Effective Management Of Water Resources Research Articles

Chemical characteristics and health risk evaluation of natural waters in the Du River Source National Nature Reserve: A case study in Zhushan County, Hubei Province, China

To study the distribution of trace elements in natural water of the Du River Source National Nature Reserve and to assess the water quality and health risks, Zhushan County in Hubei Province was selected as the study area. Element content in 361 natural water samples collected from Zhushan County were measured by ICP-MS, ICP-OES, and HG-AFS. The main anions and cations present in water samples from Zhushan County are Ca2+ and HCO3−. The water chemistry is predominantly influenced by the weathering of carbonate rocks. The water samples with high content of selenium (Se) (0∼82.9 μg/L, mean 4.6 μg/L) in natural water in Zhushan County are mainly distributed in the northern part of Zhushan. The strontium (Sr) content of 49.6% of the water samples (0.001–2.177 mg/L, mean 0.234 mg/L) reached the criteria of natural mineral water for drinking in China (Sr ≥ 0.2 mg/L), which is distributed throughout the county. The high content of metasilicic acid (H2SiO3) (0.026–35.910 mg/L, mean 12.598 mg/L) and zinc (Zn) (0∼407.218 μg/L, mean 12.406 μg/L) are concentrated in northern Zhushan County. 99.7% water samples were freshwater and 98.9% meet the criteria of “good” water quality. All of the natural water samples have low health risk and low heavy metal pollution. 6.1% water samples meet the criteria of Se-type mineral water, while 45.4% meet the criteria of Sr-type mineral water, and 4.4% water samples meet the criteria of “low sodium, high Se, and high Sr” mineral water. Zhushan County has the potential for Se-type mineral water and Sr-type mineral water development. The findings of this study hold immense significance for the public health implications of drinking water in Du River Source, thereby offering valuable insights for effective water resources management.

A GIS-based Approach to Spatially Explicit Mean Monthly Areal Rainfall Estimation of Krishna District, Andhra Pradesh, India

Rainfall analysis is pivotal for understanding hydrological systems, crucial for effective water resource management. In this study spatial rainfall analysis is carried out for Krishna District of Andhra Pradesh using ArcGIS software. Rainfall data for a period of 30 years (1992-2021) was collected from the Directorate of Economics and Statistics, Vijayawada, Andhra Pradesh. Utilizing the spatial interpolation techniques in Geographic Information System (GIS) software, mean monthly areal rainfall was found using the three methods viz., Arithmetic mean, Thiessen’s polygon and isohyet method. This study utilized Geographic Information Systems (GIS) to visualize spatial patterns of rainfall across Krishna district. To account for missing data in the rainfall record, the normal ratio method was implemented. Spatial maps were generated to depict mean monthly areal rainfall variations. The analysis yielded mean monthly areal rainfall for Krishna district as 84.72 mm, 85.22 mm and 83.97 mm using the arithmetic mean, Thiessen polygon and Isohyet methods, respectively. Isohyet method is considered to be more accurate and rainfall is the basic data required for all hydrological analysis as an input layer for runoff generation, water resources planning, evapotranspiration studies, crop planning, flood estimation and water budget estimations etc.

Utilizing Data-Driven Approaches to Forecast Fluctuations in Groundwater Table

Accurate forecasting of fluctuations in groundwater table is crucial for the effective management of regional water resources. This study explores the potential of utilizing remotely sensed satellite data to predict and forecast water table variations. Specifically, two Artificial Neural Network (ANN) models were developed to simulate water table fluctuations at two distinct well sites, namely BA Ea 18 and FR Df 35 in Maryland. One model leveraged the relationship between variations in brightness temperature and water table depth, while the other model was founded on the association between changes in soil moisture and water table depth. These models were trained and validated using recorded water table depths from the aforementioned wells, brightness temperature data acquired from the Advanced Microwave Scanning Radiometer—Earth Observing System (AMSR-E), and soil moisture information generated using the Land Data Assimilation System (LDAS). All models exhibited strong performance in predicting and forecasting water table fluctuations, with root mean square errors ranging from 0.043 m to 0.047 m for a 12-month forecasting horizon. Sensitivity tests revealed that the models displayed greater sensitivity to uncertainties in water table depth compared to uncertainties in both brightness temperature and soil moisture content. This underscores the feasibility of constructing an ANN-based water table prediction model, even in cases where high-resolution remotely sensed data is unavailable. In such situations, the model’s efficacy is contingent on the compatibility of the time series trends in data, such as brightness temperature or soil moisture, with those observed at the study site.

