Advances in Meteorology
Volume 2020 (2020), Article ID 8881118, 15 pages
https://doi.org/10.1155/2020/8881118

Research Article

Evaluation of Future Climate and Potential Impact on Streamflow in the Upper Nan River Basin of Northern Thailand

Miyuru B. Gunathilake,1,2 Yasasna V. Amaratunga,¹ Anushka Perera,¹ Imiya M. Chathuranika,¹ Anura S. Gunathilake,³ and Upaka Rathnayake¹

¹Department of Civil Engineering, Faculty of Engineering, Sri Lanka Institute of Information Technology, New Kandy Road, Malabe, Sri Lanka
²Central Engineering Services (Pvt) Limited, Bauddhaloka Mawatha, Colombo 7, Sri Lanka
³Central Engineering Consultancy Bureau, Bauddhaloka Mawatha, Colombo 7, Sri Lanka

Correspondence should be addressed to Miyuru B. Gunathilake; miyurubandaragunathilake@gmail.com

Received 27 May 2020; Revised 16 September 2020; Accepted 8 October 2020; Published 24 October 2020

Academic Editor: Roberto Coscarelli

Copyright © 2020 Miyuru B. Gunathilake et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract.

Water resources in Northern Thailand have been less explored with regard to the impact on hydrology that the future climate would have. For this study, three regional climate models (RCMs) from the Coordinated Regional Downscaling Experiment (CORDEX) of Coupled Model Intercomparison Project 5 (CMIP5) were used to project future climate of the upper Nan River basin. Future climate data of ACCESS_CCAM, MPI_ESM_CCAM, and CNRM_CCAM under Representation Concentration Pathways RCP4.5 and RCP8.5 were bias-corrected by the linear scaling method and subsequently drove the Hydrological Engineering Center-Hydrological Modeling System (HEC-HMS) to simulate future streamflow. This study compared baseline (1988–2005) climate and streamflow values with future time scales during 2020–2039 (2030s), 2040–2069 (2050s), and 2070–2099 (2080s). The upper Nan River basin will become warmer in future with highest increases in the maximum temperature of 3.8°C/year for MPI_ESM and minimum temperature of 3.6°C/year for ACCESS_CCAM under RCP8.5 during 2080s. The magnitude of changes and directions in mean monthly precipitation varies, with the highest increase of 109 mm for ACESSS_CCAM under RCP 4.5 in September and highest decrease of 77 mm in July for CNRM, during 2080s. Average of RCM combinations shows that decreases will be in ranges of −5.5 to −48.9% for annual flows, −31 to −47% for rainy season flows, and −47 to −67% for winter season flows. Increases in summer seasonal flows will be between 14 and 58%. Projection of future temperature levels indicates that higher increases will be during the latter part of the 20th century, and in general, the increases in the minimum temperature will be higher than those in the maximum temperature. The results of this study will be useful for river basin planners and government agencies to develop sustainable water management strategies and adaptation options to offset negative impacts of future changes in climate. In addition, the results will also be valuable for agriculturists and hydropower planners.

1. Introduction

The industrial revolution in 1880s and advancements of science and technology thereafter led to intensive rates of fossil fuel burning and indiscriminate use of natural resources. These activities are the main reasons for the atmospheric warming experienced since the late 20th century [1, 2]. The concentration of carbon dioxide gas in the atmosphere reached groundbreaking 414.5 parts per million in April 2020 escalating from 280 ppm recorded in 1880s. It is highly certain that these concentrations will further increase in future creating warmer atmospheric conditions [2–5]. International summits and meetings have been held with the participation of global leaders, aimed to discuss on reducing future greenhouse gas (GHG) emissions [6]. The warmer temperatures will increase water-holding capacity of air resulting in higher moisture contents, thereby creating intense rainfall and snow events [7]. The warming climate will accelerate the hydrological cycle, with changes in water balance components altering rainfall patterns and magnitude and timings of runoff. The changes in rainfall patterns are dynamic in space and time [8]. The changes in climates will create additional stress on water resources in many parts of the world [9]. The severity of the impacts varies, depending on local climate, topographical features, and the structure of water resource systems [10]. Regional and global changes in climates will affect all aspects of modern humanity including hydrology [11], hydropower generation [10, 12], agriculture [13], food security [14], human health [15], ecosystems [16, 17], groundwater [18], irrigation water requirements [19], and crop yield [20, 21].

The water availability in tropical regions is expected to change adversely due to the profound impacts of climate change [2]. Among these regions, Southeast Asia is highly vulnerable to climate change due to the region’s high dependence of economy on agriculture and water resources [22, 23]. Due to the large complexities in climates of Asia, accurate regional-scale climatic projections are essentially required for this region [24]. Thailand was ranked among the first ten countries worst affected by climatic extremes in 2017 [25]. Significant changes in rainfall patterns, atmospheric temperature, and increased frequency of extreme climatic events were observed recently in Thailand [26, 27]. IPCC [2] claims that changes in temperature and precipitation levels due to climatic change were the prime reason for floods and droughts that recently happened in the country [28–30]. The northern part of Thailand is particularly vulnerable to climate change mainly due to its fragile agroecosystem, inhabited by a resource-poor population [31].

Although many factors affect changes in streamflow, precipitation and temperature are the two dominant factors affecting streamflow in the catchment scale [32]. Due to significant impacts of climate change on hydrology, the hydrologists are keen to evaluate the impacts of climate change on streamflow in regional and local scales. Hence, many researchers including Githui et al. [33], Candela et al. [34], Ali-Safi and Sarukkalige [35], Demaria et al. [36], Salis et al. [37], and Pandey et al. [38] used climate models under different GHG emission scenarios to drive hydrological models to explore the effects of climate change. These studies indicated that severity and magnitude of the impact due to climate change on streamflow depend on the geographical area and context. Hydrologic models driven by global climate model (GCM) projections can be used to investigate possible impacts of future climate on hydrology [39]. However, GCMs limit the accurate simulations of regional climatology due to the inability in accurately simulating features of local climate including topography, cloudiness, orography, and land use due to the inherent coarse resolution ranging between 100 and 250 km [40–42]. Hence, increased tendency is witnessed in applications of regional climate models (RCMs) combined with hydrological models to examine the impact of climate change on hydrology [43]. The resolution of these RCMs is in the range of 12 to 50 km, in proximity of the watershed scale. Yet, RCM-projected climatic variables should be handled with caution since they consist of significant biases due to imperfect conceptualization, internal climatic variability, discretization, and spatial averaging within grid cells [44–46]. These factors limit direct applications of RCMs into hydrological models. GHG emission scenarios based on demographic growth, socioeconomic development, and technological growth add an uncertainty into climate change impact assessments [41]. Usage of multiple RCMs or GCMs under several Representative Concentration Pathways (RCPs) aiming at reducing uncertainties has been carried out in many studies including Minville et al. [47], Xu et al. [48], Nkomozepi and Chung [49], Agarwal et al. [50], Jha et al. [51], Jha and Gassman [52], and Babur et al. [53]. Furthermore, multimodel ensembles have also been encouraged by the Intergovernmental Panel on Climate Change (IPCC) [54]. Multiple GCMs or RCMs combined with several emission scenarios, downscaling methods, and bias correction methods are recommended for climate change modeling studies.

