Hydrological Problems Research Articles

ABSTRACT Seawater intrusion (SWI) is the most important hydrological problem in the highly populated coastal regions. SWI in the coastal aquifers is caused by the intense withdrawal of groundwater and reversal of the natural hydraulic gradient. The study focuses on geophysical and geochemical analysis to identify areas contaminated by saline water intrusion. Resistivity survey and groundwater analysis were conducted in 33 locations in the study area to identify the extent of SWI. Geochemical analysis indicated that the groundwater quality was not suitable for drinking and irrigation purposes in 64% and 85% of the study area, respectively. The areas with low resistivity and high values of water quality parameters, such as electrical conductivity, chloride, and low values of Na/Cl, indicate SWI. Resistivity study was verified by geochemical studies, and the results indicated high salinity prevailed up to 13.7 km, moderate salinity up to 16.5 km, and freshwater was present beyond 16.5 km from the coast. The study found that geophysical methods offer a valuable alternative to laborious geochemical approaches for estimating aquifer parameters and detecting saline water intrusion. The study indicated the need for proper management of coastal aquifers to control SWI.

Opencast Mining of Sedimentary deposits involve removal of huge amount of Overburden for reaching the deposit. This may cause disturbance of the underlying confined aquifers resulting in release of stored water with high pressure in the working area. This makes mining difficult and unsafe due to inrush of water in the working pit. Similar type of problems are faced by mines at Neyveli area. Mining of lignite from NLCIL’s mines in Neyveli area is faced with a unique hydrological problem due to the presence of confined aquifers below lignite seam exerting an upward pressure, if not controlled; it could jeopardize the entire mining operations. The hydro-geological investigation and pumping tests conducted in this field proved that most practical–cum-economic solution to this problem is depressurization of confined aquifer and maintaining the pressure head constantly below the lignite seam (initial technique) or just above lignite seam (positive head- improvised technique) by large scale continuous pumping operation from the series of large diameter pump well located /situated at hydrologically pre-planned /calculated distances from the active excavation zone. Therefore, the depressurization of this aquifer is being done to maintain the pressure head constantly at the lignite bottom through pumping operations from strategically located pumping wells in the lignite mines/ close to excavation zone. The pressure surface is controlled through adequate number of large diameter pumping wells (Drilling dia :36 inches/Casing dia 20inches) with capacity of 1000 & 500 gallon per minute. Effective control of Ground water pressure is achieved by shifting the location of drilling/pumping wells at lignite bund closer to lignite excavation area and overcoming earlier problems of high groundwater pressure resulting in available lignite not been extracted fully. Groundwater and Storm Water aspects have been improvised over a period of time to suit the lignite mining operations. The groundwater pumped out from the mines is being used in Thermal Power Stations for power generation and the surplus water is supplied for community use in after proper treatment. In order to pump out water from the aquifers, proper planning of pumping locations and proper methods are to be analyzed in order to make the working area suitable for excavation. This article presents a detailed analysis of various methods adopted for ground water control and management in Mines for safe and optimum mining process and also to check the efficiency for ground water control in mines.

Modern artificial intelligence methods are increasingly applied in hydrology, particularly for forecasting water inflow into reservoirs. However, their limited interpretability constrains practical deployment in critical water resource management systems. Explainable AI offers solutions aimed at increasing the transparency of models, which makes the topic relevant in the context of developing sustainable and trusted AI systems in hydrology. Articles published in leading scientific journals in recent years were selected for the review. The selection criteria were the application of XAI methods in hydrological forecasting problems and the presence of a quantitative assessment of interpretability. The main attention is paid to approaches combining LSTM, GRU, CNN, and ensembles with XAI methods such as SHAP, LIME, Grad-CAM, and ICE. The results of the review show that XAI mechanisms increase confidence in AI forecasts, identify important meteorological features, and allow analyzing parameter interactions. However, there is a lack of standardization of interpretation, especially in problems with high-dimensional input data. The review emphasizes the need to develop robust, unified XAI approaches that can be integrated into next-generation hydrological models.

