Integrating conceptual and machine learning models to enhance daily-Scale streamflow simulation and assessing climate change impact in the watersheds of the Godavari basin, India.

This study examined and addressed climate change's effects on hydrological patterns, particularly in critical places like the Godavari River basin. This study used daily gridded rainfall and temperature datasets from the Indian Meteorological Department (IMD) for model training and testing, 70% and 30%, respectively. To anticipate future hydrological shifts, the study harnessed the EC-Earth3 data, presenting an innovative methodology tailored to the unique hydrological dynamics of the Godavari River basin. The Sacramento model provided initial streamflow estimates for Kanhargaon, Nowrangpur, and Wairagarh. This approach melded traditional hydrological modeling with advanced multi-layer perceptron (MLP) capabilities. When combined with parameters like lagged rainfall, lagged streamflow, potential evapotranspiration (PET), and temperature variations, these initial outputs were further refined using the Sac-MLP model. A comparison with Sacramento revealed the superior performance of the Sac-MLP model. For instance, during training, the Nash Sutcliffe efficiency (NSE) values for the Sac-MLP witnessed an improvement from 0.610 to 0.810 in Kanhargaon, 0.580 to 0.692 in Nowrangpur, and 0.675 to 0.849 in Wairagarh. The results of the testing further corroborated these findings, as evidenced by the increase in the NSE for Kanhargaon from 0.890 to 0.910. Additionally, Nowrangpur and Wairagarh experienced notable improvements, with their NSE values rising from 0.629 to 0.785 and 0.725 to 0.902, respectively. Projections based on EC-Earth3 data across various scenarios highlighted significant shifts in rainfall and temperature patterns, especially in the far future (2071-2100). Regarding the relative change in annual streamflow, Kanhargaon projections under SSP370 and SSP585 for the far future indicate increases of 584.38% and 662.74%. Similarly, Nowrangpur and Wairagarh are projected to see increases of 98.27% and 114.98%, and 81.68% and 108.08%, respectively. This study uses EC-Earth3 estimates to demonstrate the Sac-MLP model's accuracy and importance in climate change water resource planning. The unique method for region-specific hydrological analysis provides vital insights for sustainable water resource management. This research provides a deeper understanding of climate-induced hydrological changes and a robust modeling approach for accurate predictions in changing environmental conditions.

Climate change influences on the streamflow and sediment supply to the Chao Phraya River basin, Thailand.

This study investigates the effects of climate change on the sediment loads of the Ping and Wang River basins and their contribution to the sediment dynamics of the lower Chao Phraya River basin in Thailand. The various climate models under different Representative Concentration Pathways (RCPs) scenarios are employed to project sediment loads in future. The findings indicate a significant increase in river flow approximately 20% in the Ping River (PR) and 35% in the Wang River (WR) by the mid-21st century and continuing into the distant future. Consequently, this is expected to result in sediment loads up to 0.33 × 106 t/y in the PR and 0.28 × 106 t/y in the WR. This escalation is particularly notable under the RCP 8.5 scenario, which assumes higher greenhouse gas emissions. Additionally, the research provides insights into the potential positive implications for the Chao Phraya Delta's coastal management. Without further damming in the Ping and Wang River basins, the anticipated rise in sediment supply could aid in mitigating the adverse effects of land subsidence and sea-level rise, which have historically caused extensive shoreline retreat in the delta region, particularly around Bangkok Metropolis. The paper concludes that proactive adaptation strategies are required to manage the expected changes in the hydrological and sediment regimes to protect vulnerable coastal zones and ensure the sustainable management of the Chao Phraya River Basin in the face of climate change.

Predicting phosphorus and nitrate loads by using SWAT model in Vamanapuram River Basin, Kerala, India.

Evaluations of probable environmental impacts of point and diffuse source pollution at regional sizes are essential to achieve sustainable development of natural resources such as land and water. This research focused on how nitrate and phosphorus load varied over time and space in the Vamanapuram River Basin (VRB). Phosphorus and nitrate loads have been evaluated in the VRB using the semi-distributed Soil and Water Assessment Tool (SWAT) hydrological model. SWAT Calibration and Uncertainty Programs (SWAT-CUP) have simulated the developed model using the Sequential Uncertainty Fitting, version 2(SUFI-2). The developed model was simulated for 2001 to 2008, and it was split into two-phase calibration and validation phases. Model performance was evaluated by the percentage of bias (PBAIS) and Nash-Sutcliffe efficiency coefficient (NSE). The simulated performance of nitrate was indicated as NSE = 0.22-0.59 and PBIAS = 51.86-65.88. The simulated performance of phosphorus showed NSE = 0.06-0.33 and PBIAS = 15.14-33.97. Total Phosphorus load was most sensitive to the organic Phosphorus enrichment ratio (ERORGP) and CH_N2 for streamflow simulation. This study concluded that the South-western region was a high potential for nutrient loads. This study will explain the nutrient load and guidelines for land management practice in the study area.

Comparative assessment of groundwater vulnerability using GIS-based DRASTIC and DRASTIC-AHP for Thoothukudi District, Tamil Nadu India.

