Optimal Management Of Water Resources Research Articles

Coupling SWAT and Transformer Models for Enhanced Monthly Streamflow Prediction

The establishment of an accurate and reliable predictive model is essential for water resources planning and management. Standalone models, such as physics-based hydrological models or data-driven hydrological models, have their specific applications, strengths, and limitations. In this study, a hybrid model (namely SWAT-Transformer) was developed by coupling the physics-based Soil and Water Assessment Tool (SWAT) with the data-driven Transformer to enhance monthly streamflow prediction accuracy. SWAT is first constructed and calibrated, and then its outputs are used as part of the inputs to Transformer. By correcting the prediction errors of SWAT using Transformer, the two models are effectively coupled. Monthly runoff data at Yan’an and Ganguyi stations on Yan River, a first-order tributary of the Yellow River Basin, were used to evaluate the proposed model’s performance. The results indicated that SWAT performed well in predicting high flows but poorly in low flows. In contrast, Transformer was able to capture low-flow period information more accurately and outperformed SWAT overall. SWAT-Transformer could correct the errors of SWAT predictions and overcome the limitations of a single model. By integrating SWAT’s detailed physical process portrayal with Transformer’s powerful time-series analysis, the coupled model significantly improved streamflow prediction accuracy. The proposed models offer more accurate and reliable predictions for optimal water resource management, which is crucial for sustainable economic and societal development.

Impacts of forest canopy heterogeneity on plot-scale hydrometeorological variables - Insights from an experiment in the humid boreal forest with the Canadian Land Surface Scheme

High latitude regions, including the circumpolar boreal biome, are experiencing important changes in the availability of usable surface water because of climate change. In this context, an adequate representation of the land-atmosphere interaction is critical to ensure optimal management of current and future water resources, forest management, and climate prediction. However, the task is particularly intricate in high-latitude boreal forest, as land surface model faces several challenges due to the unique environmental conditions and ecological characteristics. The objective of this study is to quantify the impact of forest landscape heterogeneity, specifically stand leaf-area index (LAI), soil texture, and drainage regime, on surface water and energy balance in a small boreal high-latitude sub-catchment. To this end, hydrometeorological conditions at seventeen 20×20 m plots in a 1-km2 boreal forest sub-basin are simulated using the Canadian Land Surface Scheme (CLASS), a land surface model, at the point scale. The subplot-scale soil texture, drainage regime, and vegetation characteristics and type are based as closely as possible on field measurements and observations for the 17 plots. The model-driven experiment comprises two sets of simulations using CLASS, each employing the same model setup and run for the 17 experimental plots. The main set employs meteorological forcing from a local micrometeorological tower within the sub-basin to investigate the plot-to-plot variability of albedo, energy fluxes, and soil state variables. A second set of simulations is conducted using meteorological forcing from the ERA5-Land reanalysis, which spans from 1986 to 2022. This data provides a longer time series, enabling a more accurate representation of the interannual climatic variability in the sub-basin. The results of the main and secondary sets of CLASS simulations are used to assess the plot-to-plot and temporal variability of several key hydrometeorological variables by calculating a monthly spread. In brief, the following conclusions and broader implications can be drawn from the findings: i) The simulated total annual evapotranspiration remains relatively uniform between plots despite notable variation in its partitioning from plot to plot. ii) In the presence of a full snowpack, the albedo exhibits substantial heterogeneity at the subplot scale, linked to the canopy's LAI. iii) Local soil properties, drainage regime, and vegetation structure and type exhibit substantial influence on the plot-to-plot variability in soil water content. iv) When parameterized with localized observations and measurements, CLASS can represent and be responsive to the complex dynamics of energy and water fluxes at the plot scale within the heterogeneous surface of boreal forests.

