Water Resources In River Basin Research Articles

Resilience assessment of water supply and demand in the Yellow River Basin based on integrated optimisation weighting–improved grey-target model

Context As a result of the combined impacts of global climate change and intensive human activities, the water-resources supply–demand imbalance has become prominent, particularly in the Yellow River Basin. Aims This study aims to accurately assess the changes in resilience of water-resource supply and demand over the past few decades. Methods This study examines the resources, economy, society, and ecology of both the supply and demand aspects in assessing the resilience of water supply and demand. The composite evaluation model based on integrated optimisation weighting and an improved grey-target model was constructed. Key results Using the Yellow River Basin as an example, from 2011 to 2020, the resilience index of the water-resource supply–demand system (RIWRSS) in the Yellow River Basin increased significantly by 69.4%. Among the 12 indicators in the Yellow River Basin, the contribution proportion of the inhibiting factors decreased by 3.7%, whereas the contribution of the driving factors increased by 70.9%. Conclusions The present study has provided insights into evaluating the RIWRSS and highlighted resilience disparities between the supply and demand sides in the Yellow River Basin. Implications This study expects to provide evidence for the improvement of water-supply conditions of water resources in the Yellow River Basin.

Impacts of planting structure adjustment on water saving in the Shiyang River Basin of Arid Region

Planting structure adjustment (PSA) affects agricultural water saving, and is an essential part of water-saving agricultural construction. This study introduced virtual water theory and innovatively constructed a model to assess the water-saving effects of PSA in Shiyang River Basin over the past 38 years, explore the relationship between planting structure and water saving, and clarify the most water-saving planting structure. The results showed that the sown area of economic crops consistently increased as food crop areas decreased in the four counties (districts) from 1980 to 2017. Being considered a “big water consumer”, wheat has lost its dominant position. The water requirements of major crops in the four counties and districts showed an increasing trend. The total area proportion of vegetables, wheat, corn, and oil-bearing crops (Abbreviated as TPVWCO) directly determined the water-saving amount. The lower the TPVWCO, the better the water-saving effect. Taking 1980 as the reference year, the most water-saving years in Gulang, Liangzhou, Yongchang, and Minqin were 2007, 1981, 2008, and 2005, respectively. Taking 2007 as the reference year, there were no water-saving years available after that due to the higher TPVWCO. Taking into account food security, ecological and economic benefits, it was recommended to control the TPVWCO at 40% in the Shiyang River Basin in the future. The land vacated should be planted with cotton in Minqin, while the land vacated in the other three counties should be planted with fruits. The research results would provide scientific basis for optimizing the planting structure and managing agricultural water resources in inland river basins in arid regions.

Predicting Monthly Runoff: A BO-CNN-LSTM Approach for Enhanced Water Resource Management

Monthly runoff prediction is critical for sustainable water resource management, flood control, and drought preparedness. Accurate forecasts assist decision-making and management of water resources, which are critical for human and environmental demands. Previous prediction algorithms frequently lack accuracy due to human-defined hyperparameters. Autonomous hyperparameter tuning is required for improved predictions. Objectives: To improve the accuracy of monthly runoff predictions, this research proposes a BO-CNN-LSTM model that integrates Bayesian optimization (BO) with a convolutional neural network-long short-term memory neural network (CNN-LSTM). Methods/Analysis: The BO algorithm is utilized to enhance the CNN-LSTM model's hyperparameters, which helps to overcome the limits of human-set parameters. Model training and validation were carried out using monthly runoff data from 1956 to 2019 from Lanxi Station in the Hulan River Basin. The model's prediction performance was assessed using root mean square error (RMSE), mean absolute error (MAE), fitting coefficient (R²), and mean relative error (MAPE), and compared with other previous models. Findings: The results show that the BO-CNN-LSTM model outperforms conventional models in prediction accuracy and reduction of errors. The BO method efficiently enhances hyperparameters, resulting in higher performance metrics. This model gives more precise and dependable monthly runoff forecasts, which are critical for handling the Hulan River Basin's water resources. Novelty/Improvement: By combining BO and CNN-LSTM, the proposed model solves the constraints of previous models, improving prediction accuracy and reducing errors. This innovative technique presents a promising novel approach for monthly runoff prediction, with possible applications in larger hydrological prediction.

