River Basin Research Articles

Species boundaries of the whiptail catfish Rineloricaria (Siluriformes: Loricariidae) from the Paraíba do Sul River drainage, southeastern Brazil, with species redescriptions and description of a new species.

Species of the catfish genus Rineloricaria are common in the Paraíba do Sul River basin, in southeastern Brazil; here we present a revision of the taxonomic diversity and geographic distribution of the species of the genus inhabiting the basin, based on novel morphologic and molecular data. Five species delimitation methods based on cytochrome C oxidase subunit 1 nucleotide sequences yielded comparable molecular operational taxonomic units. The automatic barcode gap discovery, assemble species by automatic partitioning, barcode index number, and Bayesian implementation of the Poisson tree process methods supported the recognition of five evolutionary lineages. These taxonomic units were assigned to the previously described Rineloricaria nigricauda, Rineloricaria steindachneri, Rineloricaria zawadzkii, and Rineloricaria nudipectoris, and an additional undescribed species. R. zawadzkii was further divided into two intraspecific geographically structured lineages using the generalized mixed Yule coalescent delimitation method. A maximum likelihood phylogenetic analysis revealed that the five lineages from the Paraíba do Sul have closer relationships to different species from southern and southeastern Brazil (Ribeira de Iguape, Lagoa dos Patos, Uruguay, Paraguay, and Parana river basins) than to each other. Based on the analysis of lectotypes, recently collected material, and specimens from ichthyological collections, the poorly described R. nigricauda and R. steindachneri are redescribed following current descriptive standards. The undescribed species from the middle and upper Paraíba do Sul River basin is formally described. The description of a new species, along with the description of species boundaries in R. nigricauda and R. steindachneri, contributes to the knowledge of the ichthyofauna of the Paraíba do Sul River basin and adjacent coastal drainages of southeastern Brazil. An identification key for the species of Rineloricaria occurring in the Paraíba do Sul River basin is provided.

Heavy Metal Concentrations in Surface Water of the Lower Mekong River Basin (Vietnam)

Heavy Metal Concentrations in Surface Water of the Lower Mekong River Basin (Vietnam)

Analyzing land surface temperature changes in the Danjiang River Basin: a MODIS-based reconstruction and assessment before and after the middle route of the South-to-North Water Diversion Project

Analyzing land surface temperature changes in the Danjiang River Basin: a MODIS-based reconstruction and assessment before and after the middle route of the South-to-North Water Diversion Project

Description of a new troglobitic Sinocyclocheilus (Pisces, Cyprinidae) species from the upper Yangtze River Basin in Guizhou, South China

Description of a new troglobitic Sinocyclocheilus (Pisces, Cyprinidae) species from the upper Yangtze River Basin in Guizhou, South China

A Sand Boil Database for Piping Risk Management in the Po River, Italy

Sand boil formation at the toe of river embankments is the typical manifestation of the initiation and progression of an internal erosion process known as backward erosion piping, which is recognized to be a major concern in many river systems worldwide. In Italy, more than 130 sand boils have been detected along the Po River, many of them experiencing recurrent reactivations during high-water events. In recent years, as part of the activities of the European project LIFE SandBoil, the Italian authority responsible for flood protection and flood damage reduction along the Po River has implemented a GIS-based web application to catalogue the sand boils observed in its operating area. The resulting database allows keeping records of a comprehensive and varied set of information, in terms of predisposing factors, initiation conditions and surface effects. Taking as a reference a well-documented cross-section of the Po River prone to piping, this paper describes the main features of this versatile and flexible tool, whose long-term aim is to support vulnerability studies and the development of risk maps against piping. The database, which might also accommodate data from different river basins, is thus meant to help in flood risk management, by suggesting priorities for the implementation of mitigation measures and allowing the monitoring of intervention effectiveness.

