Water Resource Management Issues Research Articles

Advanced flow simulations of estuarine water resources responding to changes in societal demands

Water resources of rivers and estuaries have traditionally been managed for local societal demands, including municipal, agricultural, and industrial usages, as well as for transportation as navigational channels. However, as societal demands have changed including urban expansion, economic growth, cultural transition, etc., the land uses also should be changed, and the civil necessities of water have been diversified, and this has raised concerns about the ecology, environment, and water quality issues. This study attempted to address three water resource management issues in South Korean rivers and estuaries, which occurred recently due to the changes in societal demands for water and explained how the most advanced scientific research techniques had been applied. The main issues were 1) agricultural water security and ecological environment restoration in Nakdong River Estuary, 2) water and soil quality degradation due to industrial effluents in Hyeongsan River Estuary, and 3) utilization of rivers as waterways and water quality and ecology issues in the ARA waterway. To find solutions to conflicts of water resources, the series of numerical simulations validated by the field observations allowed 1) assessments of the impact and extent of salt intrusion upon the opening of estuary dam gates, 2) estuarine physical processes to investigate the influence of industrial effluent, and 3) analysis water quality variations resulting from changes in the function of artificial channels. These analyses are expected to provide appropriate and science-based strategies of regional water management.

Water Resource Management Research in Benin: A Systematic Review

Benin has been the subject of numerous investigations in the complex realm of water resource management, encompassing hydrological, engineering, economic, ecological, and sociological facets. However, noticeable gaps exist, warranting a comprehensive review of the existing knowledge. This study offers a systematic review on the trends in water management research within Benin.
 Scholarly papers were carefully selected from reputable academic databases, including Web of Science, WorldCat, Google Scholar, the Directory of Open Access Journals (DOAJ), Science Direct, and Scopus. The selection criteria were stringent, focusing on English-keywords, journal articles and conference papers centered on water management or its associated challenges in the Benin context. The selection process entailed a two-phase screening protocol, commencing with an initial assessment based on titles, followed by subsequent screening based on abstracts and keywords. The chosen studies then underwent a thorough bibliometric analysis.
 The findings of this review reveal a noticeable surge in research activity related to water resource management in Benin in recent decades. These studies predominantly concentrate on rural areas. The breadth of research topics covers a wide spectrum, including water pricing reform, water policy formulation, water-related conflicts, the application of integrated water resource management, agro-dam management, challenges inherent in water resource management, water quality assessment, agricultural water usage, assessment of water availability, utilization of Geographic Information System (GIS), and the development of basin information system platforms. A majority of the examined articles emphasize water management and water availability.
 This review underscores the growing interest within the scientific community in exploring water resource management issues in rural Benin. Ineffective water management in Benin can be attributed to various factors, including inadequate financial support, deficient coordination among stakeholders in the water sector, a lack of transparency, suboptimal management of hydraulic infrastructure, inadequate dissemination and implementation of established legal and institutional frameworks, overlapping roles among actors involved in water resource management, and a notable scarcity of data.
 These findings highlight the urgent need for further research initiatives and policy interventions in these domains, with the overarching goal of enhancing the state of water management in Benin.

Development of a lightweight convolutional neural network-based visual model for sediment concentration prediction by incorporating the IoT concept

Abstract Sediment concentration (SC) monitoring has always been a pressing issue in water resource management, as many existing instruments still face challenges in accurately measuring due to environmental factors and instrument limitations. A robust technology is worth presenting to apply in the field site. This study firstly uses mean-absolute-error (MAE), root-mean-square error (RMSE), correlation coefficient (CC), and Nash–Sutcliffe efficiency coefficient (NSE) to describe the performance of the proposed convolutional neural network (CNN). Moreover, adapting the ensemble learning concept to compare the multiple machine learning (ML) approaches, the CNN presents the highest predicted accuracy, 91%, better than SVM (79%), VGG19 (63%) and ResNet50 (35%). As a result, the proposed CNN framework can appropriately apply the monitoring needs. The primary purpose is to develop a simple, accurate, and stable SC monitoring technology. Instead of some complex architectures, a simple and small neural network is adopted to implement real-time application (RTA). Via our design, such a traditional but critical issue can be improved to a new state. For example, by incorporating the concept of the Internet of Things (IoT) with our design, the distributed computing system for large-scale environmental monitoring can be realized quickly and easily.

