Abstract

Sustainable management of River Water Quality (RWQ) in tropical river systems requires an approach that addresses both spatial and temporal risks associated with hydrological dynamics, anthropogenic influences, and ecological health. This study develops an uncertainty-aware spatiotemporal risk assessment approach integrating upstream hydrological dynamics (SWAT), operational variability with Environmental Flow (Eflow), water quality simulations (QUAL2K), and probabilistic Water Quality Index (WQI) calculations. Block-Bootstrap ensembles and Monte Carlo techniques are used to quantify uncertainty propagation and construct confidence intervals within the model chain. The monthly WQI estimates under the different Eflow and pollution control scenarios are used to assess the spatial risk variability of the river system. Temporal risk was assessed with WQI using various probabilistic measures, including mean, variance, loss probability, entropy, mean excess loss, and value at risk. A unified risk ranking was developed by using Borda and Copeland aggregation techniques. Categorised spatial risk maps were created using GIS by integrating Eflows and ecological index. The results revealed significant seasonal variations in water quality, with April, March, and May identified as high-risk months due to increased pollution levels. The monthly WQI-mean in the Monte Carlo analysis (1000 iterations), as per the severity of the risk, April, March, and May months are identified as the riskiest months. Spatial risk mapping revealed distinct high-risk zones and highlighted the necessity of pollution treatment level of 25-50 % to reduce ecological risk under optimal Eflow regimes. This comprehensive framework underscores the need for both flow regulation and pollution management to protect river ecosystems, providing actionable insights for effective river health protection.

Abstract

Excessive groundwater extraction has resulted in a significant decline in water tables,
highlighting the need for continuous monitoring. Managed Aquifer Recharge (MAR) is a promising
approach to improve the groundwater supply resilience. This study presents an Internet of Things
(IoT)-based approach for real-time measurement and monitoring of groundwater levels using MAR in
borewells and recharging operations in real-time. The research focused on two borewells at IIIT
Hyderabad, India, to investigate MAR and Aquifer Storage, Transfer, and Recovery (ASTR)
operations, assessing their practical feasibility. Field deployment results indicate a strong hydraulic
connection between the higher-elevation observation borewell and the lower-elevation extraction
borewell. Rainwater recharge into the observation borewell led to immediate increases in water levels,
which then propagated to the extraction borewell. Pumping operations at the extraction borewell
created a measurable cone of depression, further confirming the interconnectivity of the aquifer. Along
with water level measurements, rainfall is also tracked to examine its influence on groundwater. These
findings demonstrate that simple IoT-enabled monitoring, combined with MAR and ASTR operations,
can significantly enhance groundwater management and sustainability in hard rock aquifer systems.

Abstract

Maintaining fresh drinking water at the point of use is critical for health and safety, but prolonged storage in building-scale coolers can lead to water quality degradation. This work presents an IoT-based methodology to estimate and track water age using only flow meter data, without relying on tracer studies or hydraulic models. We analyze an eight-month dataset collected from a cumulative water meter measuring total consumption in a drinking water cooler system containing three 80 L coolers (240 L total). The dataset was reconstructed into continuous consumption patterns from discrete refilling events using interpolation and aggregation. This reconstruction enabled analysis of water age, which revealed prolonged water age (staleness), with values exceeding 14 days nearly half the time and peaking at 30 days. Two operational strategies are evaluated to reduce prolonged water age: (i) demand-aware refilling with a reserve threshold, and (ii) a reduction in total storage capacity. Results show that both approaches decrease the mean and maximum water age compared to baseline operation, with the demand-aware strategy reducing the mean age from 13.6 days to 5.3 days and the maximum age to around 17 days. These results show that simple, data-driven interventions, enabled by IoT monitoring, can substantially improve water freshness at the point of use.