Island Water Management Cyprus Facts Finding Mission and Gap Identifications

Water management on islands faces unique challenges due to limited size, isolation, climate change impacts, and economic development needs. These factors intensify water scarcity, highlighting the need for sustainable resource development through multidisciplinary approaches. Cyprus, the third-largest Mediterranean island, exemplifies these challenges, with the lowest water availability per capita in the region and severe water scarcity exacerbated by climate change. Addressing these issues requires a comprehensive master plan that integrates technical, environmental, social, and economic factors, focusing on climate change impacts on the water cycle. The Energy, Environment, and Water Research Center (EEWRC) plays a pivotal role in global resource management, emphasizing sustainable development goals and Integrated Water Resources Management Policy. EEWRC’s key initiatives include hydrodiplomacy and WEF Nexus and Integrated Water Resource Management IWRM, monitoring sediment transport pollution, water quality monitoring of basins and aquifers, smart water management of conventional and non-conventional water, and nature-based solutions for water and land management integration. To improve water management in Cyprus, establishing a National Commission for Water Experts is recommended. This commission would provide a detailed roadmap for water management, aligning with Sustainable Development Goals (SDGs) and the Climate Change Initiative coordinated by the Cyprus Institute. Additionally, organizing a conference focused on “Cyprus Island Water Management” would help launch this concept. In conclusion, it is crucial to develop a long-term strategy for optimal water resource management in Cyprus, addressing water issues, promoting conservation, launching climate-resilient initiatives, and fostering collaboration with national and international entities for success.

Optimizing the management of multiple water resources in irrigation area under uncertainty: A novel scenario-based multi-objective fuzzy-credibility constrained programming model

Effective water resources management is vital for sustainable development, healthy ecosystems, stable socio-economic, and human well-being. However, managing water resources faces challenges due to the involvement of multiple stakeholders and uncertainties in various hydrologic fluxes. This complexity makes it rare for research to simultaneously address the hidden uncertainties in both objectives and constraints. Here, we developed a scenario-based multi-objective fuzzy-credibility constrained programming approach and applied it to the water resource management system (SMOFCP-WRMS) of the Zhaokou Yellow River Diversion Irrigation Area Phase II Project (ZKID-II). This method handles uncertainties represented by scenarios and fuzzy sets, while also resolving fuzzy uncertainty parameters within the multiple objectives and constraints of the model. We solve the SMOFCP-RWMS model by integrating the Nondominated sorting genetic algorithm III (NSGA-III) and compromise programming (CP) method. The results show that: (1) An increase in constraints violation risks (credibility level from 0.5 to 1.0) results in a decrease in water resources allocation and pollutant discharge by 25.95 × 106 m3 and 207.63 tonne, respectively, accompanied by a decline in net economic income by 68.04 × 106 CNY; (2) From extraordinary dry year to normal year (design frequencies from 95 % to 50 %) leads to a reduction in water shortage by 167.07 × 106 m3. Incorporating ‘credibility levels’ and ‘design frequencies’ would be beneficial for addressing uncertainties, including fuzzy information and discrete possibilities for the future; (3) Regarding the multi-objective model solution, the combined NSGA-III and CP approach demonstrates an average increase of 1.71 % in net economic benefits and 5.91 % in total pollution discharge, along with a 6.77 % reduction in the water deficit ration compared to the traditional CP method. Although the ecological benefits decrease slightly in the compromise-optimal solutions using the combined NSGA-III and CP method, the economic and social benefits are improved compared to those from the combined NSGA-II and CP method. This study provides valuable decision-making guidance for water resources managers in irrigated areas, aiding them in balancing economic, social, and ecological benefits, and enabling the section of an optimal water resources management scheme based on acceptable risk levels under various uncertain conditions.

