Chaliyar River Basin Research Articles

Landslide Susceptibility Modelling of Central Highland Part of Chaliyar River Basin, Kerala, India with Integrated Algorithms of Frequency Ratio and Shannon Entropy

An integrated landslide susceptibility analysis is carried out for the central highland region of the Chaliyar River Basin in Kerala, India using bivariate statistical methods, namely the Frequency Ratio (FR) and Shannon Entropy (SE). The study addresses the complex nature of landslides, influenced by natural as well as anthropogenic factors, with specific focus on assessing the landslide likelihood of the study area. The methodology involves a systematic approach of collecting the inventory data, identifying various landslide causative factors and developing their corresponding thematic maps, spatial analysis of landslide occurrence and causative factors using GIS software and generation of Landslide Susceptibility Model (LSM) employing FR and SE algorithm, followed by model validation. Various causative factors considered for the study include slope angle, slope aspect, slope curvature, elevation, lithology, drainage density, landuse and landcover (LULC), Topographic Wetness Index (TWI) and Normalized Difference Vegetation Index (NDVI). The FR and SE algorithm enable the spatial classification of the study area into four landslide susceptibility categories namely Low, Moderate, High, and Very High. Validation of both the LSMs was carried out using Landslide Density Index (LDI) and Area Under the Curve (AUC) methods. LDI demonstrate a positive fit for both the models, which is indicative of reliability of the susceptibility predictions of the study area. A slightly higher AUC value of SE model is an indication of a high accuracy rate of SE model over FR model. This research brings out a robust methodology for predicting and identifying the landslide risks of the study area.The outcomes of this study will help in developing effective strategies to manage the landslide hazards in geologically vulnerable areas. Keywords: Landslide Susceptibility, Landslide Conditioning Factors, Frequency Ratio, Shannon Entropy, Landslide Density Index(LDI), Area Under the Curve (AUC)

Quantitative Analysis of Soil Erosion in the Kurumanpuzha Sub Watershed of Chaliyar River Basin: A Remote Sensing and GIS Approach for Sustainable Resource Management

Quantitative Analysis of Soil Erosion in the Kurumanpuzha Sub Watershed of Chaliyar River Basin: A Remote Sensing and GIS Approach for Sustainable Resource Management

Landslide Susceptibility Mapping of Chaliyar River Basin by Multivariate Statistical Model

Landslides were frequently observed in nature that can result in significant property damage and fatalities. Land management in landslide-prone areas can be aided by preparing a landslide susceptibility map. The landslide susceptibility of Chaliyar river basin was evaluated using the logistic regression (LR) technique. For this, an inventory map of 592 prior landslides was created using Landsat 8 OLI satellite imagery. The inventory of landslides was then randomly split into 30% and 70% for model training and validation respectively. Fifteen landslide causative factors viz., Slope, Aspect, Curvature, Relative Relief, TWI, Distance to Road, Distance to Streams, Distance to Lineaments, Land Use Land Cover, Drainage Density, Road Density, Lineament Density, Geomorphology, Soil Texture, NDVI were considered for landslide susceptibility modelling. Utilising a Receiver Operating Characteristics Curve (ROC) and Area Under Curve (AUC) value, the resulting susceptibility maps were validated. Analysis reveals that the validation stage of the LR model had a ROC-AUC value of 0.815. The study also demonstrates that slope, soil texture and LULC play a substantial role on the occurrence of landslides in the study area. The proposed landslide susceptibility model is appropriate, taking into account the ROC-AUC (0.815), and can be applied to future land use planning and landslide mitigation in the Chaliyar basin.