Appraising meteoric-surface-groundwater interaction through analyses of hydrochemical evolution of water resources in the Lower Niger River Basin, Nigeria

Appraising meteoric-surface-groundwater interaction through analyses of hydrochemical evolution of water resources in the Lower Niger River Basin, Nigeria

Identification of dominant drivers of streamflow spatiotemporal variations in typical mountainous areas in the Hexi Corridor, China

Study regionTypical mountain areas in the Hexi Corridor, China. Study focusWater security and ecosystem sustainability of arid inland river basins are highly dependent on upstream streamflow. However, due to the complex geographical environment and limited observation data in the study region, the attribution of spatiotemporal variations in streamflow influenced by climate change and/or human activities remains unclear. Here, we used partial least squares regression (PLSR) and the Budyko framework to unravel the dominant drivers of spatiotemporal variation in streamflow over the past 30 yr. New hydrological insight for the regionPrecipitation, topographic wetness index, slope, forest land, gross primary productivity, hydrological connectivity, soil organic carbon content, silt content, relative relief, NDVI and gravel content dominated spatial variation in streamflow. Temporal variation of streamflow was sensitive to precipitation and land surface. Specifically, increased precipitation and land surface alteration dominated the increase in streamflow in 50 % of the watersheds and the decrease in streamflow in 33 % of them, respectively. Further, land surface alteration was dominated by expansion of agricultural and built-up areas, weakened hydrological connectivity, increased landscape aggregation and forest cover. Controlling agricultural and built-up areas and the scale of afforestation, and focusing on the dynamics of hydrological connectivity and landscape patterns in the upstream reaches are imperative to maintain the security and sustainability of water resources in the arid inland river basins.

X-Band Radar and Surface-Based Observations of Cold-Season Precipitation in Western Colorado’s Complex Terrain

Abstract Hydrologic processes associated with intermountain cold-season precipitation in the Upper Colorado River basin have important impacts on avalanche forecasting and water resource management. However, traditional weather radar networks struggle with observations in this complex terrain. Data collected during the Study of Precipitation, the Lower Atmosphere, and the Surface for Hydrometeorology (SPLASH) and its sister campaign, Surface Atmosphere Integrated Field Laboratory (SAIL) in the East River watershed of western Colorado, are used to examine a multistorm period from 23 December 2021 to 1 January 2022 that contributed 35% of the total winter precipitation in this watershed. Dual-polarization X-band radar and disdrometer measurements show ∼30-mm differences in precipitation amount at two sites in proximity over four distinct storm events within the period. Wind patterns, synoptic forcings, microphysical characteristics of precipitation, and surface meteorology are analyzed to explain the observed spatial variability of cold-season precipitation in complex mountainous terrain. Analysis shows that differences over time within this event are mainly accounted for by synoptic forcings, such as frontal passages; differences between sites are accounted for by the impact of variations in local wind patterns on precipitation microphysics. Patterns of surface precipitation intensity are compared and found to be correlated with X-band radar signatures; a relationship between a strong dendritic growth stage and intense low-density surface precipitation is reinforced by this study. This relationship demonstrates the importance of particle growth mechanisms on surface snowfall patterns in high-altitude complex terrain, underscoring the importance of realistic microphysical parameterizations. Significance Statement The amount and density of snowpack from western Colorado winter storms have significant impacts on water resources in the Upper Colorado River basin. Snowpack characteristics are affected by small-scale differences in how snow forms in the atmosphere. These differences are hard to study in the complex terrain of the Rockies, but data from the SPLASH and SAIL field campaigns allows us to investigate how snow crystal formation and mountain-driven wind patterns affect snow near the surface. Our study finds that snow crystal growth varies over small space and time scales and is likely controlled by the terrain beneath a given location and resultant local wind patterns. These results imply that predicting snowpack in the Rockies requires properly representing local wind patterns and crystal growth processes in models.

