Optimization of Reservoir Operation under Climate Change Scenarios for Flood Control and Irrigation: A Case Study of Kiew Kor Mah Reservoir in the Wang River Basin, Thailand
by
Ananya Suksri
Nationality: Thai Current Degree: Bachelor of Engineering in Civil Engineering-Irrigation Kasetsart University Thailand
Scholarship Donor: Ministry of Agriculture and Cooperatives (MOAC), Thailand – AIT Fellowship
May 2019
ABSTRACT
The Kiew Kor Mah reservoir has problems in the current operation. There are flooding in downstream area many times. The previous rule curve needs to develop for reduce flood damage and to supply irrigation demand. The optimization process was applied the Harmony Search algorithm for determine the optimal rule curve. The aim of this study to improve reservoir operation. There are three parts in this study. Firstly, to develop rule curve by using Harmony Search algorithm. The rule curve from Harmony Search algorithm can gain the best benefit (compare with previous rule curve and existing operation). Secondly, presents the future climate projection in the Wang River Basin which considered three climatic variables: precipitation, maximum temperature, and minimum temperature. Three RCMs were selected to address climate model uncertainty. The Linear downscaling method technique was applied for correcting the data from climate models with observed climate data. The future climate was projected for three future periods: the 2020s (2010–2039), 2050s (2040–2069), and 2080s (2070–2099) under RCP4.5 and RCP8.5 scenarios based on the baseline period (1979–2005). The future precipitation may increase during the dry season (November to April) and decrease during the wet season (May to October) under both RCP4.5 and RCP8.5 scenarios. Average annual maximum and minimum temperatures are expected to increase in the future. Lastly, assess water resources especially water inflow by using The Hydrologic Modeling System (HEC-HMS) model. Results from HEC-HMS model shows that the average annual discharge may decrease in future for both stations under climate change scenario. Keywords: Reservoir operation, Optimization reservoir operation, Flood control, Climate projection, Harmony Search algorithm, Optimal rule curve, Water availability
ii
TABLE OF CONTENTS
CHAPTER TITLE PAGE Table of Contents iv List of Figures vi List of Tables viii
1 Introduction 1 1.1 Background 1 1.2 Statement of the problems 2 1.3 Rationale 3 1.4 Objectives 3 1.5 Scope 4 1.6 Limitations of the study 4
2 Literature Review 5 2.1 Development of reservoir operation 5 2.2 Optimization reservoir operation 6 2.3 Climate change 11 2.4 Hydrology models 12
3 Study area and data collection 14 3.1 Wang river basin 14 3.2 Kiew Kor Mah dam 19 3.3 Data Collection for optimization 21 3.4 Data Collection for climate projection 21
4 Methodology 22 4.1 Conceptual framework 22 4.2 Harmony search algorithm 24 4.3 Future climate projection 28 4.4 Hydrological model 30
5 Result and Discussion 33 5.1 Historical data analysis of Kiew Kor Mah reservoir 33 5.2 Operation of the Kiew Kor Mah reservoir under 37 current climate 5.3 Climate future projection 42 5.3 Impact of climate change on Water resources 56 5.4 Operation of the Kiew Kor Mah reservoir in the 60 future
iii
TABLE OF CONTENTS
CHAPTER CHAPTER CHAPTER
6 Conclusions and Recommendations 6.1 Conclusions 69 6.2 Recommendations 71
REFERENCES 72
APPENDICES Appendix A 78 Appendix B 84 Appendix C 90 Appendix D 92
iv
LIST OF FIGURES
FIGURE TITLE PAGE
1.2-1 Water storage level of Kiew Kor Mah reservoir (Rule curve) 3 3.1-1 Reservoir storage space 14 3.1-2 Wang River Basin in Thailand and location of Kiew Kor Mah 15 Reservoir. 3.1-3 Schematic of Kiew Kor Mah Reservoir. 16 3.2-1 Top view of Kiew Kor Mah Dam 20 4.1-1 Overall research methodology 23 4.2-1 The steps in the HS algorithm 24 4.2-2 The procedure in the HS algorithm 25 4.2.2-1 Release zone of reservoir 27 4.4.1-1 Flowchart of Hydrologic Model (HEC-HMS) 30 4.4.1-2 Flowchart of Digital Elevation Model (DEM) 31 5.1.1-1 The monthly inflow of Kiew Kor Mah reservoir from 2009 to 33 2017. 5.1.1-2 The monthly outflow of Kiew Kor Mah reservoir from 2009 to 34 2017. 5.1.1-3 The monthly storage of Kiew Kor Mah reservoir from 2009 to 35 2017 5.1.1-4 The monthly water demand of Kiew Kor Mah reservoir 35 5.2-1 Reservoir rule curve (Previous) of Kiew Kor Mah reservoir 37 5.2-2 Water storage base on previous rule curve of Kiew Kor Mah 38 reservoir 5.2-3 Reservoir rule curve (Modify by Harmony Search Algorithm) 38 5.2-4 Water storage base on Modify Rule Curve of Kiew Kor Mah 39 reservoir in 2009 to 2017 5.2-5 Reservoir rule curve of Kiew Kor Mah reservoir for dry year 5.2-6 Reservoir rule curve of Kiew Kor Mah reservoir for normal year 5.2-7 Reservoir rule curve of Kiew Kor Mah reservoir for wet year 5.3.1-1 Average monthly precipitation in Wang River Basin from 1979 to 44 2005 5.3.1-2 Average monthly maximum temperature in Wang River Basin 45 from 1979 to 2005 5.3.1-3 Average monthly minimum temperature in Wang River Basin 45 from 1979 to 2005 5.3.2-1 Projected monthly precipitation in Wang River Basin under (a) 48 RCP4.5 and (b) RCP8.5 scenarios. 5.3.2-2 Projected annual precipitation in Wang River Basin under RCP4.5 49 and RCP8.5 scenarios. 5.3.3-1 Projected monthly maximum temperature in Wang River Basin 51 under (a) RCP4.5 and (b) RCP8.5 scenarios. 5.3.3-2 Projected annual maximum temperature in Wang River Basin 52 under RCP4.5 and RCP8.5 scenarios. 5.3.4-1 Projected monthly minimum temperature in Wang River Basin 54 under (a) RCP4.5 and (b) RCP8.5 scenarios 5.3.4-2 Projected annual minimum temperature in Wang River Basin 55 under RCP4.5 and RCP8.5 scenarios
v
LIST OF FIGURES
FIGURE TITLE PAGE
5.3.5 Concluding Remarks 55 5.4.1-1 Hydrology model, HEC-HMS 56 5.4.2-1 Discharge and precipitation of Wang river basin 57 5.4.3-1 Average monthly discharge under climate change scenarios. 58 5.4.3-2 Future annual discharge trend under climate change scenarios. 59 5.5.1-1 Reservoir rule curve (Modified by Harmony Search Algorithm) 60 in 2020 to 2030 under RCP4.5 of Kiew Kor Mah reservoir 5.5.1-2 Water storage base on Modified Rule Curve of Kiew Kor Mah 60 reservoir in 2020 to 2030 under RCP4.5. 5.5.1-3 Reservoir rule curve (Modified by Harmony Search Algorithm) 61 in 2020 to 2030 under RCP8.5 of Kiew Kor Mah reservoir 5.5.1-4 Water storage base on Modified Rule Curve of Kiew Kor Mah 61 reservoir in 2020 to 2030 under RCP8.5. 5.5.2-1 Reservoir rule curve (Modified by Harmony Search Algorithm) 63 in 2031 to 2040 under RCP4.5 of Kiew Kor Mah reservoir 5.5.2-2 Water storage base on Modified Rule Curve of Kiew Kor Mah 63 reservoir in 2031 to 2040 under RCP4.5 5.5.2-3 Reservoir rule curve (Modified by Harmony Search Algorithm) 64 in 2031 to 2040 under RCP8.5 of Kiew Kor Mah reservoir 5.5.2-4 Water storage base on Modified Rule Curve of Kiew Kor Mah 64 reservoir in 2031 to 2040 under RCP8.5 5.5.3-1 Reservoir rule curve (Modified by Harmony Search Algorithm) 66 in 2041 to 2050 under RCP4.5 of Kiew Kor Mah reservoir 5.5.3-2 Water storage base on Modified Rule Curve of Kiew Kor Mah 66 reservoir in 2041 to 2050 under RCP4.5 5.5.3-3 Reservoir rule curve (Modified by Harmony Search Algorithm) 67 in 2041 to 2050 under RCP8.5 of Kiew Kor Mah reservoir 5.5.3-4 Water storage base on Modified Rule Curve of Kiew Kor Mah 67 reservoir in 2041 to 2050 under RCP8.5
vi
LIST OF TABLES
TABLE TITLE PAGE
2.1-1 Summary of advantages and disadvantages of optimization 10 techniques 2.3.1-1 Intergovernmental Panel on Climate Change (IPCC) in 2007 12 publish 4 sets of scenarios called Representative Concentration Pathways (RCP) 3.1.1-1 Climate validation from weather stations of Wang river. 17 3.1.2-1 Average Monthly Reference Crop Evapotranspiration in Wang 18 River Basin. 3.1.3-1 Evaporation rate from in Wang River Basin. 18 3.2-1 The detail of Kiew Kor Mah dam 19 4.4.2-1 Reported performance rating for R2, NSE and, PBIAS of HEC- 32 HMS model 5.1.1-1 The annual inflow of Kiew Kor Mah reservoir (MCM). 34 5.2-1 Calibration: comparison volume and number of spill and water 39 shortage between Previous rule curve, Existing operation and Modify rule curve from 2009 to 2014 5.2-2 Validation: comparison volume and number of spill and water 40 shortage between Previous rule curve, Existing operation and Modify rule curve from 2015 to 2017 5.2-3 The daily maximum of flood volume in Kiew Kor Mah 40 downstream area 5.2-4 Classification of annual inflow 40 5.2-5 Volume and number of spill and water shortage under Current 41 rule curve in dry year (2012 and 2015) 5.2-6 Volume and number of spill and water shortage under Current 42 rule curve in dry year (2009, 2010, 2013, 2014 and 2016) 5.2-7 Volume and number of spill and water shortage under Current 43 rule curve in wet year (2011 and 2017) 5.3.1-1 Validation: comparison volume and number of spill and water 44 shortage between Previous rule curve, Existing operation and Modify rule curve from 2015 to 2017 5.3.1-2 The daily maximum of flood volume in Kiew Kor Mah 44 downstream area from 2020 to 2030 under RCP4.5 and RCP8.5 5.3.2-1 Comparison volume and number of spill and water shortage 45 between Previous rule curve, Existing operation and Modify rule curve from 2031 to 2040 5.3.2-2 The daily maximum of flood volume in Kiew Kor Mah 45 downstream area from 2031 to 2040 under RCP4.5 and RCP8.5 5.3.1-1 Performance evaluation of bias correction for precipitation of 46 Wang River Basin 5.3.1-2 Performance evaluation of bias correction for maximum 46 temperature of Wang River Basin 5.3.1-3 Performance evaluation of bias correction for minimum 46 temperature of Wang River Basin 5.3.2-1 Rainfall projection under RCP4.5 and RCP8.5 scenarios for three 47 future periods
vii
LIST OF TABLES
TABLE TITLE PAGE
5.3.3-1 Maximum temperature projection under RCP4.5 and RCP8.5 50 scenarios for three future periods. 5.3.4-1 Minimum temperature projection under RCP4.5 and RCP8.5 53 scenarios for three future periods. 5.4.2-1 Statistical indicators of calibration and validation 57 5.4.3-1 Average monthly discharge under climate change scenarios from 58 2010 to 2099 5.5.1-1 The volume and number of spill and water shortage from 2020 to 62 2030 under RCP4.5 and RCP8.5 scenarios. 5.5.1-2 The daily maximum of flood volume in Kiew Kor Mah 62 downstream area from 2020 to 2030 under RCP4.5 and RCP8.5 5.5.2-1 The volume and number of spill and water shortage from 2031 to 65 2040 under RCP4.5 and RCP8.5 scenarios. 5.5.2-2 The daily maximum of flood volume in Kiew Kor Mah 65 downstream area from 2031 to 2040 under RCP4.5 and RCP8.5. 5.5.3-1 The volume and number of spill and water shortage from 2041 to 68 2050 under RCP4.5 and RCP8.5 scenarios. 5.5.3-2 The daily maximum of flood volume in Kiew Kor Mah 68 downstream area from 2041 to 2050 under RCP4.5 and RCP8.5
viii
CHAPTER 1 INTRODUCTION
1.1 Background
The scarcity of freshwater in worldwide is become to the rapidly increase. Therefore, the water resources management is very important issues. The important structure for protect flood and supply water is reservoir. The important parameters are inflow, water storage, water outflow, water demand, evaporation, and capacity of downstream area. The propose of reservoir operation are gain the best benefit minimize cost when operate base on water limitation by using mass balance equation and under other constraints. The reservoir operation should supply water meet water demand. Therefore, it is does not have water shortage. The reservoir operation of multipurpose is complex to determine optimal supply because it may have some conflict between objectives. Optimal reservoir operation is used to solve problem in multipurpose reservoir.
For multiple reservoir, the optimal reservoir can improve flood prevention and hydropower generation. Depending on rainfall intensity, upper reservoirs can have water spill, while the lower reservoir cannot supply water to meet water demand. The optimization and simulation are used to solve the optimal supply under multi-reservoir. The water balance can used to assess the current situation of water in reservoir. It can help decision operator to manage reservoir in normal and flood season.
The Rule Curve used to control the reservoir water level includes water release in daily, weekly, monthly or year. The optimal reservoir purposes are maximization benefit and minimize loss under other constraints and water balance equation. There are many programs are used to optimization such as Linear programing, Non-Linear programing, Dynamic programing, Genetic Algorithm, Ant Colony Algorithm, and Harmony Search Algorithm. In simulation process, it can use by hydrological models such as HECResSim, HEC-3, MIKE11, and SOBEK (Stevinweg, 2016).
Generally, the total reservoir storage space in a multipurpose reservoir consists of dead storage zone (mainly required for irrigation demand, domestic use, supply water to lower reservoir, sediment collection, recreation, and electric hydropower). It is operation consider flood volume in downstream and supply water to meet water demand (Shrestha, S. 2018).
The Wang River basin locates in North of Thailand, which is one of the Chao Phraya River has its basin area approximately 10,791 km2 with rather narrow and long shape, this 460 km long Wang river originates in Chiang Rai province and flow past Lampang provinces. The river will meet the Ping river at Tak province, it consists of two provinces, Lampang and Tak.
Due to the increase in population, the natural resources will be decrease. It can lead to problem about water resources such as flood, drought and water quality. Overview of water resources issues in Wang river basin including firstly, water shortage is highest in dry season. Especially, Maetui River Basin, middle of Mae Wang river, Jang river, Mae Taem river and lower Wang River. Secondly, shortage of water supply in Lampang and Tak Province. Thirdly, the expansion of irrigation area beyond the potential of water resources in the watershed. The problem of water shortage for agriculture in Lampang and
1
Tak province. Fourthly, flooding in the Wang River Basin. Especially, lower Wang river in Tak province. Fifthly, water operation in the Wang River Basin. The capacity of Kiew Lom dam is not enough for water demand. Sixthly, the highest problem of water quality in Lampang province and finally, water resources are destroyed.
The operation of reservoir dam which play an important role in storing water in northern parts of Thailand and protect flood volume in downstream area. Flooding of the Wang River Basin is controlled by storing water in Kiew Kor Mah reservoir. The main purposes are supply water to meet water demand including irrigation, domestic use, and hydroelectric power.
1.2 Statement of the Problem
Many reservoirs are multiple purpose. It can have two or more purposes in the reservoir. The multipurpose is the reservoir that operate base on many purposes. The main purpose are protect flood in downstream area, supply water for irrigation demand, domestic use, generate electric hydropower, and recreation. The reservoir operation has complex problem, it has many decision variables. The optimization system is the important method that used for determine optimal rule curve.
In case of Wang River Basin, the reservoir in this place is Kiew Kor Mah reservoir. The purposes of this dam are storage water in wet and dry season. The water level is operated base on rule curve. There are upper rule curve and lower rule curve. The upper rule curve and lower rule curve are used to device the case of water release. If water storage level is occur between upper rule curve and lower rule curve, it can supply water as water demand. The rule curve may change after years of operation. Therefore, it is necessary to modified rule curve.
The Royal Irrigation Department of Thailand construct the rule curve of Kiew Kor Mah Reservoir based on the historical data. From figure 1.2.1 showed water storage in reservoir. In June2015, the amount of water decreasing until below the lower rule curve (dry year). Therefore, at the beginning 2016 started amount of water below lower rule curve.
2
Figure 1.2-1 Water storage level of Kiew Kor Mah reservoir (Rule curve) (Source: Hydro and Agro Informatics Institute)
1.3 Rationale
This study will optimize reservoir operation by considering drought control purpose as the main objective. The developed reservoir operation rule curve for Kiew Kor Mah reservoir will aim reservoir operators to guideline for decision making in dry event.
1.4 Objectives of the Research
The overall objective in this study is to improve the reservoirs operation base on flood control and irrigation in Wang river basin, Thailand • To optimize Kiew Kor Mah reservoir operation policies for flood control and irrigation under current climate; • To project the future climate of the Wang River Basin using three RCMs under two scenarios RCP 4.5 and RCP 8.5; and • To project the future inflows of Kiew Kor Mah reservoirs and optimize Kiew Kor Mah reservoir operation under climate scenarios.
3
1.5 Scope
Scope of this study is to calculate water balance in Kiew Kor Mah Reservoir Reservoir on wet and dry season. The location of dams on Wang basin. The optimization technique will be used to derive optimal reservoir policies for flood control purpose. The objective function is to minimize water shortage and the flood volume in downstream area. • Hydro-meteorological data include inflow, outflow, rainfall, Evaporation, Water demand, Currentrule curve and Reservoir inflow will be collected from Royal Irrigation Department, Thailand • Harmony search algorithms is used to optimization approach for support the operators’ decisions. • Analyzing future climate scenarios based on three RCMs model. • Using three precipitation gates stations and three temperature stations. • HEC-HMS is used to assess inflow of reservoirs. • The assess future water demand, the CROPWAT model was used.
1.6 Limitations of the study
• This study does not include water quality and sedimentation. • This study focuses on reservoir operation in Kiew Kor Mah only. • The Harmony search algorithms is used to optimization in this study. • The future in this study focuses on change in inflow reservoirs, water demand
4
CHAPTER 2 LITERATURE REVIEW
2.1 Development of reservoir operation
The reservoir operation should have optimization for gain optimal water supply. The optimization base on objective and constraint. This method can use for complex operation. Juan B. Valdes also argued that instead of focusing on optimization algorithms, basic strategy and objective systematization problem need to be explored presented by Valdes, J.B. (1995).
Ngo (2007) have two objective function in the study. First, to reduce flood in downstream area. Second, to maximize generate electric hydro power. The optimized rule curve can gain a lot of benefit more than current rule curve.
The reservoir operation under pipe line can use GA algorithm to determine pipe size. This algorithm can gain best result compare with trial and error method, Newton-Raphson, and Simulation Annealing, and Differential Evolution briefly by Kitpayung, A. (2008)
Mec (2008) compared the result between using balance water level index method and without using water level index method to shows that “a real time simulation optimization operation for determining the reservoir release at each time step during flood in order to minimize flooding in the meantime all reservoirs in the system are kept balance”. The result from using balance water level index method can gain the better benefit.
Dittmann, (2009) briefly that reservoir management system can provide optimal rule curve for use in short-term and long-term reservoir operation.
Eum (2010) shows that the integrated reservoir management system can be uses to determine the optimal rule curve in multipurpose of reservoir under climate. There are three objectives: to minimize flood, supply water for irrigation, and maximize hydro electrical.
The researcher is Chen, (2010) currentthat dynamic programming can use for determine optimal rule curve for flood control. This study consider future inflow and uncertainty of flood hydrograph. There are three modules: prerelease module, refill operation module and risk analysis module. The result from dynamic programming provide the better benefit. It can improve hydropower generation and reduce flood in downstream area.
Seahati, (2010) presents that the hydrological model can distribute the water release. The aim of this study is focused on the minimize downstream flood volume and minimize the difference between the simulated and threshold discharge by develop rule curve.
Mediero, (2010) presents that the flood control operation should operate base on three principles. “(1) the release must be less than the inflow discharge when the inflow is increasing, (2) if the inflow discharge increases, then the outflow discharge must increase consequently, (3) the higher the reservoir level, the greater should be the increment of the outflow discharge.”
5
The researcher who is Abrishamchi, (2011) study the optimization multi objective to minimize flood and maximize hydropower generation. The rule curve that was developed can provide the better benefits. It can use to operation of reservoir flood control system.
Talukdar, (2011) researched the rule curve that focus on minimize flood in downstream area and to maximize conservation zone after ending flood season. The result showed a single multipurpose reservoir performs better.
Karbowskki, (2011) develop the rule curve. It can reduce the peak of water release during the flood. Therefore, it can minimize flood in downstream area.
Tebakari, (2012) presents that the Chao Phraya river basin had many flood occur in downstream area. The develop rule curve should use in this problem because it will reduce the flood volume in downstream area of Chao Phraya river basin.
2.2 Optimization Reservoir Operation (Optimization techniques)
There are several techniques for optimize reservoir operation such as Linear programing, Non-Linear programing, Dynamic programing, Genetic algorithm, Ant Colony Algorithm, and Harmony Search Algorithm. It is used to improve the efficiency of reservoir operation in single and multiple reservoir.
The optimization techniques can device in two main types. First, the implicit stochastic optimization such as Linear programing, Non-Linear programing, and Dynamic programing. Second, the Explicit stochastic optimization such as Genetic algorithm, Ant Colony Algorithm, and Harmony Search Algorithm. Labadies, (2004) presents that “The optimization techniques for multi reservoir system rather than the single reservoir system. The difficulty in managing multi-reservoir system is complicated due the conflict in objectives and the uncertainty in future hydrologic condition including the possible climate change”.
2.2.1 The Implicit Stochastic Optimization procedure
The main parameter in Implicit Stochastic is water inflow of reservoir. This technique can used to determine the optimal release and storage. It shows the downstream effect when operate reservoir base on current rule curve (historical inflows to the reservoirs). The main disadvantage of this method show that multiple linear regressions may provide the low correlation which can disprove the operating rules.
6
- Linear programming (LP)
Yeh, (1985) summarized linear programming for reservoir management and operation models. This programming can adapt to use in high dimensional, universal optimal are obtained, no need for initial solution and it can be applied in standard computer.
Mhanis, (2001) presents that the Linear programing can used in maximize benefit and minimize loss by linear function and linear constraints. This method is easy to find the objectives functions and constrain. It can solve the large scale data and easy to develop.
- Non-Linear programming (LP)
The Non-Linear programming can analyze relationships between different variables. It can solve more complex than Linear programming. However, it difficult to develop more than Linear programming also.
