Optimal rule curves are necessary guidelines in the reservoir operation that have been used to assess performance of any reservoir to satisfy water supply, irrigation, industrial, hydropower, and environmental conservation requirements. This study applied the conditional genetic algorithm (CGA) and the conditional tabu search algorithm (CTSA) technique to connect with the reservoir simulation model in order to search optimal reservoir rule curves. The Ubolrat Reservoir located in the northeast region of Thailand was an illustrative application including historic monthly inflow, future inflow generated by the SWAT hydrological model using 50year future climate data from the PRECIS regional climate model in case of B2 emission scenario by IPCC SRES, water demand, hydrologic data, and physical reservoir data. The future and synthetic inflow data of reservoirs were used to simulate reservoir system for evaluating water situation. The situations of water shortage and excess water were shown in terms of frequency magnitude and duration. The results have shown that the optimal rule curves from CGA and CTSA connected with the simulation model can mitigate drought and flood situations than the existing rule curves. The optimal future rule curves were more suitable for future situations than the other rule curves.
Nowadays, water resource issues have become more complex, which is related to global climate change and landuse change due to population and economic growth, which are increasing rapidly. For water resource management, both demand management site and supply management site are often required to solve the problems. Improving the reservoir operation for increased efficiency is another way of supply management site, which does not require the physical development of reservoir. Normally, reservoir operation uses upper and lower rule curves to consider the release of water from the reservoir responding to downstream demands in longterm operation. The purpose of the rule curves for reservoir operation was divided into two main areas: (i) variation of hydrological conditions [
To search optimal rule curves of the reservoir is a nonlinear optimization problem. There are many optimization techniques that are applied to connect with the reservoir simulation model for searching optimal rule curves such as dynamic programming (DP), genetic algorithm (GA), and simulated annealing algorithm (SA) [
In the last decades, there are many alternative algorithms to solve complex computational problems. Tabu search is a heuristic procedure designed for solving optimization problems. It has been successfully applied to many engineering fields such as industrial engineering, electrical engineering, civil engineering, and water resources engineering [
This study proposed a conditional tabu search algorithm (CTSA) to connect with the simulation model for searching the optimal reservoir rule curves. A minimum average water shortage was used as the objective function for the searching procedure. The proposed model has been applied to determine the optimal rule curves of the Ubolrat Reservoir in the northeast region of Thailand with the historic monthly inflow, future inflow under scenario B2, water demand, hydrologic data, and physical reservoir data. Comparison of the conditional genetic algorithm (CGA) and the CTSA was shown to demonstrate the effectiveness of the proposed CTSA model.
The development of the optimal future rule curves will use data from the future inflow flowing into the Ubolrat Reservoir considering the effects of climate change using the PRECIS model. Thus, the future inflow will be produced using the SWAT hydrological model. For the future climate data in the study area, PRECIS is a regional climate model, based on the development of ECHAM4 model, displaying the data as “grid” with high solution of 22 × 22 km^{2} [
Because of the Ubolrat Reservoir and the study area located in northeastern Thailand, most of the economic characteristics are generated by the sale of major agricultural products, such as rice and sugarcane, which require water for cultivation during the rainy season as the primary source. The expansion of most urban areas in the region is slow. Therefore, this study has chosen the appropriate greenhouse gas emission projection model based on the model of socioeconomic development, population growth, and technology of the study area according to the IPCC SRES, with emphasis on regional development for the emission scenario B2—prediction of lower population growth than A2, moderatelevel economic development, and oriented toward environmental protection [
SWAT (Soil and Water Assessment Tool) [
Spatial data and observed inflow data for SWAT performance evaluation.
Data types  Period  Scale  Source 

