River Classification Research Articles

Geomorphic river classifications based on different methods coincide in predicting fish assemblage structure

Geomorphic river classifications based on different methods coincide in predicting fish assemblage structure

River flood risk quantification using reliability index method’s safety factor (Level I) and first order second moment (Level II), a case study of Plumbon River, Semarang city, Central Java

This paper presents a case study on flood risk assessment using reliability as the analysis system’s framework. The objectives of this study are to describe a flood event condition through quantitative analysis of how a river acts as the reliable hydraulic resistance and serves as an improvement from past studies’ calculations to study flood resistances for rivers using sections’ factual shapes. The study refers to the Performance Indices Ten, Safety Factor (SF) or Level I, which uses the exceedance probability as the performance index, and First Order Second Moment (FOSM) or Level II, which uses the probability for failure as the performance index that assumes all probability densities to be convertible to Gaussian distributions. Study in Plumbon River is analyzed with two methods: (a) Analyzing the river as a whole single unit to produce a single quantification; (b) Analyzing the river as separated units based on stream locations (upstream & downstream) classified by its slope level, and scored based on its rank compared to the other units. The results show that: (a) Level I and II results in similar trend and particular defined numbers for reliability; (b) River classification analyzes each section better, showing slope level is directly proportional to reliability; and (c) Trapezium-shaped assumption is not suitable for river with complex morphology.

Comparative analysis of water quality index and river classification in Kereh River, Penang, Malaysia: Impact of untreated swine wastewater from Kampung Selamat pig farms.

The Kereh River in Penang, Malaysia, has faced severe pollution for over 40 years due to untreated wastewater from swine farms in Kampung Selamat, discharged via stormwater drains. Despite official claims that all 77 swine farms treat their wastewater to meet regulatory standards, local non-governmental organizations and villagers have challenged this, though their concerns lack scientific backing. This study evaluates the river's water quality by analyzing samples from upstream (US), midstream (MS), and downstream (DS), and from Parit Cina-Parit Besar, a conduit for untreated swine wastewater. Fourteen parameters were measured against Malaysia's National Water Quality Standards (NWQS). Significant differences were found in six parameters: ammonium nitrogen (AN), biochemical oxygen demand (BOD), chemical oxygen demand (COD), dissolved oxygen (DO), total suspended solids (TSS), and oil and grease (OG). While Dunn's post hoc pairwise comparison showed no significant differences among river segments, mean values indicated increased pollution downstream, particularly after the convergence with untreated swine wastewater. River classification worsened, with water quality index dropping from 69.88 ± 11.37 score (Class III) US to 38.49 ± 12.74 and 50.44 ± 3.14 scores (Class IV) MS and downstream, respectively. A significant positive correlation between E. coli and AN (r = 0.71, p < 0.01) suggests a common point source pollutant, particularly the untreated swine wastewater. The river exhibits low oxygen levels and high organic matter and nutrient concentrations, especially MS and downstream, highlighting substantial ecological and public health risks. Effective enforcement of waste treatment regulations and enhanced monitoring are crucial for mitigating pollution and restoring the river's ecosystem. Collaboration between authorities and pig farmers is essential to improve water quality and maintain the river's ecological balance. PRACTITIONER POINTS: Severe Kereh River pollution: Untreated swine wastewater from Kampung Selamat pig farms, primarily via Parit Cina-Parit Besar, has degraded the river for over 40 years. Regulatory non-compliance: Despite official claims, untreated swine wastewater continues to pollute the river, challenging regulatory standards. Significant pollution indicators: Elevated levels of AN, BOD, COD, DO, TSS, OG, and E. coli signal severe pollution midstream and downstream. Water quality index drop: WQI scores classify midstream and downstream sections as polluted, indicating worsening conditions downstream. Urgent need for action: Enforcing regulations, improving wastewater treatment, and relocating pig farms are crucial for restoring the Kereh River.

Typology and classification of water quality in an intermittent river in a semi-arid Mediterranean climate.

