Applications In Water Resources Management Research Articles

Energy‐Aware Power Control Scheme For Water Quality Monitoring Systems

ABSTRACTThis research introduces an innovative power management approach for wireless sensor networks in aquatic environmental monitoring. The study presents a dynamic algorithm that optimizes energy consumption in long range (LoRa) communication nodes by adaptively adjusting transmission power based on sensor–gateway distances. Leveraging GPS data and a log‐normal shadowing model, the method enables efficient power allocation. Field testing in an aquaculture setting demonstrates the energy‐aware power control (EAPC) algorithm's efficacy. Nodes at 500 m from gateways achieve up to 39% reduction in power consumption compared to fixed‐power systems. At 1250 m, savings decrease to 22%, and at 1500 m, to 11%, while maintaining reliable communication. This research advances sustainable aquatic resource management, offering applications in aquaculture, environmental conservation, and water resource management. By optimizing power usage, the approach contributes to more effective long‐term monitoring of aquatic environments.

Employing the agricultural water footprint concept to enhance the sustainable management of water resources: a review

ABSTRACT Agriculture is recognized as a significant consumer of water, with projections indicating an increase in consumption due to rising food demand and the impacts of climate change. Therefore, every country must ensure the adequacy and security of water for sustainable uses. This research reviews the water footprint (WF) index as a recent water indicator that measures the human appropriation of water resources for several consumptive uses that qualify decision-makers to manage water with high efficiency in agriculture sectors. Light is shed on the WF concept, virtual water, mechanisms of water use, and the applications in water resources management, as well as methods for assessment with its three components, blue, green, and gray. The comparison between these methods was discussed, and the differences and motives for use were clarified. The good innovation in this review is linking sustainability considerations with the WF, discussing measuring the sustainability of agricultural activities, and deducing the best policies and decisions to meet environmental and economic needs without compromising the requirements of the future. This outcome confirms the effectiveness of applying the WF in enhancing sustainability.

Cyanophage-encoded auxiliary metabolic genes in modulating cyanobacterial metabolism and algal bloom dynamics

Cyanophages play a pivotal role in controlling cyanobacterial populations in aquatic environments. These dsDNA viruses harbor auxiliary metabolic genes (AMGs) that modulate the key metabolic processes of their cyanobacterial hosts, such as Photosynthesis, nutrient uptake for the optimization of viral replication. Recently, pan1~pan5 and pam1~pam5 cyanophages have been isolated from the fifth largest water resource in China; Lake Chaohu. Detailed genomic analysis of these phages revealed that these isolated cyanophages especially Pan1, Pam2 and Pam3 possess unique AMGs that significantly enhance the metabolic activities of their hosts, potentially leading to the suppression of bloom formation and stabilization of the ecological dynamics of Lake Chaohu. Our findings provide concrete evidence that cyanophages encoding AMGs could serve as effective biocontrol agents against harmful algal blooms, offering a targeted approach to manage these environmental threats. The integration of cyanophage-based management therapies with traditional methods could advance the efficiency and sustainability of controlling cyanobacterial outbreaks, paving the way for novel applications in water resource management. This review emphasizes the importance and critical need for further exploration of phage-host dynamics to fully harness the potential of cyanophages in ecosystem regulation.

Implicit EXP-RBF techniques for modeling unsaturated flow through soils with water uptake by plant roots

Modeling unsaturated flow through soils with water uptake by plan root has many applications in agriculture and water resources management. In this study, our aim is to develop efficient numerical techniques for solving the Richards equation with a sink term due to plant root water uptake. The Feddes model is used for water absorption by plant roots, and the van-Genuchten model is employed for capillary pressure. We introduce a numerical approach that combines the localized exponential radial basis function (EXP-RBF) method for space and the second-order backward differentiation formula (BDF2) for temporal discretization. The localized RBF methods eliminate the need for mesh generation and avoid ill-conditioning problems. This approach yields a sparse matrix for the global system, optimizing memory usage and computational time. The proposed implicit EXP-RBF techniques have advantages in terms of accuracy and computational efficiency thanks to the use of BDF2 and the localized RBF method. Modified Picards iteration method for the mixed form of the Richards equation is employed to linearize the system. Various numerical experiments are conducted to validate the proposed numerical model of infiltration with plant root water absorption. The obtained results conclusively demonstrate the effectiveness of the proposed numerical model in accurately predicting soil moisture dynamics under water uptake by plant roots. The proposed numerical techniques can be incorporated in the numerical models where unsaturated flows and water uptake by plant roots are involved such as in hydrology, agriculture, and water management.

