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Sea level projections for the future indicate a likely increase, raising concerns in the community due to its detrimental consequences. It has been reported by several researchers that there is considerable uncertainty in the future climate projections of Global Climate Models (GCMs), the primary tools for projecting future climate. In this study, the support vector machine (SVM) was employed to downscale sea level projections for the future from the projections of CMIP5 (Coupled Model Intercomparison Project Phase 5) GCMs. Quantile regression was employed to examine the predictors' uncertainty, and it was found that sea surface salinity (halosteric component of sea level change) is the highly uncertain variable among the three predictors, followed by sea surface temperature and mean sea level pressure. The uncertainty associated with downscaled future sea level projections under Representative Concentration Pathways (RCPs) 4.5 and 8.5, stemming from GCM structure, was investigated using Normal distribution and non-parametric kernel density estimation. Kolmogorov–Smirnov (KS) test was performed to assess the goodness of fit and found that both normal distribution and kernel density estimation satisfactorily represent the probability density function (PDF) of sea level projections for the future. The uncertainty bounds in the sea level projections under both RCPs were estimated using the bias-corrected and accelerated bootstrap algorithm and found that the lower and upper bounds of sea level projection during December 2050 are 0.529 m and 0.604 m under RCP 4.5 and 0.535 m and 0.700 m under RCP 8.5. Results of the study revealed that uncertainty is comparatively high under RCP 8.5. [ABSTRACT FROM AUTHOR]

Morphometric and hypsometric parameters provide insight into the drainage and erosional behavior of a river basin. In this work, the morphometric parameters, planforms, and hypsometry of the drainage basins of two rivers, namely, the Chaliyar River and the Thamirabarani River, originating from the windward and leeward sides respectively of the Western Ghats in Peninsular India were analyzed to compare its drainage and erosional characteristics. Geologically, both the river basins share a common lithology; however, the precipitation pattern in these two river basins is very different. The average annual precipitation in the Chaliyar River Basin is significantly higher when compared to that of the Thamirabarani River Basin. Also, the precipitation season and the temporal distribution of precipitation are different in these river basins. Mann–Kendall trend analysis of annual precipitation data for the period 1990 to 2014 for both the basins did not exhibit any significant trend. Linear, areal, and relief parameters of both the river basins exhibit considerable similarity. Hypsometric analysis of the two river basins showed that both the rivers are in the equilibrium or monadnock stage, implying that erosion is relatively less in both the river basins. From the study, it is concluded that there is no significant impact of precipitation on the drainage and erosional patterns of these river basins, but it was driven by the underlying lithology of the basins. [ABSTRACT FROM AUTHOR]

The impact of future land-use and climate changes on the surface hydrology of a watershed was studied using an event-based hydrological model. Artificial Neural Network (ANN) and Monte-Carlo Cellular Automata (CA) based model was used to generate future land-use scenarios of an urbanizing watershed. Simulations were thereafter performed using a hydrological model to assess the expected changes in runoff. The spatial data inputs for the hydrological model were generated using satellite data, field observations, and data obtained from government agencies. Sensitivity analysis, calibration, and validation of the model were carried out using the observed storm and flow hydrographs, and the model performance was assessed using various statistical performance metrics. Historical land-use data for 2005, 2013, and 2017 was prepared using Landsat satellite imageries and Google Earth images. The land-use scenario generated for the year 2021 exhibited good agreement with the actual land-use map, with a Kappa value above 0.8. Future land-use scenarios were generated to assess the short-term (2025 and 2029) and long-term (2037, 2045, and 2053) changes in the characteristics of the flow hydrographs. The expected changes in the hydrographs due to urbanization were analyzed by applying a selected storm event and considering 2005 as the baseline year using the Two-Dimensional Runoff Erosion and Export (TREX) model. Results indicate that the peak discharge is likely to increase by 29%, and the time to peak is likely to decrease by 6% in 2053 when only land use change is considered. The corresponding values in the worst case considering the combined effect of climate and land use changes are 34.63% and 8.28%, respectively. [ABSTRACT FROM AUTHOR]

River flow is closely linked to survival of human cultures, the economies providing sustainable livelihoods, and the general well-being of the society. The concept of environmental flows has been globally accepted and efforts are being made worldwide to restore and maintain riverine ecosystems. Assessment of environmental flow requirement of rivers is in an infancy stage in India; this has not been done for most of the rivers including those flowing west to the Western Ghats. In this study, the environmental flow (EF) requirements of the Chaliyar River, a major river flowing westwards of the Western Ghats (WG) in Kerala, India, were estimated using hydrologic and hydraulic methods. This was done using the Indicators of Hydrologic Alterations (IHA), and the Wetted Perimeter (WP) methods. Three major sub-basins of the Chaliyar namely, Chaliyar Main Stream, Punnapuzha, and Karimpuzha were considered in this study. This study concentrated on the low flow periods. Environmental Flow Components (EFC), and Flow Duration Curve (FDC) from the IHA, and Critical Minimum Flow (CMF) from the wetted perimeter method were compared in this study. The environmental flow requirements for the low flow season for the three streams, namely, Chaliyar Main, Punnapuzha, and Karimpuzha, computed using the IHA are 1.32, 1.33, and 0.59 m3/s respectively. The corresponding values obtained by the wetted perimeter method are 1.37, 1.25, and 0.75 m3/s respectively. This indicates that the results obtained using these two methods are in good agreement with each other. [ABSTRACT FROM AUTHOR]