Geospatial Analysis of Kurumanpuzha Sub Watershed in the Chaliyar River Basin: A Remote Sensing and GIS Approach for Geomorphological Assessment

Aims: This study aimed to determine geomorphological characteristics of Kurumanpuzha sub-watershed for understanding its hydrological behaviour, erosion susceptibility and geological characteristics.
 Study Design: Morphometric analysis of Kurumanpuzha sub-watershed was carried out using remote sensing and Geographic Information System (GIS) which is pivotal for sustainable resource management.
 Place and Duration of Study: This study was conducted at Kelappaji College of Agricultural Engineering and Technology, Tavanur, Malappuram, Kerala during 2020-2021.
 Methodology: The geomorphological characteristics of Kurumanpuzha sub-watershed were determined using GIS offering precise terrain insights to guide water resource planning, erosion control, infrastructure development and ecological conservation. Morphometric parameters were assessed based on linear, areal, and relief characteristics. USGS Earth Explorer platform was employed to download satellite images from multiple sources, including ISRO Resourcesat, Landsat, Sentinel, RADAR, and others. These images were then used to develop a Land Use Land Cover (LULC) map as well as other maps of the study area.
 Results: The sub-watershed had a dendritic drainage pattern with a mean bifurcation ratio of 2.05, indicating easier flood management due to longer durations of low peak flows. The analysis revealed fine drainage, indicating a prevalence of soft rocks prone to erosion. The watershed had high relief and steep slopes, characterized by hills, breaks, and low mountains. The hypsometric curve indicated an equilibrium stage of geomorphic evolution. Morphometric parameters were grouped into three clusters at the sub-watershed level, demonstrating spatial variability. Forest/dense vegetation were the dominant land use, followed by rubber plantations and scrubland.
 Conclusion: The findings of this study contribute to understanding of the hydrological behaviour, erosion susceptibility and geological characteristics of Kurumanpuzha sub-watershed. It is useful for effective watershed management, erosion control and informed decision-making in land use planning and engineering projects.

Performance of Qualitative and Quantitative Models in Creating Landslide Susceptibility Map of Chaliyar River Basin

A Landslide Susceptibility Map (LSM) for Kerala's Chaliyar river basin is what this study aims to provide. Several landslides occurred in this basin as a result of the floods in 2018 and 2019. There were 592 identified landslides. Using ArcGIS 10.7 software, the landslide inventory was taken from the inventories created by the National Remote Sensing Center (NRSC) and Kerala State Disaster Management Authority (KSDMA), and the future incidence of landslides was projected by linking the landslide cause variables. Landslide inventories were split into training and validation groups in this study, with the ratios fixed at 70:30. Two models, including a qualitative one called Weighted Linear Combination (WLC) and a quantitative one (a bivariate statistical model) called Weights of Evidence (WOE) model, were used to evaluate landslide susceptibility. The following factors were employed as causative parameters: Slope, Aspect, Curvature, Relative Relief, TWI, Distance to Road, Distance to Streams, Distance to Lineaments, Land Use Land Cover, Drainage Density, Road Density, Lineament Density, Geomorphology, Soil Texture, and NDVI. The performance of the models was evaluated using the Receiver Operating Characteristic (ROC)'s Area Under the Curve (AUC). In the study, the WLC model yields an AUC success rate accuracy of 59.9%, while the WOE model yields an accuracy of 70.9%. In terms of ratio of landslide validation accuracy, the WOE model outperforms the WLC model by 11%. The anticipated landslide area is included in the landslide susceptibility map, which can be incorporated to lessen the risk of landslides in this research.

Quantification of uncertainties in streamflow extremes in the Chaliyar river basin, India under climate change

Quantification of uncertainties in streamflow extremes in the Chaliyar river basin, India under climate change

Uncertainties in future monsoon flow predictions in the context of projected climate change: A study of the Chaliyar River Basin

A major part of the annual rainfall in most parts of India is received during the monsoon. The Chaliyar River Basin in the state of Kerala is no exception with more than 85% of the annual rainfall occurring during the monsoon season. Evidences pointing towards the influence of anthropogenic activities on climate change have been reported from all over the world in recent years. One of the major problems encountered in the projection of future climate is the accumulation of uncertainties arising from different sources. This, in turn, would result in uncertainties in the predicted future streamflows. In this work, uncertainties in the monsoon flow predictions for a future period (2070–2099), stemming from the use of different climate models, hydrological models, and representative concentration pathways are analyzed. Uncertainty due to each of these sources and their interactions are partitioned by performing three-way analysis of variance. Results of the study indicate that the major source of uncertainty in the monsoon flow predictions is uncertainty from the climate models, which is about 83.73% of the total uncertainty in future monsoon flow predictions. Hydrological models account for about 5.38% and RCPs account for about 4.3% of the total uncertainty. About 6.57% is attributed to interactions between these three factors. Evaluation of the uncertainties in future monsoon flow predictions would facilitate informed decision making while formulating strategies for water management in the future.

