# Dynamic Rule Curves and Streamflow under Climate Change for Multipurpose Reservoir Operation Using Honey-Bee Mating Optimization

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Materials and Methods

#### 2.1. Research Area

#### 2.2. World Climate Models

#### 2.2.1. CMIP5 Model

#### 2.2.2. Data Bias Correction

#### 2.3. SWAT Hydrological Model

_{t}is the final soil water content; SW

_{0}is the initial soil water content, t is the time (days), R

_{day}is the precipitation (mm) on the day i, Q

_{surf}is the surface water content on the day I, E

_{a}is the evaporative transpiration amount on the day I, W

_{seep}is the amount of water seeping into the basement on the day i, and Q

_{gw}is the amount of groundwater returning to the stream on the day i.

#### 2.3.1. Data Input

#### 2.3.2. Model Performance Evaluation Using SWAT-CUP

- The Coefficient of Determination (R
^{2}), as shown in Equation (6), is between 0–1, with values greater than 0.6 indicating that the two data are correlated at a level of reliability. - The Nash Sutcliffe efficiency (NSE) coefficient, as shown in Equation (7), is between −$\infty $ and 1, with values greater than 0.5 indicating that the two data are correlated at a level of reliability.

_{oi}is the i-order value, Q

_{oa}is the mean from all measurements, Q

_{si}is the i-order model, Q

_{sa}is the i-order value from all models, Q

_{s}is the calculated value from the model, and Q

_{o}is the measurement value.

#### 2.4. Application of HBMO Algorithm for Reservoir Rule Curves Generation

#### 2.4.1. HBMO Algorithm

#### 2.4.2. Water Equilibrium Simulation Model

_{υ}

_{,τ}is the amount of water discharged from the reservoir during the year υ in the month τ (τ is 1 to 12 referring to January to December); D

_{τ}is the demand for water at the bottom of the basin during month τ; x

_{τ}is the lower boundary of the rule curves of the month τ; y

_{τ}the upper boundary of the rule curves of the month τ; and W

_{υ}

_{,τ}is the amount of original water level available in the basin of the month τ.

_{υ}

_{,τ}is the amount of water stored in the reservoir at the end of the month τ; Q

_{υ}

_{,τ}is the average streamflow in the month τ; Eτ is the evaporation loss in the month τ; and DS (dead storage) is unused storage volume.

#### 2.4.3. Reservoir Rule Curves Efficiency Evaluation

## 3. Results and Discussion

#### 3.1. Streamflow Analysis Using the SWAT Model

#### 3.1.1. Model Performance Assessment

^{2}ranging from 0.62–0.88 and NSE between 0.50–0.81, which were both within the acceptable accuracy range as shown in Table 4.

