Risk assessment of a water supply system under climate variability: a stochastic approach (original) (raw)
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Risk‐based water resources planning: Incorporating probabilistic nonstationary climate uncertainties
Water Resources Research, 2014
We present a risk-based approach for incorporating nonstationary probabilistic climate projections into long-term water resources planning. The proposed methodology uses nonstationary synthetic time series of future climates obtained via a stochastic weather generator based on the UK Climate Projections (UKCP09) to construct a probability distribution of the frequency of water shortages in the future. The UKCP09 projections extend well beyond the range of current hydrological variability, providing the basis for testing the robustness of water resources management plans to future climate-related uncertainties. The nonstationary nature of the projections combined with the stochastic simulation approach allows for extensive sampling of climatic variability conditioned on climate model outputs. The probability of exceeding planned frequencies of water shortages of varying severity (defined as Levels of Service for the water supply utility company) is used as a risk metric for water resources planning. Different sources of uncertainty, including demand-side uncertainties, are considered simultaneously and their impact on the risk metric is evaluated. Supply-side and demand-side management strategies can be compared based on how costeffective they are at reducing risks to acceptable levels. A case study based on a water supply system in London (UK) is presented to illustrate the methodology. Results indicate an increase in the probability of exceeding the planned Levels of Service across the planning horizon. Under a 1% per annum population growth scenario, the probability of exceeding the planned Levels of Service is as high as 0.5 by 2040. The case study also illustrates how a combination of supply and demand management options may be required to reduce the risk of water shortages.
Hydrology and Earth System Sciences Discussions, 2015
The objective of this study was to analyze the changes and uncertainties related to water availability in the future (for purposes of this study, it was adopted the period between 2011 and 2040), using a stochastic approach, taking as reference a climate projection from the climate model Eta CPTEC/HadCM3. The study was applied to the Ijuí river basin in the south of Brazil. The set of methods adopted involved, among others, correcting the climatic variables projected for the future, hydrological simulation using Artificial Neural Networks to define a number of monthly flows and stochastic modeling to generate 1000 hydrological series with equal probability of occurrence. A multiplicative type stochastic model was developed in which monthly flow is the result of the product of four components: (i) long term trend component; (ii) cyclic or seasonal component; (iii) time dependency component; (iv) random component. In general the results showed a trend to increased flows. The mean flow for a long period, for instance, presented an alteration from 141.6 (1961-1990) to 200.3 m 3 s −1 (2011-2040). An increment in mean flow and in the monthly SD was also observed between the months of January and October. Between the months of February and June, the percentage of mean monthly flow increase was more marked, surpassing the 100 % index. Considering the confidence intervals in the flow estimates for the future, it can be concluded that there is a tendency to increase the hydrological variability during the period between 2011-2040, which indicates the possibility of occurrence of time series with more marked periods of droughts and floods.
Strategic Environmental Assessment for Municipal Water Demand Based on Climate Change
2018
Accurate urban water demand forecasting plays a key role in the planning and design of municipal water supply infrastructure. The reliable prediction of water demand is challenging for water companies, specifically when considering the implications of climate change (Zubaidi et al., 2018). Several studies have documented that weather variables drive water consumption in the short-term, and it enhances the accuracy of the prediction model when it is combined with socio-economic factors. However, the impact of climate change on the municipal water demand has yet to be challenged. To surmount this challenge, more research work is needed to accurately estimate the required quantity of water with increasing water demands. Recently, Artificial Neural Networks (ANNs) have been found to be an innovative approach to predict water demand. This PhD study aims to develop a novel methodology to forecast the impact of climate change on municipal water demands for a long-term time series based on ...
Use of climate scenarios to aid in decision analysis for interannual water supply planning
2007
This work addresses the issue of climate change in the context of water resource planning on the time scale of a few years. Planning on this time scale generally ignores the role of climate change. However, where the climate of a region has already shifted, the use of historical data for planning purposes may be misleading. In order to test this, a case study is conducted for a region, the Australian Capital Territory, where long term drought is raising concerns of a possible climate shift. The issue is cast in terms of a particular planning decision; the option to augment water supply in the next few years to hedge against the drought persisting. A set of climate scenarios are constructed for the region corresponding to the historical climate regime and to regimes where progressively greater levels of change are assumed to have already taken place (5%, 10%, 20% reductions in mean rainfall). Probabilities of the drought persisting are calculated for each of the scenarios. The results show substantial increases in the probability of the drought persisting for even moderate reductions in mean rainfall. The sensitivity of the decision to augment supply to the scenario results depends ultimately on the planners tolerable thresholds for the probability of the drought persisting. The use of different scenarios enables planners to explore the sensitivity of the decision in terms of their risk tolerance to ongoing drought and to their degree of belief in each of the scenarios tested.
