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Fushun, located in Northeast China, is prone to debris flow disasters due to its complex topographical and geological conditions. In 2013, large-scale debris flow disasters triggered by a rainstorm broke out, causing hundreds of casualties and serious economic losses. Therefore, it is significant to evaluate the susceptibility and hazard of debris flows in Fushun. Instead of adopting grid units, this research adopts the hydrological response units as the evaluation units and conducts debris flow susceptibility assessment for Fushun area with analytical hierarchical process. By combining the susceptibility with two different precipitation data, hazard assessment is further conducted. Comparison between the two hazard maps is conducted to explore the influence of precipitation on debris flow hazard assessment. Under different precipitation conditions, the debris flow hazard of the same area changes. Statistics on the accuracy of the susceptibility and hazard assessment results is conducted and further compared with the local existing debris flow disaster records, demonstrating that the evaluation results are generally in good consistency with the actual situation in Fushun. [ABSTRACT FROM AUTHOR]

This research provides a comprehensive analysis of groundwater pollution in the Lower Anayari Catchment (LAC) through δ2H and δ18O isotopic analysis, along with positive matrix factorization (PMF) and PCS-MLR receptor models. Forty groundwater samples were collected from hand-dug wells and equipped boreholes across the LAC. Flame photometry for Na+ and K+, complexometric titration for Ca2+, ion chromatography for Cl−, F−, NO3−, SO42−, and PO43−, and atomic absorption spectrometry for Mg2+, Fe, Pb, Cd, As, and Ni were analytical techniques/instruments employed. In regard to cations, Na+ has the highest average concentration of 63.0 mg/L, while Mg2+ has the lowest at 2.58 mg/L. Concerning the anions and nutrients, Cl− has the highest mean concentration of 18.7 mg/L, and Fl− has the lowest at 0.50 mg/L. Metalloids were detected in trace amount with Fe displaying the highest mean concentration of 0.077 mg/L whereas Cd and As recorded lowest (0.001 mg/L). The average values for groundwater δ18O and δ2H were − 3.64‰ and − 20.7‰, respectively; the average values for rainwater isotopic composition were − 3.41‰ for δ18O and − 17.4‰ for δ2H. It is believed that natural geological features, particularly biotite granitoid and volcanic flow/subvolcanic rocks from the Birimian Supergroup, significantly influence groundwater mineralisation. Additionally, the impact of anthropogenic activities on water quality, with urban development and agricultural practices, may be attributed to increasing levels of certain contaminants such as Fe, Ni, NO3−, and PO43−. This research contributes to the broader field of hydrological study and provides practical implications for managing and conserving water resources in similar contexts. The innovative combination of isotopic and statistical analyses sets a new standard for future studies in groundwater quality assessment, emphasising the need for comprehensive approaches that consider both geological characteristics and human impacts for sustainable water resource management. [ABSTRACT FROM AUTHOR]

Long-term gridded precipitation products (GPPs) are crucial for climatology and hydrological research to overcome the limitations of gauge observations. Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) provides long-term daily precipitation data over the globe from 1981 to near-present, but its reliability varies across regions. This review aims to summarize the performance of CHIRPS from 123 research articles that mainly published between 2015 and 2021.The findings show that the number of CHIRPS validation studies has been increased dramatically in the past few years. These studies were primarily conducted in China, Ethiopia, Kenya, Uganda, and India, while a relatively few studies in North America, Central Asia, and Europe. The performance of CHIRPS varied depending on geographical location and climate condition, with greater performance in Africa. In contrast to other GPPs, CHIRPS is always not the best product, but it is considerably well in capturing monthly precipitation and is suitable for assessing drought. But, there are some common limitations such as less effectiveness across regions with sparse gauges and complex terrain and difficulty in detecting extremely high precipitation events. Future research directions on this topic should focus on (1) enhancing CHIPRS through bias correction and downscaling, (2) validating CHIRPS for extreme indices' calculations and relate to large-scale atmospheric circulations like ENSO, (3) evaluating the capability of CHIRPS in hydrological modelling, and (4) further validating CHIRPS under various topographical and climate conditions as well as other precipitation products. This review can act as a reference to scientists who wish to apply CHIRPS in their climatology analysis and hydroclimatic modelling as well as the developers to further improve the product. [ABSTRACT FROM AUTHOR]

The gravity recovery and climate experiment (GRACE) has emerged as a crucial source of land water storage information in hydrological analysis and research. Numerous factors contribute to regional terrestrial water storage (TWS), resulting in a complex mechanism. In the Loess Plateau region, the continuous alteration of natural conditions and profound impact of human activities have posed a serious threat to the natural ecosystem, leading to an escalating trend of TWS reduction. Addressing the specific analysis of how natural conditions and human activities affect TWS represents a pressing issue. This study employed the residual analysis method to discern the contribution rates of natural conditions and human activities, elucidated the spatial and temporal changes associated with each factor, and ascertained their individual influence. The findings indicated that TWS on the Loess Plateau exhibited a downward trend of −4.89 mm·a−1 from 2003 to 2017. The combined effects of climate change and human activities accounted for alterations in water resource reserves across most areas of the Loess Plateau, with human activities predominantly driving these changes. Precipitation emerged as the primary natural factor influencing TWS variations, and NDVI demonstrated a positive feedback effect on TWS at approximately 30%. Substantial spatial disparities in TWS existed within the Loess Plateau, with human activities identified as the primary cause for the decreasing trend. Vegetation restoration plays a positive role in saving water resources in the Loess Plateau to some extent, and vegetation growth exceeding the regional load will lead to water shortage. [ABSTRACT FROM AUTHOR]

Environmental flow is an important indicator of river health as it maintains the natural flow pattern of riverine ecosystem. Although numerous researches for analyzing the hydrological alterations are there, still insightful investigation of site specific knowledge should be required for riverine ecosystem protection. In this study, the objective is to analyze the hydrological status of the Sone river basin in Bihar region, India. This study also focuses to develop a flow duration curve (FDC) to show the time duration–frequency of low-flow events. The hydrological status of the basin was analyzed using indicators of hydrologic alteration (IHA). Low flows were estimated using period of record flow duration curve (POR FDC), and design environmental flow was assessed for 10-year and 100-year return period using stochastic flow duration curve (stochastic FDC). Daily discharge data collected from Koelwar station of Sone river for 1990–2020 period were used for the hydrological analysis. Depending on the quantitative and qualitative assessment of the hydrological alterations, it was found that the hydrological status of the river basin is in a "very altered" state. The POR FDC analyzed 7-day mean discharge values (7dQ) appropriate for determining low flows, and discharge values corresponding to 95% probability of exceedance (Q95) were considered as low flow for 7dQ. Stochastic FDCs generated 7-day mean flow duration curves for 10-year (7Q10) and 100-year (7Q100) recurrence intervals. Discharge values corresponding to 95% probability of exceedance for 7Q10 range from 120 to 125 cumec and those for 7Q100 range from 135 to 140 cumec. The methodology proposed in this work to design environmental flow considering the effects of hydrological alteration can help in making the long-term strategies to protect the riverine ecosystem in Sone river basin. [ABSTRACT FROM AUTHOR]