Spatial data (model input)  
DEM  2011  30 × 30 m  Land Development Department, Thailand 
River map  2011  1 : 50,000  
Soil types  2011  1 : 50,000  
Land use map  2014  30 × 30 m  
Climate  1997–2014  Daily  Thai Meteorological Department, Thailand 
Observed inflow (model performance assessment)  
Ubolrat Dam  1997–2014  Daily  Electricity Generating Authority of Thailand 
The accuracy of the SWAT results can be evaluated by comparing the simulated data with that recorded data from the observation station (i.e., Ubolrat Reservoir). Three variables including
Model setup processes for future inflow.
Reservoir system comprises available water that flows from upstream into the reservoir and multipurpose downstream demand. The reservoir operation is performed using water usage criteria release, operating policies, and reservoir rule curves with monthly data for longterm period. A reservoir operation model was constructed on the concept of water balance, and it can be used to simulate reservoir operation effectively. The reservoir operating policies are based on the monthly rule curves of individual reservoirs and the principles of water balance equation under the reservoir simulation model. The existing standard operating policies used for the reservoir rule curves operation are presented in Figure
Standard operating rule.
In (
The released water from the reservoir was used to calculate the water shortage and excess water release situations, which can be expressed as the frequency of failures in a year and the number of excess water release, as well as the average annual shortage (as the objective function for searching the optimal rule curves in this study). The results were recorded and used to develop the CTSA model.
The connection of the CGA to the reservoir simulation model was as follows. The CGA requires an encoding format to change the decision variables into the form of chromosomes. The CGA, which consists of selection, crossover, and mutation, is executed. After this stage, the genetic operations will create new chromosomes. For this study, each decision variable represents the average monthly water storage of the rule curves in the reservoirs, which are defined as the upper bound and the lower bound. After the first set of chromosomes in the initial population have been calculated (24 decision variables, which consist of 12 values from the upper bound and 12 values from lower bound situations), the released water will be recalculated by the reservoir simulation model using these rule curves. Next, the released water is used to determine the objective function with the aim of assessing the fitness of the GA. After that, the reproduction process will create new rule curve values in the next generation. This procedure is repeated until the 24 values of rule curves are appropriate. The CGA and reservoir simulation model for searching the rule curves are described in Figure
Applying CGA and reservoir simulation for searching rule curves.
In this study, the objective function for searching the optimal reservoir rule curves is the minimum of the average water shortage (min_{(avr)}) in million cubic meters (MCM) per year, as shown in
The developed CTSA for searching rule curves is described as follows. The CTSA begins with an initial population {
Then, the process is continued until the termination criterion is satisfied as described in Figure
Applying tabu and reservoir simulation for searching rule curves.
The Ubolrat Basin is a branch of the Chi Basin located in northeastern Thailand (Figure
Location of the Ubolrat Dam.
Schematic diagram of the Ubolrat basin.
The study used CTSA in connection with a reservoir operation model to find optimal rule curves through the MATLAB toolbox. The optimal rule curve can then be applied to an actual scenario depending on whether the rule curve can be used to cover every case or event that might occur. Thus, the HEC4 model was used to create the synthetic inflow data into the monthly inflows as a synthetic data set of 500 events. This method was based on the actual historic monthly inflow of the Ubolrat Reservoir between years 1963 and 2014 (50 years) imported to the HEC4 model to generate the synthetic inflow event. (1 event is a representative period of 50 years.) Therefore, the monthly inflow data are 300,000 values (50 years × 12 months × 500 events). Then, input synthetic inflow data were used to assess the efficiency of the new rule curves and compare them with the existing rule curves and also between the CTSA and CGA model under the same conditions. Further, the new obtained rule curves from CTSA and CGA model were used to evaluate with the future situation of B2 scenario [
An evaluation on SWAT accuracy used the data found during 1997–2014 (18 years; 1997–2008 for calibration and 2009–2014 for validation) for Ubolrat Reservoir station. Practically, 8 parameter values were selected and used to analyze the flexibility score as the modified parameter values of the flexibility by adjusting the inflow volume to closely match with the data from the observed station as presented in Table
SWAT sensitivity parameters.
No.  Parameter  Range  Adjusted Values 

1  ALPHA_BF  01  0.025 
2  GWQMN  0–500  0 
3  GW_REVAP  0–500  1.25 
4  SOL_AWC  01  0.28 
5  EPCO  01  0 
6  ESCO  01  0.52 
7  CH_N2  —  0.035 
8  GW_DELAY  0–500  31 
The inflow calculated by SWAT and compared with the data from the two observed station shows the inflow during the period of model calibration and validation; meanwhile,
SWAT performance evaluation index.
Range  Average annual inflow (MCM)  Assessment index  

Observed  Simulation 

RE 


Calibration  2,858.8  2,413.1  0.89  15.6  0.80 
Validation  1,421.4  1,380.1  0.91  2.3  0.89 
The comparison between the runoff from the observed stations and the SWAT result.
The inflow at the Ubolrat Reservoir station simulated by SWAT was divided into 2 phases: (1) baseline inflow which is the climate and spatial data recorded during 1997–2014 and (2) future inflow using the climate data from PRECIS model resulted during 2015–2064. The inflow analysis indicates that an average volume of the baseline inflow was 2,736 MCM and an average volume of the future inflow was 4,580.5 MCM. When comparing those two volumes, it was noted that the future inflow seems to be increased (1,844.5 MCM or 40.3% in 50 future years). Figure
Annual future inflow flowing into Ubolrat Reservoir during 2015–2064.
Average 10year future inflow flowing into Ubolrat Reservoir compared with the baseline.
The historic data of inflow, evaporation, water requirement, and monthly rainfall were imported for processing in the CGA connected to the simulation model and CTSA model, and the optimal rule curves were obtained. These obtained rule curves are plotted in order to compare them with the existing rule curves as shown in Figure
Optimal historic rule curves of the Ubolrat Reservoir.
The obtained rule curves also indicated that the water storage levels of the CTSA and CGA lower rule curves are lower than the existing rule curves during the dry season (February–June) in order to release more water to reduce water scarcity. In the middle of rainy season (August–October), the CTSA and CGA upper curves are higher than their existing rule curves in order to increase water storage for next dry season. This will help alleviate water shortages in the next year. These patterns of the obtained curves are similar to the pattern of the other reservoirs in Thailand on the other studies [
To find the future rule curves, the average monthly inflow for the future period 2015–2064 under the B2 scenario [
Optimal future rule curves of the Ubolrat Reservoir.
The evaluation of the new historic rule curves and future rule curves generated from the CGA and CTSA model aimed to determine the performance of the rule curves with the synthetic historic inflow of 500 samples and the future inflows (B2 scenario), as shown in Tables
Situations of water shortage and excess release of the systems using historic inflow.
Situations  Rule curves  Frequency (times/year)  Magnitude (MCM/year)  Duration (year)  

