Critical Water Research Articles

Multi-Objective Synergetic Operation for Cascade Reservoirs in the Upper Yellow River

The Yellow River, a critical water resource, faces challenges stemming from increasing water demand, which has led to detrimental effects on hydropower generation and ecological balance. This paper will address the complex task of balancing the interests of hydropower generation, water supply, and ecology within the context of cascade reservoirs, specifically Longyangxia and Liujiaxia reservoirs. Employing a systemic coupling coordination approach, we constructed a multi-objective synergetic model of the upper Yellow River in order to explore synergies and competitions among multiple objectives. The results reveal that there is a weak competitive relationship between hydropower generation and water supply, a strong synergy between hydropower generation and ecology, and a strong competitive relationship between water supply and ecology. The Pareto solution set analysis indicates a considerable percentage (59%, 20%, and 8% in wet, normal, and dry years, respectively) exhibiting excellent coordination. The probability of excellent coordination decreases with diminishing inflow. The optimization scheme with the highest coupling coordination demonstrates significant improvements in power generation, water supply, and ecological benefits in the upper Yellow River without compromising other objectives, fostering the sustainable operation of hydropower generation, water supply, and ecology in the upper Yellow River.

Revealing neglected hotspots for China’s quality-induced water scarcity

A dependable assessment of quality-induced water scarcity (QualWS) is essential for tackling the issue and achieving sustainable development goals. The conventional Emission-based grey water footprint (GWF) may over- or under-estimate QualWS, as it solely focuses on local pollutant emissions while disregarding other influential factors, such as water body self-purification capacity, transboundary water flows and the potential under- or over-estimation of water pollution emissions. To address this limitation, we propose the State-based GWF to reflect the quality status of local water resources accurately. The indicator is applied in annual and monthly QualWS assessments at the provincial scale in China. In 2021, 19 provinces were identified as QualWS hotspots, comprising seven moderate and 12 slight hotspots for at least one pollutant. Notably, the State-based assessment revealed eight previously overlooked hotspots undetected by conventional methods. Furthermore, total phosphorus emerged as the most critical water pollutant, followed by total nitrogen and chemical oxygen demand. Our assessment presents an innovative perspective for understanding QualWS and establishes a scientific basis for effective aquatic environment management.

Autonomous Underwater Robot for Water Quality Measurement of Northwest Pennsylvania Lakes

The Northwestern Pennsylvania Region, notably affected by toxic algae blooms like those in Lake Erie, suffers from a lack of adequate water quality monitoring tools. This project aims to bridge this gap through the development of cost-effective underwater robots designed to gather critical water quality data. The software developed for this project facilitates the generation of analytics essential for assessing the health of aquatic environments. The robots engineered through this project are set to be deployed by the local Conservation District for routine water quality monitoring activities.

Investigation of engineering characteristics of red sand in Luanda, Angola

Red sand is widely distributed on the west coast of Africa, south of the Sahara, and is an essential material for local engineering construction. Red sand possesses orthostasis and high sensitivity to water, exhibiting characteristics that are different from ordinary sandy soil. Laboratory and in-situ tests were conducted to assess its engineering characteristics, including collapsibility, shear characteristics, and bearing capacity. The water sensitivity of red sand was analyzed along with the images from a scanning electron microscope. The findings indicate that red sand can be classified as silty sand. It demonstrates strong collapsibility, with the collapsibility coefficient decreasing as water content increases. The critical water content for red sand to avoid collapsibility is 13%. The shear strength, internal friction angle, and cohesion of undisturbed and remolded red sand decrease with increasing water content. The critical water content for red sand to lose cohesion is 13%. The bearing capacity and deformation modulus of red sand decrease with increasing water content. Red sand exhibits a softening property, with a softening coefficient of 0.4. The water sensitivity of red sand can be attributed to the cementing effects of fine-grained clay minerals adhering to the surface of large particles or aggregating at particle contacts.

A data‐driven predictive model for disinfectant residual in drinking water storage tanks

AbstractA data‐driven approach is developed and proven for ranking the risk of low disinfection residual in water distribution storage tanks, 1 month ahead. The forecasting methodology uses water quality data collected from drinking water treatment plants, storage tank outlets, and rainfall data as inputs. This methodology was developed and tested with data from a water utility serving more than 5 million people. Results show high‐risk category prediction accuracy of 75%–80%. Using a final year of unseen validation data, more than 90% of the storage tanks ranked in the top 20 by the forecasting methodology experienced low disinfectant residual in the following month. Storage tanks are critical water distribution system infrastructure that are currently managed reactively. The adoption of such readily transferable machine learning approaches enables direct proactive management strategies and efficient interventions that can help ensure drinking water quality.