Based on the characteristics of concentrated rainwater runoff in the mountainous areas of southwestern China and the low rates of rainwater infiltration into low-permeability soils. We have built a new type of sunken green space structure with a combination of a "overflow port and rainwater storage layer" and carried out model tests of storage and drainage performance under heavy rain conditions. The hydrological response of the new composite structure parameters to the sunken green space was analyzed using the HYDRUS-2D program. The results show that the new composite structure has a significant impact on runoff reduction, drainage, and rainwater storage. For the 100a return period, compared with RSL-0 (0 cm rainwater storage layer), the initial and peak drainage times of RSL-25 were delayed by 30 min and 38 min, respectively, and the rainwater storage rate increased by 13.5%. Compared with no overflow port, the peak drainage increased by 78%, the initial drainage time advanced by 73 min, and the cumulative drainage volume increased by 186%. In addition, as the height of the overflow increased, the surface rainwater absorbed by the sunken green space gradually decreased. The sunken green space with OPH-5 (overflow port height of 5 cm) could absorb more than 75% of the rainwater in the rainwater overflow layer, while the absorption capacities of OPH-7.5 and OPH-10 (overflow port height of 7.5 cm and 10 cm) were basically below 75%. In this case, the OPH-5 and the depth of the storage layer not being less than 250 cm provide the best setting for the new combined structure of the sunken green space. In conclusion, the new composite structure designed in this experiment effectively increased the hydrological performance of the layered sunken green space. [ABSTRACT FROM AUTHOR]

Forecasting discharge (Q) and water level (H) are essential factors in hydrological research and flood prediction. In recent years, deep learning has emerged as a viable technique for capturing the non-linear relationship of historical data to generate highly accurate prediction results. Despite the success in various domains, applying deep learning in Q and H prediction is hampered by three critical issues: a shortage of training data, the occurrence of noise in the collected data, and the difficulty in adjusting the model's hyper-parameters. This work proposes a novel deep learning-based Q–H prediction model that overcomes all the shortcomings encountered by existing approaches. Specifically, to address data scarcity and increase prediction accuracy, we design an ensemble learning architecture that takes advantage of multiple deep learning techniques. Furthermore, we leverage the Singular-Spectrum Analysis (SSA) to remove noise and outliers from the original data. Besides, we exploit the Genetic Algorithm (GA) to propose a novel mechanism that can automatically determine the prediction model's optimal hyper-parameters. We conducted extensive experiments on two datasets collected from Vietnam's Red and Dakbla rivers. The results show that our proposed solution outperforms current techniques across a wide range of metrics, including NSE, MSE, MAE, and MAPE. Specifically, by exploiting the ensemble learning technique, we can improve the NSE by at least 2 % . Moreover, with the aid of the SSA-based data preprocessing technique, the NSE is further enhanced by more than 5 % . Finally, thanks to GA-based optimization, our proposed model increases the NSE by at least 6 % and up to 40 % in the best case. [ABSTRACT FROM AUTHOR]

Through two case studies from the Serj and Fahs areas in north-central Tunisia, we have demonstrated the importance of biostratigraphy for hydrogeological research. Two sections of the Campanian–Maastrichtian interval, corresponding to the Abiod Formation, have been studied for their microfaunal assemblages and lithofacies variations. The Abiod Formation constitutes an interesting target aquifer, exploited in many regions of Tunisia. The biostratigraphic analysis of the Khanguet Zelga section (Serj region) has provided the following biozones: G. Elevata Zone, G. Ventricosa Zone, and A. Mayaroensis Zone in association with Ostracoda, indicating an outer platform environment. The biostratigraphic analysis of the Houita Bou Ragouba section (Fahs region) shows that the Abiod Formation is essentially composed of marls with thin carbonate intercalations. It is subdivided into the following biozones: G. Elevata Zone, G. Ventricosa Zone, G. Calcarata Zone, G. falsostuarti Zone, and G. Gansseri Zone, associated with Ostracoda, indicating a deep marine environment. The biostratigraphic study has allowed us to constrain the age of potential carbonate reservoirs and associated unconformities. The stratigraphic study highlighted that the Abiod Formation exposed in the Serj area is more interesting for hydrogeological investigation than in the Fahs area. These results are confirmed by water borehole data located near the above-mentioned sections. The lithological description and well logs corresponding to the Abiod Formation exposed in the Serj region indicate the predominance and the important thickness (25 m in average) of the limestone layers, which are characterized by low gamma ray values. The Abiod aquifer displays a relatively high flow rate of 5 l/s. In the Fahs area, the limestone reservoir layers are less developed and their thicknesses do not exceed 10 m. The flow rate is so low that the water borehole is converted into a piezometer. [ABSTRACT FROM AUTHOR]

Understanding of flood dynamics forms the basis for the leading water resource management and flood risk mitigation practices. In particular, accurate prediction of river flow during massive flood events and capturing the hysteretic behavior of river stage-discharge are among the key interests in hydrological research. The literature demonstrates that data-driven models are significant in identifying complex and hidden relationships among dependent variables, without considering explicit physical schemes. In this regard, we aim to discover the extent to which data-driven models can recognize the hidden relationships among different hydrological variables, in order to generate accurate predictions of the river flow. A secondary aim involves the detection of whether data-driven models can digest the internal features of training inputs to extrapolate severe flood records beyond the training domain. To achieve these aims, we developed a recurrent neural network (RNN) model of two hidden layers to capture the hidden relationships among the inputs, and investigated the model's predictive capability using quantitative and qualitative analyses. The quantitative analysis comprised of a comparison between model predictions, and another set of precise independent records obtained through an advanced hydroacoustic system for reference. A qualitative approach was adopted to visualize the hysteretic behavior of the stage-discharge relations of the model records, with the high-resolution records of the hydroacoustic system. The findings display the potential of data-driven models for accurately predicting river flow. Consequently, the qualitative analysis revealed moderate correlations of stage-discharge loops as compared to the reference records. Additionally, the model was tested against severe destructive flood records generated from the East Asian monsoon and tropical cyclones. Its findings suggest that data-driven models cannot extrapolate new features beyond their training dataset. Overall, this study discusses the competence of RNNs in providing reliable and accurate river flow predictions during floods. [ABSTRACT FROM AUTHOR]