A study of the influence of rainfall datasets' spatial resolution on stream simulation in Chaliyar River Basin, India

Abstract Rainfall is a vital input to model watershed hydrology, and the availability of numerous gridded and point-observed rainfall datasets poses a major challenge to the modellers to choose the appropriate data. This study compares three gridded rainfall datasets (i.e., 1° × 1°, 0.5° × 0.5°, and 0.25° × 0.25°) and point rainfall observations of the India Meteorological Department (IMD) on the simulation of streamflow of a river basin in the southern Western Ghats (India) using the Soil and Water Assessment Tool (SWAT). The results show that the different datasets lead to different optimal model parameter values and consequent water balance components, significantly in groundwater hydrology. The 0.5° × 0.5° and 0.25° × 0.25° datasets result in comparable SWAT model performances (NSE = 0.75 and 0.70, respectively), probably due to the similarity in the rain gauge network density employed for deriving the datasets and also due to the spatial discretization threshold used for sub-watershed delineation. However, the coarser resolution data (1° × 1°) results in poor performance (NSE = 0.21). The study suggests that the choice of rainfall data depends on the spatial resolution of the data and the spatial discretization threshold while compromising the computational requirement vis-à-vis simulation accuracy.

River drainage response to tectonism: Evidence from the Chaliyar river basin, southwestern India

River drainage response to tectonism: Evidence from the Chaliyar river basin, southwestern India

Development of a framework for sand auditing of the Chaliyar River basin, Kerala, India using HEC-HMS and HEC-RAS model coupling

ABSTRACT Excessive sand mining to meet the growing demands of the construction industry has resulted in the degradation of rivers, lowering of groundwater table, saltwater intrusion into coastal aquifers, and damages to the biodiversity of the riverine ecosystem, besides endangering bridges and hydraulic structures at several locations. Methods presently being employed to assess the amount of sand that can be mined from rivers lack a scientific basis and are seldom supported by systematic studies on the behaviour of riverine systems. Sand audit reports shall be prepared based on hydrologic, hydraulic, and sediment transport modelling of rivers, and shall take into account relevant environmental and ecological considerations. These reports shall clearly present details pertaining to the annual sediment yield of the river basin, reaches of the river/ locations from which sand can be extracted without disturbing the river sediment equilibrium, and the volume of sediment that can be extracted from these locations. Sand mining from rivers is a major environmental issue in Kerala and the urgent need for regulating sand extraction based on a scientific assessment of the amount of sand available is widely recognized. It is in this context that this work aimed at developing a framework to perform sand auditing was taken up. The methodology is demonstrated by applying it to the Chaliyar River in Kerala. Rainfall-runoff and soil erosion modelling, and sediment routing were performed in HEC-HMS. Soil erosion from the catchment was modelled using the Modified Universal Soil Loss Equation (MUSLE) and the sediment was routed through the river by the uniform equilibrium method. Hydraulic and sediment transport modelling was then performed in HEC-RAS. Values of the model performance criteria for both HEC-RAS and HEC-HMS indicate that these models performed reasonably well. Based on the analysis, a sand audit report and sand volume map were prepared.

Assessment of Nonstationarity and Uncertainty in Precipitation Extremes of a River Basin Under Climate Change

