(Luận văn thạc sĩ) assessing climate change impacts on surface water flow for sustainable exploitation and utilization of water resources in the srepok river basin in viet nam

INTRODUCTION

Background

Climate change poses a significant threat to humanity in the 21st century, particularly for island and coastal nations, due to its impacts on global warming and rising sea levels The severity of natural disasters has intensified, leading to increased flooding and droughts, which worsen water scarcity and contaminate water supplies (UNICEF, 2021) According to the Intergovernmental Panel on Climate Change (IPCC), Vietnam is among the countries most severely affected by these changes (IPCC, 2007) Additionally, Vietnam faces critical challenges in managing and utilizing water resources, a sector that is directly impacted by 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, socio-economic development and population growth significantly increased water demand, creating an imbalance between supply and demand This imbalance has resulted in heightened competition and a crisis in water usage.

The interplay of economic development and social security is significantly influenced by various factors, necessitating the implementation of comprehensive and targeted solutions To achieve sustainable water use in the face of climate change, it is essential to adopt a long-term strategic vision that addresses these 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 aforementioned problem on a national scale within a master's thesis poses significant challenges Therefore, the author has chosen to focus on the Srepok basin to investigate and resolve these issues effectively.

The Srepok River Basin, a sub-basin of the Lower Mekong located in Vietnam, encompasses an area of 18,264 km² across four provinces: Gia Lai, DakLak, DakNong, and Lam Dong (MONRE, 2017) Recent studies indicate that climate change is impacting the Srepok Basin, evidenced by a trend of decreasing rainfall and rising temperatures (Dao, 2013) Projections using the Statistical Downscaling Model (SDSM) suggest that while annual rainfall and temperatures are expected to increase in the future, there will be a decline in rainfall during the dry season (Dao et al.).

A study based on three climate change scenarios from the IPCC's CMIP5 program reveals a significant decline in river flow within the Srepok river basin, particularly at Duc Xuyen station, where flow decreases by nearly 80% in June This reduction heightens the risk of prolonged drought conditions in the basin, emphasizing the connection 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 challenges, including floods, droughts, and environmental degradation such as water pollution and erosion In 2016, over 70% of dams and 80% of dug wells ran dry due to prolonged drought, resulting in the loss of over 3,000 hectares of coffee and 2,200 hectares of pepper in the Central Highlands By March 2020, river flows in the region were reported to be 15–70% lower than the multi-annual average, leading to significant economic damage and adversely impacting local communities.

Rapid population growth, agricultural expansion, and industrial development have significantly increased water demand, leading to heightened competition and conflicts among users To address these challenges, it is essential to study and assess the water flow in the basin now and in the future, particularly in the context of climate change, to develop timely and effective solutions.

The research question and hypothesis

Table 1.1 shows the research questions and hypotheses of the study h

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 h

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 enhance the 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, which contribute to water shortages in various regions.

- Result 3 (R3): Solutions, recommendations for Srepok river basin

- Outcome 1 (O1): References for planning and socio-economic development of the provinces h

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 conducted a comprehensive analysis of the natural features and water resource characteristics within the Srepok River basin It identified and delineated the basin into six distinct sub-basins, each optimized for the sustainable exploitation and utilization of natural 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 h

(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 water vapor concentration, cloud cover, and ice within the atmosphere affect the Earth's radiation balance and are vital in shaping 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 The World Meteorological Organization (WMO) highlighted this issue in 1985, emphasizing the significant influence of climate change on hydrology According to the IPCC (2008), both observational data and climate projections indicate that freshwater resources are highly vulnerable to climate change, potentially facing severe damage As temperatures rise, the atmosphere's capacity to hold moisture increases, leading to alterations in the hydrological cycle Key changes in this system include shifts in the seasonal distribution, intensity, and frequency of precipitation.

7 causing changes in water availability in surface runoff and soil moisture (Judith et al.,

Research on climate change typically forecasts alterations in water resources by analyzing factors like temperature, precipitation, and evaporation However, due to the variability of regional climate change, climatologists can only provide informed predictions about future conditions As a result, experts in climate change studies often refer to these projections as "scenarios" to describe potential future climates.