Synergetic effect of green synthesized NZVI@Chitin-modified ZSM-5 for efficient oxidative degradation of tetracycline

The aggregation and limited activity of nanoscale zero-valent iron (NZVI) in aqueous media hinder its practical application. In this study, a cost-effective, environmentally friendly, robust, and efficient synthesis method for NZVI-based composite was developed. NZVI@Chitin-modified ZSM-5 (NZVI@C-ZSM) composite was facilely and greenly synthesized by loading NZVI into alkali-modified ZSM-5 molecular sieves after modifying with chitin as a surfactant and binder. NZVI@C-ZSM exhibited remarkable efficacy in TC removal, achieving a removal efficiency of 97.72% within 60 min. Compared with pristine NZVI, NZVI@C-ZSM demonstrated twice the removal efficiency, indicating that NZVI@C-ZSM effectively improved the dispersion and stability of NZVI. This enhancement provided more reactive sites for generating reactive oxygen species (ROS), significantly boosting catalytic activity and durability while reducing the potential risk of secondary pollution. An improved two-parameter pseudo-first-order kinetic model was used to effectively characterize the reaction kinetics. The mechanism for TC removal primarily involved an adsorption process and chemical oxidation-reduction reactions induced by hydroxyl radicals (•OH) and superoxide radicals (•O2−). Three potential degradation pathways for TC were suggested. Furthermore, NZVI@C-ZSM exhibited good resistance to interference, suggesting its broad potential for practical applications in complex environmental conditions. This study offers a viable material and method for addressing the issue of antibiotic-contaminated water, with potential applications in water resource management.

Research on economic growth in smart cities based on wireless sensor networks

With the acceleration of urbanization, the concept of smart city is rising gradually. Wireless sensor network as an important technical support of smart city, its application in environmental monitoring and water resources management has a profound impact on economic growth. Water resource is one of the most dependent resources for human beings. With the growth of world population and the rapid development of economy, water resource crisis is constant, water pollution, water shortage and water waste coexist. How to build a perfect water resource economic policy is a worldwide problem at present. At present, the formulation of water resources policies is often based on experience or the knowledge system of decision makers. Due to the dynamic nature of water resources utilization and the incomplete information of decision makers, there are often policy failures, which affect economic growth. Based on this, this paper uses system dynamics model to study the mechanism of water resources management policies affecting economic growth by taking Gansu, Tianjin and Zhejiang as three qualitatively representative arid areas, transitional areas and water-rich areas. The research results show that under the same water resources policy coupling, different regions also have different eco-economic effects. The effect of coupled water resources policy is better than that of single water resources management policy.

Analysis of recent decade rainfall data with new exponential-exponential distribution: Inference and applications

This study introduces the New Exponential-Exponential Distribution (NEED) within the broader New Exponential-Generating (NE-G) family of distributions, targeting enhancements in rainfall data analysis. The significance of this research lies in addressing the need for sophisticated statistical models to accurately capture the complex variability of rainfall patterns, which are critical for effective environmental planning and disaster management. Aiming to refine rainfall data modeling, we employ the NEED model, emphasizing its application across diverse climatic conditions. Our methodology encompasses a comprehensive evaluation of seven distinct parameter estimation techniques, with a particular focus on the Anderson-Darling and maximum product spacing methods. These were selected based on their performance in minimizing bias and mean square error, assessed through a rigorous Monte-Carlo simulation study. Additionally, the study utilizes rainfall data from various geographical regions to validate the model's efficacy. The major conclusion of our investigation is the demonstrable superiority of NEED over traditional models in fitting rainfall data, as evidenced by its enhanced predictive accuracy. This outcome not only contributes to the theoretical advancements in statistical meteorology but also offers practical methodologies for improved weather forecasting. The integration of NEED with contemporary machine learning algorithms further suggests potential for groundbreaking applications in climate science and water resource management.