In this review, we explore the concept, approach, and future research of hydrological connectivity and its assessment at multiscales, because according to the literature, an integrated review upon hydrological connectivity is lack. Systematic studies illustrate the effects of (i) human activities (i.e., dam construction, groundwater extraction, water flow regulation and diversion, and land management) and (ii) natural factors (i.e., climate, soil characteristics, vegetation, and topography) on hydrological connectivity. Approaches (i.e., soil water content patterns, runoff patterns and processes, numerical models, and index of hydrological connectivity) applied to evaluate hydrological connectivity are examined in detail. Lastly, hydrological connectivity at multiscales is indicated. This review concludes with a discussion of potential research trends that can improve understanding of hydrological connectivity. Reported records showed that few studies were published on hydrological connectivity from 1980 to 2003, whereas the evolution of these studies is temporally promising since 2003. We cannot define a standard concept of hydrological connectivity that works in all environments. We desire to show different concepts of hydrological connectivity in different environments. The degree and nature of hydrological connectivity are not static due to the influences of human activities and changes of natural factors. The index of hydrological connectivity and numerical models are the most significant approaches to assess the changes in hydrological connectivity. This study showed that considering hydrological connectivity in social-economical-ecological-hydrological frameworks can prevent its negative effects on surface or subsurface water quantity and quality and is beneficial for sound water sources management. [ABSTRACT FROM AUTHOR]

Hydrological models are widely used as simplified, conceptual, mathematical representatives for water resource management. The performance of hydrological modeling is usually challenged by model calibration and uncertainty analysis during modeling exercises. In this study, a multicriteria sequential calibration and uncertainty analysis (MS-CUA) method was proposed to improve the efficiency and performance of hydrological modeling with high reliability. To evaluate the performance and feasibility of the proposed method, two case studies were conducted in comparison with two other methods, sequential uncertainty fitting algorithm (SUFI-2) and generalized likelihood uncertainty estimation (GLUE). The results indicated that the MS-CUA method could quickly locate the highest posterior density regions to improve computational efficiency. The developed method also provided better-calibrated results (e.g., the higher NSE value of 0.91, 0.97, and 0.74) and more balanced uncertainty analysis results (e.g., the largest P/R ratio values of 1.23, 2.15, and 1.00) comparing with other traditional methods for both case studies. [ABSTRACT FROM AUTHOR]

Accurate long-term streamflow and flood forecasting have always been an important research direction in hydrology research. Nowadays, climate change, floods, and other anomalies occurring more and more frequently and bringing great losses to society. The prediction of streamflow, especially flood prediction, is important for disaster prevention. Current hydrological models based on physical mechanisms can give accurate predictions of streamflow, but the effective prediction period is only about 1 month in advance, which is too short for decision making. The artificial neural network (ANN) has great potential for predicting runoff and is not only good at handling non-linear data but can also make long-period forecasts. However, most of ANN models are unstable in their predictions when faced with raw flow data, and have excessive errors in predicting extreme flows. Previous studies have shown a link between the El Niño–Southern Oscillation (ENSO) and the streamflow of the Yangtze River. In this paper, we use ENSO and the monthly streamflow data of the Yangtze River from 1952 to 2018 to predict the monthly streamflow of the Yangtze River in two extreme flood years and a small flood year by using deep neural networks. In this paper, three deep neural network frameworks are used: stacked long short-term memory, Conv long short-term memory encoder–decoder long short-term memory and Conv long short-term memory encoder–decoder gate recurrent unit. The results show that the use of ConvLSTM improves the stability of the model and increases the accuracy of the flood prediction. Besides, the introduction of ENSO to the experimental data resulted in a more accurate prediction of the time of the occurrence of flood peaks and flood flows. Furthermore, the best results were obtained on the convolutional long short-term memory + encoder–decoder gate recurrent unit model. [ABSTRACT FROM AUTHOR]

Water colour is the result of its constituents and their interactions with solar irradiance; this forms the basis for water quality monitoring using optical remote sensing data. The Forel-Ule Index (FUI) is a useful comprehensive indicator to show the water colour variability and water quality change in both inland waters and oceans. In recent decades, lakes around the world have experienced dramatic changes in water quality under pressure from both climate change and anthropogenic activities. However, acquiring consistent water colour products for global lakes has been a challenge. In this paper we present the first time series FUI dataset for large global lakes from 2000–2018 based on MODIS observations. This dataset provides significant information on spatial and temporal changes of water colour for global large lakes during the past 19 years. It will be valuable to studies in search of the drivers of global and regional lake colour change, and the interaction mechanisms between water colour, hydrological factors, climate change, and anthropogenic activities. Measurement(s) color Technology Type(s) satellite imaging Factor Type(s) temporal interval Sample Characteristic - Environment water body • lake Sample Characteristic - Location global Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.13257218 [ABSTRACT FROM AUTHOR]

This paper demonstrates a climate change impact study on the hydrological process of a data-scarce Greek watershed. The Soil and Water Assessment Tool (SWAT) and, particularly, the ArcSWAT interface was used for the watershed simulation. The ERA-Interim reanalysis climate data regarding the period from 1981 to 2000 were used for the historical simulation of the watershed. The ArcSWAT simulated data were evaluated against the observed discharge data for the periods with the available data. The statistical evaluation confirmed the ArcSWAT model's capability in simulating the hydrological process of the research area. The climate change consequences on the hydrological components of the research area until the end of the twenty-first century were estimated by driving the ArcSWAT model with the Regional Climate Model Version 4 (RegCM4) forcing data under the extreme RCP 8.5 scenario, namely the simulations of the MPI and HadGEM2 general circulation models (GCMs), resulted from the spatio-temporal kriging approach. Based on the results, the increase in the minimum and the maximum temperature contributed to an increase in the actual evapotranspiration and the surface runoff. In contrast, the temperature increase caused a reduction in the infiltration. An increase (reduction) in the precipitation led to an increase (reduction) in the hydrological components. The climate change impact analysis of the Greek watershed showed that not only the precipitation changes but the temperature changes as well directly influence the water balance components of the research area and particularly the infiltration. [ABSTRACT FROM AUTHOR]

High-resolution soil moisture (SM) information is essential to many regional applications in hydrological and climate sciences. Many global estimates of surface SM are provided by satellite sensors, but at coarse spatial resolutions (lower than 25 km), which are not suitable for regional hydrologic and agriculture applications. Here we present a 16 years (2000–2015) high-resolution spatially and temporally consistent surface soil moisture reanalysis (ESSMRA) dataset (3 km, daily) over Europe from a land surface data assimilation system. Coarse-resolution satellite derived soil moisture data were assimilated into the community land model (CLM3.5) using an ensemble Kalman filter scheme, producing a 3 km daily soil moisture reanalysis dataset. Validation against 112 in-situ soil moisture observations over Europe shows that ESSMRA captures the daily, inter-annual, intra-seasonal patterns well with RMSE varying from 0.04 to 0.06 m3m−3 and correlation values above 0.5 over 70% of stations. The dataset presented here provides long-term daily surface soil moisture at a high spatiotemporal resolution and will be beneficial for many hydrological applications over regional and continental scales. Measurement(s) wetness of soil Technology Type(s) digital curation • computational modeling technique Factor Type(s) geographic location • day Sample Characteristic - Environment soil Sample Characteristic - Location Europe Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.11993547 [ABSTRACT FROM AUTHOR]