A Method for Predicting Long-Term Municipal Water Demands Under Climate Change
Water Resources Management
The accurate forecast of water demand is challenging for water utilities, specifically when considering the implications of climate change. As such, this is the first study that focuses on finding associations between monthly climate factors and municipal water consumption, using baseline data collected between 1980 and 2010. The aim of the study was to investigate the reliability and capability of a combination of techniques, including Singular Spectrum Analysis (SSA) and Artificial Neural Networks (ANNs), to accurately predict long-term, monthly water demands. The principal findings of this research are as follows: a) SSA is a powerful method when applied to remove the impact of socioeconomic variables and noise, and to determine a stochastic signal for long-term water consumption time series; b) ANN performed better when optimised using the Lightning Search Algorithm (LSA-ANN) compared with other approaches used in previous studies, i.e. hybrid Particle Swarm Optimisation (PSO-ANN) and Gravitational Search Algorithm (GSA-ANN); c) the proposed LSA-ANN methodology was able to produce a highly accurate and robust model of water demand, achieving a correlation coefficient of 0.96 between observed and predicted water demand when using a validation dataset, and a very small root mean square error of 0.025.
A Review of Models for Evaluation of Climate Change Impact on Water Resources
British Journal of Applied Science & Technology, 2015
The use of models to simulate or predict impact of climate change on water resources management is very vital due to continual increase in global warming which invariably affects most important natural resources in the environment. This paper provides an overview of the existing models used for evaluating climate change impact on water resources management. It also compares their relative advantages and drawbacks. It was found that no model can perform satisfactorily the assessment of climate change impact; hence it may be necessary to use one model to compliment the weakness of another. Global Circulation Model (GCM) is not easily accessible in developing countries due to sophistications and processes involved in running it. Moreso, the nature of available data and cost of acquiring it is high. The main advantage of Water Balance (WATBAL) model is that it can model climate change impact in water resources but its major drawback is that it requires many inputs of hydro-meteorological parameters. Regression and Artificial Neural Network (ANN) models are readily available and not too expensive. They can model climate change impact on water resources and hydropower operation. However, the drawback is that enormous data are required for ANN model calibration and operation. It is
Water Resources Research, 2008
1] Climate change may impact water resources management conditions in difficult-to-predict ways. A key challenge for water managers is how to incorporate highly uncertain information about potential climate change from global models into local-and regional-scale water management models and tools to support local planning. This paper presents a new method for developing large ensembles of local daily weather that reflect a wide range of plausible future climate change scenarios while preserving many statistical properties of local historical weather patterns. This method is demonstrated by evaluating the possible impact of climate change on the Inland Empire Utilities Agency service area in southern California. The analysis shows that climate change could impact the region, increasing outdoor water demand by up to 10% by 2040, decreasing local water supply by up to 40% by 2040, and decreasing sustainable groundwater yields by up to 15% by 2040. The range of plausible climate projections suggests the need for the region to augment its long-range water management plans to reduce its vulnerability to climate change.
Water Resources Management, 2014
Long term water demand forecasting is needed for the efficient planning and management of water supply systems. A Monte Carlo simulation approach is adopted in this paper to quantify the uncertainties in long term water demand prediction due to the stochastic nature of predictor variables and their correlation structures. Three future climatic scenarios (A1B, A2 and B1) and four different levels of water restrictions are considered in the demand forecasting for single and multiple dwelling residential sectors in the Blue Mountains region, Australia. It is found that future water demand in 2040 would rise by 2 to 33 % (median rise by 11 %) and 72 to 94 % (median rise by 84 %) for the single and multiple dwelling residential sectors, respectively under different climatic and water restriction scenarios in comparison to water demand in 2010 (base year). The uncertainty band for single dwelling residential sector is found to be 0.3 to 0.4 GL/year, which represent 11 to 13 % variation around the median forecasted demand. It is found that the increase in future water demand is not notably affected by the projected climatic conditions but by the increase in the dwelling numbers in future i.e. the increase in total population. The modelling approach presented in this paper can provide realistic scenarios of forecasted water demands which would assist water authorities in devising appropriate management strategies to enhance the resilience of the water supply systems. The developed method can be adapted to other water supply systems in Australia and other countries.