Transformations in reservoirs and flood passage in the kazakhstan part of the Esil river basin

Justify the technology for the production of hydrological and water management calculations for the passage of the maximum flow of 1% (0.1%) supply. Analyzed and summarized: methods for calculating the maximum flow in the presence and absence of anthropogenic activity; the current state and prospects of the use of water resources and the possibility of regulating and passing floods by reservoirs. At the current level, the problem of producing hydrological and water management calculations for the transformation and transmission of floods by cascade reservoirs has not been resolved. The technology for solving the transformation of floods of 1% (0.1%) of the availability of reservoirs and the possibility of combating floods with a promising level of use of water and land resources in the Esil river basin has been substantiated. It is intended for scientists, employees of design and survey and operational organizations of water management, environmental protection. The technology for calculating the transformation in reservoirs and the passage of floods in the basins of lowland rivers was developed for the first time in the Republic of Kazakhstan.

Panzara River Watershed Prioritization Based on Geomorphometric and LULC Change Analysis using Geo-Spatial Techniques

Human activities can significantly influence the quality of water flowing from a watershed, either positively or negatively. As water moves through the system, these impacts accumulate, with all land-based activities having the potential to affect the water quality and quantity experienced by downstream stakeholders. Similarly, the actions of upstream landowners impact the water that flows across others' properties. Geospatial techniques like remote sensing and geographic information systems (GIS) are invaluable tools for analysing drainage patterns within a watershed and the associated changes in land use and cover. This study focuses on the Panzara river basin, a principal tributary of the larger Tapi river basin, situated in central India between the westward-flowing Godavari and Narmada river systems, which both ultimately discharge into the Arabian Sea. The study area spans latitudes from 20°42'0" N to 21°18'0" N and longitudes from 74°06'0" E to 75°00'0" E, covering a geographical area of 2,986.05 square kilometers with a perimeter of 570.51 kilometers. The watershed delineation was carried out using Shuttle Radar Terrain Mapper (SRTM) data with a 30-meter resolution. For land use and land cover (LULC) analysis, Landsat 5 TM C2L1 and Landsat 8 OLI/TIRS C2L1 datasets, both with 30-meter resolution, were utilized. The present study conducts a morphometric analysis and assesses LULC changes within the Panzara river basin between 2000 and 2021. Morphometric parameters such as linear parameters [Drainage density (Dd), Stream frequency (Fs), Mean bifurcation ratio (Rbm), Drainage texture ratio (Dt), Length of overland flow (Lo)] and areal parameters [Elongation ratio (Re), Circulatory ratio (Cr), Form factor (Rf), Compactness coefficient (Cc)] were used to prioritize sub-watersheds. Furthermore, the study classifies the observed LULC changes between satellite imagery datasets from 2000 and 2021, quantifying the percentage changes in the respective LULC classes across the sub-watersheds over the two decades. The overall accuracy of the LULC classification was 81.82% for 2000 and 88.88% for 2021, with Kappa coefficients of 0.772 and 0.85, respectively. In terms of prioritizing sub-watersheds, common sub- watersheds such as SW-1, SW-10, and SW-15 were classified under moderate priority, while SW-5, SW-8, and SW-14 were classified under the lowest priority. The results of this study, particularly the prioritization of sub-watersheds, can be instrumental for hydraulic engineers in planning and managing water resources in the Panzara river basin.

