Water-related Research Research Articles

Of lakes and knowledges: between disciplinary enclosures and the pursuit of “full transdisciplinarity”

The article discusses the challenges and opportunities facing interdisciplinary attempts to produce knowledge about water and lake-related processes. It examines key aspects of the long-standing debates on this topic and discusses the state of the art providing empirical examples. The article argues that, notwithstanding the significant progress achieved in disciplines and fields of knowledge relevant to water-related research, the development of interdisciplinary coordination, particularly between the physical–natural and the social sciences remains underdeveloped. However, the fact that the extreme global crisis affecting water and life in the planet, more generally, has a primarily anthropogenic nature suggests that there are urgent reasons to promote greater collaboration between different forms of knowledge relevant to these processes. The main objective is contributing to raise awareness about obstacles and opportunities for enhanced interdisciplinary coordination in these areas, to tackle the urgent problems facing the socio-hydrosphere.

Cross-comparison of Landsat-8 and Landsat-9 data: a three-level approach based on underfly images

ABSTRACT The recently launched Landsat-9 has an important mission of working together with Landsat-8 to reduce the revisit period of Landsat Earth observations to eight days. This requires the data of Landsat-9 to be highly consistent with that of Landsat-8 to avoid bias caused by data inconsistency when the two satellites are simultaneously used. Therefore, this study evaluated the consistency of the surface reflectance (SR) and land surface temperature (LST) data between Landsat-8 and Landsat-9 based on five test sites from different parts of the world using synchronized underfly image pairs of both satellites. Previous cross-comparisons have demonstrated high consistency between the spectral bands of Landsat-8 and Landsat-9, with differences of around 1%. However, it is unclear whether this low deviation will be amplified in subsequent multiband calculations. It is also necessary to determine whether the difference is consistent across different land cover types. Therefore, this study used a three-level cross-comparison approach to specifically examine these concerns. Besides the commonly used band-by-band comparison, which served as the first-level comparison in this study, this approach included a second-level comparison based on the calculations of several indicators and a third-level comparison based on a composite index calculated from the indicators obtained in the second-level comparison. This three-level approach will examine whether the difference found in the first-level per-band comparison would change after the subsequent calculations in the second- and third-level comparisons. The Remote Sensing based Ecological Index (RSEI) was used for this approach because it is a composite index integrating four indicators. The results of this three-level comparison show that the first-level per-band comparison exhibited high consistency between the two satellites’ SR data, with an average absolute percent change (PC) of 1.88% and an average R 2 of 0.957 across six bands in the five test sites. This deviation increased to 2.21% in the third-level composite index-based comparison, with R 2 decreasing to 0.956. This indicates that after complex calculations, the deviation between the bands of the two satellites was amplified to some extent. However, when analyzing specific land cover types, notable differences emerged between the two satellites for the water category, with an average absolute PC ranging from 18% to 35% and an R 2 of lower than 0.6. Additionally, there were also nearly 5% differences for the built-up land category, with an average R 2 value of lower than 0.7. The comparison of LST data between both satellites also reveals that the Landsat-9 LST is on average 0.24°C lower than Landsat-8 LST across the five test areas but can be 0.58°C lower in built-up land-dominated areas and 0.42°C higher in desert environments. Overall, the SR and LST data between Landsat-8 and Landsat-9 are consistent. However, their performance varies depending on different land cover types. Caution is needed particularly for water-related research when utilizing both satellites simultaneously. Significant discrepancies may also arise in the areas characterized by deserts and built-up lands.

A year of a journal for all water-related research

A year of a journal for all water-related research

Sensor platform for assessment of water usage patterns in informal settlements

Rapid urbanization has intensified pressures on global water systems, particularly impacting informal settlements. Understanding water usage patterns within these settlements is of importance for better addressing water scarcity issues. Current methods for gaining information about water within these settings tend to lack spatio-temporal granularity and miss complex patterns of behavior related to water usage. As a consequence, there is a shortage of the reliable quantitative measurements needed to improve water management processes and modeling. Here we introduce a low-cost sensing platform for water assessment in informal settlements. Households within these types of settlements, lacking water utility connections and piping, often use storage tanks and buckets to distribute, store, and consume water; hence, the platform consists of four distinct sensor modules that can be placed on these types of water infrastructure. Evaluated in controlled settings, the sensors prove to be reliable for measuring water quantity, quality, and usage. Field testing within an informal community in Mexico reveals that the system can comprehensively track multiple tank storage levels, assess water quality, and capture bucket usage patterns without disrupting a household’s common activities or infrastructure. Our validation shows the technique’s potential to improve water management in informal communities, while opening opportunities for enhancement of water-related research and policy making through combinations of top-down and bottom-up interventions.