A STUDY OF THE HYDROGEOLOGICAL REGIME OF THE NATURAL AND TECHNICAL GEOSYSTEM: THE CASE STUDY OF THE DNIESTER GES

The primary objective of the study is to analyse changes in the hydrogeological regime before and after the facility's commissioning, taking into account the impact of human activity on water resources. To solve this problem, it is necessary to regularly measure groundwater levels. This process was carried out by systematically measuring the water level in piezometers for the period from 2010 to 2023 with a frequency of twice a week. To achieve this goal, it was necessary to analyse the geological structure and hydrogeological characteristics of the unconfined aquifers in the soil strata, as well as to investigate the permeability of rocks. Changes in the hydrogeological regime can be caused by both rock compaction due to changes in the stressed deformation state and activation of geodynamic processes, which, in turn, contribute to drainage through faults in the rock mass. The research in the article allows us to more accurately assess changes in the hydrogeological regime of the rock mass, in particular, in the reduction of the water-holding capacity of a certain horizon. The study reveals the possible causes of these changes, such as soil compaction and activation of geodynamic processes, and indicates potential consequences for the hydrogeological environment and water use, when planning and operating other energy facilities in similar conditions. The practical significance of this study is that it helps to understand changes in the hydrogeological environment and their potential impact on water resources. This is important for the development of effective strategies for managing water resources individually and preserving ecosystems in general. Studies indicate that during the period from 2010 to 2023, significant changes occurred in the hydrogeological regime of non-pressure horizons #1 and #2. The analysis of groundwater levels indicates that in certain local areas there is a decrease in the water level, which indicates the degradation of the soil massif. These changes may be related to the effects of drainage, which caused the disruption of structural bonds in soils and their transition to a loose state. In the case when the clay soil, which serves as a waterproofing layer for non-pressure horizon No. 1, loses its waterproofing properties, it can be explained with the help of the Coulomb-Mohr law, which reveals the dependence of the angle of internal cohesion (φ) on the coefficient of internal grafting (С) in soils. In simple language, when structural bonds are destroyed in clayey soils, the filtration coefficient increases [30, p. 240; 15, p. 223; 34, p. 170; 28, p. 2]. The potential cause of the destruction of structural bonds in the soil is described in works [36, p. 3]. Therefore, it can be concluded that the dynamics of groundwater levels indicates the influence of hydrogeological processes on the physical properties of the soil environment, which is important for the further management of water resources and the preservation of ecological sustainability of the region. The practical significance of this study is that it helps to understand changes in the hydrogeological environment and their potential impact on water resources. This is important for developing effective strategies for managing water resources separately and preserving ecosystems as a whole. For example, it is possible to develop models that will predict soil deformations depending on the level of groundwater, which will avoid possible destructive consequences for infrastructure and construction objects. Such data can also be used to improve hydrotechnical structures and determine optimal water resources management strategies in conditions of changing hydrogeological regime. Keywords: Geoecology, constructive geography, geosystems, river-basin systems, river-valley landscape, river natural and technical systems, landscape technical systems, landscape engineering systems, GIS technologies, Dniester PSPP, oolitic limestone, piezometer, aquifer, piezometric surface, soil base, infiltration, Neogene, aquifer.

Research on provincial water resources carrying capacity and coordinated development in China based on combined weighting TOPSIS model

With the continuous development of the economy and society, along with the sustained population growth, the issue of water resources carrying capacity in China has attracted increasing attention. This paper constructs a model for evaluating the provincial water resources carrying capacity in China from four dimensions: water, economy, society, and ecology. Utilizing this model, we analyze the spatiotemporal variations in water resources carrying capacity among 31 provinces in China from 2005 to 2021. Additionally, we delve into the coupling coordination and influencing factors of water resources carrying capacity. The study reveals an overall increasing trend in China’s water resources carrying capacity index, with the ecological indicator exhibiting the most significant growth while the water resources sub-indicator lags behind. There are notable regional differences, with higher water resources carrying capacity observed in the eastern coastal areas and relatively lower capacity in the western regions. The ecological criterion becomes a core factor constraining water resources carrying capacity from 2005 to 2015, gradually giving way to the prominence of the social criterion since 2015. The coordination degree is relatively higher in the eastern regions, more scattered in the western regions, and relatively stable in the central regions. Based on the research findings, a series of recommendations are proposed, including strengthening environmental protection policies, optimizing water resources management mechanisms, improving water use efficiency, and promoting economic structural diversification. These suggestions aim to facilitate the sustainable development of water resources carrying capacity in China.