A General Circulation Model (GCM) is a crucial tool for understanding climate and forecasting weather and climate change globally Despite their success in simulating past and future climates, GCM outputs often lack the necessary spatial and temporal resolution for local applications, necessitating the use of various scaling methods The Coupled Model Intercomparison Project Phase 5 (CMIP5) introduced Representative Concentration Pathways (RCPs), which have been validated as effective scenarios for analyzing future climatic conditions, aiding in climate change impact assessments and the formulation of mitigation strategies.

Numerous studies have demonstrated the impact of climate change on the hydrological cycle at the catchment scale globally, including in Vietnam, with notable examples such as the Rhine and Seine rivers, as well as the Red-Thai Binh river basin and the Mediterranean basin Research by Taye et al (2011) utilized downscaled data from General Circulation Models to create climate change scenarios for the upper Nile Basin, revealing an expected increase in flow for the Nyando basin by 2050, while the Tana basin exhibited uncertain discharge patterns Additionally, Kim et al (2011) employed the SRES A2 climate change scenario to analyze its effects on the flow regime of the Han River basin, indicating a trend of increasing 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 to climate change impacts in Southeast Asia (Yusuf and Francisco, 2009) Climate change effects in this area are inconsistent, with studies indicating that the dry season may become longer and more intense, while the rainy season could shorten and become more severe This variability is likely to exacerbate seasonal water shortages, flooding, and saltwater intrusion (Hoanh et al., 2003; Snidvongs et al., 2003; Chinavanno, 2004) Using CMIP5 climate predictions, Hoang et al (2018) assessed the hydrological impacts, revealing an increase in river discharge both seasonally and annually, alongside a rise in intensity and frequency of such events Conversely, projections suggest a decrease in shallow currents under changing climate conditions.

Tran (2010) utilized six climate change scenarios to evaluate the effects of climate change on water resources in the Huong River basin in Central Vietnam, revealing that climate change would lead to increased rainfall and river discharge However, it also indicated that decreased precipitation and heightened evaporation could reduce river runoff In 2011, Tran assessed the socio-economic impacts of climate change and proposed adaptation strategies for Thua Thien – Hue province Additionally, Tran's 2011 study projected flooding levels in the Dong Nai River basin for the year 2020.

In 2100, projections accounting for climate change reveal significant shifts in forecasted river flows when compared to average annual flow, based on three climate scenarios: B1, B2, and A1FI, alongside varying levels of sea-level rise.

Climate change significantly impacts both surface water and groundwater resources Bui (2013) provided detailed climate change scenarios, including variations in evaporation, temperature, rainfall, and water levels during both dry and rainy seasons, which serve as critical inputs for modeling The groundwater flow model evaluates the effects of climate change on absolute water level elevation, groundwater level reduction, and overall groundwater storage Additionally, the predicted saline gradient model analyzes how climate change influences salinity levels in groundwater.

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 in water flow To analyze these hydrological changes, various simulation tools are employed The three primary hydrological models used to assess changes in hydrology and water resources include statistical models, conceptual hydrological models, and distributed hydrologic models.

Jakimavičius and Kriaučiūnienė (2013) utilized the HBV hydrological modeling software to evaluate the impact of climate change on the water balance of the Curonian Lagoon Their findings indicate that climate change will significantly affect the Lagoon's water balance components Specifically, from 2011 to 2100, inflow from the Baltic Sea into the Lagoon is projected to decrease by 20.4%, while outflow is expected to decline by 16.6% compared to the baseline period of 1961–1990.

Uniyal et al (2015) utilized the Soil and Water Assessment Tool (SWAT) model to examine the effects of climate change on the water balance components of the Upper Baitarani River basin in Eastern India Their findings indicated that future climatic conditions are expected to have a significant impact on streamflow in this 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 due to climate change in the Dong Nai river basin.

2012) and Cau river basin (Tran et al., 2017)

Nguyen (2015) utilized the HEC-RAS hydraulic model in conjunction with the HEC-GeoRAS module to create a flood map, revealing that climate change significantly affects flooding, leading to increased frequency and extent of floods in the Nhat Le river basin Similarly, Olkeba (2016) applied this model to evaluate the impacts of climate change on water balance components in the Heeia watershed in Hawaii, with projections indicating alterations in rainfall patterns.