Predawn leaf water potential of grapevines is not necessarily a good proxy for soil moisture

BackgroundIn plant water relations research, predawn leaf water potential (Ψpd) is often used as a proxy for soil water potential (Ψsoil), without testing the underlying assumptions that nighttime transpiration is negligible and that enough time has passed for a hydrostatic equilibrium to be established. The goal of this research was to test the assumption Ψpd = Ψsoil for field-grown grapevines.ResultsA field trial was conducted with 30 different cultivars of wine grapes grown in a single vineyard in arid southeastern Washington, USA, for two years. The Ψpd and the volumetric soil water content (θv) under each sampled plant were measured multiple times during several dry-down cycles. The results show that in wet soil (Ψsoil > − 0.14 MPa or relative extractable water content, θe > 0.36), Ψpd was significantly lower than Ψsoil for all 30 cultivars. Under dry soil conditions (Ψsoil < − 0.14 MPa or θe < 0.36) Ψpd lined up better with Ψsoil. There were differences between cultivars, but these were not consistent over the years.ConclusionThese results suggest that for wet soils Ψpd of grapevines cannot be used as a proxy for Ψsoil, while the Ψpd = Ψsoil assumption may hold for dry soils.

Spatial Interpolations of Annual Rainfall in Ethiopia Using Simple and Universal Kriging Techniques

The study of spatial distribution of precipitation is an important aspect of water-related research like, hydrological modeling, disaster prediction and watershed management. But, spatial interpolations of annual average rainfall in Ethiopia using Simple and Universal Kriging type through different models has not yet investigated. This study was focused on to select the best fit semivarogram model for annual rainfall interpolation and to map the spatial distribution of rainfall of Ethiopia. In this study, long term mean annual rainfall which was collected from 931 meteorological stations were interpolated using different mathematical variogram models of Kriging to select the best fit variogram model useful to predict continuous surface values of annual average rainfall in Ethiopia. Spherical model was found to be the best fitted variogram model and most accurate model for interpolation using both Simple and Universal Kriging with the value of ME (0.005), RMSE (1.21) and MSDR (121.32) and ME (0.001), RMSE (0.88) and MSDR (144.11), respectively. Since the kriging results of spherical model indicate good model efficacy for this study, it may be useful to map spatial pattern of annual rainfall in Basin level in Ethiopia.

Water science must be Open Science

Since water is a common good, the outcome of water-related research should be accessible to everyone. Since Open Science is more than just open access research articles, journals must work with the research community to enable fully open and FAIR science

A journal for all water-related research

A journal for all water-related research

Global meta-analysis of evolution patterns for lake topics over centurial scale: A natural language understanding-based deep clustering approach with 130,000 studies

As complicated microcosms, lakes have been attracting exponentially increasing attention, resulting in plentiful interdisciplinary academic publications of more than 105. It is thereby challengeable to explore the massive unstructured text information of publications to understand lake topics from the global- and centurial-scale perspectives. However, conventional bibliometrics suffer from the limitations of non-understanding the literature. A novel approach, Natural Language Understanding-Based Deep Clustering (NLU-DC) for large text clustering, was proposed in this study for global meta-analysis of evolution patterns for lake topics. The validated NLU-DC elevated the available keywords from 24% to 70%, correcting the statistical bias in the traditional evidence synthesis. Its high performance derives from the integration of a deep learning model, cosine distance, DBSCAN clustering and changing hyperparameters. This approach is of great accuracy and efficiency for large text datasets. This study thereby identified the centurial-scale topics related to lakes using large literature datasets covering >130,000 studies. The results showed that the topics became more and more abundant but were concentrated stably towards central ones. Six evolution patterns, consisting of fluctuating, emerging in 1970, emerging after 1970, trending-up, stable and trending-down patterns, were identified with generalized linear model (GLM). We found that, in recent twenty years, few emerging topics attract significant academic attention; and the dependency between topics is catching more attention than before. To prolong the lake studies, it is essential to strengthen the integrated studies on multi-pattern topics, in particular, over emerging and trending-down topics. Our study verified that the NLU-DC, consisting of state-of-the-art deep learning models in natural language processing and efficient clustering algorithm in machine learning, is a powerful method for global meta-analysis of water-related research fields and have huge potential to be applied in all fields of academical studies.

