Water Resources Research Research Articles

CLEAN DRINKING WATER AND FUTURE PROSPECTIVE

Water is one of the most important and vital substance on earth. Without water, there would be no life on this planet (earth). Around 20% of the human population planet has access to quality drinking water. Pakistan is listed 6 th highly populated country on the earth having about 207.81 million individuals. Punjab is the most populated province of Pakistan with 110 million people. Contamination of drinking water is the most burning issue of the country. Industrialization, increase in population, intermixing of sewer in pipelines of drinking water and improper utilization of water compromise the quality and decrease the quantity of water. The physical, chemical and biological parameters of water have great detrimental effects on health. Water borne diseases in Pakistan are typhoid, diarrhea, cholera etc. The impact of poor sanitation and hygiene also have adverse effect on tourism, thereby affecting the economy of the country. Government Departments and Agencies are actively working through different interventions like chlorination to ensure facility of clean and quality drinking water to people. Chlorination is a process which is adopted to inactivate pathogens in water. Pakistan Council of Research in Water Resources (PCRWR) and Punjab Food Authority (PFA) conduct sampling and testing of the bottled water companies on periodical basis. Government of Punjab has established Aab-e-Pak Authority under Punjab Aab-e-Pak Act, 2019 for the provision of clean, safe and quality drinking water. Punjab Water Act, 2019, a comprehensive law on the subject has been passed but not implemented. Pakistan is also facing water insufficiency, water quality deterioration and demolition of freshwater resources. The world best practices regarding clean drinking water have also been analyzed. Keeping this in view, the short term, medium term and long term plans have been devised for the provision of clean and quality drinking water and improved sanitation facilities to the masses.

Field Evaluation of Conventional and Downhole TDR Soil Water Sensors for Irrigation Scheduling in a Clay Loam Soil

Highlights Soil profile water content derived from Acclima TDR-315™ sensors approximated those from NMM measurements. Soil profile water content from Campbell Scientific SoilVUE™10 sensors grossly underestimated those from the NMM. VWC values from SoilVUE10 sensors were consistently less than those reported by the TDR-315 sensors at all depths. These findings do not support SoilVUE10 use for irrigation scheduling in clay loam soils. Abstract. A field study was performed to evaluate the efficacy of two commercially available time domain reflectometry (TDR) soil water sensors for irrigation scheduling in a clay loam soil near Bushland, Texas. SoilVUE10 (Campbell Scientific Inc., Logan, Utah) and TDR-315 (Acclima Inc., Meridian, Idaho) sensors were installed within 30 cm of neutron moisture meter (NMM) access tubes in a research field planted to corn (Zea mays L) in 2020 and irrigated by a center pivot sprinkler system. Irrigation treatments included 50%, 75%, and 100% of evapotranspiration (ET) replacement with two access tubes installed in each plot, totaling six sensor evaluation sites. Semiweekly measurements with a field-calibrated NMM were used to monitor soil water status and schedule irrigation throughout the growing season. Soil profile water content values integrated over the surface to 1.1-m depth range were derived from SoilVUE10 and vertically distributed arrays of Acclima TDR-315 sensors installed at equivalent depths and were compared with those from NMM data. Average profile soil water contents from the TDR-315 sensors trended well with those from the NMM having mean bias difference (MBD) values of -9.8, -3.1, and 8.4 mm for the 50%, 75%, and 100% treatments, respectively. In contrast, soil profile water content values from the SoilVUE10 sensors grossly underestimated those from the NMM for all irrigation treatments with MBD values of -54.4, -70.5, and -89.8 mm for the 50%, 75%, and 100% treatments, respectively. Comparisons of volumetric water content (VWC) at each of the nine depths common to both electromagnetic sensor types revealed that values from the SoilVUE10 sensors were consistently less than TDR-315 values for all irrigation treatments. Underestimation at the near surface (5 and 10 cm depths) was attributed to loss of soil to electrode contact possibly associated with clay shrinkage during periodic drying following irrigation. Although soil to electrode contact can be problematic at greater depths, the explanation for chronic underestimation of VWC was less obvious except to note that underestimation occurred immediately after installation, which indicated poor electrode-soil contact after installation despite use of manufacturer guidelines and tools. Other possible reasons include challenges for accurate estimation of soil permittivity for a measured permittivity that includes the plastic sensor body. Results from this study suggest vertically distributed arrays of TDR-315 sensors can provide profile water content values adequate for monitoring soil water status for irrigation scheduling in a clay loam soil. The chronic underestimation observed for the SoilVUE10 sensors does not support their use for water resources research and irrigation management and could lead to over irrigation. Additionally, the relatively short 1 m length is less than the rooting depth of many regional crops and thus not capable of determining percolation below the root zone. Keywords: Acclima TDR-315, Campbell Scientific SoilVUE, Irrigation scheduling, Neutron moisture meter, Semi-arid, Soil water sensors, Time domain reflectometry, Volumetric water content.

