehensive benefits of the TGP, impounding in advance is a better choice than both the design operating rules and increasing the FCWL, for it can increase the energy production and the probability of full refill with almost no increase in flood risk to both the dam and the middle and lower reaches of the Yangtze River as well as smaller reduction in the sediment and P transports. The formulated detailed mathematical model is a general one that is applicable to a variety of reservoirs owning multiple operational purposes. Moreover, the achievements gained from this paper will provide important references for the managers and operators.

A flood hazard is one of the most common and destructive natural disasters. Flood risk reduction with non-engineering measures has become the primary goal for flood management. This paper proposes an approach to improve the flood control operation for cascade reservoirs by minimizing flood control risk. A framework of reservoir flood control operation coupled with risk assessment (RFCORA) and the corresponding RFCORA model are proposed to reduce risk. The model contains three sub-modules. A real-time reservoir flood control operation simulation module is developed, in which a flood discharge control chart is used to determine the flood magnitude, and flood release is obtained by operation rules. Entropy-weighted fuzzy comprehensive evaluation method is applied to assess the risk level in the flood risk assessment module. Finally, the flood release is updated according to the developed outflow adjustment optimization module aimed at minimizing flood control risk by combing future inflow information. In addition, another model without outflow adjustment optimization is set to be a comparative experiment. The water system of the Upper Yellow River is selected as a case study to verify the model. The results show that the framework and RFCORA model developed in this paper can decrease the time duration in the highest risk level without increasing the maximum water level and maximum outflow of reservoirs. The approach proposed in this paper based on flood control operation and flood risk assessment can be extended to flood mitigation in other water systems in similar situations.

Assessing flood frequency is pivotal for hydrology, with implications for many fields ranging from river science to the insurance industry. Reservoir regulation may alter the characteristics of the flood frequency distribution function. This paper incorporates reservoir impacts as an inherent variable to derive an analytical solution of outflow peaks and proposes new flood frequency distributions. Steps are as follows: (1) the original flood without reservoir impacts is assumed to follow a Pearson type III distribution, (2) linear and nonlinear reservoir models, including numerical integration and triangular area calculation methods, are proposed to simulate the relationships between inflow and outflow peaks, and (3) the flood frequency distributions resulting from reservoir impacts are derived. Derived equations are applied to the Shuibuya and Wuqiangxi reservoirs in China. Results show that: (1) although the numerical integration method with the nonlinear model is comparable with the reservoir flood routing, the triangle area calculation method with the nonlinear model is recommended for its accuracy and simplicity, (2) the reservoir regulation changes the flood frequency distribution function entirely in the nonlinear reservoir model, while only change the parameter in the linear reservoir model, and (3) downstream flood’s CV can be increased or decreased, which depends on the reservoir regulating capacity. It is indicated that altering the parameters of the flood frequency distribution is not sufficient to characterize watershed under reservoir control. This paper proposes the analytical solution about inflow and outflow peaks, which can point out the possible changes in the watershed with reservoir construction in plan.

In the United States, fluvial flood risk is managed by the National Flood Insurance Program (NFIP), which delineates areas that have a 1% chance of flooding each year (i.e. the 100-year floodplain). Development within and adjacent to riverine floodplains exacerbates flood losses by increasing peak discharge and runoff volume, shortening the time to peak, and altering the extent of the floodplain. This paper improves understanding of how future development can alter the 100-year floodplain in a watershed in Houston, TX through land use projection, hydrologic and hydraulic modeling, and implements a novel method for considering the impact of both regional trends in development and site-scale development policies on runoff response. The methodology presented in this paper integrates future development scenarios from a machine learning land use projection model with distributed hydrologic modeling and coupled 1D/2D unsteady hydraulic modeling to produce future floodplain estimates. Current site-scale detention requirements are represented within the hydrologic model to evaluate the regional effectiveness of these policies under future development conditions in 2050. Results indicate that the 100-year floodplain can expand by up to 12.5% across the watershed as a result of projected development in 2050 using current stormwater mitigation policies, and the number of parcels within the floodplain can increase by up to 18.8%. This study demonstrates how incremental land use changes can significantly alter the reality of flood risk in urbanizing watersheds, and how existing land use policies may be insufficient to mitigate impacts from future development.

In this paper, a model is proposed for the assessment of the effectiveness of various mulches in increasing deep percolation of rainfalls for enhancing arid zones aquifers. For this purpose, 8 precipitations were selected from the Intensity-Duration-Frequency (IDF) curves of the study area with a return period of 2 and 5 years. The deep percolation of these precipitations were examined in lysimeters with gravel, sand and mixed mulches and without any mulch. Then 192 data of soil moisture, daily maximum air-temperature and deep percolation were measured for driving empirical equations. Moreover, the efficiency and accuracy of these empirical equations were investigated using Coefficient of Determination (CD) and Nash–Sutcliffe Efficiency Coefficient (NSEC). The results showed that the obtained empirical equations could estimate deep percolation and evaporation with an acceptable accuracy. Using these equations and the soil-water balance equation, a soil moisture model was developed for the assessment of deep percolation. Then it was examined to evaluate the efficiency of the mulched soils in increasing soil moisture and aquifer recharging of three years’ precipitations in Shahrekord plain, Iran. The results showed that the deep percolation increased in all examined mulched soils compared to unmulched soil, its maximum and cumulative increase were in gravel mulch with 21.3% and 30%, respectively. Furthermore, groundwater modeling results showed that the mulching could improve groundwater level as 0.34 m over a three year period. Finally, this paper proposes soil mulching for enhancing groundwater resources and a model to assess it.

Mountainous headwater catchments are safeguards of quality groundwater and hence require special protection against contamination by anthropogenic sources. However, methods currently handling contamination risk fail to produce reliable results in mountainous watersheds because they overlook the influence of downhill flows and contaminant transport in the validation process. To overcome this difficulty, a new model based on so-called “concentration profiles” is presented that combines the DRASTIC framework for evaluation of intrinsic vulnerability, the categorization of land uses for evaluation of specific vulnerability to nitrate and Processing Modflow graphical user interface for simulation of nitrate transport. This model was tested in a mountainous region of Northern Portugal. The risk of groundwater contamination by nitrate was generally classified as moderate. The risky areas are regions used for agriculture and livestock production. These activities have raised nitrate concentrations of spring water (15–25 mg·L−1) downstream the risky areas. The Modflow simulations linked the risky areas (contaminant sources) to actual nitrate plumes (contaminant sinks) and modeled nitrate distributions at specific groundwater travel times. Winter plumes could be simulated for the 1-year stress period, and hence are flushable in a short time span. Spring and summer plumes could only be explained by contaminant transport during 10–20 years. In these cases, even if contaminant sources were immediately neutralized, the washout of nitrate would take decades. These results may hold back the fulfillment of sustainable development goals related to water and sanitation until 2030, and hence deserve reflection by water planners and policy makers. The modeling exercise provided extra evidence that safeguarding the catchment headwater is the keystone of groundwater quality protection in mountainous catchments. Therefore, application of this modified DRASTIC to other mountainous areas may not need to resort to Processing Modflow. The study comprises the main paper (this paper) and a MethodsX companion paper.

The need for a method of estimating streamflow with both high-quality and high spatial resolution, particularly for ungauged sites, is critical under current circumstances: intensifying competition over water resources and limited streamgages. A source of accurate and timely computations of streamflow, particularly at high spatial resolutions, has been the National Hydrograph Dataset (NHD), a comprehensive geographic information system (GIS) dataset of vector-based surface water features such as streams, lakes, and streamgages in fine detail. Utilizing the NHD with streamflow data from the U.S. Geological Survey and usage records of local water utilities, this paper proposes a simple streamflow estimation method that computes streamflow at any point on river reaches of the NHD with valid hydrograph data and tests the method in a local 613 square mile watershed. The method assumes that linearly connecting the timings of peak or bottom-base flows from upstream to downstream hydrographs leads to retracing upstream lateral inflows that have eventually arrived at a user-selected site between the upstream and downstream gauge stations of the hydrographs at any given time in a common period of record between the two hydrographs. Subsequently, the lateral inflows are added up to become the streamflow for a given site at a given time. NHD river networks and ArcGIS tools identify the locations of lateral inflows and the necessary parameters of the method. This paper assesses the results of the method by comparing them to measured streamflows through recommended evaluation statistics and visual inspection. The method has yielded promising results, including values of the Nash-Sutcliffe efficiency within 0.83 and 0.99 for daily streamflow estimates over seven years. It also credibly reconstructs missing daily streamflow over a considerable number of years.