Development of Machine Learning Algorithms for Riverside Land Cover Classification Using Synthetic Aperture Radar Satellite Imagery and Terrain Data

Riverine environments play a crucial role in maintaining the stability of river ecosystems as well as biodiversity. Furthermore, the appropriate management of small rivers has a significant impact not only on stable water supplies but also on water resource management. Wide monitoring of the riverside environment including land covers and their changes is an important issue in water resource management. This study aims to develop a high-resolution (10 m) model for classifying riverside land cover by integrating Sentinel-1 synthetic aperture radar (SAR) data and terrestrial characteristics using machine learning algorithms. We constructed a total of 3,284 landcover reference point datasets near the four major rivers of South Korea with five classes: water, barren, grass, forest, and built-up. The Random Forest and Light Gradient Boosting Machine classification models were developed using eight input variables derived from SAR signal and digital terrain data. The models showed an overall cross-validation accuracy exceeding 80% while maintaining consistent spatial distributions, except for the barren class. The false alarms on barren would be corrected through additional sampling processes and incorporating optical characteristics in further study. The high-resolution riverside land cover maps are expected to contribute to the establishment of a comprehensive management system for water resources such as riverside land cover change detection, river ecosystem monitoring, and flood hazard management. Furthermore, the utilization of the next generation medium satellite 5 (C-band SAR) would improve the performance of riverside land cover classification algorithm in the future.

Experiences, Challenges, and Initiatives on Water Resource Management of a Small Island Community: The Case of Basco, Batanes, Philippines

With the increasing water demand across different sectors: residential, commercial, agriculture, tourism, and energy, management of this finite resource is a major challenge in harnessing and sustaining its availability. The increasing demand for water from various stakeholders of Basco, Batanes puts pressure on the availability of water. These supply constraints are rooted in the current management setup aside from its biophysical characteristics. The main objective of the study is to better understand the water-related issues and concerns and the operation and implementation of water resource strategies and programs in the case of the small island community in Basco, Batanes. Specifically, it aims to i) characterize the existing water supply systems in a small island community; and ii) assess the challenges, experiences, and efforts in water resources management. Guided with the Integrated Water Resource Management principles (IWRM), the experiences, issues, challenges, and opportunities in water resources management in the small island community were assessed. The available water sources and existing water systems were characterized, and challenges and initiatives toward effective resource management were also evaluated. The methods employed were secondary data collection; detailed household, commercial, and institutional sectors surveys; Focus Group Discussions (FGD) using Strengths, Weaknesses, Opportunities, and Threats (SWOT) matrix; Key Informant Interviews (KIIs) with key officials in the Local Government Unit (LGU) to capture data on the current operation and implementation of water-related programs. Results showed that Basco, the capital of Batanes, only has two major sources of water: Racuaranum (upstream) and Miaga (downstream) springs. It has no perennial or ephemeral streams. The intermittent streams only flow for a few minutes during heavy rainfall events. Their other sources of water are deep wells and rainwater for everyday use. With the limited budget and human resources of the small island community, localized integrated water resource management highlights the importance of institutional aspects towards its success. A framework on effective resource management emphasized the importance of institutional synergies in small island community setups, such as: joint implementation of watershed management plans, enhancement of technical and financial capabilities given a limited budget and human resources, improvement, and maintenance of water facilities, etc. The complexities of the issues surrounding water resources management could be best addressed by the consolidated efforts of government institutions, the private sector, and the local community within the small island, prioritizing environmental sustainability, economic efficiency, and social equity.