Clarifying water clarity: A call to use metrics best suited to corresponding research and management goals in aquatic ecosystems

Clarifying water clarity: A call to use metrics best suited to corresponding research and management goals in aquatic ecosystems

Linear and Nonlinear Characteristics of Long-Term NDVI Using Trend Analysis: A Case Study of Lancang-Mekong River Basin

Vegetation is the main body of the terrestrial ecosystem and is a significant indicator of environmental changes in the regional ecosystem. As an essential link connecting South Asia and Southeast Asia, the Lancang-Mekong River Basin(LMRB) can provide essential data support and a decision-making basis for the assessment of terrestrial ecosystem environmental changes and the research and management of hydrology and water resources in the basin by monitoring changes in its vegetation cover. This study takes the Lancang-Mekong River Basin as the study area, and employs the Sen slope estimation, Mann–Kendall test, and Hurst exponent based on the MODIS NDVI data from 2000 to 2021 to study the spatial and temporal evolution trend and future sustainability of its NDVI. Besides, the nonlinear characteristics such as mutation type and mutation year are detected and analyzed using the BFAST01 method. Results demonstrated that: (1) In the past 22 years, the NDVI of the Lancang-Mekong River Basin generally exhibited a fluctuating upward trend, and the NDVI value in 2021 was the largest, which was 0.825, showing an increase of 4.29% compared with 2000. However, the increase rate was different: China has the most considerable NDVI growth rate of 7.25%, followed by Thailand with an increase of 7.21%, Myanmar and Laos as the third, while Cambodia and Vietnam have relatively stable vegetation changes. The overall performance of NDVI is high in the south and low in the north, and is dominated by high and relatively high vegetation coverage, of which the area with vegetation coverage exceeding 0.8 accounts for 62%. (2) The Sen-MK trend showed that from 2000 to 2021, the area where the vegetation coverage in the basin showed a trend of increase and decrease accounted for 66.59% and 18.88%, respectively. The Hurst exponent indicated that the areas where NDVI will continue to increase, decrease, and remain unchanged in the future account for 60.14%, 25.29%, and 14.53%, respectively, and the future development trend of NDVI is uncertain, accounting for 0.04%. Thus, more attention should be paid to areas with a descending future development trend. (3) BFAST01 detected eight NDVI mutation types in the Lancang-Mekong River Basin over the past 22 years. The mutations mainly occurred in 2002–2018, while 2002–2004 and 2014–2018 were the most frequent periods of breakpoints. The mutation type of “interruption: increase with negative break” was changed the most during this period, which accounts for 36.54%, and the smallest was “monotonic decrease (with negative break)”, which only accounts for 0.65%. This research demonstrates that combining the conventional trend analysis method with the BFAST mutation test can more accurately analyze the spatiotemporal variation and nonlinear mutation of NDVI, thus providing a scientific reference to develop ecological environment-related work.