Deep Learning-Based Automatic River Flow Estimation Using RADARSAT Imagery

Estimating river flow is a key parameter for effective water resource management, flood risk prevention, and hydroelectric facilities planning. Yet, traditional gauging methods are not reliable under very high flows or extreme events. Hydrometric network stations are often sparse, and their spatial distribution is not optimal. Therefore, many river sections cannot be monitored using traditional flow measurements and observations. In the last few decades, satellite sensors have been considered as complementary observation sources to traditional water level and flow measurements. This kind of approach has provided a way to maintain and expand the hydrometric observation network. Remote sensing data can be used to estimate flow from rating curves that relate instantaneous flow (Q) to channel cross-section geometry (effective width or depth of the water surface). Yet, remote sensing has limitations, notably its dependence on rating curves. Due to their empirical nature, rating curves are limited to specific river sections (reaches) and cannot be applied to other watercourses. Recently, deep-learning techniques have been successfully applied to hydrology. The primary goal of this study is to develop a deep-learning approach for estimating river flow in the Boreal Shield ecozone of Eastern Canada using RADARSAT-1 and -2 imagery and convolutional neural networks (CNN). Data from 39 hydrographic sites in this region were used in modeling. A new CNN architecture was developed to provide a straightforward estimation of the instantaneous river flow rate. Our results yielded a coefficient of determination (R2) and a Nash–Sutcliffe value of 0.91 and a root mean square error of 33 m3/s. Notably, the model performs exceptionally well for rivers wider than 40 m, reflecting its capability to adapt to varied hydrological contexts. These results underscore the potential of integrating advanced satellite imagery with deep learning to enhance hydrological monitoring across vast and remote areas.

Hydrogeological assessment and contaminant transport modelling of Enyimba landfill site in Aba, Nigeria

ABSTRACT Commercial and industrial growth in cities generates hazardous waste, often disposed of in open dump sites, contaminating groundwater. Understanding pollutant movement and numerical models are crucial for effective water resource management. This study assessed hydrogeological dynamics and pollutant transfer in the Enyimba landfill site, Nigeria, using field surveys and contaminant modelling to understand groundwater quality and arsenic contamination spread. The research technique included field and laboratory experiments, as well as water sampling during both dry and wet seasons, to evaluate groundwater quality. The geochemical examination of groundwater samples revealed the amounts of several metals using atomic absorption spectrophotometry. The study used contaminant transport modelling with Visual MODFLOW Flex 6.1 and MT3DMS software to simulate groundwater flow and solute transport within the aquifer layers. Predictive simulations over a 20-year timeframe demonstrated the progressive spread of arsenic pollution from the landfill, resulting in an ellipsoid-shaped plume stretching from its source. Arsenic levels have gradually increased, posing a serious threat to groundwater quality and human health. The research highlights the need for remedial measures to reduce groundwater pollution, including waste recovery and recycling facilities and geo-membrane use. It emphasizes proactive actions to protect groundwater supplies and promote environmental sustainability in the Enyimba landfill site.

Evapotranspiration Analysis in Central Italy: A Combined Trend and Clustering Approach

Climate change is increasingly influencing the water cycle, hindering the effective management of water resources in various sectors. Lazio, central Italy, exhibits a wide range of climatic conditions, stretching from the Tyrrhenian coast to the Apennines. This study assessed a crucial aspect of climate change, focusing specifically on reference evapotranspiration (ETo) and its associated hydrological variables. The seasonal Mann–Kendall (MK) test was used to assess trends in gridded data. The K-means algorithm was then applied to divide Lazio into four homogeneous regions (clusters), each characterized by distinct trends in hydrological variables. The analysis revealed statistically significant increasing trends (p ≤ 0.01) in temperature, solar radiation, and ETo, with more marked effects observed in the coastal and hilly clusters. In contrast, statistically significant decreasing trends (p ≤ 0.01) were observed for relative humidity, while no statistically significant trends (p > 0.01) were observed for precipitation. This study’s methodology, combining trend analysis and clustering, provides a comprehensive view of ETo dynamics in Lazio, aiding in pattern recognition and identifying regions with similar trends.

