INTRODUCTION
Background
Climate change poses a significant challenge for humanity in the 21st century, particularly for island and coastal nations like Vietnam, which is among the countries most affected by global warming and rising sea levels The Inter-governmental Panel on Climate Change (IPCC) highlights that climate change intensifies the severity of natural disasters, worsening the impacts of floods and droughts, leading to increased water scarcity and contamination of water supplies (UNICEF, 2021) Consequently, Vietnam faces critical challenges in water resource management and usage, directly linked to the adverse effects of climate change.
Climate change increases temperature and changes the seasonal distribution of rainfall, causing changes in inflows, floods frequency and drought characteristics (Tabari,
In 2020, the rapid socio-economic development and population growth have significantly increased water demand, resulting in an imbalance between supply and demand This imbalance has led to heightened competition for water resources and has triggered a crisis in water usage.
The interconnection between economic development and social security underscores the urgent need for comprehensive and targeted solutions aimed at sustainable water management, particularly in the face of climate change Implementing long-term strategies is essential to address these pressing challenges effectively.
The thesis titled "Assessing Climate Change Impacts on Surface Water Flow toward Sustainable Exploitation and Utilization of Water Resources in the Srepok River Basin in Vietnam" aims to evaluate the effects of climate change on water resources It will analyze the challenges associated with water resource usage and propose targeted solutions to address these issues effectively.
Due to time constraints and other factors, addressing the problem on a national scale within the scope of a master's thesis is challenging Therefore, the author has chosen to focus on the Srepok basin to investigate and resolve these issues.
The Srepok River Basin, a sub-basin of the Lower Mekong located in Vietnam, covers an area of 18,264 km² and spans four provinces: Gia Lai, Dak Lak, Dak Nong, and Lam Dong (MONRE, 2017) Recent studies indicate that climate change is impacting the Srepok basin, evidenced by a trend of reduced rainfall and rising temperatures (Dao, 2013) Projections using the Statistical Downscaling Model (SDSM) suggest that while annual rainfall and temperatures are expected to rise in the future, there will be a decrease in rainfall during the dry season (Dao et al.).
A study based on three climate change scenarios from the IPCC’s CMIP5 program reveals that the Srepok River basin is experiencing a significant decrease in water flow, particularly during the transition from the wet to dry season, with a nearly 80% reduction observed at Duc Xuyen station in June This decline heightens the risk of prolonged drought conditions in the basin, highlighting the implicit link between climate change and the increasing intensity and unpredictability of extreme weather events (Nguyen et al., 2018).
The Srepok River basin faces critical water resource management issues, including flooding, drought, environmental degradation, water pollution, erosion, and reservoir sedimentation Over-exploitation of groundwater and conflicts over water usage and river boundaries exacerbate the situation In 2016, prolonged drought led to over 70% of dams and 80% of dug wells running dry, resulting in the loss of more than 3,000 hectares of coffee and 2,200 hectares of pepper in the Central Highlands By March 2020, river flows in the region were 15-70% below the multi-annual average, causing significant economic damage and impacting local communities.
Rapid population growth, agricultural expansion, and industrial development have significantly increased water demand, leading to heightened competition and conflicts among water users To address these challenges, it is essential to analyze and evaluate the water flow in the basin both currently and in the context of future climate change, enabling the formulation of timely solutions.
The research question and hypothesis
Table 1.1 shows the research questions and hypotheses of the study
Table 1.1: Research question and hypothesis
How does climate change affect surface flow in the Srepok river basin?
In the future, the flow will increase in the rainy season and decrease in the dry season
How does climate change affect sustainable exploitation and use of water resources?
Under climate change, the water deficit in the Srepok river basin in the future is higher than that at present
The water resources are not enough to supply for all sectors, especially in agriculture
What options can be utilized to mitigate water shortage in the Srepok river basin under climate change?
General solutions and recommendations are given with focusing on agricultural and structural solutions, which are suitable for the Srepok river basin.
Research objectives and tasks
The research objectives and tasks are described in Table 1.2
Ob1: Simulation of the surface water flow of the study basin at present and in the future
Adopt and analyze climate change and sea-level rise scenarios for Viet Nam (RCP8.5) published in
Applying a MIKE NAM model to simulate current flow and predict the flow in the future
Ob2: Assessing the exploitation and utilization
Assessing water demand for each sector
Applying a MIKE HYDRO BASIN model to simulate the water balance at present and in the
Research objectives Tasks of water resources at present and in the future future
Assessing sustainable exploitation and utilization of water resources in the context of climate change
Ob3: Proposing solutions and recommendations to exploit and use water resources sustainably in the context of climate change
Collecting information related to solutions and recommendations to improve the effectiveness of water resources exploitation and utilization
Proposing suitable solutions, recommendations for the Srepok river basin conditions.
Objects and scope of the research
The research studies the impacts of climate change on water resources temporally
This study examines the impact of climate change on surface flow and water balance in the Srepok River basin in Vietnam, aiming to promote sustainable management and utilization of water resources.
Matrix of learning outcomes for the master's thesis
This study could lead to several results and outcomes listed below:
- Result 1 (R1): The effects of climate change on the flow at present and in the future
It is predicted that the flow will increase in the flood season and decrease in the dry season
The sustainable management and utilization of water resources in the Srepok River basin are increasingly challenged by rising water demand and the impacts of climate change As climate change exacerbates water scarcity, it is crucial to address these challenges to ensure the availability of water resources for current and future generations.
- Result 3 (R3): Solutions, recommendations for Srepok river basin
- Outcome 1 (O1): References for planning and socio-economic development of the provinces
Table 1.3: Relations between results of the Master's thesis and MCCD's
PLOs Results of the Master’s thesis Other outcomes
Contribution of the thesis
The thesis thoroughly examined the natural features and water resource characteristics of the Srepok River basin, which was divided into six sub-basins to optimize the exploitation and utilization of its natural conditions and water resources.
(ii) Setting up the MIKE NAM and the MIKE HYDRO BASIN models version 2021 for the Srepok river basin at present and in the future under climate change
- Update the latest climate change scenario
- Update new water usage information
- Update the latest regulations, standards, circulars and decisions related to water resources in the Srepok river basin
(iii) Assessment and forecast of water resources in the basin: water resources potential, surface flows, shortage water area under the impact of climate change
(iv) Based on the water shortage identification in each sub-basin and water-using sector Proposing sustainable water exploitation and use solutions in the Srepok river basin.
Framework of the Master’s thesis
Figure 1.1: Framework of the thesis
Literature review
Water significantly influences all elements of the climate system, including the atmosphere, hydrosphere, cryosphere, land surface, and biosphere Variations in atmospheric water vapor, cloud cover, and ice levels affect the Earth's radiation balance, playing a vital role in the climate's response to increasing greenhouse gas emissions (Bates et al., 2008).