The river basins of Ethiopia are fundamentally important to the nation's hydrological resources concerning agriculture, energy production, and ecosystems. However, the basins face major hydrological and environmental problems associated with water scarcity, pollution, sedimentation, and the consequences of climate variability. The following critical review examines the hydrological and environmental problems of Ethiopian river basins, underlining the interlinkages between human activities, natural processes, and policy frameworks. It identifies the most important drivers of degradation and potential mitigation strategies, including integrated watershed management, sustainable land use practices, and improved governance. Addressing these challenges using evidence-based approaches will contribute to the resilience and sustainability of river basins in Ethiopia for national development and ecological health

Relevance. The need for sensitive determination of the content of higher alcohols in water and water-oil emulsions to solve hydrological problems and tracer studies of oil deposits. Aim. Minimization of sample preparation in the gas chromatographic determination of higher alcohols based on the use of macrocyclic glycoluryl derivatives as a new sorption material. Methods. Gas chromatography, thermogravimetry, statistical methods. Results and conclusions. The authors have developed new sorbents based on the Chromaton N-AW DMCS solid carrier and macrocyclic glycoluryl derivatives in combination with fixed liquid phases OV-101, SE-52 and CBWX 20M for microextraction. The paper shows practical application of an environmentally friendly analytical technique for the gas chromatographic determination of higher alcohols in water bodies. The validation of the method was performed using calibration and minimization of matrix effect. The proposed method demonstrated low detection and quantification limits, 1,5 and 4,5 mg/L, respectively, while the accuracy of the method did not exceed 10%. At the stage of microextraction of higher alcohols, the extraction rate was >94%. The use of mixtures of macrocyclically produced glycoluryl with fixed liquid phases OV-101, SE-52 and CBWX 20M for gas chromatographic separation and identification showed a resolution of high alcohols with hydrocarbons >1.12, which confirms the selectivity of their separation. The authors studied the textural and chromatographic properties of new sorption materials, determined the thermal stability range of macrocyclic glycoluryl derivatives and the maximum operating temperature of sorbents. The paper demonstrates the possibility of practical application of the obtained new sorption materials for microextraction and gas chromatographic express determination of higher aliphatic alcohols C3-C12 at sufficiently low detection and quantification limits.

Abstract Although the concept of thermodynamic entropy due to Clausius dates back to the early 1850s, the mathematical theory of informational entropy was not developed until the pioneering work of Shannon in 1948, the development of principle of maximum entropy (POME) and theorem of concentration by Jaynes in 1957, principle of minimum cross entropy by Kullback and Leibler in 1959, and the formulation of entropy in frequency domain by Burg in 1967. The concept of informational entropy is more intuitive, because it is a measure of information or uncertainty which is encountered in daily life. Hence, its application is ubiquitous. If we peruse hydrologic problems, it becomes clear that their solutions involve either measurement of information through data collection, or extraction of information through data analysis, or maximization or minimization of information by optimization, or prediction of information through modeling, or analysis and synthesis of information by simulation, or weighing of information for decision making. Thus, solutions of hydrologic problems may involve the direct application of entropy, examples of which are monitoring network evaluation and design, water resources allocation, and model selection. Solutions of some problems involve the application of the POME, such as derivation of frequency distributions and parameter estimation, whereas solutions of other problems may involve the POME and a flux‐concentration type relation, such as modeling of hydrologic processes. There seems hardly any area in hydrology where entropy cannot be gainfully applied. This paper discusses basic ingredients for the application of entropy theory.