Learning Groundwater Contaminant Diffusion‐Sorption Processes With a Finite Volume Neural Network

AbstractImproved understanding of complex hydrosystem processes is key to advance water resources research. Nevertheless, the conventional way of modeling these processes suffers from a high conceptual uncertainty, due to almost ubiquitous simplifying assumptions used in model parameterizations/closures. Machine learning (ML) models are considered as a potential alternative, but their generalization abilities remain limited. For example, they normally fail to predict accurately across different boundary conditions. Moreover, as a black box, they do not add to our process understanding or to discover improved parameterizations/closures. To tackle this issue, we propose the hybrid modeling framework FINN (finite volume neural network). It merges existing numerical methods for partial differential equations (PDEs) with the learning abilities of artificial neural networks (ANNs). FINN is applied on discrete control volumes and learns components of the investigated system equations, such as numerical stencils, model parameters, and arbitrary closure/constitutive relations. Consequently, FINN yields highly interpretable results. We demonstrate FINN's potential on a diffusion‐sorption problem in clay. Results on numerically generated data show that FINN outperforms other ML models when tested under modified boundary conditions, and that it can successfully differentiate between the usual, known sorption isotherms. Moreover, we also equip FINN with uncertainty quantification methods to lay open the total uncertainty of scientific learning, and then apply it to a laboratory experiment. The results show that FINN performs better than calibrated PDE‐based models as it is able to flexibly learn and model sorption isotherms without being restricted to choose among available parametric models.

Bridging the Data Gap between the GRACE Missions and Assessment of Groundwater Storage Variations for Telangana State, India

Because of changing climatic conditions, uneven distribution of rainfall occurs throughout India. As a result, dependence on groundwater for irrigation has increased tremendously for industrial and domestic purposes. In India approximately 89% of agricultural demands are met through groundwater. Due to increases in population, demand for groundwater and lack of effective utilization have resulted in rapid depletion of groundwater in most parts of the country. Therefore, quantifying groundwater resources is a serious concern in populated states of India, because it is now difficult to supply enough water to every citizen, and will remain so in the future. Because of difficulties in accessing observation data, researchers have begun to depend on satellite-based remote sensing information to deal with groundwater variations. The present study deals with filling the data gap between Gravity Recovery And Climate Experiment (GRACE) and GRACE Follow On (GRACE FO) missions using multilayer perceptron’s (MLPs) during 2017–2018 to obtain a continuous terrestrial water storage anomaly (TWSA) series from 2003 to 2020 for Telangana state, India. The MLP model performed well in predicting the TWSA, with a correlation coefficient of r = 0.96 between modeled TWSA and GRACE TWSA during the test period. Telangana state observed negative TWSAs (annual) in the years 2003, 2004, 2005, 2009, 2012, 2015, and 2016–19. This TWSA series (2003–2020) was then used to evaluate regional groundwater storage anomalies (GWSAs) in Telangana state, which is considered to be one of the water stress regions in India. The TWSAs were converted to GWSAs using Global Land Data Assimilation System (GLDAS) parameters. The Telangana state experienced decreasing GWSA in the years 2005, 2009, and 2012, and from 2015 to 2019, leading to severe droughts. Groundwater well measurements were obtained from the Central Groundwater Board (CGWB) and converted to GWSA at a seasonal scale. The GWSAs obtained from GRACE (GWSAGRACE) were converted to seasonal values and compared with GWSAs obtained from observation well data (GWSAobs). The performance metrics of r = 0.74, RMSE = 5.3, and NSE = 0.62 were obtained between (GWSAGRACE) and (GWSAobs), representing a good correlation among them. Over the past decade, Telangana state has significantly relied on groundwater resources for irrigation, domestic, and industrial purposes. As a result, evaluating groundwater storage variations at a regional scale may help policy makers and water resource researchers in the sustainable utilization and management of groundwater resources.