Assessing the water potential and wastewater load capacity of the La Tinh River, Binh Dinh province by MIKE 11AD and Ecolab model combined with GIS method

The study used the NAM model of flow simulation with a time series of 14 years (2008 - 2021), statistical analysis method, and MIKE 11 AD water quality model, and Ecolab combined with GIS method to calculate wastewater reception capacity and load capacity of the basin. The model validation is highly reliable with a model efficiency factor of R2 of 0.777 and BIAS = 5%. The results of the application of the NAM model have determined the daily flow series of 14 years (2008 - 2021) and the average flow characteristics of many years of the La Tinh basin such as Qo flow is about 5.9; 6.0 m3/s, total amount of Wo = 292.1; 188.8 million m3 flow modulus Mo = 19.3 l/s-km2 flow class Yo = 611 mm and flow coefficient α= 0.3 for all sub-basins. The results of the study show that the capacity of receiving wastewater, the capacity of most river sections in the La Tinh basin is still received with five parameters at a relatively high level, the section of the Binh Tri river upstream alone has exceeded its capacity to receive. This is one of the very useful tools in integrated management and effective use management of river basin water resources.

Analysis of trends and abrupt changes in groundwater and meteorological droughts in the United Kingdom

In recent years, the escalating impact of climate change, coupled with growing concerns regarding groundwater drought, has underscored the critical importance of analyzing hydrological trends. This is a foundational step in advancing effective water resource planning and management. This study presents a regional investigation of groundwater and meteorological droughts in the UK, utilizing the Standardized Groundwater Index (SGI) and the Standardized Precipitation Index (SPI). The analysis encompasses drought duration and an evaluation of the correlation between the SGI and SPI. Subsequently, the seasonal Mann-Kendall (MK) test and the Bayesian Changepoint Detection and Time Series Decomposition (BEAST) algorithm were employed to assess overall trends for both SGI and SPI. Additionally, these methods were utilized to pinpoint trend and seasonal abrupt changes within the time series of both indices.The seasonal MK test revealed statistically significant trends in many SGI stations across the UK, spanning from Northern Scotland down to the South of England, encompassing both increasing and decreasing trends. Additionally, the MK test showed slightly positive trends at the low SPI time scale, though these were not statistically significant. In contrast, more consistent and significant increasing trends were observed throughout the entire UK, except for Northern Scotland, which exhibited negative trends. Despite indications of a progressively more humid UK, the BEAST analysis enabled the detection of decreasing abrupt changes in trends. These changes result in sudden transitions from wet to dry conditions, which can be challenging to identify using the MK test alone. However, BEAST revealed both positive and negative abrupt changes across the entire UK, as well as positive or negative variations in seasonality. This suggests a high degree of variability in seasonal patterns. In summary, this study provides a detailed assessment of groundwater and meteorological drought trends and abrupt changes, considering the impact of climate change on various regions of the UK.

Resident water conservation behaviors among different age groups in rural areas of the Weibei arid belt, China