Since the 1980s, research has increasingly focused on the impact of climate change on water resources In 1985, the World Meteorological Organization (WMO) evaluated how climate change affects hydrology According to the IPCC (2008), both observational data and climate predictions indicate that freshwater resources are highly vulnerable to climate change, which can lead to significant degradation As temperatures rise, the atmosphere's capacity to hold moisture increases, intensifying the hydrological cycle Key changes in this system involve alterations in the seasonal distribution, intensity, and frequency of precipitation.
7 causing changes in water availability in surface runoff and soil moisture (Judith et al.,
Climate change studies typically focus on how shifting temperatures, precipitation patterns, and evaporation rates affect water resources and supply in upstream areas However, due to the variability of regional climate change, climatologists can only provide informed predictions about future conditions As a result, researchers in the field often refer to potential outcomes as "scenarios" to describe the anticipated impacts of climate change.
A General Circulation Model (GCM) is a crucial tool for understanding climate and forecasting weather and climate change globally Despite their effectiveness in simulating past and future climates, GCM outputs are challenging to apply at local scales due to their coarse spatial and temporal resolution Consequently, various scaling methods are necessary The Coupled Model Intercomparison Project Phase 5 (CMIP5) introduced Representative Concentration Pathways (RCPs), which have been validated as a reliable approach for exploring future climatic conditions, facilitating climate change impact assessments, and informing mitigation strategies.
Numerous studies have demonstrated the impact of climate change on the hydrological cycle at the catchment scale, both globally and in Vietnam, including research on the Rhine and Seine rivers, as well as the Red-Thai Binh river basin and the Mediterranean basin Taye et al (2011) utilized downscaled General Circulation Model (GCM) data to create climate change scenarios for the upper Nile Basin, revealing an expected increase in flow in the Nyando basin by 2050, while the Tana basin exhibited ambiguous trends in discharge levels Additionally, Kim et al (2011) applied the SRES A2 climate change scenario to the Han River basin, indicating an increase in minimum discharge across various periods compared to current hydrological conditions.
8 climate data obtained from two Regional Climate Models (PRECIS–HADCM3Q0 and PRECIS–ECHAM05), based on the IPCC–SRES A1B scenario to simulate hydrological impacts in the Koshi River Basin, Nepal
The Mekong River Delta is identified as one of the most vulnerable regions in Southeast Asia to climate change impacts, according to Yusuf and Francisco (2009) The effects are inconsistent, with studies indicating that the dry season may become longer and more intense in some areas, while the rainy season could shorten and intensify in others This variability may lead to increased seasonal water shortages, flooding, and saltwater intrusion (Hoanh et al., 2003; Snidvongs et al., 2003; Chinavanno, 2004) Utilizing CMIP5 climate predictions, Hoang et al (2018) assessed the hydrological impacts in the delta, revealing an increase in river discharge both seasonally and annually, alongside a rise in intensity and frequency of these changes Conversely, the occurrence of shallow currents is expected to decrease under climate change conditions.
Tran (2010) assessed the impacts of climate change on water resources in the Huong River basin, Central Vietnam, using six climate change scenarios The findings indicated that climate change would increase rainfall, resulting in higher river discharge However, decreased precipitation and increased evaporation would lead to reduced river runoff In 2011, Tran evaluated the socio-economic impacts of climate change and proposed adaptation strategies for Thua Thien – Hue province Additionally, Tran (2011) forecasted flooding levels in the Dong Nai river basin for the year 2020.
By the year 2100, climate change is expected to significantly impact water flow patterns, as demonstrated by maps generated under three different climate scenarios: B1, B2, and A1FI, alongside varying levels of sea-level rise These studies reveal a noticeable shift in forecasted water flows compared to the average annual flow, highlighting the urgent need for climate adaptation strategies.
Climate change significantly impacts both surface and groundwater resources Bui (2013) outlined various climate change scenarios, including changes in evaporation, temperature, rainfall, and water levels during both dry and rainy seasons, to inform model inputs The groundwater flow model evaluated how climate change affects the absolute elevation of water levels, the extent of groundwater level decline, and overall groundwater storage Additionally, a predicted saline gradient model analyzed the repercussions of climate change on groundwater salinity.
9 salinity margin shift in aquifers Across the delta as a whole, in the 2020–2060 period, total water storage increases, but in the 2070–2100 period, total storage decreases in B1, B2 and A2 scenarios
Climate change is expected to alter global rainfall patterns and temperatures, resulting in significant changes to water flow To analyze these hydrological changes, researchers utilize hydrological simulation tools The three primary models employed for estimating variations in hydrology and water resources are statistical experience models, conceptual hydrological models, and distributed hydrologic models.
Jakimavičius and Kriaučiūnienė (2013) conducted a hydrological assessment using the HBV software to evaluate the impact of climate change on the water balance of the Curonian Lagoon Their findings indicate that, in comparison to the baseline period of 1961–1990, both inflow and outflow from the Baltic Sea to the Lagoon are projected to decrease significantly by 20.4% and 16.6%, respectively, between 2011 and 2100.
Uniyal et al (2015) employed the Soil and Water Assessment Tool (SWAT) model to assess the effects of climate change on the water balance components of the Upper Baitarani River basin in Eastern India Their findings indicate that future climatic conditions are expected to have a significant impact on streamflow in the region by the end of the twenty-first century Similarly, Quyen et al conducted research in the Srepok River basin, highlighting the broader implications of climate change on water resources.
In 2018, researchers utilized the SWAT model to evaluate the effects of climate change on water and soil resources, employing three detailed statistical scenarios from the IPCC CMIP5 program The findings revealed a decrease in runoff during dry months and an increase during rainy months Additionally, the SWAT model was used to simulate flow changes in the Dong Nai River basin due to climate change.
2012) and Cau river basin (Tran et al., 2017)
Nguyen (2015) utilized the HEC-RAS hydraulic model, along with the HEC-GeoRAS module, to create a flood map, revealing that climate change significantly affects flooding, contributing to increased frequency and extent of floods in the Nhat Le river basin Similarly, Olkeba (2016) applied the same model to evaluate the impacts of climate change on water balance components in the Heeia watershed of Hawaii, with projections indicating changes in rainfall patterns.
Recent research on the Chindwin River Basin in Myanmar, utilizing the coupled MIKE NAM and MIKE 21 models, reveals that climate change is significantly impacting hydrology and river morphology The study predicts an increase in the frequency and intensity of extreme flooding events in the future compared to historical data (Shrestha et al., 2020) These findings highlight critical hydrological changes driven by climate change, which carry substantial implications for socio-economic and ecological development in the region.
In different regions, changes in precipitation and hydrological cycles due to climate change may increase water shortages in many areas (Janet et al., 2004, Zubaidi et al.,
Overview of the study area
1.9.1 Description of study area a) Geographical location
The Srepok River, situated west of the Annamite Mountains in Vietnam's Central Highlands, is a vital component of the region's economy and tourism As a significant tributary of the Mekong River, it flows through Cambodia, ultimately merging with the Mekong at Stung Treng The Srepok river basin spans an area of 18,061 km² in Vietnam, bordered by the Sesan river basin to the north and the Ba and Cai Rivers in Nha Trang to the east, while its western boundary extends into the lower section of the basin in Cambodia.