Projections of future streamflow extremes are subjected to various sources of uncertainties. Uncertainties arising from different stages in the modelling process of extremes contribute to the overall uncertainty in the final estimates. In this study, some important sources of uncertainties that occur in the modelling chain for projection of future extremes are analysed. The contribution of climate models, hydrological models, representative concentration pathways (RCPs) and the parameters of the extreme value distribution to the overall uncertainty in the streamflow return levels of the Chaliyar river basin in Kerala, India, was quantified using analysis of variance. 5, 10, 25, and 50-year return levels for the near (2021–2050) and far (2070–2099) future periods were computed. Results of the study indicate that the median values of return levels for the near (0.6–13.5%) and far (21.5–37.3%) future periods are higher than the corresponding values for the observed data. Also the uncertainty ranges of the return levels in the near (2–2.4 times) and far (3–3.7 times) future for different return periods are higher compared to that of the observed. The contribution to uncertainties in the return levels from climate models, parameters of the extreme value distribution employed and hydrological models is higher than that from the RCPs. The contribution of parameter uncertainty increases with the return period and that of the climate models reduces with the return period for the two future periods considered in this study, indicating that parameter uncertainty is an important source of uncertainty and has to be considered while predicting the future extremes. The contribution to uncertainty by the hydrological models and by the RCPs does not exhibit much variation for different return periods in both the future time periods considered. Understanding the uncertainties in future extremes would help to devise proper adaptation strategies to mitigate the probable impacts of climate change. [ABSTRACT FROM AUTHOR]

Comparison of the performance of different rainfall-runoff models helps to identify an appropriate model for use in a specific situation at a reasonable cost. An event-based hydrologic model gives an idea about the response of a drainage basin to a single rainfall event and is useful for locations which do not have data for a long time period. Rainfall and discharge data for long periods of time are required for continuous rainfall-runoff modelling and its availability is a major challenge as most rivers and streams in India are either ungauged or poorly gauged. In this work, the rainfall-runoff process in a small tropical watershed is simulated using three event-based models—the fully distributed TREX model, the semi-distributed HEC-HMS model, and the GIUH model, which is a lumped empirical model. The study area selected is an ungauged watershed located in Thiruvananthapuram District of Kerala State, India, namely, the Moozhy sub-basin of the Karamana River. Automatic rain gauges were installed at three locations in the watershed for collecting rainfall data. Runoff from the watershed was estimated using the stage measured with a staff gauge installed at the outlet of the watershed and the velocity measured using a flow prob. SRTM DEM analysed using various GIS-based tools is a major input to all the three models along with land use and soil data. Data pertaining to three rainfall events were used to calibrate these models. The models were further validated using another set of three observed rainfall and runoff events. Results of the statistical analyses performed indicated that the performance of the TREX model was reasonably good when compared to those of the HEC-HMS and GIUH models. [ABSTRACT FROM AUTHOR]

Sea level rise is one of the serious aftermaths of global warming on the hydrosphere. The scientific community often depends on global climate models (GCMs) for projection of future sea levels. Numerous GCMs are available; thus, selecting the most appropriate GCM/GCMs is a critical task to be performed prior to downscaling. In this study, multi-criteria decision-making (MCDM) techniques, namely, Preference Ranking Organisation Method of Enrichment Evaluation (PROMETHEE-II), Elimination Et Choice Translating Reality (ELECTRE-II), and compromise programming, were used to identify appropriate GCMs whose projections can be used to downscale sea level projections at Ernakulam, Kerala, India. Support vector machine was employed to statistically downscale the sea level projections from the projections of GCMs. Five statistical metrics, namely, correlation coefficient (r ), normalized root mean square error, absolute normalized average bias, mean absolute relative error, and skill score, were adopted in this study as the performance criteria. The weightage of each criterion was computed using the entropy method. Six GCMs (GISS-E2-H, CanESM2, ACCESS1-0, CNRM-CM5, GFDL-CM3, and CMCC-CM) were considered for the analysis based on the availability of predictors. GISS-E2-H, CanESM2, and ACCESS1-0 occupied the first three positions respectively in all three MCDM techniques. [ABSTRACT FROM AUTHOR]