The typology and classification of rivers are highly relevant concepts in the field of limnology and freshwater ecology. Water body typology systematically categorizes water bodies based on their natural attributes, while water body classification groups them based on specific criteria or purposes for management, regulatory, or administrative reasons. Both concepts play important roles in understanding and managing water resources effectively. This scientific article focuses on the ZAT River in Morocco as a model for studying low-flow and intermittent rivers. The objective is to develop an accurate model for the typology and classification of small, low-flow rivers into homogeneous classes based on natural and anthropogenic factors. The study also investigates the impact of human activities on altering the uniformity and reference nature of the water body. The typology of water bodies is carried out according to the European methodology specified in The European Commission's Water Framework Directive (WFD) in 2000. The classification of water bodies is conducted by assessing their chemical and biological quality using the weighted index (WI), the Iberian Biological Monitoring Working Group (IBMWP) index, and multivariate statistical methods such as principal component analysis (PCA) for confirming water quality assessment. The results indicate the possibility of dividing the basin into four water bodies. Water bodies show homogeneity in terms of chemical quality when human influence is minimal or during periods of high river flow. However, increased human influence and decreased river flows lead to heterogeneity in chemical quality, indicating an unstable state. This study is the first of its kind in arid and semi-arid intermittent rivers, where such an approach could be suggested to determine their typology and classification.

A GIS Automated Tool for Morphometric Flood Analysis Based on the Horton–Strahler River Classification System

The development of human society over the past century has led to an explosion in population numbers and a migration of settlements to river valleys, which have become increasingly exposed to the risk of flooding. In this context, the scientific community has begun to work on identifying mathematical and spatial models that can help to identify areas at risk as quickly as possible. The present article is one that follows this objective, proposing an automatic model that can be implemented in ArcGIS and that aims to identify only areas at risk of flooding using a single file, the DEM. The novelty of this article and the usefulness of the method are given precisely by the fact that it is possible to quickly find out which areas may be exposed to flooding, i.e., water accumulations, only based on relief, which is extremely useful for local authorities. The analysis was conducted on all hierarchy orders, according to the Horton–Strahler classification system, for the entire Romanian territory. The results consist of a polygonal vector layer in shapefile format, containing an attribute table with all the initial, intermediary, and final calculations in separate numeric fields. Each parameter was normalized in order to obtain the final morphometric flood vulnerability score. Postprocessing these results involved applying a Principal Component Analysis to identify weights for the components that encompass all morphometric parameters. Each drainage basin reveals a dimensionless morphometric flood vulnerability score value that is comparable with all other basins in Romania.

An Efficient River Water Quality Prediction and Classification Model using Metaheuristics based Kernel Extreme Learning Machine

In the previous years, water quality has been susceptible to different pollutants. Also, the various environmental conditions like vegetation, climate and basin lithology affects the quality of the river water naturally. So, the prediction of water quality (WQ) becomes a major process to control and basin lithology affects the quality of the river water naturally pollution. The rise of artificial intelligence (AI) manners can be utilized for designing predictive methods for water quality index (WQI) and classification. This study focuses on the design of metaheuristics-based kernel extreme learning machine (MBKELM) for river water quality prediction and classification. The proposed MBKELM model aims to predict and classify the quality of river water into different classes. In addition, a prediction and classification model using KELM is derived to appropriately determine the water quality. Moreover, the parameter tuning of the KELM model takes place by pigeon optimization algorithm (POA). A wide range of experimental analyses was performed on benchmark datasets and the experimental outcomes reported the supremacy of the MBKELM technique over the recent techniques. The results stated that the proposed MBKELM model has accomplished minimal MSE and RMSE values. On examining the results in terms of MSE on training set, the MBKELM model has accomplished a lower MSE of 0.00257 whereas the existing model has gained a higher MSE of 0.00336. Also, on examining the results in terms of RMSE on testing set, the MBKELM manner has accomplished a lesser RMSE of 0.05070 whereas the existing model algorithm has gained a higher RMSE of 0.05800.