Figure 1.2: The Srepok river basin
The Srepok river basin features predominantly flat lowlands, with small mountains to the north of Lumphat extending eastward to the Vietnam border In the southern region, particularly west of Dak Mil, additional mountainous areas are present While the topography becomes more rugged in the southwest near Buon Ma Thuot, much of the basin in Vietnam is characterized by low altitudes, averaging around 525 meters Approximately half of the basin exhibits a gentle slope of less than 1 degree, although steep areas can be found in the upper catchment.
The Srepok river basin has a tropical monsoon climate
Temperature: Average temperature at altitudes of 500–800m arranges from 22 o C to
23 o C The average temperature in the areas at the height of below 500m ranges from
24 o C to 25 o C In the rainy season, the average temperature in many months fluctuates from 23 o C to 24.7 o C
Evaporation: The highest evaporation in the 1980–2017 period is 1,464.8 mm at Buon
Ma Thuot stations experience evaporation changes that align with annual temperature variations The lowest evaporation rates are recorded during the rainy season, particularly in August and September, with an average of only 41mm during these months.
The annual average relative humidity in the basin fluctuates between 80% and 85%, with significant variations throughout the year February and March typically experience the lowest monthly average relative humidity, while the highest levels are observed in August, September, and October.
The Srepok river basin in Vietnam comprises the primary Srepok River along with five significant tributaries: the Ia Drang, Ia Lop, Ea H'Leo, Ea Krong Ana, and Ea Krong No rivers.
Figure 1.3: The Srepok River system
Table 1.4: Characteristics of the main tributaries in the Srepok river basin in
No River Catchment area (km 2 ) Length (km)
1.9.2.2 Water resources feature a) Water level
The flood season in the SrePok river basin starts from July to December The flood usually appears from August to November; some early floods can start from June
The Cau 14 hydrological station, situated in the Srepok river basin, recorded its highest average water level of approximately 300m during the months of September, October, and November from 1980 to 2018 Conversely, the lowest mean water level was noted at 298.5m in February, March, and April, resulting in an average water level difference of up to 1.5m.
Figure 1.4: Average monthly water level at Cau 14 station in 1980–2018
(Data source used: Vietnam Meteorological and Hydrological Administration)
The Cau 14 hydrological station experiences a dry season from January to June and a flood season from July to December Notably, the highest flood water level recorded was 306.33 meters on October 30, 1992, while the lowest level reached 297.52 meters on March 21, 2005.
In Ea KrongNo river, the highest water level in 1980-2015 recorded in Duc Xuyen hydrological station is 426.46 m in October, and the lowest water level is 424.91m in April (Figure 1.5)
Figure 1.5: Average monthly water level at Duc Xuyen station in 1980–2018
Between 2011 and 2018, the average monthly water levels experienced a significant decline, primarily attributed to the impacts on Duc Xuyen Data from the Vietnam Meteorological and Hydrological Administration highlights this concerning trend.
16 discharge of Buon Tua Srah reservoir and sand mining activities The dry season in Ea KrongNo starts from January to June and the flood season from July to December
Figure 1.6: Average monthly water level at Giang Son station in 1980–2018
(Data source used: Vietnam Meteorological and Hydrological Administration)
The Giang Son hydrological station in Ea KrongAna experienced a notable decline in average water levels from 2011 to 2018, marking it as one of the most affected stations in the Central Highlands This significant drop has had a profound impact on both daily life and agricultural practices in the region.
The flood season in the Srepok river basin occurs from July to December, contributing 65–80% of the total annual flow Historical data from 1980 to 2015 indicates significant monthly flow variations, as detailed in Table 1.5.
Table 1.5: Average monthly flow in 1980-2015 in the Srepok Basin (m³/s)
Cau14 158.6 98.4 80.7 83.5 131.5 194.3 235.7 342.4 411.4 491.8 427.4 320.1 Duc Xuyen 56.2 41.5 39.7 38.2 51.3 79 97.5 164.3 181.9 208.8 136.1 95 Giang Son 58.5 28.9 18.7 19.1 31.3 45.2 48.4 73.9 94.6 140.6 188 153
(Source: Vietnam Meteorological and Hydrological Administration)
During the period from 1980 to 2015, hydrological stations observed an increase in average discharge values during the dry season, while average discharge values decreased during the flood season This trend can be attributed to the regulatory influence of upstream reservoirs on water resources.
The total amount of surface water resources in the Srepok River basin is 16.73 billion m³ (According to the calculation results from the MIKE NAM model in 1980-2015)
Figure 1.7: The percentage of annual average surface water resources of sub-basins in the Srepok river basin in 1980-2015
The Srepok river basin holds a total water capacity of 3.02 billion m³ during the dry season, representing 18.01% of its overall water resources, and 13.71 billion m³ in the rainy season, which accounts for 81.99% Among its sub-basins, the Ea Krong No sub-basin boasts the highest water resources at 4.4 billion m³, while the Ia Lop sub-basin has the least, with only 1.9 billion m³.
According to the statistical yearbook in 2017, the features of the population, agriculture, livestock, aquaculture and industry are presented as follow: a) Population
The population of the entire Srepok river basin in 2017 was 2,313,274 people The Srepok river basin population distribution is mainly concentrated in Dak Lak province,
The percentage of total surface water resources in the Srepok river basin (billion m 3 )
18 with 1,603,633 people The remaining regions include Gia Lai (289,907 people), Dak Nong (367,582 people) and Lam Dong (52,152 people) b) Agriculture
The Central Highlands features a diverse topography comprising mountains, plateaus, and plains, with 1.3 million hectares of Bazan soil ideal for cultivating industrial crops like coffee, pepper, rubber, and fruit trees However, the region faces significant challenges, including the ongoing conversion of forest land and inadequate management by authorities, resulting in a decline in forest areas Additionally, unsustainable farming practices have led to severe vegetation loss and increased soil erosion, further threatening the ecological balance of the Central Highlands.
The Srepok basin (2017) had 53,853 buffaloes, 361,642 heads of cow, 979,185 heads of pig, 96,460 heads of goat and sheep, and 12,314 thousand heads of poultry
The Srepok river basin currently had 9,484.8 ha for aquaculture The output of exploited aquatic products was 5,161 hectares in 2017 d) Industry
The production index of the whole industry in 2017 increased by 17.84% compared to
2016 Total retail sales of consumer goods and services in the Srepok basin reached 42,846 billion VND.
Overview climate change in Viet Nam and the Srepok river basin
According to the Global Climate Risk Index 2020, Vietnam ranks as the sixth most affected country by climate variability and extreme weather events over the past two decades (1999-2018) This vulnerability is intensified by Vietnam's geographic features, including its extensive coastline, heavy dependence on agriculture, and underdeveloped rural areas (World Bank, 2010).