A Deep Learning Method Based on Two-Stage CNN Framework for Recognition of Chinese Reservoirs with Sentinel-2 Images

The development of effective and comprehensive methods for mapping and monitoring reservoirs is essential for the utilization of water resources and flood control. Remote sensing has the great advantages of broad spatial coverage and regular revisit to meet the demand of large-scale and long-term tasks of earth observation. Although there already exist some methods for coarse-grained identification of reservoirs at region-level in remote sensing images, it remains a challenge to recognize and localize reservoirs accurately with insufficiency of object details and samples annotated. This study focuses on the fine-grained identification and location of reservoirs with a two-stage CNN framework method, which is comprised of a coarse classification between aquatic and land areas of image patches and a fine detection of reservoirs in aquatic patches with precise geographical coordinates. Moreover, a NIR RCNN detection network is proposed to make use of the multi-spectral characteristics of Sentinel-2 images. To verify the effectiveness of our proposed method, we construct a reservoir and dam dataset of 36 Sentinel-2 images which are sampled in various provinces across China and annotated at the instance level by manual work. The experimental results in the test set show that the two-stage CNN method achieves an average recall of 80.83% nationwide, and the comparison between reservoirs recognized by the proposed model and those provided by the China Institute of Water Resources and Hydropower Research verifies that the model reaches a recall of about 90%. Both the indicator evaluation and visualization of identification results have shown the applicability of the proposed method to reservoir recognition in remote sensing images. Being the first attempt to make a fine-grained identification of reservoirs at the instance level, the two-stage CNN framework, which can automatically identify and localize reservoirs in remote sensing images precisely, shows the prospect to be a useful tool for large-scale and long-term reservoir monitoring.

Contributors

Other| November 01 2022 Contributors Gastronomica (2022) 22 (4): x. https://doi.org/10.1525/gfc.2022.22.4.x Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Facebook Twitter LinkedIn MailTo Tools Icon Tools Get Permissions Cite Icon Cite Search Site Citation Contributors. Gastronomica 1 November 2022; 22 (4): x. doi: https://doi.org/10.1525/gfc.2022.22.4.x Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentGastronomica Search Shomit Barua is an intermedia artist and doctoral student in the School of Arts, Media, and Engineering at Arizona State University. He specializes in ecoacoustics, responsive environments, and emergent narratives. Camille Bégin is a historian of food and the senses and the author of an award-winning book, Taste of the Nation: The New Deal Search for America’s Food (University of Illinois Press, 2016). She lives and writes between Toronto and Paris. Holly Brause completed a PhD in cultural anthropology and now works as a research scientist at the New Mexico Water Resources Research Institute. Her research interests include food and agriculture, natural resources, the politics of transboundary water sharing, anthropology of the future, and the US/Mexico borderlands. Chanelle Dupuis is a PhD candidate in French and Francophone studies at Brown University. Her research focuses on smell, the senses, perception, and trauma in twentieth-century French and Francophone literature. Beyond this, she... You do not currently have access to this content.

Hydrology research articles are becoming more topically diverse

We used Natural Language Processing (NLP) to assess topic diversity in all research articles (∼75,000) from eighteen water science and hydrology journals published between 1991 and 2019. We found that individual water science and hydrology research articles are becoming increasingly diverse in the sense that, on average, the number of topics represented in individual articles is increasing, which may be a sign of increasing interdisciplinarity. This is true even though the body of water science and hydrology literature as a whole is not becoming more topically diverse. Topics with the largest increases in popularity were Climate Change Impacts, Water Policy & Planning, and Pollutant Removal. Topics with the largest decreases in popularity were Stochastic Models and Numerical Models. At a journal level, Water Resources Research, Journal of Hydrology, and Hydrological Processes are the three most topically diverse journals among the corpus that we studied.