Modelling the availability of water, energy, and food resources in transboundary river basins to achieve Sustainable Development Goals 2, 6, and 7

The Water-Energy-Food Nexus (WEFN) has gained international attention as an approach for managing water more holistically, building on the progress made by the Integrated Water Resources Management (IWRM) approach. However, modelling the availability of water, energy, and food and their interconnections is complex, with major barriers that include the availability of both data and modelling platforms capable of assessing the interconnected systems of the WEFN. There have been attempts to model resource availability in each sector generally, but few attempts to consider the WEFN in complex transboundary basins, especially considering Sustainable Development Goals (SDGs). Here, a water, energy, and food availability model was developed using a combination of remotely sensed, national government reported, and global datasets. The model explores the complex interactions between the physical availability and production of water, energy, and food for several subbasins representing different states. Results showed the potential impacts of socioeconomic development, population changes and transboundary cooperation on resources. Transboundary cooperation in the case study Ma Basin in Southeast Asia, under existing resources and operations could improve monthly food security and reduce coal power operations using excess hydropower in the basin during the dry season. These findings show that transboundary cooperation improves access to energy and food, whilst potentially reducing demands for water. Furthermore, the research has highlighted ways in which transboundary cooperation can further assist in attaining SDGs 12 and 13 on responsible production and climate action, respectively. The modelling framework is easily transferable and can support similar analysis in other basins, contributing to global attainment of several SDGs.

Multi-objective double layer water optimal allocation and scheduling framework combing the integrated surface water – groundwater model

Balancing the water consumption of agricultural and ecological is the key point of sustainable social and economic development in an inland river basin. The growth of desert riparian forests in inland river basins mainly depends on a certain phreatic water table depth (PWTD). The main object of this study was to allocate and schedule water resources to regulate the PWTD and satisfy agricultural water demand. Therefore, a multi-objective double layer optimal allocation and scheduling framework based on the computationally efficient integrated surface water-groundwater model (ISGWM), which can simulate the surface water processes, groundwater recharge and discharge processes, and PWTD changes, was constructed and applied to the mainstream of Tarim River Basin (TRB). The top layer model of the framework is an optimal ecological water allocation model, and its optimal allocation results are used as the initial solution of the bottom layer model. The results show that under 5 different inflow frequencies, the agricultural water shortage rate is 0, 17.38 %, 17.41 %, 14.06 %, and 19.94 %, respectively. The PWTD regulation has a great performance. After the optimal scheduling, the proportions of good growth of the control area behind the gate under different inflow frequencies were 98.18 %, 98.18 %, 98.18 %, 90.91 %, and 94.55 %. Agricultural water shortage is mainly due to the non-uniformity distribution of intra-annual inflow and the lack of controlling hydraulic engineering. The regulation of PWTD can guarantee the growth of desert riparian forests on both sides of the mainstream of TRB. Besides, we explored the feasibility of exploiting groundwater to supplement agricultural water consumption. The groundwater exploitation should be controlled within the scope of not causing excessive increase of PWTD (difference between PWTD and target depth <1 m), due to the groundwater exploitation to supplement agricultural water will lead to the increase of PWTD. Overall, this framework, which regulates the PWTD with the change of ecological water supply based on the ISGWM, provides a new idea for the allocation and scheduling of agricultural and ecological water resources in arid inland river basins. It also provides a new method for the coupled cooperative operation of surface water and groundwater.

Hydrochemistry and isotope hydrology of groundwater and surface water in the Sor and Gebba watershed, southwestern Ethiopia

The Sor and Gebba watershed plays a crucial role in the water resources of the Baro-Akobo River Basin. However, various factors have had a detrimental effect on the watershed. Unplanned groundwater extraction, wetland drainage, surface water pollution, and land use changes have all intensified due to population growth, urbanization, commercial farms, industrial development, poor water resources management, and improvements in living standards. This research utilized major hydrochemical ions and environmentally stable isotopes to elucidate the interaction between groundwater and surface water in the watershed, which are reliable tracers in various hydrologic processes. During the 2020 wet season and 2022 dry season, over 25 meteoric water samples were collected from different locations for hydrochemical and isotopic analysis. The hydrochemical analysis revealed that the dominant water type is Ca–Mg–HCO3−. Among the anions, HCO3− was the most dominant, followed by NO3−, Cl−, and SO42−, and those of cations Ca2+, followed by Na+, Mg2+, and K+. The spatial analysis of environmentally stable isotopes confirmed the interaction between groundwater and surface water in the watershed. The quality of groundwater is primarily influenced by rock-water interaction (Gibb's diagram) and, to some extent, by evaporation (Isotope study). A graph of the water samples closely aligned with the Addis Ababa-local meteoric water line indicates their meteoric origin. However, there was a slight deviation to the left due to the effects of distance and altitude. The isotopic correlation further supported the interaction between different water sources.