Classification, pollution characteristics and treatment strategy of the inflow rivers into Lake Yangcheng

Classification, pollution characteristics and treatment strategy of the inflow rivers into Lake Yangcheng

Hydrological and climate impacts on river characteristics of pahang river basin, Malaysia

The climate, geomorphological changes, and hydrological elements that have occurred have all influenced future flood episodes by increasing the likelihood and intensity of extreme weather occurrences like extreme precipitation events. River bank erosion is a natural geomorphic process that occurs in all channels. As modifications of sizes and channel shapes are made to transport the discharge, sediment abounds from the stream catchment, and floods are triggered dramatically. The aim of this study is to analyze the flood-sensitive regions along the Pahang River Basin and determine how climate and river changes would have an impact on flooding based on hydrometeorological data and information on river characteristics. The study is divided into three stages, namely the upstream, middle stream, and downstream of the Pahang River. The main primary hydrometeorological data and river characteristics, such as Sinuosity Index, Dominant Slope Range and Entrenchment Ratio collected as important inputs in the statistical analysis process. The statistical analyses, namely HACA, PCA, and Linear Regression applied in river classification. The result showed that the middle stream and downstream areas demonstrated the worst flooding affected by anthropogenic and hydrological factors. Rainfall distribution is one of the factors that contributed to the flood disaster. There are strong correlations between the Sinuosity Index (SI) and water level, which indicates that changes occurred at both planform and stream classification. The best management practices towards sustainability are based on the application of the outcomes that have been obtained after the analysis of Pahang River planform changes, Pahang River geometry, and the local rainfall pattern in the Pahang River Basin.

Over 200,000 kilometers of free-flowing river habitat in Europe is altered due to impoundments

European rivers are disconnected by more than one million man-made barriers that physically limit aquatic species migration and contribute to modification of freshwater habitats. Here, a Conceptual Habitat Alteration Model for Ponding is developed to aid in evaluating the effects of impoundments on fish habitats. Fish communities present in rivers with low human impact and their broad environmental settings enable classification of European rivers into 15 macrohabitat types. These classifications, together with the estimated fish sensitivity to alteration of their habitat are used for assessing the impacts of six main barrier types (dams, weirs, sluices, culverts, fords, and ramps). Our results indicate that over 200,000 km or 10% of previously free-flowing river habitat has been altered due to impoundments. Although they appear less frequently, dams, weirs and sluices cause much more habitat alteration than the other types. Their impact is regionally diverse, which is a function of barrier height, type and density, as well as biogeographical location. This work allows us to foresee what potential environmental gain or loss can be expected with planned barrier management actions in rivers, and to prioritize management actions.

Challenges of classifying and mapping perennial freshwater systems within highly variable climate zones: A case study in the Murray Darling Basin, Australia

Perennial freshwater systems are valuable natural resources that provide important ecological services globally. However, in highly variable climates, such as Australia, water availability in rivers and streams can vary greatly from year to year and from decade to decade. Further, across Australia and many other regions, perennial river systems are projected to decrease because of anthropogenic climate change, placing the ecosystems they support under additional pressure. Quantifying the potential impacts of climate change on perennial freshwater systems requires robust databases of existing water features with accurate classifications. This is a challenge for rivers that display a high degree of interannual variability since the river classification can be dependent on the period of available data. In this study, we carry out a regional scale comparison of three different spatial databases commonly used in environmental and ecological assessments of perennial systems of Australia, namely Geodata, Geofabric and Water Observations from Space (WOfS). Focusing on the southern Murray Darling Basin (MDB), due to its national and international significance and its highly variable flow regimes, we show that no single spatial database is reliable by itself in terms of perennial water classification, with notable differences likely arising from variations in the periods analysed and methods used to classify the systems. Further, an analysis of high-quality gauged streamflow data (with approximately 40-year daily records) for four sub-catchments, and long-term simulation data (>100 years) for two sub-catchments in the lower MDB, confirm that flow persistence can be non-stationary through time, with some ‘perennial’ systems exhibiting sustained periods of cease to flow (i.e. becoming non-perennial) during prolonged droughts. This study demonstrates that due consideration is required in developing baseline classification of perennial freshwater systems for assessing future changes and measuring adaptive capacity.

Optimization of the Korean river macroinvertebrate prediction and assessment System (KRIMPAS) for the biological assessment of rivers

