Evaluation of basin-scale hydrologic response to a multi-storm simulation

Yu, Zhigang; Barron, Eric J.; Yarnal, Brent; Lakhtakia, Mercedes N.; White, Richard Allen; Pollard, D.; Miller, Douglas A.

doi:10.1016/s0022-1694(01)00538-8

Cited by 25 publications

(10 citation statements)

References 25 publications

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“…A total of 9216 3.8 km pixels, covering about 130 000 km 2 , from the National Centers for Environmental Prediction (NCEP) Stage IV project (Lin and Mitchell, 2005) were used for the calibration. More than 5 million wet pixel-days were available during the 4-year calibration period (2002-2005Gagnon, 2012). The Stage IV data set covers only a small part of the southernmost region of Québec (near the study area) with 4.4 km pixels.…”

Section: Mathematical Frameworkmentioning

confidence: 99%

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Stochastic spatial disaggregation of extreme precipitation to validate a regional climate model and to evaluate climate change impacts over a small watershed

Gagnon

Rousseau

2014

Hydrol. Earth Syst. Sci.

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Abstract. Regional climate models (RCMs) are valuable tools to evaluate impacts of climate change (CC) at regional scale. However, as the size of the area of interest decreases, the ability of a RCM to simulate extreme precipitation events decreases due to the spatial resolution. Thus, it is difficult to evaluate whether a RCM bias on localized extreme precipitation is caused by the spatial resolution or by a misrepresentation of the physical processes in the model. Thereby, it is difficult to trust the CC impact projections for localized extreme precipitation. Stochastic spatial disaggregation models can bring the RCM precipitation data at a finer scale and reduce the bias caused by spatial resolution. In addition, disaggregation models can generate an ensemble of outputs, producing an interval of possible values instead of a unique discrete value.The objective of this work is to evaluate whether a stochastic spatial disaggregation model applied on annual maximum daily precipitation (i) enables the validation of a RCM for a period of reference, and (ii) modifies the evaluation of CC impacts over a small area. Three simulations of the Canadian RCM (CRCM) covering the period 1961-2099 are used over a small watershed (130 km 2 ) located in southern Québec, Canada. The disaggregation model applied is based on Gibbs sampling and accounts for physical properties of the event (wind speed, wind direction, and convective available potential energy -CAPE), leading to realistic spatial distributions of precipitation. The results indicate that disaggregation has a significant impact on the validation. However, it does not provide a precise estimate of the simulation bias because of the difference in resolution between disaggregated values (4 km) and observations, and because of the underestimation of the spatial variability by the disaggregation model for the most convective events. Nevertheless, disaggregation illustrates that the simulations used mostly overestimated annual maximum precipitation depth in the study area during the reference period. Also, disaggregation slightly increases the signal of CC compared to the RCM raw simulations, highlighting the importance of spatial resolution in CC impact evaluation of extreme events.

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Section: Mathematical Frameworkmentioning

confidence: 99%

“…The most common example is the use of a RCM to refine the resolution of global climate models (GCMs). It is also possible to run a RCM two or more times in domains nested one inside the other (Yu et al, 2002). Dynamical downscaling produces physically sound data, but it in the end requires intensive computational resources.…”

Section: Introductionmentioning

confidence: 99%

Stochastic spatial disaggregation of extreme precipitation to validate a regional climate model and to evaluate climate change impacts over a small watershed

Gagnon

Rousseau

2014

Hydrol. Earth Syst. Sci.

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“…The model has been applied successfully in numerous studies (e.g. Yu et al, 1999Yu et al, , 2002Moges et al, 2003;Fleming and Neary, 2004). …”

Section: Hydrologic Modelmentioning

confidence: 99%

Inverse flood risk modelling under changing climatic conditions

Cunderlik

Simonović́

2007

Hydrological Processes

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Abstract:One of the most significant anticipated consequences of global climate change is the increased frequency of hydrologic extremes. Predictions of climate change impacts on the regime of hydrologic extremes have traditionally been conducted using a top-down approach. The top-down approach involves a high degree of uncertainty associated with global circulation model (GCM) outputs and the choice of downscaling technique. This study attempts to explore an inverse approach to the modelling of hydrologic risk and vulnerability to changing climatic conditions. With a focus targeted at end-users, the proposed approach first identifies critical hydrologic exposures that may lead to local failures of existing water resources systems. A hydrologic model is used to transform inversely the main hydrologic exposures, such as floods and droughts, into corresponding meteorological conditions. The frequency of critical meteorological situations is investigated under present and future climatic scenarios by means of a generic weather generator. The weather generator, linked with GCMs at the last step of the proposed methodology, allows the creation of an ensemble of different scenarios, as well as an easy updating, when new and improved GCM outputs become available. The technique has been applied in Ontario, Canada. The results show significant changes in the frequency of hydro-climatic extremes under future climate scenarios in the study area.

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“…Decision makers often rely on inflow forecasts [11] to estimate potential water available for allocations, considering that water supply reservoir operations usually require scheduling releases and storage over several months. The influence of inflow forecast uncertainty should be incorporated into decision making, given that it is often difficult to precisely forecast the actual inflow at the beginning of the current time.…”

Section: Introductionmentioning

confidence: 99%

Optimal Hedging Rules for Water Supply Reservoir Operations under Forecast Uncertainty and Conditional Value-at-Risk Criterion

Zhong

Huang

et al. 2017

Water

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Abstract:Hedging rules for water supply reservoir operations provide guidelines for balancing the consequences of competing water allocations. When inflow forecast uncertainty is addressed, hedging acts as insurances for offsetting the negative influence of water shortage in the future, especially when drought is anticipated. This study used a risk-averse criterion, the conditional value-at-risk (CVaR), rather than the expected value (EV) criterion, to rationalize water delivery for overcoming the shortcomings of risk-neutral hedging rules in minimizing water shortage impacts in unfavorable realizations, in which actual inflow is less than anticipated. A two-period hedging model with the objective of maximizing the CVaR of total benefits from water delivery and water storage is established, and the optimal hedging rules using first-order optimality condition are analytically derived. Differences in hedging rules under the two criteria are highlighted by theoretical analysis and numerical experiments. The methods are applied to guide the operations of a water supply reservoir, and results show that: (1) the hedging rules under the EV criterion are special cases under the CVaR criterion; (2) water delivery in the current period would be greatly curtailed under the high influence of forecast uncertainty or the significant risk-averse attitude of decision makers; (3) hedging to maximize the CVaR of total benefit is at the cost of reducing the EV of total benefit; and (4) in real-time operations, compared with the hedging policies under the EV criterion, the hedging policies under the CVaR criterion would be more effective when applied to dry and extremely dry hydrological conditions, especially when inflow is overestimated. These implications provide new insights into rationing water supply and risk aversion.

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Evaluation of basin-scale hydrologic response to a multi-storm simulation

Cited by 25 publications

References 25 publications

Stochastic spatial disaggregation of extreme precipitation to validate a regional climate model and to evaluate climate change impacts over a small watershed

Stochastic spatial disaggregation of extreme precipitation to validate a regional climate model and to evaluate climate change impacts over a small watershed

Inverse flood risk modelling under changing climatic conditions

Optimal Hedging Rules for Water Supply Reservoir Operations under Forecast Uncertainty and Conditional Value-at-Risk Criterion

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