Combustion Completeness and Sample Location Determine Wildfire Ash Leachate Chemistry

AbstractUnderstanding past fire regimes and how they vary with climate, human activity, and vegetation patterns is fundamental to the mitigation and management of changing fire regimes as anthropogenic climate change progresses. Ash‐derived trace elements and pyrogenic biomarkers from speleothems have recently been shown to record past fire activity in speleothems from both Australia and North America. This calls for an empirical study of ash geochemistry to aid the interpretation of speleothem palaeofire proxy records. Here we present analyses of leached ashes collected following fires in southwest and southeast Australia. We include a suite of inorganic elemental data from the water‐soluble fraction of ash as well as a selection of organic analytes (pyrogenic lipid biomarkers). We also present elemental data from leachates of soils collected from sites in southwest Australia. We demonstrate that the water‐soluble fraction of ash differs from the water‐soluble fraction of soils, with trace and minor element concentrations in ash leachates varying with combustion completeness (burn severity) and sample location. Changes in some lipid biomarker concentrations extracted from ashes may reflect burn severity. Our results contribute to building a process‐based understanding of how speleothem geochemistry may record fire frequency and severity, and suggest that more research is needed to understand the transport pathways for the inclusion of pyrogenic biomarkers in speleothems. Our results also demonstrate that potential contaminant loads from ashes are much higher than from soils, with implications for the management of karst catchments, which are a critical water resource.

Theoretical perspectives on cybersecurity challenges in critical water infrastructure: Insights from Africa and the United States

This review paper examines the theoretical perspectives on cybersecurity challenges facing critical water infrastructure, with a focus on comparative insights from Africa and the United States. By exploring foundational theories of risk management, resilience, and deterrence, the paper delineates how these concepts are applied and adapted to address the cybersecurity needs of critical water systems within the distinct contexts of the two regions. The analysis identifies common challenges such as malware attacks, system vulnerabilities, human factors, and how regional differences influence technological infrastructure, regulatory environments, and cyber threat landscapes. Through a comparative analysis, the paper highlights lessons learned and best practices from both regions, emphasizing the importance of capacity building, comprehensive risk management, and the role of public-private partnerships. The paper concludes with a call for future research to develop adaptable theoretical models that address different regions' unique cybersecurity challenges, underlining theoretical understanding's critical role in enhancing the global resilience of critical water infrastructure against cyber threats.

Dynamic mechanism of check dams on evolution of river corridors based on UAV telemetry combined with numerical simulations

AbstractCheck dams are a critical soil and water conservation engineering measure in gullies that significantly influence erosion, transportation, and sediment accumulation. Check dams help reduce erosion in the upstream area, ignoring the off‐site erosion reduction capacity due to erosion dynamics, and it also alters the morphology of gullies. The morphology of the gully cross‐section from the head to the inlet of the gully is mainly “V” shaped, “V, U,” transitional shaped, “U” shaped and trapezoidal shaped. The construction of check dams can force the geomorphologic evolution of the sub‐watershed to accelerate the transition to “old age” and reduce the allocation of sediment initiation during field sub‐flooding, thus promoting sediment deposition processes. The percentage of downstream scour hours in watersheds where check dams were constructed was 0.65% and 0.80%, respectively, compared to 19.78% and 19.06% in watersheds where there are no check dams. The results reveal the role check dam constructions play in gully morphology evolution from a hydrodynamic perspective and fill the gaps in off‐site erosion reduction, providing theoretical support for assessing the role of check dams in soil and water conservation work.

WATER SECURITY AND REGIONAL STABILITY IN CENTRAL ASIA: THE CASE OF UZBEKISTAN AND AFGHANISTAN

The Amu Darya River, a critical water resource for Central Asia, frequently becomes a focal point of tensions between Uzbekistan and Afghanistan. Recent discussions regarding the Amu Darya have been reignited following the construction of a canal by the Taliban administration in 2023. This research delves into the water-related issues between Uzbekistan and Afghanistan, with a particular emphasis on the significance of the Amu Darya River to the countries in the region. Adopting a historical perspective, this study systematically examines official reports, bilateral and multilateral agreements, and existing literature through comparative analysis. The findings suggest that cooperation and consensus at the bilateral level are paramount, overshadowing the contributions of regional and international organizations in resolving this issue.