- Dynamic programming (DP)
Butcher, (1971) shows that the Dynamic programming can solve problem in multipurpose single reservoir. The historical data such as inflow, and stream flow are necessary. The result provide the better benefit.
Stedinger, (1984) presents that the dynamic programming can derive the objective functions. The historical data such as inflow is necessary. This study can expected benefits from future operations. The study area is a dam at Aswan in the Nile River Basin, operators of other reservoir systems also have available to them information other than the preceding period's inflow which can be used to develop improved inflow forecasts.
Kumar, (2009) presents that Dynamic Programming can use to develop the optimal reservoir operation for flood control in Hirakud Reservoir, India. It can solve problem in multipurposes. The result from this programing can release the flood volume in downstream area.
2.2.2 The Explicit Stochastic Optimization procedure
“The explicit stochastic optimization technique is more computationally problematic when applied to multiple reservoir systems compare with the implicit stochastic” (Labadie, J. W. 2004).
- Genetic Algorithm
Wardlaw, (1999) briefly that the main process in Genetic Algorithm including selection, crossover, and mutation. The robust is used in this algorithm. The multiple reservoir operation (four reservoirs) can be solve. It is easily to solve the complex problems. The result provides the better benefits when compare with the current situation.
7
Kumar, (2006) presents that genetic algorithm (GA) model is used to determine optimal operating policy and optimal crop water allocations from an irrigation reservoir. The objective function is to maximize the crop yield by determine the optimal water supply. The important historical data are inflow, rainfall, and water demand. The GA is similar to that obtained by linear programming. This model can be used for optimal utilization of the available water resources of any reservoir system to obtain maximum benefits.
Sharif, (2000) A genetic algorithm approach is presented for the optimization of multi reservoir systems. The approach is demonstrated through application to a reservoir system in Indonesia by considering the existing development situation in the basin and two future water resource development scenarios. A generic genetic algorithm model for the optimization of reservoir systems has been developed that is easily transportable to any reservoir system. This generality is a distinct practical advantage of the genetic algorithm approach. A comparison of the genetic algorithm results with those produced by discrete differential dynamic programming is also presented. For each case considered in this study, the genetic algorithm results are very close to the optimum, and the technique appears to be robust. Contrary to methods based on dynamic programming, discretization of state variables is not required. Model sensitivity and generalizations that can be drawn from this and earlier work by Wardlaw and Sharif are also considered.
8
- Harmony Search Algorithm
Geem, Z. (2007) who is the first researcher to use harmony search (HS) algorithm optimal reservoir operation. This study shows that this algorithm can solve the multipurpose. The process use less time than Genetic Algorithm.
Geem, Z. (2009) The harmony search (HS) algorithm has gained attention for their abilities to solve large-scale, difficult combinatorial optimization problems. Four specific optimization problems can consider in this technique: design of water distribution networks, scheduling of multi-location dams, parameter calibration of environmental models, and determination of ecological reserve location. The computational performance of the HS method on solving these four optimization problems will be compared against other optimization methods. The HS method can outperform other methods in terms of solution quality and computational time. In table below this sentence showed the number of evaluation from each algorithm, the minimum number of evaluations is 1,121 from Harmony search.
Hamid, (2015) presents that to optimal reservoir operation for flood control, the Harmony Search Algorithm can use to solve problem. It can minimize the water deficit and flood volume in downstream area. It can gain good efficiency in reservoir operation.
Algorithms Minimal Cost Number of Evaluations Genetic Algorithm 419,000 7,467 Simulated Annealing 419,000 >25,000 Shuffled Frog Leaping 419,000 11,155 Cross Entropy 419,000 35,000 Scatter Search 419,000 3,215 Harmony Search 419,000 1,121 (Source: Musician’s behavior-inspired harmony search (HS) algorithm)
9
Table 2.1-1 Summary of advantages and disadvantages of optimization techniques
Techniques Advantage Disadvantages Implicit stochastic optimization Linear programing - It is capable of solving large - It is suitable for solving the scale problem efficiency problem in which the relation - Convergence to global of variables in objective optimal solution function and constraint are - No need for initial solution linear only. - It is well-developed duality - It does not take into account for sensitivity analysis the stochastic inflows. - It is easiness of problem set up and solution -It can be applied in standard computer Non-linear programing - It can handle with a - It cannot explain the combined objective function stochastic nature likewise and non-linear constraints dynamic programing and more complexity than linear programing - It is more complicate than linear programing and consumes a lot of computation time than dynamic programing. Dynamic programing - It can take non-linear and - It is restricted by the curse of stochastic inflow into consider dimensionality - It is able to deal with the complex system by dividing the large problem into the stage. Explicit stochastic optimization Genetic algorithm - It can find fit solutions in a - It is really hard for people to less time. (fit solutions are come up with a good heuristic solutions which are good which actually reflects what we according to the defined want the algorithm to do. heuristic) - It is also hard to choose - The random mutation parameters like number of guarantees to some extent that generations, population size we see a wide range of etc. When we are working even solutions. though our heuristic was right - Coding them is really easy we were not realizing it compared to other algorithms because we were running for a which does the same job. fewer Harmony search algorithm - It can find fit solutions in a - It is also hard to choose very less time. parameters like number of - It needs fewer mathematical generations, population size requirements and does not etc. need an initial value to be set Some step, it is difficult to for the decision variables development (Source: Bhumibol reservoir performance analysis and optimal reservoir operation for flood control)
10
2.3 Climate Change
Climate change refers to general changes in climate patterns, including temperature, precipitation, winds, and other factors. Nasa, (2018) currentthat “since the late 19th century, the average temperature has risen approximately 1.62 degrees Fahrenheit (0.9 degrees Celsius), a change driven largely by increased carbon dioxide and other human- made emissions into the atmosphere. Most of the warming occurred in the past 35 years, with the five warmest years on record taking place since 2010. Not only was 2016 the warmest year on record, but eight of the 12 months that make up the year — from January through September, with the exception of June — were the warmest on record for those respective months”.
Masood, (2016) presents that the future precipitation may expect to increase. The study use the super-high-resolution MRI-AGCM3.2S with A1B scenario through three time-slice experiments; the base-period (1979–2003), the near-future (2015–2039), and the far-future (2075–2099) periods to assess the impact of climate change. This study investigates the spatio-temporal changes and the changes in the frequency of precipitation and runoff with different magnitude ranges and finds the implications for water resources management under climate change.
2.3.1 Climate scenarios
Global Circulation Modes (GCMs) and Regional Climate Models (RCMs) are mathematical models that recurrentphysical processes in the atmosphere, ocean, cryosphere and land surface and is used to simulate time series of climate variables in a large scale. The effects of physical parameters like GHGs, aerosol concentration, land use change, technology, population growth and soar constants etc. are used to simulate the change in climatic variables like temperatures, precipitation, humidity, wind speed etc.
- RCP Scenarios
The Representative Concentration Pathways (RCPs) are the new set of scenarios used for the new climate model simulations. The scenarios were carried out under framework of the Coupled Model Inter comparison Project Phase5 (CMIP5) of World Climate Research Programme. In case of RCP, Projections are based on radiative forcing components (Moss et al., 2010). Intergovernmental Panel on Climate Change (IPCC) in 2007 publish 4 sets of scenarios called Representative Concentration Pathways (RCP); selected, defined and names according to their total radiative forcing in 2100
11
Table 2.3.1-1 Intergovernmental Panel on Climate Change (IPCC) in 2007 publish 4 sets of scenarios called Representative Concentration Pathways (RCP)
RCP Description Temp Anomaly CO2 RCP8.5 Rising radiative forcing pathway 4.9˚c 1370 ppm leading to 8.5 W/m2 in 2100 RCP6.0 Stabilizing without overshoot 3.0˚c 850 ppm pathway to 6 W/m2 at Stabilizing after 2100 RCP4.5 Stabilizing without overshoot 2.4˚c 650 ppm pathway to 4.5 W/m2 at Stabilizing before 2100 RCP2.6 Peak in radiative forcing at 3 1.5˚c 490 ppm then W/m2 at Stabilizing before 2100 decline and reaches W/m2 by 2100 (Source: IPCC, 2014)
2.3.2 Bias correction to RCM data
Biases refer to the relative error or difference currentin the RCM simulated climate variable with respect to the observed dataset at local level. These errors may occur due to flawed model representation (Maraun, 2012) inappropriate adjustment of the model and incomplete observed data for model parameterization and validation (Ehret et al., 2012). The currentin the RCMs should be corrected before using it in analysis of climate change and impact studies at local scale (Salzmann et al, 2007). Many researchers have used linear scaling method for bias correction of both temperature and precipitation (Lenderink et al, 2007). Bias correction with linear scaling method tries to perfectly match the monthly mean of corrected values with the observed values.
2.4 Hydrology Models
The Hydrologic Modeling System is developed to simulate the precipitation-runoff processes of dendritic watershed systems. It is designed to be applicable in a wide range of geographic areas for solving the widest possible range of problems. This includes large river basin water supply and flood hydrology, and small urban or natural watershed runoff. Hydrographs produced be the program are used directly or in conjunction with other software for studies of water availability, urban drainage, flow forecasting, future urbanization impact, reservoir spillway design, flood damage reduction, floodplain regulation, and systems operation. (U.S. Army Corps of Engineers, 2017).
2.4.1 HEC-GeoHMS
The Geospatial Hydrologic model extension is a tool that runs on the ArcGIS platform that enable the users to prepare the inputs for Hydrologic Engineering Center’s Hydrologic Modeling System (HEC-HMS) such as delineating the sub basins and streamlines. The terrain preprocessing function offers several options such as DEM Reconditioning, filling sinks of the DEM, calculating the flow direction, flow accumulation, defining the stream, stream segmentation, catchment grid delineation, catchment polygon processing, drainage line processing and adjoin catchment processing (HEC-GeoHMS, 2013). Even though these steps can be done using GIS tools too, HEC-GeoHMS provides a user-friendly
12 interface by providing the steps in order depending on the requirement of the user. In addition to these, it also given functions for merging sub-basins the river lengths, river slopes, basin slops, longest flow paths and the centroids of each individual sub-basin (HEC-GeoHMS).
2.4.2 Hydrologic Engineering Centers Hydralogic Modeling System (HEC-HMS)
HEC-HMS is a hydrologic model which is capable of performing simulations of precipitation runoff of watersheds. In order to compute components such as evapotranspiration, rainfall, infiltration and runoff. HEC-HMS used a deterministic mathematical model. This model is quite comprehensively applicable for broad range of geographic areas to cope with problems such as large river basin water supply and flood hydrology, and small urban or natural watershed runoff. HEC-HMS includes four models for representing the runoff of the watershed (HEC-HMS, 2010).
In order to transform the excess rainfall into runoff, seven methods are available. They are SCS, Clark or Snyde unit Hydrographs, Kinematic Wave, ModClarrk and Useer Specified Unite Hydrograph. For simulating the flow in open channels, Muskingum, Kinematic Wave, lag. Modified Puls, Muskingum-Cunge and Stagger methods are available as routing methods (HEC-HMS, 2010).
2.4.3 Soil and Water Analysis Tool (SWAT)
Soil and Water Analysis Tool (SWAT) was developed by the United State Department of Agriculture – Agricultural Research Service (USDA-ARS) and Texas A&M AgriLife Research. It is a process-based continuous hydrological model. The SWAT model predicts the impact of land management practices on water, sediment and agricultural chemical yields (Arnold et al., & Srinivasan et al. 1998). This model includes different components as: climate, hydrology, erosion, soil temperature, plant growth, nutrients, pesticides, land management, channel and reservoir routing.
SWAT is a simulation catchment runoff based on estimates of daily rainfall and the topography, soils and land cover of each sub-basin. The SWAT software has the capability to investigate nutrient and sediment flows also, but there are insufficient data to establish calibrated models for these at present. Within the Lower Mekong Basin (LMB), the catchment is divided into different sub-basins to simulate this runoff according to hydrological condition and its reference station.
Bharati et al. (2014) used SWAT to assess the impact of climate change on the Koshi river basin of Nepal for the years 2030-2050, compare past spatiotemporal variations in climate variable and compare them against future projections for the Koshi river basin.
13
CHAPTER 3 STUDY AREA AND DATA COLLECTION
3.1 Wang River Basin
Wang River Basin locates in North of Thailand. The catchment area is approximately 10,791 square kilometers. The river length is approximately 460 km from the mountains. This is the shortest tributary of the Chao Phraya River. The river originates at Doi Luang, Ban Pa Wang, Phan district, Chiang Rai province. Upper part of the basin is surrounded by mountains. The middle part of the basin is a lowland and hills in capital of Lampang. The lower part is relatively flat. The river will meet the Ping river at Tak province. The basin covers two provinces including Lampang province and Tak province (In Lampang, exclude Ngao sub-district, Mae Tam sub-district, Wiangmai district and Thoen district) and (In Tak Province, exclude Sam Ngao district and Ban Tak district). The total population in the basin is approximately 767,816 people in 2003. The agricultural area is approximately 4,800,000 km2 (17.5 percent of the basin). The average income is 22,625 baht/person/year. The average annual maximum temperature is 41.6 degrees Celsius and the average annual minimum temperature is 10.5 degrees Celsius. The average annual rainfall is 1,105 mm. The average annual discharge is 1,582 million m3. The water demand is expected to increase up to 1,149 million m3 in 2024.
Figure 3.1-1 Reservoir storage space (Source: Water Engineering and Management, school of Engineering and Technology, Asian Institute of technology, Thailand.)
14
Study area
Kiew Kor Mah reservoir
Irrigation area
Figure 3.1-2 Wang River Basin in Thailand and location of Kiew Kor Mah Reservoir. (Source: Royal Irrigation Department (RID))
15
Inflow to Kiew Kor Mah Dam
Mae Moh H Kiew Kor Mah Dam Powerplant
Domestic Mae Soi River D Jaehom district Irrigation Area of Kiew Kor Mah Dam I (35km2)
Water Demand at D Downstream
SYMBOL END Inflow/Side flow
Reservoir
D Domestic use
I Irrigation area River
Figure 3.1-3 Schematic of Kiew Kor Mah Reservoir. (Source: Royal Irrigation Department (RID))
16
3.1.1 Climate and Hydrological Condition
The record of weather station in Wang river are recorded by Thai Meteorological Department (TMD) including Lampang station, Phayao station, Chiang Mai station, Phrae station and Tak station. The result of weather stations are presented on table 3.1.1-1
Table 3.1.1-1 climate validation from weather stations of Wang river.
Weather Stations Climate Validation Average Range Maximum Minimum (per year) (per month) average average (per month) (per month) Muaeng district Temperature ( ) 26.3 21.5 (Dec) – 29.9 (Apr) 38.3 (Apr) 14.9 (Jan) station Relative humidity ( ) 72.83 56.0 (Mar) – 83.0 (Sep) 96.0 (Sep) 29.0 (Mar) Lampang province Evaporation from pan (mm) 1,461.0 82.0 (Dec) – 178.0 (Apr) - - Cloudiness (0-10 oktas) 5.8 2.0 (Mar) – 8.0 (Jun) - - Wind speed (Knots) 0.8 0.4 (Oct) – 1.3 (Jul) - - Rainfall (mm) 1,717.5 101.9 (Dec) – 190.3 (Apr) - - Muaeng district Temperature ( ) 25.1 19.8 (Dec) – 28.6 (Apr) 36.0 (Apr) 13.6 (Jan) station Relative humidity ( ) 75.4 58.0 (Mar) – 84.0 (Sep) 96.0 (Sep) 31.0 (Mar) Phayao province Evaporation from pan (mm) 1,429.0 85.0 (Dec) – 174.0 (Apr) - - Cloudiness (0-10 oktas) 5.2 2.0 (Jan) – 8.0 (Jun) - - Wind speed (Knots) 0.9 0.4 (Oct) – 1.4 (Apr) - - Rainfall (mm) 1,645.3 93.7 (Dec) – 185.6 (Apr) - - Muaeng district Temperature ( ) 25.8 21.4 (Dec) – 29.2 (Apr) 36.5 (Apr) 14.8 (Jan) station Relative humidity ( ) 70.8 52.0 (Mar) – 81.0 (Sep) 94.0 (Sep) 29.0 (Apr) Chiang Mai Evaporation from pan (mm) 1,613.0 97.0 (Dec) – 187.0 (Mar) - - province Cloudiness (0-10 oktas) 4.8 2.0 (Jan) – 8.0 (Jun) - - Wind speed (Knots) 2.6 1.4 (Jan) – 3.6 (Apr) - - Rainfall (mm) 1,810.5 122.6 (Dec) – 176.5 (Apr) - - Muaeng district Temperature ( ) 26.4 21.8 (Dec) – 30.0 (Apr) 37.6 (Apr) 15.6 (Jan) station Relative humidity ( ) 75.6 61.0 (Mar) – 84.0 (Sep) 95.0 (Sep) 36.0 (Mar) Phrae province Evaporation from pan (mm) 1,614.0 96.0 (Dec) – 195.0 (Mar) - - Cloudiness (0-10 oktas) 5.3 3.0 (Jan) – 8.0 (Jun) 48.0 (Apr) - Wind speed (Knots) 1.2 0.6 (Jan) – 2.0 (Apr) - - Rainfall (mm) 1,715.9 103.3 (Dec) – 193.5 (Apr) - - Muaeng district Temperature ( ) 27.5 22.9 (Dec) – 31.5 (Apr) 38.6 (Apr) 16.8 (Dec) station Relative humidity ( ) 68.6 49.0 (Mar) – 82.0 (Oct) 94.0 (Oct) 30.0 (Mar) Tak province Evaporation from pan (mm) 1,815.0 100.0 (Nov) – 241.0 (Mar) - - Cloudiness (0-10 oktas) 5.2 2.0 (Jan) – 8.0 (Jun) - - Wind speed (Knots) 1.5 0.3 (Oct) – 2.7 (Aug) - - Rainfall (mm) 1837.2 110.5 (Dec) – 209.3 (Apr) - - (Source: Royal Irrigation Department (RID))
17
3.1.2 Reference Crop Evapotranspiration; ETo
The analysis of the Reference Crop Evapotranspiration by the Modified Penman method was based on the average monthly weather data of the Lampang Weather Station. The lowest values were 101.9 mm in December and maximum of 190.3 mm in April. The average annual was 1,717.5 mm.
Table 3.1.2-1 Average Monthly Reference Crop Evapotranspiration in Wang River Basin.
Reference Crop Evapotranspiration (mm) Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Annual 190.3 180.3 148.9 151.2 143.7 139.6 134.0 114.6 101.9 111.5 126.5 174.8 1,717.5 (Source: Royal Irrigation Department (RID))
3.1.3 Evaporation rate of reservoir
The analysis of the evaporation rate from the reservoir. The data from Meteorological Department Thailand during 2009-2017 is used.
Table 3.1.3-1 Evaporation rate from in Wang River Basin.
Evaporation rate from (mm/day) Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar 6.813 5.681 5.193 4.768 4.297 3.893 4.006 4.553 4.777 4.929 5.979 6.832 (Source: Royal Irrigation Department (RID))
18
3.2 Kiew Koh Mah dam
Table 3.2-1 The detail of Kiew Kor Mah dam
Kiew Kor Mah Country Thailand Location Pong Don Sub-district, Muang District, Lampang Main Dam Type Zoned Type Crest width 8 m. Crest length 500 m. Dam height 43.50 m. Saddle Dam Type Homogeneous Type Crest width 6 m. Crest length 300 m. Dam height 15.40 m. Reservoir Watershed area 1,275 km2 Average annual rainfall 1039 MCM Average annual inflow 265 MCM Maximum water level +352.90 MSL Retention water level +350.20 MSL Minimum water level +325.00 MSL Maximum storage capacity 208.60 MCM Normal storage capacity 170 MCM Minimum storage capacity 6.20 MCM Water available 163.80 MCM Surface area in maximum water level 15.36 km2 Surface area in retention water level 12.68 km2 Surface area in minimum water level 1.70 km2 Service spillway Type of gate Radial Gate Spillway Number of gate 3 gates Size of gate 12.50 x 7.00 m. Maximum discharge 1,209 m3/s Hydropower Power generation 5.4 MW Number of power generation 3 (2x2.5 MW and 1x0.5 MW) (Source: Royal Irrigation Department (RID))
19
) ) Top view of Kiew Kor Mah Kor Dam view 1 Top Kiew of Source:t Irrigation (RID Royal Departmen ( Figure 3 .2 -
20
3.3 Data Collection for optimization
There are five groups of data collection as below. The historical data from 2009 to 2017 is used to optimization and simulation model.
3.3.1 Inflow reservoir
The inflow reservoir is usually measured at hydro climatic station located on Wang river.
3.3.2 Outflow reservoir
The outflow of Kiew Kor Mah reservoir are provided by Royal Irrigation Department of Thailand, the data of Kiew Kor Mah is collected in daily discharge.
3.3.3 Downstream demand for irrigation of Kiew Kor Mah reservoir
The demand for irrigation sector of Wang River Basin is collected to study on the impact of applying reservoir operation on irrigation sector.
3.3.4 Physical characteristics of reservoirs
Physical characteristics including the types of dam, ancillary facilities such as controlled spillway, uncontrolled spillway, capacity of storage zone, inflow and catchment area as shown in table 3.2-1.
3.3.5 Discharge and water level at downstream station
The discharge and water level at the downstream control point is collected in daily to analyze Wang river capacity at downstream.
3.4 Data Collection for climate projection
3.4.1 Precipitation data
The precipitation data is collected by Meteorological Department of Thailand. There are 3 precipitation stations in this area. The data from 1979 until 2005 is used in RCMs models.
3.4.2 Temperature data
The temperature data is collected by Meteorological Department of Thailand. There are 3 temperature stations in this area. The data from 1979 until 2005 is used in RCMs models.
21
CHAPTER 4 METHODOLOGY
4.1 Conceptual framework This chapter showed the optimization and simulation approach in Kiew Kor Mah Reservoir located in Wang River basin. The aim of this study is to improve the reservoirs operation on water management in Wang river basin, Thailand by using Harmony Search Algorithm. The overall framework is summarized in figure 4.1-1 and the procedure of Harmony Search Algorithm showed in Figure 4.2-1
22
Objective2: To project the future climate of the study area using three RCMs under two scenarios RCP 4.5 RCM data and RCP 8.5 Bias correction of RCM
Temperature and precipitation in future Objective3: To project the future inflows of Kiew Kor Mah reservoirs. Objective1: To optimize the reservoir operation policies for flood control and HEC-HMS model irrigation of the Kiew Kor Mah reservoir. Inflow reservoir in future (2020-2050) Future water demand in future (2020-2050)
Existing data from Trend to change of 2009 to 2017 Future water demand
Domestic demand and Domestic demand and Irrigation Irrigation in future
Inflow and side flow and Inflow and side flow and Outflow reservoirs Outflow reservoirs in future
Maximum capacity and Maximum capacity and water level at D/S station. water level at D/S station.