Sedimentary records of giant landslide-dam breach events in western Sichuan, China

Introduction: Landslide-dammed lake outburst floods (LLOFs) are common natural disasters in high-mountain regions, posing serious safety threats to residents’ livelihoods and properties and causing major damage to engineering facilities. Giant landslides and river damming events commonly occur in the Eastern Tibetan Plateau in southwestern China. Dam failure generate LLOFs that form outburst deposits (ODs). This phenomenon is particularly common in some giant ancient landslide-dammed lakes.Methods: This study conducted a detailed investigation of the sedimentary characteristics of large-scale landslide-dammed lake outburst deposits in the Diexi Reach of the Upper Minjiang River and Tangjiashan Reach of the Tongkou River Basin, West Sichuan Plateau, China. Meanwhile, typical evidence of high-energy ODs was recorded.Results and Discussion: The longitudinal distribution of these ODs is similar to an elongated fan-shaped terrace along the river channel, presenting a distinctive sedimentary disordered–sub-ordered–ordered sequence from upstream to downstream. Several typical units of “sedimentary facies” are developed in the OD profiles, such as boulder units deposited by high-energy outburst flood (OF) events and the gravel and sand units representing pulsating-flow sedimentary environments during the recession stage. The grain size frequency curves are bimodal, and the granularity accumulated curves are upward convex, which reflect that the detrital characteristics of the sediment source area are mainly composed of coarse gravel and boulders. This indicates that the coarse gravel sediment gradually become decreased from upstream to downstream. Moreover, the OD hydrodynamic intensity displays a gradual weakening, and sediment sorting is improved. From upstream to the downstream, the mean particle-size and sorting of the ODs gradually decrease. The skewness become larger, and the kurtosis of the ODs is distributed in all the types. In addition, the different combinations of quartz sand surface microtextures indicate the transformation from high-to low-energy impacts over a short distance, which is controlled by flood hydrodynamics and regime.Significance: These sedimentary characteristics of ODs explain the hydrodynamic changes during the propagation of OFs, and are also important records for distinguishing between ODs, and “normal” floods.

Downscaling Future Precipitation over Mi Oya River Basin using Artificial Neural Networks

Studying future precipitation behaviour in river basins is essential for proper water resources and land-use planning within them, as this will help to reduce the risk and mitigate disasters that can occur in the future. General Circulation Models (GCMs) are used to study future precipitation fluctuations, which simulate large-scale climate variations under the effect of greenhouse gas changes. The GCM runs at a coarse spatial resolution which cannot be directly used for climate impact studies. Therefore, downscaling is required to extract the sub-grid and local scale information. This study examines the use of the Long Short-Term Memory (LSTM) neural network for climate downscaling to the Mi-Oya river basin in Sri Lanka using CNRM-CM5 and HadCM3 GCMs and observed annual data for 35 years. The precipitation data were extracted to cover Sri Lanka. Current downscaling models mostly use Convolutional Neural Networks (CNNs) to downscale GCMs. Out of 42 GCMs, two appropriate GCMs were chosen using the data analysis tool Data Integration and Analysis System (DIAS). The best predictor variables were chosen using the LASSO regression method. In this research, Machine Learning models were implemented using the Google TensorFlow platform. The Nash–Sutcliffe coefficient, Pearson correlation coefficient, and root-mean-square error performance indices were used to evaluate the performances of different downscaling models. Statistical downscaling was performed on the data at RCP 2.6, 4.5, and 8.5 using a LSTM. Subsequently, the changes that would take place by the year 2100 were analysed. The results show that precipitation will be reduced in the 2nd and 3rd decades of the 21st century, and precipitation will increase toward the 22nd century.

Spatiotemporal Variations in Snow Cover on the Tibetan Plateau from 2003 to 2020

The variations in snow cover on the Tibetan Plateau play a pivotal role in comprehending climate change patterns and governing hydrological processes within the region. This study leverages daily snow cover data and the NASA Digital Elevation Model (DEM) from 2003 to 2020 to analyze spatiotemporal snow cover days and assess their responsiveness to climatic shifts by integrating meteorological data. The results reveal significant spatial heterogeneity in snow cover across the Plateau, with a slight decreasing trend in annual average snow cover duration. Snow cover is predominantly observed during the spring and winter seasons, constituting approximately 32% of the total snow cover days annually. The onset and cessation of snow cover occur within a range of 120–220 days. Additionally, an increasing trend in snow cover duration below 5000 m altitude was observed, in addition to a decreasing trend above 5000 m altitude. Sub-basin analysis delineates the Tarim River Basin as exhibiting the lengthiest average annual snow cover duration of 83 days, while the Yellow River Basin records the shortest duration of 31 days. The decreasing trend in snow cover duration closely aligns with climate warming trends, characterized by a warming rate of 0.17 ± 0.54 °C per decade, coupled with a concurrent increase in precipitation at a rate of 3.09 ± 3.81 mm per year. Temperature exerts a more pronounced influence on annual snow cover duration variation compared to precipitation, as evidenced by a strong negative correlation (CC = −0.67). This study significantly augments the comprehension of hydrological cycle dynamics on the Tibetan Plateau, furnishing essential insights for informed decision-making in water resource management and ecological conservation efforts.