Introducing improved atom search optimization (IASO) algorithm: Application to optimal operation of multi-reservoir systems

Nowadays, optimal reservoir operation is regarded as one of the most important issues in water resources management. In this regard, several optimization algorithms have been used to evaluate the issue of optimal reservoir operation. To increase the accuracy and proximity of the solutions to the absolute optimal solution, the improved atom search optimization (IASO) algorithm was introduced with a mutation strategy to improve its global search capability, as an important factor in preventing trapping in a locally optimal solution. This article implemented the atom search optimization (ASO) algorithm and its improved model to improve the results of the objective function in multi-reservoir systems. Comparing the IASO algorithm with the basic ASO algorithm on the mathematical benchmark functions revealed good accuracy and the superiority of the IASO algorithm. Then, sensitivity analysis was performed on the effective coefficients of both algorithms, in which alpha and beta, as the depth and weight coefficients used in the calculation of atoms acceleration, were considered 50 and 0.5, respectively. The results showed the relatively good accuracy of the ASO algorithm in the case of multi-reservoir systems, but implementing and comparing the results of the improved algorithm, showed improvement so that the best IASO results were improved from 95.33% to 99.99% as close to the global four-reservoir system response of 308.29 and from 89.67 to 94.36% with the solution of the ten-reservoir system in the amount of 1194.44.

Spatial correlation of groundwater pollutants with the vulnerability index in Kano Metropolis, North-western Nigeria

Abstract This study performed a correlation analysis between groundwater quality parameters with organic and inorganic pollutants using the area under the curve (AUC) of the receiver operating characteristics (ROC) map in Kano Metropolis, North-western Nigeria. The aim is to present the spatial relationship between groundwater pollutants and the existing vulnerability condition of the groundwater resources with a view to developing a vulnerability risk map. Eight water quality parameters (Ca, Al, Cl, NO3, TH, Mg, TDS which include potassium and sodium) and pH were used to develop the water quality index to represent the general overview of the groundwater pollutants. Vulnerability parameters (depth of water, net recharge, aquifer media and groundwater conferment on sensitivity analysis) were used as reference points for preparing ROC. Inorganic pollutants and hydrocarbons representing organic pollutants were integrated into the analysis process with a view to identifying their influence on the groundwater pollution level in the area. The measured AUC under ROC revealed a positive correlation between groundwater vulnerability with organic (63.7%) and inorganic pollutants (65.6%). Vulnerability risk mapping based on hydrocarbon concentration revealed a high level of pollution risk, especially in the core centre of the metropolitan Kano. HIGHLIGHT Groundwater pollution and vulnerability studies are one of the key issues in water resources management, especially in areas with a high level of risk of groundwater pollution such as metropolitan areas (Kano Metropolis inclusive) with a high rising population.

Evaluating Three Supervised Machine Learning Algorithms (LM, BR, and SCG) for Daily Pan Evaporation Estimation in a Semi-Arid Region

Evaporation is one of the main components of the hydrological cycle, and its estimation is crucial and important for water resources management issues. Access to a reliable estimator tool for evaporation simulation is important in arid and semi-arid areas such as Iran, which lose more than 70% of their received precipitation by evaporation. Current research employs the Bayesian Regularization (BR) and Scaled Conjugate Gradient (SCG) algorithms for training the Multilayer Perceptron (MLP) model (as MLP-BR and MLP-SCG) and comparing their performance with the Levenberg–Marquardt (LM) algorithm (as MLP-LM). For this purpose, 16 meteorological variables were used on a daily scale; including temperature (5 variables), air pressure (4 variables), and relative humidity (6 variables) as input data sets, and pan evaporation as the target variable of the MLP model. The surveys were conducted during the period of 2006–2021 in Fars Province in Iran, which is a semi-arid region and has many natural lakes. Various combinations of input-target pairs were tested by several learning algorithms, resulting in seven input scenarios: (1) temperature-based (T), (2) pressure-based (F), (3) humidity-based (RH), (4) temperature–pressure-based (T-F), (5) temperature–humidity-based (T-RH), (6) pressure–humidity-based (F-RH) and (7) temperature–pressure–humidity-based (T-F-RH). The results indicated the relative superiority of the three-component scenario of T-F-RH, and a considerable weakness in the single-component scenario of RH compared with others. The best performance with a root mean square error (RMSE) equal to 1.629 and 1.742 mm per day and a Wilmott Index (WI) equal to 0.957 and 0.949 (respectively for validation and test periods) belonged to the MLP-BR model. Additionally, the amount of R2 (greater than 84%), Nash-Sutcliff efficiency (greater than 0.8) and normalized RMSE (less than 0.1) all indicate the reliability of the estimates provided for the daily pan evaporation. In the comparison between the studied training algorithms, two algorithms, BR and SCG, in most cases, showed better performance than the powerful and common LM algorithm. The obtained results suggest that future researchers in this field consider BR and SCG training algorithms for the supervised training of MLP for the numerical estimation of pan evaporation by the MLP model.