USING ARTIFICIAL INTELLIGENCE FOR HYDROLOGICAL MODELLING

Hydrological modelling plays a critical role in managing water resources, especially in arid and semi-arid regions where water scarcity is a major challenge. With the emergence of artificial intelligence (AI), hydrological modelling has experienced a significant transformation in recent years. This paper reviews the recent advances in AI-based hydrological modelling and examines its potential applications in water resource management. The study highlights the role of AI in enhancing the accuracy of hydrological models and facilitating more efficient and sustainable water management practices. The results suggest that AI-based hydrological models have the potential to revolutionize the way water resources are managed, and that future research in this area is warranted.

Water rights trading planning and its application in water resources management: A water-ecology-food nexus perspective

Nexus approach provides an effective perspective for implementing synergetic management of water resources. In this study, an interval two-stage chance-constrained water rights trading planning model under water-ecology-food nexus perspective (ITCWR-WEF) is proposed to analyze the interaction between water trading and water-ecology-food (WEF) nexus, which fills in the water resources management gaps from a novel nexus perspective. ITCWR-WEF incorporates hydrological simulation with soil and water assessment tool (SWAT), water rights configuration with interval two-stage chance-constrained programming (ITCP), and multi-criterion analysis with Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS). The developed ITCWR-WEF is applied to a real case of Daguhe watershed, which has characteristics of water scarcity, food producing areas and fragile ecosystem. Initial water rights allocation is addressed before the trading. Mechanisms analysis is designed to reveal mutual effect of water rights trading and WEF nexus. Optimal water management scenario is identified through multi-criterion analysis. Results reveal that the mechanism of water rights trading with WEF nexus under low constraint-violation risk level of water availability and environment capacity is recommended to promote the rational water resources allocation to balance the economic goals, water environment and water supply security, as well as ecological and food water demand guarantees.

Deep learning in water protection of resources, environment, and ecology: achievement and challenges.

The breathtaking economic development put a heavy toll on ecology, especially on water pollution. Efficient water resource management has a long-term influence on the sustainable development of the economy and society. Economic development and ecology preservation are tangled together, and the growth of one is not possible without the other. Deep learning (DL) is ubiquitous in autonomous driving, medical imaging, speech recognition, etc. The spectacular success of deep learning comes from its power of richer representation of data. In view of the bright prospects of DL, this review comprehensively focuses on the development of DL applications in water resources management, water environment protection, and water ecology. First, the concept and modeling steps of DL are briefly introduced, including data preparation, algorithm selection, and model evaluation. Finally, the advantages and disadvantages of commonly used algorithms are analyzed according to their structures and mechanisms, and recommendations on the selection of DL algorithms for different studies, as well as prospects for the application and development of DL in water science are proposed. This review provides references for solving a wider range of water-related problems and brings further insights into the intelligent development of water science.

Use of singular spectral analysis to reconstruct average monthly flow time series with complex behavior

Time series analysis is a useful tool for many practical water resource management applications, such as planning and anticipating conflict around water use. These analyses are also necessary for the use, design and operation of hydrotechnical works and for the protection of aquatic ecosystems. Basically, his review focuses on classic models such as Box and Jenkins. However, restrictive assumptions about data and residuals make its use difficult. As an alternative, non-parametric approaches are interesting due to their application versatility. One of the techniques that has been widely used in the analysis of flow time series is Singular Spectral Analysis (SSA). However, its application is concentrated as a pre-processor or in hybrid approaches and, because of this, many studies focus only on the model to be improved, leaving gaps in the process understanding that underlie the SSA. Within this context, and because of this, the objective of this study is to present the SSA technique in detail, through an application to a time series of monthly average flows with complex behavior (Jucu River, located in Southeast Brazil), in order to overcome gaps associated to a rigorous understanding of the procedures that make up the SSA. The Sequential SSA technique was used to decompose signal and noise components. The reconstructed series preserved the dynamics of the observed series, suggesting a strong influence of the signal component (trend and seasonality) on its behavior. It’s expected that the technique can be used with greater fluidity by specialists and contribute to the management of water resources.
 Keywords: decomposition, reconstruction, singular spectrum analysis.