Optimization reservoir operation for flood control and irrigation - Formulation of mathematical model
The optimal reservoir operation by Harmony search algorithm
Analysis the result
Figure 4.1-1 Overall research methodology
23
4.2 Harmony search algorithm
Figure 4.2-1 The steps in the HS algorithm (Source: Application of Harmony Search Algorithm to Reservoir Operation Optimization)
24
Figure 4.2-2 The procedure in the HS algorithm (Source: Application of Harmony Search Algorithm to Reservoir Operation Optimization)
25
Harmony search is a relatively new heuristic optimization algorithm inspired music and was first developed by Geem, 2001. This algorithm can be explained more in detail with the process of improvisation that takes a musician, which consists of three options: Play any song you have in your memory Play a similar composition to an existing Play a new song or randomly
4.2.1 Example of Harmony Search
2 4 2 Objective function Min f(x) = (x1-2) +(x2-3) +(x3-1) +3 Eq.4.2.1-1
Solution vector = (2, 3, 1)
Initial harmony memory
Next harmony memory
Select harmony memory
1 4 2
(Source: Transactions of The Society for Modeling and Simulation International )
26
4.2.2 The objective and constraint of Kiew Kor Mah reservoir operation
The objective function in this study is set as equation 4.2.2-1 and the constraints are shown from equation 4.2.2-2 and 4.2.2-9 respectively. The reservoir storage and reservoir release constraint as mentioned before are applied to every time step of simulation period.
- Objective function
f(x) = Min(f1(x)+ f2(x)) Eq.4.2.2-1 n f1(x)= Min(∑ spillt ) t=1 n f2 (x)= Min(∑ deficitt ) t=1
- Constraint
Figure 4.2.2-1: Release zone of reservoir (Source: Develop of Reservoir Operating Rule Curve for Mun Bon -Lam Chae Reservoir.)
Smin ≤ St ≤ Smax Eq.4.2.2-2 Rmin ≤ Rt≤ Rmax Eq.4.2.2-3 St+1 = S,t+It-Rt-Et Eq.4.2.2-4
Case1: St+1 > Smax Rt = Dt + Spillt Eq.4.2.2-5
Case2: SURC < St+1 < Smax
()SSt+1− min ()MaxRt− MaxSF tt ≥= R 2Dt Eq.4.2.2-6 ()SSURC − min
Case3: SLRC < St+1 < SURC Rt = Dt Eq.4.2.2-7
27
Case4: Smin< St+1 < SLRC
()SSDtt+1− min Entt≤= R Eq.4.2.2-8 (SSLRC − min )2 Case5: St+1 < Smin Rt = 0 Eq.4.2.2-9
Where, Deficitt = deficit at time t Spillt = spill at time t Dt = water demand at time t Rt = reservoir release at time t Smin = minimum water storage = Dead storage Smax = maximum water storage = Normal storage St = water storage at time t It = inflow to reservoir Et = evaporation of reservoir t = time of simulation MaxRt = Maximum capacity MaxSFt = Maximum side flow MaxRt = Maximum capacity Ent = Environment flow demand
4.3 Future climate projection
Future climate in the basin was projected on the basis of three selected RCMs: ACCESS1- CSIRO-CCAM, CNRM-CM5-CSIRO-CCAM, and MPI-ESM-LR-CSIRO-CCAM. The three future periods under study are the 2020s (2010–2039), 2050s (2040–2069), and 2080s (2070–2099) under two Representative Concentration Pathways (RCPs), namely RCP4.5 and RCP8.5 scenarios contained in the IPCC Fifth Assessment Report (AR5), based on observed climate data from the TMD from 1979–2005. The Linear Scaling Method was used for bias correction in this study.
4.3.1 Selection of RCM
For this research RCM were selected based on certain criteria. Smith & Hulme (1998) have suggested few criteria to select suitable RCM for the study area. They are Vintage, Resolution, Validity and Representativeness of the result.
4.3.2 Bias correction of RCM data
Bias correction method is used to despite the advancement in GCMs and RCMs to simulation the future climate, systematic errors or biases can still be seen. Bias is the correspondence between a mean forecast and mean observation average over a certain domain and time (WMO, 2009). In other words, the difference between the estimated average and the true parameter value is called bias. Sometimes these biases can be huge enough to create substantial problems during climate change impact studies
For the bias correction of the output form RCM, observed time series of monthly rainfall and temperature for past and future are required.
28
4.3.3 Linear Scaling Method of Bias Correction
Linear scaling approach operates with monthly scaling values based on the differences between observed and historical run values. It can correct biases in the mean but not the intensity and wet day frequencies. The precipitation is corrected with a scaling factor, a ratio of monthly mean observed and historical run. In case of temperature, it is corrected with adding the scaling value that is a difference between long-term monthly observed temperature and climate model’s historical run.
For precipitation
* µm(P obs (d )) Phis= Pd his () Eq.4.3.3-1 µm(P his (d ))
* µm(P obs (d )) Pfut= Pd fut () Eq.4.3.3-2 µm(P his (d ))
For temperature * This=+− Td his ( )µµm (T obs ( d ))m ( Td his ( )) Eq.4.3.3-3
* Tfut=+− Td fut ( )µµm (T obs ( d ))m ( Td his ( )) Eq.4.3.3-4
4.3.4 Performance evaluation of bias correction
The study uses three methods of statistical analysis to evaluate the performance of bias correction: the coefficient of determination (R2), standard deviation (σ), and root mean square error (RMSE).
- Coefficient of determination (R2)
The coefficient of determination (R2) was used in trend analysis to currenta value between 0 and 1. R2 represents the percentage of data closest to the line of best fit. The higher the value, the better the fit. R2 is the ratio between the explained and total variations. R2 is a function relative to the correlation coefficient (r).
2 (x−− xy )( y ) 2 = ∑ r 22 Eq.4.3.4-1 ∑(xx−− )( yy )
Where; r = the correlation coefficient x = the value of x x̅ = the mean of the x value y = the value of y y̅ = the mean of the y value
29
- Standard deviation ( σ )
The standard deviation was used to quantify the amount of variation or dispersion in the values of a dataset.
n 1 2 σ =∑()XXi − Eq.4.3.4-2 n i=1
Where; σ = the standard deviation Xi = the value X̅ = the mean of the value n = the number of value
- Root mean square error (RMSE)
The RMSE was used to compare the performance of two or more models when used for the same data set.
n 1 2 RMSE =∑() Xii − Y Eq.4.3.4-3 n i=1
Where; Xi = the value of X Yi = the value of Y n = the number of value
4.4 Hydrological model
4.4.1 Model set-up
HEC-HMS model was built up based on the available geospatial data (DEM, land use and soil maps), the hydro-meteorological data (Time series of precipitation and temperature, discharge and the detail of Wang river basin).
- Process of Rainfall-runoff model (HEC-HMS)
DEM Basin Delineation using HEC-GeoHMS Land use data Hydrological Model
Soil map HEC-HMS
Rainfall data Calibration and Validation
Discharge (Hydrograph)
Figure 4.4.1-1 Flowchart of Hydrologic Model (HEC-HMS)
30
- Process of Digital Elevation Model (DEM)
Channel survey map Streams Topo to Raster Contour line Topographic map
Elevation points DEM 1. Georeferencing WS boundary 2. Digitizing
Figure 4.4.1-2 Flowchart of Digital Elevation Model (DEM)
4.4.2 Model performance evaluation of HEC-HMS
Model calibration and validation should include multiple statistical evaluation criteria, considering that the single statistical metrics only evaluates a specific part of model performance (Moriasi et al., 2007). The HEC-HMS model performance was evaluated through graphical comparison and by concurrently using three statistical criteria for goodness-of-fit, coefficient of determination (R2), Nash-Sutcliffe Efficiency (NSE), Percent Bias (PBIAS) and Root Mean Square Value as described below.
- Coefficient of determination (R2) Coefficient of determination (R2) give the linear relationship between simulated and observed data. Its value shows in the range 0 ≤ ≤ 1. The higher the value, the better the fit. R2 is the ratio between the explained and total variations. R2 is a function relative to the correlation coefficient (r).
2 (x−− xy )( y ) 2 = ∑ r 22 Eq.4.3.4-1 ∑(xx−− )( yy )
Where; r = the correlation coefficient x = the value of x x̅ = the mean of the x value y = the value of y y̅ = the mean of the y value
31
- Nash-Sutcliffe Efficiency (NSE)
The NSE test is used to investigate the predictive power of the hydrological model. NSE indicates how the plot of observed and simulated data fits the 1:1 line. The optimal value of NSE is 1.
n obs sim 2 ∑(Yii− Y ) i=1 NSE =1 − n Eq.4.4.2-2 obs mean 2 ∑(Yii− Y ) i=1
Where, obs Yi = the observed data sim Yi = the simulated data i = the mean of observed data n =𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚 the total number of observations 𝑌𝑌 - Percent Bias (PBIAS) Percent bias (PBIAS) is the average tendency of the simulated values to be larger or smaller than observed value. A value of PBIAS is 0, meaning higher accurate simulated model. Positive value means model overestimation bias. However, negative value means underestimation bias.
n obs sim ∑(YYii− )*100 i=1 PBIAS = n Eq.4.4.2-3 Y obs ∑ i i=1
Where, Yiobs = the observed data Yisim = the simulated data
Table 4.4.2-1 Reported performance rating for R2, NSE and, PBIAS of HEC-HMS model.
Performance rating R2 NSE PBIAS Very good 0.85–1.00 0.75– 1.00 < 10% Good 0.70–0.85 0.65–0.75 10%–15% Satisfactory 0.60–0.70 0.50–0.65 15%–25% Unsatisfactory < 0.60 < 0.50 > 25% (Source: Moriasi et al., 2007)
32
CHAPTER 5 RESULTS AND DISCUSSIONS
5.1 Historical data analysis of Kiew Kor Mah reservoir
5.1.1 The historical data of Kiew Kor Mah reservoir
In order to understand and analysis the capacity of Kiew Kor Mah reservoir in terms of reservoir performance respect to various purpose. The historical Kiew Kor Mah reservoir data from 2009 to 2017 is collected. Figure 5.1.1-1 is shown that the monthly inflow to Kiew Kor Mah reservoir. The maximum average monthly inflow approximately 150 MCM in september 2011. The reservoir inflow start to increase from July to September. Therefore, the Kiew Kor Mah reservoir inflow is divided into two seasons including dry season and wet season. The dry season is on November to april and wet season is on May to October. During dry season, the main purpose of Kiew Kor Mah reservoir is release water for irrigation. In wet season, the Kiew Kor Mah reservoir is collect water as much as possible for next dry season and protecting downstream flood.
Figure 5.1.1-1 The monthly inflow of Kiew Kor Mah reservoir from 2009 to 2017. (Source: Royal Irrigation Department (RID))
The monthly inflow of Kiew Kor Mah reservoir is showed in table 5.1.1-1. The table shows that the maximum annual inflow was 490.74 MCM in 2011. Summary of inflow from August to October in many years was more than 160 MCM. The normal storage of Kiew Kor Mah reservoir is 170 MCM. It is showed that the water storage can full in three months. This reservoir was constructed for flood protection and irrigation.
33
Table 5.1.1-1 The annual inflow of Kiew Kor Mah reservoir (MCM).
Month 2009 2010 2011 2012 2013 2014 2015 2016 2017 Average 1 2.98 2.49 2.26 0.35 0.07 2.27 11.08 0.26 11.75 3.72 2 1.52 1.10 1.28 1.78 1.94 4.64 10.83 0.08 2.64 2.87 3 4.89 1.08 4.28 0.00 2.76 2.07 7.57 0.10 2.38 2.79 4 5.23 1.69 9.58 0.00 1.53 6.01 16.19 0.24 1.41 4.65 5 10.38 1.60 68.26 5.29 0.45 3.88 5.40 2.04 17.18 12.72 6 34.67 1.22 24.43 0.93 3.19 3.86 5.32 20.62 21.14 12.82 7 12.09 5.55 34.16 0.00 1.88 11.25 20.88 20.42 63.81 18.89 8 19.38 68.84 152.38 4.26 36.65 27.12 3.98 49.09 43.80 45.05 9 51.01 131.49 117.18 57.10 58.16 63.93 2.51 87.39 72.41 71.24 10 40.00 60.42 65.79 28.71 66.85 14.26 3.04 72.25 122.79 52.68 11 9.54 24.14 9.44 16.92 22.29 11.36 0.41 57.99 26.00 19.79 12 2.50 7.10 1.70 2.94 8.27 4.59 0.36 19.31 5.99 5.86 Sum 194.19 306.72 490.74 118.28 204.03 155.23 87.55 329.79 391.30 253.09 From 110.39 260.74 335.35 90.07 161.65 105.30 9.53 208.74 239.00 168.97 Aug to Oct (Source: Royal Irrigation Department (RID))
The outflow of Kiew Kor Mah reservoir is showed in Figure 5.1.1-2. In dry season, the water release is dicided base on water demand. In wet season, the water release is dicided base on the maximum capacity of downstream and target to storage water in reservoir.The maximum daily outflow was 9.82 MCM.
Figure 5.1.1-2 The monthly outflow of Kiew Kor Mah reservoir from 2009 to 2017. (Source: Royal Irrigation Department (RID))
34
The storage of Kiew Kor Mah reservoir from 2009 to 2017 is showed in Figure 5.1.1-3. There are water spill in October to January. The maximum storage is approximately 200 MCM in December 2013.
Figure 5.1.1-3 The monthly storage of Kiew Kor Mah reservoir from 2009 to 2017. (Source: Royal Irrigation Department (RID))
The monthly water demand from 2009 to 2017 is showed in Figure 5.1.1-4. The maximum average monthly water demand is approximately 30 MCM in October 2011.
Figure 5.1.1-4 The monthly water demand of Kiew Kor Mah reservoir from 2009 to 2017 (Source: Royal Irrigation Department (RID))
35
5.1.2 The result of sensitivity analysis
- Effect of Harmony population size
The effect of population size was determined the number of generation and Bandwitch probability of 2500 and 0.10 respectively. It is show that the population size probability of 20 given the best results of fitness.
- Effect of bandwitch
The effect of bandwitch probability was determined considering the harmony population size and the number of generation of 20 and 2,500 respectively. It is shown that the bandwitch probability of 0.20 given the best result of fitness.
- Effect of the number of generation
It is showed how the fitness value varies with the number of generations. The minimum fitness value is achieved when the number of generation is 2,000.
36
5.2 Operation of the Kiew Kor Mah reservoir under current climate
The current rule curve is showed in Figure 5.2-1. It was used to guideline for reservoir operation during 2012 to 2017. The existing operation is investigated base on existing rule curve. However, In that periods have some constuction in downsteam river. Therefore, sometime the operation can not base on current rule curve.
Month Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec URC Current rule curve 160 145 130 120 110 100 95 90 100 145 169 170 LRC Current rule curve 60 45 35 25 20 15 10 10 20 40 60 80
Figure 5.2-1 Reservoir rule curve (current) of Kiew Kor Mah reservoir (Source: Royal Irrigation Department (RID))
37
The water storage base on current rule curve is showed in figure 5.2-2. This rule curve can lead to have full of water storage. Therefore, the reservoir had water spill. It can lead to flood in downstream area.
Figure 5.2-2 Water storage base on current rule curve of Kiew Kor Mah reservoir (Source: Royal Irrigation Department (RID))
Figure 5.2-3 shows the rule curve, it was developed by HS algorithm base on data from 2009 to 2017. The maximum upper rule curve (URC) approximately 146 MCM in December and the maximum lower rule curve (LRC) occur in June.
Month Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec URC Current rule curve 146 132 102 84 67 71 77 80 81 90 129 146 LRC Current rule curve 71 61 36 21 11 6 11 23 24 34 52 71
Figure 5.2-3 Reservoir rule curve (Modified by Harmony Search Algorithm) of Kiew Kor Mah reservoir
38
The water storage base on modified rule curve is showed in figure 5.2-4. The water storage is less than normal storage. Therefore, the rule curve can protect all of water spill of reservoir.
Figure 5.2-4 Water storage base on Modified Rule Curve of Kiew Kor Mah reservoir in 2009 to 2017. (Source: Royal Irrigation Department of Thailand)
The volume of water spill and water shortage from 2009 to 2014 (calibration) are showed in table 5.2-1. The rule curve can lead to water spill 8,937 MCM, 4,693 MCM, and 0 MCM respectively operation base on current rule curve, existing operation, and modified rule curve. The modified rule curve can gain the best benefit.
Table 5.2-1 Calibration: comparison volume and number of spill and water shortage between Current rule curve, Existing operation and Modified rule curve from 2009 to 2014
Current rule curve Existing Operation Modified rule curve Volume of Spill (MCM) 8,937 4,693 0 Number of Spill (Day/6years) 351 315 0 Volume of Shortage (MCM) 0 258 0 Day of Shortage (Day/6years) 0 906 0
39
The table 5.2-2 is showed that the volume of water spill and water shortage from 2015 to 2017 (Validation). The rule curve can lead to water spill 11,259 MCM, 1,836 MCM, and 0 MCM respectively operation base on current rule curve, existing operation, and modified rule curve. The modified rule curve also provide the best benefit.
Table 5.2-2 Validation: comparison volume and number of spill and water shortage between Current rule curve, Existing operation and Modified rule curve from 2015 to 2017
Current rule curve Existing Operation Modified rule curve Volume of Spill (MCM) 11,259 1,836 0 Number of Spill (Day/3years) 157 191 0 Volume of Shortage (MCM) 0 287 13 Day of Shortage (Day/3years) 0 939 27
The daily maximum of flood volume was 63.92 MCM, 2.32 MCM, and 0 MCM, respectively operation base on current rule curve, existing data, and modified rule curve. Therefore, the modified rule curve can protect flood in Kiew Kor Mah downstream area. (Table 5.2-2)
Table 5.2-3 The daily maximum of flood volume in Kiew Kor Mah downstream area.
Current rule Existing Modified rule curve Operation curve Daily maximum outflow (MCM) 63.92 9.82 6.5 Daily of maximum side flow (MCM) 3.27 3.27 3.27 Maximum capacity of downstream (MCM) 10.77 10.77 10.77 Daily of flood volume (MCM) 67.19 2.32 -
The historical inflow can device in three types including dry year, normal year, and wet year. It is showed in table 5.2-4.
Table 5.2-4 Classification of annual inflow Annual inflow Annual inflow Dry year < 20 % of average annual inflow - Normal year ≤ 20 % of average annual inflow ≥ 80 % of average annual inflow Wet year - > 80 % of average annual inflow (Source: Royal Irrigation Department (RID))
The inflow of Kiew Kor Mah reservoir from 2009 to 2017 can device in three types: - Dry year: 2012, and 2015 - Normal year: 2009, 2010, 2013, 2014, and 2016 - Wet year: 2011, and 2017
40
For dry year, the rule curve is showed in figure 5.2-5. The inflow in 2012 and 2015 are less than 20% of average inflow. Because of less inflow, it should storage water as possible in reservoir.
Month Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec URC Current rule curve 157 139 134 115 111 97 87 91 96 103 139 169 LRC Current rule curve 87 59 57 35 31 26 27 27 26 31 58 96
Figure 5.2-5 Reservoir rule curve of Kiew Kor Mah reservoir for dry year
The volume of water spill and volume of water shortage in Kiew Kor Mah reservoir under dry year is showed in table 5.2-5. When operate base on the rule curve, it can provide all of water spill. However, the water shortage can occur approximately 86.01 MCM.
Table 5.2-5 Volume and number of spill and water shortage under Current rule curve in dry year (2012 and 2015)
Dry year Volume of Spill (MCM) 0 Number of Spill (Day/2years) 0 Volume of Shortage (MCM) 86.01 Day of Shortage (Day/2years) 224
41
For normal year, the rule curve is showed in figure 5.2-6. The inflow in 2009, 2010, 2013, 2014 and 2016 are more than 20% and less than 80% of average inflow.
Month Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec URC Current rule curve 149 128 114 100 86 80 74 78 81 100 148 167 LRC Current rule curve 80 49 35 21 16 11 11 11 21 41 80 90
Figure 5.2-6 Reservoir rule curve of Kiew Kor Mah reservoir for normal year
The volume of water spill and volume of water shortage in Kiew Kor Mah reservoir under normal year is showed in table 5.2-6. The water spill and water shortage are occurred. The daily maximum of outflow is 6.02 MCM. Therefore, it can protect all of flood in downstream area.
Table 5.2-6 Volume and number of spill and water shortage under Current rule curve in dry year (2009, 2010, 2013, 2014 and 2016)
Normal year Volume of Spill (MCM) 54.22 Number of Spill (Day/5years) 54 Volume of Shortage (MCM) 110.93 Day of Shortage (Day/5years) 249
42
Month Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec URC Current rule curve 130 96 83 73 72 68 67 67 63 69 108 129 LRC Current rule curve 60 41 27 20 21 21 18 18 18 18 38 66
Figure 5.2-7 Reservoir rule curve of Kiew Kor Mah reservoir for wet year
The volume of water spill and volume of water shortage in Kiew Kor Mah reservoir under wet year is showed in table 5.2-7. The water spill and water shortage are occurred. The daily maximum of outflow is 4.70 MCM. Therefore, it can protect all of flood in downstream area.
Table 5.2-7 Volume and number of spill and water shortage under Current rule curve in wet year (2011 and 2017)
Dry year Volume of Spill (MCM) 0.42 Number of Spill (Day/2years) 5 Volume of Shortage (MCM) 1.67 Day of Shortage (Day/2years) 6
5.2.1 Concluding Remarks
The current rule curve can lead to maximum volume of water spill (compare with Existing Operation and Modified rule curve. However, the current rule curve can supply water for all of water demand. The existing operation can lead to have volume of water spill and volume of shortage. The modified rule curve can protect water spill and water shortage. (Table 5.2-1 and Table 5.2-2)
In Table 5.2-3 there is flood approximately 67.19 MCM/day, 2.32 MCM/day, and no flooding respectively current rule curve, existing operation, and modified rule curve.
43
5.3 Climate future projection
The results presented in this chapter are based on simulations made with three RCMs model. more RCMs are necessary to address climate model uncertainty (Mishra et al., 2013). This chapter presents the future climate projection in the Wang River Basin, considering only three climatic variables: precipitation, maximum temperature and minimum temperature. The Linear scaling method technique was applied for correcting the data from climate models with observed climate data. The future climate was projected for three future periods: the 2020s (2010–2039), 2050s (2040–2069), and 2080s (2070–2099) under RCP4.5 and RCP8.5 scenarios based on the baseline period (1979–2005).