Assessment of Microplastic Pollution in River Ecosystems: Effect of Land Use and Biotic Indices

The proximity of freshwater ecosystems to anthropogenic activities makes them one of the most threatened environments by plastic pollution in the form of microplastics (MPs). Therefore, it is crucial to identify the primary drivers of MP dynamics in rivers to enhance their management. This work analyzed the concentration of MPs in water and sediments and evaluated the influence of land use and its relationship with the main biotic indices employed to assess the water quality of rivers. This research was carried out in four different catchments, with three sampling points established in each river basin. The results revealed that MPs were ubiquitous across all locations, with concentrations ranging from 0.10 to 35.22 items m−3 in waters and from 26 to 643 items Kg−1 in sediments. The highest concentration of MPs both in water and sediments were found in the Lagares River (35.22 items m−3 and 643 items Kg−1), while the lowest concentrations were found in the Miñor River for water (0.10 items m−3) and Tea River for sediments (138 items Kg−1). Urbanization degree was identified as the primary driver of MP pollution in water, whereas population density correlated with sediment pollution levels. These findings explain the elevated MPs abundance in the more urbanized and populated Gafos and Lagares rivers compared to the relatively pristine Miñor and Tea rivers. Furthermore, the presence of MPs in sediments was found to negatively impact the most sensitive benthic macroinvertebrate taxa, as evidenced by lower values of the IASPT and EPT indices at sampling points with higher sediment MPs concentrations (Gafos and Lagares).

Do Water Transfer Projects Promote Water Use Efficiency? Case Study of South-to-North Water Transfer Project in Yellow River Basin of China

With a huge capital and labor input influx, inter-basin water transfer (IBWT) projects have been shown to effectively mitigate water stress and ensure the water demand for social and economic development in the receiving area. Whether they have promoted the improvement of regional water use efficiency (WUE) is crucial for sustainable management of regional water resources. Targeting the South-to-North Water Transfer Project (SNWTP), the largest and most ambitious inter-basin water transfer project in China, this study establishes quantitatively econometric models to analyze the impact of different water diversion projects, specifically the eastern route of the SNWTP (ER-SNWTP), middle route of the SNWTP (MR-SNWTP), and diversion from the main stream of the Yellow River (DYR), on the regional water consumption per unit of GDP; regional water stress, water use structure, economic structure, and urbanization level are used as control variables in different types of cities in the Yellow River Basin, and some intriguing results are found. While the overall water transfer project demonstrates a positive impact on water use efficiency, the effects of the three water transfer measures vary significantly. The ER-SNWTP does not exhibit a notable positive effect on regional water use efficiency, whereas the MR-SNWTP demonstrates a significant positive impact. Interestingly, the DYR has a notable negative influence on water use efficiency in developed cities. The water use structure, shaped by the pricing, scale, and policies of different projects, emerges as a pivotal factor in explaining these differences. Finally, this paper suggests that the impact of water transfer projects on the improvement of regional water use efficiency be viewed from a more comprehensive and developmental perspective.

Estimation of Groundwater River Availability in Leang Lonrong Cave Using ARIMA Model and Econophysics Valuation Approach

This quantitative descriptive study uses the ARIMA model approach to predict groundwater river water availability. It assesses its valuation with an econophysics orientation in the Leang Lonrong Cave area of TN Bantimurung Bulusaraung. Secondary data from 2010-2023, including Water Discharge and Water Level data from the River Basin Center of Pompengan Jeneberang (BBWS) and primary data on the Valuation of Willingness to Pay, were utilized. The analysis reveals that water discharge predictions (2024-2030) in the Gua Leang Lonrong area indicate consistent annual water availability, with the highest discharge projected in December 2030 (5.08 m3/s) and the lowest in September 2024 (1.12 m3/s). Valuation of utilizing Leang Lonrong River water through willingness to pay showed residents willing to pay Rp 42,142.85/month for clean water management. At the same time, visitors are willing to contribute Rp 21,596.77/visit to enhance water quality management. Elasticity analysis modeling water discharge with willingness to pay suggests a relatively elastic relationship, with a minor impact, indicating unresponsiveness to changes in water discharge percentages. Statistical and economic methods were employed to analyze daily water discharge patterns and evaluate residents' willingness to pay for underground river water utilization. The ARIMA model (0,1,1) forecasted future water discharge patterns with a mean absolute percentage error (MAPE) of 33.7%. The study surveyed 62 respondents, finding an average willingness to pay approximately IDR 21,596.77 per visit to maintain Leang Lonrong River water quality management.

