Advances in River Corridor Research and Applications

Due to the adverse impacts of climate change, which have elicited global attention and concern, there is a pressing need to understand and address the multifaceted challenges of changing climatic conditions. The direct influence of changing climate on precipitation patterns and temperature significantly impacts streamflow dynamics through modifications in the peak and low flow conditions. The current study employs daily discharge data from ten stream gauge stations situated within the Narmada River basin, which is a prominent west-draining peninsular basin. This study examines gradual and abrupt changes in extreme streamflow indices, encompassing vital hydrological variables such as peak flows, low flows, and streamflow volumes. The study employs two robust non-parametric tests: the Modified Mann–Kendall test (MMK) for trend identification and the Pettitt test for detecting abrupt shifts. Furthermore, the true magnitude of trends is assessed using Sen’s slope (SS) estimator. The analysis reveals a consistent decreasing trend in peak flows and a significant change point across all stations. However, contrasting trends are observed in the annual streamflow volume and low flow days, indicating a mixed pattern of change, thus highlighting the complex dynamics of streamflow variations. The research aims to understand better the hydrological dynamics within the study area for efficient water resources management and flood mitigation.

There are two broad approaches to doing science: deduction and induction. While the primary objective of a deductive approach is the application of a theory to solve a problem, an inductive approach strives to develop a theory from data through generalization. Banking on Newtonian mechanics, scientific studies often prefer deductive approaches due to their perceived superiority, even though Newton himself highlighted the importance of observations. Here, we provide an overview of how hydrological science has largely progressed through inductive or observation-driven research. We also discuss recent advances in machine learning modeling and why superior performance achieved with these models cannot undermine the importance of understanding hydrological processes. In our opinion, the progress of hydrological science has been significantly hindered by the popular notion that each catchment is unique, necessitating a hydrological model to employ multiple free parameters representing its characteristics, particularly those related to soil. It is argued that efforts should be directed towards developing climate-centric models that require little or no calibration, focusing on similarities among catchments. In this regard, machine learning models have the potential to further improve our understanding of hydrological processes.

Preventing extremes of flood and drought and lending perenniality to rivers/shorter zero flow periods to intermittent rivers is an important ecosystem service provided by natural forests. Yang et al. [11] in their comprehensive assessment of long-term stationarity of annual streamflow for 11 069 catchments showed that, while long-term stationarity is evinced in almost 80% of catchments that have undergone only minimum human intervention, streamflow had remained stationary in less than 40% of catchments which have undergone substantial human interventions. Vitousek et al. [10] had already pointed out that the risk posed by anthropogenic large-scale land use changes can go beyond that of climate change. Increasingly large tracts of forest land are being cleared and brought under cultivation to supply the needs of the growing human population. Measures for reclaiming some of the ecosystem services provided by erstwhile forests include rewilding; it may be noted that the FAO UN classifies plantation forests also as forests in the matter of resources. Here, the results of a study of landscape scale mean bulk density (BD) of soil–water content (SWC) and its relation with the mean BD of soil organic carbon (SOM) and sand/silt/clay content, across the soil of 21 natural forests and a total of 100 rubber plantations set up on deforested land/converted cultivated land, randomly located over a ~700 km long narrow stretch from Shivamogga to Kanyakumari on the west coast of the Indian peninsula is presented. The implications for water resources, in the light of measures promoted for the sequestration of carbon in soil, are pointed out.

Recent rainfall-runoff modeling utilizing artificial intelligence (AI) models has shown high adaptability. The purpose of the current study was to simulate monthly runoff in the Bardha river basin using three different AI models. These models included the Long Short-Term Memory (LSTM) deep learning model, the Multilinear Regression Model (MLR), and the Support Vector Regression Model (SVR). The period from 2003 to 2007 was designated as the training phase, while 2008 to 2009 constituted the testing phase, spanning from 2003 to 2009. During the training period, the LSTM model achieved a coefficient of determination (R²) value of 0.90, a Nash–Sutcliffe simulation efficiency (NSE) value of 0.85, a root mean square error (RMSE) value of 15.5 m³/s, and an RMSE-observations standard deviation ratio (RSR) value of 0.36. Correspondingly, in the testing period, the LSTM model achieved an R² value of 0.87, an NSE value of 0.79, an RMSE value of 7.65 m³/s, and an RSR value of 0.31. Among the AI models employed, the LSTM model stood out for its notably superior accuracy.