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.

Spatio-temporal multivariable time vario-zoom network for water level forecasting based on high-resolution hydrological dataset

Water level forecasting is a crucial element in river-basin water resource management, particularly for shipping and extreme events, such as floods and droughts. However, accurately forecasting water levels is challenging due to the influence of non-stationarity hydrological processes, complex characteristic variables, and noisy data. Data-driven approaches in water level forecasting generally lack annotated high-resolution data. To address these problems, this paper presents the first hydrological dataset on the Xijiang River basin-Xijiang Water Dataset (XJWD) and a Spatio-Temporal Multivariable-based Time Vario-Zoom Network (STM- TVZN) model. XJWD combines multi-source raw hydrological data and has real-time and higher resolution data than comparable datasets. STM-TVZN is specifically tailored to enhance the discernment of salient features within key time windows for each variable by fusing spatio-temporal multivariate features through vario-zoom time windows. Finally, a novel metric, Extremum Time Offsets (ETO), is proposed to evaluate the accuracy of the occurrence of local extremum in time series, and it is significant for predicting and evaluating extremum events such as floods and droughts. Experimental results show that the proposed method outperformed existing models on XJWD dataset regarding error rate, stationarity, similarity, and ETO. Overall, the proposed method of combining time-variable focusing network design and spatio-temporal multivariate feature extraction is appealing for short-term water level forecasting in inland rivers, exhibiting potential capacity in strategic water-saving planning and extremum event forecasting caused by climate change.

Assessment of Water Resources under Climate Change in Western Hindukush Region: A Case Study of the Upper Kabul River Basin

This study aims to estimate the surface runoff and examine the impact of climate change on water resources in the Upper Kabul River Basin (UKRB). A hydrological model was developed using the Soil and Water Assessment Tool (SWAT) from 2009 to 2019. The monthly calibration was conducted on streamflow in six stations for the period from 2010 to 2016, and the results were validated from 2017 to 2018 based on available observed data. The hydrological sensitivity parameters were further prioritized using SWAT-CUP. The uncertainty of the model was analyzed by the 95% Prediction Uncertainty (95PPU). Future projections were analyzed for the 2040s (2030–2049) and 2090s (2080–2099) compared to the baseline period (1986–2005) under two representation concentration pathways (RCP4.5, RCP8.5). Four Regional Climate Models (RCMs) were bias-corrected using the linear scaling bias correction method. The modeling results exhibited a very reasonable fit between the estimated and observed runoff in different stations, with NS values ranging from 0.54 to 0.91 in the calibration period. The future mean annual surface runoff exhibited an increase in the 2040s and 2090s compared to the baseline under both RCPs of 4.5 and 8.5 due to an increase in annual precipitation. The annual precipitation is projected to increase by 5% in the 2040s, 1% in the 2090s under RCP4.5, and by 9% in the 2040s and 2% in the 2090s under RCP8.5. The future temperature is also projected to increase and consequently lead to earlier snowmelt, resulting in a shift in the seasonal runoff peak to earlier months in the UKRB. However, the shifts in the timing of runoff could lead to significant impacts on water availability and exacerbate the water stress in this region, decreasing in summer runoff and increasing in the winter and spring runoffs. The future annual evapotranspiration is projected to increase under both scenarios; however, decreases in annual snowfall, snowmelt, sublimation, and groundwater recharge are predicted in the UKRB.