This study aims to estimate the water volume of Lau Kawar Reservoir using the cylindrical shell method of integral calculus. The reservoir is assumed to be a perfect cylinder with a radius of 564 meters and a depth of 8.45 meters. The volume is calculated using both the shell method and the standard analytical formula for cylinders. The result shows a water volume of 8,437,640.45 m³. Both methods produced identical results with an absolute error of 0.00 m³. This proves that the shell method is highly accurate for symmetrical reservoir shapes. The study demonstrates how integral calculus can be applied to real-world problems in hydrology.

Relevance. The severity of the problems of managing hydrological and in-channel processes and the assessment of the social consequences of water use in rivers is steadily increasing. The study of expert and public perception of negative manifestations of hydrological and inchannel processes is an important component of improving management decisions in this area. Methods. In order to identify expert and public opinion for the management of hydrological and in-channel processes, a survey of leading experts in this field was conducted, as well as a supportive survey of representatives of the local population who are under the influence of these negative processes. The study was geographically linked to the Yakutsk Water management hub. Results. The results were obtained on the main blocks of problems of managing hydrological and in-channel processes within the Yakutsk water management hub: the nature of anthropogenic impact on the in-channel and the social effects of this impact, the effectiveness of methods of managing adverse hydrological and in-channel processes, the main problems of strategic planning of water management activities in the region. Through the study of expert opinion, the main pain points associated with the negative impact of hydrological and in-channel problems have been identified. A survey of the local population showed that their involvement in solving the problems under consideration is insufficient, and their attitude to the current situation is characterized by low awareness, superimposed on a low willingness to actively participate in the long-term solution of the problems under consideration.

ABSTRACT This work delves into the hydrological history of Lebanon. With its mountain-influenced Mediterranean climate and its geographic position, Lebanon has a rich history influenced by multiple civilizations (Romans, Arabs, Ottomans, etc.). The study, drawing from mythology, toponymy, archaeology, historical texts and maps, and grey and scientific literature, insists on the double epistemic and technological progress in terms of hydrological knowledge and of water management know how, with specific details about three old cities (Tyre, Beirut and Tripoli), rural settlements and agricultural irrigation. Curating hydrological data, information and knowledge from antiquity to the present is crucial in order to preserve legacy, feed knowledge capitalization and reinforce intelligence towards solutions for water security, in such a socio-hydrologically data-scarce and turbulent region. Insights into this corpus of retrospective water-related knowledge and management are further of interest for the wider Levant and Middle East, in line with Unsolved Problems in Hydrology (UPHs) 17 and 23.

Abstract Sparse precipitation data in karst catchments challenge hydrologic models to accurately capture the spatial and temporal relationships between precipitation and karst spring discharge, hindering robust predictions. This study addresses this issue by employing a coupled deep learning model that integrates a variation autoencoder (VAE) for augmenting precipitation and a long short‐term memory (LSTM) network for karst spring discharge prediction. The VAE contributes by generating synthetic precipitation data through an encoding‐decoding process. This process generalizes the observed precipitation data by deriving joint latent distributions with improved preservation of temporal and spatial correlations of the data. The combined VAE‐generated precipitation and observation data are used to train and test the LSTM to predict spring discharge. Applied to the Niangziguan spring catchment in northern China, the average performance of NSE, root mean square error, mean absolute error, mean absolute percentage error, and log NSE of our coupled VAE/LSTM model reached 0.93, 0.26, 0.15, 1.8, and 0.92, respectively, yielding 145%, 52%, 63%, 70% and 149% higher than an LSTM model using only observations. We also explored temporal and spatial correlations in the observed data and the impact of different ratios of VAE‐generated precipitation data to actual data on model performances. This study also evaluated the effectiveness of VAE‐augmented data on various deep‐learning models and compared VAE with other data augmentation techniques. We demonstrate that the VAE offers a novel approach to address data scarcity and uncertainty, improving learning generalization and predictive capability of various hydrological models. However, we recognize that innovations to address hydrologic problems at different scales remain to be explored.