Assessing the accuracy of OpenET satellite-based evapotranspiration data to support water resource and land management applications

Remotely sensed evapotranspiration (ET) data offer strong potential to support data-driven approaches for sustainable water management. However, practitioners require robust and rigorous accuracy assessments of such data. The OpenET system, which includes an ensemble of six remote sensing models, was developed to increase access to field-scale (30 m) ET data for the contiguous United States. Here we compare OpenET outputs against data from 152 in situ stations, primarily eddy covariance flux towers, deployed across the contiguous United States. Mean absolute error at cropland sites for the OpenET ensemble value is 15.8 mm per month (17% of mean observed ET), mean bias error is −5.3 mm per month (6%) and r2 is 0.9. Results for shrublands and forested sites show higher inter-model variability and lower accuracy relative to croplands. High accuracy and multi-model convergence across croplands demonstrate the utility of a model ensemble approach, and enhance confidence among ET data practitioners, including the agricultural water resource management community.

Control of unstable water flow in open channels

The analysis and modeling of hydrodynamic processes in open channels are crucial for effective water resource management and hydraulic structure design. This study investigates unsteady water flow in main channels using the Saint-Venant equations. Analytical and numerical methods were employed to model various flow scenarios. The developed mathematical model effectively describes and analyzes flow characteristics such as discharge, free surface elevation, and flow velocity. The obtained results have practical applications in water resource management, flood forecasting, and infrastructure design for water conservation. Overall, this work contributes to the advancement of knowledge in open channel hydraulics and provides a basis for developing more accurate and reliable water system modeling methods. These equations allow for the simulation of hydrodynamic processes in rivers, channels, and reservoirs, enabling the prediction of water level fluctuations, flow velocities, and pressure distribution under various operating conditions. This plays a vital role in water resource management and the design of hydraulic structures.

Towards an Indian land data assimilation system (ILDAS): A coupled hydrologic-hydraulic system for water balance assessments

Effective management of water resources requires reliable estimates of land surface states and fluxes, including water balance components. But most land surface models run in uncoupled mode and do not produce river discharge at catchment scales to be useful for water resources management applications. Such integrated systems are also rare over India where hydrometeorological extremes have wreaked havoc on the economy and people. So, an Indian Land Data Assimilation System (ILDAS) with a coupled land surface and a hydrodynamic model has been developed and driven by multiple meteorological forcings (0.1°, daily) to estimate land surface states, channel discharge, and floodplain inundation. ILDAS benefits from an integrated framework as well as the largest suite of observation records collected over India and has been used to produce a reanalysis product for 1981–2021 using four forcing datasets, namely, Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2), Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS), ECMWF’s ERA-5, and Indian Meteorological Department (IMD) gridded precipitation. We assessed the uncertainty and bias in these precipitation datasets and validated all major components of the terrestrial water balance, i.e., surface runoff, soil moisture, terrestrial water storage anomalies, evapotranspiration, and streamflow, against a combination of satellite and in situ observation datasets. Our assessment shows that ILDAS can represent the hydrological processes reasonably well over the Indian landmass with IMD precipitation showing the best relative performance. Evaluation against ESA-CCI soil moisture shows that MERRA-2 based estimates outperform the others, whereas ERA-5 performs best in simulating evapotranspiration when evaluated against MODIS ET. Evaluations against observed records show that CHIRPS-based estimates have the highest performance in reconstructing surface runoff and streamflow. Once operational, this system will be useful for supporting transboundary water management decision making in the region.