Understanding water conservation behaviors across various age groups is crucial for developing effective water resource management strategies particularly in arid and semiarid zones. Based on a semi-structural questionnaire survey of 445 households in China's Weibei arid belt and extended Theory of Planned Behavior Model, a Structural Equation Model (SEM) was constructed to elucidate the age-related impact of water conservation knowledge, attitudes, subjective norms, and perceived control on water-saving behaviors. The results show that (1) youth group prioritizes personal hygiene and laundry activities whereas the elderly exhibit lower such requirements. (2) Water consumption varies significantly by age, with the elderly group consumes the least, averaging 62.26 L per capita per day (Lpcd), followed by the middle-aged group at 73.95 Lpcd, and the youth at the highest consumption rate of 84.60 Lpcd. (3) Influential factors on water conservation behavior differ substantially by age: Among the youth, water conservation behavior is strongly influenced by attitude in this domain (standardized path coefficient [SPC] = 0.32). The middle-aged group is influenced by subjective norms (SPC = 0.27) and attitudes toward water conservation (SPC = 0.27). Conversely, the elder group's behavior is dominated by knowledge (SPC = 0.35) and perceived control (SPC = 0.32). Given such distinct perceptions and behaviors of household water consumption, tailored water conservation strategies are essential of water conservation education through social media for youth, tiered pricing for the middle-aged, and water-saving subsidies to the elderly in the case areas.

Deep dive into predictive excellence: Transformer's impact on groundwater level prediction

Groundwater, an essential water resource in Taiwan, is closely linked to land subsidence in the Zhuoshui River basin due to excessive exploitation. Therefore, there is a critical need for robust monitoring and predictive tools to facilitate effective management of water resources. Despite the success of Transformer neural networks in natural language processing, their potential in environmental research remains underexplored. This study explores the feasibility of employing a Transformer-based deep neural network to simultaneously predict groundwater levels at eight strategically positioned monitoring stations in the distal fan of the Zhuoshui River basin in central Taiwan. Utilizing a 20-year dataset with 10-day intervals, we compare the Transformer model with benchmarks established separately by the Convolutional Neural Network (CNN), the Long Short-Term Memory neural network (LSTM), and the Feedforward Neural Network (FFNN). Results reveal the Transformer's superior performance in predicting groundwater levels 10 and 20 days in advance. This achievement is attributed to Transformer's self-attention mechanism capable of capturing relationships between factors like rainfall and river flow, enhancing accuracy in predicting trends and peak values. This groundbreaking study underscores the Transformer model's efficacy in groundwater level prediction, emphasizing its significance for sustainable water resource management amid climate change. The findings open new avenues for environmental research and water resource management, not only in Taiwan but also globally.

Investigating hydrological dynamics and mean residence times of springs in the Tawi catchment, India: Insights into recharge mechanisms and water source variability

This study investigates the hydrological dynamics and Mean Residence Times (MRTs) of springs in the Tawi catchment, India, aiming to elucidate recharge mechanisms and water source variability. The catchment's characteristics, including altitude, geology, and land use, are examined in relation to isotopic composition and residence times of springs at six distinct locations. The study examines seasonal variations in isotopic signatures of precipitation, revealing distinct patterns during Western Disturbances (WDs) and Indian Summer Monsoon (ISM). In addition, a composite δ18O – δ2H regression line (LMWL) is developed, that suggests significant secondary evaporation from falling raindrops as a major process shaping precipitation isotopes in the catchment. The isotopic data from six stations within the catchment demonstrate specific variations influenced by local factors. Enriched rain samples in urban areas like Udhampur and Jammu show higher secondary evaporation, while depleted samples in Chenani and Mantalai suggest a canopy cover influence. Temporal and spatial variations in spring water isotopes suggest either a consistent recharge source or isotopic damping. Mean Residence Times (MRTs) of springs indicate variations in aquifer characteristics, with longer MRTs in Chenani and Mantalai indicating deeper and more complex systems. Moderate MRTs in Pangara Jagir, and Udhampur springs, suggest a balance between storage and recharge. Hydrograph separation analysis demonstrates the dominance of WDs as the precipitation source, except for Chak Rakwal, which exhibits karstified behavior with significant contribution during the ISM. The study underscores the importance of understanding recharge elevations and delineating recharge zones for sustainable groundwater management and preservation of water quality in the Tawi catchment. By elucidating these critical aspects, the study contributes to informed decision-making and effective water resource management in complex catchment areas.