Spatial and temporal change of hydrological connectivity in the Wusuli River Basin

Study regionThe study region of this paper is the Wusuli River basin, which is comprised of nine sub-basins. The basin is located on the border between China and Russia. Study focusWe investigated the hydrological connectivity of the Wusuli River basin, an international boundary river that has received little attention. The spatial and temporal evolution of hydrological connectivity over the last 38 years was characterized using a geostatistical connectivity function combined with the remote sensing technique. In particular, some key sub-basins were identified to help strengthen China-Russian cooperation on ecological protection of the basin. New hydrological insights for the regionThe results show that the hydrological connectivity in the northwestern region of the Wusuli River basin was unstable with several surges from 1984 to 2021. Analyzed from a temporal perspective, the effective range of hydrological connectivity is expanding and connectivity is showing an overall upward trend due to increased fluctuations in rainfall. Analyzed from a spatial perspective, the connectivity of the right bank of the Wusuli River is better than that of the left bank due to the influence of topography. Stable connectivity pathways are a must for efficient hydrological connectivity within a watershed. Another finding is that the capacity of the Wusuli River basin to resist extreme rainfall is weakening.

Implementing a systemic approach to water management: piloting a novel multi-level collaborative integrated water management framework in east London

ABSTRACT To address urban growth, resource competition, and environmental degradation, effective integrated water planning is crucial. In the UK, policy frameworks like the 25-Year Plan for the environment and the National Framework for Water Resources stress the need for a systemic approach. Despite efforts in stakeholder engagement and meta-models, integrating physical and human aspects in water management remains a challenge. This paper introduces a multi-level framework for regional water planning, demonstrated through London's Sub-Regional Integrated Water Management Strategy (SIWMS). The framework, depicted as an inverted triangle, starts with conceptual analysis, gathering stakeholder insights and data. Integrated modelling creates a baseline for scenario assessment, providing evidence through metrics and simulations. Integrated planning focuses on collaboration for option selection and implementation. Results show the framework's effectiveness for systems-level analysis at the river basin scale. The logical progression facilitates stakeholder engagement, enhancing shared understanding. The Water Systems Integration Modelling (WSIMOD) Framework allows simultaneous assessment of interventions on various indicators, aiding in prioritising multi-benefit schemes and identifying potential negative impacts. The study supports the prioritisation of schemes like Sustainable Drainage System (SuDS) and emphasises the importance of a multi-level collaborative approach for robust, stakeholder-supported regional water planning to achieve effective implementation of planning and environmental policies.

Refining ICESAT-2 ATL13 Altimetry Data for Improving Water Surface Elevation Accuracy on Rivers

The application of ICESAT-2 altimetry data in river hydrology critically depends on the accuracy of the mean water surface elevation (WSE) at a virtual station (VS) where satellite observations intersect solely with water. It is acknowledged that the ATL13 product has noise elevations of the adjacent land, resulting in biased high mean WSEs at VSs. Earlier studies have relied on human intervention or water masks to resolve this. Both approaches are unsatisfactory solutions for large river basins where the issue becomes pronounced due to many tributaries and meanders. There is no automated procedure to partition the truly representative water height from the totality of the along-track ICESAT-2 photon segments (portions of photon points along a beam) for increasing precision of the mean WSE at VSs. We have developed an automated approach called “auto-segmentation”. The accuracy of our method was assessed by comparing the ATL13-derived WSEs with direct water level observations at 10 different gauging stations on 37 different dates along the Lower Murray River, Australia. The concordance between the two datasets is significantly high and without detectable bias. In addition, we evaluated the effects of four methods for calculating the mean WSEs at VSs after auto-segmentation processing. Our results reveal that all methods perform almost equally well, with the same R2 value (0.998) and only subtle variations in RMSE (0.181–0.189 m) and MAE (0.130–0.142 m). We also found that the R2, RMSE and MAE are better under the high flow condition (0.999, 0.124 and 0.111 m) than those under the normal-low flow condition (0.997, 0.208 and 0.160 m). Overall, our auto-segmentation method is an effective and efficient approach for deriving accurate mean WSEs at river VSs. It will contribute to the improvement of ICESAT-2 ATL13 altimetry data utility on rivers.