Recent research on the Chindwin River Basin in Myanmar, utilizing the coupled MIKE NAM and MIKE 21 model, reveals that climate change is expected to increase the frequency and intensity of extreme flood events in the future This study highlights significant hydrological changes that could impact 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 crucial component of the region's economy and tourism As one of the major tributaries of the Mekong River, alongside the Sekong and Se San rivers, it originates in the Central Highlands and flows through Cambodia before merging with the Mekong River at Stung Treng The Srepok River basin spans an area of 18,061 km², bordered by the Sesan River basin to the north, and the Ba and Cai Rivers in Nha Trang city to the east, while its western boundary meets the lower section of the Srepok basin in Cambodia.

Figure 1.2: The Srepok river basin

The Srepok river basin features predominantly flat lowlands, with small mountains located north of Lumphat extending towards the Vietnam border In the southern part of the basin, west of Dak Mil, several mountains are present The topography becomes more mountainous in the southwest near Buon Ma Thuot, yet much of the basin in Vietnam maintains a low average altitude of 525 meters Approximately half of the basin exhibits a slope of less than 1 degree, although steeper 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 experiences distinct evaporation patterns that align with annual temperature fluctuations The lowest evaporation rates are recorded during the rainy season, particularly in August and September, averaging around 41mm during these months.

The annual average relative humidity in the basin fluctuates between 80% and 85%, with significant seasonal variations February and March experience the lowest monthly average relative humidity, while August, September, and October record the highest levels.

The Srepok river basin in Vietnam encompasses 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 h

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 In contrast, the lowest mean water level was observed 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 306.33m on October 30, 1992, while the lowest level was 297.52m 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

(Data source used: Vietnam Meteorological and Hydrological Administration)

The average monthly water level in the 2011–2018 period decreased markedly compared to the whole period The leading causes are the Duc Xuyen affected by the h

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 local daily life and agricultural practices.

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 the average monthly flow during this period, 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) h

During the period from 1980 to 20158, hydrological stations observed an increase in average discharge values during the dry season, while average discharge values decreased in the flood season This trend can be attributed to the regulatory effects 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 volume of 3.02 billion m³ during the dry season, representing 18.01% of its overall resources, and 13.71 billion m³ in the rainy season, accounting 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 contains 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, including mountains, plateaus, and plains, with approximately 1.3 million hectares of Bazan soil ideal for cultivating industrial crops like coffee, pepper, rubber, cashew, tea, and various fruits However, the region faces significant environmental challenges due to the extensive conversion of forest land for other uses and inadequate management by authorities, resulting in a continuous decline in forest areas Additionally, unscientific farming practices have led to severe erosion of vegetation, exacerbating soil erosion issues in 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 is the sixth most impacted country by climate variability and extreme weather events over the past two decades (1999–2018) This vulnerability is heightened by Vietnam's geographic characteristics, 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 h

The South Central Coast is experiencing a notable increase in temperature and precipitation, although the rise in rainfall is minimal and does not meet the 10% significance level This trend is accompanied by a clear shift in extreme weather events, with an increase in hot days and a decrease in cold days Additionally, maximum daily rainfall and the frequency of heavy rainy days have risen across most climates, while the occurrence of active storms in the Southern seas has also intensified (Phan and Ngo, 2013).

According to the 2016 MONRE report on climate change and sea-level rise scenarios for Vietnam, the RCP8.5 scenario predicts a significant increase in annual average temperatures By the early 21st century, temperatures are expected to rise by 0.8–1.1°C, with an overall increase of 1.8 to 2.3°C by mid-century This results in an average temperature rise of 2.0 to 2.3°C in the North and 1.8 to 1.9°C in the South By the end of the century, temperatures are projected to increase by 3.3–4.0°C in the North and 3.0–3.5°C in the South.