5.3.1 Bias Correction
The bias correct the RCMs model is use linear scaling method with the observation data provided by the Thai Meteorological Department (TMD). There are three rain gauge and three temperature stations in Wang River Basin. The climate data from 1979 to 2005. The three RCMs are used to project the future climate: ACCESS1-CSIRO-CCAM, CNRM- CM5-CSIRO-CCAM, and MPI-ESM-LR-CSIRO-CCAM
This study uses four RCMs: ACCESS1-CSIRO-CCAM, CNRM-CM5-CSIRO-CCAM, and MPI-ESM-LR-CSIRO-CCAM to project the future climate in the Wang River Basin under climate change scenarios. Figure 5.3.1-1 to 5.3.1-3 shows the average monthly precipitation, average monthly maximum temperature and average monthly minimum temperature in the basin under four RCMs from 1979–2005.
Figure 5.3.1-1 Average monthly precipitation in Wang River Basin from 1979 to 2005 (Source: Thai Meteorological Department (TMD)
44
Figure 5.3.1-2 Average monthly maximum temperature in Wang River Basin from 1979 to 2005 (Source: Thai Meteorological Department (TMD))
Figure 5.3.1-3 Average monthly minimum temperature in Wang River Basin from 1979 to 2005 (Source: Thai Meteorological Department (TMD))
45
Table 5.3.1-1 Performance evaluation of bias correction for precipitation of Wang River Basin
Station RCM R2 Annual Rainfall (mm.) Stdev RMSE His Cor His Obs His Cor His Obs His Cor His His Cor His Wang Nue ACCESS 0.002 0.004 3.166 1.460 3.166 8.758 4.690 9.721 9.895 12.657 CCAM 0.003 0.006 3.166 1.547 3.166 8.758 4.393 9.281 9.717 12.274 MPI 0.005 0.007 3.166 1.520 3.166 8.758 4.206 9.632 9.585 12.473 Wang Nue2 ACCESS 0.003 0.007 3.129 1.460 3.129 8.719 4.690 9.647 9.799 12.461 CCAM 0.003 0.006 3.129 1.547 3.129 8.719 4.393 9.211 9.677 12.188 MPI 0.005 0.008 3.129 1.520 3.129 8.719 4.206 9.585 9.539 12.380 Jae Hom ACCESS 0.005 0.009 2.862 1.494 2.862 7.734 3.632 7.137 8.410 10.009 CCAM 0.003 0.006 2.862 1.511 2.862 7.734 3.869 7.287 8.573 10.192 MPI 0.005 0.009 2.862 1.494 2.862 7.734 3.632 7.137 8.410 10.009
Table 5.3.1-2 Performance evaluation of bias correction for maximum temperature of Wang River Basin
Station RCM R2 Mean of Tmax (°C) Stdev RMSE His cor His Obs His cor His Obs His cor His His cor His Lampang ACCESS 0.137 0.192 33.534 31.985 33.534 3.305 4.112 4.179 4.493 4.037 CCAM 0.142 0.172 33.534 31.678 33.534 3.305 4.235 4.231 4.664 4.152 MPI 0.142 0.184 33.534 31.958 33.534 3.305 4.021 4.096 4.422 4.011 Chiang Mai ACCESS 0.125 0.165 32.131 31.095 32.131 3.093 4.169 4.114 4.347 4.017 CCAM 0.131 0.146 32.131 30.636 32.131 3.093 4.281 4.160 4.533 4.129 MPI 0.147 0.175 32.131 31.018 32.131 3.093 4.087 4.042 4.222 3.931 Pa-Yao ACCESS 0.129 0.159 31.654 31.074 31.654 3.342 4.207 4.197 4.372 4.194 CCAM 0.157 0.165 31.654 30.656 31.654 3.342 4.335 4.251 4.414 4.206 MPI 0.148 0.170 31.654 30.965 31.654 3.342 4.114 4.123 4.242 4.097
Table 5.3.1-3 Performance evaluation of bias correction for minimum temperature of Wang River Basin
Station RCM R2 Mean of Tmin (°C) Stdev RMSE His cor His Obs His cor His Obs His cor His His cor His Lampang ACCESS 0.471 0.541 20.705 20.523 20.705 4.034 4.557 4.312 3.442 3.044 CCAM 0.496 0.552 20.705 20.268 20.705 4.034 4.735 4.354 3.461 3.021 MPI 0.474 0.532 20.705 20.463 20.705 4.034 4.608 4.333 3.459 3.091 Chiang Mai ACCESS 0.447 0.529 20.752 19.682 20.752 3.869 4.579 4.177 3.660 2.986 CCAM 0.456 0.515 20.752 19.405 20.752 3.869 4.793 4.239 3.835 3.065 MPI 0.447 0.513 20.752 19.592 20.752 3.869 4.651 4.214 3.728 3.061 Pa-Yao ACCESS 0.499 0.565 20.215 19.600 20.215 4.418 4.810 4.579 3.608 3.173 CCAM 0.466 0.528 20.215 19.324 20.215 4.418 4.994 4.649 3.888 3.360 MPI 0.482 0.539 20.215 19.517 20.215 4.418 4.933 4.634 3.754 3.306
46
5.3.2 Precipitation Projection
Future annual precipitation in the Wang River Basin under RCP4.5 and RCP8.5 scenarios is decreasing from baseline. The mean annual precipitation was 1114.89 mm from 1979– 2005 and decrease to 1038.74 mm, 1034.57 mm, and 1035.47 mm for the 2020s, 2050s, and 2080s, respectively under the RCP4.5 scenario, and 1104.61 mm, 1098.80 mm, and 1103.48 mm for the 2020s, 2050s, and 2080s, respectively under the RCP8.5 scenario (Table 5.3.2-1).
Table 5.3.2-1 Rainfall projection under RCP4.5 and RCP8.5 scenarios for three future periods. Period Precipitation (mm) RCP 4.5 RCP 8.5 Baseline (1979 – 2005) 1114.89 1114.89 2020s (2010 – 2039) 1038.74 1104.61 2050s (2040 – 2069) 1034.57 1098.80 2080s (2070 – 2099) 1035.47 1103.48
47
(a)
(b)
Figure 5.3.2-1 Projected monthly precipitation in Wang River Basin under (a) RCP4.5 and (b) RCP8.5 scenarios.
48
Figure 5.3.2-2 Projected annual precipitation in Wang River Basin under RCP4.5 and RCP8.5 scenarios.
49
5.3.3 Maximum Temperature Projection
The future maximum temperature in the basin under RCP4.5 and RCP8.5 scenarios is continually increasing. The mean annual maximum temperature was 32.44°C from 1979– 2005, potentially rising to 33.35 °C, 33.39 °C, and 33.40 °C for the 2020s, 2050s, and 2080s, respectively under the RCP4.5 scenario, and 33.40 °C, 33.42 °C, and 33.44 °C for the 2020s, 2050s, and 2080s, respectively under the RCP8.5 scenario, as presented in Table 5.3.3-1
Table 5.3.3-1 Maximum temperature projection under RCP4.5 and RCP8.5 scenarios for three future periods. Period Temperature (°C) RCP 4.5 RCP 8.5 Baseline (1979 – 2005) 32.44 32.44 2020s (2010 – 2039) 33.35 33.40 2050s (2040 – 2069) 33.39 33.42 2080s (2070 – 2099) 33.40 33.44
50
(a)
(b)
Figure 5.3.3-1 Projected monthly maximum temperature in Wang River Basin under (a) RCP4.5 and (b) RCP8.5 scenarios.
51
Figure 5.3.3-2 Projected annual maximum temperature in Wang River Basin under RCP4.5 and RCP8.5 scenarios.
52
5.3.4 Minimum Temperature Projection
The future minimum temperature in the basin under RCP4.5 and RCP8.5 scenarios is continually increasing. The mean minimum temperature was 20.58 °C from 1979–2005, and can rise to 21.31 °C, 21.33 °C, and 21.35 °C for the 2020s, 2050s, and 2080s, respectively under the RCP4.5 scenario, and 21.36 °C, 21.38 °C, and 21.41 °C for the 2030s, 2055s, and 2080s, respectively under the RCP8.5 scenario. The trend of minimum temperature under both RCP4.5 and RCP8.5 scenarios is presented in Table 5.3.4-1.
Table 5.3.4-1 Minimum temperature projection under RCP4.5 and RCP8.5 scenarios for three future periods. Period Temperature (°C) RCP 4.5 RCP 8.5 Baseline (1979 – 2005) 20.58 20.58 2020s (2010 – 2039) 21.31 21.36 2050s (2040 – 2069) 21.33 21.38 2080s (2070 – 2099) 21.35 21.41
53
(a)
(b)
Figure 5.3.4-1 Projected monthly minimum temperature in Wang River Basin under (a) RCP4.5 and (b) RCP8.5 scenarios.
54
Figure 5.3.4-2 Projected annual minimum temperature in Wang River Basin under RCP4.5 and RCP8.5 scenarios.
5.3.5 Concluding Remarks
Future climate projections for the Wang River Basin are based on three Regional Circulation Models (RCMs). There are three climatic parameters: maximum and minimum temperature and rainfall were projected for three future periods: the 2020s (2010–2039), 2050s (2040–2069), and 2080s (2070–2099) under RCP4.5 and RCP8.5 scenarios. RCMs were corrected with observed climate data obtained from the Thai Meteorological Department (TMD) for 1979–2005. The evaluation of bias correction shows high performance, with maximum and minimum temperatures expected to increase. The future temperature is expected to be higher in all seasons under both climate change scenarios. The future rainfall pattern may change, with future rainfall increasing in the dry season and decreasing in the wet season under both climate change scenarios and there may be fewer rainy days in future.Thailand’s climate is gradually changing. TMD reported that the historical average temperature is continuously rising, with rainfall becoming more variable, showing an increasing trend.
55
5.4 Impact of climate change on Water resources
Climate change (change in temperature and precipitation pattern) has both a positive and negative impact on water resources in Thailand (e.g. Shrestha, 2014b; Sharma and Babel, 2013). This chapter presents an assessment of the climate change impact on water resources in the Wang River Basin, Thailand using a hydrological model (HEC-HMS) under climate change scenarios. The calibration and validation based on discharge gauge stations.
5.4.1 Hydrological model, Calibration and validation of HEC-HMS
Wang River Basin was generated from 30m ASTER GLODEL DEM to delineate river basin and sub basin. This model use simple canopy method for surface area and SCS curve number to input runoff curve number of each sub basin. Snyder unit hydrograph was used for channel routing. As a result, the simulation of total flow from each basin can checked by the relationship between observed and simulated data at discharge stations. Calibration period was included for five years (2010-2014) and validation period was included for three years (2015-2017). Using standard sensitivity indicators include coefficient of determination (R2), Nash-Sutcliffe Efficiency (NSE), Percent Bias (PBIAS), and Root Mean Square Value. Those indicators can show performance of model and accuracy of simulation discharge.
Figure 5.4.1-1 Hydrology model
56
5.4.2 Calibration and Validation of discharge
The model based on observed discharge gauge stations for the period from 2010–2017. The periods from 2010–2014 (four years) were used for the calibation and 2015–2017 (two years) were used for the validation (Figure 5.4.2-1). NSE, R2, and PBIAS were applied to test the model performance.
Calibration Validation
Figure 5.4.2-1 Discharge and precipitation of Wang river basin
- Performance Evaluation of the HEC-HMS Model
Table 5.4.2-1 Statistical indicators of calibration and validation
Parameter Station Calibration Validation 1 Coefficient of determination (R2) 0.70 0.66 2 Nash- Sutcliffe efficiency (NSE) 0.65 0.66 3 Percent Bias (PBIAS)% 5.17 -6.46
The model performance evaluation shows a high performance (Table 5.4.2-1). The R2 values ranged is 0.70 and 0.66 during the calibration and validation periods, with R2 values greater than 0.5 considered acceptable (Moriasi et al., 2007). The NSE values are 0.65– 0.66 (good) during the calibration and validation periods for both stations. The model performance for streamflow ranged from good to very good. The average magnitude (PBIAS) for calibration and validation are very good range during both calibration and validation periods.
57
5.4.3 Climate Change Impact on Water Availability
This study projected the future water availability using a future climate ensemble for the Wang River Basin. The study considered discharge gauge stations. The future monthly discharge prediction for the Kiew Kor Mah discharge gauge station shows that the future discharge may increase from April to August and reduce from September to March.
Table 5.4.3-1 Average monthly discharge under climate change scenarios from 2010 to 2099.
BASELINE RCP4.5 RCP8.5 (m3/s) (m3/s) (m3/s) Jan 44.17 79.72 81.67 Feb 35.14 54.62 57.43 Mar 29.27 56.52 58.82 Apr 53.03 124.36 128.12 May 150.61 277.91 266.59 Jun 116.76 273.79 278.21 Jul 228.52 306.54 296.83 Aug 558.62 416.61 408.57 Sep 853.81 610.54 619.40 Oct 628.04 266.11 268.85 Nov 243.85 226.82 217.10 Dec 72.70 172.98 160.48
Figure 5.4.3-1 Average monthly discharge under climate change scenarios.
58
Figure 5.4.3-2 Future annual discharge trend under climate change scenarios.
5.4.4 Concluding Remarks
The impact of climate change on the Wang River Basin was investigated using The Hydrologic Modeling System (HEC-HMS) hydrological model under climate change scenarios. Model calibration and validation were based on data from 2010 to 2017 by Royal Irrigation Department (RID), Thailand. The HEC-HMS model shows high performance. The R2 is 0.70 and 0.66 for calibration and validation. Average future annual discharge for the two discharge gauge stations may not change much under climate change scenarios. However, future discharge may increase from April to August and reduce from September to March under climate change scenarios.
Climate change can have both a positive and negative impact on water resources. Boonwichai ,S. (2018) presents that “changing temperature will not significantly affect the runoff. On the other hand, rainfall, relative humidity, and evaporation are the parameters for considering runoff change. Therefore, average future annual discharge may not change much because future annual rainfall is not expected to change under climate change scenarios.”
59
5.5 Operation of the Kiew Kor Mah reservoir in the future
5.5.1 Future rule curve from 2020 to 2030
The rule curve from 2020 to 2030 under RCP4.5 is showed in figure 5.5.1-1. It was developed by HS algorithm base on future climate under RCP4.5 scenario.
Month Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec URC Current rule curve 160 135 115 105 100 95 91 90 120 140 145 152 LRC Current rule curve 80 54 35 25 20 20 10 10 23 50 65 71
Figure 5.5.1-1 Reservoir rule curve (Modified by Harmony Search Algorithm) in 2020 to 2030 under RCP4.5 of Kiew Kor Mah reservoir
The figure 5.5.1-2 presents the water storage with rule curve from 2020 to 2030 under RCP4.5 scenario. This figure shows that the rule curve can protect all of water spill because no have water storage more than normal storage.
Figure 5.5.1-2 Water storage base on Modified Rule Curve of Kiew Kor Mah reservoir in 2020 to 2030 under RCP4.5.
60
The rule curve from 2020 to 2030 under RCP8.5 is showed in figure 5.5.1-3. It was developed by HS algorithm base on future climate under RCP8.5 scenario.
Month Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec URC Current rule curve 160 135 115 105 100 95 91 90 120 140 145 152 LRC Current rule curve 80 54 35 25 20 20 10 10 23 50 65 71
Figure 5.5.1-3 Reservoir rule curve (Modified by Harmony Search Algorithm) in 2020 to 2030 under RCP8.5 of Kiew Kor Mah reservoir
The water storage with rule curve from 2020 to 2030 under RCP4.5 scenario is showed in figure 5.5.1-4. This figure shows that the rule curve can protect all of water spill because no have water storage more than normal storage.
Figure 5.5.1-4 Water storage base on Modified Rule Curve of Kiew Kor Mah reservoir in 2020 to 2030 under RCP8.5.
61
The volume of water spill and water shortage from 2020 to 2030 are showed in table 5.5-1- 1. The rule curve can lead to water spill 0 MCM, and 0 MCM respectively operation base on modified rule curve under RCP4.5 and RCP8.5 scenarios. Therefore, the modified rule curve can protect all of water spill. However, the rule curve can not protect all of water deficit.
Table 5.5.1-1 The volume and number of spill and water shortage from 2020 to 2030 under RCP4.5 and RCP8.5 scenarios.
RCP4.5 RCP8.5 Volume of Spill (MCM) 0 0 Number of Spill (Day) 0 0 Volume of Shortage (MCM) 246.68 352.46 Day of Shortage (Day) 822 764
Table 5.5.1-2 shows the daily flood volume in downstream area from 2020 to 2030 when operation base on RCP4.5 and RCP8.5 scenarios. It is shows that both of rule curve can protect flood in downstream area.
Table 5.5.1-2 The daily maximum of flood volume in Kiew Kor Mah downstream area from 2020 to 2030 under RCP4.5 and RCP8.5
RCP4.5 RCP8.5 Daily maximum outflow (MCM) 5.84 4.08 Daily of maximum side flow (MCM) 3.27 3.27 Maximum capacity of downstream (MCM) 10.77 10.77 Daily of flood volume (MCM) - -
62
5.5.2 Future rule curve from 2031 to 2040
The rule curve from 2031 to 2040 under RCP4.5 is showed in figure 5.5.2-1. It was developed by HS algorithm base on future climate under RCP4.5 scenario.
Month Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec URC Current rule curve 146 134 113 106 105 103 80 76 88 108 125 137 LRC Current rule curve 68 57 42 30 30 30 14 13 15 30 46 57
Figure 5.5.2-1 Reservoir rule curve (Modified by Harmony Search Algorithm) in 2031 to 2040 under RCP4.5 of Kiew Kor Mah reservoir
The figure 5.5.2-2 presents the water storage with rule curve from 2031 to 2040 under RCP4.5 scenario. This figure shows that the rule curve can protect all of water spill because no have water storage more than normal storage.
Figure 5.5.2-2 Water storage base on Modified Rule Curve of Kiew Kor Mah reservoir in 2031 to 2040 under RCP4.5
63
The rule curve from 2031 to 2040 under RCP 8.5 is showed in figure 5.5.2-3. It was developed by HS algorithm base on future climate under RCP8.5 scenario.
Month Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec URC Current rule curve 146 134 113 106 105 103 80 76 88 108 125 137 LRC Current rule curve 68 57 42 30 30 30 14 13 15 30 46 57
Figure 5.5.2-3 Reservoir rule curve (Modified by Harmony Search Algorithm) in 2031 to 2040 under RCP8.5 of Kiew Kor Mah reservoir
The water storage with rule curve from 2031 to 2040 under RCP8.5 scenario is showed in figure 5.5.2-4. This figure shows that the rule curve can protect all of water spill because no have water storage more than normal storage.
Figure 5.5.2-4 Water storage base on Modified Rule Curve of Kiew Kor Mah reservoir in 2031 to 2040 under RCP8.5
64
The volume of water spill and water shortage from 2031 to 2040 are showed in table 5.3-2- 1. The rule curve can lead to water spill 0 MCM, and 0 MCM respectively operation base on modified rule curve under RCP4.5 and RCP8.5 scenarios. However, the water shortage will occur when operate base on both RCP4.5 and RCP8.5 scenarios.
Table 5.5.2-1 The volume and number of spill and water shortage from 2031 to 2040 under RCP4.5 and RCP8.5 scenarios.
RCP4.5 RCP8.5 Volume of Spill (MCM) 0 0 Number of Spill (Day) 0 0 Volume of Shortage (MCM) 144.91 148.73 Day of Shortage (Day) 376 497
Table 5.5.2-2 shows the daily flood volume in downstream area from 2031 to 2040 when operation base on RCP4.5 and RCP8.5 scenarios. It is shows that both of rule curve can protect flood in downstream area.
Table 5.5.2-2 The daily maximum of flood volume in Kiew Kor Mah downstream area from 2031 to 2040 under RCP4.5 and RCP8.5.
RCP4.5 RCP8.5 Daily maximum outflow (MCM) 6.11 5.45 Daily of maximum side flow (MCM) 3.27 3.27 Maximum capacity of downstream (MCM) 10.77 10.77 Daily of flood volume (MCM) - -
65
5.5.3 Future rule curve from 2041 to 2050
The rule curve from 2041 to 2050 under RCP4.5 is showed in figure 5.5.3-1. It was developed by HS algorithm base on future climate under RCP4.5 scenario.
Month Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec URC Current rule curve 150 135 121 113 101 96 95 94 106 125 144 153 LRC Current rule curve 69 58 44 33 31 26 26 26 27 47 67 73
Figure 5.5.3-1 Reservoir rule curve (Modified by Harmony Search Algorithm) in 2041 to 2050 under RCP4.5 of Kiew Kor Mah reservoir
The figure 5.5.3-2 presents the water storage with rule curve from 2041 to 2050 under RCP4.5 scenario. This figure shows that the rule curve can protect all of water spill because no have water storage more than normal storage.
Figure 5.5.3-2 Water storage base on Modified Rule Curve of Kiew Kor Mah reservoir in 2041 to 2050 under RCP4.5
66
The rule curve from 2041 to 2050 under RCP8.5 is showed in figure 5.5.3-3. It was developed by HS algorithm base on future climate under RCP8.5 scenario.
Month Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec URC Current rule curve 150 135 121 113 101 96 95 94 106 125 144 153 LRC Current rule curve 69 58 44 33 31 26 26 26 27 47 67 73
Figure 5.5.3-3 Reservoir rule curve (Modified by Harmony Search Algorithm) in 2041 to 2050 under RCP8.5 of Kiew Kor Mah reservoir
The water storage with rule curve from 2041 to 2050 under RCP8.5 scenario is showed in figure 5.5.3-4. This figure shows that the rule curve can protect all of water spill because no have water storage more than normal storage.
Figure 5.5.3-4 Water storage base on Modified Rule Curve of Kiew Kor Mah reservoir in 2041 to 2050 under RCP8.5
67
The volume of water spill and water shortage from 2041 to 2050 are showed in table 5.3-3- 1. The rule curve can lead to water spill 0 MCM, and 0 MCM respectively operation base on modified rule curve under RCP4.5 and RCP8.5 scenarios. However, the water shortage will occur when operate base on RCP8.5 scenario.
Table 5.5.3-1 The volume and number of spill and water shortage from 2041 to 2050 under RCP4.5 and RCP8.5 scenarios
RCP4.5 RCP8.5 Volume of Spill (MCM) 0 0 Number of Spill (Day) 0 0 Volume of Shortage (MCM) 224.63 360.02 Day of Shortage (Day) 511 908
Table 5.5.3-2 shows the daily flood volume in downstream area from 2041 to 2050 when operation base on RCP4.5 and RCP8.5 scenarios. It is shows that both of rule curve can protect flood in downstream area.
Table 5.5.3-2 The daily maximum of flood volume in Kiew Kor Mah downstream area from 2041 to 2050 under RCP4.5 and RCP8.5
RCP4.5 RCP8.5 Daily maximum outflow (MCM) 4.80 6.38 Daily of maximum side flow (MCM) 3.27 3.27 Maximum capacity of downstream (MCM) 10.77 10.77 Daily of flood volume (MCM) - -
5.5.4 Concluding Remarks
The modified rule curve was investigated using Harmony Search algorithms. The maximum capacity of downstream is 10.77 MCM and the daily of maximum side flow is 3.27 MCM. Therefore, the daily maximum outflow should lower than 7.5 MCM (No flooding in downstream area). The modified rule curve can protect flood in downstream area under RCP4.5 and RCP8.5 for future 2020 to 2050.