Over the past half-century, the average annual temperature had increased by about 0.5ºC nationwide Unlike temperature, annual rainfall tends to decrease in the North
In the South, particularly in the South Central Coast, there is a notable increase in temperature and precipitation, although the rainfall growth is minimal and does not reach a 10% significance level This trend has led to clear changes in extreme weather events, with a rise in the number of hot days and a decline in cold days Additionally, the maximum daily rainfall and the frequency of heavy rainy days have increased across various climates, while the Southern seas have experienced a rise in active storm occurrences.
According to the 2016 MONRE report on climate change and sea-level rise scenarios for Vietnam, the RCP8.5 scenario predicts an annual average temperature increase of 0.8–1.1°C at the start of the 21st century By mid-century, the temperature is expected to rise by 1.8 to 2.3°C, with the North experiencing an increase of 2.0 to 2.3°C and the South 1.8 to 1.9°C By the end of the century, the forecast indicates a temperature rise of 3.3 to 4.0°C in the North and 3.0 to 3.5°C in the South.
The RCP8.5 scenario predicts an increase in annual rainfall across the country, ranging from 3% to 10% at the start of the century However, winter rainfall is expected to decrease slightly in the North and Central Highlands Spring rainfall may decline by up to 8% in most regions, while summer rainfall is projected to rise between 5% and 15% Autumn rainfall is also anticipated to increase, potentially rising by 10% to 20% By the end of the 21st century, the maximum increase in rainfall could exceed 20% in much of the North and parts of the South and Central Highlands.
Over the past 30 years (1977–2007), the Srepok river basin has experienced an annual average temperature increase of approximately 0.5ºC to 1ºC This rise in temperature is not uniform throughout the year; it is more pronounced during the dry season compared to the rainy season, as illustrated in Figure 1.8.
Figure 1.8: Temperature in some stations in the Srepok river basin ( o C)
(Data source used: Vietnam Meteorological and Hydrological Administration) The tendency of rainfall to change is revealed in Figure 1.11
Figure 1.9: Rainfall in some stations in the Srepok river basin (mm)
(Data source used: Vietnam Meteorological and Hydrological Administration)
From 1980 to 2017, the Srepok River basin experienced a general decline in annual rainfall, though some stations reported increases This resulted in an uneven distribution of rainfall throughout the year, with 80-85% occurring during the rainy season and only 15-20% in the dry months Consequently, this irregular rainfall pattern has contributed to water shortages in certain areas of the Srepok River basin.
MATERIALS AND METHODS
Data collection
The river network, land use, land cover change maps, hydro-meteorological stations were used to divide the sub-basin and delineate the basins to the reservoir
Location coordinates and parameters of 18 reservoirs with a volume over 0.5 million m 3 and five hydroelectric power plants would be selected for calculation and simulation
The Digital Elevation Model (DEM) was sourced from the 30m x 30m SRTM-DEM, and the coordinates of 18 reservoirs were utilized to delineate the basin using ArcGIS software Following the delineation process, the reservoir catchment areas were verified against the design documents, confirming complete consistency.
The thesis analyzed daily rainfall data from 17 stations, including Pleiku, Lak, Mdrak, and others, covering the period from 1980 to 2017 The stations monitored are located in various regions such as Kontum, EaSup, and Da Lat, providing a comprehensive overview of rainfall patterns in the area.
Observed evaporation daily data were collected from Buon Ho, Buon Ma Thuot, Pleiku, Mdrak, Dak Nong and Da Lat meteorological stations in 1980–2017
Observed discharge daily data were collected from three hydrological stations Cau 14, Duc Xuyen, Giang Son in 1980–2015 on the basin
The hydro-meteorological stations in Srepok river basin were expressed in Table 2.1 and Figure 2.1
Figure 2.1: The hydro-meteorological stations, reservoirs and hydropower plants network
(Data source used for making the map: DWRPIS, 2015; Vietnam Meteorological and
Table 2.1: List of rainfall stations used in MIKE NAM
2.1.2.2 The climate change and sea-level rise scenarios for Viet Nam report in 2016 and projected rainfall data
The thesis analyzes the percentage changes in rainfall across four provinces in Vietnam—Gia Lai, Dak Lak, Dak Nong, and Lam Dong—based on the climate change and sea-level rise scenarios outlined in the 2016 MONRE report under RCP 8.5 The study categorizes changes in average annual rainfall relative to the baseline period of 1986-2005 into three distinct phases: 2016-2035, 2046-2065, and 2080-2099, as detailed in Tables 2.2 and 2.3.
Table 2.2: The variation of annual average rainfall compared to the baseline period in four provinces in the Srepok river basin
Under the RCP8.5 scenario, the Srepok River basin is projected to experience an increase in annual rainfall at the start of the 21st century The provinces of Gia Lai, Dak Lak, Dak Nong, and Lam Dong are expected to show significant changes in annual rainfall compared to the baseline period of 1986-2005, as illustrated in Table 2.3.
Table 2.3: Changes in seasonal rainfall (%) compared with the baseline period in four provinces in the Srepok river basin
Climate change and sea-level rise predictions are inherently uncertain, as they rely on greenhouse gas emission scenarios and our incomplete understanding of climate systems (Nguyen, 2016) Thus, it is crucial to thoroughly analyze and consider all potential future climate scenarios when conducting climate change impact assessments.
This thesis employs the RCP 8.5 scenario, characterized by the highest greenhouse gas concentrations, to illustrate the most significant impacts of extreme climate change on water resources from 2016 to 2035 This timeframe aligns with the data collected for the study Additionally, there is a growing trend towards investing in sustainable solutions aimed at adapting to and mitigating climate change effects Implementing these sustainable strategies in the face of severe conditions is deemed a long-term effective approach to enhancing resilience and adaptive capacity against climate-related hazards and natural disasters globally.
“Integrate climate change measures into national policies, strategies and planning” (SDGs13)
The detailed rainfall calculation results for 17 rainfall stations in the Srepok river basin in 2016-2035 are presented in Appendix D
The water demand projections for the provinces of Gia Lai, Dak Lak, Dak Nong, and Lam Dong, aligned with their socio-economic and water supply plans through 2030, are based on the 2015 water resources plan for the Srepok River basin by the Division for Water Resources Planning and Investigation for the South of Vietnam (DWRPIS) This assessment encompasses six key water usage categories: industry, agriculture, aquaculture, domestic use, tourism, and livestock, which were subsequently recalibrated for the specific needs of the six sub-basins within the Srepok River basin.
Table 2.4: Water demand in the Srepok river basin (million m 3 )
Domestic Industry Agriculture Livestock Aquaculture Tourism
Domestic Industry Agriculture Livestock Aquaculture Tourism
The 2021 decision No 1354/QD-BTNMT by MONRE establishes that the minimum flow value downstream of the Krong No 3 reservoir is set at 9.3 m³/s for hydraulic constructions and hydropower plants.
Circular No 64/2017/TT–BTNMT, issued by the Ministry of Natural Resources and Environment in 2017, outlines the criteria for determining the minimum flow in rivers, streams, and downstream areas of reservoirs and weirs It establishes that the environmental flow should range from the minimum monthly discharge to the average discharge of the lowest three months (measured in m³/s) Consequently, the minimum flow at a specific location is calculated based on the minimum monthly flow that corresponds to a 95% frequency, particularly during extreme drought conditions.