In Thailand, several studies attempted to project future climate and evaluate the impact of future-climate soil erosion [55–57], reservoir inflows [58], rice production [59, 60], irrigation water requirements [61], future precipitation extremes [62], monsoon seasonal precipitation [63], and groundwater [64, 65]. In addition to these studies, Shrestha et al. [66], Sharma and Babel [67], Sharma and Babel [68], Supakosol and Kangrang [69], Sangmanee et al. [70], Ponpang-Nga and Techamahasaranont [71], Deb et al. [72], and Shrestha [73] attempted to evaluate the impact of climate change on streamflow in the basin scale and national level of Thailand. Plangoen and Babel [57] and Plangoen et al. [56] projected future climate based on the Special Report on Emission Scenarios (SRES) of Coupled Model Intercomparison Project 3 (CMIP-3) [74] to evaluate the impact on rainfall erosivity in upper Nan. Hence, the present study is the first study which modeled the upper Nan River basin (UNRB) with the Hydrological Engineering Center-Hydrological Modeling System (HEC-HMS) [75] with the latest GHG emission scenarios from RCPs and climate models from CMIP 5. Reliable estimates of the impact of future climate on hydrology are imperative to formulate adaptation measures to enhance water security and to promote sustainable water management strategies, especially for a developing nation such as Thailand. Acknowledging the needs of fine-scale climate model simulations, three RCMs from the Coordinated Regional Downscaling Experiment (CORDEX) under RCP4.5 (medium-level GHG emission scenario) and RCP8.5 (high-level GHG emission scenario) were used to project future climate in the UNRB in this study. The bias-corrected future climate was then used to drive the HEC-HMS model to investigate the potential impact of future climate on streamflow. Thereafter, the projected future climate and streamflow in near future (2020–2039), midfuture (2040–2069), and far future (2070–2099) were analyzed and compared with baseline conditions (1988–2005) for monthly, seasonal, and annual scales.

The structure of the remaining parts of the paper is organized as follows. The second section provides a detailed description of the study area, data used for this study, and subsequently, the research methodology. Results and discussion of the study are discussed through the third section. The summary, outcomes, and conclusions of this work are discussed in the fourth section of this paper.

2. Materials and Methods

2.1. Study Area

Figure 1 provides the distribution of rainfall gauging, meteorological, and streamflow stations in the upper Nan River basin.

Figure 1: Rainfall stations, meteorological stations, and streamflow stations in the upper Nan River basin of Northern Thailand.

The upper Nan River basin (UNRB) is located between 100°06′30″–101°21′48″E and 17°42′12″–19°37′48″ N in Northern Thailand. It is one of the four major subbasins of the greater Chao Phraya basin [76]. The UNRB drains a geographical area of 13,000 km² [77]. A high amount of rainfall is normally received during July and August [56]. The climate of this region is tropical monsoon and has three well-defined seasons: a rainy season or southwest monsoon season (from mid-May to late-Oct), a winter season with a relatively dry climate (from early-Nov to late-Feb), and a summer season of relatively warmer temperature levels (from mid-Feb to mid-May). The mountainous regions in the north of the UNRB receive an annual rainfall of more than 1000 mm, while the relatively flat regions in the middle receive a rainfall between 600 and 1000 mm. Nearly, 80% of the annual rainfall which is received during the rainy season is delivered by the southwest monsoon developed in the Indian Ocean [56]. The average annual temperature variation is between 20 and 34°C in the UNRB [77, 78].

The topographical variation, soil cover, and land use of the UNRB are illustrated through Figures 2(a)–2(c), respectively. The elevation ranges between 0 and 2020 m above mean sea level. The dominant soil type is slope complex which is often found in forested steep slopes having low permeability rates [79]. The other main soil types found in the UNRB are of silty clay and clay loam nature in Hang Chat and Mae Rim. Nakom Pathom is fine textured [77]. Mae Sai is of poorly drained medium-textured type soils [80]. The forested mountain areas and highlands are dominated in the upper part covering nearly eight five percent of the land area. Forests and agricultural lands account for 71 and 15% of the total land area. The rest of the land comprises grasslands, water bodies, and urban areas. The UNRB provides livelihood for 477,000 inhabitants of indigenous communities [79]. Importantly, the UNRB provides home for 108 fish species amounting 600 types found in Thailand [81].

(a)

(b)

(c)

Figure 2: (a) Topography, (b) soil cover, and (c) land use in the upper Nan River basin.

2.2. Data

Table 1 presents the temporal and spatial data used in this study. Detailed information of observed, gridded climatic datasets, climate models used for future climate projections, and spatial datasets used is given.

Table 1: Temporal and spatial data used.

Data
Spatial/temporal resolution
Source

Temporal datasets

Observed temperature
Point/daily
TMD

Observed rainfall
Point/daily
TMD and RID

Gridded rainfall
0.5° × 0.5°/daily
NOAA-ESRL

Observed streamflow
Point/daily
RID

Spatial datasets

Topography
150 m × 150 m
RTSD

Soil cover
500 m × 500 m
LDD

Land use
90 m × 90 m
LDD

RCMs under RCP4.5 and RCP8.5

RCM
Spatial/temporal resolution
Contributing CORDEX modeling center

ACCESS_CCAM
0.5° × 0.5°/daily
CSIRO

MPI_ESM_CCAM
0.5° × 0.5°/daily
CSIRO

CNRM_CCAM
0.5° × 0.5°/daily
CSIRO

Daily temperature and rainfall records at two meteorological stations with corresponding station IDs 331201 (Nan Meteorological Station) and 331401 (Tha Wang Pha) were obtained from the Thai Meteorological Department (TMD). Daily rainfall data records at five rain gauging stations with station IDs 280022 (Wiang Sa station), 280032 (Na Noi station), 280042 (Pua station), 280102 (Chiang Klang station), and 280143 (Nan Agrometeorological Station) were obtained from the Royal Irrigation Department (RID) of Thailand. All climatic data records were obtained for 1976–2014. In addition, observed daily streamflow data during 1988–2014 at station N.13A (Ban Bun Nak, Wiang Sa station) were obtained from the RID. Furthermore, gridded rainfall data from Climate Prediction Center Global Precipitation (CPC-GP) datasets (available at https://climatedataguide.ucar.edu/) developed by the National Oceanic and Atmospheric Administration Earth System Research Laboratory (NOAA-ESRL) which showed a fairly good correlation with observed data were used to fill missing rainfall days in some of the rain gauging stations.

The digital elevation model (DEM) of resolution 150 m × 150 m was obtained from the Royal Thai Survey Department (RTSD). A land use map of 500 m resolution and a scale of 1 : 50,000 and soil cover map with 1 km resolution and a scale of 1 : 100,000 for the year 2010 were obtained from the Land Development Department (LDD) of Thailand.

Three RCMs, ACCESS_CCAM, MPI_ESM_CCAM, and CNRM_CCAM from CORDEX platform developed by the Commonwealth Scientific and Industrial Research Organization (CSIRO) under RCP4.5 and RCP8.5, were used to project future climate. The driving GCMs of these RCMs are ACCESS 1.0, MPI_ESM_LR, and CNRM_CM5, respectively. The grid domain of RCMs used was CORDEX South Asia 1391M. The units of precipitation and temperature in CORDEX are mm and K, respectively. RCMs from CORDEX [38, 82] of 50 km resolution were used in this study. The climatic data availability of RCMs was 1976–2100 comprising a historical period between 1976 and 2005 and a future period between 2006 and 2100. ACCESS_CCAM, MPI_CCAM, and CNRM_CCAM are referred to as ACCESS, MPI, and CNRM hereafter in the text.