68
CHAPTER 6 CONCLUSIONS AND RECOMMENDATIONS
6.1 Conclusions
Management of water storage in reservoir to provide sufficiency of flood space is one of the main problems in reservoir operation. The optimization technique is used to advice decision-makers. In addition to measure flood damages are important because they can help decision-makers to model scenarios to minimize damages during a real disaster. There are three parts in this study.
Firstly, this part presents a HS algorithm for minimizing the flood damages and water shortages downstream of a reservoir, applied to a single, multi-purpose reservoir operation, assess the climate projection, and assess the impact of climate on water resources. The HS algorithm has been developed to determine the optimal rule curves for reservoir operation at Kiew Kor Mah reservoir in Wang River Basin. Kiew Kor Mah reservoir was constructed for supply water to irrigation and flood protection in Lampang province. HS process requires reservoir data (Inflow, outflow, evaporation, water storage, water demand) and constraints. There are three main parameters in HS process including population size, bandwidth, and number of generations. These parameters were adjusted for determining the best fitness. This study considers reservoir data from 2009 to 2017 (historical data) and 2018 to 2050 (future data). In optimization process, the results shown that HS can gain the minimum flood and minimum deficit in downstream area. In the past, water spill was occurred many times because they can not predict the future inflow. Therefore, there were floods in downstream area especially during heavy rainfall. The HS can reduce water deficit base on water demand. Secondly, the climate projection was presented. The important effect on water resources is climate. It can reduce and increase water inflow of reservoirs.
Secondly, this part project the future precipitation, future maximum temperature, and minimum temperature in Wang River Basin, Thailand. The future climate in Wang River Basin was projected on the basis of three Regional Circulation Models (RCMs) under climate change scenarios. An ensemble of three RCMs (ACCESS1-CSIRO-CCAM, CNRM-CM5-CSIRO-CCAM, and MPI-ESM-LR-CSIRO-CCAM) was used to address uncertainty in the climate models. The future climate was projected for three periods (2020s: 2010–2039, 2050s: 2040–2069, 2080s: 2070–2099) under two climate change scenarios (RCP4.5 and RCP8.5) based on the baseline period (1979–2005). The linear downscaling method technique was applied for bias correction. Three rain gauge stations in the basin and three temperature stations were used to recurrentthe climate in the basin. The future annual rainfall may decrease by 1039, 1035, and 1036 mm for the 2020s, 2050s, and 2080s, respectively under the RCP4.5 scenario, and 1105, 1099, and 1103 mm for the 2020s, 2050s, 2080s, respectively under the RCP8.5 scenario. However, rainfall may experience seasonal changes. Future precipitation may increase during the dry season (November to April) and decrease during the wet season (May to October) under both scenarios. Average annual maximum and minimum temperatures are expected to increase in the future. The baseline maximum temperature is 32.44 °C. The maximum temperature can increase by 33.35, 33.39, and 33.40 °C for the 2020s, 2050s, and 2080s, respectively under the RCP4.5 scenario, and 33.40, 33.42, and 33.44°C for the 2020s, 2050s, 2080s, respectively under the RCP8.5 scenario. The minimum temperature can increase by 21.31, 21.33, and 21.35 40 °C for the 2020s, 2050s, and 2080s, respectively under the RCP4.5
69 scenario, and 21.36, 21.38, and 21.41°C for the 2020s, 2050s, 2080s, respectively under the RCP8.5 scenario for baseline 20.58 °C.
Thirdly, this part assess the impact of climate change on water resources especially water inflow of Kiew Kor Mah reservoir. The impact of climate change on water resources in Wang River Basin was investigated using The Hydrologic Modeling System (HEC-HMS) model. The calibration period from 2010–2014 and the validation period from 2010–2014. There are three statistical indicators: The results performance is showed good model performance: Coefficient of determination (R2), Nash- Sutcliffe efficiency (NSE), and Percent Bias (PBIAS). The R2 are 0.70 and 0.66, NSE are 0.65 and 0.66. PBIAS are 5.17 and -6.46 respectively calibration and validation. The average annual discharge may decrease in future for both stations under climate change scenarios due to consistent future precipitation and future temperature.
70
6.2 Recommendations
For study reservoir operation - There is water spill occur in Kiew Kor Mah reservoir, the operators should study for distributing this excess water to other catchment. - It should have more discharge station in downstream area. - The officers should check the efficiency of irrigation canal and equipment. - Water availability is projected to reduce. Structural and non-structural measures may be identified to manage the water resources in future periods.
For study future climate projection - As only three RCMs was used in this study under RCP4.5 and RCP8.5. In this context, it is recommended to use more RCMs for the analysis of wide range of uncertainties.
For future study - The Harmony Search process is difficult to developed, the researcher should understand visual basic before use this algorithm.
71
REFERENCES
Agarwal, A., Babel, M. S., & Maskey, S. (2014). Analysis of future precipitation in the Koshi river basin, Nepal. Journal of Hydrology, 513, 422-434.
Afshar, A., Haddad, O. B., Mariño, M. A., & Adams, B. J. (2007). Honey-bee mating optimization (HBMO) algorithm for optimal reservoir operation. Journal of the Franklin Institute, 344(5), 452-462.
Afshar, M. H. (2013). A cellular automata approach for the hydro-power operation of multi-reservoir systems. Water Manage, 166(9), 465-478.
Allen, R. G., Pereira, L. S., Raes, D., & Smith, M. (1998). Crop evapotranspiration- Guidelines for computing crop water requirements-FAO Irrigation and drainage paper 56. Fao, Rome, 300(9), D05109.
Arunkumar, R., & Jothiprakash, V. (2012). Optimal reservoir operation for hydropower generation using non-linear programming model. Journal of The Institution of Engineers (India): Series A, 93(2), 111-120.
Azadivar, F., & Shu, J. V. (1998, December). Use of simulation in optimization of maintenance policies. In 1998 Winter Simulation Conference. Proceedings (Cat. No. 98CH36274) (Vol. 2, pp. 1061-1067). IEEE.
Bashiri-Atrabi, H., Qaderi, K., Rheinheimer, D. E., & Sharifi, E. (2015). Application of harmony search algorithm to reservoir operation optimization. Water Resources Management, 29(15), 5729-5748.
Boonwichai, S., Shrestha, S., Mukand, S. B., Weesakul, S., & Datta, A. (2018). Assessment of the Climate Change Impact and Evaluation of Adaptation Strategies for Water Resources and Rice Production in the Songkhram River Basin, Thailand. (Doctoral dissertation, Asian Institute of Technology).
Butcher, W. S. (1971). Stochastic dynamic programming for optimum reservoir operation. Journal of the American Water Resources Association, 7(1), 115-123.
Chaleeraktrakoon, C., & Kangrang, A. (2007). Dynamic programming with the principle of progressive optimality for searching rule curves. Canadian Journal of Civil Engineering, 34(2), 170-176.
Chang, F. J., Chen, L., & Chang, L. C. (2005). Optimizing the reservoir operating rule curves by genetic algorithms. Hydrological Processes: An International Journal, 19(11), 2277-2289.
Cheng, C. T., Liao, S. L., Tang, Z. T., & Zhao, M. Y. (2009). Comparison of particle swarm optimization and dynamic programming for large scale hydro unit load dispatch. Energy Conversion and Management, 50(12), 3007-3014.
Duan, Q., Sorooshian, S., & Gupta, V. (1992). Effective and efficient global optimization for conceptual rainfall‐runoff models. Water resources research, 28(4), 1015-1031.
72
Ellis, J. H., & ReVelle, C. S. (1988). A searable linear algorithm for hydropower optimization. Journal of the American Water Resources Association, 24(2), 435- 447.
Flood, T. (2012). Rapid Assessment for Resilient Recovery and Reconstruction Planning. World Bank Publication. Available online: www. car. chula. ac. th, Retrieved September, 15.
Fontaine, T. A., Klassen, J. F., Cruickshank, T. S., & Hotchkiss, R. H. (2001). Hydrological response to climate change in the Black Hills of South Dakota, USA. Hydrological Sciences Journal, 46(1), 27-40.
Geem, Z. W. (2007, June). Optimal scheduling of multiple dam system using harmony search algorithm. In International Work-Conference on Artificial Neural Networks (pp. 316-323). Springer, Berlin, Heidelberg.
Geem, Z. W., Tseng, C. L., & Williams, J. C. (2009). Harmony search algorithms for water and environmental systems. In Music-Inspired Harmony Search Algorithm (pp. 113-127). Springer, Berlin, Heidelberg.
Goldberg, D. E., & Deb, K. (1991). A comparative analysis of selection schemes used in genetic algorithms. In Foundations of genetic algorithms (Vol. 1, pp. 69-93). Elsevier.
Goldberg, D. E., & Holland, J. H. (1988). Genetic algorithms and machine learning. Machine learning, 3(2), 95-99.
Haleeraktrakoon, C., Chaipuriphat, A., and YingKrainkrai, P. (2001) Management of Available Water at the Bhumiphol and Sirikit Dams Base on Rule Curves. Thailand : Institute of Thailand under H.M. The King's Patronage
Henderson, S. G., & Nelson, B. L. (Eds.). (2006). Handbooks in operations research and management science: simulation (Vol. 13). Elsevier.
Hormwichian, R., Kangrang, A., Lamom, A., Chaleeraktrakoon, C., & Patamatamkul, S. (2013). Coupled-operations model and a conditional differential evolution algorithm for improving reservoir management. International Journal of Physical Sciences, 7(42), 5701-5710.
Jalali, M. R., Afshar, A., & Marino, M. A. (2006). Reservoir operation by ant colony optimization algorithms.
Jiang, Z., Qin, H., Wu, W., & Qiao, Y. (2017). Studying operation rules of cascade reservoirs based on multi-dimensional dynamics programming. Water, 10(1), 20.
Kalita, D. N. (2008, August). A study of basin response using HEC-HMS and subzone reports of CWC. In Proceedings of the 13th National Symposium on Hydrology. National Institute of Hydrology, Roorkee, New Delhi.
73
Kangrang, A., Compliew, S., & Hormwichian, R. (2011, January). Optimal reservoir rule curves using simulated annealing. In Proceedings of the Institution of Civil Engineers-Water Management (Vol. 164, No. 1, pp. 27-34). Thomas Telford Ltd.
Kangrang, A., & Lokham, C. (2013). Optimal reservoir rule curves considering conditional ant colony optimization with simulation model. Journal of applied sciences, 13(1), 263-267.
Kasemsup, J. & Supriyasilp, T. (2018). Operation of Bhumibol Reservoir for Flood Mitigation by HEC-ResSim Model. Department of Civil Engineering, Faculty of Engineer, Chiang Mai University, Chiang Mai, 50200, Thailand.
Khin, K. H. (1991). Optimal Operation of the Ubolratana Reservoir Using Stochastic Dynamic Programming (Doctoral dissertation, Asian Institute of Technology).
Kiruthiga, D., & Amudha, T. (2016). Optimal reservoir release for hydropower generation maximization using particle swarm optimization. In Innovations in Bio-Inspired Computing and Applications (pp. 577-585). Springer, Cham.
Kleopa, X. A. (1990). Optimal reservoir operation for drought management (Doctoral dissertation, Ohio University).
Klipsch, J. D., & Hurst, M. B. (2003). HEC-ResSim: Reservoir system simulation. US Army Corps of Engineers, Davis, CA, USA.
Komsan, C. (2002). Reservoir rule curve study of Lam Pao Dam. Royal Irrigation Department, Thailand.
Kougias, I. P., & Theodossiou, N. P. (2013). Application of the harmony search optimization algorithm for the solution of the multiple dam system scheduling. Optimization and Engineering, 14(2), 331-344.
Kumar, D. N., & Reddy, M. J. (2006). Ant colony optimization for multi-purpose reservoir operation. Water Resources Management, 20(6), 879-898.
Kumar, D. N., Baliarsingh, F., & Raju, K. S. (2010). Optimal reservoir operation for flood control using folded dynamic programming. Water resources management, 24(6), 1045-1064.
Lee, K. S., & Geem, Z. W. (2005). A new meta-heuristic algorithm for continuous engineering optimization: harmony search theory and practice. Computer methods in applied mechanics and engineering, 194(36-38), 3902-3933.
Lin, N. M., & Rutten, M. (2016). Optimal operation of a network of multi-purpose reservoir: A review. Procedia Engineering, 154, 1376-1384.
Lu, Y., Zhou, J., Qin, H., Wang, Y., & Zhang, Y. (2010). An adaptive chaotic differential evolution for the short-term hydrothermal generation scheduling problem. Energy Conversion and Management, 51(7), 1481-1490.
74
Lu, Y., Zhou, J. (2012, March). Optimal scheduling of cascade hydropower system using grouping differential evolution algorithm. In Computer Science and Electronics Engineering (ICCSEE), 2012 International Conference on (Vol. 2, pp. 625-629). IEEE.
Mahdavi, M., Fesanghary, M., & Damangir, E. (2007). An improved harmony search algorithm for solving optimization problems. Applied mathematics and computation, 188(2), 1567-1579.
Mantawy, A. H., Soliman, S. A., & El-Hawary, M. E. (2001). An innovative simulated annealing approach to the long-term hydro scheduling problem. In LESCOPE 01. 2001 Large Engineering Systems Conference on Power Engineering. Conference Proceedings. Theme: Powering Beyond 2001 (Cat. No. 01ex490) (pp. 79-85). IEEE.
Masood, M., & Takeuchi, K. (2016). Climate change impacts and its implications on future water resource management in the Meghna Basin. Futures, 78, 1-18.
Melillo, J. M. (2014). Climate change impacts in the United States: the third national climate assessment. Government Printing Office.
Michalewicz, Z. (2013). Genetic algorithms+ data structures= evolution programs. Springer Science & Business Media.
Moeini, R., & Afshar, M. H. (2013). Extension of the constrained ant colony optimization algorithms for the optimal operation of multi-reservoir systems. Journal of Hydroinformatics, 15(1), 155-173.
Mousavi, S. J., Nakhaei, P., Sadollah, A., & Kim, J. H. (2017, February). Optimization of hydropower storage projects using harmony search algorithm. In International Conference on Harmony Search Algorithm (pp. 261-270). Springer, Singapore.
Niknam, T., Taheri, S. I., Aghaei, J., Tabatabaei, S., & Nayeripour, M. (2011). A modified honey bee mating optimization algorithm for multiobjective placement of renewable energy resources. Applied Energy, 88(12), 4817-4830.
Oliveira, R., & Loucks, D. P. (1997). Operating rules for multireservoir systems. Water resources research, 33(4), 839-852.
Peraza, C., Valdez, F., & Castillo, O. (2015). A harmony search algorithm comparison with genetic algorithms. In Fuzzy Logic Augmentation of Nature-Inspired Optimization Metaheuristics (pp. 105-123). Springer, Cham.
Pinthong, P. and Weesakul, S. (2008). A hybrid genetic-neurofuzzy system based decision modeling for real-time reservoir operation. Thailand : Institute of Thailand under H.M. The King's Patronage.
Pinthong, P., Gupta, A. D., Babel, M. S., & Weesakul, S. (2009). Improved reservoir operation using hybrid genetic algorithm and neurofuzzy computing. Water resources management, 23(4), 697.
75
Pinthong, P. (2014). Modeling of Multiple Reservoir Operation System for Water Supply using Genetic Algorithm. (n.p.).
Puttrawutichai, S. (2017). Optimal Operation of a Multipurpose Multi-Reservoir System in The Upper Chao Phraya River Basin. Thailand : Kasetsart University.
Rewtragulpaibul, C., Tingsanchali, T., Mukand, S. B., Dutta, D., & Parnickun, M. (2005). Application of genetic algorithm for analysis of Pasak Jolasid reservoir operation, Thailand. Asian Institute of technology, school of Engineering and Technology, Thailand.
Ritma, A. (2002) Develop of Reservoir Operating Rule Curve for Mun Bon -Lam Chae Reservoir
Sharif, M., & Wardlaw, R. (2000). Multireservoir systems optimization using genetic algorithms: case study. Journal of Computing in Civil Engineering, 14(4), 255-263.
Shashank, R. B., Shrestha, S., & Taravudh, T. (2015). Assessment of HEC-HMS model to simulate river discharge in Koshi basin, Nepal. Asian Institute of technology, school of Engineering and Technology, Thailand.
Shen, J., Cheng, C., Cheng, X., & Wu, X. (2014). A hybrid nonlinear optimization method for operation of large-scale cascaded hydropower plants. Scientia Sinica Technologica, 44(3), 306-314.
Shrestha, S., Khatiwada, M., Babel, M. S., & Parajuli, K. (2014). Impact of climate change on river flow and hydropower production in Kulekhani hydropower project of Nepal. Environmental Processes, 1(3), 231-250.
Stedinger, J. R., Sule, B. F., & Loucks, D. P. (1984). Stochastic dynamic programming models for reservoir operation optimization. Water resources research, 20(11), 1499-1505.
Stonefelt, M. D., Fontaine, T. A., & Hotchkiss, R. H. (2000). Impacts of climate change on water yield in the upper wind river basin. Journal of the American Water Resources Association, 36(2), 321-336.
Sudesh, K. M., Ricardo, H., Guna, N. P., & Huynh, N. P. (1991). Optimal operation of Kulekhani reservoir in Nepal. Asian Institute of technology, school of Engineering and Technology, Thailand. Tayebiyan, A., Ali, T. A. M., Ghazali, A. H., & Malek, M. A. (2016). Optimization of exclusive release policies for hydropower reservoir operation by using genetic algorithm. Water resources management, 30(3), 1203-1216.
Teegavarapu, R. S., & Simonovic, S. P. (2002). Optimal operation of reservoir systems using simulated annealing. Water Resources Management, 16(5), 401-428.
76
Thaikheaw, S., Shrestha, S., Mukand, S. B., Weesakul, S. & Jourdain, D. (2013). Bhumibol reservoir performance analysis and optimal reservoir operation for flood control. Asian Institute of technology, school of Engineering and Technology, Thailand.
Tospornsampan, J., Kita, I., Ishii, M., & Kitamura, Y. (2005). Optimization of a multiple reservoir system using a simulated annealing--A case study in the Mae Klong system, Thailand. Paddy and Water Environment, 3(3), 137-147.
Uysal, G., Akkol, B., Topcu, M. I., Sensoy, A., & Schwanenberg, D. (2016). Comparison of Different Reservoir Models for Short Term Operation of Flood Management. Procedia Engineering, 154, 1385-1392.
Walsh, G. R. (1985). An introduction to linear programming (pp. 74-138). New York: Wiley.
Wardlaw, R., & Sharif, M. (1999). Evaluation of genetic algorithms for optimal reservoir system operation. Journal of water resources planning and management, 125(1), 25-33.
Weatherhead, E. K., & Knox, J. W. (2000). Predicting and mapping the future demand for irrigation water in England and Wales. Agricultural Water Management, 43(2), 203-218.
Win, K. M. (1991). Analysis of Flood Control Reservoir Operation Models Under Different Alternative Objective Functions (Doctoral dissertation, Asian Institute of Technology).
Yoo, J. H. (2009). Maximization of hydropower generation through the application of a linear programming model. Journal of hydrology, 376(1-2), 182-187.
Zambelli, M. S., Luna, I., & Soares, S. (2009, June). Long-term hydropower scheduling based on deterministic nonlinear optimization and annual inflow forecasting models. In 2009 IEEE Bucharest PowerTech (pp. 1-8). IEEE.
Zhang, Z., Zhang, S., Wang, Y., Jiang, Y., & Wang, H. (2013). Use of parallel deterministic dynamic programming and hierarchical adaptive genetic algorithm for reservoir operation optimization. Computers & Industrial Engineering, 65(2), 310- 321.
Zhang, X., Yu, X., & Qin, H. (2016). Optimal operation of multi-reservoir hydropower systems using enhanced comprehensive learning particle swarm optimization. Journal of hydro-environment research, 10, 50-63.
77
APPENDIX A.
DAILY INFLOW OF KIEW KOR MAH RESERVOIR (MCM)
78
The daily inflow of Kiew Kor Mah reservoir in 2009.
2009 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1 1.31 0.00 0.00 0.00 0.00 12.18 0.00 0.12 1.07 1.20 0.57 0.26 2 0.20 0.00 0.00 0.22 0.10 2.45 0.16 0.17 0.93 0.97 0.47 0.25 3 0.09 0.00 0.00 0.15 0.11 1.20 0.04 0.24 0.91 0.98 0.42 0.15 4 0.11 0.00 0.02 0.16 0.21 0.65 0.04 0.18 0.85 0.98 0.53 0.16 5 0.00 0.00 0.03 0.15 0.41 0.72 0.04 0.19 0.80 3.35 0.47 0.15 6 0.12 0.00 0.00 0.17 0.21 3.16 0.38 0.18 1.27 2.05 0.37 0.15 7 0.01 0.00 0.04 0.16 0.00 0.66 1.34 0.24 0.75 2.97 0.36 0.15 8 0.02 0.00 0.32 1.01 0.00 0.67 2.20 0.19 0.75 2.69 0.48 0.15 9 0.13 0.00 0.00 0.00 0.45 0.46 1.11 0.00 1.55 1.44 0.36 0.05 10 0.25 0.00 0.00 0.00 0.05 0.41 0.59 0.30 1.14 1.17 0.37 0.05 11 0.00 0.00 0.82 0.00 0.04 1.24 0.42 0.14 0.95 1.56 0.30 0.15 12 0.04 0.66 0.00 0.00 0.00 0.29 0.22 0.24 0.59 1.38 0.37 0.00 13 0.16 0.00 0.04 0.00 0.25 0.29 0.23 0.36 0.65 1.20 0.37 0.05 14 0.16 0.00 0.00 0.03 0.25 0.29 0.10 0.25 1.28 1.59 0.36 0.04 15 0.00 0.23 0.00 0.79 0.91 0.59 0.03 0.36 1.85 1.02 0.26 0.05 16 0.18 0.00 0.00 0.19 1.29 0.21 0.16 3.04 1.63 1.03 0.26 0.05 17 0.08 0.00 0.18 0.00 0.47 1.42 0.04 1.78 1.38 0.83 0.26 0.05 18 0.09 0.00 0.21 0.62 1.20 3.07 0.40 1.02 3.31 0.74 0.26 0.05 19 0.00 0.00 0.10 0.00 0.26 0.35 0.03 0.85 4.96 1.14 0.26 0.05 20 0.00 0.00 1.10 0.04 0.31 2.06 0.66 0.63 4.01 0.95 0.26 0.06 21 0.00 0.00 0.00 0.00 0.26 0.55 0.91 0.41 1.35 1.24 0.26 0.05 22 0.00 0.00 0.11 0.31 0.26 0.28 0.03 0.63 1.12 0.96 0.26 0.04 23 0.02 0.00 0.12 0.31 0.21 0.22 0.28 0.63 2.24 1.15 0.26 0.04 24 0.00 0.00 0.12 0.28 0.05 0.28 0.47 2.20 2.73 0.97 0.26 0.04 25 0.00 0.00 0.00 0.00 0.05 0.33 0.41 1.27 3.04 1.88 0.26 0.04 26 0.00 0.00 0.06 0.10 0.05 0.06 0.24 0.83 1.68 1.18 0.16 0.04 27 0.00 0.00 0.07 0.00 0.00 0.00 0.22 0.65 1.56 0.88 0.25 0.04 28 0.00 0.64 0.38 0.00 0.52 0.07 0.23 0.48 2.66 0.66 0.15 0.04 29 0.00 0.00 0.26 0.99 0.07 0.00 0.48 2.50 0.78 0.26 0.05 30 0.00 0.14 0.26 1.48 0.46 0.88 0.48 1.51 0.47 0.15 0.04 31 0.00 1.03 0.00 0.23 0.82 0.58 0.04 The daily inflow of Kiew Kor Mah reservoir in 2010.