A review of the application of machine learning in water quality evaluation

With the rapid increase in the volume of data on the aquatic environment, machine learning has become an important tool for data analysis, classification, and prediction. Unlike traditional models used in water-related research, data-driven models based on machine learning can efficiently solve more complex nonlinear problems. In water environment research, models and conclusions derived from machine learning have been applied to the construction, monitoring, simulation, evaluation, and optimization of various water treatment and management systems. Additionally, machine learning can provide solutions for water pollution control, water quality improvement, and watershed ecosystem security management. In this review, we describe the cases in which machine learning algorithms have been applied to evaluate the water quality in different water environments, such as surface water, groundwater, drinking water, sewage, and seawater. Furthermore, we propose possible future applications of machine learning approaches to water environments. • Machine learning is widely used in water quality monitoring and prediction. • The performance of 45 machine learning algorithms is evaluated and discussed. • The challenges and opportunities of machine learning in water system are described.

A Rapid Review of Environmental Health Gaps in Antimicrobial Resistance and Water-Related Research from 1990-2020.

Antimicrobial resistance (AMR) is a pervasive global health threat linked to human antimicrobial misuse and abuse, food production, and broader environmental contamination. While global agencies promote a multi-sectoral One Health system approach to equitably combat human, animal, and environmental health AMR risks, it is widely acknowledged that the human and animal sectors dominate discussions. Given this disproportionate focus, identification of critical research gaps is needed to develop stewardship plans that equitably address One Health AMR threats. This review used natural language processing and term frequency algorithms to classify 12,638 records from 1990–2020 thematically in order to highlight sectoral prioritization and gaps. It also specifically assessed water-related gaps as water is recognized as both a primary environmental dissemination pathway and key means of intervention. Drawing from systemic health and integrated water management lenses, this review found that themes related to plant, wildlife, and environmental-related AMR threats—in particular, the role that environmental (ambient) waters play in AMR development, transmission, and spread—are under-prioritized as compared to human and food animal health concerns regardless of geographic region or income level. Further prioritization of these themes is needed to strengthen the environmental dimension of One Health AMR responses and systemically protect global health.

A comprehensive analysis of evolution and underlying connections of water research themes in the 21st century

This work aimed to reflect the advancements in water-related science, technology, and policy and shed light on future research opportunities related to water through a systematic overview of Water Research articles published in the first 21.5 years of the 21st century. Specific bibliometric analyses were performed to i) reveal the temporal and spatial trends of water-related research themes and ii) identify the underlying connections between research topics. The results showed that while top topics including wastewater (treatment), drinking water, adsorption, model, biofilm, and bioremediation remained constantly researched, there were clear shifts in topics over the years, leading to the identification of trending-up and emerging research topics. Compared to the first decade of the 21st century, the second decade not only experienced significant uptrends of disinfection by-products, anaerobic digestion, membrane bioreactor, advanced oxidation processes, and pharmaceuticals but also witnessed the emerging popularity of PFAS, anammox, micropollutants, emerging contaminants, desalination, waste activated sludge, microbial community, forward osmosis, antibiotic resistance genes, resource recovery, and transformation products. On top of the temporal evolution, distinct spatial evolution existed in water-related research topics. Microplastics and Covid-19 causing global concerns were hot topics detected, while metagenomics and machine learning were two technical approaches emerging in recent years. These consistently popular, trending-up and emerging research topics would most likely attract continuous/increasing research input and therefore constitute a major part of the prospective water-related research publications.

Systems approach and performance-based water resources management

In 1975 I was walking with Professor Ven Te Chow, the founding father of the International Water Resources Association (IWRA), asking about future water-related research directions to guide my inte...