Abstract

This study evaluated the performance of 36 Coupled Model Inter-comparison Project Phase 6
(CMIP6) Global Climate Models (GCMs) in simulating extreme precipitation conditions across India
using an integrated bias correction and multi-criteria decision-making framework. Daily precipitation
data spanning 1975-2014 from CMIP6 GCMs were bias-corrected against Indian Meteorological
Department (IMD) gridded observations at 0.25° spatial resolution using three distinct methods: (i)
monthly quantile mapping applying empirical cumulative distribution function transformation on
monthly aggregated data, (ii) daily quantile mapping using complete daily time series with linear
interpolation for intermediate quantiles, and (iii) ISIMIP3b parametric trend-preserving quantile
mapping employing gamma distribution fitting with mixed trend preservation strategy. Three extreme
precipitation indices were calculated from bias-corrected data: total annual precipitation on wet days
(PRCPTOTAL), 99th percentile of daily precipitation (R99p), frequency of days exceeding 99th
percentile (R99pf). Performance evaluation employed Taylor Skill Score incorporating correlation and
variance ratios, Pearson correlation coefficient (PCC), root-mean-square error (RMSE), and normalised
standard deviation against IMD reference data. For the R99p index, PCC values ranged from -0.045 to
0.628, RMSE varied from 29.86 to 58.23 mm, and Taylor Skill Scores ranged from 0.72 to 0.17 across all
GCMs. For R99p frequency, PCC ranged from 0.242 to 0.663, RMSE varied from 0.526 to 1.380, and
Taylor Skill Scores ranged from 0.83 to 0.59. Multi-criteria decision-making integration used Analytic
Hierarchy Process (AHP) with expert judgment-based pairwise comparison matrices for criteria
weighting, followed by Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and
VlseKriterijumska Optimizacija Kompromisno Resenje (VIKOR) ranking methods with entropy-based
objective weighting. The comprehensive ranking revealed EC-Earth3-Veg as the best performing GCM
with a combined TOPSIS score of 0.890, followed by EC-Earth3-CC (0.880) and EC-Earth3 (0.875).
MIROC6 achieved the highest Taylor Skill Score (0.717) for R99p intensity simulation, while GFDL-CM4
demonstrated consistent performance across both intensity and frequency metrics.

Abstract

This study presents a comprehensive multi-criteria ranking framework for evaluating 36 CMIP6 global climate models based on their simulation of temperature extreme indices over the Indian subcontinent. The research focuses on comparative assessment of Ta95p (95th percentile, hot extremes) and Ta5p (5th percentile, cold extremes) across critical monsoon (JJAS) and post-monsoon (OND) seasons. The methodology employs a systematic data processing pipeline beginning with IMD observational data (0.25°×0.25° resolution, 1975-2014) as reference, followed by CMIP6 model data preprocessing including spatial cropping to Indian domain (6.5°N-38.5°N, 66.5°E-100.0°E), bilinear interpolation to IMD resolution, and quantile mapping bias correction. Seasonal temperature extreme indices are calculated using robust percentile estimation methods for both seasons. The ranking framework integrates Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and VIKOR (VIseKriterijumska Optimizacija I Kompromisno Resenje) multi-criteria decision-making techniques with AHP-derived weights (Pearson Correlation Coefficient, PCC: 0.652, Root Mean Square Error, RMSE: 0.239, Standard Deviation Normalization StdNorm: 0.109). TOPSIS calculates distances to ideal positive and negative solutions in multi-dimensional performance space, while VIKOR employs compromise programming emphasizing balanced performance across criteria. Final rankings aggregate both methods using mean rank averaging. Comparative analysis reveals distinct seasonal and index-specific performance patterns: TaiESM1 and CMCC-CM2-SR5 dominate hot extreme (Ta95p) simulation in both seasons, while EC-Earth3-CC and GFDL-ESM4 excel in monsoon cold extremes (Ta5p), and IPSL-CM5A2-INCA leads post-monsoon cold extreme representation. Performance metrics demonstrate substantial inter-model variability with PCC ranging 0.791-0.994, RMSE spanning 0.290-1.989°C, and StdNorm varying 0.835-1.213 across different indices and seasons.

Abstract

This study presents a comprehensive analysis of a statistical downscaling framework developed to predict daily rainfall over India. Utilizing outputs from the Coupled Model Intercomparison Project Phase 6 (CMIP6) and the National Centers for Environmental Prediction (NCEP) reanalysis data, the study employs a hybrid machine learning approach combining a classification model for broad meteorological zones with a cell-by-cell regression pipeline. The performance of six regression models- Lasso, Ridge, MLP Regressor, Random Forest, XGBoost, and Support Vector Regressor (SVR)--were evaluated using standard metrics. The findings reveal that while the framework successfully generates high-resolution rainfall projections, the models demonstrate limited explanatory power, with R² values ranging from 0.03 to 0.154. A critical discrepancy between the Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) indicates a significant failure to accurately predict extreme rainfall events, a key limitation for practical applications. The analysis concludes that while the framework provides a valuable baseline, a move towards more sophisticated architectures, such as deep learning and hybrid statistical-dynamical methods, is essential to capture the complex, non-linear spatiotemporal dynamics of precipitation.