2.3. HEC-HMS Model Description

The HEC-HMS model developed by the United States Army Corps of Engineers is a conceptually based model which can perform event-based and long-term simulations [75]. This study used HEC-HMS 4.3. The HEC-HMS model consists of four model components: the basin model, the meteorological model, time specification model, and the input data model. The HEC-HMS offers 11 precipitation loss, 7 direct runoff, 5 baseflow, and 6 routing methods. In addition, the model also simulates canopy, surface, and loss/gain mechanisms. More details on the development of the model can be found in Pinto [83],and in the studies of Feldman [75] and Scharffenberg and Fleming [84]. Availability in the public domain and user-friendly geographical information system has resulted in extensive applications in different regions of the world. The HEC-HMS model has been used in studies related to climate change impact assessment on streamflow [85, 86], reservoir spillway capacity studies [87], urbanization impact on hydrology [88], and operationalizing a flood forecasting decision support system [89].

2.4. Coordinated Regional Downscaling Experiment (CORDEX)

The CORDEX, an initiative of the World Climate Research Program, aimed at regional climate modeling through dynamically downscaled Coupled Model Intercomparison Project 5 (CMIP5) Atmospheric-Ocean General Circulation Model outputs using multiple RCMs. CORDEX climate datasets are available at https://www.cordex.org/. The RCPs are four greenhouse gas emission concentration trajectories adopted by the IPCC [2]. The four RCPs, RCP2.5, RCP4.5, RCP6.0, and RCP8.5, are named after a possible range of radiative forcing values relative to preindustrial levels. More details on RCP scenarios can be found in Van Vuuren et al. [90]. Different RCPs relate to the radiative forcing, which is a measure of overall change in the Earth’s energy balance due to external perturbation [2]. CORDEX climatic data had been previously used for climate change studies by Sharannya et al. [43], Endris et al. [91], Solman [92], Virgilio et al. [93], and Prein et al. [94] in Asia, South America, Europe, North America, Africa, and Australia.

3. Methodology

3.1. Watershed Model Development

In this study, the DEM was fed into the Hydrological Engineering Center-Geospatial Hydrological Modeling Extension tool in the Arc Geographical Information System to develop the basin model. Watershed characteristics such as river length, basin slope, basin centroid, elevation of basin centroid, flow directions, and streamlines were automatically calculated through this process. The areal rainfall distributions were calculated by the Thiessen polygon method [95]. Manual calibration was performed by satisfying the goodness-of-fit criterion by maximizing Nash–Sutcliffe efficiency (NSE) between observed and simulated discharges. It was ensured that a physically meaningful set of parameters were fixed between the ranges outlined by Feldman [75]. The hydrological model developed through this study was calibrated at station N13.A operated by the RID which drains a geographical area of 8573 km². The watershed model was calibrated between 1988 and 2004 (17 years) and validated between 2005 and 2014 (10 years). The precipitation losses, direct runoff transformation, baseflow, and routing were simulated by the soil moisture accounting method, Clark unit hydrograph, recession method, and Muskingum methods [75].

The soil moisture accounting method was used in conjunction with canopy and surface methods. Interested readers are encouraged to refer Ouédraogo et al. [96] and De Silva et al. [97] for detailed information on the soil moisture accounting method. Maximum infiltration which reflects the hydraulic conductivity and GW1 percolation rate (refer Table 2) were adjusted based on the properties of soil distributed in the study area. All other parameters were adjusted based on the trial-and-error method. Table 2 provides the final calibrated values used in the soil moisture accounting method. The values of parameters in direct runoff transformation, baseflow, and routing were fixed based on the guidelines of De Silva et al. [97], Pinto [83], Scharffenberg and Fleming [84], and Feldman [75].

Table 2: Calibrated values of parameters of the soil moisture accounting model.

Parameter
Value

Soil (%)
70

Ground water 1 (%)
45

Ground water 2 (%)
82

Max infiltration (mm/hr)
10

Imperviousness (%)
20

Soil storage (mm)
125

Tension storage (mm)
75

Soil percolation (mm/h)
0.75

Ground water 1 storage (mm)
100

Ground water 1 percolation (mm/h)
1

Ground water 1 coefficient (h)
100

Ground water 2 storage (mm)
150

Ground water 2 percolation (mm/h)
1

Ground water 2 coefficient (h)
1

3.2. Future Climate Projection and Analysis

This study used three RCMs of the CORDEX as mentioned earlier in Section 2.2. The selection of RCMs used to project future climate in the UNRB was based on their representativeness of simulating climate in Thailand and the Southeast Asian region previously reported by Shrestha et al. [66], Adhikari et al. [98], Shrestha et al. [99], Shrestha et al. [100], Shrestha et al. [101], and Shrestha et al. [66]. The ensemble mean method calculated by simple arithmetic average [102] was used to assess the future climate in the UNRB which was previously used by Shrestha et al. [103] and Bhatta et al. [8]. Furthermore, the bias-corrected future climate was analyzed under three time windows. The future time period was sliced as near future (2020–2040), midfuture (2041–2069), and far future (2070–2099).

3.3. Bias Correction of Rainfall and Temperature

Bias correction methods are used to remove biases in future climatic data due to imperfect conceptualization, internal climatic variability, discretization, and spatial averaging within grid cells [44]. Bias correction can be done through linear scaling, local intensity scaling, variance scaling, distribution mapping, power transformation, quantile mapping, and delta change [104]. Readers are encouraged to refer Teutschbein and Seibert [39] for detailed information on these methods. Bias correction can also be applied to other meteorological variables such as relative humidity, solar radiation, and wind speed [105]. The bias of future climate of RCMs was removed by using observed data compared with the raw hindcast climate of RCMs (1976–2005).

The linear scaling method (LS) [106] is used in this study to remove biases in climate data. The linear scaling approach assumes that correction algorithm and parameterization of historical climate will remain stationary for future climatic conditions. Climate studies carried out in different regions of the globe demonstrate that the LS approach performs well for coarse temporal scale analysis as more complicated methods such as quantile mapping, delta change, and power transformation [107, 108]. Equations for bias correction in precipitation are provided by equations (1) and (2) and for temperature by equations (3) and (4). In this study, the performance of the linear scaling method is evaluated using three statistical indicators: coefficient of determination (R²), root mean square error (RMSE), and standard deviation (SD). Higher R², lower RMSE, and close SD to those of observed data indicate improved performance after bias correction [109, 110]. The linear scaling correction was applied at individual stations.

The notations used in equations (1)–(4) are given as follows.

µm: long-term monthly mean; : bias-corrected; his: raw RCM hindcast; obs: observed data; sim: raw RCM corrected; : precipitation; T: temperature; d: daily.

3.4. Evaluation of the Impact of Future Climate on Streamflow

Climate change impact on streamflow was analyzed under annual, monthly, and seasonal scales with respect to the baseline period. In addition to that, flow quantiles (Q5, Q50, and Q95) were also calculated using FDC software available at https://www.hydrooffice.org/Tool/FDC). Detailed analysis of different flow-related indices will allow studies related to drought and hydropower operations. Q5 is low flow, Q50 is median flow, and Q95 high flow. Determination of the future flow duration curve will be important for designing and improving structures of reservoirs and hydropower plants.