An overview of streamflow prediction using random forest algorithm

Since the first application of Artificial Intelligence in the field of hydrology, there has been a great deal of interest in exploring aspects of future enhancements to hydrology. This is evidenced by the increasing number of relevant publications published. Random forests (RF) are supervised machine learning algorithms that have lately gained popularity in water resource applications. It has been used in a variety of water resource research domains, including discharge simulation. Random forest could be an alternate approach to physical and conceptual hydrological models for large-scale hazard assessment in various catchments due to its inexpensive setup and operation costs. Existing applications, however, are usually limited to the implementation of Breiman's original algorithm for extrapolation and categorization issues, even though several developments could be useful in handling a variety of practical challenges in the water sector. In this section, we introduce RF and its variants for working water scientists, as well as examine related concepts and techniques that have earned less attention from the water science and hydrologic communities. In doing so, we examine RF applications in water resources, including streamflow prediction, emphasize the capability of the original algorithm and its extensions, and identify the level of RF exploitation in a variety of applications.

Spatial and temporal variation characteristics and attribution analysis of land water storage changes in China

AbstractThe quantitative study of terrestrial water storage change (TWSC) and its spatial and temporal dynamics has been an important topic in water resources research and management. In this study, we invert the characteristics of TWSC in China based on the GRACE time-varying gravity field model and explore its attribution analysis, using Theil- Sen Median trend, Mann-Kendall test and geographic detector, as well as the geographically and temporally weighted regression model (GTWR), combined with meteorological and drought indicators and other data to analyze the relationship between their drivers. The results are as follows: (1) The influencing factors of TWSC's high effect are minimum temperature, precipitation and maximum temperature, and the explanatory power of TWSC through factor interaction is much higher than that of a single factor; (2) The GTWR model is better than GWR models in determining the relationship between influencing TWSC and drivers, and there are temporal and local differences in the influence of each driver on TWSC; (3) In summer and autumn, the rise of water storage in central China is higher than that in east and west China. In spring and winter, the overall water storage in China shows a deficit, and some regions in Guizhou, Guangdong and Guangxi have a small surplus compared with other regions.

Featured Collection Introduction: Severe Sustained Drought Revisited: Managing the Colorado River System in Times of Water Shortage 25 Years Later — Part I

Featured Collection Introduction: Severe Sustained Drought Revisited: Managing the Colorado River System in Times of Water Shortage 25 Years Later — Part I

HydroLang: An open-source web-based programming framework for hydrological sciences

This paper introduces HydroLang, an open-source and integrated community-driven computational web framework for hydrology and water resources research and education. HydroLang employs client-side web technologies and standards to carry out various routines aimed at acquiring, managing, transforming, analyzing, and visualizing hydrological datasets. HydroLang consists of four major high-cohesion low-coupling modules: (1) retrieving, manipulating, and transforming raw hydrological data, (2) statistical operations, hydrological analysis, and model creation, (3) generating graphical and tabular data representations, and (4) mapping and geospatial data visualization. To demonstrate the framework's capabilities, portability, and interoperability, two detailed case studies (assessment of lumped models and construction of a rainfall disaggregation model) have been presented. HydroLang's unique modular architecture and open-source nature allow it to be easily tailored into any use case and web framework, and it encourages iterative enhancements with community involvement to establish the comprehensive next-generation hydrological software toolkit.

A bibliometric analysis and review of water resources carrying capacity using rené descartes’s discourse theory

Water Resources Carrying Capacity is an important indicator of water sustainable and economic development, yet few studies investigated a bibliometric analysis of Water Resources Carrying Capacity research. In this research, we proposed a four-staged bibliometric analysis method for Water Resources Carrying Capacity studies following the René Descartes’s Discourse on the Method guidelines which makes the bibliometric analysis process more systematic. Our four-staged bibliometric analysis method contains a refined screening process of the records, which can successfully delete poorly correlated data from tens of thousands of data within a short period of time and determined the subject-related data. The screening results shortlisted 271 records from an initial 16,769. We further conducted a complete bibliometric, statistical and meta-analysis of the 271 records. The results showed that China is the major country that conducts research on Water Resources Carrying Capacity. Modeling in various forms and system dynamics are the mainstream methods of Water Resources Carrying Capacity research. Water Resources Carrying Capacity is intrinsically linked to population carrying capacity, groundwater resources, urbanization and water shortage. Based on a comprehensive analysis of the research of Water Resources Carrying Capacity, we divided the research progress into five stages lastly. The method proposed in this research can provide reference for future bibliometric studies.