Application of artificial intelligence models for prediction of groundwater level fluctuations: case study (Tehran-Karaj alluvial aquifer).

The nonlinear groundwater level fluctuations depend on the interaction of many factors such as evapotranspiration, precipitation, groundwater abstraction, and hydrogeological characteristics, making groundwater level prediction a complex task. Groundwater level changes are among the most critical issues in water resource management, which can be predicted to effectively provide management solutions to conserve renewable water resources. Understanding the aquifer status using numerical models is time-consuming and also is associated with inherent uncertainty; therefore, in recent decades, the application of artificial intelligence methods to predict water table fluctuations has significantly gained momentum. In this study, artificial neural network (ANN), fuzzy logic (FL), adaptive neuro-fuzzy inference system (ANFIS), and least square support vector machine (SVM) methods were utilized to predict groundwater level (GWL) with 1-, 2-, and 3-month lead time in Tehran-Karaj plain. Several input scenarios were developed considering groundwater levels, average temperature, total precipitation, total evapotranspiration, and average river flow on a monthly interval. The four error criteria, the correlation coefficient (R), root mean squared error (RMSE), Nash-Sutcliffe efficiency (NSE), and mean absolute error (MAE), were the basis to evaluate the models. Results showed that all the applied methods could provide acceptable GWL prediction, but the ANFIS was the most accurate. However, the ANFIS model showed slightly better performance by yielding R = 0.98 for the training stage and R = 0.98 for the testing stage in the P84 observation well and the second combination of inputs and 1-month lead time. The outcomes also revealed that all the approaches mentioned above could appropriately predict GWL for the leading time of 1 and 2months, but the models provided unsatisfactory results for a 3-month leading time.

Climate change impact on water balance and hydrological extremes in the Lower Mekong Basin: a case study of Prek Thnot River Basin, Cambodia

Abstract Climate change alters hydrological cycles and streamflow regimes at the local, regional and global levels. In this study, we aimed to assess the change in water balance change and hydrological extremes in the Prek Thnot River Basin of the Lower Mekong in Cambodia through a hydrological model (SWAT) under the two climate change scenarios (RCP2.6 and RCP8.5) following three different GCMs. An ensemble of 3 GCMs included GFDL-CM3, GISS-E2-R-CC and IPSL-CM5A-MR models and was applied to a well-calibrated SWAT model through climate change factors. Annual precipitation under RCP2.6 likely decreases by 0.1–0.5% for the near future (2021–2040) and mid-future (2051–2070) and decreases by 0.2–1.3% under RCP8.5. The decrease in precipitation will lead to reductions in water yield by 1–4% (RCP2.6) and 2–5% (RCP8.5). However, peak flow is expected to increase, while the low flow was projected to decrease (1–2% for RCP2.6 and 8–9% for RCP8.5). The study further found that high flow events will increase in both magnitude and frequency. The finding highlights water resources management issues in the Prek Thnot River Basin, including the frequency of future flood events.