Soil, one of the non-renewable resources on this planet, is being depleted at an alarming rate due to various anthropogenic activities. The effect of soil erosion could range from on-site ecosystem fragmentation to off-site reservoir storage capacity loss. Though soil erosion is caused by various agents such as wind, water, etc., water erosion is the major type. Heavy rainfall during the monsoons and the relatively thin cover of soil over the bedrock aggravates the damage potential of this form of erosion in the state of Kerala, India. Water-induced soil erosion in the Bharathapuzha river basin in Kerala, which is blessed with cultivable land resources and intensive agricultural activities, has been undertaken for quite long and has started impacting agricultural activities, drinking water supply, and reservoir storage capacity in the basin. In this background, this study was taken up to assess the effects of climate and land use–land cover dynamics on soil erosion in the Bharathapuzha river basin over the last three decades. Soil erosion from the river basin was estimated by the Revised Universal Soil Loss Equation (RUSLE), and these estimates were verified with the observed sediment load in the Bharathapuzha river at the Kumbidi river gauging station. The storage capacity loss of some of the reservoirs located in the river basin, estimated by the Integrated Bathymetric Survey (IBS), was compared with the likely storage capacity loss of these reservoirs estimated based on soil erosion from the catchment areas upstream. Soil loss from the river basin for a future period was predicted using projections of future climate under two Representative Concentration Pathways (RCP 4.5 and RCP 8.5) from four regional climate data experiments and future land use–land cover. Annual soil loss estimation maps for 2020 and 2035 under RCP4.5 and RCP8.5 reveal the urgent necessity to plan and implement soil conservation measures in the erosion hotspots, especially in the Malampuzha, Anamala, and Attapady sub-basins. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Anthropogenic interventions have altered the natural environment and affected many of its physical, chemical, and biological characteristics. Changes in land use–land cover (LULC) are one of the main drivers that alter the hydrologic cycle and cause significant impacts on local, regional, and even the global climate system. It is now widely recognised and accepted that climate change is one of the gravest problems that our planet Earth is facing at present. This study analyses the impact of LULC dynamics on the spatial and temporal variation of land surface temperature (LST) in an inter-state river basin, which also happens to be the largest river basin in the state of Kerala, India, viz. the Bharathapuzha river basin, during the period 1990–2017. LST time-series analysis (derived from Landsat) revealed that 98% of the river basin experienced LST less than 298 K in January 1990. Over time, along with changes in LULC, LST also increased; in 2017, about 7.82% of the river basin experienced LST greater than 312 K . A notable change in LULC that occurred during this period was the drastic increase in areas with high albedo. The seasonal curves of LST derived from MODIS data are strong evidence of the devastating impacts of change in LULC on LST and, in turn, on climate change. The major spatial and temporal components of change in LST in the study region were identified by principal component analysis (PCA). The results of this spatiotemporal analysis spread over a period of 28 years can be used for formulating sustainable development policies and mitigation strategies against extreme climatic events in the river basin. [ABSTRACT FROM AUTHOR]

Climate change is expected to adversely affect the coastal ecosystem in many ways. One of the major consequences of climate change in coastal areas is sea level rise. In order to manage this problem efficiently, it is essential to obtain reasonably accurate estimates of future sea level. This study focuses essentially on the identification of climatic variables influencing sea level and sea level prediction. Correlation analysis and wavelet coherence diagrams were used for identifying the influencing variables, and support vector machine (SVM) and hybrid wavelet support vector machine (WSVM) techniques were used for sea level prediction. Sea surface temperature, sea surface salinity, and mean sea level pressure were observed to be the major local climatic variables influencing sea level. Halosteric effect is found to have a major impact on the sea level. The variables identified were subsequently used as predictors in both SVM and WSVM. WSVM employs discrete wavelet transform to decompose the variables before being input to the SVM model. The performance of both the models was compared using statistical measures such as root mean square error (RMSE), correlation coefficient (r), coefficient of determination (r2), average squared error, Nash–Sutcliffe efficiency, and percentage bias along with graphical indicators such as Taylor diagrams and regression error characteristic curves. Results indicate that the WSVM model predicted sea level with an RMSE of 0.029 m during the training and 0.040 m during the testing phases. The corresponding values for SVM are 0.043 m and 0.069 m, respectively. Also, the other statistical measures and graphical indicators suggest that WSVM technique outperforms the SVM approach in the prediction of sea level. [ABSTRACT FROM AUTHOR]