Hydrogeochemical characterization and water quality evaluation for drinking and irrigation purposes of coastal aquifers of Middle Andaman

A comprehensive study to characterize the hydrogeochemistry of the region and the impact of saltwater intrusion on groundwater was conducted along the eastern coast of Middle Andaman of Andaman and Nicobar (A&N) Islands. The escalating population growth and intensified tourism activities have resulted in the over-extraction of groundwater. Seismic activities led to the opening of lineaments to the sea and the dissolution of limestone in the influence of seawater. 24 groundwater samples and 1 reference sample from sea were taken from various locations of middle Andaman. The analysis involved the determination of major cations, anions, and heavy metals using Inductively Coupled Plasma Optical Emission spectroscopy (ICP), spectrophotometry, and flame photometry. Furthermore, X-ray diffraction analysis, binary diagrams, Chloro-alkaline indices (CAI), Gibbs Plot, correlation matrix, Piper plot, Chadha’s plot and Principal Component Analysis (PCA) to the major ions data indicated rock-water interactions, strong correlations among alkali and alkaline earth metals, and interactions between seawater and carbonate minerals respectively. The water quality index indicated “very poor to unsuitable for drinking purposes” in 24% of the samples. Additionally, indices for irrigation suitability; total hardness (TH), residual sodium carbonate (RSC), and Magnesium adsorption ratio (MAR) were found to be detrimental for irrigation in 80%, 08%, and 12% of the water samples, respectively. These results highlight the importance of implementing effective water resource management techniques, such as groundwater extraction rates, adopting appropriate water treatment technologies to mitigate the adverse impacts of heavy metals, saltwater intrusion and maintain water quality for human consumption and agricultural use in the region.

Evaluation of water shortage and instream flows of shared rivers in South Korea according to the dam operations in North Korea

Abstract The Korean Peninsula's mountainous terrain poses challenges to effective water resource management. Notably, two significant river basins, North Han River and Imjin River basins, are essentially shared rivers originating in North Korea. After the construction of various dams in North Korea, billions of tons per year of water annually decreased from the upper reaches of these rivers of North Korea to South Korea. This study conducted an impact analysis on two major river basins affected by dam operations in North Korea. Both before and after the Imnam Dam operation, significant reduction (27.7%) in the average monthly inflow was observed, and the total release of the Hwacheon Dam in South Korea decreased by 40.2%. The analysis of instream flow indicated that the operation of dams in North Korea had a substantial impact on fulfilling instream flow requirements for dams located in North Han River and Imjin River basins. To ensure instream flow, this study proposed two plans. The first plan involved the utilization of existing dams in the North Han River basin, while the second plan suggested connecting the dams in North Korea, taking into account the shared rivers.

Water-Level Prediction Analysis for the Three Gorges Reservoir Area Based on a Hybrid Model of LSTM and Its Variants

The Three Gorges Hydropower Station, the largest in the world, plays a pivotal role in hydroelectric power generation, flood control, navigation, and ecological conservation. The water level of the Three Gorges Reservoir has a direct impact on these aspects. Accurate prediction of the reservoir’s water level, especially in the dam area, is of utmost importance for downstream regions’ safety and economic development. This study investigates the application and performance of four distinct deep-learning models in predicting water levels. The models evaluated include the Long Short-Term Memory (LSTM), Bidirectional Long Short-Term Memory (BiLSTM), Convolutional Neural Network–Long Short-Term Memory (CNN–LSTM), and Convolutional Neural Network–Attention–Long Short-Term Memory (CNN–Attention–LSTM). The performance of these models was assessed using several metrics, namely the Coefficient of Determination (R2), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE). The findings indicate that the CNN–Attention–LSTM model outperforms the others in all metrics, achieving an R2 value of 0.9940, MAE of 0.5296, RMSE of 0.6748, and MAPE of 0.0032. Moreover, the CNN–LSTM model exhibited exceptional predictive accuracy for lower water levels. These results underscore the potential of deep-learning models in water-level forecasting, particularly highlighting the efficacy of attention mechanisms in enhancing predictive accuracy. Precise water-level predictions are instrumental in optimizing hydropower generation and providing a scientific basis for effective flood control and water resource management.