Accurate streamflow estimation is essential in several water resources management applications. Therefore, hydrologists aim to develop a simple, robust and calibration-free rainfall–runoff model. In a typical rainfall–runoff model, soil is at the centre and is assumed to play an important role in runoff generation. However, the recently proposed Dynamic Budyko (DBv1) model predicts daily streamflow using only climatic inputs. Furthermore, the DBv1 model framework was recently modified for effective rainfall simulation. The study reports that meteorological inputs contain sufficient information for runoff prediction. However, the modified DBv1 (DBv2) model has not been tested for peninsular river basins in India, which is crucial for water resources management applications. Therefore, in this study, three basins from India were selected as the study area. The DBv2’s runoff generation module was used for effective rainfall estimation, and linear reservoirs (LR) connected in parallel were used to obtain streamflow at the basin outlet. Thus, this model (DBv2_LR) has a calibration-free runoff generation module, while routing requires calibration. We compared the performance of DBv2_LR with the well-known Probability Distributed Model (PDM), which is fully calibrated. In the case of PDM, the same routing module (reservoirs connected in parallel) was used to obtain streamflow at the basin outlet (PDM_LR). The models, DBv2_LR and PDM_LR, were evaluated for Nash–Sutcliffe efficiency (NSE). The results indicate that the DBv2_LR model shows performance very similar to the PDM_LR. Overall, this study suggests that even in arid or semi-arid regions like India, climate information is sufficient for effective rainfall estimation.

The river's hydrodynamics can be directly influenced by the temporary flooding resulting from the dam's reservoir. This, even without backwater inundation, leads to changes in the river's structure that can further affect its initial hydrodynamics. Over recent years, the repetitive flooding of the Kelo River has caused significant damage to the upper section of the Kelo Dam. Located on the Kelo River in Chhattisgarh, the Kelo Dam primarily contributes to backwater flooding and hydraulic responses in this region, resulting in substantial losses in terms of property, infrastructure, and livelihoods. The primary objective of this investigation is to assess the impact of backwater on the river under both stable and flood conditions. The study focused on the Kelo River, employing a steady-flow analysis through a geo-referenced HEC-RAS model. The hydrodynamic reactions of the Kelo Dam can be determined by studying the changes in the water surface profile, velocity distribution, and water coverage area with and without the dam. The findings indicate that the influence of backwater upstream of the Kelo Dam extends up to 8 km. In this region, the velocity decreases from 3.5 to 0.7 m/s. The results demonstrate the potential submersion of the Chirpaini area during high flood events. This study will be valuable for future planning aimed at minimizing the impact of backwater in the surrounding area.

Rivers have historically served as the birthplace of human civilization. The study of channel planform and geo-morphological characteristics is of utmost importance in investigating the effects of climate change and alterations in land use on the overall well-being of rivers. This study examines the temporal variations in the geo-morphological characteristics of the upper reaches of Krishna River during three decades (1991–2021). Spatial data is obtained from several satellite missions and afterward processed using Remote Sensing and Geographic Information Systems (GIS) to assess alterations in the active river channel's extent, erosion, and accretion areas and the sinuosity index. The river channel has been segmented into seven sub-reaches, and it has been observed that some sections of the bank line require prompt attention from the relevant authorities to carry out bank protection measures. The findings indicate that the left bank exhibits a greater tendency towards erosion and shifting compared to the right bank. Additionally, the river has undergone geo-morphological alterations due to the construction of hydraulic structures and the occurrence of numerous flood events within the basin. The sinuosity index of the river provides evidence of its meandering nature. This study offers valuable insights into the dynamic behavior of the Upper Krishna River, hence providing useful information for authorities and decision-makers involved in river training initiatives and future development projects.