Analyzing Water Resources Management Issues Along the US Midwest Region

The US Midwest region known for its vast network of arable farmland ranks high as the food basket of the nation and a global agricultural hub. The area stretches through a multiplicity of states and sub regions. With the intense level of farming and good soil therein, water access and security are overly crucial considering the region’s water dependent sectors from agriculture, industry, residential, energy, commercial establishments, and others with notable benefits to the communities. Notwithstanding all that, there exists a wide range of challenges to hydrological security deeply rooted in water management in the US Midwest region. The emergent worries center around declining water quality triggered by decaying infrastructure, pollution, the risks of flooding emanating from changing climate and access deprivation with spillovers to minority communities. Despite its vast network of lakes and reservoirs together with global and regional capacity as key water source, the zone faces vast concentration of usage in a few sectors. This comes in the wake of policies centered on continual access to key water assets germane to the communities and states in the region, mostly to agriculture, as well as industrial and manufacturing sectors. At the same time, very little in the literature exists on the analysis of water resource use issues in the zone via a mix scale approach anchored in Geographic Information System (GIS) and descriptive statistics. Accordingly, this enquiry will fill that void in research using mix scale methods to assess the trends. With emphasis on the issues, trends, factors, impacts, and the efforts of institutions. The results point to abundance of water assets in the zone and visible changes in usage over time in the form of gains and declines. In terms of the impacts, the region saw degradation, declines, and stress from climate change. In the process, a GIS mapping of the trends pinpointed a gradual dispersion of water use patterns of varying dimensions clustered in the region. Given that the forces of change reflect socio-economic, ecological, physical, and political factors located within the larger hydrological structure. The paper proffered solutions ranging from education, monitoring, sustainability, the design of a regional water information system and the enactment of sound policy.

Assessing the Vulnerability of Water Resources System Using VSD-SD Coupling Model: A Case of Pearl River Delta

Water plays an essential role in social and economic sustainable development, and the relationship between socio-economic development and water resources sustainable utilization has been an important issue in water resources management. We aim to propose a water resources vulnerability assessment index with the dimensions of exposure, sensitivity and adaptability, and establish a water resources system model over the Pearl River Delta network river region based on the Vulnerability Scoping Diagram (VSD) framework and System Dynamics (SD) model. The city of Zhongshan, which is located in the Pearl River Delta is used as a case study. The vulnerability of the water resources in Zhongshan from 2021 to 2050 is simulated in four different scenarios (normal, technical innovation, social economic improvement, and comprehensive development models). The results showed that the vulnerability in all four scenarios span three grades in 30 years, including moderately vulnerable, slightly vulnerable, and not vulnerable. Among them, similar trends were found between scenarios 1 and 3, and between scenarios 2 and 4. Furthermore, the vulnerability level in scenarios 2 and 4 was lower than that in scenarios 1 and 3. The vulnerability of both scenarios 2 and 4 decreased first and then increased, with the average values of 24.64 and 27.63, respectively. Scenario 2 experienced 7 years of not vulnerable (2034 to 2040) and 23 years of slightly vulnerable (2021–2033, 204–2050), scenario 4 experienced 5 years of not vulnerable (2032–2036), 21 years of slightly vulnerable (2021–2031, 2037–2046), and 4 years of moderately vulnerable (2047–2050). Although the vulnerability of scenario 4 was slightly worse than scenario 2, its adaptability to economic and social development, water resources, and water environment was much higher than scenario 2. Considering the extent of socio-economic development and the level of adaptability of the local water resources and water environment, the study concluded that the comprehensive development model is more suitable for cities in the network river area. In this scenario, sustainable water use and management can be made possible through policy regulation that encourages higher water efficiency, sewage reuse rate, and centralized sewage treatment rate.

Evaluation of cooperative and non-cooperative game theoretic approaches for water allocation of transboundary rivers

Efficient water allocation in a transboundary river basin is a complex issue in water resources management. This work develops a framework for the allocation of transboundary river water between the countries located in the river basin to evaluate the characteristics of allocation approaches. The allocation of river water is obtained based on initial-water conditions, cooperative, and non-cooperative game-theoretic approaches. The initial-conditions water allocation approach assigns 34, 40, and 26% of the Harirud River flow to Afghanistan, Iran, and Turkmenistan, respectively. The game-theoretic cooperative approach assigns 36, 42, and 22% of the river flow to Afghanistan, Iran, and Turkmenistan, respectively. The non-cooperative game-theoretic approach establishes that the most stable water allocation was 42, 38, and 20% of the Harirud River flow for Afghanistan, Iran, and Turkmenistan, respectively. Human and agricultural water-stress criteria are used to evaluate the water allocations in the Harirud River basin. The criterion of human water stress has the largest influence in Iran, and the criterion of agricultural water stress has the smallest influence in Afghanistan. This work’s results indicate the initial-conditions water allocation approach favors Turkmenistan, whereas the cooperative and the non-cooperative game-theoretic approaches favors Iran and Afghanistan, respectively. The results show that the priorities of each country governs water allocation, and cooperation is shown to be necessary to achieve sustainable development.