The role of continental water in polar motion excitation can be illustrated by determining Hydrological Angular Momentum calculated from terrestrial water storage (TWS). In this paper we compare global and regional changes in TWS computed using Coupled Model Intercomparison Project Phase 5 climate models, Global Land Data Assimilation System (GLDAS) land hydrology models and observations from the Gravity Recovery and Climate Experiment (GRACE) satellite mission. We also compare hydrological excitation functions derived from models with those obtained from the GRACE mission and the hydrological signal in observed polar motion excitation (the so-called geodetic residuals). The results confirm that GLDAS models of seasonal and non-seasonal TWS change are more consistent with GRACE data than climate models; on the other hand, none of the considered models are fully consistent with GRACE data or geodetic residuals. In turn, GRACE observations are most consistent with the non-seasonal hydrological signal in observed excitation. A detailed study of the contribution of different TWS components to the hydrological excitation function shows that soil moisture dominates. [ABSTRACT FROM AUTHOR]

Projections of future climate relying on the use of climate models are still uncertain but decision makers are compelled to analyse the potential impacts of climate variability. The methodology presented in this paper can be widely used with a very large set of climate projections that are available today to the scientific community to generate input data at the watershed scale for the simulation and evaluation of water reservoir systems operation considering the implementation of ecological flows. The conceptual framework was applied to the analysis of Odeleite-Beliche multi-reservoir system in southern Portugal. In the selected case study, climate projections of monthly precipitation and temperature for the period 2021-2050 were obtained from the EURO-CORDEX initiative database; adjustment of climate projections was made with the quantile mapping method; natural inflows to the reservoirs were estimated with the Temez hydrological model; AQUATOOL was used in water system simulation; and reliability, resilience, vulnerability and dimensionless maximum deficit were used as performance criteria indicators. Two management perspectives were considered by prioritizing the water supply or the ecological flows. The results showed greater variation in the impacts on the water supply due to differences in the natural inflows generated from diverse climate projections compared to the results obtained with a different prioritization scheme. Failures in meeting current maximum withdrawals on Odeleite-Beliche multi-reservoir system with the implementation of ecological flows were registered only with climate projections that anticipate reductions of the natural inflows higher than 10% compared to natural inflows estimated by hydrological modelling using historical meteorological data. [ABSTRACT FROM AUTHOR]

Freshwater and the services it provides are vital to both natural ecosystems and human needs; however, extreme climates and their influence on freshwater availability can be challenging for municipal planners and engineers to manage these resources effectively. In Arctic Canada, financial and human capital limitations have left a legacy of freshwater systems that underserve current communities and may be inadequate in the near future under a warming climate, growing population, and increasing demand. We address this challenge to community water resource planning by applying several novel water supply forecasting methods to evaluate the Apex River as an alternative freshwater source for Iqaluit, Nunavut (Canada). Surveys of water isotope composition of the Apex River and tributaries indicated that rainfall is the main source of water replenishment. This information was utilized to calibrate a water resource assessment that considered climate forecasting scenarios and their influence on supply, and alternative scenarios for freshwater management to better adapt to a changing climate. We found that under current climate and demand conditions, the freshwater supply of Iqaluit would be in a perpetual state of drawdown by 2024. Analysis of current infrastructure proposals revealed significant deficiencies in the supply extensions proposed whereby the Apex replenishment pipeline would only provide a 2-year extension to current municipal supply. Our heuristic supply forecast methods allowed for several alternative supply strategies to be rapidly evaluated, which will aid the community planning process by specifically quantifying the service life of the city’s current and future primary water supply. [ABSTRACT FROM AUTHOR]

Purpose: In recent history, human development overbalanced towards economic growth has often been accompanied by the degradation and reduction of freshwater resources at the expense of freshwater dependent ecosystems. For their subsistence and correct functioning, understanding environmental water requirements (EWR) represents an area of great interest for life cycle impact assessment (LCIA) and it has been only marginally explored. The aim of this paper is to investigate how this concept has evolved in ecological and hydrological literature and how it can be better integrated in LCIA, to identify potential options for improvement of LCIA indicators in the short, mid and long term.Methods: To address the limitations of existing LCIA approaches in modelling EWR, four families of EWR methods have been reviewed, namely hydrological, hydraulic, habitat simulation and holistic methods. Based on existing scientific literature and their broad application, 24 methods have been selected and their suitability to be adopted in LCIA has been evaluated against nine criteria, with regard to data management issues, accuracy, scientific robustness, and potential for future development. A semi-quantitative performance score has been subsequently assigned for each criterion, showing the main strengths and weaknesses of selected methods.Results and discussion: The underlying rationale of the chosen approaches is markedly different, likewise the input information needed and results applicability. Hydrological methods are well suited for the development of global models and they are the only ones currently considered in LCIA, although their applicability remains limited to water stress indicators. Habitat modelling is identified as an essential step for the development of mechanistic LCIA models and endpoint indicators. In this respect, hydraulic, habitat simulation and holistic methods are fit for the purpose. However, habitat simulation methods represent the best compromise between scientific robustness and applicability in LCIA. For this reason, a conceptual framework for the development of habitat-based characterization factors has been proposed. Among the evaluated habitat simulation methods, ESTIMHAB showed the best performance and was the method retained for the development of an LCIA model that will assess the consequences of water consumption on stream ecosystems.Conclusions: This study identifies the advantages of specific modelling approaches for the assessment of water requirements for ecosystems. Selected methods could support the development of LCIA models at different levels. In the short-term for improving environmental relevance of water stress indicators, and in the mid/long-term to build up midpoint habitat indicators relating water needs of ecosystems with new endpoint metrics. [ABSTRACT FROM AUTHOR]

The study is focused on the species composition, distribution, population dynamics, biomass, and production of zooplankton in the lower reaches of the Pregolya R., subject to the effect of specific hydrological conditions. The characteristics of the seasonal dynamics of zooplankton population, biomass, and production in autumn (October, November) were found to be high because of euryhaline crustaceans Eurytemora affinis and Acartia sp., which enter the river with water setups from the bay. The total zooplankton production over the vegetation season in river medial and riparian zones were similar (8.3 and 7.1 kcal/m3, respectively), the production rates were at the level typical of the riparian systems. [ABSTRACT FROM AUTHOR]

Water resources of lakes with natural and regulated hydrological regime are evaluated, and estimates are given to the water resources of artificial water bodies in three federal districts in the Asian part of RF (Ural, Siberian, and Far-Eastern) and the constituent entities of the Federation they contain. The estimates were made by a new procedure, incorporating up-to-date satellite data and functions of Google Earth project. In the Asian part of RF, ~3140000 natural water bodies were analyzed, including ~1170000 lakes >1 ha in area and ~6000 artificial water bodies. Lake water resources in the Asian part of RF amount to 24537 km3, of which ~96% are contained in Lake Baikal. Other 638 km3 are contained in artificial water bodies. [ABSTRACT FROM AUTHOR]