In this study, an uncertainty analysis of extreme precipitation return levels was performed for the Chaliyar river basin, India, under representative concentration pathways (RCPs) 4.5 and 8.5. Weighted average projections of various climate models (for RCPs 4.5 and 8.5) using reliability ensemble averaging were used in the analysis for projecting the future extremes. To start with, the presence of nonstationarity in the observed annual maximum precipitation (AMP) series and the future ensemble averaged AMP projections were investigated. For this purpose, three generalized extreme value (GEV) models—one stationary model with constant parameters and two nonstationary models with trends in location and scale parameters—were applied to assess the goodness of fit using Akaike information criterion and likelihood ratio test. The best fit model was used in the uncertainty analysis, and the confidence bounds of extreme precipitation return levels were estimated. A nonparametric bootstrapping approach was followed in the uncertainty analysis. Results of the study suggest that a nonstationary GEV distribution with linear trend in location parameter and constant scale and shape parameters are the best fit distribution for the AMP series under the RCP scenarios, whereas the stationary GEV distribution fits the observed AMP series the best. The expected values and confidence bounds of return levels obtained from the uncertainty analysis reveal that precipitation extremes in the river basin would intensify under the projected climate change scenarios. Compared with the RCP4.5 scenario, the confidence intervals of return levels under the RCP8.5 scenario were wider, implying that uncertainty in the latter scenario is higher.

IMPACTS OF CLIMATE CHANGE ON IRRIGATED AGRICULTURE IN THE CHALIYAR RIVER BASIN IN KERALA, INDIA

Impact assessment of regional climate change is very important as change in climate has emerged as one of the major threats to water resource systems and would significantly affect streamflow, soil moisture and water availability. The study used output of the Regional Climate Model (RCM) Remo2009 (Max-Planck-Institute (MPI)) to analyze the potential impacts of climate change on irrigated agriculture in the Chaliyar river basin, India. Streamflow and evapotranspiration were simulated using validated Hydrologic Engineering Center’s Hydrologic Modeling System (HEC-HMS) model. The estimation of irrigation water requirement (IWR) was performed using Food and Agriculture Organization (FAO) method for the period 2021–2030 and 2051–2060. Results show that projected streamflow increases during June to September and decreases during October to December and January to May in future. Crop water requirement and IWR showed an increase during dry season and decrease during wet season. The increase/decrease in streamflow and IWR during wet/dry season is more in the far future than near future and for RCP 8.5 scenario than RCP 4.5 scenario.

Downscaling future projections of monthly precipitation in a catchment with varying physiography

Projections of monthly precipitation data in the Chaliyar river basin for the future scenarios are downscaled. Observed precipitation data at two rain gauge stations located in mid land and high land were compared and found to be highly varying and hence downscaling with respect to the individual stations was adopted. Based on the values of the correlation coefficient between the large-scale atmospheric predictors and observed precipitation data, potential predictors were selected. Predictor-predictand correlation was not similar at the two stations and hence separate sets of potential predictors were selected for both the stations and for three different seasons. Artificial Neural Network-based transfer functions were derived between the potential predictors and the observed precipitation data for each station and for each season and these were subsequently validated. The validated models were used to downscale the data projected by three GCMs, viz. CGCM3, BCM2 and FUB-EGMAM, for the scenarios SRES A1B, A2 and B1 at two rain gauge stations for the period from 2001 to 2100. Results show an increase in precipitation at both the stations for the scenarios SRES A1B, A2 and B1 during the south-west monsoon period. In the north-east monsoon and pre-monsoon, more number of extreme events was present.

Isotopic characterization and mass balance reveals groundwater recharge pattern in Chaliyar river basin, Kerala, India

Abstract Study region The Chaliyar river basin, Kerala State, India. Study focus Detailed understanding about spatio-temporal variation in the interaction and exchange of water between surface and sub-surface reservoirs is important for effective watershed management. Spatio-temporal variations in the oxygen isotopic composition ( δ 18 O) were used to understand the interaction between groundwater and river water, and to estimate the groundwater recharge from river water in the Chaliyar river basin. New hydrological insights for the region Based on the spatio-temporal variation in δ 18 O values of river and groundwater and fluctuation in ground water levels, following important inferences are made: (1) estimated river water contribution to post-monsoon groundwater recharge is ∼16% in the lowland coastal area of the Chaliyar river basin and 29% in midland region; (2) northeast winter monsoon rains contribute to the groundwater of Chaliyar river basin only in an insignificant manner, and with a delayed response; (3) unlike river water samples which exhibit both seasonal and spatial variation of more than 3‰, the groundwater samples vary only marginally (∼1‰) between the seasons and across the physiographic zones; (4) groundwater samples exhibit inverse altitude gradient in δ 18 O values in the highland zone, in all the three seasons. This may be due to flow of the isotopically depleted groundwater down the gradient and evaporation of residual water in the upper reaches of the basin.