Flood Monitoring Using Enhanced Resolution Passive Microwave Data: A Test Case over Bangladesh

Monitoring floods is a major issue in water resources management and risk mitigation, especially in the Global South. Optical and radar observations, even providing a fine spatial resolution, are still limited by cloud cover interaction or insufficient temporal resolution. On the other hand, passive microwave (PMW) sensors collect information on a daily frequency with minor cloud cover interaction, but they have been historically limited in terms of spatial resolution. Here, we evaluate the capability of an enhanced spatial resolution PMW dataset (3.125 km) in monitoring spatio-temporal evolution of flood events, focusing on a major flood event that occurred in October 2005 in Bangladesh. We apply an algorithm aimed to remove the seasonal variability of surface temperature from the PMW timeseries, exploiting the difference in emissivity between dry and water-covered pixels. We assess the capability of the algorithm in capturing flood evolution and extension through the comparison with quantities obtained from optical data collected by the Moderate Resolution Imaging Spectroradiometer (MODIS) and water level measurements. We also compare the enhanced product with the historical coarser resolution dataset by means of a variogram-based analysis to evaluate the improvements in terms of spatial representation. Finally, we evaluate the possibility to extract the water fraction within a single pixel by using an Advanced Microwave Scanning Radiometer—Earth Observing System (AMSR-E) emissivity dataset and compare the estimates with MODIS-derived water fractions. Our results show that the enhanced PMW product outperforms the coarser one when compared to flood mapped from optical data based on information content, indicating that it is possible to integrate such a product into the mapping of floods at a global scale on a daily basis.

Nutrients across Time: Relationships with Climate, Hydrology, and Land Use in Four Rivers of the Pacific Northwest

AbstractUnderstanding the temporal dynamics and drivers of dissolved inorganic nitrogen (DIN) and dissolved inorganic phosphorus (DIP) provides a critical link to better management of nutrient‐related impacts such as eutrophication and harmful algal blooms (HABs). DIN and DIP are a primary control on persistent eutrophication and HABs in the Pacific Northwest (PNW). An understudied phenomenon, this paper examines multi‐decadal trends in DIN and DIP concentrations and loads, and their relationships to climatic and hydrologic factors (e.g., stream and air temperature, discharge, precipitation) in the PNW. Dissolved constituents act as a broad sentinel of linkages between watershed and in‐stream mechanisms such as nitrification, denitrification, nutrient use efficiency, evapotranspiration, hydrologic connectivity, groundwater extraction, irrigation, and land uses. As opposed to the total N and P often used in individual, autochthonous, lentic systems, DIN and DIP are used here as measures of multiscaled processes in allochthonous lotic systems with diverse flow paths. Time‐series data from public agencies were used for up to 20 years in river outlets from the Willamette, Salmon, Spokane, and Yakima watersheds. Seasonal Mann Kendall (SMK) tests suggest significant decreasing multi‐decadal trends in DIN and DIP loads for three out of four watersheds (for DIN, SMK = −0.104; for DIP, SMK = −0.081, −0.181, and −0.213), significant decreasing trends in DIN concentrations for one of the four watersheds (SMK = −0.144), and significant decreasing trends in DIP concentrations in three of the four watersheds (SMK = −0.120, −0.135, and −0.157). Multivariate regressions found significant relationships for concentrations, loads, and ratios when regressed against stream and air temperatures, precipitation, and discharge (16 significant regressions, with adjusted R2 values between 0.016 and 0.65). Highlights of these regression results are as follows: (1) precipitation, discharge, and water and air temperatures help to explain DIN and DIP concentrations and loads, (2) changes in DIN concentrations are sensitive to more hydroclimatic variables than DIP concentrations, and (3) DIP concentrations are positively correlated with stream temperature while DIP loads are negatively correlated with stream temperature. Furthermore, seasonal changes in nutrients — and their potential to alter aquatic productivity during a year — has received little attention in the literature. Regressions established significant seasonality or monthly variation of DIN and DIP concentrations and loads in all four watersheds (20 significant regressions, with adjusted R2 values between 0.038–0.65). Nutrient thresholds of DIN (0.3–0.5 mg/L) and DIP (0.05–0.005 mg/L) concentrations were used to analyze N‐ and P‐limitation. P‐limitation is known to occur in lakes, and N‐limitation is known to occur in rivers. Surprisingly, except for one watershed (Salmon), nutrient concentrations for both DIN and DIP in all watersheds were shown to be above the limitation thresholds across multiple seasons. In certain situations, such as where significant decreasing trends continue, the DIN:DIP ratio suggests seasonal switching between N‐ and P‐limited could create ideal conditions for HABs. The findings of this study have important implications for water resource management issues such as agriculture, land use development, fish populations, timber harvests, water quality, and public health.