The biotic community predictive model proposed in the River Invertebrate Prediction And Classification System which compares observed values at test sites with expected values in reference sites is used to perform biological assessments of rivers. In 2021, a Korean River Macroinvertebrate Prediction and Assessment System (KRIMPAS) for river integrity assessment was developed. However, since it was the early stage of development, improvement of the accuracy and precision of assessment was required. Therefore, benthic macroinvertebrates and six environmental variables collected from 357 reference sites between 2010 to 2021 were used to compare the prediction mothods and attempt to optimize the KRIMPAS. Based on the number of taxa (species and family levels) and six biological indices, the existing KRIMPAS method (Original method) was compared with two weighted average methods (W-Avg 1 and 2) according to the probability that a test site was assigned. In terms of statistical significance, observed to expected (O/E) values of the number of taxa, but, there were difference for most of biological indices. When the probability density function based on the log-normal distribution for each derivation method was applied, no difference was observed in the number of taxa; however, there were differences in the mean, median, mode, and variance for each biological index. A fit verification based on the normalized root-mean-square error (NRMSE) for each derivation method was conducted, and the overall fit tended to be higher with the W-Avg 1 and 2 when compared with the Original method. Thus, to correct the current biological assessments of Korean rivers we propose the application of the W-Avg method as this would improve the derivation of the predictive model.

Formation of Zaminsuv River and Distribution Through the Years.

This article presents the results and analysis of the formation and classification of floodplain rivers over the years. Key terms, methodology, research objectives, and approaches are identified to analyze the formation of floodplain rivers. The article is based on research conducted to understand the classification of floodplain rivers over the years. The obtained results and their interpretation are presented. Furthermore, the main findings related to the formation of floodplain rivers and their classification over the years are discussed. The analysis includes the examination of factors indicating the affiliation of rivers to a particular type based on the conditions of river convergence.

Geodiversity as a potential indicator of stream health in ecological quality assessment systems

AbstractStream geodiversity is a novel concept that is used to describe the variety of geological, geomorphological and hydrological features at a reach‐scale level. In this study, we investigated the relationship between geodiversity and ecological indicators based on fishes, benthic macroinvertebrates and diatoms used for the ecological classification of rivers in line with the Water Framework Directive (WFD) 2000/60/EC. We examined whether geodiversity can be used as a proxy indicator of ecological quality, and we further tested if geodiversity can explain a significant amount of taxa richness variation. We hypothesized that undisturbed or minimally disturbed rivers will be more hydrologically and geomorphologically diverse, and as such, the ecological quality of streams will improve with geodiversity. We also attempted to quantify the probability of achieving ‘good’ ecological quality in relation to geodiversity levels through ordinal regression analysis. Although we did not find a significant relationship between geodiversity and taxonomic richness for any of the three freshwater groups, our results showed positive effects of geodiversity on all three ecological indicators. We also found that the likelihood of achieving the WFD target increases significantly with higher geodiversity. Our findings indicate that geodiversity can potentially serve as a proxy of ecological quality and highlight the need to consider the inclusion of geodiversity measures in ecological quality assessment systems.

Evaluating the Feasibility of the Liuxihe Model for Forecasting Inflow Flood to the Fengshuba Reservoir

Because of differences in the underlying surface, short flood confluence times, extreme precipitation, and other dynamic parameters, it is difficult to forecast an inflow flood to a basin reservoir, and traditional hydrological models do not achieve the forecast accuracy required for flood control operations. This study of the Fengshuba Reservoir in China evaluated the capacity of the Liuxihe model, which is based on a physically distributed hydrological model, to predict inflow floods in the Fengshuba Reservoir. The results show that the Liuxihe model has good applicability for flood forecasting in the basin. The use of different river classifications influenced the simulation results. The Liuxihe model can take into account the temporal and spatial inhomogeneity of precipitation and model parameters can be optimized using particle swarm optimization; this greatly improves the accuracy. The results show that the Liuxihe model can be used for real-time flood forecasting in the Fengshuba Reservoir watershed.

Classification of Rivers of Brahmaputra Basin in Assam, India Based on Designated Best Use Concept

River water quality is an important issue as rivers are one of the major sources of fresh water for drinking, agriculture, residential use, hydroelectric power plant, tourism, transportation, and others. In spite of abundance of surface water in Brahmaputra basin, only 47% rural population of Assam has access to potable water. It is important to check water quality at regular intervals and prevent and control their pollution. An attempt was made to study water quality of rivers of the Brahmaputra basin in Assam focusing designated best use concept. Water quality status was evaluated from analysis of water samples and secondary data from reports of Central Water Commission and Pollution Control Board, Assam. It is found that water of most of the rivers fall under ‘B’ and ‘C’ class (as per designated best use concept, class ‘B’ water can be used for bathing and class ‘C’ water can be used as drinking water source with conventional treatment followed by disinfection). Most of the rivers of the Brahmaputra basin have moderate or low pollution, mainly due to rejuvenated life every year during monsoon. However, with contemporary issues of population explosion, increasing demand for water, flood, growing pollution and climate change, it is imperative to take precautionary approach to the pristine as well as ecologically challenged rivers of Brahmaputra basin in India.