4. Results and Discussion

4.1. Hydrological Model Development

The monthly hydrographs obtained during calibration and validation at streamflow gauging station N.13A (refer Figure 1) are illustrated through Figures 3(a) and 3(b). The flow hydrographs are given in the format of discharge per drainage area (units in mm) for easy comparison to the corresponding rainfall. The visual comparisons and statistical indicators strongly inferred that the developed HEC-HMS model is capable of tracking monthly streamflow of the upper Nan River basin reasonably well although, with few discrepancies. However, increases in rainfall through 1988–1993, 2009, and through 2012–2014 did not produce any relative changes in observed discharge rates. These mismatches might be due to localized storm events. Coefficient of determination (R²), Nash–Sutcliffe efficiency (NSE), and percentage error in volume (P.E.V.) were 0.68, 0.67, and 12.89% and 0.71, 0.70, and 7.4% during calibration and validation, respectively. The performance of the developed hydrological model is acceptable [111]. The model accurately captured peaks in 1997-1998, 2000 and 2004, and 2005 and 2008 with a reasonable accuracy. However, some peaks were slightly ahead of time, while some peaks were slightly late in time. Since there is a good agreement between observed and simulated flows, the calibrated model is adequate to simulate the availability of water resources in the long-term run.

(a)

(b)

Figure 3: (a) Hydrographs during calibration (1988–2004) and (b) validation (2005–2014).

4.2. Future Climate Projections

The bias-corrected future climate of ACCESS, CNRM, and MPI was examined under three time windows with reference to baseline as mentioned earlier in Section 2.2. However, before future projections, the performance of the linear scaling method was examined. The hindcast RCM data were compared with observed historical data, and monthly correction factors were applied to raw RCM data as explained in Section 3.3. Increased R² (coefficient of determination), decreased RMSE (root mean square error), and close standard deviations (SD) to observed data after bias correction have indicated the improved performance of the bias correction method. The ranges of statistical indicators used for evaluation of bias correction before are provided through Table 3.

Table 3: Results of bias correction.

Variable
Statistical indicator
Raw
Bias-corrected

Maximum temperature
R²
0.41–0.43
0.51–0.58

RMSE (°C)
2.23–2.47
1.76–1.98

SD (°C)
2.56–2.82
2.55–2.80

Observed SD (°C) = 2.37–2.42

Minimum temperature
R²
0.67–0.68
0.84–0.86

RMSE (°C)
2.30–2.51
1.56–1.65

SD (°C)
3.78–4.22
3.87–4.25

Observed SD (°C) = 3.92–4.24

Precipitation
R²
0.26–0.38
0.37–0.55

RMSE (°C)
85.95–119.4
72.64–105.53

SD (°C)
41.70–49.98
86.35–127.99

Observed SD (mm) = 87.94–119.2

Maximum temperature, minimum temperature, and precipitation are hereafter referred to as Tmax, Tmin, and in the text. Baseline Tmax, Tmin, and and changes with respect to 2030s, 2050s, and 2080s are given through Table 4.

Table 4: Projected changes in annual average ensemble maximum temperature (Tmax), minimum temperature (Tmin), and precipitation ().

Time period
Baseline Tmax (°C)
Change in Tmax (°C)
Baseline Tmin (°C)
Change in Tmin (°C)
Baseline (mm)
Change in (mm)

RCP4.5

32.53

20.41

1255.0

2030s

0.86

0.42

−56.84

2050s

1.18

1.03

22.16

2080s

1.84

1.97

47.05

RCP8.5

2030s

0.93

0.73

−13.39

2050s

1.88

1.76

21.86

2080s

3.02

3.41

51.87

Baseline average annual ensemble Tmax, Tmin, and are 32.53°C, 20.41°C, and 1255 mm, respectively. The increases in average annual ensemble Tmax are 0.86, 1.18, and 1.84°C under RCP4.5 and 0.93, 1.88, and 3.02°C under RCP8.5 during 2030s, 2050s, and 2080s, respectively. The increases in average annual ensemble Tmin are 0.42, 1.03, and 1.97°C under RCP4.5 and 0.73, 1.76, and 3.41°C under RCP8.5, respectively. Towards the end of the 20st century, increases in Tmin are significantly higher compared to Tmax with higher increases for RCP8.5. Figure 4 depicts the changes in temperature and precipitation levels under different RCMs under RCP4.5 and RCP8.5.

(a)

(b)

(c)

(d)

(e)

(f)

Figure 4: Temperatures and precipitation under RCP4.5 and RCP8.5. (a) For Tmax under RCP4.5. (b) For Tmax under RCP8.5. (c) For Tmin under RCP4.5. (d) For Tmin under RCP8.5. (e) For under RCP4.5. (f) For under RCP8.5.

As seen through Figure 4, the trends in annual temperature (for both maximum and minimum) for all RCMs are unidirectional in increasing order. The highest increases will be during 2080s followed by 2050s and 2030s. Although annual ensemble temperature levels are steadily increasing, annual precipitation levels do not show fixed trends. The annual precipitation ensemble changes vary slightly within different time periods considered (−4.5 to 4.1%). Annual average rainfall will decrease in MPI for all time periods. Annual rainfall will decrease for all RCMs under RCP4.5 during 2030s. It is noteworthy that, under RCP8.5, during 2030s, all RCMs will decrease except ACCESS. With the exception in 2030s, precipitation projections in ACCESS will increase. Different directions and magnitudes in changes in RCMs indicate that there are a wide range of uncertainties associated in future climate projections.

Figure 5 illustrates the changes in ensemble temperature and precipitation in the monthly scale with respect to baseline conditions.

(a)

(b)

(c)

(d)

(e)

(f)

Figure 5: Changes in mean monthly ensemble. (a) For Tmax under RCP4.5. (b) For Tmax under RCP8.5. (c) For Tmin under RCP4.5. (d) For Tmin under RCP8.5. (e) For under RCP4.5. (f) For under RCP8.5.

Highest increases of 4.2°C for Tmin will be during November in 2080s under RCP8.5. Highest increase in Tmax of 2.8°C will occur during August of 2080s under RCP8.5. It is noteworthy that a slight decrease in the minimum temperature during January is observed. However, minimum and maximum temperatures under both RCP4.5 and RCP8.5 show that increases in temperatures are in the ascending order along the timeline. In other words, Tmin in 2080s are the highest, while Tmin in 2050s are higher than 2030s.. The results of this study are consistent with previous studies carried out in the Southeast Asian region by Shrestha et al. [66], Aung et al. [110], and Trang et al. [112]. The ranges of increase in Tmax under RCP4.5 in 2030s, 2050s, and 2080s ranged between (0.45–0.75°C), (0.8–1.7°C), and (1.4–2.4°C) under RCP8.5 (0.58–1.60°C), (1.3–2.5°C), and (2.03–3.1°C). For Tmin, these changes were between (−0.08–0.83°C), (0.24–1.5°C), and (1.8–3.2°C) under RCP4.5 and (0.1–1.2°C), (1–2.3°C), and (2.7–4.2°C) under RCP8.5, respectively.

The highest ensemble increase of mean monthly precipitation is 109 mm during September under ACCESS, while the highest decrease is 77 mm in CNRM. Both highest increase and decrease are under RCP8.5 during 2080s. Increases in rainfall during February, April, and May and decreases during March and July will be expected under both scenarios. The variations in individual RCM projections are shown in Figure 6. The results indicate that the peak rainfall in future time periods can be expected in September in contrast to the current peak rainfall events reported in July and August.

(a)

(b)

Figure 6: Mean monthly precipitations under different RCMs. (a) Under RCP4.5. (b) Under RCP8.5.