Spatial and temporal evolution characteristics of water resources in the Hanjiang River Basin of China over 50 years under a changing environment

In the formulation of scientific water resource management systems and strategies for improving water resource utilization efficiency, the effects of land use change and climate change on the temporal and spatial distribution of water resources cannot be overlooked. And the Hanjiang basin is the main source of water supply to eastern Guangdong and southwestern Fujian, China. However, the basin is experiencing a significant imbalance between water resource supply and demand. This study investigated the spatiotemporal distribution of water resources in the Hanjiang River Basin under changing environmental conditions using the Soil and Water Assessment Tool model. The findings reveal that, from 1980 to 2020, changes in water resources significantly varied among sub-basins, reflecting spatial heterogeneity. Moreover, sub-basins with severe land use changes showed significant changes in water resources. From 1970 to 2020, the water resources of each sub-basin changed with climate change, indicating temporal variability. Under the combined effects of land use change and climate change, the amount of water resources decreased and its spatial distribution changed dramatically. At the same time, the evolution of water resources under climate change was consistent with that under the combined influence of both land use change and climate change, indicating that climate change is the primary driver. The findings provide theoretical guidance for water resource research and management.

Runoff Estimation in Ungauged Watershed and Sensitivity Analysis According to the Soil Characteristics: Case Study of the Saint Blaise Vallon in France

Water Resources Research announced that, as a result of global warming, the amount of extreme torrential rain globally has increased steadily since the middle of the last century. To cope effectively with climate change, it is important to use consistent and scientific water information of water resources. In this study, we use a hydrological analysis of the Saint Blaise Vallon area to indicate how the damage from natural disasters that may come in the future may be minimized. In addition, a hydrological analysis and a numerical simulation model were implemented to estimate runoff and runoff coefficients derived from a heavy rainfall event that caused serious damage to river flooding. A runoff sensitivity analysis was conducted according to the soil parameters. In particular, a method using the hydrological model and the hydrological sensitivity analysis were applied to the target watershed, and the results of the peak outlet discharge were analyzed in time series so that they could be used for flood prediction and disaster management. In addition, the flood discharge and runoff coefficients during the flooding of the target watershed were presented through the study.

A Review of GNSS/GPS in Hydrogeodesy: Hydrologic Loading Applications and Their Implications for Water Resource Research.

Hydrogeodesy, a relatively new field within the earth sciences, is the analysis of the distribution and movement of terrestrial water at Earth's surface using measurements of Earth's shape, orientation, and gravitational field. In this paper, we review the current state of hydrogeodesy with a specific focus on Global Navigation Satellite System (GNSS)/Global Positioning System measurements of hydrologic loading. As water cycles through the hydrosphere, GNSS stations anchored to Earth's crust measure the associated movement of the land surface under the weight of changing hydrologic loads. Recent advances in GNSS‐based hydrogeodesy have led to exciting applications of hydrologic loading and subsequent terrestrial water storage (TWS) estimates. We describe how GNSS position time series respond to climatic drivers, can be used to estimate TWS across temporal scales, and can improve drought characterization. We aim to facilitate hydrologists' use of GNSS‐observed surface deformation as an emerging tool for investigating and quantifying water resources, propose methods to further strengthen collaborative research and exchange between geodesists and hydrologists, and offer ideas about pressing questions in hydrology that GNSS may help to answer.

