Climate change induced risk in water quality control problems

Rehana, S.; Mujumdar, P. P.

doi:10.1016/j.jhydrol.2012.03.042

Cited by 50 publications

(15 citation statements)

References 40 publications

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“…Wilby et al 2006;Tu 2009), there are also predictions of much smaller changes. As examples, Tong et al (2012) report changes in mean daily nitrogen concentrations changes of typically \5 % for a range of climate scenarios which is consistent with our predictions; Rehana and Mujumdar (2012) predict small changes in the probability of low dissolved oxygen conditions in accordance with our results. In addition, note that most published studies represent Northern Hemisphere examples where concentrations of nutrients are an order of magnitude greater than the system reported here.…”

Section: Discussionsupporting

confidence: 90%

The effects of climate change on ecologically-relevant flow regime and water quality attributes

Dyer

Elsawah

Croke

et al. 2013

Stoch Environ Res Risk Assess

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The management of freshwater ecosystems is usually targeted through the regulation of water quantity (limiting diversions and providing environmental flows) and regulation of water quality (setting limits or targets for constituent concentrations). Climate change is likely to affect water quantity and quality in multiple ways and the future management of freshwater ecosystems requires predictions of plausible future conditions. We use a suite of ecologically-relevant hydrological indicators to determine the significance of projected climate-driven hydrological changes in the Upper Murrumbidgee River Catchment in south eastern Australia in relation to river regulation. We also determine the possible water quality changes (in relation to guidelines for aquatic ecosystem protection) associated with the climate change projections to identify the combined effects of hydrological and water quality changes. The results of this study suggest that river regulation has resulted in greater changes to ecologically-relevant streamflow characteristics than climate change scenarios that involve a 1 and 2°C temperature rise in the Upper Murrumbidgee River Catchment. In contrast to the projected hydrological changes, Bayesian Network modelling suggests very small changes to violations of water quality thresholds designed to protect aquatic ecosystems as a result of climate change. By identifying key components of the flow and water quality regimes that may be affected by climate change, we are able to provide managers with information that assists in developing adaptation initiatives.

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Section: Discussionsupporting

confidence: 90%

The effects of climate change on ecologically-relevant flow regime and water quality attributes

Dyer

Elsawah

Croke

et al. 2013

Stoch Environ Res Risk Assess

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show abstract

“…After data processing, statistically significant climate variables (predictors) were considered to predict the precipitation over various grid points of the basin. The predictor variables which have shown significant correlation coefficients with precipitation were identified as surface air temperature, wind speed, humidity, etc., which were also found as potential predictors in other studies over India (Rehana & Mujumdar 2012;Salvi et al 2013). NCEP/NCAR reanalysis data for surface air temperature, mean sea level pressure, specific humidity at 500 mb pressure level, zonal and meridional wind velocity at the surface level is extracted for the latitudes in the range of 12.5-20°N and the longitudes in the range of 72.5-82.5°E, surrounding the entire Krishna basin.…”

Section: Climate Change Projection Model: Statistical Downscaling Modmentioning

confidence: 81%

Modelling hydrological responses under climate change using machine learning algorithms – semi-arid river basin of peninsular India

2020

Self Cite

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Catchment scale conceptual hydrological models apply calibration parameters entirely based on observed historical data in the climate change impact assessment. The study used the most advanced machine learning algorithms based on Ensemble Regression and Random Forest models to develop dynamically calibrated factors which can form as a basis for the analysis of hydrological responses under climate change. The Random Forest algorithm was identified as a robust method to model the calibration factors with limited data for training and testing with precipitation, evapotranspiration and uncalibrated runoff based on various performance measures. The developed model was further used to study the runoff response under climate change variability of precipitation and temperatures. A statistical downscaling model based on K-means clustering, Classification and Regression Trees and Support Vector Regression was used to develop the precipitation and temperature projections based on MIROC GCM outputs with the RCP 4.5 scenario. The proposed modelling framework has been demonstrated on a semi-arid river basin of peninsular India, Krishna River Basin (KRB). The basin outlet runoff was predicted to decrease (13.26%) for future scenarios under climate change due to an increase in temperature (0.6 °C) compared to a precipitation increase (13.12%) resulting in an overall reduction in water availability over KRB.

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“…The other important factor influencing river water quality is streamflow, which defines the rate of pollutant transport and dilution of pollutant loads. Low water quality (LWQ) risks under future climate change scenarios are quantified by Rehana and Mujumdar (2012), who assessed the impacts of climate change for river water quality management in the Tunga-Bhadra River basin. To arrive at local future estimates of hydro-climatic variables that influence river water quality (inflow, rainfall, maximum and minimum temperatures, relative humidity and wind speed), they used a multi-variable statistical downscaling model involving principal component analysis and Canonical Correlation Analysis (CCA).…”

Section: Water Quality Under Climate Changementioning

confidence: 99%

Regional hydrological impacts of climate change: implications for water management in India

Mondal¹,

Mujumdar²

2015

Proc. IAHS

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Abstract. Climate change is most likely to introduce an additional stress to already stressed water systems in developing countries. Climate change is inherently linked with the hydrological cycle and is expected to cause significant alterations in regional water resources systems necessitating measures for adaptation and mitigation. Increasing temperatures, for example, are likely to change precipitation patterns resulting in alterations of regional water availability, evapotranspirative water demand of crops and vegetation, extremes of floods and droughts, and water quality. A comprehensive assessment of regional hydrological impacts of climate change is thus necessary. Global climate model simulations provide future projections of the climate system taking into consideration changes in external forcings, such as atmospheric carbon-dioxide and aerosols, especially those resulting from anthropogenic emissions. However, such simulations are typically run at a coarse scale, and are not equipped to reproduce regional hydrological processes. This paper summarizes recent research on the assessment of climate change impacts on regional hydrology, addressing the scale and physical processes mismatch issues. Particular attention is given to changes in water availability, irrigation demands and water quality. This paper also includes description of the methodologies developed to address uncertainties in the projections resulting from incomplete knowledge about future evolution of the human-induced emissions and from using multiple climate models. Approaches for investigating possible causes of historically observed changes in regional hydrological variables are also discussed. Illustrations of all the above-mentioned methods are provided for Indian regions with a view to specifically aiding water management in India.

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Climate change induced risk in water quality control problems

Cited by 50 publications

References 40 publications

The effects of climate change on ecologically-relevant flow regime and water quality attributes

The effects of climate change on ecologically-relevant flow regime and water quality attributes

Modelling hydrological responses under climate change using machine learning algorithms – semi-arid river basin of peninsular India

Regional hydrological impacts of climate change: implications for water management in India

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