4.3. Impact of Future Climate on Streamflow

Future climate impact on streamflow on annual, monthly basis, and seasonal scales was assessed. Since 85% of annual rainfall occurred during the rainy season, the other two seasons were less influential in streamflow. Due to significant contributions of rainfall during the rainy season, decreases in rainy season precipitation (as seen through Figure 5) will decrease annual streamflow. Significant decreases in streamflow in rainy seasons under both RCP scenarios have been observed in this Southeast Asian region by Shrestha et al. [66] and Kim et al. [113]. During the baseline period, daily average streamflow is 217 m³/s, for the rainy season, this is 1080 m³/s, for the summer season, this is 138 m³/s, and for the winter season, this is 37 m³/s. All RCMs under both emission scenarios decreased, while the highest decreases were in MPI in most of the cases. Average of RCM combinations shows that decreases in streamflow will be in ranges of −5.5 to −48.9% for annual flows, −31 to −47% for rainy season flow, and −47 to −67% for winter season flows. Increases in summer seasonal flows will be between 14 and 58%. The highest decreases for streamflows are in MPI (refer Figures A1 and A2 in Appendix in Supplementary Materials).

Figure 7 provides ensemble mean monthly discharges under RCP4.5 and RCP8.5. Mean monthly ensemble streamflow will decrease under all time horizons throughout July–February for RCP4.5 and RCP8.5. Increases in mean monthly ensemble streamflow will be in May-June of the rainy season. Increases will also be there in April and June of summer months. Decreases in streamflow will occur during the entire winter season (November–January). Flow duration curves for ensemble streamflow during 2030s, 2050s, and 2080s are given by Figure A3 for (a) RCP4.5 and (b) RCP8.5 in Appendix. The FDCs imply that decreases are anticipated in high flows while increases in low flows. The discharges corresponding to Q5, Q50, and Q95 are provided through Table A1 in Appendix in Supplementary Materials.

Figure 7: Ensemble mean monthly discharges under RCP4.5 and RCP8.5.

5. Conclusions

This study evaluated the future climate and its impacts on streamflow of the upper Nan River basin using three bias-corrected RCMs and the HEC-HMS hydrological model. The level of changes in precipitation and temperature varies within individual RCMs.

The expected reductions in streamflow rates will have adverse impact on many sectors including aquatic ecosystems, domestic users, irrigation water, and hydropower. The results of the study highlight the necessity of implementing sustainable water management strategies, which is timely needed ahead of future anticipated changes in the climate. The temporal shifts of future peak rainfalls should be taken into consideration by the agricultural sector, and awareness should be created among local farmers. The changes in the flow duration curve should also be taken into account for the future development of hydropower. Since there will be increases in rainfall in the nonrainy season in future, water received during this period can be used for agriculture. The authors recommend further studies to be carried out in the region with different GCMs and RCMs and hydrological models. Furthermore, the results of this study highlight the needs of fine-scale RCMs which can replicate the local climate regimes. Proper adaptation options are recommended for this study area for future. This study did not account for changes in land use. However, since these are also expected to change in future, more uncertainties will be included in the future climate change models.

Data Availability

All the data used in this study are available from the corresponding author upon request.

Conflicts of Interest

The authors declare no conflicts of interest.

Acknowledgments

The authors of this manuscript are grateful to all data provision government agencies of Thailand for allowing them to carry out the work successfully. They are also thankful to the CORDEX and US Army Corps of Engineers. The authors are extremely grateful to Professor Mukand S. Babel for motivating them to carry out the research work. Moreover, the support given by Mr. Binod Bhatta is also acknowledged. The authors are also thankful to Sri Lanka Institute of Technology, Malabe, for providing the research environment to carry out this work in Upaka Rathnayake's Research Laboratory.

Supplementary Materials

Figure A1: ensemble changes in seasonal flows. Figure A2: changes in streamflow. Figure A3: FDCs (a) under RCP4.5 and (b) under 8.5. Table A1: corresponding discharges (in m³/s) for Q5, Q50, and Q95. (Supplementary Materials)

References

B. M. Campbell, S. J. Vermeulen, P. K. Aggarwal et al., “Reducing risks to food security from climate change,” Proceedings of the 2nd International Global Food Security Conference C. Barrett and C. Palm, Eds., , vol. 11, no. 1–92, pp. 34–43, 2016.

IPCC, “Climate change 2014: synthesis report,” in Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, R. K. Pachauri and L. A. Meyer, Eds., p. 151, IPCC, Geneva, Switzerland, 2014.

G. Bala, “Digesting 400 ppm for global mean CO₂ concentration,” Current Science, vol. 104, no. 11, pp. 1471-1472, 2013.

Earth’s CO₂ Homepage, 2020, https://www.co2.earth/.

M. D’Oria, M. Ferraresi, and M. G. Tanda, “Historical trends and high-resolution future climate projections in northern Tuscany (Italy),” Journal of Hydrology, vol. 555, pp. 708–723, 2017.

F. M. Pulselli and M. Marchi, “Global warming potential and the net carbon balance,” Reference Module in Earth Systems and Environmental Sciences, 2015.

K. Trenberth, “Changes in precipitation with climate change,” Climate Research, vol. 47, no. 1, pp. 123–138, 2011.

B. Bhatta, S. Shrestha, P. K. Shrestha, and R. Talchabhadel, “Evaluation and application of a SWAT model to assess the climate change impact on the hydrology of the Himalayan River Basin,” Catena, vol. 181, p. 104082, 2019.

M. Azari, H. R. Moradi, B. Saghafian, and M. Faramarzi, “Climate change impacts on streamflow and sediment yield in the North of Iran,” Hydrological Sciences Journal, vol. 61, no. 1, pp. 123–133, 2016.

P. Qin, H. Xu, M. Liu et al., “Climate change impacts on Three Gorges Reservoir impoundment and hydropower generation,” Journal of Hydrology, vol. 580, Article ID 123922, 2020.

Y. Luo, D. L. Ficklin, X. Liu, and M. Zhang, “Assessment of climate change impacts on hydrology and water quality with a watershed modeling approach,” Science of The Total Environment, vol. 450-451, pp. 72–82, 2013.

S. Shrestha, M. Shrestha, and M. S. Babel, “Modelling the potential impacts of climate change on hydrology and water resources in the Indrawati River Basin, Nepal,” Environmental Earth Sciences, vol. 75, no. 4, p. 280, 2016.

D. Gunasekara, K. Yeon, T. Catherine, and F. Melanie, “Climate change: impacts on Australian agriculture,” Australian Commodities: Forecasts and Issues, vol. 14, no. 4, pp. 657–676, 2007.

T. S. Masipa, “The impact of climate change on food security in South Africa: current realities and challenges ahead,” Jàmbá: Journal of Disaster Risk Studies, vol. 9, no. 1, 2017.

J. A. Patz, D. Campbell-Lendrum, T. Holloway, and J. A. Foley, “Impact of regional climate change on human health,” Nature, vol. 438, no. 7066, pp. 310–317, 2005.

T. Cui, T. Yang, C.-Y. Xu, Q. Shao, X. Wang, and Z. Li, “Assessment of the impact of climate change on flow regime at multiple temporal scales and potential ecological implications in an alpine river,” Stochastic Environmental Research and Risk Assessment, vol. 32, no. 6, pp. 1849–1866, 2018.

H. Reid, “Climate change and biodiversity in Europe,” Conservation and Society, vol. 4, no. 1, pp. 84–101, 2006.

J. Scibek and D. Allen, “Comparing modelled responses of two high-permeability, unconfined aquifers to predicted climate change,” Global and Planetary Change, vol. 50, no. 1-2, pp. 50–62, 2006.