Improving Hourly Precipitation Estimates for Flash Flood Modeling in Data-Scarce Andean-Amazon Basins: An Integrative Framework Based on Machine Learning and Multiple Remotely Sensed Data

Accurate estimation of spatiotemporal precipitation dynamics is crucial for flash flood forecasting; however, it is still a challenge in Andean-Amazon sub-basins due to the lack of suitable rain gauge networks. This study proposes a framework to improve hourly precipitation estimates by integrating multiple satellite-based precipitation and soil-moisture products using random forest modeling and bias correction techniques. The proposed framework is also used to force the GR4H model in three Andean-Amazon sub-basins that suffer frequent flash flood events: upper Napo River Basin (NRB), Jatunyacu River Basin (JRB), and Tena River Basin (TRB). Overall, precipitation estimates derived from the framework (BC-RFP) showed a high ability to reproduce the intensity, distribution, and occurrence of hourly events. In fact, the BC-RFP model improved the detection ability between 43% and 88%, reducing the estimation error between 72% and 93%, compared to the original satellite-based precipitation products (i.e., IMERG-E/L, GSMAP, and PERSIANN). Likewise, simulations of flash flood events by coupling the GR4H model with BC-RFP presented satisfactory performances (KGE* between 0.56 and 0.94). The BC-RFP model not only contributes to the implementation of future flood forecast systems but also provides relevant insights to several water-related research fields and hence to integrated water resources management of the Andean-Amazon region.

Bright and blind spots of water research in Latin America and the Caribbean

Abstract. Water resources management in Latin America and the Caribbean is particularly threatened by climatic, economic, and political pressures. To assess the region's ability to manage water resources, we conducted an unprecedented literature review of over 20 000 multilingual research articles using machine learning and an understanding of the socio-hydrologic landscape. Results reveal that the region's vulnerability to water-related stresses, and drivers such as climate change, is compounded by research blind spots in niche topics (reservoirs and risk assessment) and subregions (Caribbean nations), as well as by its reliance on an individual country (Brazil). A regional bright spot, Brazil, produces well-rounded water-related research, but its regional dominance suggests that funding cuts there would impede scientifically informed water management in the entire region.

A Novel Method to Assess the Impact of a Government’s Water Strategy on Research: A Case Study of Azraq Basin, Jordan

Water scarcity drives governments in arid and semi-arid regions to promote strategies for improving water use efficiency. Water-related research generally also plays an important role in the same countries and for the same reason. However, it remains unclear how to link the implementation of new government strategies and water-related research. This article’s principal objective is to present a novel approach that defines water-related research gaps from the point of view of a government strategy. The proposed methodology is based on an extensive literature review, followed by a systematic evaluation of the topics covered both in grey and peer-reviewed literature. Finally, we assess if and how the different literature sources contribute to the goals of the water strategy. The methodology was tested by investigating the impact of the water strategy of Jordan’s government (2008–2022) on the research conducted in the Azraq Basin, considering 99 grey and peer-reviewed documents. The results showed an increase in the number of water-related research documents from 37 published between 1985 and 2007 to 62 published between 2008 and 2018. This increase should not, however, be seen as a positive impact of increased research activity from the development of Jordan’s water strategy. In fact, the increase in water-related research activity matches the increasing trend in research production in Jordan generally. Moreover, the results showed that only about 80% of the documents align with the goals identified in the water strategy. In addition, the distribution of the documents among the different goals of the strategy is heterogeneous; hence, research gaps can be identified, i.e., goals of the water-strategy that are not addressed by any of the documents sourced. To foster innovative and demand-based research in the future, a matrix was developed that linked basin-specific research focus areas (RFAs) with the MWI strategy topics. In doing so, the goals that are not covered by a particular RFA are highlighted. This analysis can inspire researchers to develop and apply new topics in the Azraq Basin to address the research gaps and strengthen the connection between the RFAs and the strategy topics and goals. Moreover, the application of the proposed methodology can motivate future research to become demand-driven, innovative, and contribute to solving societal challenges.