According to the RCP8.5 scenario, annual rainfall across the country is projected to rise by 3% to 10% at the beginning of the century However, winter rainfall may slightly decrease in the North and Central Highlands, while spring rainfall is expected to decline by up to 8% in many regions In contrast, summer rainfall is anticipated to increase by 5% to 15%, and autumn rainfall could rise significantly by 10% to 20% By the end of the 21st century, the maximum increase in rainfall may exceed 20% in most northern areas 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 varies by season, with more significant increases observed during the dry season compared to the slower warming in 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 h

Figure 1.9: Rainfall in some stations in the Srepok river basin (mm)

(Data source used: Vietnam Meteorological and Hydrological Administration)

Between 1980 and 2017, the Srepok River basin experienced a general decline in annual rainfall, although some stations reported increases This has led to an uneven distribution of rainfall throughout the year, with 80-85% occurring during the rainy season and only 15-20% during the dry months Consequently, this irregular rainfall pattern has resulted in 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 SRTM-DEM with a resolution of 30m x 30m, and the coordinates of 18 reservoirs were utilized to delineate their basins using ArcGIS software Following this delineation, the reservoir catchments were verified against the design documents, confirming complete consistency.

The thesis analyzed daily rainfall data from 17 stations, including Pleiku, Lak, Mdrak, KrongKma, Kontum, EaSup, EaKnop, EaKmat, EaH’leo, Da Lat, Dak Nong, Dakmin, Chuse, Chupong, Buon Ma Thuot, Buon Ho, and AyunPa, covering the period from 1980 to 2017.

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 h

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 changes in annual rainfall percentages 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, specifically RCP 8.5 The study categorizes these changes into three distinct phases: 2016–2035, 2046–2065, and 2080–2099, comparing them to the baseline period of 1986–2005 (refer to Tables 2.2 and 2.3 for detailed data).

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 show notable changes in annual rainfall compared to the baseline period of 1986-2005, as detailed 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 due to their reliance on greenhouse gas emission scenarios and our limited understanding of climate systems at both global and regional levels (Nguyen, 2016) Consequently, it is essential to thoroughly evaluate and analyze all potential future climate scenarios when conducting climate change impact assessments.

This thesis utilizes the RCP 8.5 scenario, characterized by the highest greenhouse gas concentrations, to illustrate the severe 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 most challenging conditions is deemed a long-term effective approach to enhance 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 analysis for Gia Lai, Dak Lak, Dak Nong, and Lam Dong provinces, aligned with socio-economic and water supply plans up to 2030, is 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 analysis encompasses six water use categories: industry, agriculture, aquaculture, domestic, tourism, and livestock, and has been recalibrated to fit 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

In 2021, the Ministry of Natural Resources and Environment (MONRE) issued decision No 1354/QD-BTNMT, which establishes that the minimum flow value downstream of hydraulic constructions and hydropower plants, specifically after the Krong No 3 reservoir, is set at 9.3 m³/s.

Circular No 64/2017/TT–BTNMT, issued by the Ministry of Natural Resources and Environment in 2017, outlines the criteria for determining minimum flow levels in rivers, streams, and downstream areas of reservoirs and weirs It specifies that the environmental flow should be assessed within the range of 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 guidelines for managing reservoir operations It is essential to maintain a minimum flow of 27m³/s in the mainstream of the Srepok River across the border to ensure environmental sustainability and compliance with regulatory standards.

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) h

The thesis focused on simulating water flow from 1985 to 2015 and forecasting future flow from 2016 to 2035 These flow data will serve as inputs for the MIKE HYDRO BASIN model to assess water balance The outcomes highlight 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 applied in various hydrological and climatic contexts globally, including studies in Nepal (Talchabhadel and Shakya, 2015) and Malaysia (Shamsudin and Hashim, 2002) In Vietnam, it has been extensively researched across multiple river systems, including the Vu Gia Thu Bon river basin (Truong, 2019), the Ca river basin (Nguyen, 2020), and the inter-reservoir operations of 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 approaches This lumped model treats each catchment as an individual unit, allowing parameters and variables to represent average values for 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 h

30 calibration against the time series of hydrological data is required for the final parameter estimate

The MIKE NAM model mimics the land phase of the hydrological cycle by simulating the rainfall-runoff process through four interconnected storages: snow storage, surface storage, lower or root zone storage, and groundwater storage This continuous accounting of water content in these storages effectively represents various physical features of the watershed (DHI, 2021).

Figure 2.3 Structure of NAM model

The MIKE NAM model utilizes meteorological data, including precipitation and evaporation, to analyze catchment runoff and assess various factors such as transpiration, soil moisture, groundwater recharge, and groundwater levels This versatile engineering tool has been successfully implemented in numerous catchments globally, accommodating 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 h

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 in question shapes the peaks of the hydrograph, utilizing two linear reservoirs with identical time constants (CK 1 = CK 2) High and low peaks are simulated using small and large time constants, respectively, with typical values ranging from 3 to 48 hours.