Comparison of different drought monitoring indices in different climatic conditions in Iran

This study evaluates drought in different climate zones (Rasht, Shiraz, and Birjand) in Iran, using meteorological, agricultural, and remote sensing drought indices. For this purpose, NDVI, SAVI, and SR were extracted from Landsat images for 2002 and 2014-2020. Then, these indices were compared with the SPI, SPEI, and PDSI. The results indicate an increase in drought and a decrease in vegetation cover in the study area. In Rasht, where the vegetation cover is high, NDVI and SAVI were equal. In Shiraz and Birjand, where the soil effect is more significant, the distance between these two indices increased, which shows that SAVI performs better than NDVI for Shiraz and Birjand. The results also show that the drought severity could grow with decreasing rainfall and more water demand due to temperature increases, according to SPI, SPEI, and PDSI criteria. The comparison of drought indices showed that the highest correlations were between NDVI plus SAVI and SPI in Rasht, SR and SPEI in Shiraz, and NDVI and SPEI in Birjand. Based on the results of the Mann-Kendall test, the increasing trend of drought in the studied area is confirmed based on the SPI, SPEI, and PDSI. Therefore, it is suggested that remote sensing techniques combined with drought indices can be considered a suitable tool for optimal management of water resources, land use planning, and reduction of costs due to drought.

Development of a new rotational irrigation model in an arid basin based on ecological zoning and sluice regulation

Precise and efficient allocation of existing water resources is of great significance to protect and restore desert riparian forests in inland river basins. However, the lack of detailed ecological zoning in most basins hinders the optimization of the current ecological sluice regulation scheme, leading to low effectiveness in vegetation restoration and water resources management. To address this problem, we proposed for the first time an ecological zoning method that integrates the vegetation gradient model and control of ecological sluices. Specifically, the mainstream of Tarim River basin (China) was classified into three types of ecological functional zones, i.e., key protection, key restoration, and ecologically sensitive zones, based on the kernel NDVI (kNDVI) gradient detection framework. On this basis, the basin was divided into 27 sluice group control areas determined by the control range of ecological sluices and the study area was further divided into five grades zones of ecological rotation irrigation. Based on the results of ecological zoning and the priorities of rotational irrigation, this study proposes a new rotational irrigation model in the basin to further promote vegetation recovery. The results showed that guarantee rate of ecological water supply to vegetation protection and restoration increased from 82.3% to 101.7% before and after rotational irrigation, respectively, indicating the effectiveness of this new method. The results of this study have a practical value and may significantly contribute to the optimal allocation and management of water resources in global arid basins.

Assessing the efficiency of the irrigation system in a horticulture field through time-lapse electrical resistivity tomography

AbstractThe characterization by means of geophysical techniques of agricultural soils subjected to continuous irrigation cycles makes it possible to study the heterogeneity of a soil and the preferential pathways of water flows without disturbing soil and plants. A better knowledge of soil heterogeneity enables optimal water resource management in terms of crop, yield, and sustainability. In this study, time-lapse monitoring using electrical resistivity tomographies (ERT) is proposed as a reliable and non-invasive technique to quantify the movement of water flows and thus the variation of soil water content during the irrigation process. ERT surveys have been conducted in melon-growing soils in southern Tuscany (Italy). Five survey campaigns have been carried out between June and August 2022, in which ERT data have been collected by taking measurements before (T0), during (T1), and after (T2) the irrigation phase. The interpretation of the ERT results provided information on the spatial and temporal distribution of water fluxes in the soil and root zone of melons during the irrigation phases. The investigation made it possible to identify the preferential pathways of infiltration of irrigation water, the points where water is absorbed by the roots, and the points where water follows a preferential pathway instead distributing itself entirely below the root growth zone. Thus, this research suggests that the ERT technique can be used to evaluate the efficiency of the irrigation system in order to achieve optimal management of the water resource, avoiding preferential flow paths that lead to less water availability for the plant.