Considering ecological flow in multi-objective operation of cascade reservoir systems under climate variability with different hydrological periods

The trade-off between ecological and socioeconomic benefits in the reservoir operation has become a focus issue in the watershed water resource management. However, finding a suitable reservoir ecological operation scheme in the multi-objective cascade reservoir systems remains unclear. At present, most ecological operation models are designed on the basis of water quantity balance, neglecting the dynamic variability of the hydrological process. This study proposed a multi-objective ecological operation system, which coupled a two-dimensional hydrodynamic model with a rainfall–runoff model, and integrated the ecological operation scheme into the hydrodynamic simulation system considering ecological flow. Moreover, the applicability of the operation scheme under climate variability with different hydrological periods was evaluated. Results indicated that multi-reservoir joint operation had the largest effect in normal years; the variation in the monthly hydrological magnitude, extreme events and their duration, temporal change and frequency of streamflow were significantly reduced after reservoir ecological operation. The SAM0-UNICON model performed better than the two other climate models, the ecological deficit (ED) under the Representative Concentration Pathway (RCP) 8.5 climate change scenario was larger than other scenarios with different operation schemes. Future climate change will have a larger impact on discharge change in the wet season than in other hydrological periods. This study emphasises the comprehensive application of the hydrological and hydrodynamic methods, which is of considerable importance for decision-making in basin water resource management and reservoir regulation.

Modelling of Nutrient Pollution Dynamics in River Basins: A Review with a Perspective of a Distributed Modelling Approach

Nutrient pollution is one of the major issues in water resources management, which has drawn significant investments into the development of many modelling tools to solve pollution problems worldwide. However, the situation remains unchanged, even likely to be exacerbated due to population growth and climate change. Effective measures to alleviate the issues are essential, dependent upon existing modelling tools’ capacities. More complex models have been developed with technological advancement, though applications are mainly limited to academic reach. Hence, there is a need for a paradigm shift in policymaking that looks for a reliable modelling approach. This paper aims to assess the capacity of existing modelling tools in the context of process-based modelling and provide a future direction in research. The article has categorically divided models into plot scale to basin-wide applications for evaluation and discussed the pros and cons of conceptual and process-based modelling. The potential benefits of distributed modelling approach have been elaborated with highlights of a newly developed distributed model and its application in catchments in Japan and Australia. The distributed model is more adequate for predicting the realistic details of pollution problems in a changing environment. Future research needs to focus on more process-based modelling.