In a recent paper published in this journal, Bretreger et al. (2020) estimate irrigation water use from satellite remotely sensed estimates of actual evapotranspiration in five irrigated districts of the Murray-Darling Basin (southeast Australia). They used three models that scale crop reference evapotranspiration with vegetation indices acquired by recent Landsat satellites: (i) IrriSAT, (ii) Kamble and (iii) CMRSET. In their paper, irrigation water use computed with CMRSET generally overestimated observed irrigation water use, sometimes fivefold. Based on these results, Bretreger et al. (2020) discouraged the use of CMRSET for irrigation monitoring. In this comment, we reproduce the experiments in Bretreger et al. (2020), and demonstrate that their overestimation was because they used an incorrect Landsat band in their implementation of CMRSET. CMRSET was originally calibrated using both MODIS-derived Enhanced Vegetation Index (EVI) and Global Vegetation Moisture Index (GVMI). To calculate GVMI, a shortwave infrared (SWIR) band with a wavelength of ∼1.6 μm was used, which for MODIS is named SWIR2, and for Landsat is named SWIR1 (i.e., different sensors have different bands and a different number of bands and SWIR1 in MODIS has a wavelength of ∼1.2 μm). In their Landsat CMRSET implementation, Bretreger et al. (2020) computed GVMI using the Landsat SWIR2 band, which has a wavelength of ∼2.1 μm (see their Table 3). We show that CMRSET implemented with the Landsat SWIR1 band (i.e., with a wavelength of ∼1.6 μm, so being the correct Landsat band to calculate GVMI) yields similar results (both for temporal patterns and magnitude) when compared to the other two remote sensing actual evapotranspiration models in Bretreger et al. (2020). For the irrigation districts, these similar results meant that the mean water year (i.e., July to next June from 2010 to 2017) actual evapotranspiration mean absolute relative difference was 15.3% (with a 5.4%–26.5% range) and the water year irrigation water use mean absolute relative difference was 4.1% (with a 9.8%–18.9% range). Conversely, we show that a Landsat CMRSET implementation using the incorrect Landsat band for the calculation of GVMI (i.e., with a wavelength of ∼2.1 μm) led to a large overestimation agreeing with the CMRSET results reported by Bretreger et al. (2020), both for water year actual evapotranspiration (mean absolute relative difference of 64.6%, with a 51.1%–83.6% range) and irrigation water use (mean absolute relative difference of 44.1%, with a 31.4%–65.1% range). The use of the incorrect Landsat band means that the Bretreger et al. (2020) recommendation specific to their Landsat CMRSET implementation is invalid. A demonstration of Bretreger et al.’s (2020) incorrect Landsat CMRSET implementation and the correct one can be accessed at: https://jorgepena.users.earthengine.app/view/comment-on-bretreger-2020-joh-paper . Despite Bretreger et al.’s (2020) shortcomings in their implementation of the Landsat CMRSET actual evapotranspiration model, timely monitoring of irrigation water use via satellite remote sensing is required at Landsat resolutions (i.e., 30 m) or higher due to the heterogeneous nature of irrigation in most agricultural landscapes, and we applaud them for pursuing this line of research.

Water supply management for hydroclimatic extremes has commonly been analysed using the cumulative difference between inflow and demand, a concept known as the Residual Mass Curve. This paper extends the residual mass concept to develop a new method to quantify water availability, termed the Residual Mass Severity Index (RMSI). The RMSI improves on the Residual Mass Curve by allowing extreme deficit periods to be easily identified without the need for manual inspection of the Residual Mass Curve. Another advantage of the RMSI is that it does not require a temporal window to be specified to characterise extreme deficit periods, which is needed for many drought indices such as the Standardized Precipitation Index (SPI). This paper develops the method for calculating the RMSI using precipitation time series and compares it to the SPI for different drought durations across Australia. Major hydroclimatic extremes during the instrumental period are assessed using RMSI, SPI, and by comparing the RMSI estimates with streamflow data from 467 catchments with varying sizes and climate across Australia. RMSI is a useful index for water managers interested in assessing water availability with potential applicability in summarising the features of historical hydroclimatic extremes as well as future changes in water availability due to anthropogenic climate change.

Long-term forecasting of any hydrologic phenomena is essential for strategic environmental planning, hydrologic and other forms of structural design, agriculture, and water resources management. Climate mode indices, utilising machine learning methods, are frequently considered as predictor variables in order to forecast several different hydrological variables. In this study, a feature selection algorithm based on two different deep learning models, i.e., long short-term memory and a gated recurrent unit, is applied to improve the forecasting capability of streamflow water levels at six gauging stations in the Murray Darling Basin of Australia. This paper therefore aggregates the significant antecedent lag memory of climate mode indices, rainfall, and the monthly factor based on the periodicity as the predictor variables to attain significantly accurate stream water level forecasts. This novel method identifies an improved relationship between the stream water level and climate mode indices through the aggregation of the significant lagged datasets capturing the historical features to predict the future streamflow water level. The boruta feature selection algorithm (BRF) was then applied in a two phase process before and after attaining the significant lagged inputs to screen the optimum predictor variables. The merits of the forecast models were evaluated through different performance evaluation criteria. The results show that the accumulated significant lagged inputs based on climate mode indices, along with the rainfall and periodicity factors are seen to provide improved forecasting of the SWL over the non-BRF deep learning approaches where no prior feature selection was applied. The hybrid LSTM method (i.e., BRF-LSTM model) achieved a unique advantage in terms of SWL forecasting, particularly attaining over 98% of the predictive errors lying within a band of +/-0.015 m with relatively low relative errors (RRMSE ≈1.30% and RMAE ≈ 0.882%), outperforming all of the benchmark models. It is also found that the periodicity factor has a potential influence on the accuracy of the forecast models for the four monitored study stations. This study concludes that the newly developed hybrid deep learning approaches, coupled with the BRF feature selection, provide improved forecasting performance. The hybrid approach developed in this paper can therefore be used to provide a strong provide predictive response algorithm for the hydrological variables that were influenced by the low-frequency variability of the climate model indices in respect to streamflow water level.

The increase in dynamic interactions between climate and human activities threatens water security in terms of water quantity and quality. Most water security studies have focused on water quantity compared to water quality, while both are equally important and vital for maintaining a healthy ecosystem and human well-being. The first part of the paper provides a review of the potential impacts of climate-related extreme events (i.e., drought, flood, and wildfires) on different water quality indicators and the potential impact of cascading extreme events (e.g., drought-flood regimes) on dynamics of water quality behavior. In the second part of the paper, we demonstrate the cascading impact of severe drought and an extreme historical flood event (October 1–4, 2015) in South Carolina (USA) on water quality variables. The effect of drought on water quality in contrasting land-use settings is investigated. Finally, water quality data was collected over a period of time in three types of land-use settings to study the dynamics of multiple flood and drought events on microbial communities. Flooding conditions result in high levels of bacteria associated with fecal contamination, especially in the stream setting, where large differences between drought and flooding occur in the microbial communities. The results highlight the significant impact of cascading events on water quality and microbial communities. The effect of drought on water quality indicators in different land-use settings can be different, highlighting the dominant role of watershed characteristics. Overall, it is essential to develop quantitative frameworks in the context of sustainability science to quantify the interaction between climate, watershed, and anthropogenic variables that control stream water quality. This study highlights the importance of understanding the relationships between extreme events and water quality indicators as an important step to improve ecosystem health and sustainability. Finally, some remarks are made on the knowledge gaps which need to be addressed in future studies.

The paper presents issues relating to the influence of time distribution of rainfall on the required storage capacity of stormwater reservoirs. The research was based on data derived from simulations of existing drainage systems. The necessary models of catchments and the drainage system were prepared using the hydrodynamic modelling software SWMM 5.0 (Storm Water Management Model). The research results obtained were used to determine the critical rainfall distribution in time which required reserving the highest capacity of stormwater reservoir. In addition, it can be confirmed based on the research that dimensioning of enclosed structures should rely on using the critical precipitation generated as the characteristics of a synthetically developed rainfall vary dynamically in time. In the final part of the paper, the results of the analyses are compared and followed with the ensuing conclusions. The results of the research will have impact on the development of methodologies for dimensioning retention facilities in drainage systems.