2010 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1 0.05 0.05 0.00 0.00 0.21 0.00 0.00 0.11 2.92 1.00 0.58 0.81 2 0.04 0.06 0.00 0.00 0.21 0.09 0.00 0.00 1.99 9.53 2.07 0.38 3 0.00 0.05 0.00 0.00 0.21 0.04 0.20 0.51 1.56 7.05 1.79 0.52 4 0.05 0.05 0.00 0.00 0.00 0.03 0.20 1.16 1.40 0.71 1.06 0.51 5 0.00 0.06 0.00 0.00 0.02 0.04 0.20 0.35 0.94 0.85 1.50 0.08 6 0.05 0.05 0.00 0.00 0.02 0.03 0.04 0.71 0.66 0.28 1.00 0.22 7 0.35 0.05 0.00 0.00 0.03 0.03 0.20 0.36 0.48 0.28 0.97 0.43 8 0.25 0.06 0.00 0.03 0.00 0.04 2.21 0.59 0.38 0.56 0.58 0.45 9 0.15 0.00 0.00 0.00 0.00 0.00 0.03 0.51 0.00 0.56 1.02 0.48 10 0.15 0.05 0.00 0.43 0.00 0.38 0.03 2.25 1.44 0.56 0.58 0.91 11 0.04 0.00 0.00 0.18 0.00 0.04 0.03 1.33 1.84 0.56 0.72 0.29 12 0.05 0.05 0.19 0.20 0.00 0.00 0.09 3.00 12.38 0.28 0.46 0.15 13 0.05 0.00 0.00 0.21 0.00 0.00 0.03 5.76 7.89 0.28 0.43 0.29 14 0.15 0.00 0.00 0.23 0.00 0.00 0.03 5.58 5.70 0.28 0.57 0.15 15 0.14 0.00 0.00 0.00 0.00 0.00 0.15 3.25 4.94 0.71 1.00 0.15 16 0.00 0.00 0.00 0.10 0.00 0.00 0.03 2.96 12.28 0.98 0.67 0.17 17 0.05 0.00 0.00 0.09 0.27 0.00 0.00 1.93 12.62 0.29 0.66 0.01 18 0.00 0.06 0.00 0.10 0.40 0.00 0.00 1.37 12.62 1.13 1.17 0.01 19 0.00 0.06 0.89 0.10 0.00 0.00 0.16 1.91 6.37 0.85 0.67 0.01 20 0.04 0.02 0.00 0.03 0.03 0.00 0.10 1.81 2.15 2.12 0.81 0.15 21 0.00 0.01 0.00 0.00 0.03 0.03 0.10 1.35 0.67 3.12 0.66 0.01 22 0.05 0.03 0.00 0.00 0.03 0.00 0.15 1.30 0.52 3.38 0.67 0.15 23 0.05 0.19 0.00 0.00 0.03 0.20 0.00 7.23 2.08 2.57 1.09 0.15 24 0.05 0.19 0.00 0.00 0.03 0.15 0.00 4.89 2.54 1.83 0.48 0.01 25 0.05 0.00 0.00 0.00 0.03 0.03 0.00 2.65 1.60 1.76 0.40 0.15 26 0.06 0.00 0.00 0.00 0.00 0.03 0.00 3.44 14.59 1.08 0.68 0.15 27 0.05 0.00 0.00 0.00 0.00 0.03 0.04 2.32 1.03 1.08 0.82 0.01 28 0.15 0.00 0.00 0.00 0.00 0.03 0.52 1.99 1.41 0.86 0.12 0.15 29 0.15 0.00 0.00 0.03 0.00 0.14 1.87 13.06 1.57 0.54 0.15 30 0.15 0.00 0.00 0.00 0.04 0.57 3.16 3.44 10.91 0.38 0.01 31 0.05 0.00 0.03 0.30 3.19 3.39 0.01
79
The daily inflow of Kiew Kor Mah reservoir in 2011.
2011 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1 0.05 0.05 0.00 0.00 0.21 0.00 0.00 0.11 2.92 1.00 0.58 0.81 2 0.04 0.06 0.00 0.00 0.21 0.09 0.00 0.00 1.99 9.53 2.07 0.38 3 0.00 0.05 0.00 0.00 0.21 0.04 0.20 0.51 1.56 7.05 1.79 0.52 4 0.05 0.05 0.00 0.00 0.00 0.03 0.20 1.16 1.40 0.71 1.06 0.51 5 0.00 0.06 0.00 0.00 0.02 0.04 0.20 0.35 0.94 0.85 1.50 0.08 6 0.05 0.05 0.00 0.00 0.02 0.03 0.04 0.71 0.66 0.28 1.00 0.22 7 0.35 0.05 0.00 0.00 0.03 0.03 0.20 0.36 0.48 0.28 0.97 0.43 8 0.25 0.06 0.00 0.03 0.00 0.04 2.21 0.59 0.38 0.56 0.58 0.45 9 0.15 0.00 0.00 0.00 0.00 0.00 0.03 0.51 0.00 0.56 1.02 0.48 10 0.15 0.05 0.00 0.43 0.00 0.38 0.03 2.25 1.44 0.56 0.58 0.91 11 0.04 0.00 0.00 0.18 0.00 0.04 0.03 1.33 1.84 0.56 0.72 0.29 12 0.05 0.05 0.19 0.20 0.00 0.00 0.09 3.00 12.38 0.28 0.46 0.15 13 0.05 0.00 0.00 0.21 0.00 0.00 0.03 5.76 7.89 0.28 0.43 0.29 14 0.15 0.00 0.00 0.23 0.00 0.00 0.03 5.58 5.70 0.28 0.57 0.15 15 0.14 0.00 0.00 0.00 0.00 0.00 0.15 3.25 4.94 0.71 1.00 0.15 16 0.00 0.00 0.00 0.10 0.00 0.00 0.03 2.96 12.28 0.98 0.67 0.17 17 0.05 0.00 0.00 0.09 0.27 0.00 0.00 1.93 12.62 0.29 0.66 0.01 18 0.00 0.06 0.00 0.10 0.40 0.00 0.00 1.37 12.62 1.13 1.17 0.01 19 0.00 0.06 0.89 0.10 0.00 0.00 0.16 1.91 6.37 0.85 0.67 0.01 20 0.04 0.02 0.00 0.03 0.03 0.00 0.10 1.81 2.15 2.12 0.81 0.15 21 0.00 0.01 0.00 0.00 0.03 0.03 0.10 1.35 0.67 3.12 0.66 0.01 22 0.05 0.03 0.00 0.00 0.03 0.00 0.15 1.30 0.52 3.38 0.67 0.15 23 0.05 0.19 0.00 0.00 0.03 0.20 0.00 7.23 2.08 2.57 1.09 0.15 24 0.05 0.19 0.00 0.00 0.03 0.15 0.00 4.89 2.54 1.83 0.48 0.01 25 0.05 0.00 0.00 0.00 0.03 0.03 0.00 2.65 1.60 1.76 0.40 0.15 26 0.06 0.00 0.00 0.00 0.00 0.03 0.00 3.44 14.59 1.08 0.68 0.15 27 0.05 0.00 0.00 0.00 0.00 0.03 0.04 2.32 1.03 1.08 0.82 0.01 28 0.15 0.00 0.00 0.00 0.00 0.03 0.52 1.99 1.41 0.86 0.12 0.15 29 0.15 0.00 0.00 0.03 0.00 0.14 1.87 13.06 1.57 0.54 0.15 30 0.15 0.00 0.00 0.00 0.04 0.57 3.16 3.44 10.91 0.38 0.01 31 0.05 0.00 0.03 0.30 3.19 3.39 0.01 The daily inflow of Kiew Kor Mah reservoir in 2012.
2012 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1 0.05 0.05 0.00 0.00 0.21 0.00 0.00 0.11 2.92 1.00 0.58 0.81 2 0.04 0.06 0.00 0.00 0.21 0.09 0.00 0.00 1.99 9.53 2.07 0.38 3 0.00 0.05 0.00 0.00 0.21 0.04 0.20 0.51 1.56 7.05 1.79 0.52 4 0.05 0.05 0.00 0.00 0.00 0.03 0.20 1.16 1.40 0.71 1.06 0.51 5 0.00 0.06 0.00 0.00 0.02 0.04 0.20 0.35 0.94 0.85 1.50 0.08 6 0.05 0.05 0.00 0.00 0.02 0.03 0.04 0.71 0.66 0.28 1.00 0.22 7 0.35 0.05 0.00 0.00 0.03 0.03 0.20 0.36 0.48 0.28 0.97 0.43 8 0.25 0.06 0.00 0.03 0.00 0.04 2.21 0.59 0.38 0.56 0.58 0.45 9 0.15 0.00 0.00 0.00 0.00 0.00 0.03 0.51 0.00 0.56 1.02 0.48 10 0.15 0.05 0.00 0.43 0.00 0.38 0.03 2.25 1.44 0.56 0.58 0.91 11 0.04 0.00 0.00 0.18 0.00 0.04 0.03 1.33 1.84 0.56 0.72 0.29 12 0.05 0.05 0.19 0.20 0.00 0.00 0.09 3.00 12.38 0.28 0.46 0.15 13 0.05 0.00 0.00 0.21 0.00 0.00 0.03 5.76 7.89 0.28 0.43 0.29 14 0.15 0.00 0.00 0.23 0.00 0.00 0.03 5.58 5.70 0.28 0.57 0.15 15 0.14 0.00 0.00 0.00 0.00 0.00 0.15 3.25 4.94 0.71 1.00 0.15 16 0.00 0.00 0.00 0.10 0.00 0.00 0.03 2.96 12.28 0.98 0.67 0.17 17 0.05 0.00 0.00 0.09 0.27 0.00 0.00 1.93 12.62 0.29 0.66 0.01 18 0.00 0.06 0.00 0.10 0.40 0.00 0.00 1.37 12.62 1.13 1.17 0.01 19 0.00 0.06 0.89 0.10 0.00 0.00 0.16 1.91 6.37 0.85 0.67 0.01 20 0.04 0.02 0.00 0.03 0.03 0.00 0.10 1.81 2.15 2.12 0.81 0.15 21 0.00 0.01 0.00 0.00 0.03 0.03 0.10 1.35 0.67 3.12 0.66 0.01 22 0.05 0.03 0.00 0.00 0.03 0.00 0.15 1.30 0.52 3.38 0.67 0.15 23 0.05 0.19 0.00 0.00 0.03 0.20 0.00 7.23 2.08 2.57 1.09 0.15 24 0.05 0.19 0.00 0.00 0.03 0.15 0.00 4.89 2.54 1.83 0.48 0.01 25 0.05 0.00 0.00 0.00 0.03 0.03 0.00 2.65 1.60 1.76 0.40 0.15 26 0.06 0.00 0.00 0.00 0.00 0.03 0.00 3.44 14.59 1.08 0.68 0.15 27 0.05 0.00 0.00 0.00 0.00 0.03 0.04 2.32 1.03 1.08 0.82 0.01 28 0.15 0.00 0.00 0.00 0.00 0.03 0.52 1.99 1.41 0.86 0.12 0.15 29 0.15 0.00 0.00 0.03 0.00 0.14 1.87 13.06 1.57 0.54 0.15 30 0.15 0.00 0.00 0.00 0.04 0.57 3.16 3.44 10.91 0.38 0.01 31 0.05 0.00 0.03 0.30 3.19 3.39 0.01
80
The daily inflow of Kiew Kor Mah reservoir in 2013.
2013 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1 0.00 0.04 0.00 0.00 0.00 0.00 0.00 0.50 3.27 1.59 1.19 0.73 2 0.00 0.04 0.00 0.00 0.00 0.00 0.00 0.36 2.71 1.05 0.93 0.42 3 0.00 0.04 0.00 0.00 0.00 0.05 0.00 0.96 1.46 1.35 0.73 0.42 4 0.00 0.07 0.02 0.00 0.13 0.00 0.00 0.74 0.92 2.93 0.69 0.58 5 0.00 0.23 0.25 0.00 0.13 0.33 0.00 0.58 0.84 6.15 0.71 0.58 6 0.00 0.42 0.14 0.00 0.00 0.00 0.01 0.52 0.84 3.06 0.56 0.27 7 0.00 0.13 0.27 0.11 0.10 0.00 0.00 0.30 1.49 4.40 0.74 0.27 8 0.00 0.12 0.25 0.28 0.02 0.00 0.00 1.02 1.69 3.59 0.45 0.27 9 0.00 0.13 0.33 0.27 0.00 0.00 0.22 2.01 2.52 1.72 0.39 0.42 10 0.00 0.00 0.11 0.18 0.00 0.00 0.07 1.29 5.30 1.05 0.65 0.30 11 0.00 0.00 0.28 0.29 0.00 0.46 0.00 2.11 4.34 0.65 0.65 0.18 12 0.00 0.00 0.07 0.00 0.03 0.90 0.00 3.52 2.57 0.51 0.52 0.33 13 0.00 0.00 0.00 0.06 0.00 0.20 0.00 3.81 2.17 0.78 0.49 0.18 14 0.00 0.00 0.00 0.09 0.00 0.27 0.00 1.66 3.44 0.40 0.52 0.18 15 0.00 0.00 0.18 0.16 0.03 0.00 0.00 1.15 2.68 0.40 0.25 0.15 16 0.00 0.03 0.05 0.00 0.00 0.00 0.00 1.04 2.76 0.40 0.38 0.86 17 0.00 0.00 0.13 0.00 0.00 0.14 0.41 2.08 2.86 0.40 0.25 0.00 18 0.00 0.02 0.00 0.00 0.00 0.68 0.21 1.52 2.05 0.91 0.93 0.23 19 0.00 0.02 0.12 0.10 0.00 0.00 0.08 0.31 1.34 2.50 0.52 0.66 20 0.04 0.00 0.04 0.00 0.00 0.00 0.06 0.96 0.93 3.51 1.56 0.03 21 0.00 0.36 0.00 0.00 0.00 0.00 0.04 0.39 0.53 4.54 1.71 0.19 22 0.00 0.22 0.10 0.01 0.00 0.00 0.00 1.12 0.64 7.34 1.20 0.19 23 0.00 0.08 0.18 0.00 0.00 0.00 0.00 1.04 0.63 3.53 0.79 0.19 24 0.00 0.00 0.00 0.00 0.02 0.00 0.08 1.04 0.03 2.25 0.79 0.19 25 0.00 0.00 0.27 0.00 0.00 0.00 0.03 1.30 0.73 2.15 0.38 0.00 26 0.00 0.00 0.00 0.00 0.01 0.00 0.25 0.23 0.61 2.15 1.24 0.00 27 0.00 0.00 0.00 0.00 0.00 0.15 0.00 1.43 1.91 1.71 0.11 0.21 28 0.00 0.00 0.00 0.00 0.00 0.01 0.05 1.35 2.34 1.44 0.73 0.05 29 0.00 0.00 0.00 0.00 0.00 0.11 0.93 2.34 1.22 0.73 0.21 30 0.00 0.00 0.00 0.00 0.00 0.05 0.68 2.24 1.78 1.51 0.00 31 0.04 0.00 0.00 0.22 0.74 1.39 0.00 The daily inflow of Kiew Kor Mah reservoir in 2014.
2014 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1 0.04 0.00 0.00 0.00 0.00 0.10 0.00 0.42 1.21 0.67 0.24 0.20 2 0.00 0.16 0.00 0.42 0.31 0.18 0.00 0.26 3.64 1.19 0.30 0.10 3 0.02 0.00 0.00 0.06 0.00 0.00 0.07 0.42 9.35 0.92 0.20 0.00 4 0.02 0.00 0.00 0.13 0.00 0.00 0.16 1.83 11.51 0.16 0.46 0.00 5 0.00 0.49 0.02 0.18 0.00 0.06 0.00 0.61 7.01 0.14 0.65 0.10 6 0.02 1.82 0.02 0.02 0.47 0.06 0.00 0.53 5.67 0.85 0.54 0.38 7 0.43 0.00 0.00 0.39 0.47 0.04 0.00 0.85 2.77 0.34 0.44 0.22 8 0.29 0.93 0.00 0.00 0.38 0.00 0.00 0.70 2.00 0.03 0.68 0.47 9 0.00 0.00 0.00 0.00 0.00 0.00 0.26 0.70 1.34 0.74 1.15 0.11 10 0.00 0.02 0.00 0.57 0.00 0.00 0.00 1.96 0.93 0.64 0.64 0.00 11 0.00 0.00 0.00 0.00 0.52 0.00 0.00 0.28 1.14 0.29 0.42 0.47 12 0.03 0.00 0.00 0.00 0.00 0.02 1.16 1.07 0.73 0.19 0.30 0.23 13 0.00 0.48 0.32 0.17 0.11 0.02 0.09 0.48 0.54 0.39 0.60 0.00 14 0.36 0.00 0.04 0.31 0.24 0.34 0.42 0.31 0.68 0.59 0.40 0.00 15 0.00 0.00 0.00 0.17 0.00 0.26 0.01 0.82 0.98 0.29 0.30 0.13 16 0.00 0.00 0.41 0.87 0.00 0.86 0.01 0.39 0.59 0.29 0.40 0.56 17 0.32 0.00 0.00 0.47 0.00 0.00 0.42 0.03 1.14 0.57 0.30 0.35 18 0.05 0.56 0.00 0.81 0.00 1.11 0.34 0.00 0.62 0.20 0.20 0.43 19 0.00 0.00 0.00 0.20 0.42 0.62 0.18 0.20 0.60 0.30 0.30 0.00 20 0.00 0.00 0.00 0.19 0.00 0.18 0.32 0.45 0.61 0.30 0.40 0.00 21 0.32 0.10 0.00 0.18 0.09 0.00 0.38 3.80 1.32 0.20 0.30 0.66 22 0.05 0.01 0.00 0.25 0.07 0.02 0.42 5.09 0.52 0.30 0.30 0.00 23 0.00 0.00 0.93 0.01 0.00 0.03 0.01 1.00 0.91 0.29 0.20 0.12 24 0.00 0.04 0.00 0.28 0.00 0.00 0.67 0.84 0.55 0.20 0.30 0.02 25 0.00 0.00 0.00 0.08 0.26 0.00 0.92 0.52 1.92 0.20 0.20 0.00 26 0.11 0.03 0.32 0.23 0.00 0.00 0.34 0.28 1.20 0.15 0.30 0.04 27 0.11 0.00 0.00 0.00 0.00 0.00 0.11 0.31 0.78 0.97 0.10 0.00 28 0.11 0.00 0.01 0.00 0.00 0.00 0.28 0.39 1.11 0.95 0.20 0.00 29 0.00 0.00 0.00 0.16 0.00 1.90 0.93 1.25 1.15 0.20 0.00 30 0.00 0.00 0.00 0.24 0.00 2.13 0.54 1.34 0.55 0.30 0.00 31 0.00 0.00 0.15 0.66 1.15 0.24 0.00
81
The daily inflow of Kiew Kor Mah reservoir in 2015.
2015 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1 0.41 0.30 0.19 0.31 0.23 0.15 0.15 0.20 0.09 0.09 0.07 0.00 2 0.41 0.39 0.24 0.31 0.18 0.15 0.15 0.23 0.09 0.09 0.07 0.00 3 0.38 0.42 0.24 0.29 0.18 0.15 0.15 0.23 0.09 0.09 0.07 0.00 4 0.38 0.41 0.24 0.27 0.18 0.00 0.00 0.23 0.09 0.09 0.07 0.00 5 0.38 0.14 0.26 0.26 0.18 0.00 0.00 0.23 0.09 0.09 0.04 0.00 6 0.38 0.29 0.24 0.26 0.20 0.14 0.14 0.20 0.09 0.09 0.02 0.00 7 0.41 1.13 0.23 0.26 0.23 0.14 0.14 0.18 0.09 0.09 0.02 0.00 8 0.41 1.54 0.23 0.26 0.23 0.14 0.14 0.10 0.09 0.11 0.02 0.00 9 0.40 0.65 0.23 0.26 0.22 0.16 0.16 0.06 0.09 0.11 0.02 0.00 10 0.34 0.34 0.23 0.65 0.21 0.16 0.16 0.06 0.09 0.11 0.02 0.00 11 0.28 0.28 0.11 0.91 0.21 0.10 0.10 0.00 0.09 0.11 0.00 0.00 12 0.28 0.23 0.06 0.95 0.21 0.10 0.10 0.00 0.07 0.11 0.00 0.00 13 0.29 0.01 0.25 0.95 0.21 0.16 0.16 0.15 0.05 0.11 0.00 0.00 14 0.29 0.20 0.27 0.95 0.21 0.16 0.16 0.18 0.05 0.10 0.00 0.00 15 0.36 0.39 0.27 0.95 0.18 0.16 0.16 0.20 0.09 0.08 0.00 0.00 16 0.39 0.39 0.33 0.95 0.18 0.16 0.16 0.22 0.09 0.08 0.00 0.00 17 0.38 0.39 0.38 0.94 0.18 0.16 0.16 0.19 0.09 0.08 0.00 0.00 18 0.38 0.39 0.31 0.94 0.07 0.16 0.16 0.20 0.09 0.08 0.00 0.00 19 0.38 0.39 0.31 0.94 0.09 0.16 0.16 0.21 0.09 0.08 0.00 0.00 20 0.38 0.39 0.31 0.99 0.14 0.22 0.22 0.14 0.09 0.10 0.00 0.00 21 0.38 0.39 0.31 0.78 0.18 0.25 0.25 0.10 0.09 0.11 0.00 0.00 22 0.38 0.39 0.31 0.97 0.18 0.24 0.24 0.06 0.09 0.11 0.00 0.00 23 0.38 0.39 0.31 0.27 0.15 0.18 0.18 0.06 0.10 0.11 0.00 0.00 24 0.38 0.39 0.31 0.13 0.15 0.18 0.18 0.07 0.10 0.11 0.00 0.00 25 0.38 0.21 0.24 0.27 0.15 0.27 0.27 0.09 0.06 0.11 0.00 0.00 26 0.28 0.13 0.29 0.25 0.15 0.29 0.29 0.06 0.06 0.11 0.00 0.08 27 0.28 0.13 0.18 0.23 0.16 0.29 0.29 0.06 0.06 0.11 0.00 0.08 28 0.28 0.13 0.11 0.23 0.15 0.29 0.29 0.06 0.06 0.11 0.00 0.08 29 0.28 0.15 0.23 0.15 0.29 0.29 0.06 0.08 0.11 0.00 0.06 30 0.35 0.18 0.23 0.15 0.28 0.28 0.06 0.09 0.11 0.00 0.01 31 0.42 0.24 0.15 0.08 0.08 0.06 The daily inflow of Kiew Kor Mah reservoir in 2016.