J.-L. Lee and W.-C. Huang, “Impact of climate change on the irrigation water requirement in northern Taiwan,” Water, vol. 6, no. 11, pp. 3339–3361, 2014.

Y. Kang, S. Khan, and X. Ma, “Climate change impacts on crop yield, crop water productivity and food security-a review,” Progress in Natural Science, vol. 19, no. 12, p. 1665, 2009.

L. Shrestha and N. K. Shrestha, “Assessment of climate change impact on crop yield and irrigation water requirement of two major cereal crops (rice and wheat) in Bhaktapur district, Nepal,” Journal of Water and Climate Change, vol. 8, no. 2, pp. 320–335, 2017.

B. Yuen and L. Kong, “Climate change and urban planning in Southeast Asia,” Surveys and Perspectives Integrating and Environment and Society, vol. 2, no. 3, 2009.

IPCC, in Climate Change 2007: the Physical Science Basis, Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, S. Solomon, D. Qin, M. Manning et al., Eds., Cambridge University Press, Cambridge, UK, 2007.

S. Pfister, A. Koehler, and S. Hellweg, “Assessing the environmental impacts of freshwater consumption in LCA,” Environmental Science & Technology, vol. 43, no. 11, pp. 4098–4104, 2009.

D. Eckstein, M. L. Hutfils, and M. Winges, in Global Climate Risk Index 2019. Who Suffers Most from Extreme Weather Events? Weather-Related Loss Events in 2017 and 1998 to 2017, Germanwatche.V. Office, Bonn, Germany, 2018.

Thailand Meteorological Department, Rainfall and Severe Flooding over Thailand in 2011, Climatological Center, Meteorological Development Bureau, Bangkok, Thailand, 2011.

S. Supharatid, “Skill of precipitation projectionin the Chao Phraya river Basinby multi-model ensemble CMIP3-CMIP5,” Weather and Climate Extremes, vol. 12, pp. 1–14, 2016.

P. Wichitarapongsakun, C. Sarin, P. Klomjek, and S. Chuenchooklin, “Rainfall prediction and meteorological drought analysis in the Sakae Krang River basin of Thailand,” Agriculture and Natural Resources, vol. 50, no. 6, pp. 490–498, 2016.

K. L. Ebi and K. Bowen, “Extreme events as sources of health vulnerability: drought as an example,” Weather and Climate Extremes, vol. 11, pp. 95–102, 2016.

E. L. Gale and M. A. Saunders, The 2011 Thailand Flood: Climate Causes and Return Periods in the Proceedings of AOGS 2014, Sapporo, Japan, 2014.

K. Buncha, “The impacts of climate change in Chiangrai,” in Summary of the Seminar on the Impact of Climate Change in the City of Chiang Rai, Asian Cities Climate Change Resilience Network (ACCCRN), Bangkok, Thailand, 2010.

L. Liu, Z. Liu, X. Ren, T. Fischer, and Y. Xu, “Hydrological impacts of climate change in the Yellow River Basin for the 21st century using hydrological model and statistical downscaling model,” Quaternary International, vol. 244, no. 2, p. 211, 2011.

F. Githui, W. Gitau, F. Mutua, and W. Bauwens, “Climate change impact on SWAT simulated stream flow in Western Kenya,” International Journal of Climatology, vol. 29, no. 12, pp. 1823–1834, 2009.

L. Candela, K. Tamoh, G. Olivares, and M. Gomez, “modelling impacts of climate change on water resources in ungauged and data-scarce watersheds,” Application to the Siurana Catchment (NE Spain), vol. 440, pp. 253–260, 2012.

H. I. J. Al-Safi and P. R. Sarukkalige, “Evaluation of the impacts of future hydrological changes on the sustainable water resources management of the Richmond River catchment,” Journal of Water and Climate Change, vol. 9, no. 1, pp. 137–155, 2018.

E. M. C. Demaria, E. P. Maurer, B. Thrasher, S. Vicuña, and F. J. Meza, “Climate change impacts on an alpine watershed in Chile: do new model projections change the story?” Journal of Hydrology, vol. 502, pp. 128–138, 2013.

H. H. C. D. Salis, A. M. D. Costa, J. H. M. Vianna et al., “Hydrologic modeling for sustainable water resources management in urbanized karst areas,” International Journal Environmental Research Public Health, vol. 16, no. 14, p. 2542, 2019.

V. P. Pandey, S. Dhaubanjar, L. Bharati, and B. R. Thapa, “Hydrological response of Chamelia watershed in Mahakali Basin to climate change,” Science of The Total Environment, vol. 650, pp. 365–383, 2019.

C. Teutschbein and J. Seibert, “Bias correction of regional climate model simulations for hydrological climate-change impact studies: review and evaluation of different methods,” Journal of Hydrology, vol. 456-457, pp. 12–29, 2012.

M. Moriondo and M. Bindi, “Comparison of temperatures simulated by GCMs, RCMs and statistical downscaling: potential application in studies of future crop development,” Climate Research, vol. 30, no. 2, pp. 149–160, 2006.

A. Anandhi, V. V. Srinivas, R. S. Nanjundiah, and D. Nagesh Kumar, “Downscaling precipitation to river basin in India for IPCC SRES scenarios using support vector machine,” International Journal of Climatology, vol. 28, no. 3, pp. 401–420, 2008.

K. Parajuli and K. Kang, “Application of statistical downscaling in GCMs at constructing the map of precipitation in the Mekong River basin,” Russian Meteorology and Hydrology, vol. 39, no. 4, pp. 271–282, 2014.

T. M. Sharannya, A. Mudbhatkal, and A. Mahesha, “Assessing climate change impacts on river hydrology-a case study in the Western Ghats of India,” Journal of Earth System Science, vol. 127, no. 6, p. 78, 2018.

C. Teutschbein and J. Seibert, “Regional climate models for hydrological impact studies at the catchment scale: a review of recent modeling strategies,” Geography Compass, vol. 4, no. 7, pp. 834–860, 2010.

O. Varis, T. Kajander, and R. Lemmelä, “Climate and water: from climate models to water resources management and vice versa,” Climatic Change, vol. 66, no. 3, pp. 321–344, 2004.

J. H. Christensen, F. Boberg, O. B. Christensen, and P. Lucas‐Picher, “On the need for bias correction of regional climate change projections of temperature and precipitation,” vol. 35, no. 20, 2008.

M. Minville, F. Brissette, and R. Leconte, “Uncertainty of the impact of climate change on the hydrology of a nordic watershed,” Journal of Hydrology, vol. 358, no. 1-2, pp. 70–83, 2008.

Y.-P. Xu, X. Zhang, Q. Ran, and Y. Tian, “Impact of climate change on hydrology of upper reaches of Qiantang River Basin, East China,” Journal of Hydrology, vol. 483, pp. 51–60, 2013.

T. Nkomozepi and S.-O. Chung, “The effects of climate change on the water resources of the Geumho river basin, Republic of Korea,” Journal of Hydro-Environment Research, vol. 8, no. 4, p. 358, 2014.

A. Agarwal, M. S. Babel, and S. Maskey, “Estimating the impacts and uncertainty of climate change on the hydrology and water resources of the Koshi river basin,” in Managing Water Resources under Climate Uncertainty, S. Shrestha, A. Anal, P. Salam, and M. van der Valk, Eds., Springer Water, Cham, Switzerland, 2015.

M. Jha, J. G. Arnold, P. W. Gassman, F. Giorgi, and R. R. Gu, “Climate chhange sensitivity assessment on upper Mississippi river basin streamflows using swat,” Journal of the American Water Resources Association, vol. 42, no. 4, pp. 997–1015, 2006.