A new metaheuristic optimizer combined with artificial neural networks is proposed for streamflow prediction. Hence, the study aimed to forecast monthly streamflow of the main rivers in Urmia, Iran, by considering data shortage and using artificial neural network (ANN) models. By combining three variables: temperature, precipitation, and streamflow, we formulated five patterns, where 70% of the data were used for model training, and 30% for model testing. To improve the performance of ANN, we evaluated a new optimization algorithm, reptile search algorithm (RSA), and compared the results with combinations of ANN, particle swarm optimization algorithm (PSO), and whale optimization algorithm (WOA) models. The results of the ANN + RSA were promising at most stations and patterns. At Band station streamflow simulation testing gave RMSE, MAE, and NSE of 1.65, 1.21 MCM/month, and 0.80, respectively. At Babaroud station they were 4.01, 3.0 MCM/month and 0.68, respectively, at Nazlo station 5.62, 3.79 MCM/month, and 0.69, respectively, and at Tapik station 5.69, 3.82 MCM/month, and 0.59, respectively. However, the results of the ANN + PSO hybrid model were better than ANN + RSA. The impact of different parameters on the accuracy of streamflow prediction varied depending on model and streamflow station, indicating that the models do not perform consistently across different locations, times, and conditions. The inclusion of lagged monthly streamflow in the model was an influential input parameter. The results demonstrated that the new algorithm consistently improved predictions, enhancing the performance of traditional algorithms. The findings of this study highlight advantage of the ANN + RSA hybrid model for specific areas, suggesting its potential application in other similar hydrological problems for further validation.

Estuarine areas have experienced varying levels of pollution globally due to increased industrial and social development. Water diversion projects can mitigate water pollution in estuaries, but it is necessary to consider freshwater runoff and tidal variations and their effects on water resource scheduling in the tidal reaches. In this study, a multi-objective optimal scheduling model of a tidal-sluice pump based on this flexible freshwater reservoir was established with the aim of mean water level of diversion port, water diversion quantity, and ecological flow. The tidal reach was generalized as a flexible freshwater reservoir, and a hydrodynamic model was used to construct the water level and discharge capacity curves. The advantage of this model is that it transforms complex multidimensional hydrodynamic problems into hydrological problems for water resource scheduling and reduces the complexity of model coupling. In the decision-making application example of the Hanjiang–Rongjiang–Lianjiang Water Transfer Project, the model’s average annual diversions under typical rich, average, and low-flow day schemes all satisfied the minimum diversion requirement of 5.50 × 108 m3. In addition, the average ecological discharge flows all met the ecological discharge requirements of 112 m3/s for the Rongjiang River, and electricity costs were reduced by at least 6.8568 million CNY. This model simplifies the calculation process, improves scheduling efficiency, and maximizes the benefits of the project, providing a new approach for solving water resource scheduling problems in tidal reaches.

ABSTRACT Stable isotope techniques (δ 18Ο, δ 2Η of H2O) were applied in the transboundary Prespa Lakes (Great and Little Prespa) to assess the water dynamics and the evolution of evaporation losses over the past several decades. The lakes currently experience high evaporation losses to inflows (E/I > 60%), which are significantly higher than in the 1980s. The results showed that the Great Prespa Lake (GPL) water level decline could be due to a drastic decrease in lake inflows over evaporation over the years due to climate change and water abstractions. River runoff contributed more (~57%) to the recharge of the GPL in the wet period, whereas in the dry period direct precipitation was more significant. Our work highlights the advantage of using stable isotope techniques to address hydrological problems in comparison to conventional methods, and the need for collaborative efforts between countries to ensure sustainable usage of transboundary water resources.