The groundwater is very precious in the world. Rapid urbanization and industrialization create tremendous stress on groundwater quality and quantity. Unscientific groundwater extraction and waste disposal methods impact the groundwater aquifer's susceptibility in the coastal area. This research examines how industrial waste, seawater intrusion, and solid waste dumping affect the Thoothukudi District, located on the southwest coast of Tamil Nadu, India. The groundwater vulnerability potential is determined using the DRASTIC and analytical hierarchy process (AHP)-based DRASTIC model. DRASTIC-AHP method's weights and ranks are determined using multi-criteria decision analysis (MCDA)-based pairwise comparison method. Remote sensing (RS) and geographical information system (GIS) are implemented to prepare the input layers for DRASTIC and DRASTIC-AHP. The findings reveal a very high category of vulnerability along the coastline that is covered in sand and loose sediments from an aquifer. Similar conditions exist on the southeast side, which is covered with gravel, sand, and sandstone with shale and has relatively low-slope topography. This enables higher contaminant percolation into the groundwater and raises the possibility for pollution. The DRASTIC-AHP method's results reveal that the southeast side has a significant possibility of contamination. The water table, net recharge, vadose zone, and conductivity greatly impacted the DRASTIC vulnerability assessment due to their stronger weight than theoretical weight. It may be stated that the DRASTIC technique is more cost-effective and time-efficient in analyzing a wide range of regional groundwater risks while avoiding sloppy, uncontrolled land development and other unwanted activities.

Impact of climate change on hydrology components using CORDEX South Asia climate model in Wunna, Bharathpuzha, and Mahanadi, India.

Detecting the probable impact of climate change responses on hydrological components is most important for understanding such changes on water resources. The impact of climate change on virtual parameters of water was assessed through hydrological modeling of the Wunna, Mahanadi (Middle), and Bharathpuzha watersheds. In this article, future hydrological component responses under two Representative Concentration Pathway (RCP) 4.5 and 8.5 scenarios were considered for investigating the runoff, sediment, and water storage components. RegCM4 CSIRO-Mk3.6.0 CORDEX South Asia of RCM model was used which is specially downscaled for the Asian region by IITM-India. Delta change method was adopted to remove bias correction in RCM data. Hydrological simulation for current and future periods was performed by GIS interfaced Soil Water and Assessment Tool (SWAT) model. The surface runoff of Wunna and Bharathpuzha watersheds and the yield of sediment are expected to increase further under RCP8.5 than RCP4.5 and in contrast to Mahanadi watershed. Both blue water storage (BW) and green water storage (GWS) of Wunna watershed are expected to decline under RCP4.5, and rise under RCP8.5 scenario. Both BW and GWS of Bharathpuzha are expected to increase in the future except in western region under RCP4.5 scenario. BW of Mahanadi is expected to increase in the future. However, GWS will decrease in some of the sub-basins. The model-generated results will be helpful for future water resources planning and development.

Extreme precipitation indices over India using CMIP6: a special emphasis on the SSP585 scenario.

In this study, a comparison of 17 Coupled Model Intercomparison Project Phase 6 (CMIP6) data sets with Indian Meteorological Department (IMD) data in terms of finding out extreme precipitation indices obtained by the Expert Team on Climate Change Detection and Indices (ETCCDI) is done. The extreme indices considered were the consecutive dry days (CDD), consecutive wet days (CWD), maximum 1-day precipitation (RX1DAY), maximum 5-day precipitation (RX5DAY), precipitation > 2.5 mm (RR2.5), heavy precipitation > 10 mm (R10MM), very heavy precipitation > 20 mm (R20MM), and simple daily intensity (SDII) that have been calculated for 17 CMIP6 datasets from 1950 to 2014 with IMD-gridded precipitation datasets over India. Rankings were assigned using the TOPSIS method with evaluation metrics as CC and RMSE as conditions. Almost all datasets performed well for indices, i.e., CWD, R10MM, R20MM, and RR2.5. The top 5 performing models are EC-Earth3, EC-Earth3-Veg, MRI-ESM2-0, GFDL-ESM4, and MIROC6 which were ensembled and projected for future periods in the near (2015-2040), middle (2041-2070), and far future (2070-2100), and extreme indices were calculated under Shared Socioeconomic Pathways 126 (SSP126), SSP245, SSP370, and SSP585 scenarios. The ensemble mean shows that RX1DAY, RX5DAY, R10MM, R20MM, and CWD are observed to increase in western ghats and northeastern regions of India. Central India exhibits a dynamic influence on precipitation indices in different climate change scenarios. The temporal variation for the SSP585 scenario predicts significant increases of about 45.41%, 149.40%, 52.26%, and 45.92% in R10MM, R20MM, RX1DAY, and RX5DAY over the current climate. Future extreme precipitation indices help flood modelers and hydrologists with watershed management.

Coastal vulnerability assessment for the coast of Tamil Nadu, India-a geospatial approach.