#### 3.1.2. Forecasting of Future Streamflow Volumes

#### 3.2. Optimal Reservoir Rule Curves with HBMO Algorithm Technique

#### 3.2.1. Optimal Reservoir Rule Curves by HBMO Algorithm

#### 3.2.2. Reservoir Rule Curves Efficiency Evaluation

## 4. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## References

- Ehsani, N.; Vörösmarty, C.J.; Fekete, B.M.; Stakhiv, E.Z. Reservoir operations under climate change: Storage capacity options to mitigate risk. J. Hydrol.
**2017**, 555, 435–446. [Google Scholar] [CrossRef] - Ehteram, M.; Mousavi, S.F.; Karami, H.; Farzin, S.; Singh, V.P.; Chau, K.W.; El-Shafie, A. Reservoir operation based on evolutionary algorithms and multi-criteria decision-making under climate change and uncertainty. J. Hydroinform.
**2018**, 20, 332–355. [Google Scholar] [CrossRef] - Gorguner, M.; Kavvas, M.L. Modeling impacts of future climate change on reservoir storages and irrigation water demands in a Mediterranean basin. Sci. Total Environ.
**2020**, 748, 141246. [Google Scholar] [CrossRef] [PubMed] - Abera, F.F.; Asfaw, D.H.; Engida, A.N.; Melesse, A.M. Optimal operation of hydropower reservoirs under climate change: The case of Tekeze reservoir, Eastern Nile. Water
**2018**, 10, 273. [Google Scholar] [CrossRef] [Green Version] - Carvalho-Santos, C.; Monteiro, A.T.; Azevedo, J.C.; Honrado, J.P.; Nunes, J.P. Climate change impacts on water resources and reservoir management: Uncertainty and adaptation for a mountain catchment in northeast Portugal. Water Resour Manag.
**2017**, 31, 3355–3370. [Google Scholar] [CrossRef] [Green Version] - Moazzam, M.F.; Lee, B.G.; Rahman, G.; Waqas, T. Spatial Rainfall Variability and an Increasing Threat of Drought, According to Climate Change in Uttaradit Province, Thailand. Atmos. Clim. Sci.
**2020**, 10, 357. [Google Scholar] [CrossRef] - Tebakari, T.; Dotani, K.; Kato, T. Historical change in the flow duration curve for the upper nan River Watershed, Northern Thailand. J. Jpn. Soc. Hydrol. Water Resour.
**2018**, 31, 17–24. [Google Scholar] [CrossRef] [Green Version] - Sharma, D.; Babel, M.S. Assessing hydrological impacts of climate change using bias-corrected downscaled precipitation in Mae Klong basin of Thailand. Meteorol. Appl.
**2018**, 25, 384–393. [Google Scholar] [CrossRef] - Petpongpan, C.; Ekkawatpanit, C.; Kositgittiwong, D. Climate change impact on surface water and groundwater recharge in Northern Thailand. Water
**2020**, 12, 1029. [Google Scholar] [CrossRef] [Green Version] - Kamworapan, S.; Surussavadee, C. Evaluation of CMIP5 global climate models for simulating climatological temperature and precipitation for Southeast Asia. Adv. Meteorol.
**2019**, 2019, 1067365. [Google Scholar] [CrossRef] - Bhatta, B.; Shrestha, S.; Shrestha, P.K.; Talchabhadel, R. Evaluation and application of a SWAT model to assess the climate change impact on the hydrology of the Himalayan River Basin. Catena
**2019**, 181, 104082. [Google Scholar] [CrossRef] - Azari, M.; Oliaye, A.; Nearing, M.A. Expected climate change impacts on rainfall erosivity over Iran based on CMIP5 climate models. J. Hydrol.
**2021**, 593, 125826. [Google Scholar] [CrossRef] - Saharia, A.M.; Sarma, A.K. Future climate change impact evaluation on hydrologic processes in the Bharalu and Basistha basins using SWAT model. Nat. Hazards
**2018**, 92, 1463–1488. [Google Scholar] [CrossRef] - Chuenchooklin, S.; Pangnakorn, U. Hydrological Study Using SWAT and Global Weather, a Case Study in the Huai Khun Kaeo Watershed in Thailand. Int. J. Environ. Prot. Pol.