The inter-reservoir operation process in the Srepok River basin adheres to Decision No 1612/QD–TTG issued by the Prime Minister in 2019, which establishes procedures for managing reservoir operations To protect the environment, the river flow across the border must maintain a minimum rate of 27m³/s.
2.1.4 Reservoirs and hydropower plants data
Information about reservoirs and hydropower plants used in the model is described in Table 2.5 and Appendix C
Table 2.5: Reservoirs and hydropower plans
# Reservoir In operation since Type Branch
3 BuonTuaSrah 2009 HPP/Reservoir Ea KrongNo
5 Krong No 3 HPP/Reservoir Ea KrongNo
17 Ia Glai – Reservoir Ia Lop
18 Hoang An – Reservoir Ia Drang
Rainfall-runoff and water balance models
The thesis used the MIKE NAM model version 2021 and MIKE HYDRO BASIN model version 2021 to simulate the rainfall-runoff process and water balance in the Srepok river basin (Figure 2.2)
The thesis focuses on simulating water flow from 1985 to 2015 and forecasting future flow from 2016 to 2035 These flow data serve as inputs for the MIKE HYDRO BASIN model, which is utilized for water balance simulations The outcomes highlight the current and future water deficits in various sub-basins.
Figure 2.2: The process and concept for the MIKE NAM and MIKE HYDRO BASIN models 2.2.1 MIKE NAM model
The MIKE NAM model is a widely utilized rainfall-runoff model that has been applied in various hydrological and climatic contexts around the globe, including Nepal (Talchabhadel and Shakya, 2015) and Malaysia (Shamsudin and Hashim, 2002) In Vietnam, it has been extensively studied across multiple river systems, such as the Vu Gia Thu Bon river basin (Truong, 2019), the Ca river basin (Nguyen, 2020), and the inter-reservoir operations in Ta Trach, Binh Dien, Huong Dien, and A Luoi along the Huong river (National Key Laboratory of River and Marine Dynamism, 2015) This thesis employs the MIKE NAM model version 2021 to simulate current and future flow conditions in the Srepok river basin.
DHI (2021) describes MIKE NAM as a conceptual model that integrates physical structures and equations with semi-empirical methods This lumped model treats each catchment as an individual unit, allowing parameters and variables to represent average values across the catchment Consequently, physical data from the catchment can be utilized to estimate certain model parameters effectively.
Water deficit in sub-basins
Real time/Forecasted meteorological data
Catchments Information Water use Reservoir and HPP information Sub-basin discharge
30 calibration against the time series of hydrological data is required for the final parameter estimate
The MIKE NAM model simulates the rainfall-runoff process by mimicking the land phase of the hydrological cycle, effectively managing water content across four interconnected storage types: snow storage, surface storage, lower or root zone storage, and groundwater storage (DHI, 2021) This comprehensive approach enables accurate representation of various physical features within the watershed, as illustrated in Figure 2.3.
Figure 2.3 Structure of NAM model
The MIKE NAM model utilizes meteorological data, including precipitation and evaporation, to analyze catchment runoff and various hydrological factors such as transpiration, soil moisture, and groundwater levels This versatile engineering tool has been successfully implemented across diverse catchments globally, effectively representing a wide range of hydrological regimes and climatic conditions (DHI, 2021).
Basic parameters of the NAM model
The basic parameters of the MIKE NAM model are presented in Table 2.6
Table 2.6: MIKE NAM model parameter
Maximum water content in surface storage (Umax)
Storage encompasses the water content found in interception storage on vegetation, surface depression storage, and the top few centimeters of soil, with typical values ranging from 10 to 20 mm.
Maximum water content in root zone storage (Lmax)
Lmax can be interpreted as the maximum soil moisture content in the root zone available for vegetative transpiration Typical values are between 50 – 300 mm
CQOF is an essential parameter, determining the extent to which excess rainfall runs off as overland flow and the magnitude of infiltration Values range between 0.0 and 1.0
The time constant for interflow
Determines the amount of interflow, which decreases with larger time constants Values in the range of 500–1000 hours are expected
Time constants for routing overland flow (CK1, 2)
The parameter influences the shape of hydrograph peaks, with routing conducted through two linear reservoirs that share the same time constant (CK 1 = CK 2) Small and large time constants are utilized to simulate high and low peaks, respectively, typically ranging from 3 to 48 hours.
Root zone threshold value for overland flow
TOF, or Threshold Overland Flow, represents a critical value for overland flow generation, indicating that no flow occurs when the relative moisture content in the lower zone storage is below this threshold The acceptable range for TOF is between 0 and 0.7% of Lmax, with a maximum permissible value set at 0.99.
Root zone threshold value for inter flow (TIF)
The moisture content in the root zone (L/Lmax) is a key factor in determining when interflow occurs However, this parameter is often considered insignificant and can typically be assigned a value of zero in most situations.
The time constant for routing base flow (CKBF)
The time constant for routing base flow (CKBF) can be determined from the hydrograph recession in dry periods
Root zone threshold value for ground water recharge (TG)
The water storage capacity of the ground is assessed relative to the root zone's L/Lmax, with a threshold value ranging from 0 to 0.7% of Lmax The maximum permissible value is 0.99 inches (DHI, 2021).
The thesis divided the Srepok river basin into 6 sub-basins: Ia Drang, Ia Lop, EaH’leo,
Ea KrongAna, Srepok and Ea KrongNo, to assess the amount of water deficit as the impact of climate change on sustainable water exploitation and use in the area (Table 2.7)
Table 2.7: The sub-basins in the Srepok river basin
No Sub-basins River Reservoir
1 Ia Drang Ia Drang Hoang An
2 Ia Lop Ia Lop IaGlai
3 EaH’leo EaH’leo EaSoupThuong, EaSoupHa
4 Kongana Kongana Eauy, Krongbukha, Yangreh,
5 Srepok Srepok Buonyong, Srepok 3, Srepok4,
6 Ea KrongNo Ea KrongNo BuonTuaSrah, Ea KrongNo3
Figure 2.4: Sub-basin in MIKE NAM model
The impact of 18 reservoirs on the river basin led to the division of six sub-basins into 22 distinct water balance units for effective water balance calculations The arrangement of these sub-basins and water balance units is illustrated in Figure 2.4 Additionally, rainfall calculations were conducted to support this analysis.
The thesis used the observed rainfall data collected from 17-rainfall stations and six evaporation stations in 1980-2015, as mentioned in 2.1 Data collection
The thesis analyzes annual rainfall variations in four provinces of Vietnam—Gia Lai, Dak Lak, Dak Nong, and Lam Dong—based on the climate change scenarios and sea-level rise report by MONRE published in 2016, specifically using the RCP 8.5 model It calculates detailed rainfall data for 17 stations within the Srepok River basin for the period from 2016 to 2035, with results available in Appendix D.