Machine Learning-Based Estimation of Daily Cropland Evapotranspiration in Diverse Climate Zones

The accurate prediction of cropland evapotranspiration (ET) is of utmost importance for effective irrigation and optimal water resource management. To evaluate the feasibility and accuracy of ET estimation in various climatic conditions using machine learning models, three-, six-, and nine-factor combinations (V3, V6, and V9) were examined based on the data obtained from global cropland eddy flux sites and Moderate Resolution Imaging Spectroradiometer (MODIS) remote sensing data. Four machine learning models, random forest (RF), support vector machine (SVM), extreme gradient boosting (XGB), and backpropagation neural network (BP), were used for this purpose. The input factors included daily mean air temperature (Ta), net radiation (Rn), soil heat flux (G), evaporative fraction (EF), leaf area index (LAI), photosynthetic photon flux density (PPFD), vapor pressure deficit (VPD), wind speed (U), and atmospheric pressure (P). The four machine learning models exhibited significant simulation accuracy across various climate zones, reflected by their global performance indicator (GPI) values ranging from −3.504 to 0.670 for RF, −3.522 to 1.616 for SVM, −3.704 to 0.972 for XGB, and −3.654 to 1.831 for BP. The choice of suitable models and the different input factors varied across different climatic regions. Specifically, in the temperate–continental zone (TCCZ), subtropical–Mediterranean zone (SMCZ), and temperate zone (TCZ), the models of BPC-V9, SVMS-V6, and SVMT-V6 demonstrated the highest simulation accuracy, with average RMSE values of 0.259, 0.373, and 0.333 mm d−1, average MAE values of 0.177, 0.263, and 0.248 mm d−1, average R2 values of 0.949, 0.819, and 0.917, and average NSE values of 0.926, 0.778, and 0.899, respectively. In climate zones with a lower average LAI (TCCZ), there was a strong correlation between LAI and ET, making LAI more crucial for ET predictions. Conversely, in climate zones with a higher average LAI (TCZ, SMCZ), the significance of the LAI for ET prediction was reduced. This study recognizes the impact of climate zones on ET simulations and highlights the necessity for region-specific considerations when selecting machine learning models and input factor combinations.

Copulas and hydro-economic models for assessing the impacts of climate change in irrigated agriculture

Climate change significantly affects water resources through alterations in rainfall patterns, reduced rainfall quantities, rising temperatures, and prolonged drought cycles. Consequently, the escalating demand for water coupled with diminishing water availability is anticipated to lead to a critical water scarcity issue in the future unless proactive and innovative strategies are implemented.The impact of climate change on water resources is globally recognized as an increasingly vital concern, given its intricate interconnections with various sectors, notably agriculture, energy, and the provision of drinking water. Consequently, the scientific community has dedicated substantial efforts to devise optimal water resource management strategies in response to the challenges posed by climate change. Over the past four decades, hydro-economic models (HEM) have been instrumental in proposing solutions to adapt to evolving climatic conditions.This paper presents a newhydro-economic optimization model accounting for climatic uncertainties. The problem is formulated as a chance-constrained program in which the dependence structure between hydrologic and meteorologic variables is modeled using copula theory. The novelty of the approach lies in the capacity of the model to optimize the water resources, taking into account the dependence between agronomic, socio-economic, and hydrologic systems and climatic uncertainties.

Quantifying multi-dimensional services of water ecosystems and breakpoint-based spatial radiation of typical regulating services considering the hierarchical clustering-based classification

This study proposes a set of water ecosystem services (WES) research system, including classification, benefit quantification and spatial radiation effect, with the goal of promoting harmonious coexistence between humans and nature, as well as providing a theoretical foundation for optimizing water resources management. Hierarchical cluster analysis was applied to categorize WES taking in to account the four nature constraints of product nature, energy flow relationships, circularity, and human social utility. A multi-dimensional benefit quantification methodology system for WES was constructed by combining the emergy theory with multidisciplinary methods of ecology, economics, and sociology. Based on the theories of spatial autocorrelation and breaking point, we investigated the spatial radiation effects of typical services in the cyclic regulation category. The proposed methodology has been applied to Luoyang, China. The results show that the Resource Provisioning (RP) and Cultural Addition (CA) services change greatly over time, and drive the overall WES to increase and then decrease. The spatial and temporal distribution of water resources is uneven, with WES being slightly better in the southern region than the northern region. Additionally, spatial radiation effects of typical regulating services are most prominent in S County. This finding suggests the establishment of scientific and rational intra-basin or inter-basin water management systems to expand the beneficial impacts of water-rich areas on neighboring regions.