Assessment of the impact of land cover changes on sediment yield: a case study

Abstract Land use is one of the most important variables impacting river basin water and land resource planning and management. Sediment transport in streams is an important issue in the assessment of sediment deposition in dam reservoirs and its impact on operational activities. Evaluation of sediment yield contributes to decision-makers for effective water management and protection of the dam body and its elements. In this study, land use cover changes, in particular in forest areas, agricultural areas, arable lands, permanent crops, heterogeneous agricultural areas, and pastures in the Çakıt and Körkün sub-basins of the Seyhan River basin were examined between 1990 and 2018 using CORINE land use cover (CLC) data. The effect of land use/land cover (LULC) modifications on sediment yield was studied using suspended sediment observation data and CLC data. It was determined that the forest area in the Çakıt sub-basin drainage area increased between 1990 and 2018, and decreased in the Körkün sub-basin. It was determined that the increase in forest area in the Çakıt sub-basin drainage region until 2018 helped to reduce sediment movement. The regression values between forest area and suspended sediment concentration in Çakıt and Körkün sub-basins were found to be 0.9521 and 0.4431, respectively.

Assessing ecological health in a semi-arid basin: a case study of the Wei River Basin, China.

A healthy water ecosystem within a river basin is essential for maintaining ecological security, preserving species diversity, and ensuring sustainable socio-economic development. Unfortunately, human activities have significantly threatened the health of water ecosystems in various basins. Consequently, timely restoration and targeted protection of damaged river ecosystems have become crucial objectives in watershed management. As a prerequisite and cornerstone for river protection and management, assessing river ecological health has emerged as a primary focus in current research. In this study, we selected the Wei River Basin, a representative area of the Yellow River Basin, as our research subject. We identified multiple influencing factors, including society, biology, water quality, and habitat, which collectively impact this semi-arid region. To assess the overall impact of these factors on ecological health, we developed a comprehensive River Ecological Health Assessment Index (REHAI) system. The research findings indicate that the Wei River system, as a whole, is currently in a healthy state, while the Jing and Luo River systems are classified as sub-healthy. Furthermore, we observed variations within the Wei River system itself; the upper reaches of the Wei River exhibit higher levels of health compared to the middle reaches, whereas the water environment in the lower reaches is the most compromised. This degradation can be attributed to downstream subsidence, increased pollution, and rapid urbanization. By establishing a river ecosystem health assessment methodology and conducting a comprehensive evaluation of the health status of river ecosystems, this paper puts forward management recommendations for river basins. These findings provide a scientific basis for the sustainable utilization of water resources in river basins and promote the harmonious coexistence of humanity and nature.

Morphometry, Water Availability and Sediment Transport in the River Renato Watershed, Southern Amazon

The reduction in surface water availability in the Brazilian Amazon biome is associated with anthropic activities and climate change, facts that have driven hydrological monitoring studies. Such studies seek to understand the hydrological responses to changes in land use and land cover, aiming at the proper management of water resources in Amazonian river basins. Thus, the objective of the present study was to determine the physiographic characteristics and establish liquid and solid rating curves of the Renato river sub-basin, a tributary of the Teles Pires river, under conditions of restricted data. Measurements of quota, liquid flow (QL) and solid flows in suspension (Qss) and drag (Qsa) took place monthly between February 2019 and November 2022, composing 28 and 16 (fluviometric station 1) and 21 and 16 (station fluviometric 2) measurements of QL, Qss and Qsa, respectively. Physiographic characteristics were determined using the QGIS 3.16.11 software. The Renato River sub-basin had a regular drainage density = 0.505 km km-2, an elongated shape and low probability of flooding (Kf = 0.19; Kc = 2.17 and Ic = 0.21) and the relief varied from flat to strongly undulating (0 to 30%). Regardless of the hydrological section, the statistical performance of the regression models for suspended and total solid flows (Qst) could have been better. It is concluded that the Renato River watershed is little susceptible to extreme flood events due to its elongated shape and regular drainage density. The evaluated regression models adjusted satisfactorily for QL and unsatisfactorily for Qss and Qst. Therefore, there is still a need to continue studies with hydrosedimentological monitoring to build rating curves that accurately estimate the liquid and solid flows.