M. K. Jha and P. W. Gassman, “Changes in hydrology and streamflow as predicted by a modelling experiment forced with climate models,” Hydrological Processes, vol. 28, no. 5, pp. 2772–2781, 2014.

M. Babur, M. Babel, S. Shrestha, A. Kawasaki, and N. Tripathi, “Assessment of climate change impact on reservoir inflows using multi climate-models under RCPs-the case of Mangla dam in Pakistan,” Water, vol. 8, no. 9, p. 389, 2016.

R. Knutti, G. Abramowitz, M. Collins et al., “Good practice guidance paper on assessing and combining multi model climate projections,” in Meeting Report of the Intergovernmental Panel on Climate Change Expert Meeting on Assessing and Combining Multi Model Climate Projections, T. F. Stocker, D. Qin, G.-K. Plattner, M. Tignor, and P. M. Midgley, Eds., University of Bern, Bern, Switzerland, 2010.

P. Plangoen and P. Udmale, “Impacts of climate change on rainfall erosivity in the Huai Luang watershed, Thailand,” Atmosphere, vol. 8, no. 12, p. 143, 2017.

P. Plangoen, M. Babel, R. Clemente, S. Shrestha, and N. Tripathi, “Simulating the impact of future land use and climate change on soil erosion and deposition in the Mae nam nan sub-catchment, Thailand,” Sustainability, vol. 5, no. 8, pp. 3244–3274, 2013.

P. Plangoen and M. S. Babel, “Projected rainfall erosivity changes under future climate in the upper nan watershed, Thailand,” Journal of Earth Science and Climatic Change, vol. 5, no. 10, 2014.

T. Thongwan, A. Kangrang, R. Techarungreungsakul, and R. Ngamsert, “Future inflow under land use and climate changes and participation process into the medium-sized reservoirs in Thailand,” Advances in Civil Engineering, vol. 1–17, 2020.

S. Boonwichai, S. Shrestha, M. S. Babel, S. Weesakul, and A. Datta, “Evaluation of climate change impacts and adaptation strategies on rainfed rice production in Songkhram River Basin, Thailand,” Science of the Total Environment, vol. 652, no. 189, pp. 189–201, 2019.

S. Shrestha, R. Chapagain, and M. S. Babel, “Quantifying the impact of climate change on crop yield and water footprint of rice in the Nam Oon irrigation project, Thailand,” Science of the Total Environment, vol. 599-600, pp. 689–699, 2017.

S. Boonwichai, S. Shrestha, M. S. Babel, S. Weesakul, and A. Datta, “Climate change impacts on irrigation water requirement, crop water productivity and rice yield in the Songkhram River Basin, Thailand,” Journal of Cleaner Production, vol. 198, pp. 1157–1164, 2018.

R. T. Cooper, “Projection of future precipitation extremes across the bangkok metropolitan region,” Heliyon, vol. 5, no. 5, p. e01678, 2019.

N. Singhrattna and M. Singh Babel, “Changes in summer monsoon rainfall in the upper Chao phraya river basin, Thailand,” Climate Research, vol. 49, no. 2, pp. 155–168, 2011.

P. Tanachaichoksirikun, U. Seeboonruang, and P. Saraphirom, “Impact of climate change on the groundwater sustainability in the lower Chao Phraya basin, Thailand,” in Proceedings of the 4th International Conference on Engineering, Applied Sciences and Technology (ICEAST 2018) Exploring Innovative Solutions for Smart Society, Phuket, Thailand, 2018.

U. Seeboonruang, “Impact assessment of climate change on groundwater and vulnerability to drought of areas in Eastern Thailand,” Environmental Earth Sciences, vol. 75, no. 1, p. 42, 2016.

S. Shrestha, B. Bhatta, M. Shrestha, and P. K. Shrestha, “Integrated assessment of the climate and landuse change impact on hydrology and water quality in the Songkhram River Basin, Thailand,” Science of The Total Environment, vol. 643, pp. 1610–1622, 2018.

D. Sharma and M. S. Babel, “Assessing hydrological impacts of climate change using bias-corrected downscaled precipitation in Mae Klong basin of Thailand,” Meteorological Applications, vol. 25, no. 3, pp. 384–393, 2018.

D. Sharma and M. S. Babel, “Application of downscaled precipitation for hydrological climate-change impact assessment in the Upper Ping River Basin of Thailand,” Climate Dynamics, vol. 41, no. 9-10, pp. 2589–2602, 2013.

J. Supakosol and A. Kangrang, “Climate change impact on water quality in song Khram basin, Thailand,” International Review of Civil Engineering (IRECE), vol. 7, no. 6, p. 176, 2016.

C. Sangmanee, G. Wattayakorn, and S. Pramot, “Simulating changes in discharge and suspended sediment loads of the Bangpakong River, Thailand driven by future climate change,” Maejo International Journal of Science and Technology, vol. 7, pp. 72–84, 2013.

P. Ponpang-Nga and J. Techamahasaranont, “Effects of climate and land use changes on water balance in upstream in the Chao Phraya River basin, Thailand,” Agriculture and Natural Resources, vol. 50, no. 4, pp. 310–320, 2016.

P. Deb, M. S. Babel, and A. F. Denis, “Multi-GCMs approach for assessing climate change impact on water resources in Thailand,” Modeling Earth Systems and Environment, vol. 4, no. 2, pp. 825–839, 2018.

S. Shrestha, “Assessment of water availability under climate change scenarios in Thailand,” Journal of Earth Science & Climatic Change, vol. 5, no. 3, p. 184, 2014.

L. Touzé-Peiffer, A. Barberousse, and H. L. Treut, “The Coupled Model Intercomparison Project: history, uses, and structural effects on climate research,” WIRES Climate Change, vol. 11, no. 3, 2020.

A. D. Feldman, Hydrologic Modeling System HEC-HMS: Technical Reference Manual, US Army Corps of Engineers, Hydrologic Engineering Center HEC, Davis, CA, USA, 2000.

T. Sayama, Y. Tatebe, and S. Tanaka, “An emergency response-type rainfall-runoff-inundation simulation for 2011 Thailand floods,” Journal of Flood Risk Management, vol. 10, no. 1, pp. 65–78, 2017.

K. Pakoksung and M. Takagi, “Effect of satellite based rainfall products on river basin responses of runoff simulation on flood event,” Modeling Earth Systems and Environment, vol. 2, no. 3, p. 143, 2016.

Y. Trisurat, R. Alkemade, and E. Arets, “Projecting forest tree distributions and adaptation to climate change in northern Thailand,” Journal of Ecology and Natural Environment, vol. 1, no. 3, pp. 55–63, 2009.

W. Wangpimool, K. Pongput, C. Sukvibool, S. Sombatpanit, and P. W. Gassman, “The effect of reforestation on stream flow in Upper Nan river basin using Soil and Water Assessment Tool (SWAT) model,” International Soil and Water Conservation Research, vol. 1, no. 2, pp. 53–63, 2013.

Land Development Department of Thailand (LDD), 2020, http://www.ldd.go.th/ldd_en/.

A. Lothongkham, “Species diversity of fishes in the Nan River basin (the Chao Phraya river system) in Nan province, northern Thailand,” Kasetsart University, Bangkok, Thailand, 2008, M.S. Thesis.

W. Cabos, D. V. Sein, A. Durán-Quesada et al., “Dynamical downscaling of historical climate over CORDEX Central America domain with a regionally coupled atmosphere-ocean model,” Climate Dynamics, vol. 52, no. 7-8, pp. 4305–4328.