One way to quantify the various morphometric features of a river is through morphology. The North–south-flowing Ramis River is a major tributary of Lake Titicaca, one of the world’s largest lakes. The Ramis has been exhibiting marked changes in its morphology, loss of cultivation areas, and limitations in the operational performance of its associated public infrastructure. Thus, this study aims to present the fluvial classification of the Ramis River. To this end, the basin was examined to establish the various morphometric parameters along thirteen representative sections, which do not include the river delta, through the analysis of the planimetry, sediment properties, river dynamics, cross sections, longitudinal profiles, and hydraulic behavior. Our results indicate that the upper Ramis exhibits a rocky-alluvial valley bed relatively straight and becomes more sinuous downstream. The study stretch is classified as types E5, D4c, C4b, C4, C4c, B4c, and B4cm according to the Rosgen stream classification scheme. Positive correlations were found between stream cross-sectional flow area, bank width, and discharge concerning the size of the watershed. The Ramis River exhibits morphologic units associated with debris flows and channel processes (e.g., point bars, and mid-channel deposits). The southern Ramis exhibits lower efficiency due to a decline in slope and competition, the northern Ramis contains more energy for the conveyance of bed load. According to the Ramis’ stability report, segments 11 to 13 of the stream are deteriorating, the intermediate portions indicate a relationship related to a braided river, and the lower reaches undergo a process of aggradation.

River morphology and hydrology are extremely important in finding location, waterway, afflux and scour in bridges. Although there are a number of IRC codes for design guidelines, there are several ambiguities in the codes. In this paper, author has pointed out some of the deficiencies of the existing IRC codes and suggested improvements needed.

Understanding erosion and accretion, which are critical geomorphic processes, is essential for effective river management and conservation. Erosion by removing soil and rock changes the river's shape, depth, and course. Accretion, conversely, involves the deposition and accumulation of sediment, shaping features like riverbanks and floodplains. Focused on a 30 km stretch of the Netravati River, in the southwestern region of India, this study used Survey of India toposheets and Landsat images to track changes over time (1973, 1998, 2022). The Normalised Difference Water Index (NDWI) and image classification were employed for the analysis which revealed notable spatiotemporal variations in these processes. From 1973 to 2022, the analysis estimated a total erosion of 510.43 hectares and an accretion of 317.71 hectares. The years 1973–1998 witnessed more accretion (417.6 hectares) than erosion (229.08 hectares). And, from 1998 to 2022, erosion dominated at 438.37 hectares, with only 56.97 hectares of accretion. These variations can be attributed to both natural processes and human interventions. Notably, the construction of a vented dam in 1993 at Thumbe, followed by the subsequent dam in 2016, 50 m downstream of the old dam, influenced the sediment dynamics and flow patterns in the Netravati River, potentially impacting erosion and accretion processes. This research adds to our understanding of erosion and sediment changes in the Netravati River over time. The dams and hydraulic structure upstream along with geospatial techniques offer researchers and river managers a unique opportunity to examine river shape impacts and thus develop sustainable strategies for river preservation.

This laboratory study is aimed to assess the impact of different plant types and varying rainfall levels on the runoff and sediment erosion on slopes. The study employed a 2 × 1 m soil bed in conjunction with a rainfall simulator. Various plant layouts were tested including scenarios with no vegetation, full vegetation coverage, and distinct patterns such as upstream, downstream, and checkerboard. Rainfall was applied at three different intensities (100, 150, and 200 mm/h) for a total duration of 16 min, with data collection continuing until runoff ceased into the collection tank. This comprehensive approach enabled the researchers to gain insights into how different plant arrangements influenced erosion dynamics. Upon drying the sediment for 24 h, results of the study revealed that vegetation cover played a significant role in reducing both runoff and sediment. Intriguingly, downstream vegetation was found to be more effective than upstream vegetation in mitigating erosion. Furthermore, the placement of vegetation on the lower part of the slope proved more efficient in erosion prevention than having it higher up. These findings hold substantial importance for the development of erosion control and land management strategies, particularly in regions with diverse plant configurations. Notably, the absence of vegetation (bare soil) resulted in the highest sediment output, while a uniform vegetation pattern across the area exhibited a significant reduction in sediment compared to bare soil. Among the various layouts tested, the scenario with uniform vegetation (at a density of 2.5 cm) emerged as the most effective in sediment reduction. When vegetation was placed on one side with bare soil on the other (either upside or downside), a moderate reduction in sediment was observed compared to entirely bare soil. However, the checkerboard pattern had only a marginal effect on sediment reduction and proved less effective than the uniform vegetation layout.