The Kamenushka Formation, exposed in the northern part of South Primorye (Kamenushka-1 and Kamenushka-2 sections), is one of the few localities in the world with richly fossiliferous Lower-Upper Olenekian sedimentary successions. Lower to Middle Triassic ammonoid-, brachiopod- and conodont-bearing silty-clayey deposits of the Kamenushka-1 and Kamenushka-2 sections have been isotope-geochemically investigated in detail. As a result, these sections, together with the previously investigated Abrek Section, exposed in the southern part of South Primorye, provide almost complete 15Norg- and 13Corg- records for the Lower Triassic of this region. Nine N-isotope intervals and the five negative C-isotope excursions, reflecting, apparently, unstable climatic and hydrological conditions, have been distinguished in the Lower Triassic of South Primorye. On the basis of the new C-isotope data the Mesohedenstroemia bosphorensis Zone (upper part), Shimanskyites shimanskyi and Neocolumbites insignis zones of South Primorye are correlated now with the Lower Smithian part of the Yinkeng Formation, the Upper Smithian part of the Helongshan Formation and the Middle Spathian part of the Nanlinghu Formation in South China, respectively, as has been observed in the Abrek, Kamenushka-2, West Pingdingshan and Majiashan sections. [ABSTRACT FROM AUTHOR]

The variation and tendency of hydrological extremes in the context of climate change have received extensive concern. However, there is still no clear understanding toward the evolution of hydrological extremes, and the impacts of climate change on flood disaster and risk have been explored very poorly. This study investigated the trends in flood-related variables of extreme precipitation, extreme river flow (1955-2012), and flood damages (1990-2014) in Yangtze River Basin. To further explore the impacts of climate change, the relations of changes in extreme river flow and precipitation were evaluated using wavelet transform analysis, and the trends in normalized flood damages were re-examined with the elimination of changes in exposure. The results showed that flood damages decreased significantly in the middle and lower reaches, while extreme precipitation and river flow still had increasing trends, indicating the potential of increasing flood risk. For upper basin, extreme precipitation increased significantly in the west and northeast part, while significant decreasing trends can also be found in the central part. The wavelet analysis revealed significant covarying relations for extreme discharge and precipitation, demonstrating the substantial influence of climate variability on extreme river flow. Meanwhile, the normalized flood damages showed increasing trend in Sichuan province, indicating significant climate change impacts on flood disaster. This study suggests the need for more effective measures to mitigate flood risk and better adaptation for climate change in Yangtze River Basin, especially in the middle and lower basin and northeast of the upper basin. [ABSTRACT FROM AUTHOR]

Nutrient discharge into peri-urban streams and reservoirs constitutes a significant pressure on environmental management, but quantitative assessment of non-point source pollution under climate variability in fast changing peri-urban watersheds is challenging. Soil and Water Assessment Tool (SWAT) was used to simulate water budget and nutrient loads for landscape patterns representing a 30-year progression of urbanization in a peri-urban watershed near Tianjin metropolis, China. A suite of landscape pattern indices was related to nitrogen (N) and phosphorous (P) loads under dry and wet climate using CANOCO redundancy analysis. The calibrated SWAT model was adequate to simulate runoff and nutrient loads for this peri-urban watershed, with Nash-Sutcliffe coefficient (NSE) and coefficient of determination (R2) > 0.70 and percentage bias (PBIAS) between −7 and +18 for calibration and validation periods. With the progression of urbanization, forest remained the main “sink” landscape while cultivated and urban lands remained the main “source” landscapes with the role of orchard and grassland being uncertain and changing with time. Compared to 1984, the landscape use pattern in 2013 increased nutrient discharge by 10%. Nutrient loads modelled under wet climate were 3-4 times higher than that under dry climate for the same landscape pattern. Results indicate that climate change could impose a far greater impact on runoff and nutrient discharge in a peri-urban watershed than landscape pattern change. [ABSTRACT FROM AUTHOR]

Flooding associated with landing tropical cyclones (TCs) is one of the major natural hazards in the coastal region of Vietnam. Annually, approximately 5 or 6 TCs make landfall in Vietnam, bringing heavy rains and inducing flooding, particularly to the central coastal region because of its topography and geographic configuration. This study focuses on the modelling of typhoon-induced floods that have resulted in widespread damage to agriculture over the central Thua Thien Hue Province of Vietnam by coupling two well-known hydrological models, KINEROS2 and HEC-RAS (Daniel et al. in Open Hydrol J 5(1), 2011), and using GSMaP (Global Satellite Mapping of Precipitation) data as the satellite rainfall input. Landsat imagery and GIS are also used for mapping and analysing the inundated areas. The discharge and water level from the KINEROS2 and HEC-RAS models displayed acceptable results for the floods modelled from three selected typhoons; both the Nash-Sutcliffe simulation efficiency coefficient (NSE) and the coefficient of determination (R2) were greater than 0.6. The simulated inundation maps of these typhoon-induced floods were compared with those extracted from the Landsat imagery to assess consistency. The result revealed a similar spatial extension of the inundated agricultural areas. This information, together with the forecasted TC movements and associated rainfalls, will be helpful to plan methods for mitigating potential typhoon-induced flooding and damage, particularly damage to agricultural regions. [ABSTRACT FROM AUTHOR]

Western Himalaya is a strategically important region, where the water resources are shared by China, India and Pakistan. The economy of the region is largely dependent on the water resources delivered by snow and glacier melt. The presented study used stable isotopes of water to further understand the basin-scale hydro-meteorological, hydrological and recharge processes in three high-altitude mountainous basins of the western Himalayas. The study provided new insights in understanding the dominant factors affecting the isotopic composition of the precipitation, snowpack, glacier melt, streams and springs. It was observed that elevation-dependent post-depositional processes and snowpack evolution resulted in the higher isotopic altitude gradient in snowpacks. The similar temporal trends of isotopic signals in rivers and karst springs reflect the rapid flow transfer due to karstification of the carbonate aquifers. The attenuation of the extreme isotopic input signal in karst springs appears to be due to the mixing of source waters with the underground karst reservoirs. Basin-wise, the input–output response demonstrates the vital role of winter precipitation in maintaining the perennial flow in streams and karst springs in the region. Isotopic data were also used to estimate the mean recharge altitude of the springs. [ABSTRACT FROM AUTHOR]

The temporal resolution of observed data is a critical element in determining the parameters, prediction performance, and applicability of hydrological models. In this study, runoff simulations were performed at different temporal resolutions using the Xinanjiang model to evaluate the influence of temporal resolution on the model parameters and performance. Based on the sensitivity analysis of the model parameters, the posterior distribution of the sensitive parameters was derived using the Bayesian method and Differential Evolution Adaptive Metropolis (DREAM) algorithm at different temporal resolutions. The transformation functions of the model parameters were put forward to transform the parameters according to the regulatory between the parameters and time-steps on the basis of the parameters posterior distribution. The model performance and uncertainty for runoff simulation were compared and discussed at each temporal resolution. The results show that (1) the parameters related with the process of the water balance and runoff routing are identified as sensitive to the temporal resolutions, and there exist linear or power function relationships between parameter values at different temporal resolutions; and (2) the quantitative relationship equations have been verified to have good capacity for model simulation when the model parameters are transformed from other temporal resolution. [ABSTRACT FROM AUTHOR]