**2018**, 6, 36. [Google Scholar] [CrossRef] [Green Version] - Prasanchum, H.; Sirisook, P.; Lohpaisankrit, W. Flood risk areas simulation using SWAT and Gumbel distribution method in Yang Catchment, Northeast Thailand. Geogr. Tech.
**2020**, 15, 29–39. [Google Scholar] [CrossRef] - Ekkawatpanit, C.; Pratoomchai, W.; Khemngoen, C.; Srivihok, P. Climate change impact on water resources in Klong Yai River Basin, Thailand. Proc. Int. Assoc. Hydrol. Sci.
**2020**, 383, 355–365. [Google Scholar] [CrossRef] - Thongwan, T.; Kangrang, A.; Techarungreungsakul, R.; Ngamsert, R. Future inflow under land use and climate changes and participation process into the medium-sized reservoirs in Thailand. Adv. Civ. Eng.
**2020**, 2020, 5812530. [Google Scholar] [CrossRef] - Prasanchum, H.; Kangrang, A. Optimal reservoir rule curves under climatic and land use changes for Lampao Dam using Genetic Algorithm. KSCE J. Civ. Eng.
**2018**, 22, 351–364. [Google Scholar] [CrossRef] - Kumar, N.; Singh, S.K.; Srivastava, P.K.; Narsimlu, B. SWAT Model calibration and uncertainty analysis for streamflow prediction of the Tons River Basin, India, using Sequential Uncertainty Fitting (SUFI-2) algorithm. Model. Earth Syst. Environ.
**2017**, 3, 30. [Google Scholar] [CrossRef] - Abeysingha, N.S.; Islam, A.; Singh, M. Assessment of climate change impact on flow regimes over the Gomti River basin under IPCC AR5 climate change scenarios. J. Water Clim. Chang.
**2020**, 11, 303–326. [Google Scholar] [CrossRef] - Tayebiyan, A.; Mohammad, T.A.; Al-Ansari, N.; Malakootian, M. Comparison of optimal hedging policies for hydropower reservoir system operation. Water
**2019**, 11, 121. [Google Scholar] [CrossRef] [Green Version] - Akbarifard, S.; Sharifi, M.R.; Qaderi, K. Data on optimization of the Karun-4 hydropower reservoir operation using evolutionary algorithms. Data Br.
**2020**, 29, 105048. [Google Scholar] [CrossRef] [PubMed] - Kangrang, A.; Chaleeraktrakoon, C. Suitable Conditions of Reservoir Simulation for Searching Rule Curves. J. Appl. Sci.
**2008**, 8, 1274–1279. [Google Scholar] [CrossRef] [Green Version] - Kangrang, A.; Lokham, C. Optimal Reservoir Rule Curves Considering Conditional Ant Colony Optimization with. J. Appl. Sci.
**2013**, 13, 154–160. [Google Scholar] [CrossRef] - Kangrang, A.; Srikamol, N.; Hormwichian, R.; Prasanchum, H.; Sriwanphen, O. Alternative Approach of Firefly Algorithm for Flood Control Rule Curves. Asian J. Sci. Res.
**2019**, 12, 431–439. [Google Scholar] [CrossRef] - Sinthuchai, N.; Kangrang, A. Improvement of Reservoir Rule Curves using Grey Wolf Optimizer. J. Eng. Appl. Sci.
**2019**, 14, 9847–9856. [Google Scholar] [CrossRef] [Green Version] - Marchand, A.; Gendreau, M.; Blais, M.; Guidi, J. Optimized operating rules for short-term hydropower planning in a stochastic environment. Comput. Manag. Sci.
**2019**, 16, 501–519. [Google Scholar] [CrossRef] - Thongwan, T.; Kangrang, A.; Prasanchum, H. Multi-objective future rule curves using conditional tabu search algorithm and conditional genetic algorithm for reservoir operation. Heliyon
**2019**, 5, e02401. [Google Scholar] [CrossRef] - Haddad, O.B.; Afshar, A.; Mariño, M.A. Honey-bee mating optimization (HBMO) algorithm in deriving optimal operation rules for reservoirs. J. Hydroinform.