Groundwater mapping of urban and coastal areas in Brunei

Groundwater mapping plays an important role in effective water resource management, sustainable development, and environmental protection. In the tropical Brunei Darussalam (north Borneo Island), groundwater mapping is yet to be provided. The aim of the study is to provide groundwater mapping of Brunei, particularly for the urban and coastal areas of the Brunei-Muara capital district. The study uses a GIS interpolation technique to generate a groundwater contour map based on groundwater data from 572 sacrificial boreholes located in the study area. Remote sensing data and published maps from secondary sources were digitised in ArcGIS software to produce thematic layers for further hydrological evaluations. Results showed that groundwater levels in the study area are generally high and shallow, ranging from 0 to 18 m below ground level with a mean value of 2.9 m. According to the evaluation of geo-thematic layers and groundwater contours, groundwater flows towards the South China Sea in the coastal areas and towards the Brunei River further inland. Hydraulic gradients towards the South China Sea also vary between 0.004 and 0.08. Thus, assuming surface aquifer thickness in the weathered zone between 10 to 20 m, hydraulic conductivities ranges from 1∙10−5 to 1∙10−4 m∙s−1, a submarine groundwater discharge (SGD) flux between 4.7∙10−7 to 4.0∙10−4 m3∙s−1 per unit width can be estimated for the shallow aquifer. This study provides valuable insights into the groundwater system dynamics so important, which are critical for its future utilisation and protection, aiming to contribute to the national water security in Brunei Darussalam.

Dynamic monitoring of surface area and water volume of reservoirs using satellite imagery, computer vision and deep learning

The development of surface area and estimated volume measurements for water reservoirs using advanced computational techniques has become an increasingly important task for reservoir operation. Thus, the objective of this study is to determine the surface area and estimate the volume of water reservoirs through the segmentation of multispectral satellite images using a Fully Convolutional Neural Network (FCN). This paper introduces a universally applicable framework that utilizes deep convolutional neural networks for segmenting water bodies, particularly focusing on areas in Paraíba State, Northeast Brazil, susceptible to severe droughts. The study utilized Sentinel-2 satellite data obtained from Google Earth Engine for the period between January 2019 and September 2023. The extraction of water surface area from satellite images was achieved using the U-Net model, a deep learning framework based on FCN. The accuracy of water surface area extraction was analyzed using the Intersection-Over-Union (IoU) metric, also known as Jaccard Index. Subsequently, reservoir volumes were calculated using elevation-area-volume curves. The evaluation metrics for the results included root mean square error (RMSE), mean absolute percentage error (MAPE), coefficient of determination (R2), and Pearson correlation coefficient (r). The network demonstrated high accuracy, achieving a 95% IoU on the test set. This methodology was applied to four reservoirs, yielding promising results that closely aligned the monitored reservoir curves derived from satellite imagery with observed field data. The findings of this study are significant for research on semiarid region changes, extending their relevance to a broader understanding of global environmental dynamics. This work not only contributes to the field of remote sensing in hydrology but also offers practical insights for effective water resource management in drought-prone regions.

Projected changes in extreme daily precipitation linked to changes in precipitable water and vertical velocity in CMIP6 models

Understanding the drivers of precipitation and their changes in a non-stationary climate is crucial for effective climate adaptation and water resource management, as it helps us anticipate and respond to shifting precipitation patterns and their impacts. Here, analysing simulations from the Coupled Model Intercomparison Project Phase 6 (CMIP6) we show that the conditional probability of extreme daily precipitation given joint extremes of two drivers (precipitable water and vertical velocity) will be stable in a 3 °C warmer future. Consistent with earlier work, we find that the near-global increase in precipitable water (thermodynamic influence) is the baseline for changes in extreme precipitation, which are modulated by changes in vertical velocity (dynamic influence). Thus, in regions where vertical velocity increases, the effect of the two drivers is additive and their changes contribute to an increase in extreme precipitation. The changes of the two drivers are opposite where vertical velocity decreases, resulting in only small increases in extreme precipitation or even a decrease. Furthermore, we reveal that there are moderate changes in the dependence between the drivers, which are larger over the ocean than over landmasses, but they contribute only little to the overall changes in extreme precipitation. We conclude that the use of two very simple drivers that are readily available from climate models can be of great utility for evaluating precipitation extremes in models and understanding their projected changes.