The use of radar measurements for the space time estimation of precipitation has for many decades been a central topic in hydro-meteorology. In this paper we are interested specifically in daily and sub-daily extreme values of precipitation at gauged or ungauged locations which are important for design. The purpose of the paper is to develop a methodology to combine daily precipitation observations and radar measurements to estimate sub-daily extremes at point locations. Radar data corrected using precipitation-reflectivity relationships lead to biased estimations of extremes. Different possibilities of correcting systematic errors using the daily observations are investigated. Observed gauged daily amounts are interpolated to unsampled points and subsequently disaggregated using the sub-daily values obtained by the radar. Different corrections based on the spatial variability and the subdaily entropy of scaled rainfall distributions are used to provide unbiased corrections of short duration extremes. Additionally a statistical procedure not based on a matching day by day correction is tested. In this last procedure as we are only interested in rare extremes, low to medium values of rainfall depth were neglected leaving a small number of L days of ranked daily maxima in each set per year, whose sum typically comprises about 50% of each annual rainfall total. The sum of these L day maxima is first iterpolated using a Kriging procedure. Subsequently this sum is disaggregated to daily values using a nearest neighbour procedure. The daily sums are then disaggregated by using the relative values of the biggest L radar based days. Of course, the timings of radar and gauge maxima can be different, so the method presented here uses radar for disaggregating daily gauge totals down to 15 min intervals in order to extract the maxima of sub-hourly through to daily rainfall. The methodologies were tested in South Africa, where an S-band radar operated relatively continuously at Bethlehem from 1998 to 2003, whose scan at 1.5 km above ground [CAPPI] overlapped a dense (10 km spacing) set of 45 pluviometers recording in the same 6-year period. This valuable set of data was obtained from each of 37 selected radar pixels [1 km square in plan] which contained a pluviometer not masked out by the radar foot-print. The pluviometer data were also aggregated to daily totals, for the same purpose. The extremes obtained using disaggregation methods were compared to the observed extremes in a cross validation procedure. The unusual and novel goal was not to obtain the reproduction of the precipitation matching in space and time, but to obtain frequency distributions of the point extremes, which we found to be stable.

Summary The paper presents the set-up and functioning of a flood alert system based on SURFEX–TOPODYN platform for the cross-border Arda River basin. The system was built within a Bulgarian-Greek project funded by the European Territorial Cooperation (ETC) Programme and is in operational use since April 2014. The basin is strongly influenced by Mediterranean cyclones during the autumn–winter period and experiences dangerous rapid floods, mainly after intensive rain, often combined with snow melt events. The steep mountainous terrain leads to floods with short concentration time and high river speed causing damage to settlements and infrastructure. The main challenge was to correctly simulate the riverflow in near-real time and to timely forecast peak floods for small drainage basins below 100 km 2 but also for larger ones of about 1900 km 2 using the same technology. To better account for that variability, a modification of the original hydrological model parameterisation is proposed. Here we present the first results of a new model variant which uses dynamically adjusted TOPODYN river velocity as function of the computed partial streamflow discharge. Based on historical flooding data, river sections along endangered settlements were included in the river flow forecasting. A continuous hydrological forecast for 5 days ahead was developed for 18 settlements in Bulgaria and for the border with Greece, thus giving enough reaction time in case of high floods. The paper discusses the practical implementation of models for the Arda basin, the method used to calibrate the models’ parameters, the results of the calibration-validation procedure and the way the information system is organised. A real case of forecasted rapid floods that occurred after the system’s finalisation is analysed. One of the important achievements of the project is the on-line presentation of the forecasts that takes into account their temporal variability and uncertainty. The web presentation includes a comparison of the forecasted river flow to three predefined alert levels.

International audience; Surface runoff is the hydrological process at the origin of phenomena such as soil erosion, floods out of rivers, mudflows, debris flows and can generate major damage. This paper presents a method to create maps of surface runoff susceptibility. The method, called IRIP (Indicator of Intense Pluvial Runoff, French acronym), uses a combination of landscape factors to create three maps representing the susceptibility (1) to generate, (2) to transfer, and (3) to accumulate surface runoff. The method input data are the topography, the land use and the soil type. The method aims to be simple to implement and robust for any type of study area, with no requirement for calibration or specific input format. In a second part, the paper focuses on the evaluation of the surface runoff susceptibility maps. The method is applied in the Lézarde catchment (210 km², northern France) and the susceptibility maps are evaluated by comparison with two risk regulatory zonings of surface runoff and soil erosion, and two databases of surface runoff impacts on roads and railways. Comparison tests are performed using a standard verification method for dichotomous forecasting along with five verification indicators: accuracy, bias, success ratio, probability of detection, and false alarm ratio. The evaluation shows that the susceptibility map of surface runoff accumulation is able to identify the concentrated surface runoff flows and that the susceptibility map of transfer is able to identify areas that are susceptible to soil erosion. Concerning the ability of the IRIP method to detect sections of the transportation network susceptible to be impacted by surface runoff, the evaluation tests show promising probabilities of detection (73-90%) but also high false alarm ratios (77-92%). However, a qualitative analysis of the local configuration of the infrastructure shows that taking into account the transportation network vulnerability can explain numerous false alarms. This paper shows that the IRIP method can be a valuable tool to facilitate field analysis and perform surface runoff zonings and opens interesting prospects for the use of the IRIP method in a context of risk management.

This paper describes a Bayesian statistical model for estimating flood frequency by combining uncertain annual maximum (AMAX) data from a river gauge with estimates of flood peak discharge from various historic sources that predate the period of instrument records. Such historic flood records promise to expand the time series data needed for reducing the uncertainty in return period estimates for extreme events, but the heterogeneity and uncertainty of historic records make them difficult to use alongside Flood Estimation Handbook and other standard methods for generating flood frequency curves from gauge data. Using the flow of the River Eden in Carlisle, Cumbria, UK as a case study, this paper develops a Bayesian model for combining historic flood estimates since 1800 with gauge data since 1967 to estimate the probability of low frequency flood events for the area taking account of uncertainty in the discharge estimates. Results show a reduction in 95% confidence intervals of roughly 50% for annual exceedance probabilities of less than 0.0133 (return periods over 75 years) compared to standard flood frequency estimation methods using solely systematic data. Sensitivity analysis shows the model is sensitive to 2 model parameters both of which are concerned with the historic (pre-systematic) period of the time series. This highlights the importance of adequate consideration of historic channel and floodplain changes or possible bias in estimates of historic flood discharges. The next steps required to roll out this Bayesian approach for operational flood frequency estimation at other sites is also discussed.

An essential task of performance assessment of radioactive waste repositories is to predict radionuclide release into the environment. For such a quantitative assessment, the Channel Network Model and the corresponding computer program, CHAN3D, have been used to simulate radionuclide transport in crystalline bedrocks. Recent studies suggest, however, that the model may tend to underestimate the rock retarding capability, because it ignores the presence of stagnant water zones, STWZs, situated in the fracture plane. Once considered, the STWZ can provide additional surface area over which radionuclides diffuse into the rock matrix and thereby contribute to their retardation. The main objective of this paper is to extend the Channel Network Model and its computer implementation to account for diffusion into STWZs and their adjacent rock matrices. In the first part of the paper, the overall impact of STWZs in retarding radionuclide transport is investigated through a deterministic calculation of far-field releases at Forsmark, Sweden. Over the time-scale of the repository safety assessments, radionuclide breakthrough curves are calculated for increasing STWZ width. It is shown that the presence of STWZs enhances the retardation of most long-lived radionuclides except for 36 Cl and 129 I. The rest of the paper is devoted to the probabilistic calculation of radionuclide transport in fractured rocks. The model that is developed for transport through a single channel is embedded into the Channel Network Model and new computer codes are provided for the CHAN3D. The program is used to (I) simulate the tracer test experiment performed at Aspo HRL, STT-1 and (II) investigate the short- and long-term effect of diffusion into STWZs. The required data for the model are obtained from detailed hydraulic tests in boreholes intersecting the rock mass where the tracer tests were made. The simulation results fairly well predict the release of the sorbing tracer 137 Cs. It is found that over the short time-scale of the tracer experiment, the effect of diffusion into STWZs is not as pronounced as that of matrix diffusion directly from the flow channel, and the latter remains the main retarding mechanism. Predictions for longer time-scale, tens of years and more, show that the effect of STWZs becomes strong and tends to increase with transport time. It is shown that over the long times of interest for safety assessment of radioactive waste repositories, STWZs can substantially contribute to radionuclide retardation, though for the short time-scales the impact is not very strong and is not expected to affect the results of short-term field experiments.