**2008**, 10, 257–264. [Google Scholar] [CrossRef] - Kangrang, A.; Prasanchum, H.; Hormwichian, R. Active future rule curves for multi-purpose reservoir operation on the impact of climate and land use changes. J. Hydro-Environ. Res.
**2019**, 24, 1–13. [Google Scholar] [CrossRef] - Ferguson, C.R.; Pan, M.; Oki, T. The effect of global warming on future water availability: CMIP5 synthesis. Water Resour. Res.
**2018**, 54, 7791–7819. [Google Scholar] [CrossRef] - Climate Change in Australia. List of Global Climate Models. Available online: https://www.climatechangeinaustralia.gov.au/en/overview/methodology/list-models/ (accessed on 20 February 2022).
- Zhou, T.; Yu, Y.; Liu, Y.; Wang, B. (Eds.) Flexible Global Ocean-Atmosphere-Land System Model: A Modeling Tool for the Climate Change Research Community; Springer: Berlin/Heidelberg, Germany, 2014. [Google Scholar]
- Chaowiwat, W. Impact of climate change assessment on agriculture water demand in Thailand. Naresuan Univ. Eng. J.
**2016**, 11, 35–42. [Google Scholar] - Sharma, D. Selection of suitable general circulation model precipitation and application of bias correction methods: A case study from the Western Thailand. In Environmental Management of River Basin Ecosystems; Springer: Cham, Switzerland, 2015; pp. 43–63. [Google Scholar]
- Arnold, A.G.; Srinivasan, R.; Muttiah, R.S.; Williams, J.R. Large area hydrological modeling and assessment pert I: Model development. J. Am. Water Resour. Assoc.
**1998**, 34, 73–89. [Google Scholar] [CrossRef] - Prasanchum, H.; Kangrang, A. Analyses of climate and land use changes impact on runoff characteristics for multi-purpose reservoir system. In Proceedings of the Conference on The AUN/SEED-Net Regional Conference 2016 on Environmental Engineering (RC-EnvE 2016), Chonburi, Thailand, 23–24 January 2017. [Google Scholar]
- Khalid, K.; Ali, M.F.; Abd Rahman, N.F.; Mispan, M.R.; Haron, S.H.; Othman, Z.; Bachok, M.F. Sensitivity analysis in watershed model using SUFI-2 algorithm. Procedia. Eng.
**2016**, 162, 441–447. [Google Scholar] [CrossRef] [Green Version] - Shivhare, N.; Dikshit, P.K.; Dwivedi, S.B. A comparison of SWAT model calibration techniques for hydrological modeling in the Ganga river watershed. Engineering
**2018**, 4, 643–652. [Google Scholar] [CrossRef] - Moriasi, D.N.; Gitau, M.W.; Pai, N.; Daggupati, P. Hydrologic and water quality models: Performance measures and evaluation criteria. Trans. ASABE
**2015**, 58, 1763–1785. [Google Scholar] - Zhang, S.; Li, Z.; Lin, X.; Zhang, C. Assessment of climate change and associated vegetation cover change on watershed-scale runoff and sediment yield. Water
**2019**, 11, 1373. [Google Scholar] [CrossRef] [Green Version] - Haddad, O.B.; Afshar, A.; Mariño, M.A. Honey-bees mating optimization (HBMO) algorithm: A new heuristic approach for water resources optimization. Water Resour. Manag.
**2006**, 20, 661–680. [Google Scholar] [CrossRef] - Rodriguez, L.B.; Cello, P.A.; Vionnet, C.A.; Goodrich, D. Fully conservative coupling of HEC-RAS with MODFLOW to simulate stream–aquifer interactions in a drainage basin. J. Hydrol.
**2008**, 353, 129–142. [Google Scholar] [CrossRef] - Techarungruengsakul, R.; Kangrang, A. Application of Harris Hawks Optimization with Reservoir Simulation Model Considering Hedging Rule for Network Reservoir System. Sustainability
**2022**, 14, 4913. [Google Scholar] [CrossRef]