Root zone threshold value for overland flow

TOF, or Threshold Overland Flow, represents a critical value that determines whether overland flow occurs, contingent on the relative moisture content of the lower zone storage This threshold ranges from 0 to 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, represented as (L/Lmax), plays a role in determining the threshold for interflow generation However, this parameter is often deemed insignificant and can typically be assigned a value of zero in most scenarios.

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 quantity of water that can be stored in the ground is determined by the root zone's L/Lmax value, with a threshold range between 0 and 0.7% of Lmax The maximum allowable value is 0.99” (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 h

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 water balance units for accurate water balance calculations The configuration of these sub-basins and water balance units is illustrated in Figure 2.4 Additionally, rainfall calculations are integral to 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

This thesis analyzes the annual rainfall percentage changes 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 published by MONRE in 2016 under RCP 8.5 The study calculates detailed rainfall data for 17 rainfall 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 h

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 employed three hydrological stations—Duc Xuyen, Giang Son, and Cau14—to calibrate and validate the model To accurately assess the rainfall in each sub-basin influenced by these stations, the Thiessen method was applied using MIKE NAM The methodology involved several key steps to ensure precise calculations.

Mean precipitation and evaporation for the sub-basin at the Duc Xuyen hydrological station were derived using data from three rainfall stations (Lak, Da Lat, and Dak Nong) and two meteorological stations (Dak Nong and Da Lat), utilizing the Thiessen method for accurate calculations.

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, Krong Kmar, Da Lat, Dak Nong, Lak, Ea Kmat, Buon Ma Thuot, and Ea H’leo—along with four evaporation stations: Buon Ho, Mdrak, Da Lat, and Buon Ma Thuot The methodology employed for this analysis was the Thiessen method, with the findings compiled 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 underwent calibration from 1983 to 1993 and was subsequently tested between 1994 and 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 Furthermore, the MIKE NAM model is designed for user-friendliness, aiding users in their assessments effectively.

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

In the MIKE NAM model, the most sensitive parameters are identified by adjusting individual model values while keeping others constant (Aneljung, 2007) Each parameter was systematically varied, with calibration values modified by 10% and 20% increments (Teshome et al., 2020) Sensitivity analysis revealed that the maximum water content in surface storage (Umax) and root zone storage (Lmax) are the most critical factors influencing streamflow simulation (Q) Additionally, the overland flow runoff coefficient (CKOF), interflow time constant (CKIF), and routing time constants (CK1, CK2) significantly impact flow dynamics and flood peak responses.

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 h

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 highlight critical issues These discrepancies are unacceptable for addressing water resource exploitation and utilization challenges, necessitating further calibration of Umax and Lmax.

The simulation series demonstrates that the simulated discharge does not reach the observed peak values, showing good agreement at baseline but poor performance during the flood season While the Correlation Coefficient (R), Total Volume Ratio, and Nash-Sutcliffe index indicate satisfactory results, the RMSE and Standard Deviation reveal shortcomings If the simulation's objective is to assess total flow for water resource management, these results are acceptable; however, further calibration is required for accurately modeling flood peak processes.

To effectively evaluate simulation results in hydrology, multiple criteria should be considered rather than relying on a single statistical indicator Hydrological models are inherently generalizations and simplifications of real-world conditions, making it unrealistic to expect perfect simulations of the hydrological cycle Additionally, uncertainties in data and model parameters contribute to the likelihood of unexpected outcomes in simulations.

The thesis emphasizes that the coincidence flow process between actual observations and simulations is more critical than matching individual hydrographic shapes This approach has been widely applied in various plans, including this thesis The calibration and validation criteria at three hydrological stations confirm the effectiveness of the procedures, yielding satisfactory results that fulfill the requirements for flow simulation in both the sub-basins leading to the reservoir and those downstream Detailed results can be found 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 rainfall data A sparse network of rainfall stations leads to less reliable data for regions between these stations Given that rainfall is the primary factor influencing hydrological and water balance models, the uncertainty in rainfall measurements significantly affects the overall reliability of these models.