Classification of Pollution Sources and Their Contributions to Surface Water Quality Using APCS-MLR and PMF Model in a Drinking Water Source Area in Southeastern China

Identifying the potential pollution sources of surface water pollutants is essential for the management and protection of regional water environments in drinking water source areas. In this study, absolute principal component score-multiple linear regression (APCS-MLR) and positive matrix factorization (PMF) models were applied to assess water quality and identify the potential pollution sources affecting the surface water quality of Xin’an River Basin. For this purpose, a 10-year (2011–2020) dataset of eight water quality indicators (including pH, EC, DO, COD, NH3-N, TN, TP, and FC) covering eight monitoring stations and 7248 monthly observations was used. The results indicated that Pukou section had the worst water quality among the eight monitoring stations, and TN was the most serious water quality index. Both the APCS-MLR and PMF models identified agricultural nonpoint source pollution, urban nonpoint source pollution and rural domestic pollution, and meteorological factors. The sum of these three sources was very close, accounting for 60% and 58%, respectively. The APCS-MLR results demonstrated that for EC, COD, and NH3-N, the major pollution sources were urban nonpoint sources and rural domestic pollution. The major contamination source of TN was agricultural nonpoint source pollution (30.4%). Meanwhile, the major pollution sources of pH, DO, TP, and FC were unidentified factors. The PMF model identified five potential sources, and pH and DO were affected by meteorological factors. NH3-N and TP were influenced mainly by agricultural nonpoint source pollution. Atmospheric deposition was the major pollution source (87.9%) of TN. FC was mostly derived from livestock and poultry breeding (88.3%). EC and COD were mostly affected by urban nonpoint sources and rural domestic pollution. Therefore, receptor models can help managers identify the major sources of pollution in watersheds, but the major factors affecting different pollutants need to be supplemented by other methods.

Influence of climatic phenomena on rainfall in the Tapajós River Basin

Climate change results in weather phenomena such as those that are currently being observed, affecting precipitation, possibly more intensely in the Amazon region. This research is justified by the scarcity of climate risk mitigation measures in the Amazon. Rainfall data from 1992 to 2021 were analyzed, revealing long occurrences of dipole anomalies for consecutive months. Based on the climate indices, the best correlation results occurred in the years with high rainfall, coinciding with the occurrence of negative dipole and of La Niña. Therefore, climate indices can indicate the possible predictive capacity of extreme rainfall events. The results show that the influence of climatic phenomena is more pronounced in the northern region of the Tapajós River Basin.

Research on power generation prediction of hydropower in river basin based on multi-head attention graph convolutional neural network

For the power generation prediction of traditional hydropower stations, most of them only use time series prediction and neglect to study the spatial topological relationship of hydropower stations in the river basin, so that it is difficult to fully explore the characteristic relationship of space power stations. In this paper, a research method for power generation prediction of hydropower in river basin hydropower stations based on multi-head attention map convolutional neural network is proposed. This method establishes a first-level node neighborhood feature map based on the spatial geographic distribution information of hydropower stations in the basin, and uses the method of graph convolution to carry out node feature mining and feature learning, so as to transform the power generation capacity evaluation problem of the hydropower station in the basin into the node prediction problem in the graph, which is different from the global normalization rule. the multi-head attention mechanism introduced further improves the information aggregation quality of the graph node, and uses the historical temperature, power generation, electricity price, unit status and other data of each hydropower station in the basin for training. The reasoning results show that the proposed method achieves higher accuracy than other schemes, and the power prediction method is conducive to the formulation of power plans of hydropower stations in the basin, and can also play a positive role in guiding the site selection of hydropower plants.