**Figure 4.**Comparison of streamflow between the data from Ubolratana Dam Station and the calculated results from the SWAT model during 2011–2019.

**Figure 5.**Annual streamflow from the base year SWAT model 2011–2019 and under the forecast of RCP 4.5 between 2020–2049.

**Figure 6.**Monthly streamflow from the base year SWAT model 2011–2019 and under the forecast of RCP 4.5 between 2020–2049.

**Figure 7.**Annual streamflow from the base year SWAT model 2011–2019 and under the forecast of RCP 8.5 between 2020–2049.

**Figure 8.**Monthly streamflow from the base year SWAT model 2011–2019 and under the forecast of RCP 8.5 between 2020–2049.

**Figure 9.**Rule curves of Ubolratana reservoir developed using HBMO algorithm technique based on climate change impacts under the RCP4.5 projection case.

**Figure 10.**Rule curves of Ubolratana reservoir developed using HBMO algorithm technique based on climate change impacts under the RCP8.5 projection case.

Data Type | Period | Scale | Source |
---|---|---|---|

DEM | 2015 | 30 × 30 m | Land Development Department, Thailand |

Soil type map | 2015 | 1:50,000 | |

River map | 2020 | 1:50,000 | |

Land use map | 2015 | 30 × 30 m | |

Climate | 2011–2019 | Daily | Thai Meteorological Department, Thailand |

Observed inflow | 2011–2019 | Daily | Royal Irrigation Department, Thailand; Electricity Generating Authority, Thailand |

No. | Parameter | Range | Adjusted Values |
---|---|---|---|

1 | ALPHA_BF.gw | 0–1 | 0.367 |

2 | GW_DELAY.gw | 0–500 | 19.500 |

3 | GWQMN.gw | 0–500 | 179.500 |

4 | ESCO.hru | 0–1 | 0.881 |

5 | GW_REVAP.gw | 0–500 | 129.500 |

6 | SOL_AWC.sol | 0–1 | 0.393 |

7 | CN2.mgt | −0.2–0.2 | −0.104 |

8 | EPCO.hru | 0–1 | 0.819 |

Level | R^{2} | NSE |
---|---|---|

Very good | 0.80 < R^{2} ≤ 1.00 | 0.75 < NSE ≤ 1.00 |

Good | 0.70 < R^{2} ≤ 0.80 | 0.65 < NSE ≤ 0.75 |

Satisfactory | 0.60 < R^{2} ≤ 0.70 | 0.50 < NSE ≤ 0.65 |

Unsatisfactory | R^{2} ≤ 0.60 | NSE ≤ 0.50 |

**Table 4.**Index values for evaluating the accuracy of SWAT calculation results comparing streamflow volumes from measurement stations.

Assessment Index | R^{2} | NSE |
---|---|---|

E68A Station (Lam Pha Niang River Basin) | 0.82 | 0.52 |

E29 Station (Upper Phong River Basin) | 0.79 | 0.76 |

Ubolratana Dam Station | 0.88 | 0.81 |

E85 Station (Lam Nam Choen River Basin) | 0.62 | 0.50 |

Period | RCP | GCM | May–November (Wet Season) (MCM) | December–April (Dry Season) (MCM) | ||
---|---|---|---|---|---|---|

Average | Difference (%) | Average | Difference (%) | |||

Baseline (2011–2019) | 2257.67 | 127.89 | ||||

2020–2049 | RCP4.5 | Overall | 2930.95 | 29.82 | 232.53 | 81.82 |

MIROC_ESM | 4479.10 | 98.40 | 255.87 | 100.07 | ||

BNU | 2658.62 | 17.76 | 246.90 | 93.06 | ||

CanESM | 2516.67 | 11.47 | 242.13 | 89.33 | ||

MIROC5 | 3533.11 | 56.49 | 356.00 | 178.36 | ||

FGOALS_g2 | 1467.22 | −35.01 | 61.73 | −51.73 | ||

RCP8.5 | Overall | 3551.80 | 57.32 | 401.32 | 213.81 | |

MIROC_ESM | 4902.41 | 117.14 | 926.05 | 624.11 | ||

BNU | 3409.38 | 51.01 | 294.67 | 130.41 | ||

CanESM | 3126.56 | 38.49 | 293.06 | 129.15 | ||

MIROC5 | 3654.94 | 61.89 | 304.12 | 137.80 | ||

FGOALS_g2 | 2665.69 | 18.07 | 188.71 | 47.56 |

**Table 6.**Estimated results of water shortage and overflow events of the Ubolratana reservoir rule curves from the MIROC_ESM model under the RCP4.5 projection case.