2016 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1 0.02 0.00 0.00 0.00 0.00 0.36 3.00 1.68 1.32 1.79 1.79 1.42 2 0.00 0.01 0.00 0.00 0.00 0.56 0.64 0.74 1.40 1.16 1.85 0.96 3 0.00 0.00 0.00 0.00 0.04 1.31 0.72 0.30 0.78 1.43 1.36 1.23 4 0.05 0.00 0.01 0.01 0.00 1.37 0.68 0.36 0.55 2.91 1.36 0.95 5 0.01 0.00 0.00 0.00 0.00 1.56 0.27 1.19 0.48 4.57 0.84 0.56 6 0.00 0.00 0.01 0.00 0.00 4.21 0.23 2.29 1.11 4.56 0.96 1.09 7 0.00 0.00 0.00 0.00 0.00 3.82 0.12 1.24 0.01 4.10 0.96 0.95 8 0.00 0.00 0.00 0.00 0.00 1.33 1.45 0.70 0.48 3.66 0.78 0.82 9 0.00 0.00 0.00 0.00 0.00 0.62 1.31 1.41 2.52 2.09 1.01 0.69 10 0.00 0.00 0.00 0.00 0.00 0.49 0.71 0.68 1.41 1.95 1.06 0.48 11 0.00 0.01 0.00 0.00 0.00 0.35 0.57 0.35 1.10 1.51 1.71 0.35 12 0.00 0.00 0.00 0.00 0.00 0.35 0.57 0.19 1.33 1.76 11.42 0.22 13 0.00 0.00 0.00 0.00 0.00 0.22 0.32 0.13 8.54 5.47 6.08 0.62 14 0.00 0.00 0.08 0.00 0.00 0.27 0.31 0.18 8.50 4.76 2.94 0.36 15 0.00 0.00 0.00 0.00 0.00 0.18 0.31 0.40 4.00 1.95 2.42 0.40 16 0.00 0.00 0.00 0.00 0.00 0.18 0.28 0.29 5.15 1.45 2.42 0.44 17 0.00 0.00 0.00 0.00 0.00 0.13 0.61 0.13 5.00 1.18 1.87 0.37 18 0.00 0.00 0.00 0.00 0.00 0.18 0.46 0.13 3.00 1.00 1.59 0.34 19 0.00 0.00 0.00 0.00 0.00 0.04 0.00 0.35 6.25 0.91 1.57 0.34 20 0.00 0.03 0.00 0.00 0.00 0.09 0.00 0.31 5.00 0.53 1.41 0.47 21 0.00 0.00 0.00 0.00 0.39 0.13 2.32 4.68 3.22 0.68 1.12 0.44 22 0.00 0.00 0.00 0.20 0.25 0.04 1.13 5.65 3.36 0.44 1.23 0.36 23 0.01 0.00 0.00 0.00 0.14 0.11 1.09 2.07 3.03 0.39 1.08 0.44 24 0.00 0.00 0.00 0.00 0.20 0.04 0.79 2.55 2.35 0.56 1.05 0.48 25 0.01 0.00 0.00 0.00 0.10 0.22 0.20 2.95 2.64 1.64 0.98 0.41 26 0.00 0.00 0.00 0.01 0.07 0.01 0.17 2.18 2.79 4.80 0.63 0.53 27 0.13 0.01 0.00 0.01 0.07 0.56 0.03 1.78 5.12 5.32 1.12 0.67 28 0.01 0.01 0.00 0.00 0.03 0.17 0.28 4.65 2.59 2.96 1.78 0.87 29 0.01 0.00 0.00 0.00 0.07 0.11 0.14 4.20 1.62 1.65 2.21 0.85 30 0.00 0.00 0.00 0.10 1.59 0.10 3.59 2.75 1.65 1.41 0.83 31 0.00 0.00 0.60 1.62 1.76 3.44 0.39
82
The daily inflow of Kiew Kor Mah reservoir in 2017.
2017 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1 0.00 0.08 0.00 0.02 0.00 0.65 0.34 1.13 5.64 3.55 0.62 0.49 2 0.63 0.12 0.00 0.00 0.00 0.37 0.09 1.03 3.27 1.87 1.23 0.36 3 0.17 0.00 0.00 0.03 0.08 1.02 0.16 0.63 2.15 1.49 0.97 0.49 4 0.24 0.23 0.00 0.00 0.00 1.12 0.29 0.50 1.45 4.55 0.48 1.03 5 0.21 0.21 0.02 0.07 0.00 0.65 0.34 0.33 1.13 4.62 0.85 0.36 6 0.44 0.00 0.00 0.08 0.00 0.37 0.33 0.53 0.76 5.31 0.67 0.54 7 0.56 0.00 0.00 0.01 0.00 0.47 0.23 0.53 1.91 6.74 1.13 0.00 8 0.53 0.00 0.00 0.01 0.00 1.49 0.23 0.73 3.64 4.11 0.74 0.11 9 0.38 0.43 0.00 0.05 0.00 4.67 0.12 0.53 6.36 8.58 1.27 0.22 10 0.69 0.15 0.00 0.00 0.00 2.41 0.20 0.53 5.13 7.83 0.88 0.00 11 0.73 0.07 0.00 0.00 0.00 1.61 0.01 0.84 6.04 3.69 1.01 0.08 12 1.14 0.07 0.05 0.00 0.00 0.70 0.06 0.32 2.95 9.15 0.88 0.22 13 0.98 0.07 0.05 0.00 0.00 0.70 0.12 0.59 2.68 8.07 1.01 0.00 14 0.72 0.00 0.05 0.23 0.00 0.30 0.08 0.39 1.48 6.04 1.02 0.09 15 0.53 0.10 0.00 0.52 0.00 0.61 1.06 0.39 1.08 6.18 1.02 0.09 16 0.51 0.09 0.00 0.14 0.00 0.51 1.06 0.72 1.29 4.35 0.89 0.00 17 0.22 0.00 0.00 0.21 0.15 0.50 1.23 0.92 1.68 4.25 1.02 0.06 18 0.62 0.14 0.00 0.04 3.26 0.35 3.72 1.04 2.88 3.70 0.90 0.00 19 0.36 0.00 0.00 0.00 2.44 0.14 7.33 1.43 2.17 2.93 0.77 0.02 20 0.23 0.22 0.00 0.00 1.59 0.33 5.25 2.79 1.70 3.07 0.90 0.00 21 0.24 0.00 0.01 0.00 0.88 0.26 2.55 2.51 1.37 2.22 0.90 0.02 22 0.24 0.00 0.11 0.00 0.71 0.16 3.47 3.11 1.14 2.40 0.77 0.00 23 0.13 0.00 0.27 0.00 0.71 0.15 3.40 2.71 1.12 2.38 0.77 0.09 24 0.13 0.14 0.38 0.00 0.00 0.23 7.79 1.69 1.26 2.38 1.04 0.00 25 0.13 0.13 0.29 0.00 1.41 0.01 4.81 1.83 1.13 2.73 0.78 0.09 26 0.30 0.13 0.40 0.00 1.06 0.00 5.12 1.45 0.92 2.86 0.65 0.22 27 0.17 0.13 0.40 0.00 2.38 0.46 4.60 1.42 1.53 2.20 0.65 0.22 28 0.17 0.13 0.25 0.00 0.74 0.18 3.57 2.39 1.67 1.93 1.04 0.22 29 0.17 0.07 0.00 0.47 0.20 2.45 1.98 1.84 1.13 0.68 0.22 30 0.11 0.04 0.00 0.47 0.49 2.36 2.50 5.06 1.38 0.50 0.26 31 0.11 0.00 0.84 1.45 6.27 1.14 0.50
83
APPENDIX B.
DAILY WATER STORAGE OF KIEW KOR MAH RESERVOIR (MCM)
84
The daily water storage of Kiew Kor Mah reservoir in 2009.
2009 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1 135.27 103.96 85.14 53.34 40.60 54.18 63.02 51.58 50.82 94.10 125.73 127.75 2 134.16 103.16 84.41 52.25 40.44 56.35 62.50 50.80 51.75 94.83 125.94 127.85 3 132.94 102.37 84.41 51.64 40.29 57.27 61.86 50.25 52.28 95.57 126.10 127.85 4 131.74 101.58 82.30 50.77 40.24 57.64 61.22 49.56 52.75 96.31 126.37 127.75 5 130.43 100.80 79.97 49.90 40.39 58.08 60.59 48.88 53.17 99.42 126.47 127.64 6 129.25 100.02 79.97 49.05 40.34 58.39 60.52 48.20 54.06 101.23 126.58 127.53 7 127.96 99.34 79.97 48.20 40.03 58.76 61.41 47.58 54.42 103.96 126.68 127.32 8 126.68 98.57 79.97 47.30 39.78 59.14 63.15 46.91 54.78 106.40 126.90 127.21 9 125.52 97.81 79.97 46.63 39.98 59.32 63.80 46.36 56.05 107.59 127.00 127.00 10 124.48 97.06 76.64 45.92 39.78 59.45 63.93 45.81 56.91 108.51 127.11 126.79 11 123.13 96.23 76.64 45.42 39.57 60.14 63.67 45.10 57.58 109.82 127.21 126.68 12 121.89 95.49 75.76 44.99 39.32 60.40 63.21 44.50 57.89 110.95 127.32 126.37 13 120.77 94.75 75.76 44.50 39.32 60.59 62.76 44.02 58.26 111.90 127.43 126.16 14 119.65 93.94 75.76 44.12 39.32 60.59 62.18 43.43 59.26 113.24 127.53 125.94 15 118.34 93.22 75.76 44.55 39.98 60.65 61.54 42.95 60.90 114.01 127.53 125.73 16 117.25 92.50 69.25 44.28 41.01 60.52 61.03 45.37 62.31 114.79 127.53 125.52 17 116.06 91.79 69.25 43.75 41.22 61.60 60.40 46.52 63.47 115.37 127.53 125.31 18 114.89 91.00 69.25 43.96 42.16 64.33 59.89 46.91 66.56 115.86 127.53 125.10 19 114.01 90.23 66.43 43.48 42.16 65.57 59.01 47.13 71.30 116.75 127.53 124.89 20 113.24 90.23 66.43 43.11 42.21 66.03 58.76 47.13 75.08 117.45 127.53 124.69 21 112.37 90.23 66.43 42.63 42.21 66.23 58.39 46.91 76.20 118.44 127.53 124.48 22 111.61 90.23 66.43 42.63 42.21 66.16 57.89 46.58 77.09 119.15 127.53 124.27 23 110.95 90.23 66.43 42.68 42.16 66.03 57.27 47.30 79.10 120.05 127.53 124.06 24 110.10 90.23 61.54 42.42 41.95 65.96 57.84 48.48 81.60 120.77 127.53 123.85 25 109.35 90.23 62.00 42.05 41.74 65.83 56.35 49.27 84.41 122.40 127.53 123.64 26 108.60 85.14 60.46 41.89 41.53 65.43 55.69 49.62 85.86 123.33 127.43 123.23 27 107.77 85.14 58.45 41.63 41.27 64.97 55.02 49.79 87.19 123.96 127.53 123.23 28 106.94 85.14 58.45 41.37 41.53 64.58 54.36 49.79 89.61 124.37 127.53 123.02 29 106.21 58.45 41.11 42.26 64.19 53.70 49.79 91.87 124.89 127.64 122.82 30 105.49 58.45 41.11 43.48 63.60 52.99 49.79 93.14 125.10 127.64 122.61 31 104.68 55.42 49.16 52.34 50.13 125.42 122.61 The daily water storage of Kiew Kor Mah reservoir in 2010.
2010 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1 122.20 116.55 106.67 83.70 59.89 52.75 47.41 39.37 104.68 168.39 198.02 184.89 2 121.99 116.36 105.85 82.99 59.64 52.63 47.19 38.46 106.67 169.23 196.61 183.62 3 121.68 116.16 105.04 82.30 59.39 52.46 46.97 38.16 108.23 169.94 196.04 182.50 4 121.48 115.96 104.23 81.60 59.14 52.28 46.80 38.51 109.63 170.64 194.76 181.36 5 121.17 115.77 103.43 80.92 58.95 52.11 46.63 38.86 110.57 171.49 194.76 179.81 6 120.97 115.57 102.55 80.24 58.76 51.93 46.47 38.76 111.23 171.77 194.48 178.40 7 121.07 115.37 101.58 79.50 58.58 51.75 46.47 38.31 111.71 172.05 194.90 177.83 8 121.07 115.18 100.63 78.91 58.20 51.58 48.48 38.71 112.09 172.62 195.19 177.41 9 120.97 114.89 99.85 78.12 57.95 51.35 48.31 39.22 112.09 172.62 195.90 177.27 10 120.87 114.69 99.08 77.41 57.71 51.52 48.14 39.47 113.53 173.18 196.18 176.99 11 120.66 114.40 98.23 76.45 57.27 51.35 47.97 40.80 115.37 173.74 196.61 176.99 12 120.46 114.20 97.56 75.51 56.78 51.11 47.86 43.80 127.75 174.03 196.89 176.85 13 120.26 113.91 96.81 74.58 56.35 50.88 47.69 49.56 135.38 174.31 197.17 176.85 14 120.16 113.53 96.06 73.68 56.05 50.65 47.57 55.14 140.82 174.59 197.59 176.70 15 120.05 113.24 95.32 71.09 55.75 50.30 47.47 58.39 144.42 175.29 197.31 176.56 16 119.75 112.95 94.51 70.07 55.44 50.07 47.30 61.35 150.14 176.00 196.32 176.56 17 119.55 112.66 93.78 69.04 55.50 49.84 46.97 63.28 156.14 176.14 195.33 176.42 18 119.25 112.47 92.98 68.03 55.69 49.62 46.63 64.65 156.14 177.13 194.90 176.42 19 118.95 112.28 93.22 67.03 55.44 49.39 46.47 66.56 156.63 177.83 193.92 176.28 20 118.74 111.90 92.50 65.96 55.26 49.16 46.25 68.37 155.38 179.67 193.07 176.28 21 118.44 111.51 91.79 65.24 55.08 48.99 46.03 69.72 155.38 182.50 192.08 176.14 22 118.24 111.14 91.00 64.65 54.90 48.77 45.86 71.02 155.51 184.19 191.10 176.14 23 118.04 110.76 90.23 64.06 54.72 48.76 45.21 78.25 157.54 184.19 190.53 176.14 24 117.84 110.38 89.46 63.47 54.54 48.71 44.34 83.14 160.08 183.06 190.81 176.00 25 117.64 109.91 88.62 62.82 54.36 48.54 43.48 85.79 160.08 183.06 190.25 175.99 26 117.45 109.44 87.87 63.47 54.12 48.37 42.63 89.23 158.68 183.76 189.40 175.99 27 117.25 108.51 87.12 61.73 53.88 48.20 41.84 91.55 156.91 184.47 188.84 175.86 28 117.15 107.59 86.45 61.22 53.64 48.03 41.53 93.54 157.16 185.03 188.14 175.86 29 117.05 85.79 60.65 53.46 47.80 40.85 95.41 167.96 186.30 187.01 175.86 30 116.95 85.14 60.08 53.23 47.64 40.60 98.57 167.57 195.90 185.74 175.72 31 116.75 84.41 53.05 40.08 101.76 198.02 175.72
85
The daily water storage of Kiew Kor Mah reservoir in 2011.
2011 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1 175.77 170.29 146.48 119.69 97.52 100.45 97.64 115.05 133.32 164.19 187.79 190.91 2 175.72 169.51 145.41 119.13 97.72 98.55 98.33 135.46 132.71 166.21 187.79 190.91 3 175.72 168.98 144.33 118.48 97.95 98.55 99.31 143.80 129.98 167.68 188.20 191.05 4 174.50 168.33 143.26 117.83 99.09 95.37 100.30 148.36 127.96 165.94 188.60 191.05 5 174.98 167.55 142.18 116.90 99.62 93.74 100.98 149.03 126.25 165.27 188.87 191.05 6 174.85 166.67 141.11 116.90 100.68 93.27 101.59 148.13 128.47 165.67 189.28 191.05 7 174.85 165.67 140.33 111.89 103.47 93.43 101.89 144.33 132.92 167.28 189.42 191.05 8 174.72 164.86 139.52 110.98 104.53 93.58 102.12 142.18 135.66 168.72 189.01 190.91 9 174.72 163.93 138.81 110.27 104.97 93.43 102.35 140.74 136.57 170.16 188.20 190.91 10 174.59 163.12 138.00 110.27 106.56 93.04 102.59 140.84 138.20 171.59 188.20 190.78 11 174.59 163.12 137.08 108.77 117.93 92.65 103.38 141.51 138.91 172.90 188.47 190.78 12 174.46 162.99 136.17 107.80 121.12 92.10 104.00 145.25 139.32 173.81 188.87 190.64 13 174.46 162.85 135.25 106.92 120.72 92.02 104.44 146.88 146.21 174.72 189.01 190.37 14 174.33 162.05 134.34 106.65 119.60 92.26 104.53 146.35 154.67 175.11 189.01 190.23 15 174.33 161.24 133.32 105.94 120.53 92.26 105.06 143.80 157.09 175.50 189.01 189.83 16 174.33 160.44 132.71 105.15 121.43 92.33 105.77 140.43 157.62 176.16 189.01 189.55 17 174.20 159.37 132.10 104.27 122.03 92.41 106.74 137.69 157.89 177.07 188.87 189.55 18 174.20 158.43 131.39 103.47 123.64 92.33 107.80 135.25 159.10 178.76 189.01 189.15 19 174.07 157.49 130.68 102.77 123.44 92.33 108.68 136.27 159.90 181.40 189.42 188.74 20 174.07 156.55 129.98 102.20 123.24 92.41 109.21 137.18 160.98 183.03 189.42 187.92 21 173.94 155.48 129.27 101.29 121.93 90.85 109.83 136.67 165.67 184.12 189.55 188.06 22 173.81 154.40 128.47 100.53 120.43 88.66 110.45 135.76 165.67 184.80 189.69 188.06 23 173.55 153.20 127.76 99.19 118.58 88.35 111.65 135.66 164.73 185.34 189.83 188.06 24 173.16 151.99 126.96 99.26 116.72 88.35 111.65 134.44 163.12 185.75 190.10 188.06 25 172.90 150.78 126.15 98.55 114.58 88.43 112.17 133.63 162.32 186.16 190.10 187.92 26 172.51 149.57 125.35 98.33 112.35 89.83 113.75 132.20 163.12 186.56 190.23 187.92 27 172.24 148.50 124.54 98.10 110.27 94.99 114.40 132.71 163.52 186.84 190.51 187.79 28 171.85 147.56 123.64 97.57 108.15 97.87 114.40 135.76 162.99 186.70 190.64 187.79 29 171.46 122.63 97.04 106.03 98.33 113.00 136.57 164.86 185.48 190.64 187.52 30 171.07 121.63 97.26 104.62 97.26 113.00 135.25 165.67 185.61 190.78 187.38 31 170.68 120.62 102.27 110.36 135.25 186.84 187.11 The daily water storage of Kiew Kor Mah reservoir in 2012.
2012 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1 186.84 175.24 130.48 112.72 92.88 88.27 75.15 56.33 53.26 124.44 148.63 165.13 2 186.70 174.85 130.28 112.26 92.18 88.12 74.25 56.17 53.48 125.85 148.76 165.53 3 186.43 174.33 129.17 111.52 91.40 88.27 73.60 55.84 54.85 128.17 148.90 165.80 4 186.29 173.81 128.57 110.98 90.69 88.35 73.35 55.56 56.33 129.98 151.18 166.34 5 186.02 172.90 127.96 110.27 89.99 88.19 73.27 55.18 61.38 131.49 153.06 166.88 6 185.75 170.55 127.26 109.56 89.68 87.88 73.11 54.79 68.39 132.81 154.40 167.14 7 185.48 167.68 126.66 108.95 89.37 87.80 71.97 54.25 71.40 135.25 155.08 167.14 8 185.21 166.34 126.05 108.24 89.91 87.80 70.91 53.70 77.03 138.91 155.61 167.14 9 184.80 162.05 125.45 107.62 90.38 88.12 69.90 53.20 82.75 140.23 156.28 167.28 10 184.25 158.70 124.85 106.92 90.46 88.12 69.01 53.20 89.05 141.51 156.82 167.28 11 183.57 157.62 124.54 106.30 90.30 87.96 68.19 53.59 92.41 142.45 157.22 167.28 12 182.89 154.94 124.24 105.68 90.07 87.57 67.31 53.42 95.29 143.12 157.62 167.14 13 182.22 151.99 123.84 104.97 89.76 87.18 66.41 53.15 96.73 143.66 158.03 167.01 14 181.40 149.03 123.54 104.35 89.37 86.60 65.66 52.93 97.72 144.33 158.56 167.01 15 181.13 145.54 123.44 103.65 88.97 85.93 64.90 52.65 101.21 145.00 159.37 166.74 16 180.86 142.99 123.14 103.03 88.43 85.34 64.15 52.27 106.21 145.27 160.04 166.61 17 180.59 140.84 122.73 102.42 87.95 84.93 63.47 51.94 108.50 145.27 160.31 166.47 18 180.46 139.72 122.23 101.74 87.41 84.26 62.73 51.61 110.01 145.54 160.71 166.21 19 180.20 138.81 121.73 101.13 86.93 83.34 61.92 51.39 110.98 145.68 160.84 166.21 20 180.07 137.89 121.22 100.45 86.35 82.92 61.18 51.34 111.52 145.94 161.11 166.07 21 179.68 136.88 120.43 99.84 85.93 82.25 60.50 51.39 112.26 146.08 161.38 165.94 22 179.29 136.07 119.78 99.24 85.34 81.50 59.69 51.12 114.21 146.35 161.91 165.80 23 178.76 135.05 119.04 98.63 84.93 80.83 59.15 51.12 115.42 146.62 162.32 165.53 24 178.24 134.34 118.39 97.95 84.51 80.25 58.48 51.34 116.07 147.02 162.58 165.40 25 177.85 133.63 117.74 97.34 84.17 79.49 58.14 51.67 116.63 147.15 163.12 165.13 26 177.33 133.12 117.09 96.66 84.34 78.91 57.94 52.76 117.09 147.56 163.26 165.00 27 176.81 132.41 116.44 95.97 84.34 78.17 57.60 53.04 117.74 147.82 163.52 164.60 28 176.81 131.80 115.79 95.21 87.57 77.43 57.40 52.98 118.86 147.96 163.52 164.33 29 176.42 131.19 114.95 94.52 87.96 76.70 57.06 53.04 120.43 148.23 163.79 164.06 30 175.50 114.21 93.74 88.35 75.88 56.88 53.09 123.34 148.36 164.60 163.79 31 175.50 113.47 88.27 56.66 53.20 148.36 163.52
86
The daily water storage of Kiew Kor Mah reservoir in 2013.