As the efficiency of the long-running inter basin water transfer (IBWT) projects declines, the increasing water crisis in the intake area and the deteriorating ecosystem problems in the transferred area have gradually worsened. In this paper, a new form of IBWT, the dual-IBWT (DIBWT), is defined, in which the original transferred area (OTA) and additional transferred area (ATA) are integrated to transfer water resource from two basins to a corporate intake area (CIA). Unlike a typical IBWT, the OTA is one of the transferred areas for the CIA as well as the recipient area of the ATA and plays a dual role in this DIBWT system. A multi-objective DIBWT model involving interconnected OTA and ATA submodels is developed for fulfilling joint transfer targets, flattening water transfer fluctuations, and replenishing ecological flow demands. Then, a three-step framework is presented to derive the multi-resource joint operation rules coordinating the water-supply, water-transfer and water-receipt rules together. In particular, the simulation-optimization approach (SO) is applied to obtain the optimal solution based on a time series of 56 years. The Jialing across Han River to Wei River Water Transfer (JHW-DIBWT) project in western China is used as a case study to assess the performance of the DIBWT compared to a typical IBWT. The results indicate that the JHW-DIBWT has significant advantages in alleviating the conflicts of transfer-supply-demand and restoring the river ecosystem with the aid of additional water resources. Meanwhile, its superiority is revealed by the corresponding joint operation rules of the water-supply, the water-transfer and the water-receipt rule curves, which also can serve as a practical guide for multi-objective and multi-resource water system management. Therefore, the main focus of this paper is not limited to compare the performance between these two IBWTs, but more importantly, it provides a promising framework for extending the study of different form of IBWTs.

The Great 2011 Thailand flood is one of the most catastrophic flood disasters recorded worldwide in modern history, which covered about 100,000 km2 of the country and resulted in 813 casualties and incurring US$ 46 billion in economic losses. In this paper, we re-evaluate the flood event to identify the root source of the catastrophe. By analyzing hydrological data from gauge stations along the Chao Phraya River (CPR) and remote sensing data, we decoupled the volumetric contributions of river overflow and local rainfall to the lowland reach of the basin and found that the latter contributed most to the flooding. More specifically, out of the total of 77.6 km3 of floodwaters estimated, 73.7% was from precipitation, while only 26.3% was from river discharges. This finding differs from the official reports or previous studies, which attributed the main cause of the river overbank flow. Therefore, it is also inferred that the upstream dam operations would have been only marginally helpful in mitigating the flood since rainfall made up most of the floodwaters in the downstream reach of the river. Our finding offers a new perspective that the local rainfall could be a significant source of the floodwater, rather than river overflow in the lower reaches of a large monsoonal river system in Southeast Asia. Thus, this paper contributes to the understanding of complex flood processes in large river basins and provides fresh insights for efficient flood control and stormwater management.

Climate change will impact every aspect of biophysical systems and society. However, unlike other components of the climate system, the impact of climate change on the groundwater system has only recently received attention. This focus is due to the realization that groundwater is a vital freshwater resource crucial to global food and water security, and is essential in sustaining ecosystems and human adaptation to climate variability and change. This paper synthesizes findings on the direct and indirect impacts of climate change on the entire groundwater system and each component. Also, we appraise the use of coupled groundwater-climate and land surface models in groundwater hydrology as a means of improving existing knowledge of climate change-groundwater interaction, finding that most models anticipate decreases in groundwater recharge, storage and levels, particularly in the arid/semi-arid tropics. Reducing uncertainties in future climate projections and improving our understanding of the physical processes underlying models to improve their simulation of real-world conditions remain a priority for climate and Earth scientists. Despite the enormous progress made, there are still few and inadequate local and regional aquifer studies, especially in less developed regions. The paper proposes two key considerations. First, physical basis: the need for a deeper grasp of complex physical processes and feedback mechanism with the use of more sophisticated models. Second, the need to understand the socioeconomic dimensions of climate-groundwater interaction through multidisciplinary synergy, leading to the development of better groundwater-climate change adaptation strategies and modeling.

The role of extreme rains over Peninsular Florida (PF) in modulating the seasonal rainfall characteristics is investigated in this study. The paper is motivated on its potential implication on the seasonal predictability of the hydroclimate of PF by relatively coarse global seasonal climate models. A majority of these climate models are unable to resolve the weather events like tropical cyclones that produce such extreme rain events. Therefore, a legitimate question to ask is if this limits the model’s seasonal predictability of the hydroclimate of PF. In this paper, extreme rain events over PF within a season are defined as days with daily rain amount at or above the 95th percentile over 39 years from 1979 to 2017 at the grid resolution of the observed rainfall dataset (0.5° × 0.5°). The thresholds for extreme rain days range from 16 mmday−1 to 36 mmday−1 depending on the season and the location over PF, while the heaviest rainfall range from 58 mmday−1 to 278 mmday−1. These extreme rain events occur most often across PF in the boreal summer season followed by the fall season with the least in the boreal winter season. Our study reveals that removing the days of extreme rain events has the largest impact on the corresponding seasonal anomalies and daily rainfall distribution in the dry winter season and least in the wet summer season. The impact of El Nino and the Southern Oscillation (ENSO) on the extreme rain events was evaluated by contrasting the differences in the shape and the scale parameters of the fitted Gamma distribution on daily rainfall in winter/spring seasons during warm and cold phases. Results revealed that the warm ENSO phases make the tails of the daily rainfall distribution over PF heavier and longer relative to the cold ENSO phases in the winter and spring seasons. In essence, our study reveals that the extreme rain events that are critical for the overall seasonal distribution of rainfall over PF in the first half of the year is modulated by large-scale phenomenon (e.g., ENSO). In the latter half of the year (summer and fall), the extreme rain events are not as critical to the seasonal rainfall anomaly or the overall seasonal distribution of rainfall over PF. Therefore, resolving the extreme rain events need not be as critical for the seasonal predictability of the hydroclimate of PF.

Climate change, which impacts the spatial and temporal distribution of water resources, has a significant influence on the future hydropower generation. Studying the future evolution pattern of hydropower generation under climate change is of great significance for the medium- and long-term hydropower prediction. The objective of this paper is to predict the future hydropower generation of large-scale reservoir groups under climate change. The innovation of this paper is that the macro-scale distributed hydrological model combined with the optimal operation model of large-scale reservoirs was proposed for hydropower generation prediction. The established model considers the specific operation processes of large-scale reservoir groups, including 62 reservoirs in the case study. First, the Statistical Downscaling Model (SDSM) was built, and the evolution trend of future rainfall and temperature was predicted. Second, the macro-scale distributed Variable Infiltration Capacity (VIC) model was built to predict the future runoff. Finally, the optimal operation generation model of large-scale reservoir groups was established to predict the trends of hydropower generation under climate change. Results demonstrate that under RCP2.6 scenario, there is no significant increase or decrease trend of hydropower generation in the future. But under RCP4.5 and RCP8.5 scenarios, the hydropower generation shows a growing trend, and the increase trend under RCP8.5 scenario is more obvious than that under RCP4.5 scenario. Thus, the development of hydropower generation is sensitive to climate change. This study can provide a reference for the long-term prediction of hydropower generation capacity in the upper Yangtze River Basin.

Summary Mulched drip irrigation for cotton field is an effective measure for the utilization of saline water, and the regulation of soil water and salt. However, the reasonable methods for quantifying actual evapotranspiration (ET) and deep percolation of recharge to groundwater are still not very well understood, which restricts the accurate regulation of soil water and salt for cotton growth in oasis. In this paper, a set of experiments of mulched drip irrigation with brackish water were conducted in a typical arid region of Tarim basin in southern Xinjiang, China. The irrigation events were recorded, and ET and fluctuations of groundwater table were carefully measured for two consecutive irrigation periods of flowering and bolling stages. A group of upscaling conversion methods were used to quantify the ET, in which canopy structure was considered to estimate the transpiration from leaf scale to a unit of field scale. The groundwater table had a significant response to the irrigation events, thus the deep percolation was estimated using water-table fluctuation method (WTF). Results showed that during the two irrigation events of flowering and bolling stages, the total ET was 31.1 mm with the soil surface evaporation of only 0.4 mm. The total percolation of recharge to groundwater was 48.2 mm which contributed to the groundwater run-off of 22.1 mm. Transpiration of 30.7 mm accounted for 98.6% of the total ET of 31.1 mm and 34.3% of the irrigation water of 90.6 mm. Compared with transpiration, the deep percolation accounted for 53.2% of irrigation water, indicating a serious excessive irrigation that recharged to groundwater. Soil salt budget showed that the salt leached into groundwater was 1.56 times of the input from brackish irrigation water and fertilization during the two irrigation periods. Even for the irrigation practice with brackish water, the accumulated salt of soil profile could also be leached out under large amount of irrigation water (e.g. 90.6 mm for the two irrigation periods, 10 days). However, the waste of enormous water which for instance occupied 53.2% of the irrigation water in this study was not conducive to the sustainable utilization of water resources in the arid oasis. Furthermore, the methods introduced in this paper for ET and deep percolation calculation of cotton filed could be used to quantify the oasis hydrologic cycle of micro-irrigation, to gain a better understanding of the ecological process.