Situations | Rule Curves | Frequency (Times/Year) | Magnitude (MCM/Year) | Duration (Year) | ||
---|---|---|---|---|---|---|

Average | Maximum | Average | Maximum | |||

Water shortage | Existing | 0.2 | 23.43 | 478.00 | 1.7 | 2.0 |

MIROC_ESM | 0.1 | 10.93 | 215.00 | 1.5 | 2.0 | |

BNU | 0.1 | 14.87 | 264.00 | 2.0 | 2.0 | |

CanESM | 0.1 | 14.17 | 295.00 | 1.5 | 2.0 | |

MIROC5 | 0.1 | 21.90 | 351.00 | 1.3 | 2.0 | |

FGOALS_g2 | 0.1 | 13.97 | 268.00 | 2.0 | 2.0 | |

Excess water release | Existing | 1.0 | 3235.04 | 8570.84 | 14.5 | 19.0 |

MIROC_ESM | 1.0 | 3181.27 | 8213.26 | 14.5 | 26.0 | |

BNU | 1.0 | 3187.92 | 8124.91 | 14.5 | 26.0 | |

CanESM | 1.0 | 3204.33 | 8284.15 | 14.5 | 19.0 | |

MIROC5 | 1.0 | 3216.58 | 8551.56 | 30.0 | 30.0 | |

FGOALS_g2 | 1.0 | 3207.96 | 8585.07 | 14.5 | 26.0 |

**Table 7.**Estimated water shortage and overflow events of the Ubolratana reservoir rule curves from the MIROC5 model under the RCP8.5 projection case.

Situations | Rule Curves | Frequency (Times/Year) | Magnitude (MCM/Year) | Duration (Year) | ||
---|---|---|---|---|---|---|

Average | Maximum | Average | Maximum | |||

Water shortage | Existing | 0.23 | 36.67 | 449.00 | 1.40 | 2.00 |

MIROC_ESM | 0.17 | 13.90 | 233.00 | 1.67 | 2.00 | |

BNU | 0.07 | 7.77 | 195.00 | 2.00 | 2.00 | |

CanESM | 0.13 | 12.77 | 259.00 | 2.00 | 2.00 | |

MIROC5 | 0.10 | 7.13 | 169.00 | 1.50 | 2.00 | |

FGOALS_g2 | 0.17 | 16.00 | 250.00 | 1.67 | 2.00 | |

Excess water release | Existing | 0.97 | 2460.08 | 6281.34 | 14.5 | 21 |

MIROC_ESM | 0.93 | 2460.26 | 5983.39 | 14 | 20 | |

BNU | 0.87 | 2441.62 | 6165.43 | 8.667 | 15 | |

CanESM | 0.93 | 2466.88 | 6055.38 | 9.333 | 15 | |

MIROC5 | 0.87 | 2424.31 | 6436.28 | 8.667 | 15 | |

FGOALS_g2 | 0.93 | 2452.14 | 6098.75 | 14 | 20 |

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**MDPI and ACS Style**

Songsaengrit, S.; Kangrang, A.
Dynamic Rule Curves and Streamflow under Climate Change for Multipurpose Reservoir Operation Using Honey-Bee Mating Optimization. *Sustainability* **2022**, *14*, 8599.
https://doi.org/10.3390/su14148599

**AMA Style**

Songsaengrit S, Kangrang A.
Dynamic Rule Curves and Streamflow under Climate Change for Multipurpose Reservoir Operation Using Honey-Bee Mating Optimization. *Sustainability*. 2022; 14(14):8599.
https://doi.org/10.3390/su14148599

**Chicago/Turabian Style**

Songsaengrit, Songphol, and Anongrit Kangrang.
2022. "Dynamic Rule Curves and Streamflow under Climate Change for Multipurpose Reservoir Operation Using Honey-Bee Mating Optimization" *Sustainability* 14, no. 14: 8599.
https://doi.org/10.3390/su14148599