Deep learning models for river classification at sub-meter resolutions from multispectral and panchromatic commercial satellite imagery

Remote sensing of the Earth’s surface water is critical in a wide range of environmental studies, from evaluating the societal impacts of seasonal droughts and floods to the large-scale implications of climate change. Consequently, a large literature exists on the classification of water from satellite imagery. Yet, previous methods have been limited by (1) the >10 m spatial resolution of public satellite imagery, (2) classification schemes that operate at the pixel level, and (3) the need for multiple spectral bands. We advance the state-of-the-art by (1) using commercial satellite imagery with panchromatic and multispectral resolutions of ∼ 30 cm and ∼ 1.2 m, respectively, (2) developing multiple fully convolutional neural networks (FCN) that can learn the morphological features of water bodies in addition to their spectral properties, and (3) FCN that can classify water even from panchromatic imagery. This study focuses on rivers in the Arctic, using images from the Quickbird-2, WorldView-1, WorldView-2, WorldView-3, and GeoEye satellites. Because no training data are available at such high resolutions, we construct those manually. First, we use the red, green, blue, and near-infrared bands of the 8-band multispectral sensors. Those trained models all achieve excellent precision and recall over 90% on validation data, aided by on-the-fly preprocessing of the training data specific to satellite imagery. In a novel approach, we then use results from the multispectral model to generate training data for FCN that only require panchromatic imagery, of which considerably more is available. Despite the smaller feature space, these models still achieve a precision and recall of over 85%. We provide our open-source codes and trained model parameters to the remote sensing community, which paves the way to a wide range of environmental hydrology applications at vastly superior accuracies and 1–2 orders of magnitude higher spatial resolution than previously possible.

Ecological Quality Assessment of Greek Lowland Rivers with Aquatic Macrophytes in Compliance with the EU Water Framework Directive

Aquatic macrophytes are one of the four biological quality elements (BQE) used for assessing the ecological status of inland waters according to the EU Water Framework Directive (WFD 2000/60). With this article, we present the methodological approach for the implementation of a WFD compliant macrophyte index to the riverine systems of Greece. In addition to the definition and harmonization of the ecological quality class boundaries, the results from the pilot application of the index and the ecological classification of the monitored river reaches are also presented. Aquatic plants and environmental parameters were sampled from 93 river reaches between 2012 and 2015. A multivariate analysis with optimal scaling (MVAOS) was conducted to define the main stressor gradient and to identify the least disturbed sites and the reference conditions that are required for the derivation of the ecological quality classes. The Macrophyte Biological Index IBMR for Greek rivers (IBMRGR) was calculated for all the sites and the boundaries for the five quality classes were derived according to the methodology proposed by the Mediterranean Geographic Intercalibration Group (MedGIG). The main findings showed that the hydromorphological modifications were the main environmental stressors that correlated strongly with the IBMRGR, whereas physicochemical stressors were of lesser importance. More specifically, the first principal component explained 51% of the total variance of the data, representing a moderately strong gradient of hydromorphological stress, whereas the second component explained 22.5%, representing a weaker gradient of physicochemical stress. In addition, the ecological assessment showed that almost 60% of the sites failed the WFD target of the “Good” ecological quality class, which agrees with classification assessments based on other BQEs for Greece and many Mediterranean countries. Overall, this work provides a first assessment of the ecological classification of Greek rivers with the BQE of aquatic macrophytes with significant implications for ecological monitoring and decision making within the frame of the WFD implementation.