Quantifying soil erosion and soil organic carbon conservation services in indian forests: A RUSLE-SDR and GIS-based assessment

Indian forests, having sixteen types of vegetation, provide various ecosystem services. Forests reduce water erosion of soil, reduce floods and conserve low river flow during dry seasons. Present study estimated annual sedimentation, texture, soil organic carbon control service of forests of India using the Revised Universal Soil Loss Equation (RUSLE) and Sediment Delivery Ratio (SDR) approach through GIS technique along with identifying important drivers responsible for erosion in forests. The soil erosion control service was based on five factors viz., rainfall erosivity, soil erodibility, slope, crop management, conservation practice factor and SDR across the various forest dominated river basins. Geo-detector modelling was performed to evaluate environmental drivers responsible for soil erosion. Valuation of soil control service of the India’s forests was estimated based on damage and replacement cost method. Annual soil loss rate was maximum in Tropical Thorn Forest and in Ganga River basin. Maximum sediment control and SOC conservation against the soil erosion control service by India’ forests were due to Tropical Dry Deciduous Forest with a total annual sedimentation control and SOC conservation of 33,056,560 tonnes and 3,31,115.06 tonnes, respectively. The conservative estimate of the economic value of soil conservation service by the forests of India was US$ 535.6 M with an average of 4.40 US$ ha−1 and a maximum share of value for Tropical Dry Deciduous Forest i.e. 294.18 million US$ and average maximum value for Littoral and Swamp Forest (28.91 US$ ha−1). The soil erosion in Tropical and Sub-tropical Forest was influenced by the interactive effects between NDVI and Bulk Density. In contrast, soil erosion in the temperate and alpine area was influenced by anthropogenic activity in combination with climatic factors. Present study accounts the role of India’s forests for the soil conservation service and may provide inputs to implement forest and reservoir management strategies besides providing information for Integrated Watershed management and Catchment Treatment Plans.

Climate change adaptation for drinking water and ecological flows through sustainable agricultural practices

ABSTRACT Analysing the impacts of climate change on water resources is crucial to identify vulnerabilities and prioritize actions. We investigated climate change impacts on drinking water supply, emphasizing sustainable agriculture as an adaptation strategy, using the SWAT model in the Cávado River basin, Portugal. Our study highlights an increase in months with river discharge below ecological flow post water abstraction due to climate change. Notably, nitrate concentration was more influenced by sustainable agriculture practices than climate change. Our study highlights the vital role of adaptive strategies, especially sustainable agriculture, in securing water resources amidst challenges posed by climate change.

Interpretable baseflow segmentation and prediction based on numerical experiments and deep learning

Baseflow is a crucial water source in the inland river basins of high-cold mountainous region, playing a significant role in maintaining runoff stability. It is challenging to select the most suitable baseflow separation method in data-scarce high-cold mountainous region and to evaluate effects of climate factors and underlying surface changes on baseflow variability and seasonal distribution characteristics. Here we attempt to address how meteorological factors and underlying surface changes affect baseflow using the Grey Wolf Optimizer Digital Filter Method (GWO-DFM) for rapid baseflow separation and the Long Short-Term Memory (LSTM) neural network model for baseflow prediction, clarifying interpretability of the LSTM model in baseflow forecasting. The proposed method was successfully implemented using a 63-year time series (1958–2020) of flow data from the Tai Lan River (TLR) basin in the high-cold mountainous region, along with 21 years of ERA5-land meteorological data and MODIS data (2000–2020). The results indicate that: (1) GWO-DFM can rapidly identify the optimal filtering parameters. It employs the arithmetic average of three methods, namely Chapman, Chapman-Maxwell and Eckhardt filter, as the best baseflow separation approach for the TLR basin. Additionally, the baseflow significantly increases after the second mutation of the baseflow rate. (2) Baseflow sources are mainly influenced by precipitation infiltration, glacier frozen soil layers, and seasonal ponding. (3) Solar radiation, temperature, precipitation, and NDVI are the primary factors influencing baseflow changes, with Nash-Sutcliffe efficiency coefficients exceeding 0.78 in both the LSTM model training and prediction periods. (4) Changes in baseflow are most influenced by solar radiation, temperature, and NDVI. This study systematically analyzes the changes in baseflow and response mechanisms in high-cold mountainous region, contributing to the management of water resources in mountainous basins under changing environmental conditions.