Average  Maximum  Average  Maximum  
Water shortage  RC1 (existing)  0.857  554.918  1,594  6.000  22.000 
RC2 (CGA)  0.878  402.633  1,352  8.600  25.000  
RC4 (CTSA)  0.959  455.776  1,324  15.667  34.000  


Excess release  RC1 (existing)  0.612  578.319  3,323.422  3.000  7.000 
RC2 (CGA)  0.408  423.555  2,891.616  2.857  5.000  
RC4 (CTSA)  0.469  478.789  3,000.471  2.556  6.000 
Situations of water shortage of the systems using synthetic inflow from historic data.
Rule curves  Frequency (times/year)  Magnitude (MCM/year)  Duration (year)  

Average  Maximum  Average  Maximum  
RC1 (existing) 

0.889  532.169  1,482.370  9.573  19.780 

0.037  25.134  144.076  3.414  6.538  


RC2 (CGA) 

0.883  381.586  1,465.750  9.248  20.190 

0.039  29.268  180.698  3.424  7.595  


RC3 (CGA) 

0.918  427.853  1,431.100  12.542  24.320 

0.032  27.612  188.318  5.252  8.507  


RC4 (CTSA) 

0.728  391.612  1,453.790  4.776  11.000 

0.051  28.749  177.480  1.348  4.306  


RC5 (CTSA) 

0.911  404.437  1,442.930  11.918  23.690 

0.034  28.146  179.992  5.266  8.304 
Note:
Situations of excess water release of the systems using synthetic inflow from historic data.
Rule curves  Frequency (times/year)  Magnitude (MCM/year)  Duration (year)  

Average  Maximum  Average  Maximum  
RC1 (existing) 

0.648  550.036  3,511.112  2.817  7.140 

0.045  33.939  825.296  0.534  1.939  


RC2 (CGA) 

0.472  390.421  3,247.282  2.304  5.590 

0.051  39.386  808.863  0.461  1.843  


RC3 (CGA) 

0.495  438.704  3,310.449  2.163  5.330 

0.050  36.914  785.299  0.410  1.596  


RC4 (CTSA) 

0.482  400.120  3,197.957  2.296  5.380 

0.050  38.579  814.755  0.475  1.600  


RC5 (CTSA) 

0.504  412.739  3,225.344  2.457  5.850 

0.051  37.803  801.793  0.525  1.866 
Note:
Situations of water shortage and excess release of the systems using future inflow.
Situations  Rule curves  Frequency (times/year)  Magnitude (MCM/year)  Duration (year)  

Average  Maximum  Average  Maximum  
Water shortage  RC1 (existing)  0.140  23.200  660.000  1.167  2.000 
RC2 (CGA)  0.240  9.340  412.000  1.200  2.000  
RC4 (CTSA)  0.340  32.860  418.000  1.417  4.000  
RC3 (CGA)  0.020  5.800  290.000  1.000  1.000  
RC5 (CTSA)  0.340  32.860  418.000  1.417  4.000  


Excess release  RC1 (existing)  0.980  2,085.463  4,711.864  24.500  27.000 
RC2 (CGA)  0.980  2,056.182  4,697.881  24.500  27.000  
RC4 (CTSA)  0.980  2,085.965  4,702.331  24.500  27.000  
RC3 (CGA)  0.980  2,045.096  4,689.417  24.500  27.000  
RC5 (CTSA)  0.980  2,085.965  4,702.331  24.500  27.000 
Tables
In the case of future situation (Table
This study proposed an alternative algorithm for searching optimal reservoir rule curves. The conditional tabu search algorithm (CTSA) and reservoir simulation model were applied to search the optimal rule curves of the Ubolrat Reservoir under historic monthly inflow and future inflow under the scenario B2. The future inflow and synthetic inflow data of reservoirs were used to simulate reservoir system for evaluating situations of water shortage and excess release. The results found that the new obtained rule curves from CTSA are more suitable for reservoir operating than the existing rule curves. The frequency and magnitude of water shortage and excess water release for using new obtained rule curves are lower than the existing rule curves. When comparing the new obtained rule curves from CTSA with the rule curves of the CGA method as well as the existing simulation method, it was found that these rule curves are similar. The proposed CTSA model is an effective method for application to find optimal reservoir rule curves. This reveals that the CTSA and GA model with future inflow are effective methods for searching optimal reservoir rule curves that are suitable for using in the future situations.
The authors declare that there are no conflicts of interest regarding the publication of this paper.
This research was financially supported by Mahasarakham University and National Research Council of Thailand Grant Year 2018.