Calculation of average precipitation and evaporation of the basin
The thesis uses the direct Thiesson method in MIKE NAM according to formula 2.5 to calculate average rainfall and evaporation for the sub-basins: ̄ ∑
Where fi is the area where the rainfall at i (Xi) station; n is the number of affected rain stations in the basin
The study utilized three hydrological stations—Duc Xuyen, Giang Son, and Cau14—to calibrate and validate the model To achieve this, rainfall calculations for each sub-basin influenced by the hydrological stations were performed using the Thiessen method within the MIKE NAM framework The detailed steps for this process are outlined as follows.
Mean precipitation and evaporation for the Duc Xuyen hydrological station sub-basin were calculated using data from three rainfall stations (Lak, Da Lat, and Dak Nong) and two meteorological stations (Dak Nong and Da Lat) through the Thiessen method.
The data was processed to ensure the period from 1980 to 2015 The results of station selection are summarized in Table 2.8 and Figure 2.5
Table 2.8: The evaporation and rainfall stations in the area of Duc Xuyen station
The influence rainfall station according to Thiessen
The influence evaporation station according to Thiessen
Figure 2.5: Thiessen polygon calculates the average rainfall of the basin to the Duc
Xuyen hydrological station Cau 14 station:
Mean precipitation and evaporation for the sub-basin of the Cau 14 hydrological station were determined using data from ten rainfall stations—Buon Ho, EaKnop, Mdrak, KrongKmar, Da Lat, Dak Nong, Lak, EaKmat, Buon Ma Thuot, and EaH’leo—along with four evaporation stations: Buon Ho, Mdrak, Da Lat, and Buon Ma Thuot The Thiessen method was employed for these calculations, with the findings detailed in Table 2.9 and illustrated in Figure 2.6.
Table 2.9: The evaporation and rainfall stations in the area of Cau14 station
The influence rainfall station according to Thiessen
The influence evaporation station according to Thiessen
Buon Ho (0.0405); EaKnop (0.0538) Mdrak (0.0356); KrongKmar (0.186)
Da Lat (0.0874); Dak Nong (0.197) Lak (0.274); EaKmat (0.0631)
Figure 2.6: Thiessen polygon calculates the average rainfall of the basin to the Cau 14 hydrological station
RESULTS AND DISCUSSIONS
Calibration and validation of MIKE NAM model
The MIKE-NAM model was calibrated between 1983 and 1993 and subsequently tested from 1994 to 2004 at three hydrological stations: Giang Son, Duc Xuyen, and Cau 14 The calibration and validation results are illustrated in Figures 3.1, 3.2, and 3.3.
Figure 3.1: Calibration and validation in Giang Son station
Figure 3.2: Calibration and validation in Duc Xuyen station
Figure 3.3: Calibration and validation in Cau 14 station
The calibration and testing results of the model align well with the thesis objectives, demonstrating satisfactory performance Additionally, the MIKE NAM model is designed for user-friendliness, effectively supporting users in their assessments.
49 to zoom in to the sub-basin level (DHI, 2021) Therefore, the use of the MIKE NAM model is correct and appropriate
The parameters of calibration and validation in in MIKE NAM model was showed in Table 3.1
Table 3.1: The parameter of calibration and validation model
Station Umax Lmax CQOF CKIF CK1 TOF TIF TG CKBF
The MIKE NAM model's sensitivity analysis reveals that the most critical parameters are identified by varying individual model parameters while maintaining others constant (Aneljung, 2007) Calibration involved adjusting these parameters by ±10% and ±20% (Teshome et al., 2020) Results show that the maximum water content in surface storage (Umax) and the maximum water content in root zone storage (Lmax) significantly influence streamflow simulation (Q) Additionally, the overland flow runoff coefficient (CKOF), interflow time constant (CKIF), and routing time constants for overland flow (CK1, CK2) also play vital roles in shaping flow processes and flood peaks.
Usually, there are some problems in model validation and calibration, such as simulation flow being larger than actual measurements, or phase delay or no peak tracking
Figure 3.4: Examples in simulation flow at Giang Son station
The simulated flow patterns closely align with observed flows, although they consistently show higher values While the calibration criteria indicate reasonable Correlation Coefficient (R), Bias, RMSE, and Standard Deviation, the low Nash-Sutcliffe index and significant total flow error are concerning These issues hinder effective solutions for water resource management, highlighting the need for further calibration of Umax and Lmax parameters.
The simulation results indicate that while the model aligns well with baseline discharge values, it fails to accurately represent peak values during flood seasons Evaluation metrics such as the Correlation Coefficient (R), Total Volume Ratio, and Nash-Sutcliffe Index show positive results, whereas RMSE and Standard Deviation reveal shortcomings If the primary goal of the simulation is to assess total flow for effective water resource management, these findings are deemed acceptable However, for accurate flood process modeling, further calibration of flood peak values is essential.
The evaluation of simulation results requires multiple criteria rather than relying on a single statistical indicator In hydrological calculations, it is understood that models serve as generalizations and simplifications of real-world scenarios, making it impossible for any model to perfectly replicate all aspects of the hydrological cycle Additionally, uncertainties related to data and model parameters can lead to unexpected simulation outcomes.
The thesis emphasizes the importance of the coincidence flow process path between actual observations and simulations over individual hydrographic shape matching This approach has been widely applied in various plans, including this thesis The calibration and validation evaluation criteria at three hydrological stations confirm the effectiveness of the procedures, yielding relatively good results that satisfy the flow simulation requirements for both the sub-basins leading to the reservoir and those following it, as detailed in Table 3.2.
Table 3.2: Results of calibration and validation of MIKE– NAM model at three hydrological stations Giang Son, Duc Xuyen and Cau 14 in the study basin
Station/Par Period BIAS RMSE Standard
The total volume ratio was close to 1, and the BIAS coefficient, RMSE and Standard deviation were relatively small in Giang Son and Duc Xuyen but slightly high at Cau
The accuracy of a calibrated model is heavily dependent on the quality of its inputs, particularly the density of rainfall stations A sparse network of rainfall stations leads to unreliable rainfall data for regions between those stations Since rainfall is the primary factor influencing both the hydrological model and the water balance model, any uncertainty in rainfall measurements significantly impacts overall model reliability.
The daily rainfall time series significantly influences model results, but errors in monitoring station records can negatively impact model performance if left undetected Additionally, model parameters are affected by the resolution and inaccuracies of the gauged data used for calibration Research by Teshome et al (2020) assessed the reliability of the MIKE NAM model using the correlation coefficient (R) and the Nash–Sutcliffe coefficient With an R value exceeding 0.8 and a Nash–Sutcliffe coefficient above 0.7, the evaluation criteria indicated that the model performs well, demonstrating its capability to effectively simulate basin runoff.
Enhancing the rainfall-runoff calculations of the MIKE NAM model can be achieved by establishing a higher density network of rainfall stations and increasing the number of meteorological and hydrological stations Despite its simplicity and low data requirements, the MIKE 11-NAM model remains effective for simulating streamflow, particularly in areas with limited data availability (Teshome, 2020) Therefore, utilizing the MIKE NAM model is both correct and appropriate for such scenarios.