Impacts of the Covid-19 pandemic on the South African water sector

An analysis of the impacts of the Covid-19 pandemic on the South African water sector is presented. The study identified and focused on three main themes: water revenue collection, water service delivery, and water sector operations. The Covid-19 pandemic negatively impacted these aspects and exacerbated pre-existing problems in the water provision landscape of South Africa. Business continuity management was identified as a top priority for water sector institutions in South Africa to ensure the continued provision of critical water services. In-depth case study research is recommended to establish how water sector institutions in South Africa dealt with the Covid-19 pandemic and how this can inform their disaster preparedness and management going forward.

Assessing groundwater quality in semi-arid conditions: a geographic information systems-integrated approach using water quality index

Meeting freshwater demands in water-scarce regions is imperative, given challenges like hydric stress, urbanization, aridity, and population growth. This study focuses on the Tebessa aquifer system, a critical water source facing escalating demands. A primary objective is to identify actionable management strategies to improve groundwater quality dynamics in semi-arid regions. The methodology involves a comprehensive assessment of groundwater health using the Water Quality Index (WQI) approach. This method integrates diverse parameters influenced by intensive agricultural and industrial activities. Additionally, spatial relationship analysis, facilitated by Geographic Information System (GIS) technology, is employed to gain insights into complex hydrogeological processes. The multi-parameter strategy implemented results in water quality maps that highlight various ions, revealing spatial disparities and assigning quality priority classes from 'Good' to 'Poor' across the groundwater. The research, conducted in the semi-arid Tebessa-Morsott Plain, encompasses field and laboratory investigations to delineate hydrochemical traits, including alkalinity, salinity, and heightened ion concentrations. The results of the study contribute to scientific understanding by uncovering interrelationships and spatial intricacies, offering valuable insights for water resource governance. The effectiveness of the methodology, incorporating WQI and GIS, is demonstrated in the comprehensive evaluation of groundwater quality. Ultimately, this research provides a foundation for informed decision-making and sustainable management of groundwater resources in semi-arid regions.

A review on hydrothermal treatments for solid, liquid and gaseous fuel production from biomass

The rise in the population and rapid industrialization has resulted in a rise in the global energy consumption. In order to minimize the load on the conventional energy sources, various studies are being conducted for the production of biofuels by hydrothermal operations. Unlike conventional processes of biofuel production, wet biomass can be directly utilised without drying in turn reducing the energy consumption. Feedstocks such as agricultural residue, forest residue, energy crops, algae, sludge, litter and food waste can be utilised for the production of biofuels. The operation intensities (temperature and pressure) can be varied from pressurized hot water to supercritical water. Hydrothermal operations depending on the operating parameters are further subcategorised into four types namely wet torrefaction (WT), hydrothermal carbonization (HTC), hydrothermal liquefaction (HTL) and hydrothermal gasification (HTG). Even though the operating conditions of wet torrefaction and hydrothermal carbonization lie in similar categories, the difference is clearly visible in the level of carbonization. Due to the wide range of operating temperature and pressure, mainly three different products are produced through hydrothermal operations. The temperature range for wet torrefaction can be limited between 150 and 220 °C, whereas the HTC process can be between 200 and 260 °C. At higher temperatures (260 – 370 °C) in hydrothermal liquefaction (HTL), increased isomerization, depolymerization and repolymerization of organic compounds within the biomass occurred, causing liquid product (bio-oil) to be formed as the major product. Hydrothermal gasification can be further subcategorised into three types: namely aqueous phase refining, near critical water gasification and supercritical water gasification (SCWG). This paper has reviewed different hydrothermal operations based on biofuel production from different biomass.

Unravelling the influencing hydrogeological factors contributing to land subsidence in the Tianjin Plain of China using a multi-scale geographically weighted regression model and monitoring data

The establishment of a quantitative relationship between land subsidence and its influencing factors is a crucial task in developing prevention strategies of land subsidence in specific areas. In this study we examined the dynamic patterns of land subsidence before and after establishing the South-to-North Water Division Project (SNWDP) in the Tianjin Plain (TJP) that aims to reduce groundwater extractions in certain areas. Statistical analyses were used to determine the key attributing factors contributing to land subsidence that were subsequently used to derive the so-called ‘critical water level of land subsidence’(CWLS), which were used to manage groundwater extractions with the objective of minimizing further land subsidence. The main results obtained were as follows: under the influence of the SNWDP, the groundwater level in the TJP stopped declining and the water level rose in most areas. The area of ‘strong’ subsidence, defined as the area with a subsidence rate greater than 50 mm a −1 , decreased from 16.0% before the SNWDP to 2.5% after. The results of the multi-scale geographically weighted regression (MGWR) model revealed that groundwater drawdown in the second and third confined aquifer was the main contributor to the areas classified as ‘strong’ and ‘medium’ land subsidence areas. Following the reduction in groundwater extractions, the behaviour of surface deformation can be grouped into two categories: first, a rebound in surface elevation; and, second, continuous compression with a lower subsidence rate. The first mode occurs mainly in the area of ‘medium’ and ‘weak’ subsidence, and the second mode occurs mainly in the ‘strong’ subsidence area. Supplementary material : The result of the MGWR model is available at https://doi.org/10.6084/m9.figshare.c.7103611