Improving rainfall-runoff modeling in the Mekong river basin using bias-corrected satellite precipitation products by convolutional neural networks

Accurate rainfall-runoff (RR) modeling is crucial for effective Mekong River Basin (MRB) water resource management. Satellite precipitation products (SPPs) can offer valuable data for such modeling; however, these products often exhibit biases that may adversely affect hydrological simulations. This study aimed to improve RR modeling using bias-corrected SPPs and the Soil and Water Assessment Tool (SWAT) model for MRB. A convolutional neural network-based deep learning framework was employed to correct biases in four SPPs (TRMM, PERSIANN-CDR, CHIRPS, and CMORPH), resulting in four respective bias-corrected SPPs (ADJ_TRMM, ADJ_CDR, ADJ_CHIR, and ADJ_CMOR). The bias-corrected products were compared against a gauge-based dataset in terms of rainfall analysis, and their performance within the SWAT model was assessed over calibration (2004–2013) and validation (2014–2015). Bias-corrected products demonstrated superior performance in rainfall analysis, with ADJ_TRMM outperforming other products. The SWAT model calibration results showed satisfactory performance across all stations, with a Nash-Sutcliffe Efficiency (NSE) ranging from [0.76–0.87]. Integrating bias-corrected SPPs into the SWAT model significantly increased the RR simulations in the MRB, indicated by higher NSE values [0.72–0.85] compared to uncorrected SPPs [-0.37 to 0.85] at the Kratie station. Besides, the inconsistent performance of bias-corrected products between rainfall analysis and RR modeling was observed, with ADJ_CDR outperforming ADJ_TRMM in the SWAT model. These results highlight the significance of using bias-corrected SPPs in hydrological modeling applications, especially in areas with limited ground-based precipitation data, and highlight the need for further research to refine bias correction methods and address the limitations of the SWAT model.

Evaluating the Impact of Rising Temperature on Water Demand in the Klamath River Basin, a Watershed in Western United States

This study investigates the impact of climate change on water availability within the Klamath River Basin, a significant watershed in the western United States. Critical factors including precipitation, snowpack, and potential evapotranspiration (PET) was explored to understand how climate change dynamics may exacerbate water scarcity, and stakeholder conflicts in the region. The study uses 40 years of historical climate data of these sites (Big Red Mountain and Silver Lake), findings indicate a slight but statistically inconclusive decrease in both precipitation and snowpack, despite a significant positive correlation between temperature and PET. Observations from the study highlight the vulnerabilities of the Klamath River Basin's water resources, especially with warmer temperatures that may shift precipitation patterns from snow to increased rainfall, creating concerns for seasonal water storage and availability within the region. The impact of these shifts poses significant risks to agricultural, environmental, and cultural water needs, with implications for regional water management and climate adaptation strategies. The study emphasizes the urgent need for collaborative, data-informed approaches to equitable water allocation amidst growing climate pressures.

Integrating multi-criteria decision analysis (MCDA) with kriging and entropy methods for optimising streamflow measurement in a scantly monitored river basin

ABSTRACT Streamflow data is one of the most important inputs for the management of water resources in river basins. In this study, a GIS-based multi-criteria decision analysis (MCDA) was integrated with kriging and entropy methods for optimising a streamflow monitoring network in the upper Tekeze River basin, north-western Ethiopia. An initial evaluation based on the WMO (2008) guideline showed that the density of existing streamflow monitoring network for the upper Tekeze River basin was inadequate. Hence, 41 new streamflow monitoring stations were needed. The most important criteria for identifying suitable streamflow monitoring sites in the upper Tekeze River basin were stream order, accessibility (distance to roads), slope gradient, land use/cover and soil texture. The required number of new streamflow monitoring stations for the upper Tekeze River basin was reduced from 41 to 33 after an optimisation using ordinary kriging and entropy. The optimal number of monitoring stations for the upper Tekeze River basin including the existing ones was therefore 47. An integrated application of MCDA, kriging and entropy methods aids to optimise the location and number of streamflow monitoring stations in a catchment. The use of optimisation techniques for designing streamflow monitoring network is essential for collecting adequate streamflow data in scantly monitored basins.