Integrating machine learning and empirical evapotranspiration modeling with DSSAT: Implications for agricultural water management

The availability of accurate reference evapotranspiration (ETo) data is crucial for developing decision support systems for optimal water resource management. This study aimed to evaluate the accuracy of three empirical models (Hargreaves-Samani (HS), Priestly-Taylor (PT), and Turc (TU)) and three machine learning models (Multiple linear regression (LR), Random Forest (RF), and Artificial Neural Network (NN)) in estimating daily ETo compared to the Penman-Monteith FAO-56 (PM) model. Long-term data from 42 weather stations in Florida were used. Moreover, the effect of ETo model selection on sweet corn irrigation water use was investigated by integrating simulated ETo data from empirical and ML models using the Decision Support System for Agrotechnology Transfer (DSSAT) model at two locations (Citra and Homestead) in Florida. Furthermore, a linear bias correction calibration technique was employed to improve the performance of empirical models. Results were consistent in that the NN and RF models outperformed the empirical models. The empirical models tended to underestimate and overestimate small and high daily ETo values, respectively, with the HS model exhibiting the least accuracy. However, calibrated PT and TU models performed comparably to the ML models. Results also revealed that using an inappropriate ETo model could lead to over-irrigation by up to 54 mm during a single crop season. Overall, ML models have proven reliable alternatives to the PM model, especially in regions with access to long-term data due to their site-independent performance. In areas without long-term data for ML model training and testing, calibrating empirical models is viable, but site-specific calibration is needed. It is important to highlight that distinct plant species exhibit varying transpiration characteristics and, consequently, have different water requirements. These differences play a pivotal role in shaping the overall impact of ETo models on crop water use.

Quantitative source tracking for organic foulants in ultrafiltration membrane using stable isotope probing approach

Quantitatively identifying the primary sources of organic membrane fouling is essential for the effective implementation of membrane technology and optimal water resource management prior to the treatment. This study leveraged carbon stable isotope tracers to estimate the quantitative contributions of various organic sources to membrane fouling in an ultrafiltration system. Effluent organic matter (EfOM) and aquatic natural organic matter (NOM), two common sources, were combined in five different proportions to evaluate their mixed effects on flux decline and the consequent fouling behaviors. Generally, biopolymer (BP) and low molecular weight neutral (LMWN) size fractions - abundantly present in EfOM - were identified as significant contributors to reversible and irreversible fouling, respectively. Fluorescence spectroscopy disclosed that a protein-like component notably influenced overall membrane fouling, whereas humic-like components were predominantly responsible for irreversible fouling rather than reversible fouling. Fluorescence index (FI) and biological index (BIX), common fluorescence source tracers, showed promise in determining the source contribution for reversible foulants. However, these optical indices were insufficient in accurately determining individual source contributions to irreversible fouling, resulting in inconsistencies with the observed hydraulic analysis. Conversely, applying a carbon stable isotope-based mixing model yielded reasonable estimates for all membrane fouling. The contribution of EfOM surpassed 60 % for reversible fouling and increased with its content in DOM source mixtures. In contrast, aquatic NOM dominated irreversible fouling, contributing over 85 %, regardless of the source mixing ratios. This study emphasizes the potential of stable isotope techniques in accurately estimating the contributions of different organic matter sources to both reversible and irreversible membrane fouling.