Spatio-temporal variability of Turbidity over Ukai Reservoir, by using RS2/R2A LISS III Satellite datasets

Turbidity is an optical determination of water clarity. It is one of the most important optically active water parameter to assess the water quality through the remote sensing observations. Turbidity measurements come from suspension of sediment such as silt or clay, inorganic materials, or organic matter such as algae, plankton and decaying material. Turbidity and total suspended matter often overlap each other. However, it is not a direct measurement of the total suspended materials in water. Instead, as a measure of relative clarity, turbidity is often used to indicate changes in the total suspended solids concentration in water without providing an exact measurement of solids. Through remote sensing we can monitor the turbidity in large water bodies, rives, coastal areas etc. An algorithm has been developed to estimate the turbidity (in NTU: Nephelometric Turbidity Unit) over inland waters (Ukai reservoir) using empirical relationship between normalized Green and Red bands (NDTI : Normalized Difference Turbidity Index) of Resourcesat-2 and Resourcsat-2A Linear Imaging Self Scanning-III (RS2 and R2A LISS-III) dataset. Derived algorithm shows a strong coefficient of determination (R2 = 0.97) with the in-situ turbidity measurements. The field measurements were carried out over Ukai reservoir on 27-28th March 2018, where synchronous in situ water leaving reflectance and turbidity were measured. Model was derived between in situ measured turbidity and NDTI derived from spectral reflectance of band 2 (Green) and band 3 (Red) of RS2 and R2A LISS-III. The model was applied to derive the turbidity maps of Ukai reservoir for pre-monsoon (March, April and May months) season during the period 2012 to 2018. Overall turbidity ranges from 1.47-20 NTU during the field data collection of pre-monsoon season and overall scene derived turbidity ranges are between 2 – 33 NTU. The highest observed turbidity value was more than fourteen times greater than the lowest value that shows the natural variability within the reservoir for the same season. Remotely sensed data sets can increase the abilities of water resources researchers and decision making persons to monitor waterbodies more effectively and frequently.

Study of water resources parameters using artificial intelligence techniques and learning algorithms: a survey

Qualitative analysis of water resources is one of the most widely used topics in water resources research today. Researchers use various analysis methods of water parameters to achieve the desired goals in this field. This research uses artificial intelligence (AI), learning machine (LM), data mining, and mathematical techniques to simulate water behavior and estimate its parametric changes. The proposed model used in this study was a Self-adaptive Extreme learning machine (SAELM) to estimate hydrogeological parameters of the Meghan wetland located in Markazi province in Iran. In addition, SAELM simulation results were compared to Least square support vector machine (LSSVM), Multiple linear regression (MLR), and Adaptive Neuro-fuzzy inference system (ANFIS) models. The simulated parameters were Electrical Conductivity (EC), Total Dissolved Solids (TDS), Groundwater Level (GWL), and salinity. This information was related to sampling for 175 months in the study area. Finally, after simulation operation, four models were introduced as superior models. Mentioned exceptional models were SAELM in GWL modeling, SAELM in modeling the EC, MLR in salinity simulation, and LSSVM in the simulation of TDS parameters. Moreover, by five approaches, the models' performance was evaluated. Suggested strategies were performance evaluation by statistical indicators, Wilson score method uncertainty analysis (WSMUA), response & correlation plots, discrepancy ratio charts, and distribution error diagrams. Based on statistical indicators, the SAELMGWL model was the most accurate model with RMSE, MAPE, and R2 indices equal to 0.1496, 0.0043, and 0.9933, respectively. The ANFIS model had the worst results in simulation.

Thank You to Our 2021 Reviewers

On behalf of the editorial board of Water Resources Research (WRR) and the entire water science community, we want to express our most heartfelt gratitude to all who reviewed manuscripts for the journal in 2021. Your great efforts have ensured and improved the high quality and impact of the WRR papers and generally of research in our field. In 2021, the 2,960 individuals listed below have contributed 4,788 peer reviews with the 432 people indicated in italics, reviewing three or more papers for the journal. The editors, associate editors, and authors of WRR sincerely thank all our reviewers and look forward to the exciting advances that will be published in WRR in 2022.