Knowledge of pathogen removal in stormwater biofilters (also known as stormwater bioretention systems or rain gardens) has predominately been determined using bacterial indicators, and the removal of reference pathogens in these systems has rarely been investigated. Furthermore, current understanding of indicator bacteria removal in these systems is largely built upon laboratory-scale work. This paper examines whether indicator organism removal from urban stormwater using biofilters in laboratory settings are representative of the removal of pathogens in field conditions, by studying the removal of Escherichia coli (a typical indicator microorganism) and Campylobacter spp. (a typical reference pathogen) from urban stormwater by two established field-scale biofilters. It was found that E. coli log reduction was higher than that of Campylobacter spp. in both biofilters, and that there was no correlation between E. coli and Campylobacter spp. log removal performance. This confirms that E. coli behaves significantly differently to this reference pathogen, reinforcing that single organisms should not be employed to understand faecal microorganism removal in urban stormwater treatment systems. The average reduction in E. coli from only one of the tested biofilters was able to meet the log reduction targets suggested in the current Australian stormwater harvesting guidelines for irrigating sports fields and golf courses. The difference in the performance of the two biofilters is likely a result of a number of design and operational factors; the most important being that the biofilter that did not meet the guidelines was tested using extremely high influent volumes and microbial concentrations, and long antecedent dry weather periods. As such, the E. coli removal performances identified in this study confirmed laboratory findings that inflow concentration and antecedent dry period impact overall microbial removal. In general, this paper emphasizes the need for the validation of stormwater harvesting systems, namely, the testing of treatment systems under challenging operational conditions using multiple indicators and reference pathogens.

Summary Satellite altimetry has proven a valuable resource of information on river and lake levels where in situ data are sparse or non-existent. In this study several new methods for obtaining stable inland water levels from CryoSat-2 Synthetic Aperture Radar (SAR) altimetry are presented and evaluated. In addition, the possible benefits from combining physical and empirical retrackers are investigated. The retracking methods evaluated in this paper include the physical SAR Altimetry MOde Studies and Applications (SAMOSA3) model, a traditional subwaveform threshold retracker, the proposed Multiple Waveform Persistent Peak (MWaPP) retracker, and a method combining the physical and empirical retrackers. Using a physical SAR waveform retracker over inland water has not been attempted before but shows great promise in this study. The evaluation is performed for two medium-sized lakes (Lake Vanern in Sweden and Lake Okeechobee in Florida), and in the Amazon River in Brazil. Comparing with in situ data shows that using the SAMOSA3 retracker generally provides the lowest root-mean-squared-errors (RMSE), closely followed by the MWaPP retracker. For the empirical retrackers, the RMSE values obtained when comparing with in situ data in Lake Vanern and Lake Okeechobee are in the order of 2–5 cm for well-behaved waveforms. Combining the physical and empirical retrackers did not offer significantly improved mean track standard deviations or RMSEs. Based on these studies, it is suggested that future SAR derived water levels are obtained using the SAMOSA3 retracker whenever information about other physical properties apart from range is desired. Otherwise we suggest using the empirical MWaPP retracker described in this paper, which is both easy to implement, computationally efficient, and gives a height estimate for even the most contaminated waveforms.

Summary This paper presents the modelling results of several tracer-tests performed in the cave system of Han-sur-Lesse (South Belgium). In Han-sur-Lesse, solute flows along accessible underground river stretches and through flooded areas that are rather unknown in terms of geometry. This paper focus on the impact of those flooded areas on solute transport and their dimensioning. The program used (One-dimensional Transport with Inflow and Storage: OTIS) is based on the two-region non equilibrium model that supposes the existence of an immobile water zone along the main flow zone in which solute can be caught. The simulations aim to replicate experimental breakthrough curves (BTCs) by adapting the main transport and geometric parameters that govern solute transport in karst conduits. Furthermore, OTIS allows a discretization of the investigated system, which is particularly interesting in systems presenting heterogeneous geometries. Simulation results show that transient storage is a major process in flooded areas and that the crossing of these has a major effect on the BTCs shape. This influence is however rather complex and very dependent of the flooded areas geometry and transport parameters. Sensibility tests performed in this paper aim to validate the model and show the impact of the parametrization on the BTCs shape. Those tests demonstrate that transient storage is not necessarily transformed in retardation. Indeed, significant tailing effect is only observed in specific conditions (depending on the system geometry and/or the flow) that allow residence time in the storage area to be longer than restitution time. This study ends with a comparison of solute transport in river stretches and in flooded areas.

Summary When there is a sudden and uniform release of contaminant over the depth of wetland, the centroid of resulted solute cloud will travel downstream at the cross-sectional mean velocity of the flow. However, if the initial release is not uniform, there will be an additional longitudinal displacement of the centroid, which is important for predicting the concentration distribution but cannot be revealed by the classical one-dimensional Taylor dispersion model. For the most typical case of an initial point source release at the free-water-surface of the wetland, an idealized case modeling accidental leakage of toxic chemicals in waters, in the present paper we analytically deduce the longitudinal displacement by the method of concentration moment. The result is then incorporated in the analytical solutions of concentration distribution, which are further verified by our numerical simulations. The effects of the longitudinal displacement on the concentration distribution are analyzed in detail. It is shown that without considering the displacement, for vertical planes close to the edges of the contaminant cloud, the analytical solution can over- or under-estimate the vertical distribution of concentration for over 20% of the maximum concentration in the plane even at a large dimensionless time of t ∗ = 5 . The longitudinal displacement is shown to decrease with the increase of the important damping factor α, which characterizes the effects of vegetation in wetlands. A simple application is given at the end of this paper to illustrate the evolution of the additional longitudinal displacement.

Summary This paper presents a recently developed software, ArcGIS-based Nitrogen Load Estimation Toolkit (ArcNLET), for estimating nitrogen loading from septic systems to surface water bodies. The load estimation is important for managing nitrogen pollution, a world-wide challenge to water resources and environmental management. ArcNLET simulates coupled transport of ammonium and nitrate in both vadose zone and groundwater. This is a unique feature that cannot be found in other ArcGIS-based software for nitrogen modeling. ArcNLET is designed to be flexible for the following four simulating scenarios: (1) nitrate transport alone in groundwater; (2) ammonium and nitrate transport in groundwater; (3) ammonium and nitrate transport in vadose zone; and (4) ammonium and nitrate transport in both vadose zone and groundwater. With this flexibility, ArcNLET can be used as an efficient screening tool in a wide range of management projects related to nitrogen pollution. From the modeling perspective, this paper shows that in areas with high water table (e.g. river and lake shores), it may not be correct to assume a completed nitrification process that converts all ammonium to nitrate in the vadose zone, because observation data can indicate that substantial amount of ammonium enters groundwater. Therefore, in areas with high water table, simulating ammonium transport and estimating ammonium loading, in addition to nitrate transport and loading, are important for avoiding underestimation of nitrogen loading. This is demonstrated in the Eggleston Heights neighborhood in the City of Jacksonville, FL, USA, where monitoring well observations included a well with predominant ammonium concentrations. The ammonium loading given by the calibrated ArcNLET model can be 10–18% of the total nitrogen load, depending on various factors discussed in the paper.

Summary Increasing demands from emergency responders for accurate flood prediction, particularly in cities, have motivated consistent increases in the resolution of urban drainage models. Such models are now primarily limited by the accuracy and resolution of the initialising rainfall field. Surface rainfall estimates from radar, traditionally derived at scales of order 1 km, are now requested at grid lengths of 100 m to drive improvements in the outputs of these models. Deriving radar precipitation products on grids at the sub-kilometre scale introduces new requirements for the processing of reflectivity measurements into surface rainfall rates. A major source of uncertainty is the physical distance between the radar measurement and the surface onto which precipitation falls. Whilst adjustments to account for inhomogeneity in the vertical reflectivity profile have been extensively investigated, the effects of horizontal displacement have not. This paper discusses the issue of wind drift, first by outlining the need for correction, and then by evaluating the corrections available for impact at the required scale. One correction is detailed and its sensitivity evaluated with respect to the assumptions necessary in its derivation. These sensitivities are verified by trials on the Met Office operational radar processing system, where errors on wind drift displacement estimates are shown to be of order 1 km or more. This is significantly greater than the grid length desired by hydrological users. The paper therefore concludes by suggesting further research necessary to ensure the accuracy of radar precipitation estimates at sub-kilometre resolution.