Sea level rise (SLR) is one of the prime consequences of global warming as pointed out by the Intergovernmental Panel on Climate Change (IPCC). SLR adversely affects coastal regions; triggers coastal erosion, inundation, and affects the freshwater–seawater interface as well. This paper presents the results of a study in which a coastal aquifer under changing climate was simulated using a three-dimensional groundwater model. The study area covers a part of the coastal aquifer in Ernakulam district in the State of Kerala, India. Support Vector Machine (SVM) was used for projection of future sea levels under the representative concentration pathways (RCPs) 4.5 and 8.5, based on the projections of Phase 5 of the Coupled Model Intercomparison Project (CMIP5). Both thermosteric and halosteric components were taken into account in the projection of sea level. It was observed that sea level changes are significantly influenced by the halosteric effect. Results indicate that SLR in the year 2050 with respect to the levels in 2014 will be about 8.64 cm and 12.96 cm under RCPs 4.5 and 8.5, respectively. The repercussions of this rise in sea level on seawater intrusion into the coastal aquifer were evaluated by performing simulations with SEAWAT. Results of the study indicate that the effect of this SLR on seawater intrusion is negligible. Highlights: The impact of projected climate change on seawater intrusion in a densely populated coastal region was assessed using a variable density model Local climatic variables influencing sea level were identified Machine learning approach was employed for statistical downscaling of sea level using climatic variables Both thermosteric and halosteric effects were taken in to account in the projection of sea level rise [ABSTRACT FROM AUTHOR]

This study investigated the variation of extreme precipitation on a catchment under climate change. Extreme value analysis using generalized extreme value distribution was used to characterize the extreme precipitation. Reliability ensemble average of annual maximum precipitation projections of five global climate model–regional climate model (GCM–RCM) combinations was used to analyse the precipitation extremes under the representative concentration pathways, RCPs 4.5 and 8.5. In order to tackle the nonstationarity present in the bias-corrected ensemble-averaged annual maximum precipitation series under RCPs 4.5 and 8.5, it was split in such a way that the resulting blocks were stationary. Here the analysis was performed for three blocks 2010–2039, 2040–2069 and 2070–2099, each of which were individually stationary. Uncertainty analysis was done to estimate the ranges of extreme precipitation corresponding to return periods of 10, 25 and 50 years. Results of the study indicate that the extreme precipitation corresponding to these return periods in the three time blocks under the RCPs 4.5 and 8.5 exhibit an increasing trend. Extreme precipitation for these return periods are obtained as higher for the RCP scenarios compared to that obtained using observations. Also the extreme precipitation under RCP8.5 is higher compared to that under RCP4.5 scenario. [ABSTRACT FROM AUTHOR]

The Bharathapuzha river basin, once endowed with dense vegetation and abundant water, has been experiencing acute water shortage and extreme climatic conditions in recent times. To understand the influence of human interventions on the natural environmental conditions, including the problems mentioned above, it is essential to critically examine the changes in land use/cover over these years. The objective of this study is to assess land use/cover change in the Bharathapuzha river basin, Kerala during the period 1990–2017 using LANDSAT series satellite images. The dynamics of land use/cover change were quantified and mapped using geospatial techniques. The multi-temporal LANDSAT images were classified by supervised maximum likelihood method to generate the corresponding land use/cover maps; changes in land use/cover in the river basin were subsequently detected by the post-classification technique. Results of the study revealed a drastic change in land use/cover in the period 1990–2017; the primary causes of this were deforestation and urbanization. The near- and long-term future land use/cover maps of the basin for 2020 and 2035 were generated from the historically retrieved land use/cover change pattern. Multi-Layer Perceptron Neural Network and Markov chain techniques were used to generate future land use/cover maps. These maps reveal that the predominant land use/cover class in the basin will be barren land and about 46.13% of the existing (in 2017) dense vegetation will diminish by 2035. The efficiency of sustainable watershed management activities in the river basin can be improved based on the critical observations from this study. [ABSTRACT FROM AUTHOR]

Pharmaceuticals and personal care products (PPCPs) are contaminants of emerging concern and have been detected worldwide in water bodies in trace concentrations. Most of these emerging contaminants are not regulated in water quality standards except a few in the developed countries. In the case of developing countries, research in this direction is at a nascent stage. For the effective management of Pharmaceutical contaminants (PC) in developing countries, the relevance of PCs as an emerging contaminant has to be analyzed followed by regular monitoring of the environment. Considering the resource constraints, this could be accomplished by identifying the priority compounds which is again region specific and dependent on consumption behavior and pattern. In this work, relevance of pharmaceutical compound as emerging contaminant in water for a developing country like India is examined by considering the data pertaining to pharmaceutical consumption data. To identify the critical Pharmaceutical Contaminants to be monitored in the Indian environment, priority compounds from selected prioritization methods were screened with the compounds listed in National List of Essential Medicine (NLEM), India. Further, information on the number of publications on the compound as an emerging contaminant, data on monitoring studies in India and the number of brands marketing the compound in India were also analyzed. It is found that out of 195 compounds from different prioritization techniques, only 77 compounds were found relevant to India based on NLEM sorting. [ABSTRACT FROM AUTHOR]