Abstract

Contributing catchment land use patterns under rapid urbanization, agricultural practices and climatic variability in tropical regions significantly influence the sediment load of inland waterbodies such as reservoirs. This study presents a modeling framework to estimate qualitative turbidity dynamics in two tropical reservoirs (Bhadra and Tungabhadra) within same river basin but different landscape characteristics, using Sentinel-2 satellite data from 2016 to 2021 using Google Earth Engine (GEE). The framework demonstrates a robust capability of Sentinel-2 data, in detecting, mapping spatiotemporal trends and classifying turbidity levels from low to high along with impact of potential catchment characteristics. The Normalized Difference Turbidity Index (NDTI) values have a strong correlation with in-situ data (Earth Observing and Mapping EOMAP and field-based observations) having values > 10 NTU (nephelometric turbidity unit) with R2 = 0.81 and standard Error Estimate (SEE) of 5.3 respectively. In addition, the correlation between red band reflectance values with <10 NTU, has demonstrated a strong relation with an R2 and SEE of 0.74 and 1.7 respectively. Thus, the combination of NDTI and single red band reflectance values were used further for classifying the different turbidity levels (low, moderate and high). It was also observed that diverse land use patterns, particularly high proportions of urban and agriculture area along with rainfall, impact the annual and seasonal fluctuation of turbidity level in the reservoirs. The study demonstrates that remote sensing (RS) approach can effectively estimate turbidity levels in tropical reservoirs, highlighting the impact of catchment characteristics and supporting improved monitoring and sustainable water resource management.

Abstract

Sewage Treatment Plants (STPs) are critical for public health and environmental sustainability. However, Traditional water quality monitoring in globally heavily relies on labour-intensive, lab-based methods, which pose significant challenges but in recent years, IoT-based sensors have emerged as a promising alternative. However, these sensors face challenges including sensor fouling and frequent manual maintenance, which compromise long-term accuracy and operational efficiency. This study presents an innovative IoT enabled Self-Cleaning Water Quality Monitoring System (SC-WQMS) to address these challenges through automation and precision. The proposed system integrates advanced features, including self-cleaning mechanisms. Key water quality parameter, such as Total Dissolved Solids (TDS) is analysed in real-time by using IoT-based sensors. The proposed IoT-enabled SC-WQMS demonstrates significantly better accuracy over traditional non-cleaning devices. Lab report data over a two-month period reveals that the SC-WQMS maintains an average accuracy of 95%-98.5% for TDS measurements, compared to 68%-75% accuracy observed in traditional IoT-based non cleaning devices due to issues like algae formation and particle buildup over the same period. This substantial difference highlights the SC-WQMS's effectiveness in ensuring reliable data transmission to a cloud server. By mitigating the challenges faced by traditional methods. The proposed SC-WQMS represents a scalable, efficient, and sustainable solution for modern water treatment facilities, addressing the operational challenges of traditional systems while promoting better resource management and environmental protection.

Abstract

Urban stormwater management involves strategies to mitigate the adverse effects of urban runoff. These strategies aim to reduce flood risk and enhance the environmental values and sustainability of urban water systems. But the effective urban stormwater management is highly dependent on the factors such as spatial variability of urban watershed characteristics as well as the scale of the area under consideration. This insight has prompted increasing utilization of physically based urban watershed models such as the Environmental Protection Agency (EPA) Stormwater Management Model (SWMM). Thus, in this study we utilized SWMM for modeling the dynamic runoff response in a stormwater zone of Hyderabad, India. The study results are validated for a particular observed 48-hour storm event occurred on 3rd and 4th of October 2015. Five years (2010-2014) of hourly rainfall data have been used to estimate the design storms with 2, 5, 10, and 25 years return periods using Gumbel distribution. The results indicate that the existing drainage system is insufficient to withstand the generated runoff by the design storm of 2-year return period and causes notable flooding. The water elevation profile generated from the initial node, representing the beginning of the drainage system, to the outfall indicates that the flooding occurred at the outfall during the peak precipitation event. Also, the hydrographs which were developed through SWMM at different links and outfalls are crucial in designing the sumps or ponds and determining pump capacities, aiding in effective stormwater management practices.

Abstract

Multivariate drought indices including various hydrological processes into account can be more valuable under climatic and anthropogenic changes. Standardized Precipitation Evapotranspiration Index (SPEI) and Standardized precipitation Actual Evapotranspiration Index (SPAEI) are the drought indices used to estimate drought index, considering precipitation, and evapotranspiration (ET) into account. Many studies used empirical, machine learning, and process-based model estimates of ET for the calculation of drought indices of SPEI and SPAEI. However, the sensitivity of ET estimates on drought characteristics at the catchment scale is highly complex. The present study aimed to include and analyze the sensitivity of various approaches of empirical (Budyko, Penman-Monteith, Hargreaves, and Turc), modeled (SWAT), and remote sensing (MODIS) in the drought characterization using SPEI and SPAEI. The present methodology was tested on a dry-sub-humid river catchment of India, the Tunga-Bhadra River catchment for the period of 2000-2012. The performance of statistical indicators (Nash-Sutcliffe Efficiency and R2) for SPEI values by various empirical methods of Potential Evapotranspiration (PET) (i) Penman-Monteith (ii) Hargreaves against remote sensing PET were 0.93 and 0.95, respectively, which are high in comparison with SWAT simulated PET-based SPEI values, which shows NSE values of 0.85 against remote sensing PET-based SPEI.