A Comprehensive Survey of Machine Learning Methodologies with Emphasis in Water Resources Management

This paper offers a comprehensive overview of machine learning (ML) methodologies and algorithms, highlighting their practical applications in the critical domain of water resource management. Environmental issues, such as climate change and ecosystem destruction, pose significant threats to humanity and the planet. Addressing these challenges necessitates sustainable resource management and increased efficiency. Artificial intelligence (AI) and ML technologies present promising solutions in this regard. By harnessing AI and ML, we can collect and analyze vast amounts of data from diverse sources, such as remote sensing, smart sensors, and social media. This enables real-time monitoring and decision making in water resource management. AI applications, including irrigation optimization, water quality monitoring, flood forecasting, and water demand forecasting, enhance agricultural practices, water distribution models, and decision making in desalination plants. Furthermore, AI facilitates data integration, supports decision-making processes, and enhances overall water management sustainability. However, the wider adoption of AI in water resource management faces challenges, such as data heterogeneity, stakeholder education, and high costs. To provide an overview of ML applications in water resource management, this research focuses on core fundamentals, major applications (prediction, clustering, and reinforcement learning), and ongoing issues to offer new insights. More specifically, after the in-depth illustration of the ML algorithmic taxonomy, we provide a comparative mapping of all ML methodologies to specific water management tasks. At the same time, we include a tabulation of such research works along with some concrete, yet compact, descriptions of their objectives at hand. By leveraging ML tools, we can develop sustainable water resource management plans and address the world’s water supply concerns effectively.

Significant impacts of river inputs on the distributions and transports of mercury and methylmercury in nearshore and open seas – Simulation based on field surveys and mass balance modeling

Rivers are important sources of Hg for adjacent seas, and seafood from nearshore waters is a major source of Hg exposure for humans. There is thus a key scientific concern regarding how much riverine Hg inputs influence Hg loads in nearshore waters as well as how far the impact range can extend from the river to the open sea. In addition, it is important to understand the influence of anthropogenic hydro-facilities and activities on Hg levels in downstream seas. Because of the concise mass exchange pattern between the seas and the previously demonstrated intensive Hg inputs under anthropogenic regulation from the Yellow River, the Bohai and Yellow Seas, which are key fishery and marine breeding areas for China, are an ideal research area for exploring the impacts of riverine Hg on nearshore and adjacent open seas. Field surveys were conducted in eight major rivers and two seas, and 433 water samples were collected. The main Hg input and output terms (rivers, ocean currents, underground discharge, sewage, coastal erosion, atmospheric deposition, surface evasion, sedimentation, and fisheries) were quantified in the Bohai and Yellow Seas. Owing to the high inputs from the Yellow and Yalu Rivers, elevated THg concentrations were found. Apart from direct MeHg discharge, riverine nutrients may also seemingly affect nearshore MeHg. Using mass balance models, we found that the Yellow River (9.8 t) was the dominant Hg source in the Bohai Sea, which accounted for more than half of all contributions, and the Bohai Sea played the role of a secondary source of Hg to the Yellow Sea, with a flux of 3.3 t. Anthropogenic hydro-activities in large rivers could significantly influence Hg outputs and loads in the nearshore and even open seas. This study provides useful information for water resource management applications to reduce potential MeHg risks.

Precipitation Estimation Based on Infrared Data with a Spherical Convolutional Neural Network

Abstract Precipitation is a vital process in the water cycle. Accurate estimation of the precipitation rate underpins the success of hydrological simulations, flood predictions, and water resource management. Satellite infrared (IR) data, with high temporal resolution and wide coverages, have been commonly used in precipitation inversion. However, existing IR-based precipitation retrieval algorithms suffer from various problems such as overestimation in dry regions, poor performance in extreme rainfall events, and reliance on an empirical cloud-top brightness–rain rate relationship. To resolve these problems, we construct a deep learning model using a spherical convolutional neural network to properly represent Earth’s spherical surface. With data input directly from IR bands 3, 4, and 6 of the operational Geostationary Operational Environmental Satellite (GOES), our new model of Precipitation Estimation based on IR data with Spherical Convolutional Neural Network (PEISCNN) was first trained and tested with a 3-month-long dataset, and then validated in a 2-yr period. Compared to the commonly used IR-based precipitation product PERSIANN-CCS (Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks–Cloud Classification System), PEISCNN showed significant improvement in the metrics of POD, CSI, RMSE, and CC, especially in the dry region and for extreme rainfall events. Decomposed with the four-component error decomposition (4CED) method, the overestimation of PEISCNN was averaged 47.66% lower than the CCS at the hourly scale. The PEISCNN model may provide a promising way to produce an improved IR-based precipitation product to benefit a wide range of hydrological applications. Significance Statement An IR-based precipitation algorithm is irreplaceable in satellite precipitation inversion, since an IR sensor can provide observations of high frequency, fine temporal resolution, and wide coverage. Considering the spherical nature of Earth’s surface which has been overlooked in previous IR-based precipitation retrieval algorithms, we proposed a new deep learning model PEISCNN, which can address the problems that exist in IR-based precipitation estimations such as overestimation in dry regions, deficiency in extreme rainfall events, and reliance on the empirical cloud-top brightness–rain rate relationship. PEISCNN provides a new insight to improve the accuracy of the satellite IR-based or multisensor-based precipitation estimation, and it has great potential to benefit a range of related hydrological research, applications in water resource management, and flood predictions.