Long-term over-exploitation and unsustainable practices have caused severe groundwater depletion and deterioration across many regions throughout the world. This has triggered a series of negative impacts on human populations and the natural environment. To understand groundwater-related processes and explore ways to mitigate pertaining issues, large numbers of groundwater-related studies have been performed by the research community. In this study, all research papers published between 1978 and 2017 incorporating “groundwater” in the title, keywords, or abstract, were retrieved from the Web of Science Core Collection (WSCC). Of these, a total of 80,763 papers were identified as being groundwater-related publications. Our analysis revealed that groundwater research output increased significantly during this period, at average annual rate of 10.1%, exceeding the general trend for the WSCC (3.4%). Institutions based in developed countries have held predominance in publishing groundwater research, however, research output from developing countries, such as China and India, experienced much growth in recent years. Development trends were assessed among the research topics of groundwater depletion, contamination, remediation technology, and sustainable management, all of which experienced increased research output. Among sub-topics, groundwater depletion and sustainable management research have increased as a percentage of total groundwater studies, while studies of arsenic, chlorinated solvents, BTEX, bioremediation, and natural attenuation have dropped off in recent years. Additionally, as groundwater-related research output has increased rapidly, robust secondary analyses and meta-analyses are called for in order to make sense of the rapidly expanding body of groundwater research literature. Moreover, owing to the many complexities surround groundwater issues, greater interdisciplinary collaboration is also called for.

The paper presents the results of long-term (1996–2017) studies of the chemical composition of rain, snow and sleet in one of the cities with the highest concentrations of particulate matter in the air in Europe. The study included measurements of 45 chemical constituents of precipitation as well as its pH and EC. Concentrations of PM10, SO2 and meteorological parameters were also analysed. Differences in Cl and Na concentrations were found depending on the type of precipitation (snow or rain). These differences are most probably caused by anthropogenic factors, however, to a small extent (~18–19%), they are associated with the total variability of the concentrations of these chemical constituents. The differences between the concentrations of the remaining constituents of precipitation analysed are of lesser or negligible importance. The main processes affecting the chemical composition of precipitation are the chemical weathering of suspended dust and the formation of mineral acid aerosols. These processes shape the chemical composition of both rain and snow in a similar manner. The differences between the chemical composition of rain and snow are the result of the concentrations of and mutual relations between selected chemical constituents and precipitation pH. An important factor affecting the pH of precipitation is the effectiveness of the chemical weathering of suspended dust, which may make the precipitation more alkaline. It is very likely that pH-buffering will occur, which can prevent the pH of precipitation from decreasing, especially in cold periods. Long-term observations of variability in the concentrations of the chemical constituents indicate a slight, but rather constant decrease in concentrations of most of the chemical constituents analysed, as well as in the concentrations of PM10 and SO2 in the air in Krakow. The directions of these changes reflect, to a certain extent, the complex transformations of industrial, economic and social conditions in Krakow within that period. The typical ranges of variability of the recorded values of concentrations/measurement values of 22 chemical constituents and the physical characteristics of rain, sleet and snow presented in the paper can be used as reference ranges of these parameters, typical for an area with a significant degree of atmospheric dust pollution.

This paper presents a new framework for quantitatively identifying, characterizing and analyzing systematic hydrological cycles resulting from streamflow variability in a way that integrates water supply and water demand. The hydrological cycles in question are measures of the most severe drought and pluvial events in a historical record of streamflow, along with their respective durations. The metrics developed here to quantify such episodes are based on an extended sequent peak algorithm that tracks the dynamic shifts in hydrological behavior of streamflow by accounting for water supply and demand with respect to streamflow. In the interest of being able to analyze the largest possible scope of hydrological cycles and behavior, we apply the quantitative methods developed in this paper to streamflow reconstructions in the Upper Missouri River Basin (UMRB) as a case study. We find that the duration of dry periods increase conspicuously as a function of increasing demand levels, the duration of pluvial events decrease as a function of increasing demand levels, and that the general tendency is for streamflow gauges on or near the main stem of the river to have shorter dry spell durations and typically lower drought severity. On the other hand, being on or near the main stem tends to result in longer-duration pluvial events, though these pluvials are typically less severe than those off the main stem. Persistence and spatial variability of streamflow reconstructions were also analyzed to shed further light on the spatial patterns identified earlier, and to see if this variability and persistence may have an influence on the behavior of the streamflow as quantified by the metrics defined in this paper. Finally, it was found that there is a stochastic dependence between the length of a drought and the time to recovery from that drought, and this dependence is used to create simple conditional probability curves to help water managers prepare for future extreme events.

Flood hazard in urban areas is usually assessed by the estimations of parameters like flood extent, water depths, flow velocities and other related quantities. These hydrodynamic variables can be computed using flood inundation numerical models, the complexity of which has been analyzed in several papers in the literature. However, the question of how detailed a model should be in order to give reasonable results is still an open issue and no shared and general conclusions have been reached so far. One of the main reasons for this is that model benchmarking is very often carried out with reference to the flooded areas extent only, sometimes analyzing also the simulated water depths values and, quite rarely, evaluating the computed velocities. This is in contrast with the main purpose of the simulation models which are, from a practical point of view, advanced tools for flood hazard and vulnerability assessments. Finally, the lack of sufficient data on inundation extent and depths for the calibration and validation of the numerical models has been recognized, especially in complex situations that often characterize the flood evolution in a given area due to bridge obstructions, interaction with buildings and so on. In this paper, the performances of three different approaches to two-dimensional flood modeling (fully dynamic, diffusive and porosity approaches) have been evaluated with reference not only to the hydrodynamic variables, water depths and velocities, but also focusing the attention on their product and, thus, to the consequences associated to their different estimations on the vulnerability assessment. This analysis has been focused first on a flash flood flow experiment in a simplified urban district and then to the event which occurred in the city of Crotone (Calabria, Italy) in 1996. The results highlight that fully-dynamic modeling should be the unavoidable reference tool when the goal of the urban flood mapping activity is not limited to the evaluation of the flood-prone areas extent but involves also the local estimation of flood hazard/vulnerability.

Soil water characteristic curve (SWCC) is a critical parameter for unsaturated soil. However, few study can be found in literature for SWCC of loess with high soluble salt content, in particular SWCC fitting model. This paper targets on Ili loess in Xinjiang in China, which has a high level of soluble salts and is a typical loess in central Asia. Centrifuge method and filter paper method are used to conduct SWCC tests of Ili loess under different salt conditions. The total and matric suction of Ili loess under different soluble salt contents are obtained. The results of two methods are compared. The corresponding influence of soluble salt on SWCC is studied. At the same time, based on the Gardner model, a new SWCC fitting model of total and matric suction for Ili loess is proposed, which is able to consider the influences of soluble salts. The test results show that the compression during centrifuge method contributes certain increase of matric suction, especially at high volumetric water content conditions. Soluble salts have large effect on total and osmotic suction of Ili loess, however limited effect on matric suction. The alteration of total and osmotic suction is caused by the change of solution concentration in the sample. Total suction is mainly affected by osmotic suction. The alteration of matric suction is caused by the precipitation of the salts. It is proved that the proposed SWCC model is capable of characterizing the total and matric suction by simultaneously considering volumetric water content and soluble salt content.

Summary In situ ground observation measurement of precipitation is difficult in vast and sparsely populated areas, with poor road networks. This paper examines the use of remote sensors installed in satellites and evaluates the accuracy of TRMM 3B42 annual maximum daily precipitation estimates in Angola, in West Africa, a region where ground monitoring networks are generally. TRMM 3B42 estimates of annual maximum daily precipitation are compared to ground observation data from 159 locations. As a direct comparison between the two datasets for a common specific period and sites is not possible, a statistical approach was adopted to test the hypothesis that the TRMM 3B42 estimates and the ground monitoring records exhibit similar statistical characteristics. The study shows that the annual maximum daily precipitation estimates obtained from TRMM 3B42 slightly underestimate the quantiles obtained from the in situ observations. The use of remote sensing products to estimate extreme precipitation values for engineering design purposes is however promising. A maximum daily precipitation map for a return period of 20 years was computed and in the future, as the length of the remote sensing data series increases, it may be possible to estimate annual maximum daily precipitation estimates exclusively from these datasets for larger return periods. The paper also presents maps of the P d T / P D T ratios, where P d T is the annual maximum precipitation for a duration d and a return period of T years, and P D T is the annual maximum daily precipitation for a return period of T years. In conjunction with these maps it is possible to estimate the maximum precipitation for durations between 3 h and 5 days.