Detection and Attribution of Climate Change Signals in Precipitation in the Chaliyar River Basin, Kerala, India

Scientific studies have yielded evidence to support the common perception that climatic variables and associated natural resources and human systems are being affected by external forcings. Detection and attribution (D&A) of climate change provides a formal tool to decipher the complex causes of climate change. This work aims to statistically detect such climatic change signals, if any, in the monthly precipitation data of the Chaliyar river basin, Kerala, India and to evaluate the factors contributing to it. Data employed for the study includes monthly mean precipitation observations, National Centre for Environmental Prediction and National Centre for Atmospheric Research (NCEP/NCAR) reanalysis data sets and results of several climate model runs based on natural internal variability and anthropogenic effects. Precipitation data from the GCMs were statistically downscaled to river basin scale using ANN based models and the potential predictors were identified by correlation coefficient analysis.Five widely used General Circulation Models were chosen based on availability of the predictor data in this study. GCM projections for three SRES scenarios, A1B, A2 and B1 were downscaled and the t test at 99% confidence interval was performed on the downscaled monthly precipitation data. Results show that there is consistency in the observed data and anthropogenically forced data. Using a standard fingerprint approach, it was noticed that the observed trends in precipitation over the second half of the 20th century lie outside the range expected from natural internal climate variability alone at 95% confidence level for most of the GCMs. To determine the reason for this climate change signal, attribution study was performed based on correlation coefficient analysis and this yielded a positive relationship between the downscaled A2 scenario precipitation and the observed precipitation. The detection and attribution analysis can be further extended to consider the effects of non climatic localized influences like land use and land cover changes, urbanization etc.

Prediction of the likely impact of climate change on monthly mean maximum and minimum temperature in the Chaliyar river basin, India, using ANN-based models

In this work, an approach based on Artificial Neural Networks (ANN) has been employed to assess the likely impact of climate change on mean monthly maximum and minimum temperature (T max and T min) in the Chaliyar river basin, Kerala, India. ANN is trained to downscale temperature from the General Circulation Model (GCM) from a coarser resolution to the required resolution of the river basin. The work aims to estimate the GCMs’ output to the scales compatible with that employed in a hydrologic model of the river basin. In order to satiate this purpose, predictor variables were obtained from the National Centre for Environmental Prediction and National Centre for Atmospheric Research (NCEP/NCAR) reanalysis data; this was utilized for training the ANN using a feed-forward network with a back-propagation algorithm. These models were validated further and used to downscale CGCM3 GCM simulations for the scenarios outlined in the IPCC Special Report on Emission Scenarios (SRES). Results showed that both T max and T min are increasing consistently in all the scenarios. T max exhibited an average increase of maximum 3 °C during the dry season (December–May) and 1 °C during the wet season (June–November) by the year 2100, while T min showed an average increase of 2.5 °C in the dry season and 0.5 °C in the wet season.

Tectonic effects on the longitudinal profiles of the Chaliyar River and its tributaries, southwest India

Chaliyar River, a west-flowing river, originates at about 2300m elevation in the Western Ghat hill ranges in the southern part of India. We have studied geomorphic aspects of this river by examining longitudinal profiles and drainage pattern in order to understand the rock uplift and river incision. Chaliyar River and its tributaries display uneven longitudinal profiles with numerous knickpoints along the profiles. River concavity and river morphology were analysed to better understand the influence of tectonics and rock uplift on the fluvial and topographic system in the Chaliyar River basin. Wide variability in the concavity index of the tributaries of the Chaliyar River reflects the role of tectonism in carving the present river profiles. Steepness and concavity indices computed for the longitudinal profiles suggest that the rate of uplift is exceeding the rate of incision and are independent of lithology. River incision is not uniform in the Chaliyar River. The streams become graded (absence of knickpoints) towards the river mouth, suggesting that the uplift and incision are in equilibrium.