Microplastics (MPs) are anthropogenetic pollutants, with particle sizes between 5 mm and 1 μm. These particles enter riverine ecosystem through sources like discharge from wastewater treatment plants, surface runoff from landfills and plastic recycling plants. Movement of the MP particles in the water column is influenced by changes in river flow, leading to deposition and re-suspension from the riverbed. These dynamic actions have considerable impact on the benthic ecosystem. The present study aims to understand how the deposited MP particles affect the hydraulic conductivity of the riverbed. For this, experiments were conducted using the constant head permeability test setup with gravels (mean particle size, d₅₀ = 14 mm) and pebbles (d₅₀ = 13 mm). The MPs used in the present study were collected from PVC recycling plant in Karaikal, Puducherry, India. The collected sample predominantly constituted of fragment (82%) and fibre (18%) shaped particles, with a d₅₀ of 2 mm. The intrusion effect was analysed by altering the concentration of MPs and the unit weight of the aggregate. Results show that the hydraulic conductivity is inversely proportional to the concentration of MPs and positively correlated with a decrease in aggregate unit weight. Consequently, the hydraulic conductivity of gravels and pebbles is significantly due to the presence of MPs, with a more marked effect observed for gravels than pebbles. Moreover, the particle shape and texture properties also influenced the hydraulic conductivity. These findings are crucial to environmental managers and decision-makers seeking to understand MPs’ impact on riverbed and groundwater quality.

Dams constructed along rivers serve as barriers to the migration of fish. Consequently, the demise of many fish species populations across the globe has been noticed. Many conventional fishways are developed to restore the longitudinal connectivity in the river. However, for non-target species such as cyprinid, carp, and all non-salmonid species, their efficiency is very poor. Recently, nature-like fishways have gained popularity as they are ecologically viable by attracting diverse fish species and are economical due to less construction and management costs. Among all NLFs, step-pool bed morphology in the mountainous stream shows varying flow hydrodynamics and high energy dissipation. This feature of step-pools makes them very suitable for use in ecological environments such as creating close-to nature like step-pool fishways. Hence, a comprehensive study of flow hydrodynamics and turbulence associated with nature like step-pool-type fishways is essential. However, understanding fish behavior in the flow field is a prerequisite for developing and incorporating them into the design guidelines for a fishway. This paper reviews the geometry, flow field, and performance of a nature-like step-pool fishway and the future work to be done. Also, a review of how fish behavior and fish response to the flow field inside the fishway is essential to designing a fishway is presented. Finally, the significance of experiments with high-frequency instruments to capture accurate flow velocity, limitations, and future scopes of work is discussed.

Arsenic (As) contamination of shallow alluvial aquifers in deltas of major rivers (Ganga, Meghna, Brahmaputra, Sutlej, Indus, etc.) in south Asia is the result of the microbially mediated reductive dissolution of iron (Fe)- and As-rich sediments, which are eroded from Himalayan rocks and transported to the deltas by rivers. The reductive dissolution is fueled by labile sedimentary and dissolved organic matter (OM). However, a very limited number of studies investigated the interactions between Fe, As, and DOC or OM in the Himalayan region. We hypothesize that the sediments transported by the Himalayan rivers shall contain elevated concentrations of Fe and As. We collected and analyzed riverbank sediment, river water, and sediment pore water samples from six locations along the Beas River in Himachal Pradesh (India), a major contributor to Sutlej-Indus River delta. Our results showed that the river sediments contained 12 ± 3 g/kg of total Fe, 4 ± 1 mg/kg of As, and 264 ± 122 mg/kg of Mn as measured by XRF. These As concentrations are approximately twice the crustal abundance of As, which is 2 mg/kg. The findings of this study will advance our understanding of how As is mobilized from the source to the delta.