M. J. Pinto, Hydrologic Modeling System HEC-HMS: Quick Start Guide, US Army Corps of Engineers, Hydrologic Engineering Center HEC, Davis, CA, USA, 2013.

W. A. Scharffenberg and M. J. Fleming, Hydrologic Modeling System HEC–HMS User’s Manual, U.S. Army Corps of Engineers, Institute for Water Resources, Hydrologic Engineering Center, Davis, CA, USA, 2006.

U. Ghimire, M. S. Babel, S. Shrestha, and G. Srinivasan, “A multi-temporal analysis of streamflow using multiple CMIP5 GCMs in the Upper Ayerawaddy Basin, Myanmar,” Climatic Change, vol. 155, no. 1, pp. 59–79, 2019.

R. Mahmood, S. Jia, and M. Babel, “Potential impacts of climate change on water resources in the Kunhar river basin, Pakistan,” Water, vol. 8, no. 1, p. 23, 2016.

J. Bhatt, P. H. Pandya, and H. M. Gandhi, “Hydrological model for design flood estimation for the Bhadar Dam,” in Proceedings of the National Conference on Recent Advances in Civil and Structural Engineering, (RACSE-’14), Maharashtra, India, 2014.

S. Suriya and B. V. Mudgal, “Impact of urbanization on flooding: the Thirusoolam sub watershed-a case study,” Journal of Hydrology, vol. 412-413, pp. 210–219, 2012.

A. Agarwal, U. Ghimire, H. H. Than et al., “Operationalizing a flood forecasting decision support system for Ayeyarwady river, Myanmar,” International Journal of River Basin Management, vol. 1, 2020.

D. P. Van Vuuren, J. Edmonds, M. Kainuma et al., “The representative concentration pathways: an overview,” Climatic Change, vol. 109, no. 1-2, p. 5, 2011.

H. S. Endris, P. Omondi, S. Jain et al., “Assessment of the performance of CORDEX regional climate models in simulating east African rainfall,” Journal of Climate, vol. 26, no. 21, pp. 8453–8475, 2013.

S. A. Solman, “Regional climate modeling over South America: a review,” Advances in Meteorology, vol. 2013, p. 1, 2013.

G. D. Virgilio, J. P. Evans, A. D. Luca et al., “Evaluating reanalysis-driven CORDEX regional climate models over Australia: model performance and errors,” Climate Dynamics, vol. 53, pp. 2985–3005, 2019.

A. F. Prein, M. S. Bukovsky, L. O. Mearns, C. L. Bruyère, and J. M. Done, “Simulating north American weather types with regional climate models,” Frontiers in Environmental Science, vol. 7, 2019.

A. H. Thiessen, “District no. 10, great basin,” Monthly Weather Review, vol. 39, no. 7, p. 1082, 1911.

W. Ouédraogo, J. Raude, and J. Gathenya, “Continuous modeling of the mkurumudzi river catchment in Kenya using the HEC-HMS conceptual model: calibration, validation, model performance evaluation and sensitivity analysis,” Hydrology, vol. 5, no. 3, p. 44, 2018.

M. M. G. T. De Silva, S. B. Weerakoon, and S. Herath, “Modeling of event and continuous flow hydrographs with HEC-HMS: case study in the kelani river basin, Sri Lanka,” Journal of Hydrologic Engineering, vol. 19, no. 4, pp. 800–806, 2014.

R. K. Adhikari, S. Mohanasundaram, and S. Shrestha, “Impacts of land-use changes on the groundwater recharge in the Ho Chi Minh city, Vietnam,” Environmental Research, vol. 185, Article ID 109440, 2020.

S. Shrestha, S. K. Gunawardana, T. Piman, and M. S. Babel, “Assessment of the impact of climate change and mining activities on streamflow and selected metal’s loading in the Chindwin River, Myanmar,” Environmental Research, vol. 181, Article ID 108942, 2020.

S. Shrestha, N. Imbulana, T. Piman, S. Chonwattana, S. Ninsawat, and M. Babur, “Multimodelling approach to the assessment of climate change impacts on hydrology and river morphology in the Chindwin River Basin, Myanmar,” Catena, vol. 188, Article ID 104464, 2020.

M. Shrestha, S. C. Acharya, and P. K. Shrestha, “Bias correction of climate models for hydrological modellin are simple methods still useful?” Meteorological Applications, vol. 24, no. 3, pp. 531–539, 2017.

S. J. Lambert and G. J. Boer, “CMIP1 evaluation and intercomparison of coupled climate models,” Climate Dynamics, vol. 17, no. 2-3, pp. 83–106, 2001.

S. Shrestha, A. R. Bajracharya, and M. S. Babel, “Assessment of risks due to climate change for the upper Tamakoshi hydropower project in Nepal,” Climate Risk Management, vol. 14, pp. 27–41, 2016.

J. Chen, F. P. Brissette, D. Chaumont, and M. Braun, “Finding appropriate bias correction methods in downscaling precipitation for hydrologic impact studies over North America,” Water Resources Research, vol. 49, no. 7, pp. 4187–4205, 2013.

G. H. Fang, J. Yang, Y. N. Chen, and C. Zammit, “Comparing bias correction methods in downscaling meteorological variables for a hydrologic impact study in an arid area in China,” Hydrology and Earth System Sciences, vol. 19, no. 6, pp. 2547–2559, 2015.

G. Lenderink, A. Buishand, and W. van Deursen, “Estimates of future discharges of the River Rhine using two scenario methodologies: direct versus delta approach,” Hydrology and Earth System Sciences, vol. 11, no. 3, pp. 1145–1159, 2007.

P. Stanzel and H. Kling, “From ENSEMBLES to CORDEX: evolving climate change projections for Upper Danube River flow,” Journal of Hydrology, vol. 563, pp. 987–999, 2018.

U. Ghimire, G. Srinivasan, and A. Agarwal, “Assessment of rainfall bias correction techniques for improved hydrological simulation,” International Journal of Climatology, vol. 39, no. 4, pp. 2386–2399, 2019.

M. S. Babel, S. P. Bhusal, S. M. Wahid, and A. Agarwal, “Climate change and water resources in the Bagmati river basin, Nepal,” Theoretical and Applied Climatology, vol. 115, no. 3-4, pp. 639–654, 2014.

M. T. Aung, S. Shrestha, S. Weesakul, and P. K. Shrestha, “Multi-model climate change projections for Belu river basin, Myanmar under representative concentration pathways,” Journal of Earth Science and Climatic Change, vol. 7, no. 1, p. 323, 2016.

D. Moriasi, J. G. Arnold, M. W. Van Liew, R. L. Binger, R. D. Harmel, and T. L. Veith, “Model evaluation guidelines for systematic quantification of accuracy in watershed simulations,” Transactions of ASABE 2007, vol. 50, pp. 885–900, 2007.

N. T. T. Trang, S. Shrestha, M. Shrestha, A. Datta, and A. Kawasaki, “Evaluating the impacts of climate and land-use change on the hydrology and nutrient yield in a transboundary river basin: a case study in the 3S River Basin (Sekong, Sesan, and Srepok),” Science of The Total Environment, vol. 576, p. 586, 2017.

J. Kim, J. Choi, C. Choi, and S. Park, “Impacts of changes in climate and land use/land cover under IPCC RCP scenarios on streamflow in the Hoeya River Basin, Korea,” Science of The Total Environment, vol. 452-453, pp. 181–195, 2013.