Research progress on structure and hydrological processes in the karst critical zone of southwest China

The southwestern region of China is the largest exposed karst area in the world and serves as an important ecological security barrier for the upstream of Yangtze River and Pearl River. Different from the critical zone of non-karst areas, the epikarst, formed by an interwoven network of denudation pores, is the core area of karst critical zone. Water is the most active component that participates in internal material cycle and energy flow within the critical zone. We reviewed relevant research conducted in the southwestern region from three aspects: the characte-rization of critical zone structure, the hydrological processes of soil-epikarst system, and their model simulations. We further proposed potential research hotpots. The main approach involved multi-scale and multi-method integrated observations, as well as interdisciplinary collaboration. Precisely characterizing the eco-hydrological processes of the vegetation-soil-epikarst coupling system was a new trend in the future research. This review would provide scientific reference for further studies on hydrological processes in critical zones and regional hydrological water resource management in karst areas.

Impact of revegetation and agricultural intensification on water storage variation in the Yellow River Basin

The Yellow River Basin (YRB) is experiencing a critical water resource shortage due to climate change and human activities. Thoroughly understanding water resource dynamics under the dual pressures of revegetation and agricultural intensification presents significant challenges. Development of satellite-based observations for vegetation and water storage has greatly facilitated large-scale and high-resolution watershed analysis, and enables understanding their dynamics. In this study, the effects of revegetation and agricultural intensification were assessed by incorporating multiple vegetation indices and phenological methods. Regression models were developed to determine how much water variation could be attributed to vegetation change driven by these human activities, using an expanded set of vegetation features to analyze seasonal water storage variation. The results indicate that both revegetation and agricultural intensification played important roles in the decline of water storage in the YRB and showed significant heterogeneity across the basin. In semi-humid and semi-arid regions, revegetation can explain over 60 % of groundwater reduction throughout the year, while in arid areas, the primary impact was particularly revealed in summer soil moisture, with the explanation rate reaching 70 %. Water storage in the downstream croplands decreased at a rate of 18 mm·yr−1 due to a combination of intensified irrigation and reduced precipitation. In the upstream croplands, crop structure changes and irrigation reduction led to a reduction in spring and winter water storage at a rate of 2 mm·yr−1. Consequently, the security of water, food, and ecosystems is challenged. This study benefits sustainable management of the YRB by providing comprehensive vegetation features and their impact on water storage.

Monitoring 36-year water clarity dynamics in turbid waters of the Pearl River Estuary, China, using Landsat data

Water clarity, commonly determined by Secchi disk depth (SDD), is a critical water quality parameter for assessing estuarine ecosystems heath. Since the 1980s, significant changes in SDD have been observed in the Pearl River Estuary (PRE), a robust, economical, and densely populated estuary in southern China. However, its long-term patterns and associated drivers have not yet been systematically investigated. In this study, three novel semi-analytical algorithms suitable for Landsat 5, 7, and 8 images were applied to 36-year (1987–2022) Landsat series data to explored the spatiotemporal heterogeneity of SDD in the PRE. The results indicated that the proposed SDD estimation algorithms yielded well-accepted performances and few errors, with mean absolute percentage errors <27% and root mean square errors <0.105 m. Spatially, the SDDs in the Western regions of the PRE (0.25 ± 0.1 m) were significantly lower than that in the Open areas (0.55 ± 0.15 m). In addition, the SDD showed an overall increasing trend from 1987 to 2022 with a rate of 0.032 m/y over the entire area, indicating gradually improved water quality in the PRE, owing to the less anthropogenic perturbations and landscape change. This research not only produces a 36-year historical SDD dataset for estuarine areas, but also imparts new insights into the complex mechanisms involved in watershed management.