Nature-based solutions play an essential role in enhancing urban soil hydrology by improving water retention properties, reducing surface runoff, and improving water infiltration. This bibliometric analysis study reviewed the literature and identified the current trends in research related to nature-based solutions in urban soil hydrology. The study has the potential to highlight current research areas and future hot topics in this specific field. The research used the Scopus database to collect published articles from 1973 to 2023. The keywords (“trees” OR “vegetation” OR “green infrastructure” OR “blue green infrastructure” OR “greenery” OR “nature-based solutions” AND “hydrolog*” AND “urban” OR “city” OR “soil”) were searched in the Scopus database, and 13,276 articles were retrieved. The obtained publications were analyzed for bibliometric analysis by using Bibliometrix (v4.3.0) and VOSviewer (v1.6.20) software. The maximum number of publications (970) related to nature-based solutions and urban soil hydrology was published in 2023. Additionally, countries such as the United States and China published 54.2% of articles of the global research in the field of nature-based solutions and urban soil hydrology, with 36% from the USA and 18.2% of articles from China. The bibliometric analysis depicted that Beijing Normal University led this specific research field with 540 articles. The top country in terms of collaboration was the USA, with 26.17% as compared to the global countries. The most productive researcher identified was Jackson, T.J., as he had the highest number of publications, showing his considerable contribution to the field. Furthermore, the most frequent keywords used in this research area were hydrology, ecosystem services, urban hydrology, remote sensing, nature-based solutions, climate change, runoff, stormwater management, water quality, vegetation, green roof, bioretention, and land use. The early research trending topics in this field from 2015 to 2023 were remote sensing, soil moisture, climate change, drought, green infrastructure, machine learning, and nature-based solutions. The bibliometric analysis identified limited interdisciplinary research integrations, not using well-significant and standardized methodologies for the evaluation of urban soil hydrology, and under-representation of research from developing countries as current research gaps. Future research directions highlight advanced methods such as combining data-driven technologies with traditional hydrological approaches, and increasing international collaboration, specifically in developing nations, to address urban soil hydrological problems properly.

Contemporary distributed hydrological models are detailed and mathematically rigorous, but their calibration and testing can be still an issue. Often it is based on the quadratic measure of the calculated and observed hydrographs proximity at one outlet gauge station, typically on the Nash-Sutcliffe model efficiency coefficient (NSE). This approach seems insufficient to calibrate a model with hundreds of spatial elements. This paper presents using a multi-dimensional estimator of modeling quality, being a natural generalization of the traditional NSE but which would aggregate data from several hydrological stations using Principal Component Analysis (PCA). The method was tested on the ECOMAG model developed for a sub-basin (24,400 km2, with 15 gauges) of the Ussuri River in Russia. The results show that the presented version of the multi-dimensional NSE with PCA in calibration of spatially-distributed hydrological models has a number of advantages compared to other methods: the reduced dimensionality without loss of important information, straightforward data analysis and the automated calibration procedure; objective separation of the deterministic signal from the noise, calibration using the “informational kernel” of data, leading to more accurate parameters’ estimates. Additionally, the introduced notion of the “compact” dataset allow to interpret physical-geographical homogeneity of the basins in mathematic manner, which can be valuable for hydrological zoning of the basins, hydrological fields analysis, and structuring the models of large basins. There is no doubt that further development and testing of the proposed methodology is advisable in solving spatial hydrological problems based on distributed models, such as managing a cascade of reservoirs, creating hydrological reanalyses, etc.

Abstract Understanding the change in soil hydraulic conductivity with temperature is key to predicting groundwater flow and solute transport in cold regions. The most commonly used models for hydraulic conductivity during freeze‒thaw cycles only consider the flow of capillary water in the soil and neglect water flowing along thin films around the particle surface. This paper proposed a new hydraulic conductivity model of frozen soil via the Clausius–Clapeyron equation based on an unsaturated soil hydraulic conductivity model over the entire moisture range using an analogy between freeze‒thaw and dry‒wet processes in soils. The new model used a single equation to describe the conductivity behaviors resulting from both capillary and adsorption forces, thus accounting for the effect of both capillary water and thin liquid film around soil. By comparison with other existing models, the results demonstrated that the new model is applicable to various types of soils and that the predicted hydraulic conductivity is in the highest agreement with the observed data, while reducing the root mean square error by 38.9% compared to the van Genuchten–Mualem model. Finally, our new model was validated with thermal–hydrological benchmark problem and laboratory experiment result. The benchmark results indicated that the advective heat transfer was more significant, and the phase change was completed earlier when considering both capillary and adsorption forces than when only considering capillary forces. Furthermore, the coupled flow–heat model with the new hydraulic conductivity expression replicated well the results from a laboratory column experiment.

