Abstract

Ensuring the freshness of drinking water at the point of use remains a challenge in building-scale storage systems, where intermittent demand can lead to prolonged storage. This paper presents a data-driven framework for estimating and managing water age using IoT-enabled flow monitoring, eliminating the need for tracer studies or detailed hydraulic models. An eight-month dataset collected from a cumulative flow meter installed on a system of three 80 L drinking water coolers is used to reconstruct continuous consumption behavior from discrete refilling events through interpolation and event aggregation. The reconstructed demand profile enables continuous estimation of water age, revealing persistent staleness under baseline operation, with water age exceeding 14 days for a significant portion of the monitoring period and reaching peaks of approximately 30 days. To address this, two operational strategies are investigated: demand-aware refilling based on observed hourly consumption patterns, and reduction of storage capacity to promote faster turnover. Both approaches demonstrate substantial reductions in water age; specifically, demand-aware refilling reduces the mean water age from 13.6 days to 5.3 days and the peak from approximately 30 days to 17 days, while capacity reduction to 120 L reduces the mean to 6.9 days. The results highlight the potential of IoT-based monitoring combined with simple control strategies to enhance water freshness in decentralized storage systems, offering a scalable and low-cost solution for smart water management.

Abstract

Abstract
Accurately representing the precipitation extremes is a core mandate for both flood risk
assessment and water resource planning in India. This paper evaluates the performance of 36
CMIP6 Global Climate Models in simulating the key characteristics of Indian precipitation by
using a bias-corrected, multi-criteria evaluation framework. The daily precipitation outputs from
the CMIP6 historical simulations (1975-2014) were bias-corrected against the 0.25° gridded
observations provided by the India Meteorological Department through the Daily Quantile
Mapping technique. Two representative precipitation indices have been analyzed: a) Annual Total
Precipitation (PRCPTOTAL), reflecting the mean-state water availability; b) the 99th percentile
precipitation intensity (R99p), regarding extreme rainfall relevant to flood risk. The performance
of the models was evaluated using multiple complementary metrics, such as Pearson correlation,
root mean square error, normalized standard deviation, and the Taylor Skill Score. Then, an
MCDM approach based on the Technique for Order of Preference by Similarity to Ideal Solution
(TOPSIS) was used in order to derive objective rankings of the CMIP6 models for both indices.
Results indicate substantial inter-model variability even after bias correction. The best
performance for PRCPTOTAL was seen for the model EC-Earth3-AerChem, while MIROC6 was
found to be the best among those showing extreme precipitation intensity, R99p. Results highlight
that the best performing GCMs vary with applications, and different models provide the best
performance for mean precipitation and extreme intensity simulations. The developed framework
here offers a transparent and reproducible platform for the selection of suitable CMIP6 models
for hydrological and climate impact studies over India and allows for an easy extension to more
analyses, introducing new indices and bias-correction methods in future works.
Keywords: CMIP6 GCMs; Extreme Precipitation; Bias Correction; MCDM ranking; Climate
model evaluation; Quantile Mapping

Abstract

Urban cities have been intensely prone to floods during extreme rainfall events and water
scarcity issues during dry periods in recent years. In this context, identifying rainwater har
vesting potential (RWHP) regions in urban environments provides a sustainable approach
to mitigate both urban flooding and water security, thereby improving urban stormwater
management. Geospatial mapping of RWHP has tried to consider various hydrometeoro
logical, topographical and other geospatial datasets, but integrating socio-economic factors
over urban environments has not beenexplored much. Thepresentstudyintegrated remote
sensing and hydrological-based information, such as slope, soil type, drainage density,
geomorphology, topographic wetness index (TWI), land use land cover (LULC), rainfall,
runoff coefficient, proximity to roads, and proximity to settlements for geospatial mapping
of RWHpotential zones for Hyderabad city using multi-criteria decision analysis (MCDA)
and weighted overlay analysis (WOA). The resulting RWH potential map indicates that
80.20% of the area falls within the "low" potential category, 17.53% as "moderate", 2.0% as
"very low", and only 0.25% as "high" potential, mainly in the southeastern portion near the
Hussain Sagar outlet. These categories are spatially verified using Sentinel-2 LULC and
Google Earth imagery to assess the qualitative plausibility of the mapped RWH potential
zones. Northwestern areas, with loamy soils and mild slopes, demonstrate suitability for
rooftop collection and percolation structures, highlighting the effectiveness of the proposed
modelling framework for sustainable stormwater management for urban environments.

Abstract

River water quality assessment has traditionally been conducted using univariate or
threshold-based approaches; however, the exploration of extremes assessment under bivari
ate water quality variables has been limited by manystudies. Understandingthecompound
extremes of low river discharge (Q) and elevated river water temperatures (RWTs) resulting
from climatic variability is essential for effective water quality management and protection
of the river. This study investigates the joint behaviour of RWTs and Q in six Indian rivers:
Kaveri, Mahi, Sabarmati, Vardha, Bhadra, and Yamuna. TheWeibull-3PandGeneralisedEx
treme Value (GEV-3P) distributions best fit for Q and RWTs, respectively. The adequacy of
eighteen different parametric copula classes was evaluated. The Gaussian copula provided
the best fit for the Vardha River, the Frank copula for Bhadra, and the BB8 copula for the
Yamuna River. The evaluation of joint return periods (RPs) and conditional distributions
has identified notable spatial variability in compound hydrological and thermal extreme
hazards. The semi-arid Vardha River showed the shortest RPs for simultaneous low Q
and high RWTs, indicating a greater likelihood of combined extremes. Conversely, the
monsoon-fed Bhadra River displayed moderate hazard levels, while the Himalayan-fed
Yamuna River had the longest joint RPs and the lowest conditional probabilities. This
suggests that simultaneous extreme drought and heat events are less likely in the Yamuna
basin, although significant risks remain for less severe thresholds.

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.