2013 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1 163.26 155.61 142.72 120.16 80.50 69.83 65.11 53.15 87.65 138.50 183.30 197.87 2 162.99 155.61 142.05 119.04 79.58 69.49 64.83 53.31 90.15 139.32 182.62 198.18 3 162.72 155.61 141.38 117.83 78.91 69.35 64.56 53.97 91.40 140.43 182.08 198.49 4 162.45 155.48 140.84 116.53 78.82 69.08 64.01 54.47 92.10 143.12 181.94 198.95 5 162.05 155.21 140.54 115.14 78.74 69.21 63.81 54.79 92.72 149.03 182.22 199.42 6 161.91 155.08 140.13 113.19 78.17 68.94 63.67 55.07 93.35 151.85 182.49 199.57 7 161.78 154.54 139.62 111.42 77.84 68.60 63.40 55.18 94.60 156.01 183.03 199.73 8 161.65 154.00 139.01 109.83 77.68 68.25 63.20 55.95 96.05 159.37 183.30 199.88 9 161.38 153.60 138.50 108.20 77.43 67.84 62.93 57.67 98.33 160.84 183.57 200.19 10 161.24 153.06 137.79 106.56 77.11 67.50 62.39 58.68 103.38 161.65 184.12 200.35 11 160.98 152.52 137.28 104.97 76.54 67.78 61.58 60.57 107.62 162.05 184.66 200.35 12 160.44 151.99 136.57 103.03 76.37 68.53 60.77 64.08 110.09 162.32 185.07 200.50 13 160.17 151.45 135.76 101.59 76.05 68.60 60.03 67.84 112.17 162.99 185.48 200.50 14 159.90 150.91 134.95 100.30 75.48 68.73 59.29 69.42 115.51 163.26 185.88 200.50 15 159.50 150.38 134.44 99.09 75.31 68.60 58.54 70.44 118.02 163.52 186.02 200.35 16 159.23 149.97 133.83 97.64 74.82 67.84 57.80 71.40 120.53 163.79 186.29 200.81 17 158.96 149.44 133.32 96.28 74.58 67.84 57.60 73.27 123.14 164.06 186.43 200.35 18 158.70 149.03 132.61 94.75 74.17 68.39 57.19 74.58 124.95 164.86 187.24 200.19 19 158.43 148.63 131.80 93.50 73.76 68.25 56.66 74.66 126.05 167.28 187.65 200.50 20 158.43 147.96 130.78 92.02 73.44 67.98 56.11 75.39 126.76 170.68 189.15 200.19 21 158.03 147.69 129.78 90.62 73.03 67.71 55.67 75.56 127.06 175.11 190.78 200.04 22 157.76 147.29 129.07 89.29 72.70 67.43 55.29 76.46 127.46 181.13 191.86 199.88 23 157.62 146.75 128.47 87.88 72.29 67.09 54.79 77.27 127.86 182.89 192.54 199.73 24 157.49 146.08 127.76 86.52 72.13 66.82 54.52 78.09 127.66 183.30 193.22 199.57 25 157.22 145.41 127.46 85.96 71.89 66.48 54.19 79.16 128.17 183.57 193.50 199.26 26 156.95 144.74 126.76 84.84 71.72 66.27 54.08 79.99 128.67 183.85 194.62 198.64 27 156.68 144.06 125.85 84.09 71.40 66.27 53.53 81.33 130.48 183.57 194.62 198.34 28 156.28 143.39 124.64 83.25 71.07 66.13 53.31 82.59 132.71 183.03 195.24 197.87 29 156.01 123.54 82.25 70.74 65.59 53.15 83.42 134.95 182.49 195.86 197.56 30 155.61 122.43 81.42 70.44 65.31 52.93 84.01 137.08 183.44 197.25 196.63 31 155.61 121.32 70.10 52.87 84.59 183.71 195.55 The daily water storage of Kiew Kor Mah reservoir in 2014.
2014 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1 194.62 177.46 147.15 121.02 84.76 77.52 73.76 75.88 96.20 135.96 125.15 127.16 2 193.63 176.03 146.75 119.88 84.43 77.52 73.44 75.88 99.54 136.27 124.44 127.16 3 192.68 174.07 146.21 118.76 83.67 77.03 73.27 76.05 108.59 136.67 123.64 126.76 4 191.73 171.59 145.68 117.28 82.67 76.78 73.19 77.60 119.88 136.47 123.03 125.85 5 190.51 168.98 145.41 116.35 82.00 76.62 72.95 77.92 126.66 136.27 122.63 124.64 6 189.55 167.68 145.14 115.33 81.83 76.46 72.54 78.17 132.10 136.78 122.13 123.24 7 189.01 162.32 144.47 114.12 81.83 76.29 72.13 78.74 134.64 136.78 121.83 121.22 8 188.33 160.31 143.53 112.26 81.58 76.05 71.72 79.16 136.37 136.47 122.13 119.69 9 187.52 158.03 142.99 110.54 81.16 75.72 71.72 79.58 137.39 136.88 122.93 117.93 10 186.97 157.35 142.45 109.56 80.75 75.48 71.40 81.25 138.00 137.18 123.24 115.98 11 186.29 156.28 142.05 107.98 81.00 75.23 70.99 81.25 138.81 137.08 123.34 114.58 12 185.88 155.34 141.24 106.30 80.66 75.07 71.89 82.17 139.21 136.88 123.54 114.12 13 185.34 155.34 140.97 106.03 80.50 74.91 71.72 82.50 139.52 136.88 124.04 113.00 14 185.21 154.67 140.23 105.41 80.41 75.07 71.89 82.67 140.03 137.08 124.34 111.52 15 184.53 153.87 139.32 104.00 80.08 75.15 71.64 83.34 140.84 136.98 124.54 110.27 16 183.98 153.06 138.81 103.30 79.83 75.88 71.40 83.51 141.11 136.88 124.85 109.39 17 183.98 152.12 137.49 102.20 79.49 75.72 71.56 83.25 141.91 136.57 125.05 108.24 18 183.71 152.12 136.27 101.44 79.16 76.70 71.64 82.92 142.18 135.56 125.15 107.18 19 183.17 150.91 135.25 100.07 79.41 77.19 71.56 82.84 141.65 134.64 125.35 105.41 20 182.62 149.70 134.54 98.71 79.24 77.19 71.64 83.00 140.33 133.73 125.65 103.83 21 182.62 149.44 133.63 97.34 79.16 76.95 71.80 86.52 139.62 132.71 125.85 103.03 22 182.35 149.17 132.20 96.05 79.07 76.70 71.97 91.32 138.20 131.80 126.05 102.42 23 182.08 148.90 131.80 94.52 78.74 76.46 71.72 92.02 138.00 130.88 126.15 101.97 24 181.81 148.76 129.98 93.27 78.58 76.13 72.13 92.57 137.59 129.88 126.35 101.59 25 181.54 148.50 128.97 91.94 78.40 75.80 72.13 92.80 136.88 128.87 126.46 101.13 26 181.54 148.36 128.06 90.77 78.58 75.48 71.70 92.80 136.27 127.96 126.66 100.83 27 181.54 148.09 126.65 89.37 78.25 75.07 71.80 92.96 136.98 127.76 126.66 100.53 28 181.54 147.55 125.45 88.19 77.76 74.66 71.80 93.19 136.47 127.56 126.76 100.22 29 181.13 124.64 87.10 77.68 74.42 73.44 93.97 135.96 127.56 126.86 99.92 30 180.72 123.34 85.93 77.68 74.17 75.31 94.36 136.17 126.96 127.06 99.62 31 178.89 122.13 77.60 75.72 95.29 126.05 99.24
87
The daily water storage of Kiew Kor Mah reservoir in 2015.
2015 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1 98.71 91.01 80.08 69.21 53.15 49.30 41.91 19.27 16.72 21.79 22.55 26.62 2 98.17 90.54 79.58 68.94 52.87 48.98 41.27 19.00 16.49 21.90 22.51 26.65 3 98.02 90.46 79.33 68.53 52.71 48.76 40.58 18.77 16.35 21.98 22.47 26.69 4 97.79 90.22 78.99 68.19 52.49 48.76 39.60 18.51 16.92 22.40 22.44 26.95 5 97.49 90.46 78.74 67.84 52.21 48.87 38.47 18.27 17.71 22.40 22.44 27.07 6 97.19 90.30 78.50 67.57 51.83 48.70 37.30 17.94 17.98 22.36 22.40 27.14 7 96.81 89.68 78.25 67.16 51.56 48.48 36.17 17.74 18.08 22.17 22.40 27.22 8 96.43 88.66 77.92 66.75 53.15 48.21 35.11 17.71 18.08 22.17 22.66 27.29 9 95.97 88.19 77.68 66.48 52.93 47.99 34.14 17.68 18.04 22.13 22.74 27.38 10 95.67 87.80 77.43 65.86 52.60 47.66 33.08 17.61 17.98 22.13 22.78 27.47 11 95.37 87.49 77.35 64.77 52.38 47.49 31.75 17.61 17.98 22.17 22.97 27.51 12 96.58 87.18 77.11 63.54 52.10 47.33 30.99 17.68 18.01 22.25 23.08 27.56 13 96.35 87.33 76.62 62.73 51.83 47.60 30.14 17.78 18.08 22.25 23.62 27.60 14 96.73 87.18 76.13 62.39 51.61 47.49 29.21 17.58 18.08 22.47 23.80 27.65 15 96.58 86.85 75.80 62.19 51.23 47.27 28.45 17.35 18.17 22.59 24.40 27.69 16 96.28 86.35 75.23 61.65 50.95 47.11 27.38 17.05 18.21 22.58 24.67 27.69 17 96.05 85.51 74.91 61.11 50.95 46.89 26.54 16.72 18.24 22.97 24.82 27.69 18 95.75 84.84 74.66 60.37 51.23 46.56 25.53 16.85 18.31 23.01 24.82 27.74 19 95.52 84.17 74.09 59.56 51.23 46.39 23.88 16.42 18.57 23.08 25.30 27.74 20 95.14 83.59 73.44 58.61 51.12 45.96 22.97 16.42 18.67 23.12 25.49 27.78 21 94.75 83.09 72.95 57.67 50.90 45.66 22.78 16.42 19.88 23.12 25.64 27.78 22 94.44 82.50 72.54 56.50 51.17 45.32 22.51 16.39 19.96 23.08 25.75 27.78 23 94.13 81.92 72.21 56.17 51.34 45.02 22.09 16.42 20.68 23.01 25.87 27.78 24 93.74 81.42 71.80 56.06 51.67 44.78 21.75 16.39 20.91 23.01 25.94 27.83 25 93.43 81.25 71.48 55.62 51.39 44.38 21.33 16.92 21.10 22.93 26.05 27.83 26 93.19 80.83 70.99 55.18 51.12 44.03 20.99 16.95 21.22 22.89 26.20 27.74 27 92.88 80.66 70.44 54.85 50.79 43.64 20.61 16.95 21.45 22.85 26.28 27.69 28 92.57 80.33 70.38 54.41 50.51 43.19 20.30 16.95 21.78 22.74 26.39 27.47 29 92.26 70.17 53.97 50.29 42.85 20.11 16.92 21.67 22.70 26.54 27.29 30 91.79 69.90 53.59 50.02 42.45 19.88 16.92 21.75 22.66 26.54 27.29 31 91.32 69.49 49.74 19.63 16.82 22.63 27.25 The daily water storage of Kiew Kor Mah reservoir in 2016.
2016 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1 27.25 25.45 23.54 20.76 15.93 17.98 37.84 47.77 89.99 161.78 177.72 181.94 2 27.10 25.42 23.46 20.68 15.93 18.54 37.89 48.21 91.08 161.65 177.85 181.54 3 26.99 25.34 23.43 20.61 15.96 19.84 38.03 48.21 91.55 161.78 177.46 181.40 4 27.03 25.30 23.43 20.61 15.96 21.22 38.18 48.26 91.86 163.39 177.07 180.99 5 27.03 25.27 23.39 20.57 15.93 22.78 37.93 49.30 92.18 166.74 176.16 180.20 6 26.99 25.23 23.39 20.49 15.93 26.99 37.64 51.45 93.11 170.81 175.37 179.94 7 26.95 25.19 23.35 20.45 15.86 30.81 37.40 52.54 92.96 173.29 174.59 179.55 8 26.92 25.15 23.20 20.42 15.82 32.15 38.62 53.09 93.27 174.46 175.37 179.03 9 26.88 25.12 23.01 20.38 15.79 32.77 39.75 54.36 95.52 168.59 175.24 179.03 10 26.84 25.08 22.78 20.30 15.79 33.25 40.29 54.74 96.66 167.81 174.59 179.29 11 26.80 25.08 22.59 20.00 15.75 33.61 40.68 54.79 97.49 167.14 174.59 179.42 12 26.77 25.04 22.40 19.70 15.75 33.96 41.07 54.68 98.55 166.34 185.21 179.42 13 26.65 25.00 22.17 19.33 15.68 34.18 41.22 54.52 106.83 169.25 189.42 179.81 14 26.54 24.97 22.25 18.90 15.65 34.45 41.32 54.41 115.05 171.46 189.96 179.94 15 26.39 24.93 22.21 18.14 15.65 34.62 41.42 54.52 118.76 170.68 189.96 180.07 16 26.24 24.89 22.17 17.58 15.65 34.80 41.61 54.52 123.64 169.38 189.96 180.07 17 26.02 24.85 22.13 17.02 15.65 34.93 42.01 54.36 128.37 167.81 189.42 180.07 18 25.83 24.74 22.09 16.52 15.61 35.11 42.26 55.34 131.09 166.07 188.60 180.07 19 25.79 24.63 22.05 16.31 15.61 35.15 41.81 55.40 137.18 165.00 187.79 180.07 20 25.75 24.55 22.02 16.00 15.61 35.24 41.52 55.56 142.18 164.86 186.84 180.20 21 25.72 24.37 21.98 15.89 16.00 35.37 43.59 60.10 145.41 165.27 185.61 180.20 22 25.68 23.92 21.94 16.07 16.24 35.42 44.43 65.59 148.76 165.27 184.53 180.07 23 25.68 23.77 21.90 16.03 16.39 35.51 45.27 67.50 151.05 165.00 183.30 179.68 24 25.64 23.73 21.86 16.00 16.59 35.55 45.81 69.90 151.45 164.73 182.08 178.89 25 25.64 23.65 21.83 15.96 16.69 35.77 45.76 72.54 152.66 165.40 180.86 177.72 26 25.60 23.62 21.67 15.96 16.75 35.20 45.66 74.42 154.40 169.25 179.81 176.55 27 25.72 23.62 21.52 15.96 16.82 35.20 45.42 75.88 158.29 173.29 179.81 175.37 28 25.72 23.62 21.37 15.93 16.85 34.84 45.42 80.25 159.50 174.85 180.46 174.33 29 25.72 23.58 21.18 15.93 16.92 34.40 45.27 84.17 159.77 175.11 181.54 173.29 30 25.68 20.99 15.93 17.02 35.42 45.07 87.49 161.24 175.37 181.81 172.24 31 25.57 20.80 17.61 46.39 88.97 177.46 170.42
88
The daily water storage of Kiew Kor Mah reservoir in 2017.
2017 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1 167.94 158.43 150.38 122.83 96.66 114.49 124.85 127.46 139.11 168.72 173.68 180.07 2 166.34 158.16 149.97 121.22 96.66 114.86 123.03 126.46 139.21 168.72 173.68 180.20 3 164.33 157.76 149.57 119.88 96.73 115.88 120.92 126.05 138.50 168.20 172.90 180.46 4 162.45 157.62 149.03 118.48 96.73 117.00 118.86 126.05 137.79 170.29 171.59 181.26 5 160.57 157.49 148.76 116.04 96.73 117.65 116.81 125.95 137.79 171.46 170.68 181.40 6 158.96 157.09 148.36 115.33 96.73 118.02 114.77 126.05 137.89 173.16 170.29 181.67 7 157.62 156.68 148.09 113.65 96.73 118.48 112.63 126.15 139.21 176.16 170.94 181.40 8 157.09 156.15 147.82 111.98 96.73 119.97 110.80 126.56 142.05 170.29 171.20 181.26 9 156.55 156.28 147.42 110.36 96.73 124.64 109.39 126.66 146.62 181.13 171.98 181.26 10 156.68 156.15 147.15 108.68 96.73 127.06 108.15 126.76 150.11 185.07 172.37 180.99 11 156.95 155.88 147.02 107.00 96.73 128.67 107.27 127.16 154.67 184.66 172.90 180.86 12 157.62 155.61 147.02 105.33 96.73 129.37 107.00 127.36 156.15 189.69 173.29 180.86 13 158.43 155.34 147.02 103.56 96.73 130.08 106.92 127.86 157.35 192.95 173.81 180.33 14 158.96 154.94 147.02 102.12 96.73 130.38 106.74 128.17 157.35 192.95 174.33 180.20 15 159.23 154.67 146.88 100.98 96.73 130.99 107.53 128.47 156.95 192.95 174.85 180.07 16 159.50 154.40 146.62 99.46 96.73 131.49 108.33 128.87 155.48 190.37 175.24 179.55 17 159.50 153.73 146.08 98.02 96.88 131.59 109.30 129.17 154.00 187.92 175.77 179.29 18 159.90 153.46 145.68 97.26 100.15 131.49 112.26 129.27 153.73 185.48 176.16 178.89 19 160.04 152.93 144.74 97.04 102.59 131.19 118.67 129.57 153.87 182.62 176.42 178.63 20 160.04 152.79 143.39 96.88 104.18 131.09 122.63 130.78 154.40 180.33 176.81 178.37 21 160.04 152.39 141.91 96.81 105.06 130.88 123.14 131.19 155.21 177.33 177.20 178.11 22 160.04 151.85 140.23 96.81 105.77 130.58 124.34 131.49 155.88 174.59 177.46 177.85 23 159.90 151.45 138.61 96.81 106.47 130.28 126.35 131.09 156.55 171.98 177.72 177.72 24 159.77 151.32 136.88 96.73 106.47 130.08 131.49 130.18 157.35 169.51 178.24 177.46 25 159.63 151.18 135.25 96.73 107.89 129.67 132.92 129.57 158.03 167.81 178.50 177.33 26 159.63 151.05 133.63 96.66 108.95 129.17 134.44 129.47 158.56 168.07 178.63 177.33 27 159.50 150.91 132.10 96.66 111.33 128.67 135.46 130.48 159.77 168.98 178.76 177.33 28 159.37 150.78 130.38 96.66 112.07 127.76 135.46 131.39 161.11 170.29 179.29 177.33 29 159.23 128.47 96.66 112.54 126.96 134.03 131.49 162.32 171.20 179.55 177.33 30 158.96 126.35 96.66 113.00 126.15 132.20 132.71 166.74 172.37 179.81 177.33 31 158.70 124.24 113.84 129.78 136.57 173.29 177.59
89
APPENDIX C.
MONTHLY WATER DEMAND OF DOWNSTREAM AREA (MCM)
90
The monthly water demand of downstream area from 2009 to 2017.
month 2009 2010 2011 2012 2013 2014 2015 2016 2017 1 32.05 6.21 4.46 2.49 0.92 16.12 10.97 0.90 23.26
2 21.13 8.09 18.69 30.81 13.59 29.46 10.73 0.94 9.82
3 28.99 23.48 25.51 3.72 18.62 23.42 7.68 1.37 27.25
4 15.83 23.25 26.74 4.84 37.00 40.54 16.12 0.35 27.46
5 7.08 5.87 46.63 4.39 6.32 9.28 5.35 1.50 1.50
6 10.20 4.81 34.43 4.35 4.60 7.06 5.53 3.39 8.77 7 24.11 11.69 17.58 5.00 11.79 8.60 20.71 8.91 60.19 8 23.31 5.09 107.96 3.77 6.11 7.54 3.84 7.67 37.00 9 7.63 25.22 75.98 0.81 5.62 22.59 5.58 16.08 42.25 10 6.12 20.66 33.65 1.77 21.65 24.64 3.03 52.14 116.24 11 5.38 32.27 11.98 0.69 7.26 9.31 0.34 53.39 19.48 12 5.97 12.71 6.77 0.79 10.27 30.88 0.37 31.69 11.26
91
APPENDIX D.
EXAMPLE OF ASSESS FUTURE IRRIGATION WATER DEMAND
92
For 2020 to 2030 under RCP4.5
- Input data in CROPWAT model
93
Output data from CROPWAT model
- Wet season
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Precipitation deficit 1. Riceth 0 0 0 0 0 0 208 21.4 6.9 10.7 0 0 2. cornTh 0 0 0 0 0 0 0 16.2 22.2 50.3 0 0 3. Small Vegetables 0 0 0 0 0 0 0 0 0 16.3 0 0
Net scheme irr.req. in mm/day 0 0 0 0 0 0 3.6 0.5 0.4 0.7 0 0 in mm/month 0 0 0 0 0 0 110.2 16.5 10.7 22.1 0 0 in l/s/h 0 0 0 0 0 0 0.41 0.06 0.04 0.08 0 0
Irrigated area 0 0 0 0 0 0 53 85 85 87 0 0 (% of total area)
Irr.req. for actual area 0 0 0 0 0 0 0.78 0.07 0.05 0.09 0 0 (l/s/h)
- Dry season
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Precipitation deficit 1. Riceth 126.4 100.1 3.3 0 0 0 0 0 0 0 151 81.6 2. cornTh 130.2 128.9 30.9 0 0 0 0 0 0 0 0 48.7 3. Small Vegetables 81.9 82.7 16.8 0 0 0 0 0 0 0 0 50
Net scheme irr.req. in mm/day 4.1 3.7 0.2 0 0 0 0 0 0 0 4.5 2.5 in mm/month 125.9 102.3 6 0 0 0 0 0 0 0 134.4 78 in l/s/h 0.47 0.42 0.02 0 0 0 0 0 0 0 0.52 0.29
Irrigated area 100 100 100 0 0 0 0 0 0 0 89 100 (% of total area)
Irr.req. for actual area 0.47 0.42 0.02 0 0 0 0 0 0 0 0.58 0.29 (l/s/h)
94