Summary Retrospective forecasts of precipitation, temperature, and streamflow were generated with the Hydrologic Ensemble Forecast Service (HEFS) of the U.S. National Weather Service (NWS) for a 20-year period between 1979 and 1999. The hindcasts were produced for two basins in each of four River Forecast Centers (RFCs), namely the Arkansas-Red Basin RFC, the Colorado Basin RFC, the California-Nevada RFC, and the Middle Atlantic RFC. Precipitation and temperature forecasts were produced with the HEFS Meteorological Ensemble Forecast Processor (MEFP). Inputs to the MEFP comprised “raw” precipitation and temperature forecasts from the frozen (circa 1997) version of the NWS Global Forecast System (GFS) and a climatological ensemble, which involved resampling historical observations in a moving window around the forecast valid date (“resampled climatology”). In both cases, the forecast horizon was 1–14 days. This paper outlines the hindcasting and verification strategy, and then focuses on the quality of the temperature and precipitation forecasts from the MEFP. A companion paper focuses on the quality of the streamflow forecasts from the HEFS. In general, the precipitation forecasts are more skillful than resampled climatology during the first week, but comprise little or no skill during the second week. In contrast, the temperature forecasts improve upon resampled climatology at all forecast lead times. However, there are notable differences among RFCs and for different seasons, aggregation periods and magnitudes of the observed and forecast variables, both for precipitation and temperature. For example, the MEFP-GFS precipitation forecasts show the highest correlations and greatest skill in the California Nevada RFC, particularly during the wet season (November–April). While generally reliable, the MEFP forecasts typically underestimate the largest observed precipitation amounts (a Type-II conditional bias). As a statistical technique, the MEFP cannot detect, and thus appropriately correct for, conditions that are undetected by the GFS. The calibration of the MEFP to provide reliable and skillful forecasts of a range of precipitation amounts (not only large amounts) is a secondary factor responsible for these Type-II conditional biases. Interpretation of the verification results leads to guidance on the expected performance and limitations of the MEFP, together with recommendations on future enhancements.

Summary In the present paper multi-temporal Lidar (Light detection and ranging) data and Landsat images are used to assess the spatial variability of snow at the end of the accumulation season (April–May) in a glacierized catchment (167 km 2 ) in Tyrol, Austria. Snow cover characteristics in the Tyrolean Alps have been analysed using regular snow measurements and snow course data. Results are used for the conversion of basin-wide Lidar snow depth into snow water equivalent (SWE). When considering different possible error sources, uncertainties of the mean basin-wide SWE obtained from Lidar are between 12% and 16%. Available distributions of SWE and snow covered area (SCA) in the catchment are used for the calibration and validation of the fully distributed hydrological model SES. The study focuses especially on the simulation of snow accumulation and the corresponding variability of snow. Observed accumulation patterns are related to the topography (elevation, slope and curvature), and according parameter settings of the hydrological model are derived by means of Monte Carlo simulations. The majority of the model runs simulates SCA for various datasets with an accuracy of 85–95%. The paper demonstrates that using SWE data is superior to SCA for constraining model parameter ranges. Results at the watershed scale are in agreement with respect to the total water volume of the snow cover with deviations lower than 5% between SWE from Lidar or from the hydrological model.

Summary The effects of climate change on water resources have been studied extensively throughout the world through the use of hydrologic models coupled with General Circulation Model (GCM) output or climate sensitivity scenarios. This paper examines the effects of hydrologic model parameterization uncertainty or equifinality, where multiple unique hydrologic model parameter sets can result in adequate calibration metrics, on hydrologic projections from downscaled GCMs for three snowmelt-dependent watersheds (upper reaches of the Clearwater, Gunnison, and Sacramento River watersheds) in the western United States. The hydrologic model used in this study is the Soil and Water Assessment Tool (SWAT) and is calibrated for discharge at the watershed outlet in each watershed. Despite achieving similar calibration metrics, a majority of hydrologic projections of average annual streamflow during the 2080s were statistically different, with differences in magnitude and direction (increase or decrease) compared to historical annual streamflows. At the average monthly time-scale, a majority of the hydrologic projections varied in peak streamflow timing, peak streamflow magnitude, summer streamflows, as well as overall increases or decreases compared to the historical monthly streamflows. Snowmelt projections from the SWAT model also widely varied, both in depth and snowmelt peak timing, for all watersheds. Since a large portion of the runoff-producing regions in the western United States is snowmelt-dependent, this has large implications for the prediction of the amount and timing of streamflow in the coming century. This paper shows that hydrologic model parameterizations that give similar adequate calibration metrics can lead to statistically significant differences in hydrologic projections under climate change. Therefore, researchers and water resource managers should account for this uncertainty by assembling ensemble projections from both multiple parameter sets and GCMs.

Summary Retrospective forecasts of precipitation, temperature, and streamflow were generated with the Hydrologic Ensemble Forecast Service (HEFS) of the U.S. National Weather Service (NWS) for a 20-year period between 1979 and 1999. The hindcasts were produced for two basins in each of four River Forecast Centers (RFCs), namely the Arkansas–Red Basin RFC, the Colorado Basin RFC, the California–Nevada RFC and the Middle Atlantic RFC. In a companion paper, temperature and precipitation hindcasts were produced with the Meteorological Ensemble Forecast Processor (MEFP) and verified against observed temperature and precipitation, respectively. Inputs to the MEFP comprised raw precipitation and temperature forecasts from the frozen (circa 1997) version of the NWS Global Forecast System (MEFP-GFS) and a conditional or “resampled” climatology (MEFP-CLIM). For this paper, streamflow hindcasts were produced with the Community Hydrologic Prediction System and were bias-corrected with the Ensemble Post-processor (EnsPost). In order to separate the meteorological and hydrologic uncertainties, the raw streamflow forecasts were verified against simulated streamflows, as well as observed flows. Also, when verifying the bias-corrected streamflow forecasts, the total skill was decomposed into contributions from the MEFP-GFS and the EnsPost. In general, the streamflow forecasts are substantially more skillful when using the MEFP-GFS together with the EnsPost than using the MEFP with resampled climatology alone. However, both the raw and bias-corrected streamflow forecasts have lower biases, stronger correlations and are more skillful in CB- and CN-RFCs than AB- and MA-RFCs. In addition, there are strong variations in forecast quality with streamflow amount, forecast lead time, season and aggregation period. The relative importance of the meteorological and hydrologic uncertainties also varies between basins and is modulated by the same controls on forecast quality. For example, the MEFP-GFS accounts for the majority of skill in the CNRFC basins. This is associated with the greater predictability of large storms in the North Coast Ranges during the winter months. In CBRFC, much of the skill in the streamflow forecasts originates from the hydrologic modeling and the EnsPost, particularly during the snowmelt period. In AB- and MA-RFCs, the contributions from the MEFP and the EnsPost are more variable. This paper summarizes the verification results, describes the expected performance and limitations of the HEFS for short- to medium-range streamflow forecasting, and provides recommendations for future research.