Soil Erosion Risk Assessment Using Corine Model: A Case Study in Shwan Sub basin, Kirkuk, Iraq

Water erosion is one of the most important problems and challenges facing the optimal management of natural resources, especially soil and water resources, because of its impact on soil degradation. This study aimed to classify the risk of soil water erosion and clarify its distribution in the Shwan Basin in Kirkuk Governorate - Iraq based on the Corine model.In the first stage of the work, the soil erodibilty, erosivity and slope factors were determined, classified and mapped according to the degree of their impact on soil erosion.The potential soil erosion risk map was prepared using Arc GIS 10.4.1.In the second stage, the land cover factor was calculated and classified according to the degree of protection. In the last stage, the actual soil erosion risk map was prepared by multiplying the class of land cover with the map of potential soil erosion risk over the entire study area.The study showed that 28.18% of the studied area is classified under a high risk, 39.57% represents as a medium risk and 32.25% is a low risk .The areas of severe soil erosion risk were concentrated in the southwestern part and some northern and central parts, and the areas of medium risk were concentrated in the northern, central and southern parts, while regarding the low actual risk, they were concentrated in the western parts and in random areas in the center and east of the study area.The results also showed that the Corine model for soil water erosion risk mapping is a highly effective and cost-effective approach.

Optimization of Dam Operation and Interaction with Groundwater: An Overview Focusing on Greece

The optimization of dam operations to transform them into multi-objective facilities constitutes a challenge for both hydrology, hydrogeology, and hydropower generation. However, the use of the optimal algorithm for such transformation is critically important. Additionally, the literature has highlighted that dams might negatively influence the recharge of groundwater. Within this study, we provide an overview of the available algorithms for the optimization of dam operations. Additionally, an overview focusing on hydropower generation in Greece illustrates the high potential of the Mediterranean region for hydropower generation and the application of MAR. The water quality of the reservoirs is also highlighted as a critical parameter. Within this study, we present indices for water quality monitoring in dam reservoirs, while the most prevailing index is the SRDD. This study constitutes a guide for researchers in choosing the optimal tools for the optimization of dam operations and the water quality monitoring of reservoirs. The present study suggests a meta-heuristic optimization methodology using the harmony search algorithm. The model uses a geometric model of the reservoir and calculates the level–supply curve. Furthermore, a multi-criteria optimization model was developed with two objective functions: the maximum power output from the hydroelectric power plant turbines and the optimal groundwater recharge. The model with appropriate parameter modifications can be applied to any small dam as it is a decision- and policy-making methodology, independent of local conditions. A further step is the application of these approaches dealing with field data and the numerical modeling of case studies. The interdisciplinary approach of this study links deferent aspect and scientific perceptions, providing a comprehensive guide to optimal water resource management and environmental sustainability.

Assessing soil moisture levels using visible UAV imagery and machine learning models

The estimation of soil moisture (SM) as an important variable in the hydrological cycle of nature is necessary for the optimal management of water and soil resources. One of the indirect methods to estimate SM is using visible imagery with unmanned aerial vehicles (UAVs). This study aims to evaluate the potential of visible UAV imagery for estimating SM in a bare soil field in Iran. In this study, M5 tree (M5P), random forest (RF), sequential minimal optimization regression (SMOreg), and multilayer perceptron (MLP) methods have been used for SM modeling from RGB (Red, Green and Blue) bands and brightness and intensity indices of aerial imagery. Three evaluation methods were used to assess the accuracy of the models, including the coefficient of determination (R2), root mean square error (RMSE), and mean absolute error (MAE). Four different indices, including difference index (DI), ratio index (RI), normalized difference index (NDI), and perpendicular index (PI), were used to estimate SM. The green and red bands pair were found to be the optimal bands for SM estimation. The findings showed that the PI index provided the most accurate SM estimates (R2 = 0.51). The RF model predicted SM most accurately among the machine learning models tested (R2 = 0.67). However, all models underestimated SM content in high-moisture areas and overestimated it in low-moisture areas, with the MLP model showing the most significant overestimation. All the indices were saturated beyond 25% SM. In general, this study highlighted the potential of aerial RGB imagery and associated indices for assessing SM levels within bare soil fields. However, it should be noted that the use of individual bands and indices alone is not sufficient to make an accurate estimate of SM.