Improving Fish Quality and Yield: An Automated Monitoring System for Intensive Aquaculture

The growing interest in the fish farming industry is driven by the depletion of natural fish stocks in the market. However, intensive aquaculture systems, which involve raising fish in artificial tanks and cages, can lead to challenges such as low-quality fish and increased mortality rates, depending on the species being cultivated. To address these issues and maximize yield, this paper proposes a fish quality monitoring system with automatic correction. The system focuses on monitoring and maintaining critical water quality parameters essential for fish growth, including temperature, water level, and pH level. The system comprises an Arduino connected to sensors and a web-based application for data collection and monitoring. Correction devices such as an aquarium heater, a valve, and a water pump are integrated into the system to maintain these parameters at optimal levels for fish development. To assess the system's efficiency and reliability, two fish monitoring setups were compared: one using the proposed controlled system and the other using a traditional setup. Results indicate that the controlled system increased efficiency, reduced stress on fish farmers, decreased fish mortality rates, and improved product quality compared to the traditional setup.

Deep learning of estuary salinity dynamics is physically accurate at a fraction of hydrodynamic model computational cost

AbstractSalinity dynamics in the Delaware Bay estuary are a critical water quality concern as elevated salinity can damage infrastructure and threaten drinking water supplies. Current state‐of‐the‐art modeling approaches use hydrodynamic models, which can produce accurate results but are limited by significant computational costs. We developed a machine learning (ML) model to predict the 250 mg L−1 Cl− isochlor, also known as the “salt front,” using daily river discharge, meteorological drivers, and tidal water level data. We use the ML model to predict the location of the salt front, measured in river miles (RM) along the Delaware River, during the period 2001–2020, and we compare predictions of the ML model to the hydrodynamic Coupled Ocean–Atmosphere‐Wave‐Sediment Transport (COAWST) model. The ML model predicts the location of the salt front with greater accuracy (root mean squared error [RMSE] = 2.52 RM) than the COAWST model does (RMSE = 5.36); however, the ML model struggles to predict extreme events. Furthermore, we use functional performance and expected gradients, tools from information theory and explainable artificial intelligence, to show that the ML model learns physically realistic relationships between the salt front location and drivers (particularly discharge and tidal water level). These results demonstrate how an ML modeling approach can provide predictive and functional accuracy at a significantly reduced computational cost compared to process‐based models. In addition, these results provide support for using ML models in operational forecasting, scenario testing, management decisions, hindcasting, and resulting opportunities to understand past behavior and develop hypotheses.

Water yield response to forest treatment patterns in a sierra nevada watershed

Study regionSierra Nevada Study focusSnow dominated forests serve as source water supplies to much of the western United States. In recent years, these forests have experienced an increase in both drought and wildfire, which threaten critical water resources. By reducing forest density, treatments offer a promising solution to reducing fuel loads and potentially increasing surface water yield. However, the amount of fuel load reduction necessary to produce a change in water yield is not well-characterized. The objectives of the current study are to: 1) calibrate a distributed parameter, fully-integrated and physically-based hydrologic model to available watershed data and evaluate predicted hydrologic changes for a range of fuel treatment scenarios in a heavily forested experimental basin in the Sierra Nevada (Sagehen Creek basin near Truckee, California, USA), and 2) determine the extent (threshold) of forest treatments necessary to produce substantial changes, i.e. a 25% increase, in surface runoff. New hydrological insights for the regionUsing DHI’s physically-distributed code, MIKESHE, to develop the model for Sagehen Basin, twenty forest treatment scenarios with varying canopy density reductions (CDRs) and areas of treatment were simulated for a five-year period. Statistical testing showed that significant change in runoff occurred for every developed scenario at the annual scale (99% confidence interval). Surface water yield is highly correlated with precipitation patterns, however, treatments have a compounding effect over the years and the basin has a more dramatic response to higher treatment intensities. Increasing treatment CDR was more effective at increasing water yield than increasing treatment area. Results suggest that as forest managers implement fuel treatments, water yield response depends on: 1) the degree to which treatments are implemented by either area or CDR, 2) future climate variability including extended periods of drought, and 3) basin storage conditions and how this might buffer disturbance response in the basin.

Beyond Local Structures in Critical Supercooled Water through Unsupervised Learning.

The presence of a second critical point in water has been a topic of intense investigation for the last few decades. The molecular origins underlying this phenomenon are typically rationalized in terms of the competition between local high-density (HD) and low-density (LD) structures. Their identification often requires designing parameters that are subject to human intervention. Herein, we use unsupervised learning to discover structures in atomistic simulations of water close to the liquid-liquid critical point (LLCP). Encoding the information on the environment using local descriptors, we do not find evidence for two distinct thermodynamic structures. In contrast, when we deploy nonlocal descriptors that probe instead heterogeneities on the nanometer length scale, this leads to the emergence of LD and HD domains rationalizing the microscopic origins of the density fluctuations close to criticality.