Abstract When using machine learning to model environmental systems, it is often a model’s ability to predict extreme behaviors that yields the highest practical value to policy makers. However, most existing error metrics used to evaluate the performance of environmental machine learning models weigh error equally across test data. Thus, routine performance is prioritized over a model’s ability to robustly quantify extreme behaviors. In this work, we present a new error metric, termed Reflective Error, which quantifies the degree at which our model error is distributed around our extremes, in contrast to existing model evaluation methods that aggregate error over all events. The suitability of our proposed metric is demonstrated on a real-world hydrological modeling problem, where extreme values are of particular concern.

Abstract The lack of accurate information on the spatiotemporal variations of snow water equivalent (SWE) in mountain catchments remains a key problem in snow hydrology and water resources management. This is partly because there is no sensor to measure SWE beyond local scale. At Mt. Zugspitze, Germany, a superconducting gravimeter senses the gravity effect of the seasonal snow, reflecting the temporal evolution of SWE in a few kilometers scale radius. We used this new observation to evaluate two configurations of the Alpine3D distributed snow model. In the default run, the model was forced with meteorological station data. In the second run, we applied precipitation correction based on an 8 m resolution snow depth image derived from satellite observations (Pléiades). The snow depth image strongly improved the simulation of the snowpack gravity effect during the melt season. This result suggests that satellite observations can enhance SWE analyses in mountains with limited infrastructure.

The existence of the hydrological problem is having great global repercussions as well as in the case of Mexico and the various federal entities that make it up, such as the State of Mexico, specifically the municipality of Ecatepec de Morelos, where the Universidad Estatal del Valle de Ecatepec (UNEVE) is located. UNEVE’s educational offering includes seven graduate and two postgraduate degrees. Humanidades-Empresa, which is carrying out this research work, takes into consideration the ODS recommendations in its study plan. Therefore, it is necessary to pay attention to the fulfillment of objective 6 “Clean water and sanitation”, given that the municipality where the institution is located has had the problem of water shortage for several years now. The measures that need to be implemented to avoid waste and shortages require the involvement of everyone, to present proposals and solutions. The results of this research show the interest of students, teachers, administrative and management staff in participating. This is an element that favors the generation of actions aimed to improve the consciousness of water saving and will allow this idea to be transferred to other educational divisions and administrative areas, so that it can be extended among the society.

The implementation of large-scale water infrastructure projects, such as the São Francisco River Integration Project with the Northeastern Hydrographic Basins (PISF), causes territorial transformations of great proportions, constituting a new hydrosocial cycle, resulting in hydrosocial territories in which not only water but also power relations and socioeconomic and physical resources circulate. In this sense, this article aims to historically analyze the interconnection between water and land issues in the Brazilian Northeast, shedding light on the territorial dynamics produced by the new hydrosocial cycle constituted by the implementation of the largest water project in the country. To this end, we interviewed actors from different groups and in various locations, conducted on-site field research, and collected and systematically analyzed important documents from the institutional websites of the Brazilian government. The main results of the research indicate that since the implementation of the PISF, a context of deep and historical political and socioeconomic inequalities in hydrosocial territories has been impacted, which has deepened conflicts over land and water and increased socio-spatial injustices. Contrary to most available approaches, which consider water scarcity a ‘natural’ (hydrological) problem that requires technical and administrative solutions, the water problem reflects the profound asymmetry of power consolidated over time and the strength of the reactionary agrarian sectors that control the State apparatus.