Bias Correction for RCM Predictions of Precipitation and Temperature in the Chaliyar River Basin

Global climate models (GCMs) relate greenhouse gas (GHG) forcing to future potential climate states and enable development of climate projections for the future. GCMs exhibit some limitations when focusing on smaller scales (regional to local) or to resolve the processes caused by topography and land use. To overcome this problem, regional climate models (RCMs) and other downscaling methods have been developed. The Indian Institute of Tropical Meteorology (IITM), Pune, in collaboration with the Hadley Centre, UK has developed future climate scenarios for India. Climate data (precipitation and temperature) for the Chaliyar river basin in Kerala for both A2 and B2 scenarios were utilized in this study. Bias correction was performed to ensure that important statistics (coefficient of variation, mean and standard deviation) of the downscaled output matched the corresponding statistics of the observed data. This method of bias correction does not correct for the fraction of wet and dry days and lag inverse autocorrelation. But, it was observed that a relatively simple non-linear correction, adjusting both the biases in the mean and variability, leads to better reproduction of observed extreme daily and multi-daily precipitation amounts. A marked improvement was achieved with a nonlinear transformation, adjusting the mean as well as the coefficient of variation of daily precipitation. Predictions show that annual rainfall in the Chaliyar river basin may decrease by about 20-25% from the present day annual average value in the A2 scenario. In the B2 scenario, the decrease was in the range of 10-15%. Annual average temperature may increase by 3.3oC and 1.8oC from the present day average values in the A2 and the B2 scenarios respectively.

Impact of anticipated climate change on direct groundwater recharge in a humid tropical basin based on a simple conceptual model

AbstractA simple conceptual semi‐distributed modelling approach for assessing the impacts of climate change on direct groundwater recharge in a humid tropical river basin is investigated. The study area is the Chaliyar river basin in the state of Kerala, India. Many factors affecting future groundwater recharge include decrease or increase in precipitation and temperature regimes, coastal flooding, urbanization and changes in land use. The model is based on the water‐balance concept and links the atmospheric and hydrogeologic parameters to different hydrologic processes. It estimates daily water‐table fluctuation and is calibrated and validated using 10 years of data. Data for the first 6 years (2000 to 2005) is used for model calibration, and data for the remaining four years (2006 to 2009) is used for validation. For assessing the impact of predicted climate change on groundwater recharge during the period 2071–2100, temperature and precipitation data in two post climate change scenarios, A2 and B2, were predicted using the Regional Climate Model (RCM), PRECIS (Providing Regional Climates for Impact Studies). These data were then corrected for biases and used in a hydrologic model to predict groundwater recharge in the post climate change scenario. Due to lack of reliable data and proper knowledge as to the magnitude and extent of future climatic changes, it may not be possible to include all the possible effects quantitatively in groundwater recharge modelling. However, the study presents a scientific method to assess the impact of predicted climate change on groundwater recharge and would help engineers, hydrologists, administrators and planners to devise strategies for the efficient use as well as conservation of freshwater resources. Copyright © 2011 John Wiley & Sons, Ltd.

Influence of Scale on SWAT Model Calibration for Streamflow in a River Basin in the Humid Tropics

The Soil Water Assessment Tool (SWAT) was applied to the 2,530 km2 Chaliyar river basin in Kerala, India to investigate the influence of scale on the model parameters. The study was carried out in this river basin at two scales. Parameters such as land use, soil type, topography and management practices are similar at these scales. The model was initially calibrated for streamflow and then validated. Critical parameters were the curve number (CN2), soil evaporation compensation factor (ESCO), available water holding capacity (SOL_AWC), average slope length (SLSUBBSN), and base flow alpha factor (ALPHA_BF). Using the optimized value of various parameters, stream flow was estimated from parts of the basin at two different scales—an area of 2,361.58 km2 and an area of 1,013.15 km2. The streamflow estimates at both these scales were statistically analysed by computing the coefficient of determination (R 2) and the Nash–Sutcliffe efficiency (ENS). Results indicate that the SWAT model could simulate streamflow at both scales reasonably well with very little difference between the observed and computed values. However, the results also indicate that there may be greater uncertainty in SWAT streamflow estimates as the size of the watershed increases.