Evaluating the necessity of geographical locality for patterning biological integrity and its responses to multiple stressors in river systems

Water and sediment quality influence biotic communities, and relationships between the chemical composition and community structure provide important information about the regulation of river systems. We evaluated the effectiveness of clustering based on stress-response relationships and relationships with geographical location for the appropriate classification of rivers and streams. To address this issues, we applied clustering results using a self-organizing map (SOM) and geo-self-organizing map (Geo-SOM) as random effects in linear mixed-effect models (LMMs). The dataset was composed of 16 water quality variables and 11 sediment quality variables with indices of benthic diatoms (TDI), macroinvertebrates (BMI), and fishes (FAI) surveyed at 84 stations along rivers of the Republic of Korea over 2 years. The clustering results of SOM were spatially sporadic based on the relationships between the variables, while those of Geo-SOM were based on these relationships as well as the geographical characteristics. The inclusion of random effects of SOM and Geo-SOM clustering improved the performance of all LMMs; however, the LMMs with random effects of Geo-SOM-based clustering were best for all three indices. Based on the variable importance from the Geo-SOM training and LMMs, the most influential water quality variables were phosphate and chlorophyll a, and the most influential sediment quality variables were sedimentary phosphate and Cr. The correlation coefficients for heavy metals from the Geo-SOM training and contribution to selected LMMs were generally lower than those of nutrient-associated variables, indicating that the rivers and streams of Korea were more influenced by organic pollution than by heavy metal pollution. Cluster-specific conditions were significantly affected by land-use patterns, showing negative relationships with the percentage of rice paddy area and positive relationships with the percentage of forest. This approach can be applied to the ecological classification of river basins and provides fundamental information for the implementation of sustainable management.

A Simple Index of Lake Ecosystem Health Based on Species-Area Models of Macrobenthos

An effective biological index should meet two criteria: (1) the selected parameters have clear relationships with ecosystem health and can be measured simply by standard methods and (2) reference conditions can be defined objectively and simply. Species richness is a widely used estimate of ecosystem condition, although it is increased by nutrient enrichment, a common disturbance. Based on macrobenthos data from 91 shallow Yangtze lakes disconnected from the mainstem, we constructed an observed species (SO)-area (A) model to predict expected species richness (SE), and then developed an observed to expected index (O/E-SA) by calculating the SO/SE ratio. We then compared O/E-SA with three other commonly used indices regarding their ability to discriminate cultivated and urban lakes: (1) River Invertebrate Prediction and Classification System (RIVPACS; O/E-RF), (2) Benthic Index of Biotic Integrity (B-IBI), and (3) Average Score Per Taxon (ASPT). O/E-SA showed significant positive linear relationships with O/E-RF, B-IBI and ASPT. Quantile regressions showed that O/E-SA and O/E-RF had hump-shape relationships with most eutrophication metrics, whereas B-IBI and ASPT had no obvious relationships. Only O/E-SA, O/E50 and B-IBI significantly discriminated cultivated from urban lakes. O/E-SA had comparable or higher performance with O/E-RF, B-IBI and ASPT, but was much simpler. Therefore, O/E-SA is a simple and reliable index for lake ecosystem health bioassessment. Finally, a framework was proposed for integrated biological assessment of Yangtze-disconnected lakes.

Land use Land Cover Classification of Burhner River Watershed Using Remote Sensing and GIS Technique

Information on Land Use Land Cover (LULC) pattern is very important for developing management strategies for land use planning. Remotely sensed satellite data has proved to be an unmatched source of information that can deliver LULC information with good accuracy especially in regions where acquiring LULC information through intensive ground surveys seems to be impractical. The present study aims at performing LULC classification of Burhner river watershed situated in Mandla, Balaghat and Dindori districts of Madhya Pradesh, India by adopting unsupervised classification. It also discusses merits and demerits of using unsupervised classification for a high resolution satellite image along with key factors responsible for using it. Sentinel-2B satellite imagery with spatial resolution of 10 m was used in the present investigation. A total number of 6 LULC classes were identified in the study area namely agricultural land, fallow/open land, forest, habitation, wasteland and waterbodies using ERDAS IMAGINE® 2011. Accuracy assessment of the LULC classified image from reference (ground truth) data using error matrix revealed an overall accuracy of 95.72% with kappa coefficient of 0.94. Furthermore, the error matrix also aided in computing classified image producer’s and user’s accuracy which were under acceptable limits. The LULC statistics of study area indicated that highest area is covered by forest (53.01%), followed by fallow/open land (24%), agricultural land (19.44%) and least area is covered by waterbodies (1.38%) and habitation (0.19%). A major portion of study area under fallow/open land category pointed that underutilized land resource potential exist in watershed. Such land can be further utilized for crop production and plantation purposes in order to maximize output from the available natural resources in a sustainable manner.