Explainable machine learning model for multi-step forecasting of reservoir inflow with uncertainty quantification

We propose an explainable machine learning (ML) model with uncertainty quantification (UQ) to improve multi-step reservoir inflow forecasting. Traditional ML methods have challenges in forecasting inflows multiple days ahead, and lack explainability and UQ. To address these limitations, we introduce an encoder–decoder long short-term memory (ED-LSTM) network for multi-step forecasting, employ the SHapley Additive exPlanation (SHAP) technique for understanding the influence of hydrometeorological factors on inflow prediction, and develop a novel UQ method for prediction trustworthiness. We apply these methods to forecast 7-day inflow in snow-dominant and rain-driven reservoirs. The results demonstrate the effectiveness of the ED-LSTM model, with high forecasting accuracy for short lead times. Our UQ method provides reliable uncertainty estimates, covering 90% of data with a 90% confidence level. The SHAP analysis reveals the importance of historical inflow and precipitation as influential factors. These findings and methods may support reservoir operators in optimizing water resources management decisions.

WLP-VBL: A Robust Lightweight Model for Water Level Prediction

Accurate and reliable water level prediction plays a crucial role in the optimal management of water resources and reservoir scheduling. Water level data have the characteristics of volatility and temporality; a single water level prediction model can only be applied to specific hydrological conditions and reservoirs. Therefore, in this paper, we present a robust lightweight model for water level prediction, namely WLP-VBL, by using a combination of VMD, BA, and LSTM. The proposed WLP-VBL model consists of three steps: first, the water level dataset is decomposed by EMD to obtain a number of decomposition layers K, and then VMD is used to decompose the original water level dataset into K intrinsic modal functions (IMFs) to produce a clearer signal. Next, the IMF data are sent to an LSTM neural network optimized by BA for prediction, and finally each component is superimposed to obtain the predicted value. In order to evaluate the effectiveness of the model, experiments were carried out on water level data for the Gan River. The results indicate that: (1) Compared with state-of-the art methods, e.g., LSTM, VMD-LSTM, and EMD-LSTM, WLP-VBL exhibited the best performance. The MSE and MAE of WLP-VBL decreased by 69.6~74.7% and 45~98.5%, respectively. (2) The proposed model showed stronger robustness for water level prediction, and was able to handle highly volatile and noisy data.

Simulating the effects of vegetation restoration and climate change on the long-term soil water balance on the Loess Plateau, 2021–2050

In water-limited areas, soil water content (SWC) is a critical factor for plant growth and ecosystem stability. To understand the long-term effects of three different land uses (Vigna radiata: VR, Stipa bungeana: SB, and Medicago sativa: MS) and two climate scenarios (representative concentration pathway 4.5 (RCP45) and nonclimate change (Non_CC)) on water budgets and the mechanisms of dried soil layer (DSL) formation, a new complete multilayered model, OpenKarHydro, was developed. The results showed that the model adequately captured the water budget for different land uses, with root mean square errors (RMSEs) of 0.058, 0.046, and 0.046 and coefficients of determination (R2) of 0.615, 0.605, and 0.582, respectively, for SWC dynamics. This model performance was also demonstrated by the verification of runoff. Sensitivity analysis indicated that the soil porosity (n) was the most sensitive parameter in all land uses. The DSL was primarily caused by higher evapotranspiration (VR: 83.16% vs. 82.48%, SB: 80.62% vs. 77.69%, and MS: 79.93% vs. 76.26%) and canopy interception (VR: 11.92% vs. 11.99%, SB: 17.56% vs. 23.15% and MS: 22.60% vs. 26.19%) in the Non_CC climate pattern than in the RCP45 climate pattern. Climate change has caused an increase in evapotranspiration and interception for all land uses, reducing bottom leakage. Rainfall is sufficient to meet the water demand in the VR plot regardless of climate change; however, the additional water consumption caused by climate warming cannot be offset by the increased rainfall for the SB and MS plots. Compared to Non_CC, RCP45 resulted in increases of 0%, 54.19%, and 57.62% in proportion with extreme drought and decreases of 4.70%, 38.74%, and 5.24% in proportion with no drought for the soil profiles of VR, SB, and MS, respectively. For MS, RCP45 (2026) caused the DSL to penetrate the soil profile (>400 cm) 4 years earlier than in the Non_CC (2030), which should take no more than 5 and 9 years, respectively. OpenKarHydro is a useful tool for optimal water resource management and ecological restoration in water-limited areas.