A coastal region is a section of land that borders a significant body of water, often the sea or ocean. Despite their productivity, they are sensitive to even little alterations in the outside environment. This study aims to develop a spatial coastal vulnerability index (CVI) map for the Tamil Nadu coast of India, which has diverse coastal and marine environments that are ecologically fragile zones. Climate change is expected to increase the intensity and frequency of severe coastal hazards, such as rising sea levels, cyclones, storm surges, tsunamis, erosion, and accretion, severely impacting local environmental and socio-economic conditions. This research employed expert knowledge, weights, and scores from the analytical hierarchy process (AHP) to create vulnerability maps. The process includes the integration of various parameters such as geomorphology, Land use and land cover (LULC), significant wave height (SWH), rate of sea level rise (SLR), shoreline change (SLC), bathymetry, elevation, and coastal inundation. Based on the results, the very low, low, and moderate vulnerability regions comprise 17.26%, 30.77%, and 23.46%, respectively, whereas the high and very high vulnerability regions comprise 18.20% and 10.28%, respectively. The several locations tend to be high and very high due to land-use patterns and coastal structures, but very few are contributed by geomorphological features. The results are validated by conducting a field survey in a few locations along the coast. Thus, this study establishes a framework for decision-makers to implement climate change adaptation and mitigation actions in coastal zones.

A comparative analysis on groundwater vulnerability models-fuzzy DRASTIC and fuzzy DRASTIC-L.

Groundwater vulnerability assessment using the fuzzy logic technique is attempted in this study. A hierarchical fuzzy inference system is created to serve the selected objective. The parameters considered in this study are similar to the seven parameters used in conventional DRASTIC methods; however, the effect of land use and land cover is studied by including it as an additional parameter in a model. A hierarchy is created by comparing two input parameters, say (D and R), and the output of the same is paired as an input with the third parameter (A) and so on using the fuzzy toolbox in MATLAB. Thus, the final output of fuzzy inference systems six and seven (FI6 and FI7) is defuzzified and mapped using ArcGIS to obtain the groundwater vulnerability zones by fuzzy DRASTIC and fuzzy DRASTIC-L. Each map is grouped into five vulnerability classes: very high, high, moderate, low, and very low. Further, the results were validated using the observed nitrate concentration from 51 groundwater sampling points. The receiver operating curve (ROC) technique is adopted to determine the best suitable model for the selected study. From this, area under the curve is estimated and found to be 0.83 for fuzzy DRASTIC and 0.90 for fuzzy DRASTIC-L; the study concludes that fuzzy DRASTIC-L has a better value of AUC suits best for assessing the groundwater vulnerability in Thoothukudi District.

Assessment of land use and land cover change detection and prediction using remote sensing and CA Markov in the northern coastal districts of Tamil Nadu, India.

The study on land use and land cover (LULC) changes assists in analyzing the change and regulates environment sustainability. Hence, this research analyzes the Northern TN coast, which is under both natural and anthropogenic stress. The analysis of LULC changes and LULC projections for the region between 2009-2019 and 2019-2030 was performed utilizing Google Earth Engine (GEE), TerrSet, and Geographical Information System (GIS) tools. LULC image is generated from Landsat images and classified in GEE using Random Forest (RF). LULC maps were then framed with the CA-Markov model to forecast future LULC change. It was carried out in four steps: (1) change analysis, (2) transition potential, (3) change prediction, and (4) model validation. For analyzing change statistics, the study region is divided into zone 1 and zone 2. In both zones, the water body shows a decreasing trend, and built-up areas are in increasing trend. Barren land and vegetation classes are found to be under stress, developing into built-up. The overall accuracy was above 89%, and the kappa coefficient was above 87% for all 3 years. This study can provide suggestions and a basis for urban development planning as it is highly susceptible to coastal flooding.

Mamdani fuzzy based decision support system for prediction of groundwater quality: an application of soft computing in water resources.

Groundwater is a primary source of living which also requires preservative measures for furture generations. Due to the lack of effective management technologies, the wastewater generated by rapid urbanization and industrialization is being disposed untreated, leading to groundwater contamination, caused by infiltration and accumulation. This problem has become more intense in major cities of India. The present work is based on determining the water quality using fuzzy index developed for the Perambalur district, Tamilnadu, India, from where 30 groundwater samples were collected from bore well as well as dug well sources. The research focusses mainly on chemical parameters like total hardness (T.H.), total dissolved solids (TDS.), potential hydrogen (pH), calcium (Ca2+), magnesium (Mg2+), potassium (K), sulphates (SO42-), total nitrates (NO3 + NO2), fluoride (F), bicarbonate (HCO3), carbonate (CO32-) and chloride (Cl2-). These parameters were assessed for fuzzy water quality index (FWQI) model, and the index was designed concerning Mamdani fuzzy inference system. Five FIS models with different linguistic variables were developed based on triangular membership function with the implementation of 189 numbers of rules. Finally, fuzzy model was classified into five categories, such as excellent, good, poor, very poor and not-suitable. Based on the results obtained from this model, 6 samples were classified into excellent, 8 samples into good, 12 to poor, 3 to very poor and 1 to not-suitable. In connection with that, the results of proposed model were compared with the output obtained from the deterministic method.