Following calibration and validation, the thesis employed standard parameters to simulate both current and future flow conditions Initially, it utilized observed rainfall and evaporation data from 1980 to 2015 to model present flow Subsequently, rainfall projections for the period of 2016 to 2035 were applied to forecast future flow scenarios.
Flow calculation for selected frequencies (P = 85% and P = 50%)
In 2012, the Ministry of Agriculture and Rural Development established Standard No 04–05:2012/BNNPTNT, which outlines the national technical regulations for hydraulic structures, emphasizing the importance of an 85% design frequency for determining irrigation water supply needs Furthermore, the Ministry of Natural Resources and Environment issued Circular No 04/2020/TT–BTNMT in 2020, which focuses on technical regulations for the general planning of interprovincial river basins and water sources, highlighting the necessity of thorough assessments in these areas.
The assessment of surface water quantity must be conducted at varying frequencies of 50%, 85%, and 95% This study utilizes the RCP8.5 climate change scenario for Vietnam, focusing specifically on the 50% and 85% frequencies to analyze the impact of climate change on water usage Consequently, the MIKE NAM flow data from 1980 to 2015 will be adjusted to reflect the flow at the 85% probability level, while the MIKE NAM flow for 2016 to 2035 will be converted to both the 85% and 50% probability levels This approach aims to elucidate how changes in discharge will influence future water usage.
Figure 3.5: Frequency curve and respective flow values calculation by computer program FFC 2008
The resulting flow with P = 85% and P = 50% will be the input to the sub-basins in the MIKE HYDRO BASIN model The results are presented in Appendix A.
Calibration and validation of MIKE HYDRO BASIN model
The MIKE HYDRO BASIN model simplifies real-world scenarios by making assumptions about water usage in balance, intake, and discharge processes It is important to anticipate and accept less-than-perfect model performance due to uncertainties in model data and parameters The calibration criteria selected depend on the model's intended application However, data limitations hinder the thesis from accurately representing all irrigation activities and water uses within the river basin Consequently, other model parameters are influenced by the resolution of data sources and errors associated with the observed data used for calibration and validation.
The thesis utilized various criteria, including BIAS, RMSE, Standard Error, R, Volume Ratio, and Nash, for the calibration and validation of the model, as illustrated in Figure 3.6 and detailed in Table 3.3.
Giang Son station Cau 14 Station
Figure 3.6: Observed and simulated flow at Giang Son station and Cau 14 station
Table 3.3: Criteria evaluation result of calibration and validation of MIKE HYDRO BASIN model at 2 hydrological stations Giang Son and Cau 14
The reliability assessment of the MIKE HYDRO BASIN utilized various metrics including BIAS, RMSE, Standard Error, Volume Ratio, correlation coefficient (R), and the Nash–Sutcliffe coefficient The findings indicated that BIAS, RMSE, and Standard Error values were relatively low, with the Volume Ratio approximately equal to 1 Additionally, the correlation coefficient exceeded 0.9, and the Nash–Sutcliffe coefficient surpassed 0.81, demonstrating strong model performance.
It revealed that the evaluation criteria had a good result, which means the model is considered valid and good enough to simulate the water balance in the basin
The effectiveness of a calibrated model largely depends on the quality of its inputs To enhance model performance, it is essential to focus on the available data to set baseline conditions and carry out accurate calibration and validation processes.
Establishing a higher density network of rainfall stations will enhance the accuracy of water balance calculations in the MHB Additionally, increasing the number of meteorological and hydrological stations is essential for improving model performance.
To enhance model performance, it is recommended to gather additional reservoir data Incorporating smaller reservoirs into the MHB model, once sufficient knowledge is obtained, will improve its application in water balance assessments at the sub-basin level.
The calibration and testing outcomes of the MIKE NAM and MIKE HYDRO BASIN models demonstrate satisfactory results, confirming their suitability for the thesis objectives Numerous studies highlight the user-friendly interface of these models, which offer various tools to aid in assessments and deliver reliable simulation results Consequently, the integrated application of MIKE NAM and MIKE HYDRO BASIN ensures consistent and dependable performance.
Impacts of climate change on flow
The Srepok River basin features numerous reservoirs and hydropower plants that significantly alter water flow The MIKE NAM model simulates a natural basin without accounting for these reservoirs, and calibration and validation were conducted during periods when reservoirs were absent In contrast, the MIKE HYDRO BASIN model will incorporate detailed assessments of structures and reservoirs Consequently, evaluating the impacts of climate change on flow will focus on basins without reservoirs, akin to reservoir catchments The findings from the MIKE NAM model will be analyzed accordingly.
The flow to reservoir catchments on the Srepok River is analyzed for two distinct periods based on the 2016 climate change scenario for Vietnam: the baseline period of 1986–2005 and the future period of 2016–2035 This analysis includes calculations of precipitation and potential evaporation, assessing the percentage changes in rainfall and temperature projected under climate change conditions.
Figure 3.7: Average annual flow in 1986–2005 and 2016–2035 periods
From 2017 to 2035, the average flow rate in all basins feeding into the reservoir shows a slight increase, with most sub-basins experiencing a rise of approximately 0.05 to 0.2 m³/s Notably, the Srepok4 and DraHlinh sub-basins exhibit a more significant increase of around 0.25 m³/s, while the Easupthuong catchment follows closely with a rise of about 0.2 m³/s compared to the baseline period.
Figure 3.8: The average annual flow change in 1986–2005 and 2017–2035 periods
12 The average annual flow in 2 stages (m 3 /s)
0.3 The average annual flow change in 2 stages (m 3 /s)
3.3.2 The average flow in the rainy season
The rainy season in the Srepok River basin occurs from July to December Research has analyzed the average flow rates with an 85% frequency discharge during this period, focusing on two distinct timeframes: 1860–2005 and 2016–2035.
Figure 3.9: The average flow years in the rainy season
Figure 3.10: The average flow years change in the rainy season
During the rainy season from 2016 to 2035, average discharge levels across all basins have significantly increased compared to the baseline period of 1986 to 2005 The EaSupThuong sub-basin experienced the most notable rise, with an average increase of approximately 0.7 m³/s Other sub-basins recorded average increases ranging from 0.05 to 0.25 m³/s, with the lowest increase at 0.02 m³/s These findings indicate that, due to climate change, flood season flows are expected to rise in the future compared to previous years.
20 The average annual flow in rainy season (m 3 /s)
0.8 The average annual flow change in rainy season (m 3 /s)
3.2.3 The average flow in the dry season
During the dry season (December to May), the average discharge in the period 2016–
By 2035, most basins are projected to experience a decrease in average discharge compared to the baseline period of 1986–2005, with the EaSupThuong sub-basin facing the most significant reduction of approximately 0.25 m³/s Other sub-basins are expected to see an average decline of about 0.05 m³/s, while a few, including Eakao, EaUy, and Srepok4, may experience minimal increases of 0.02 m³/s This indicates that climate change will significantly impact dry season flow in various regions, leading to a notable decrease in average discharge values over the years compared to historical data.