Excessive nitrogen (N) discharge from agriculture causes widespread problems in aquatic ecosystems. Knowledge of spatiotemporal patterns and source attribution of N pollution is critical for nutrient management programs but is poorly studied in headwaters with various small water bodies and mini-point pollution sources. Taking a typical small watershed in the low mountains of Southeastern China as an example, N pollution and source attribution were studied for a multipond system around a village using the Hydrological Simulation Program-Fortran (HSPF) model. The results exhibited distinctive spatio-seasonal variations with an overall seriousness rank for the three indicators: total nitrogen (TN) > nitrate/nitrite nitrogen (NO -N) > ammonia nitrogen (NH-N), according to the Chinese Surface Water Quality Standard. TN pollution was severe for the entire watershed, while NO -N pollution was significant for ponds and ditches far from the village, and the NH-N concentrations were acceptable except for the ponds near the village in summer. Although food and cash crop production accounted for the largest source of N loads, we discovered that mini-point pollution sources, including animal feeding operations, rural residential sewage, and waste, together contributed as high as 47% of the TN and NH-N loads in ponds and ditches. So, apart from eco-fertilizer programs and concentrated animal feeding operations, the importance of environmental awareness building for resource management is highlighted for small farmers in headwater agricultural watersheds. As a first attempt to incorporate multipond systems into the process-based modeling of nonpoint source (NPS) pollution, this work can inform other hydro-environmental studies on scattered and small water bodies. The results are also useful to water quality improvement for entire river basins. [ABSTRACT FROM AUTHOR]

It has been argued that rainfall-runoff model calibration based solely on streamflow is not sufficient to evaluate the realism of a hydrological model to represent the internal fluxes. Therefore, model calibration has evolved to evaluating model performance using a number of hydrological signatures that link the model to the underlying processes. However, this approach uses goodness-of-fit measures, unable to describe the entire dynamic of time series, to evaluate model consistency and to simulate hydrological signatures. The present paper develops a stepwise multicriteria calibration using hydrograph partitioning and calibration criteria defined on the basis of Functional Data Analysis (FDA), a statistical tool that conserves all important features of the hydrograph by approximating times series as a single function. The aim of this approach is to improve model realism by scrutinizing model components and by evaluating its ability to reproduce the entire flow dynamic. The proposed approach is compared to a calibration against daily streamflow only. The stepwise calibration improved the estimation of the flood curve, the annual peak volume as well as the performance of the model at sites other than the calibration station. [ABSTRACT FROM AUTHOR]

Climate change and other future developments can influence the availability of groundwater resources for drinking water. The uncertainty of the projected impact is a challenge given the urgency to decide on adaptation measures to secure the drinking water supply. Improved understanding on how climate change affects the groundwater system is necessary to develop adaptation strategies. AZURE is used, a detailed, well-calibrated hydrological model to study the projected impact of climate change scenarios on the large Veluwe aquifer in the Netherlands. The Veluwe area is an important source of drinking water. However, some existing groundwater extractions in the area affect nearby groundwater-dependent ecosystems. Redistribution of the licensed extraction volumes of these sites is considered to reduce the impact on these ecosystems. The projected impact of climate change and redistribution to groundwater levels is studied. The research shows that in a slowly responding large aquifer the projected climate change may cause rising groundwater levels despite the projected increase in summer dryness. The results indicate that this impact may exceed the impact of redistribution of extraction volumes. In addition, it is shown that the combined effect strongly depends on local conditions, thus highlighting the need for high-resolution modelling. [ABSTRACT FROM AUTHOR]

The paper analyses complex hydrological behaviour of Skradinski Buk, the tufa waterfall, formed on the downstream part of the Krka River located in the coastal part of the Croatian Dinaric karst. This waterfall is the central point of the Krka National Park (KNP) proclaimed in 1985. Skradinski Buk is the seventh, final and the largest tufa barrier on the Krka River. Morphology of Skradinski Buk is composed of 17 unevenly distributed steps and dozens of small depressions. Natural waterfall morphology strongly influenced the complex waterfall hydrological regime. Additionally, hydrological regime is disturbed by the operation of the hydroelectric power plant (HEPP) Jaruga. This paper analyses characteristic (minimum, mean, maximum) annual water levels and discharges monitored at two hydrological stations (Skradinski Buk Gornji-SBG and Nacionalni Park-NP). SBG controls the inflow of water into the waterfall, while the NP is established in order to control the use of water from the HEPP Jaruga operation. It is concluded that the HEPP Jaruga does not comply with the agreement signed between the KNP and the HEPP Jaruga. The goal of the paper is to give detailed hydrological analysis of the Skradinski Buk, which should play the crucial role in the protection of this wonderful and very vulnerable phenomenon. [ABSTRACT FROM AUTHOR]

An introduction is presented in which the editor discusses articles in the issue on topics including Selenga-Baikal Basin, hydrological changes occurring there and also the environmental changes.

Application of artificial neural network (ANN) models has been reported to solve variety of water resources and environmental related problems including prediction, forecasting and classification, over the last two decades. Though numerous research studies have witnessed the improved estimate of ANN models, the practical applications are sometimes limited. The black box nature of ANN models and their parameters hardly convey the physical meaning of catchment characteristics, which result in lack of transparency. In addition, it is perceived that the point prediction provided by ANN models does not explain any information about the prediction uncertainty, which reduce the reliability. Thus, there is an increasing consensus among researchers for developing methods to quantify the uncertainty of ANN models, and a comprehensive evaluation of uncertainty methods applied in ANN models is an emerging field that calls for further improvements. In this paper, methods used for quantifying the prediction uncertainty of ANN based hydrologic models are reviewed based on the research articles published from the year 2002 to 2015, which focused on modeling streamflow forecast/prediction. While the flood forecasting along with uncertainty quantification has been frequently reported in applications other than ANN in the literature, the uncertainty quantification in ANN model is a recent progress in the field, emerged from the year 2002. Based on the review, it is found that methods for best way of incorporating various aspects of uncertainty in ANN modeling require further investigation. Though model inputs, parameters and structure uncertainty are mainly considered as the source of uncertainty, information of their mutual interaction is still lacking while estimating the total prediction uncertainty. The network topology including number of layers, nodes, activation function and training algorithm has often been optimized for the model accuracy, however not in terms of model uncertainty. Finally, the effective use of various uncertainty evaluation indices should be encouraged for the meaningful quantification of uncertainty. This review article also discusses the effectiveness and drawbacks of each method and suggests recommendations for further improvement. [ABSTRACT FROM AUTHOR]

Copyright of Hydrogeology Journal is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Germany does not only have a long tradition in water research, but a very active community of scientists and practitioners working on a vast range of 'water topics.' This thematic issue, which was initiated by four water-related research associations (German Hydrological Society; German Limnological Society; Hydrological Sciences Commission within German Water Association; Working Group Hydrology within German Geographical Society), is a testimony of both the quality and diversity of the water research currently undertaken by Germany's scientific community. Key topics include hydrology and hydromorphology; water quality; aquatic and riparian ecosystems; water in agriculture and forestry; and water management and supply. The manuscripts contained in this thematic issue do not only cover a period of more than two millennia, but also address all types of water resources and a multitude of both established and newly developed methods that help us to better understand the processes governing the hydrological cycle, aquatic ecosystems and the management and operation of various water infrastructures. [ABSTRACT FROM AUTHOR]

An introduction is presented in which the editor discusses various reports within the issue on topics including water resources management, impact of hydrological processes on water management and groundwater level predictions.