Changes in extreme precipitation over Mpologoma catchment in Uganda, East Africa

Changes of five Extreme Precipitation Indices (EPIs) over Mpologoma catchment were examined. The EPIs were obtained from the long-term (1948–2016) gridded (0.25° × 0.25°) Princeton Global Forcing (PGF) daily precipitation dataset. The number of days with precipitation intensity greater than 5 mm/day (NWD5) and 10 mm/day (NWD10) were part of the five EPIs. Trend and variability analyses were by the nonparametric technique based on the cumulative sum of the difference between exceedance and non-exceedance counts of data. The simple multiplicative bias correction method was employed to bias correct the PGF dataset using observed rainfall from nine weather stations. The number of wet days were decreasing although insignificant (p > 0.05). On the other hand, the annual total precipitation considering days with rainfall intensities greater than 5 mm/day (SPre5) exhibited a significant (p < 0.05) decrease. However, annual maxima rainfall (ANMS) increased even though insignificant. This indicates that less frequent rains are received over the study area but some events having high intensity. The insights from the findings of this study constitute a good baseline for predictive planning of water resources management applications across Mpologoma catchment.

A survey on applications of machine learning algorithms in water quality assessment and water supply and management

AbstractManaging water resources and determining the quality of surface and groundwater is one of the most significant issues fundamental to human and societal well-being. The process of maintaining water quality and managing water resources well involves complications due to human-induced errors. Therefore, applications that facilitate and enhance these processes have gained importance. In recent years, machine learning techniques have been applied successfully in the preservation of water quality and the management and planning of water resources. Water researchers have effectively used these techniques to integrate them into public management systems. In this study, data sources, pre-processing, and machine learning methods used in water research are briefly mentioned, and algorithms are categorized. Then, a general summary of the literature is presented on water quality determination and applications in water resources management. Lastly, the study was detailed using machine learning investigations on two publicly shared datasets.

GIS-AHP based site selection to identify the optimum number of meteorological stations: Karasu Watershed case study

The density of meteorological stations in most watersheds across the globe is far lower than recommended by the World Meteorological Organization (WMO). However, for some basins, including those used as pilot, an adequate quantity of weather stations is crucial for collecting high-accuracy data. This study aimed to 1) estimate the optimum number of meteorological stations and 2) demarcate the most appropriate sites for their installation considering physical and environmental factors directly and indirectly influencing both objectives, i.e. to develop a well-optimized weather station network. The Weighted Overlay method and six (6) environmental factors –- precipitation variance, slope, elevation, proximity of existing stations, land cover and land use, as well as distance from roads –- were applied to delineate the potential locations. All parameters were mapped out separately and then reclassified for scoring (0 to 100 scale) based on their significance. The Analytic Hierarchy Process (AHP) method was applied to determine the impact of each factor. Based on the analysis, the precipitation variance received 38% weight, while the distance from road was computed to reach only 3% weight. The Weighted Overlay map of the Karasu Watershed was delineated into corresponding highly suitable, moderately suitable, suitable, marginally suitable, and not suitable zones. Finally, the recommended station locations were validated using a hypsometric curve to ensure proper coverage of different elevations. The research will improve the climate change and water resource management applications by informing them with sufficient climatic data about the entire target area including all variations, as well as will help addressing the challenge of data shortage and thus increase the quality of future thematic research.