Figure 3.11: The average annual flow in the dry season
Figure 3.12: The average annual flow years in the dry season
4 The average annual flow in dry season (m3/s)
The average annual flow change in dry season (m 3 /s)
Assessing the capacity of sustainable exploitation and using water resources
Water demand in Gia Lai, Dak Lak, Dak Nong, and Lam Dong Provinces is based on comprehensive socio-economic development planning and local water supply strategies, as well as water resource planning for the Srepok River basin (DWRPIS, 2015) This demand was subsequently recalibrated to align with the specific needs of six sub-basins: Ia Drang, Ia Lop, EaH’leo, Srepok, Krong Ana, and Krong No.
In general, the water demands on the Srepok river basin is different between months
During the dry season from January to June, water demand increases significantly compared to the rainy months of July to December, with the highest demand observed in the Srepok and Ea KrongAna regions, while the Ia Drang area experiences the lowest water demand.
By 2030, water demand is projected to increase by approximately 1.3 times compared to 2017 levels, with the Ia Drang river basin experiencing the most significant rise at 1.6 times In contrast, the KrongNo area will see a modest increase of only about 1.07 times due to its low population density and less developed agriculture resulting from its topographical characteristics Meanwhile, the Ia Lop, EaH'leo, Krong Ana, and Srepok river basins are expected to experience an average increase in water demand of 1.24 times compared to 2017.
By 2030, domestic water demand is projected to increase significantly, reaching 1.5 times the levels of 2017 The provincial socio-economic plans for Gia Lai, Dak Lak, and Dak Nong emphasize industrial development, leading to an anticipated 2.4-fold rise in industrial water demand compared to 2017 Despite a modest increase of only 1.01 times from 2017, agriculture remains the largest consumer of water in the Srepok River basin.
Figure 3.13: The water demand in 2017
Figure 3.14: The water demand in 2030
Figure 3.15: The water demand by sector in 2017
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
The water demand in 2017 (million m 3 )
Ia Drang Ia Lop Ea H'leo Srepok Krongana Krongno
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
The water demand in 2030 (million m 3 )
Ia Drang Ia Lop Ea H'leo Srepok Krongana Krongno
The water demand by sector in 2017 (million m 3 )
Tourism Aquaculture Livestock Agriculture Industry Domestic
Figure 3.16: The water demand by sector in 2030
By 2030, water demand across various sectors in the Srepok River basin is expected to rise significantly, particularly in industry, aquaculture, and agriculture Additionally, a decrease in rainfall during the dry season will further intensify the need for water during this period.
3.4.2 Assessing the capacity of sustainable exploitation and using water resources under the context of climate change
In 2017, the Srepok basin experienced a significant water deficit of 125.27 million m³, with the most severe shortages occurring in the Ea KrongAna area, particularly during the dry season In contrast, the Ia Drang and Ea KrongNo basins reported no water shortages during the same period.
Table 3.4: Water demand deficit in 2017 (million m 3 )
Ia Drang Ia Lop EaH’leo Ea KrongAna Srepok Ea KrongNo Total
The water demand by sector in 2030 (million m 3 )
Tourism Aquaculture Livestock Agriculture Industry Domestic
Ia Drang Ia Lop EaH’leo Ea KrongAna Srepok Ea KrongNo Total
Table 3.5: Water demand deficit in 2017 by sector (million m 3 )
Domestic Industry Livestock Tourism Aquaculture Agriculture
Agriculture and aquaculture face significant water deficits, totaling 124.23 million m³ and 0.66 million m³, respectively Among all sectors, the Ea Krong Ana and Srepok basins experience the most severe water shortages.
Table 3.6: Water demand deficit in 2030 with P% (million m 3 )
Ia Drang Ia Lop EaH’leo Ea KrongAna Srepok Ea KrongNo Total
Ia Drang Ia Lop EaH’leo Ea KrongAna Srepok Ea KrongNo Total
Table 3.7: Water demand deficit in 2030 with P% by sector (million m 3 )
Domestic Industry Livestock Tourism Aquaculture Agriculture
By 2030, the total water deficit in the basin is projected to reach 183.82 million m³, a significant increase of 58.55 million m³ compared to 2017 This water shortage is primarily observed during the dry season and gradually alleviates in the rainy season Contributing factors include climate change, characterized by heightened evaporation and reduced rainfall, alongside a substantial rise in population and water demand for agriculture and industry, particularly in dry periods Additionally, the number of sub-basins experiencing water deficits, such as Ia Drang and Ea KrongNo, has also risen.
Table 3.8: Water demand deficit in 2030 with PP% (million m 3 )
Ia Drang Ia Lop EaH’leo Ea KrongAna Srepok Ea KrongNo Total
Table 3.9: Water demand deficit in 2030 with PP% by sector (million m 3 )
Domestic Industry Livestock Tourism Aquaculture Agriculture
By 2030, the Srepok River basin is projected to experience a total water deficit of 169.11 million m³, indicating a reduction in shortages compared to previous years Despite this improvement, water scarcity will predominantly affect the agricultural sector, driven by the rising demand for irrigation in the future.
The Srepok River basin is likely to experience water shortages, particularly during dry years, exacerbated by the impacts of climate change Prioritizing water supply for industrial use is essential to address these challenges.
By 2030, water demand is projected to increase by 35%-40% compared to 2017, resulting in significant shortages, particularly during the dry season from January to June, with peak deficits occurring in February and March Areas most affected include the Ea KrongAna and Srepok basins, where agriculture is the primary consumer, representing approximately 90% of the total water shortage The Srepok river basin is characterized by rural and mountainous regions with a low population density, heavily reliant on agriculture, which drives nearly 90% of the overall water demand in the sector.
The economic value of water use in the primary sector reveals that while irrigation demands the most water, it generates less economic value compared to livestock and industry (Do and Nguyen, 2019) Consequently, it is essential to prioritize adequate water supply for domestic needs and industries, while ensuring that livestock and aquaculture receive the minimum necessary water to maintain production Any remaining water can then be allocated for irrigation; if this supply proves insufficient, alternative measures must be implemented to guarantee sustainable water management and usage.
According to Do and Nguyen (2019) and Luyen (2013), a balanced approach to water management in the Srepok River basin involves ensuring a full water supply for daily life, prioritizing industrial needs, and allocating 85% of water resources for agriculture Consequently, it is recommended to reduce the percentage of water demand in agriculture, as illustrated in Table 3.10.
Table 3.10: The percentage reduces to meet the 85% water supply in agriculture
Ia Drang Ia Lop EaH’leo Ea KrongAna Srepok Ea KrongNo
Utilizing only surface water for agricultural needs in the Srepok River basin can decrease water demand by approximately 15-27% in each sub-basin.
66 85% water supply The detailed measures to sustainable exploitation and use will be presented in the next chapter