In the winter low-water season in March and the first week of April 2019, comprehensive hydrological and hydrochemical studies were carried out at the mouths of two small rivers of the White Sea catchment basin: (the Mudyuga River, which flows into Dvina Bay, and the Tamitsa River, which flows into Onega Bay). In the nearshore of the first river, microtidal conditions are observed (the size of the spring tide in summer is about 1 m); in the nearshore of the second river, mesotidal conditions are observed (the size of the spring tide in summer is 2.4 m). [ABSTRACT FROM AUTHOR]

This study investigated the effects of urbanization predicted using the SLEUTH urban growth model (an acronym taken from Slope, Landuse, Exclusion, Urban extent, Transportation and Hillshade) under four landuse policy scenarios on the hydrological response of Ayamama watershed using the Hydrologic Engineering Center Release 1 (HEC-1) hydrological model. The SLEUTH model was calibrated based on the Brute Force Monte Carlo iteration technique using the urban extents of Istanbul in 1987, 2000, 2009 and 2013 and was verified by considering Kappa coefficient as evaluation criteria. HEC-1 was calibrated and verified using observed rainfall-runoff event and based on the coefficient of determination (R), Nash-Sutcliffe coefficient of efficiency (CE) and percentage of bias (PBIAS) as performance indicators. The urbanization prediction results showed that the urban extent of Ayamama watershed would reach 50.3 km, 44 km, 63 km and 60 km under Scenarios 1, 2, 3 and 4, respectively, in 2050. The hydrological simulation results under these urban extents showed that the urban extent of Ayamama watershed under Scenario-3, a scenario that allows unrestricted growth with the implementation of Project Canal Istanbul (PCI), resulted in the highest peak discharge and the shortest time to peak. Such an increase in the peak discharge and reduction in the time to peak will increase the risk of flooding and, therefore, extreme care needs to be taken before and during the implementation of PCI. [ABSTRACT FROM AUTHOR]

The reconstruction of glacio-hydrological records for the data deficient Himalayan catchments is needed in order to study the past and future water availability. The study provides outcomes of a glacio-hydrological model based on the degree-day approach. The model simulates the discharge and mass balance for glacierised Shaune Garang catchment. The degree-day factors for different land covers, used in the model, were estimated using daily stake measurements on Shaune Garang glacier and they were found to be varying between 2.6 ± 0.4 and 9.3 ± 0.3 mm °Cday. The model is validated using observed discharge during ablation season of 2014 with coefficient of determination (R) 0.90 and root mean square error (RMSE) 1.05 m sec. The model is used to simulate discharge from 1985 to 2008 and mass balance from 2001 to 2008. The model results show significant contribution of seasonal snow and ice melt in total discharge of the catchment, especially during summer. We observe the maximum discharge in July having maximum contribution from snow and ice melt. The annual melt season discharge shows following a decreasing trend in the simulation period. The reconstructed mass balance shows mass loss of 0.89 m we per year between 2001 and 2008 with slight mass gain during 2000/01 and 2004/05 hydrological years. [ABSTRACT FROM AUTHOR]

Recently some generalized autoregressive conditional heteroskedasticity (GARCH) models are proposed and applied to various hydrologic variables to capture and remove the ARCH effect, which has been observed frequently in the residuals from linear autoregressive moving average (ARMA) models fitted to hydrologic time series. As a nonlinear phenomenon of variance behavior, the ARCH effect reveals partially nonstationarity and nonlinearity of hydrological processes. This paper deals with the variation of a river basin using the ARMA-GARCH error model, which combines an ARMA model for modelling the mean behavior and a GARCH model for modelling the variance behavior of the residuals from the ARMA model. Based on the heteroscedasticity of hydrological variable series, the time-varying regional variance is proposed to check the variation of a river basin for the first time. As a study case, the method is applied to four deseasonalized daily discharge series from the middle reach of Yangtze River, China. Through the analyses of the conditional variance in different streamflow series, it is concluded that: (1) The ARCH effect exists in all the studied series which means the stream processes is nonstationary in terms of the variance; (2) The variations of time-varying variances are similar for the series from adjacent hydrological stations, and the similarity degree increases from upstream to downstream; (3) The regional variance is time-varying and can be used for further regional research. [ABSTRACT FROM AUTHOR]

Hydropower is the most important source of electricity in Brazil. It is subject to the natural variability of water yield. One building block of the proper management of hydropower assets is the short-term forecast of reservoir inflows as input for an online, event-based optimization of its release strategy. While deterministic forecasts and optimization schemes are the established techniques for short-term reservoir management, the use of probabilistic ensemble forecasts and multi-stage stochastic optimization techniques is receiving growing attention. The present work introduces a novel, mass conservative scenario tree reduction in combination with a detailed hindcasting and closed-loop control experiments for a multi-purpose hydropower reservoir in a tropical region in Brazil. The case study is the hydropower project Três Marias, which is operated with two main objectives: (i) hydroelectricity generation and (ii) flood control downstream. In the experiments, precipitation forecasts based on observed data, deterministic and probabilistic forecasts are used to generate streamflow forecasts in a hydrological model over a period of 2 years. Results for a perfect forecast show the potential benefit of the online optimization and indicate a desired forecast lead time of 30 days. In comparison, the use of actual forecasts of up to 15 days shows the practical benefit of operational forecasts, where stochastic optimization (15 days lead time) outperforms the deterministic version (10 days lead time) significantly. The range of the energy production rate between the different approaches is relatively small, between 78% and 80%, suggesting that the use of stochastic optimization combined with ensemble forecasts leads to a significantly higher level of flood protection without compromising the energy production. [ABSTRACT FROM AUTHOR]