Error Correction of Multi-Source Weighted-Ensemble Precipitation (MSWEP) over the Lancang-Mekong River Basin

The demand for accurate long-term precipitation data is increasing, especially in the Lancang-Mekong River Basin (LMRB), where ground-based data are mostly unavailable and inaccessible in a timely manner. Remote sensing and reanalysis quantitative precipitation products provide unprecedented observations to support water-related research, but these products are inevitably subject to errors. In this study, we propose a novel error correction framework that combines products from various institutions. The NASA Modern-Era Retrospective Analysis for Research and Applications (AgMERRA), the Asian Precipitation Highly-Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE), the Climate Hazards group InfraRed Precipitation with Stations (CHIRPS), the Multi-Source Weighted-Ensemble Precipitation Version 1.0 (MSWEP), and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Records (PERSIANN) were used. Ground-based precipitation data from 1998 to 2007 were used to select precipitation products for correction, and the remaining 1979–1997 and 2008–2014 observe data were used for validation. The resulting precipitation products MSWEP-QM derived from quantile mapping (QM) and MSWEP-LS derived from linear scaling (LS) are evaluated by statistical indicators and hydrological simulation across the LMRB. Results show that the MSWEP-QM and MSWEP-LS can better capture major annual precipitation centers, have excellent simulation results, and reduce the mean BIAS and mean absolute BIAS at most gauges across the LMRB. The two corrected products presented in this study constitute improved climatological precipitation data sources, both time and space, outperforming the five raw gridded precipitation products. Among the two corrected products, in terms of mean BIAS, MSWEP-LS was slightly better than MSWEP-QM at grid-scale, point scale, and regional scale, and it also had better simulation results at all stations except Strung Treng. During the validation period, the average absolute value BIAS of MSWEP-LS and MSWEP-QM decreased by 3.51% and 3.4%, respectively. Therefore, we recommend that MSWEP-LS be used for water-related scientific research in the LMRB.

Water-related technology research landscape in India: Current status and perspectives

While seventy-one per cent of the earth’s surface is covered with water, equitable access to safe and drinking water remains a challenge across countries. Especially developing countries, like India, have been identified as water-stressed. In India, various institutions are participating and joining hands to deliver social and technological solutions to resolve prevalent water-related challenges. This study reviews national policy frameworks and identifies the institutions participating in water-related technology research. Besides government interventions to formulate water-related policies, both private and government institutions are participating in water-related technology research. Along with this, local community initiatives and heterodox modes of innovations have introduced a means to address the issues surrounding water quality and availability. The Indian Institutes of Technology (IITs), Council for Scientific and Industrial Research (CSIR), University Grants Commission (UGC), and Department of Science and Technology (DST) are some of the prominent institutions that have been playing a pivotal role in establishing water security through technological and ecological solutions. National Innovation Foundation (NIF) and Atal Innovation Mission have documented innovative solutions developed by individual innovators for addressing local water-related challenges.

Prediction of leaf water potential and relative water content using terahertz radiation spectroscopy.

Increases in the frequency and severity of droughts across many regions worldwide necessitate an improved capacity to determine the water status of plants at organ, whole plant, canopy, and regional scales. Noninvasive methods have most potential for simultaneously improving basic water relations research and ground‐, flight‐, and space‐based sensing of water status, with applications in sustainability, food security, and conservation. The most frequently used methods to measure the most salient proxies of plant water status, that is, water mass per leaf area (WMA), relative water content (RWC), and leaf water potential (Ψleaf), require the excision of tissues and laboratory analysis, and have thus been limited to relatively low throughput and small study scales. Applications using electromagnetic radiation in the visible, infrared, and terahertz ranges can resolve the water status of canopies, yet heretofore have typically focused on statistical approaches to estimating RWC for leaves before and after severe dehydration, and few have predicted Ψleaf. Terahertz radiation has great promise to estimate leaf water status across the range of leaf dehydration important for the control of gas exchange and leaf survival. We demonstrate a refined method and physical model to predict WMA, RWC, and Ψleaf from terahertz transmission across a wide range of levels of dehydration for given leaves of three species, as well as across leaves of given species and across multiple species. These findings highlight the powerful potential and the outstanding challenges in applying in vivo terahertz spectrometry as a remote sensor of water status for a range of applications.

Fungal Aquaporins in Ectomycorrhizal Root Water Transport.

Ectomycorrhizal fungi influence root water transport of host plants. To delineate the exact mechanisms of how fungal partner alters root water relations, it is important to understand the functions of fungal transmembrane water channels, i.e., aquaporins, the key component in the symplastic pathways. In this paper, we discussed what roles the fungal aquaporins may play in root water transport. We also highlighted the opportunities of using integrated approaches to address rising questions in future hotspots of aquaporin and root water relations research.