Arsenic (As) contamination within the shallow aquifers of the Bengal basin continues to pose a serious health risk to millions of people who rely on the groundwater for drinking purposes. Elevated dissolved As concentrations in the aquifers are attributed to the reductive dissolution of As-bearing Fe-oxides. Within the hyporheic zone (HZ), interactions between oxygen-rich river water and reducing groundwater causes the precipitation of Fe-oxides, which act as a sink of As. Surficial fine sediment has been proposed to limit this reaction. Once formed, the As-bearing Fe-oxides may dissolve under reducing conditions to further contaminate the adjacent aquifer. In this preliminary study, sediments from silt-capped and sandy riverbanks along the Hooghly River (West Bengal, India) were investigated to understand the factors controlling the mobility of As within the HZ. Bulk elemental concentrations were measured by X-Ray Fluorescence and the relative proportion of Fe(III) was estimated by diffuse reflectance spectroscopy. The silt-capped riverbanks had As and Fe concentrations of 3.6 mg/kg and 19.0 g/kg, respectively, which were more closely associated with clay minerals as shown by the ΔR which is a proxy for Fe(III) (ΔR at 520 nm = 0.2). The sands had As and Fe concentrations of 3.6 mg/kg and 12.5 g/kg, respectively, with higher proportions of Fe present as Fe-oxides (ΔR at 520 nm = 0.37). The results indicate that the distribution of As and Fe differs between the sandy and silt-capped riverbanks, indicating that the hydrological and chemical reactions impacting As mobility varies between the riverbanks.

Floods cause physical damage and impact the availability of food, water, and crops. Effective disaster management and disaster risk reduction strategies require a quick and accurate mapping of these phenomena. The study area selected is the Krishnaraja Nagar taluk, Mysore districts, Karnataka having an area of 608 Km². In this study, the analysis of a flood event was conducted using the temporal GRDH SAR pictures in C-band from Sentinel-1. Additionally, the co-polarized Vertical transmit, and Vertical received (VV) Synthetic Aperture Radar (SAR) images were utilized to map the extent of the flooded area. Two methods of change detection are applied to the temporal SAR images: Otsu's Automatic thresholding method using Matlab R2020a, utilizing a pre-flood image dated 02 August 2018 that shares identical image characteristics with the flood images captured on 14 August 2018; and flood mapping based on Normalized Difference Flood Index (NDFI) using Sentinel Application Platform (SNAP) software. By dividing the SAR image's non-water and open-water regions, the threshold approach was used to extract the flooded areas. In order to identify the actual flooded region, permanent water bodies were later removed from the open water. An analysis of the overlay flood maps was conducted to determine the total area inundated. After processing the SAR data and conducting threshold operations, the flooded area estimates from NDFI is 28.10 km², and by Otsu's method flooded area is 21.92 km². It is concluded from the study that the SAR information, sideways with GIS, can be used efficiently for floodwater plotting, real-time analysis, and analysing the spread of floodwater in a flood-prone zone.

Cryospheric components are sensitive to changes in climate and monitoring them is a challenging task in the rugged topography of the Himalaya. Many studies were carried out to assess the changes in Cryosphere like glacial retreat, mass balance, changes in runoff and weathering effect in glaciated valleys. But very few studies have been carried out on the assessment of Snow Line Altitude (SLA), which is an important parameter to understand the sensitivity of glaciers to climate change. Mapping and monitoring the SLAs of glaciers using in situ methods is a crucial task since it is a zonal feature and has high spatio-temporal variability. Remote sensing-based feature tracking is the alternative way for mapping the snowline altitudes of glaciers. With the recent development of various machine learning algorithms, segregation of identical features on the multispectral imagery and tracking the position of SLA’s has become easy. Few of them are Otsu, K-means, cascade K-means, random forest, minimum distance, smile cart and support vector machine algorithms. Thus, this study aims to understand the applicability of machine learning algorithms to map and analyse the variations in SLAs of glaciers in the Chandra River basin, a tributary of the Sutlej River. During the analyses, SLAs are mapped and snowline altitudes are extracted. It indicates that the SLAs of glaciers vary from 4100 to almost 5260 ± 30 m. All the glaciers are completely covered with seasonal snow during the accumulation period starting from November to April, and then snowline starts retreating from the lower altitude i.e., 3800 m.a.s.l, and reaches a maximum altitude by the end of the ablation period (in this study it is 5259 ± 30 m.a.s.l). Analyses and observations suggest that all the applied methods performed similarly except the support vector machine and minimum distance methods. Estimated uncertainties be within ± 30 m. The regional SLA is observed higher during the hydrological year 2003–04 (estimated approximately to 525 m) and vice versa in the years 2019–2020 (where SLA is estimated to 5206 m). The observations on seasonal snow extents and altitudes of snowlines from this study can be used to reconstruct the mass balance and the hydrological budget of the Chandra river basin.

