Evaluation of Climatic-Change Impacts on Multiobjective Reservoir Operation with Multiobjective Genetic Programming

Ashofteh, Parisa‐Sadat; Bozorg‐Haddad, Omid; Loáiciga, Hugo A.

doi:10.1061/(asce)wr.1943-5452.0000540

Cited by 118 publications

(32 citation statements)

References 47 publications

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“…Ashofteh, Bozorg Haddad, and Loaiciga () found that the ranges of vulnerability and reliability indices were larger under climate change conditions, in response to larger flow variability. They concluded that modified reservoir's rule curves under climate change may improve performance scores as compared with control period rules.…”

Section: Discussionmentioning

confidence: 99%

Operation of two major reservoirs of Iran under IPCC scenarios during the XXI century

Akbari‐Alashti

Soncini

Dinpashoh

et al. 2018

Hydrological Processes

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We assess the effects of prospective climate change until 2100 on water management of two major reservoirs of Iran, namely, Dez (3.34 × 109 m3) and Alavian (6 × 107 m3). We tune the Poly‐Hydro model suited for simulation of hydrological cycle in high altitude snow‐fed catchments. We assess optimal operation rules (ORs) for the reservoirs using three algorithms under dynamic and static operation and linear and non‐linear decision rules during control run (1990–2010 for Dez and 2000–2010 for Alavian). We use projected climate scenarios (plus statistical downscaling) from three general circulation models, EC‐Earth, CCSM4, and ECHAM6, and three emission scenarios, or representative concentration pathways (RCPs), RCP2.6, RCP4.5, and RCP8.5, for a grand total of nine scenarios, to mimic evolution of the hydrological cycle under future climate until 2100. We subsequently test the ORs under the future hydrological scenarios (at half century and end of century) and the need for reoptimization. Poly‐Hydro model when benchmarked against historical data well mimics the hydrological budget of both catchments, including the main processes of evapotranspiration and streamflows. Teaching–learning‐based optimization delivers the best performance in both reservoirs according to objective scores and is used for future operation. Our projections in Dez catchment depict decreased precipitation along the XXI century, with −1% on average (of the nine scenarios) at half century and −6% at the end of century, with changes in streamflows on average −7% yearly and −13% yearly, respectively. In Alavian, precipitation would decrease by −10% on average at half century and −13% at the end of century, with streamflows −14% yearly and −18% yearly, respectively. Under the projected future hydrology, reservoirs' operation would provide lower performance (i.e., larger lack of water) than now, especially for Alavian dam. Our results provide evidence of potentially decreasing water availability and less effective water management in water stressed areas like Northern Iran here during this century.

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Section: Discussionmentioning

confidence: 99%

Operation of two major reservoirs of Iran under IPCC scenarios during the XXI century

Akbari‐Alashti

Soncini

Dinpashoh

et al. 2018

Hydrological Processes

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“…The decision variables in Equation are the parameters that define the hedging rule for reservoir release introduced in Equation ; LSR = the long‐term shortage ratio; RSPH t = reservoir release (regulated release plus spill flow) calculated based on hedging rule during period t ; T = length of the operation interval; and D = average water demand during the operation interval.The hedging release rule depends on the reservoir's water availability according to Equation :

{RSPH}_{t} = f ((), {AW}_{t}) t = 1, 2, \dots ., T

in which f (AW t ) = hedging rule calculated with the TGP and LGP approaches; and AW t = available water during period t , which is calculated with Equation assuming linear approximation of reservoir evaporation (Fallah‐Mehdipour et al ., ; Ashofteh et al ., , b):

{AW}_{t} = S_{t} + Q_{t} - \frac{e_{t} \cdot ((), {aS}_{t} + b)}{1000} t = 1, 2, \dots ., T

in which S t = reservoir storage at the beginning of period t ; Q t = reservoir inflow during period t ; e t = depth of reservoir evaporation during period t ; and a and b = constants in the surface storage function of the reservoir.…”

Section: Methodsmentioning

confidence: 99%

“…In other research by Ashofteh et al . () multi‐objective genetic programming (MO‐GP) was implemented to calculate optimal reservoir‐operating rules under baseline and climatic change conditions. The objective functions were the maximization of the reliability of meeting irrigation demand and the minimization of the vulnerability to irrigation deficits under baseline condition and future conditions.…”

Section: Introductionmentioning

confidence: 99%

Logical Genetic Programming (LGP) Development for Irrigation Water Supply Hedging Under Climate Change Conditions

Ashofteh

Bozorg‐Haddad

Loáiciga

2017

Irrigation and Drainage

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Traditional genetic programming (TGP) is herein enhanced by the addition of logical operators to form logical genetic programming (LGP). The LGP approach is applied to calculate hedging reservoir‐operation rules for the Aidoghmoush single‐purpose reservoir (north‐eastern Iran) to supply irrigation water during a 14‐year baseline operation period (1987–2000) and a climatically changed condition (2026–2039). The objective function of the hedging rule is to minimize the long‐term shortage ratio (LSR). Our results show that the LGP‐obtained hedging rule compares favourably with that obtained with the TGP approach, so that the former approach's objective function is 25 and 6% better than the latter's approach with the baseline and climate change conditions, respectively. The results obtained concerning the reliability, vulnerability and resiliency of water supply indicate that the LGP hedging operating rule decreases the water supply reliability by 34%, increases the vulnerability by 58%, and decreases the resiliency by 29% during climate change conditions compared with baseline conditions. Copyright © 2017 John Wiley & Sons, Ltd.

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“…Some studies have used a single GCM (Ashofteh et al, 2015;Fortier and Mailhot, 2015) or an ensemble mean from multiple climate models (Benestad, 2003;Smith et al, 2009;Tebaldi and Knutti, 2007). Using each approach, only one "likely" outcome was produced, but no information regarding a range of possible outcomes.…”

Section: Gcm Performance and Convergencementioning

confidence: 99%

“…The question here is how to choose an appropriate subset of GCMs. Studies choose a subset of GCMs based on results from earlier studies on the performance of the GCMs in simulating the local climate, future climate, or both (Ashofteh et al, 2015;Elhakeem et al, 2015;Singh et al, 2015). The number of GCMs selected in these studies ranges from one to nine.…”

Section: Gcm Performance and Convergencementioning

confidence: 99%

Synthetic Scenarios from CMIP5 Model Simulations for Climate Change Impact Assessments in Managed Ecosystems and Water Resources: Case Study in South Asian Countries

2016

Trans. ASABE

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Evaluation of Climatic-Change Impacts on Multiobjective Reservoir Operation with Multiobjective Genetic Programming

Cited by 118 publications

References 47 publications

Operation of two major reservoirs of Iran under IPCC scenarios during the XXI century

Operation of two major reservoirs of Iran under IPCC scenarios during the XXI century

Logical Genetic Programming (LGP) Development for Irrigation Water Supply Hedging Under Climate Change Conditions

Synthetic Scenarios from CMIP5 Model Simulations for Climate Change Impact Assessments in Managed Ecosystems and Water Resources: Case Study in South Asian Countries

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