Summary This paper analyzes the spatial patterns of surface soil moisture of alpine meadows and pastures in the Matsch/Mazia Valley in the Italian Alps by comparing estimations from three different sources of information: (I) RADARSAT 2 synthetic aperture radar (SAR) images; (II) simulations by using the GEOtop hydrological model and (III) ground observations, derived from a network of fixed stations and field campaigns with mobile devices. The aim of this paper is to assess the added value of RADARSAT 2 products with respect to a distributed hydrological model in capturing soil moisture patterns in mountain areas, which is a challenging environment with a high degree of spatial variability. Moreover, the physical controls of the observed soil moisture patterns are analyzed by using the hydrological model. Results show that the model, once calibrated for soil and vegetation parameters, predicts the plot-scale temporal dynamic in station locations and the spatial averages with sufficient accuracy. However, the model output shows lower spatial variability with respect to the ground surveys, with a limited capability of reproducing moist areas in irrigated meadows. Differences arise due to difficulties in knowing soil model parameters and irrigation amounts with accurate spatial detail. RADARSAT 2 soil moisture maps well reproduce the spatial ground surveys, as well as over-irrigated meadows. However, SAR products are limited to slopes with a favorable viewing angle, to bare soil or to grassland areas. Moreover, the signal penetration depth is restricted to the soil surface layer. The major control on RADARSAT 2 patterns is land use. Irrigated meadows in the bottom of the valley have moister conditions, with respect to pastures along the upper hillslopes. In this case, model simulations suggest that differences in soil type could have a relevant impact on soil moisture estimation. A secondary control is topography, with increased moisture in convergent locations with a high topographic wetness index. Results suggest that the capability of RADARSAT 2 products to reproduce small-scale (20 m pixels size) surface soil moisture patterns in mountain grassland areas could complement the ability of the hydrological model to predict variations of soil moisture continuously in space and time. Therefore, RADARSAT 2 products can give useful information to improve spatial parameterization and validation of distributed hydrological models in mountain grassland areas, also in the perspective of implementing data integration procedures for operational soil moisture monitoring.

Unraveling the interaction between water users in a river basin is essential for sound water resources management, particularly in a context of increasing water scarcity and the need to save water. While most attention from managers and decision makers goes to allocation and withdrawals of surface water resources, reuse of non-consumed water gets only marginal attention despite the potentially significant volumes. As a consequence, claims of water saving are often grossly exaggerated. It is the purpose of this paper to explore the processes associated with water reuse in a river basin among users of varying nature and review existing methods for directly or indirectly describing non-consumed water, recoverable flow and/or water reuse. First a conceptual representation of processes surrounding water withdrawals and associated definitions is discussed, followed by a section on connectivity between individual withdrawals and the complex dynamics arising from dependencies and tradeoffs within a river basin. The current state-of-the-art in categorizing basin hydrological flows is summarized and its applicability to a water system where reuse occurs is explored. The core of the paper focuses on a selection and demonstration of existing indicators developed for assessing water reuse and its impacts. It is concluded that although several methods for analyses of water reuse and recoverable flows have been developed, a number of essential aspects of water reuse are left out of existing indicators. Moreover, a proven methodology for obtaining crucial quantitative information on recoverable flows is currently lacking. Future studies should aim at spatiotemporal tracking of the recoverable portion of water withdrawals and showing the dependency of multiple water users on such flows to water policy makers.

Where surface and groundwater are managed conjunctively, the stress on water supplies from climate change can significantly influence water use patterns as well as the economic value and sustainability of those uses. However, aquifer protection can be an expensive proposition because water uses that currently rely on aquifer pumping may produce considerable economic value that would be lost if protection measures are carried out. Evidence from climate-stressed regions has attracted research addressing the costs and benefits of aquifer protection plans. Despite these efforts, few peer-reviewed papers have examined water use patterns that minimize the economic costs of aquifer protection. This work presents an original approach to address that gap by developing and applying a basin scale hydroeconomic optimization model of North America’s Middle Rio Grande Basin to explore impacts of new policies not yet implemented supporting aquifer protection. It also gives model access to readers or stakeholders to experiment with their own scenarios to assess impacts of alternative aquifer protection plans. The model accounts for surface and groundwater storage, irrigation, urban, environmental, and recreational demands, surface water inflows under various climate scenarios, groundwater pumping and recharge, substitute water prices, crop water use, evaporation, as well as institutional constraints governing water use. The objective is implemented by finding the optimized discounted net present value of economic benefits summed over uses, sectors, and regions from use of surface water and connected aquifers. Results are shown for each of six water supply scenarios, two substitute water prices, and two system operation rules. To address impacts of aquifer protection targets, groundwater sustainability targets are specified and enforced as constraints for each of the region’s two major aquifers. We assess total and marginal cost of achieving two targeted aquifer protection levels by identifying optimized surface use and groundwater pumping for each of 24 scenarios. Results show that climate change, in the form of reduced and highly variable inflows, considerably drives up the cost of protecting aquifer sustainability, amplified by the conjunctive nature of the system. Future work points to a need to assess economic performance of various water conservation measures as well as reducing costs of substitute water through measures such as technical advance in desalination, recycling and reuse, substitution of other resources for water, better characterization of existing aquifers, and development of new groundwater supplies.

Accurate and reliable measurements of field-saturated hydraulic conductivity (Kfs) are essential for accurate determinations of soil and solution water fluxes. Kfs is a crucial parameter to design drainage, irrigation systems and many other environmental, agricultural and industrial installations Reynolds et al. (1983). There is a high demand for Kfs data. Its evaluation is preferable to be done in situ. A popular device to measure the Kfs is the head well permeameter such as the Guelph Permeameter. However, it is an analog equipment based on a Mariotte’s bottle that requires full attention and training to be manually operated, where the reading of the flux values (Q, in cm3/min) is time-consuming and costly. In this paper, we describe a device, with a digital readout, to measure the steady state rate of water level change Rs, in order to obtain Qs and Kfs. The method is based on the monitoring of the vacuum variation of the Mariotte’s bottle. Our results show no statistical differences between the measurements values of manual and automatic recording using paired t-test. In addition, our device identifies measurement artifacts due to the variation of the ambient temperature during measurements. The necessary accuracy for the digitalization method becomes feasible due to the availability of accurate piezoelectric transducers (Micro-Electro-Mechanical System – MEMS). Automating and digitizing these processes will reduce time, costs and increase the accuracy and availability of data. The device can be directly adapted to commercially available head well permeameters as shown in our experimental results. The more widespread use and understanding of this parameter can improve the rational use of water for irrigation and use in risk models for forecasting available soil water and therefore crop productivity.

Groundwater resources are increasingly exploited for industrial and agricultural purposes in many arid/semi-arid regions globally. In order to gain a harmonious development among groundwater resources and economic benefits, urgent investigation of the impacts of anthropogenic activities on the groundwater chemistry is required. Understanding hydrochemical mechanism of groundwater in Yinchuan Plain, arid and semi-arid area in Northwestern China, plays a significant role in water resources planning in this ecologically sensitive area. In this paper, a total of 460 phreatic water samples were collected, including 58 water samples containing isotopic data (D and 2 H). Results demonstrate that water chemistry type and isotopic data of phreatic water presents a zonation characteristic from west to east and from south to north. In the eastern part of the Helan Mountain (A 1 zone), the type of hydrochemistry is mainly HCO 3 ·SO 4 -Ca·Mg, SO 4 ·HCO 3 -Ca·Mg, which is mainly recharged by the bedrock fissure water of Helan Mountain. However, in the southern Qingtongxia (A 2 zone) are HCO 3 -Na HCO 3 -Ca·Mg, which receives irrigation and spring water recharge. Phreatic aquifer in Multi-layered structure zone (A 3 zone) reflect multiple recharge sources to the aquifer, the water chemistry demonstrates diverse variations. The anion was dominated by the HCO 3 , while the cation is dominated by sodium. The predominant controlling factors of phreatic water hydrochemistry are divided into anthropogenic activities and natural conditions. Natural conditions mainly include the existence form and lithology of the aquifer, sources of groundwater recharge, soil salinity, and evaporation, mineral dissolution, ion exchange and adsorption and mixing effect. Meanwhile, among natural factors, minerals dissolution in groundwater is the main process dominating the hydrochemical evolution of the groundwater. The other is anthropogenic activities which can be classified as groundwater pollution, excessive exploitation or excretion of groundwater, and excessive groundwater recharge.

Urban flooding is still frequently treated as a direct consequence of excess rainfall, without considering the watershed as an interdependent system connected with the development of its territory. The traditional flood control approach implies in continuous corrective interventions, usually of local character, and resulting from post-events responses. This process requires increasingly large investments to implement (or retrofit) structures capable to accommodate the runoff generated by new urbanized areas. These efforts have not prevented floods from continuing to cause high damages worldwide. Thus, there is a need to change stormwater management strategies, shifting to a risk management approach, not only considering cost-benefit analysis, but also internalizing the residual risk accounting for inherent uncertainties, such as climate change and fast and uncontrolled urban growth. This paper proposes a multi-criteria index, the Urban Flood Resilience index-UFRI, to measure quantitatively urban resilience to floods, supported by a hydrodynamic mathematical model and socio-economic indicators, resulting in spatialized maps as a communicating tool. A case study of an urban watershed in Rio de Janeiro/Brazil is used to demonstrate UFRI potential.