Multi-purpose reservoir development have been always a big challenge for the management of water resources. This paper describes an integrated approach for investigating catchment hydrology in the development of a hydropower and a canal irrigation system based on model analyses. The investigation aims to adequately determine an optimal domestic and irrigation water resources allocation scheme based on an assessment of the reservoir water balance and capacity for hydropower. The soil and water assessment tool (SWAT) which characterizes basin hydrology and the water management and planning model MODSIM which provides a decision support system for water allocation optimization, were used in this study. The integrated approach was applied to Prek Te River basin in Cambodia. The water demand aspect was examined based on domestic water use, irrigation water, environmental flow, and water losses. An operational rule curve was developed for hydropower operation with respect to a power potential of 13 MW. Hydrologic modeling revealed 90 % dependable water of about 2.7 m/s during the dry season and 214.3 m/s during the wet season, indicative of a wet-season dependent reservoir for storage. Results from the 26-years simulation period also showed that diversions for domestic water and irrigation water supply were 92.3 % dependable for a 13 MW capacity hydropower development. The integrated approach was shown to be a valuable decision support tool for water resources management with the determination of an optimum policy for multi-purpose reservoir operation based on available basin water supply. [ABSTRACT FROM AUTHOR]

The unavailability of proper hydrological data quality combined with the complexity of most physical based hydrologic models limits research on rainfall-runoff relationships, particularly in the tropics. In this paper, an attempt has been made to use different resolutions of DEM generated from freely available 30 m-based ASTER imagery as primary input to the topographically-based hydrological (TOPMODEL) model to simulate the runoff of a medium catchment located in the tropics. Response surface methodology (RSM) was applied to optimize the most sensitive parameters for streamflow simulation. DEM resolutions from 30 to 300 m have been used to assess their effects on the topographic index distribution (TI) and TOPMODEL simulation. It is found that changing DEM resolutions reduces the TOPMODEL simulation performance as the resolutions are varied from 30 to 300 m. The study concluded that the ASTER 30 m DEM can be used for reasonable streamflow simulation of a data scarce tropical catchment compared with the resampled DEMs. [ABSTRACT FROM AUTHOR]

Involving a limited resource, the assessment of groundwater aquifers is of utmost importance. A key component of any such assessment is the determination of key properties that permit water resource managers to estimate aquifer drawdown and safe yield. This paper presents a particle filtering approach to estimate aquifer properties from transient data sets, leveraging recently published analytically-derived models for confined aquifers and using sample-based approximations of underlying probability distributions. The approach is examined experimentally through validation against three common aquifer testing problems: determination of (i) transmissivity and storage coefficient from non-leaky confined aquifer performance tests, (ii) transmissivity, storage coefficient, and vertical hydraulic conductivity from leaky confined aquifer performance tests, and (iii) transmissivity and storage coefficient from non-leaky confined aquifer performance tests with noisy data and boundary effects. On the first two well-addressed problems, the results using the particle filter approach compare favorably to those obtained by other published methods. The results to the third problem, which the particle filter approach can tackle more naturally than the previously-published methods, underscore the flexibility of particle filtering and, in turn, the promise such methods offer for a myriad of other geoscience problems. [ABSTRACT FROM AUTHOR]

A simple stochastic model of meteorological droughts (meteo-droughts) is presented. All its components are analyzed and described, along with their probabilistic structures. The model is composed of ten defining descriptive parameters and each of them is a random variable. The statistical explanation of the entire probabilistic process of meteo-droughts provides the base from which the procedure of developing the m-year synthetic meteo-drought is derived. From the meteorologist's and consulting engineer's point of view this model can be used efficiently to explain meteo-drought phenomenon in a quantifiable manner. This model is an approximate, not exact theoretical method of the probabilistic meteo-drought analysis. The method is intended for practical use by various professionals, such as irrigation, agricultural and forest engineers, hydrologists and meteorologists. The method has been applied in this paper to data observed at Sombor meteorological station, Serbia. Each defined meteorological drought be it past, present or future, can be described quantitatively and completely by the presented stochastic method. In other words, each meteo-drought is given its 'identification card' (ID card). The application of the procedure has shown that the method is simple, yet complete, reliable and efficient. [ABSTRACT FROM AUTHOR]

The eco-hydrological model SWAT is used worldwide for simulating hydrology and water quality of agricultural catchments. One of the main water uses is irrigation, predominantly in arid and semi-arid regions. Climate impact simulations show that a future increase of irrigation demand can be expected for humid regions. Options for adaptation include the improvement of irrigation techniques and the modification of crop patterns. In our study we investigate the application of SWAT for the development of water saving irrigation control strategies in a humid river catchment in Northern Germany. We developed different scenarios using both soil moisture deficit control and plant water demand control. The results show plausible changes of irrigation amounts when changing the trigger points of both control methods. By deficit control strategies, the water consumption could be reduced with only a moderate decrease of crop yield. Differences between soil characteristics were well shown in the SWAT simulations, but the model consistently overestimated irrigation values. Furthermore we found a high variability of the model errors between the different years, even if the long term average values are considered acceptable. Future research is needed to improve the model accuracy in automatic irrigation control. [ABSTRACT FROM AUTHOR]

The increasing frequency of extreme events in large rivers may affect not only their flow, but also their water quality. In the present study, spatial and temporal changes in fluvial physico-chemical variables were analyzed in a mega-river delta during two extreme hydrological years (La Niña-El Niño) and related to potential explanatory factors. Basic water variables were evaluated in situ at 13 points (distant 2-35 km from each other) in watercourses of the Delta Biosphere Reserve (890 km) in the Lower Paraná River (Argentina) in nine surveys (October 2008-July 2010) without meteorological tides. Samples for laboratory analyses were collected from each main river. Multivariate tests by permutations were applied. The period studied was influenced by a drought, within a long period dominated by low flows combined with dry weather and wildfires, and a large (10 years of recurrence) and prolonged (7 months) flood. The hydrological phase, followed by the season and the hydrological year (according to the ENSO event) were the principal explanatory factors of the main water quality changes, whereas the drainage sub-basin and the fluvial environment (river or stream) were secondary explanatory factors. During the drought period, conductivity, turbidity, and associated variables (e.g., major ions, silicon, and iron concentrations) were maximal, whereas real color was minimal. In the overbanking flood phase, pH and dissolved oxygen concentration were minimal, whereas real color was maximal. Dissolved oxygen saturation was also low in the receding flood phase and total major ion load doubled after the arrival of the overbanking stage. The water quality of these watercourses may be affected by the combination of several influences, such as the Paraná River flow, the pulses with sediments and solutes from the Bermejo River, the export of the Delta floodplain properties mainly by the flood, the season, and the saline tributaries to the Lower Paraná River. The high influence of the hydrology of this large river on the Delta fluvial water quality emphasizes the relevance of changes in its flow regime in recent decades, such as the seasonality attenuation. Considering that the effects of extreme events differ among and within fluvial systems, specific ecohydrological evaluations and powerful appropriate statistics are key tools to gain knowledge on these systems and to provide bases for suitable management measures in a scenario of climate change and increasing human alterations and demands. [ABSTRACT FROM AUTHOR]

Copyright of Hydrogeology Journal is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Hydrogeology Journal is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)