Spatio-temporal controls of C–N–P dynamics across headwater catchments of a temperate agricultural region from public data analysis

Abstract. Characterizing and understanding spatial variability in water quality for a variety of chemical elements is an issue for present and future water resource management. However, most studies of spatial variability in water quality focus on a single element and rarely consider headwater catchments. Moreover, they assess few catchments and focus on annual means without considering seasonal variations. To overcome these limitations, we studied spatial variability and seasonal variation in dissolved C, N, and P concentrations at the scale of an intensively farmed region of France (Brittany). We analysed 185 headwater catchments (from 5–179 km2) for which 10-year time series of monthly concentrations and daily stream flow were available from public databases. We calculated interannual loads, concentration percentiles, and seasonal metrics for each element to assess their spatial patterns and correlations. We then performed rank correlation analyses between water quality, human pressures, and soil and climate features. Results show that nitrate (NO3) concentrations increased with increasing agricultural pressures and base flow contribution; dissolved organic carbon (DOC) concentrations decreased with increasing rainfall, base flow contribution, and topography; and soluble reactive phosphorus (SRP) concentrations showed weaker positive correlations with diffuse and point sources, rainfall and topography. An opposite pattern was found between DOC and NO3: spatially, between their median concentrations, and temporally, according to their seasonal cycles. In addition, the quality of annual maximum NO3 concentration was in phase with maximum flow when the base flow index was low, but this synchrony disappeared when flow flashiness was lower. These DOC–NO3 seasonal cycle types were related to the mixing of flow paths combined with the spatial variability of their respective sources and to local biogeochemical processes. The annual maximum SRP concentration occurred during the low-flow period in nearly all catchments. This likely resulted from the dominance of P point sources. The approach shows that despite the relatively low frequency of public water quality data, such databases can provide consistent pictures of the spatio-temporal variability of water quality and of its drivers as soon as they contain a large number of catchments to compare and a sufficient length of concentration time series.

Modeling reference evapotranspiration using a novel regression-based method: radial basis M5 model tree

In the current study, an ability of a novel regression-based method is evaluated in modeling daily reference evapotranspiration (ET0), which is an important issue in water resources management and planning. The method was developed by hybridizing radial basis function and M5 model tree and called as radial basis M5 model tree (RM5Tree). The new model results were compared with traditional M5 model tree (M5Tree), response surface method (RSM), and two neural networks (multi-layer perceptron neural networks, MLPNN & radial basis function neural network, RBFNN) with respect to several statistical indices. Daily climatic data (relative humidity, RH, solar radiation, SR, wind speed, air temperature, T) recorded at three stations in Turkey, Mediterranean Region, were used. The effect of each weather data on ET0 was also investigated by utilizing three different input scenarios with various combinations of input variables. On the whole, the RM5Tree provided the best results (Nash and Sutcliffe efficiency, NES > 0.997) followed by the MLPNN (NES > 0.990), and M5Tree (NES > 0.945) in modeling daily ET0. The SR was observed as the most effective input parameter on ET0 which was followed by the T and RH. However, the findings of the third modeling scenario revealed that taking into account of all variables would considerably increase models’ accuracies for the three stations.

Social learning diffusion and influential stakeholders identification in socio-hydrological environments

In this paper, six critical topics in social learning (SL) definition and applications are discussed: 1- Clarification of the SL definition; 2- The role of SL in the management of shared water resources; 3- The definition of “learning” in SL; 4- The quantification of SL; 5- The relation of SL with outputs of Social Networks Analysis (SNA) and stakeholder Analysis (SA); 6- Identifying and ranking of key stakeholders (i.e., influencers) for SL diffusion. After defining SL, we present SL's role using a well-known game theory problem. We have addressed the term “learning” in SL using an existing method, namely reinforcement learning. We present a new approach for quantifying SL within a hydrological environment and finding key stakeholders for the SL diffusion. For the first time, we consider a combination of SL, SA, and SNA to propose a comprehensive social-based approach to investigate SL diffusion. The western part of Tehran city, which is suffering from some water and environmental issues such as overexploitation of groundwater and land subsidence, is the case study of this paper. The results show a significant relationship between SL and the outputs of SNA. The suggested method for identifying key stakeholders has been able to identify key stakeholders in terms of various water resources management issues in the study area. The results can be used for developing effective policies towards sustainable management of water and environmental resources in the studied socio-hydrological system.