Rainfall Repercussions: Assessing Climate Change Influence on Iraq Precipitation Patterns

The unequal spatial and temporal distribution of precipitation, exacerbated by climate change, has received significant attention. Rainfall is pivotal for crop growth and environmental health, crucial in the water cycle, and essential to replenishing surface water sources vital for drinking water supplies. Consequently, understanding this phenomenon is critical for future planning. Evaluating spatial and temporal variations in rainfall is essential for the effective management of water resources. This study employs a statistical analysis of rainfall data from 16 rain gauge stations to identify annual rainfall trends, Linear Regression Equations, Coefficients of Determination (R²), Precipitation Concentration Index (PCI), and Rainfall Variability Index (RVI). The findings of the study come to show as the PCI has indicated strongly irregular rainfall concentration in Iraq. RVI identifies 2017 and 1983 as notably dry years, while 2018 stands out as a particularly wet year within the 40-year period from 1980 to 2019. RVI also highlights that normal rainfall years predominate, with very dry years ranging from 2 to 13, dry years from 3 to 11, wet years from 1 to 7, and very wet years from 6 to 10 within the study period.

Quantifying the Interactions of Noah‐MP Land Surface Processes on the Simulated Runoff Over the Tibetan Plateau

AbstractThe quantification of uncertainties in runoff over the Tibetan Plateau (TP), simulated by land surface models (LSMs), is of paramount importance for effective water resources management within this region. However, the interactions of land surface processes on simulated runoff, where the effectiveness of one process depends on the chosen scheme for another, have rarely been studied. To address this gap, we conducted ensemble simulations with the Noah‐MP (Noah with multiparameterization) LSM by varying the optional parameterization schemes of six land surface processes and quantified the sensitivities of the simulated runoff to these processes. Results showed that the simulated runoff over the TP was most sensitive to the RUN (runoff‐groundwater) process. The interplay of RUN and FRO (frozen soil permeability) accounted for up to 30% of the variation in the annual mean surface runoff in the TP's permafrost regions. The interactions of RUN and VEG (dynamic vegetation) on summer and autumn subsurface runoff exceeded 10% in the southeast TP. In regions where these interactions among land surface processes significantly affected simulated runoff, we observed elevated model errors and reduced model controllability. Therefore, this study underscores the imperative need to categorize land regions based on the interactions of land surface processes as a foundational step toward enhancing the performance of LSMs. Prioritizing improvements in model physics should be particularly directed toward regions marked by high interactions.

Modelling runoff components and hydrological processes in glaciated catchments of the inner Tien-Shan, Kyrgyzstan

Accurately quantifying runoff sources and understanding hydrological processes in glaciated mountain basins is essential for effective water resource management in the face of climate change. This study aimed to determine the contributions from various runoff sources in glaciated basins within the inner Tien-Shan mountains of Kyrgyzstan by utilizing integrated land surface, glacier energy-mass balance, and river routing models. To account for local topographic influences on solar radiation and cloud transmissivity processes, gridded meteorological forcing data were downscaled. The integrated models were then evaluated against observed discharge, glacier mass balance, and snow water equivalent, with a focus on the Kara-Batkak glacier reference site. Shortwave radiation correction was particularly important for improving the accuracy of model simulations. The results indicate that the peak glacier melt contribution occurs in July and August, with some basins reaching up to 54%. On an annual basis, the average contribution from glaciers across the basins was found to be 19%, while the ratios of snowmelt and rainfall were 58% and 23%, respectively. This study highlights the utility of integrated modelling approaches in understanding and quantifying runoff components in data-scarce high mountain regions. The incorporation of observed glacier data proved to be crucial for accurately representing hydrological processes under current climatic conditions. These findings underscore the significance of considering glacier dynamics and their influence on water resources to inform effective water management strategies in glaciated mountain basins.