A novel low-cost underwater remotely operated vehicle (UROV) has been designed and developed to meet the demand for underwater explorations, bathymetry study and monitoring of rivers, lakes and wetlands from the prospective of research. The design of the UROV is made by considering three broad aspects of low cost, low weight, which can be customized according to various demands in terms of space allocation and sensor integration. Considering these aspects, the outer frame of the UROV is made of aluminium extrusion rods where the hull, battery module, four thrusters and buoyancy blocks are mounted. This frame can be easily assembled and dismantled. The hull of the UROV is made of industrial-grade PVC pipes where an acrylic slot is inserted inside the hull. This slot is housed with all the electronic components on both sides of it. The slot is then water-tight in the hull enclosure with gas foil rings and industrial adhesive. Various sensors such as temperature, pressure, sonar, GPS, camera with gimble and inertial measuring unit are integrated in the slots inside the hull to get the live video and sensor data while manoeuvring in the water bodies. The design of the UROV is made symmetric and neutrally buoyant. The developed UROV has been successfully tested several times acquiring live critical parameters from the lakes, the Brahmaputra River and other water bodies. Individuals and small-scale marine companies may find this UROV, to be a highly valuable resource for underwater explorations and bathymetry study.

The dynamic ecosystems of water bodies like rivers and lakes are heavily dependent on parameters like temperature, turbidity, and dissolved oxygen. Continuous monitoring of these parameters is very much required to maintain the good health of aquatic ecosystems. In this work, a new water quality measurement device has been developed to monitor the health of lakes and rivers. The developed system can be used as a standalone unit and can also be used as an array of multiple interconnected systems through a cloud-based communication protocol. The device runs on the Atmel328 microcontroller with integrated sensors. It is capable of measuring, recording, displaying, and transmitting the sensor data to a cloud-based data storage facility. It is powered by a rechargeable Li-ion battery, chargeable with an onsite 20W solar-powered unit. The first prototype has five sensors, viz., pH, temperature, turbidity, pressure, and total dissolved solids, to measure corresponding water quality parameters. An enclosure has been designed from ABS plastic to safeguard all the electronic components. This in-house-developed watertight enclosure has an IP67 rating under laboratory testing conditions. In its current form, the developed device can be deployed in any remotely located water body for monitoring and measuring critical water quality parameters. The module-based design allows for easy installation and integration of desired sensors. The lightweight and portable design of the developed device also facilitates the user carrying it to any aquatic body for measuring its water quality easily.

Effects of randomly spaced, non-uniformly sized roughness elements on the flow structure and energy loss in a supercritical open-channel flow were investigated using numerical models and compared with experimental data. A three-dimensional numerical model for complex flow simulations was evaluated using experimental data. Simulations were performed at large Froude numbers varying from 2.5 to 2.8. The overall nature of the water surface profile was well simulated but a maximum of 30% error in water level was noticed closer to the roughness elements. The simulated velocity profiles matched well with the observed data in the upstream locations of roughness elements but a minor discrepancy was observed in the wake region (downstream side) near to bed. The numerical model did not accurately resolve the local flow features such as the heights and lengths of water jumps over obstacles. The maximum difference between the observed and predicted jump lengths was 73.4%. However, it satisfactorily simulated the general flow features, including the total energy loss.