The daily rainfall time series significantly influences modeling outcomes, but the accuracy of records from monitoring stations can vary greatly, leading to potential errors that negatively impact model performance if not detected and corrected Additionally, the resolution of source data and errors in gauged data used for calibration affect other model parameters Research by Teshome et al (2020) assessed the reliability of the MIKE NAM model, finding a correlation coefficient (R) exceeding 0.8, indicating strong correlation Furthermore, the Nash–Sutcliffe coefficient, which ranges from 0 to 1, was over 0.7, suggesting that the model performs well and is adequate for simulating basin runoff.

Enhancing the MIKE NAM model's accuracy in rainfall-runoff calculations can be achieved by implementing a higher density network of rainfall stations and increasing the number of meteorological and hydrological stations Despite its simplicity and minimal data needs, 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 Initially, it utilized observed rainfall and evaporation data from 1980 to 2015 for the current flow simulation Subsequently, rainfall projections for 2016 to 2035 were applied to forecast future flow.

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 that the design frequency for assessing water supply needs in irrigation systems is set at 85% Furthermore, the 2020 Circular No 04/2020/TT–BTNMT from the Ministry of Natural Resources and Environment introduced technical regulations for the general planning of interprovincial river basins and water sources, highlighting the importance of thorough assessments in these areas.

The study examines surface water quantity under varying frequencies of 50%, 85%, and 95%, focusing on the climate change scenario for Vietnam (RCP8.5), which represents the highest concentration of greenhouse gases It primarily evaluates the impacts of climate change on water usage by analyzing MIKE NAM flow outputs from 1980 to 2015, converting them to the flow at P = 85% Additionally, the flow data from 2016 to 2035 will be adjusted to reflect both P = 85% and P = 50%, highlighting how changes in discharge will influence future water use.

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 approximating water usage in balance, intake, and discharge processes It is important to acknowledge that model performance may be imperfect due to uncertainties in data and parameters The calibration criteria selected should align with the model's intended application However, due to data limitations, the thesis may not fully represent all irrigation activities and water usage within the river basin Additionally, the accuracy of other model parameters is influenced by the resolution of data sources and errors in the observed data utilized for calibration and validation.

The thesis employed various criteria, including BIAS, RMSE, Standard Error, R, Volume Ratio, and Nash, to effectively calibrate and validate the MIKE NAM 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 statistical metrics, including BIAS, RMSE, Standard Error, Volume Ratio, correlation coefficient (R), and Nash–Sutcliffe coefficient The findings indicated that BIAS, RMSE, and Standard Error values were relatively low, with the Volume Ratio approximating 1 Additionally, the correlation coefficient exceeded 0.9, and the Nash–Sutcliffe coefficient was above 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 h

The effectiveness of a calibrated model largely depends on the quality of its inputs To enhance model performance, several strategies can be implemented regarding the available data, which are essential for establishing baseline conditions and facilitating accurate calibration and validation.

Establishing a denser network of rainfall stations will enhance the accuracy of water balance calculations in the MHB Additionally, increasing the number of meteorological and hydrological stations can significantly boost model performance.

To enhance model performance, it is recommended to gather additional information on reservoirs Incorporating smaller reservoirs into the MHB model, once sufficient knowledge is obtained, will improve its application for 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 indicate that these models feature user-friendly interfaces and offer a variety of tools to aid users in their assessments while delivering high-quality simulation results Consequently, the integrated application of MIKE NAM and MIKE HYDRO BASIN yields consistent and reliable effects.

Impacts of climate change on flow

The Srepok river basin is characterized by numerous reservoirs and hydropower plants that significantly alter its flow dynamics The MIKE NAM model, which simulates a natural basin, does not account 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 these structures Consequently, the evaluation of climate change impacts on flow will focus on basins without reservoirs, similar 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, 1986–2005 as the baseline and 2016–2035 as a future period influenced by climate change, based on the 2016 climate change scenario for Vietnam This study assesses changes in precipitation and potential evaporation, expressed as a percentage change in rainfall and temperature, to understand the impact of climate change on water resources in the region.

Figure 3.7: Average annual flow in 1986–2005 and 2016–2035 periods

Between 2017 and 2035, the average flow rate in all basins feeding into the reservoir shows a slight increase, with most sub-basins experiencing an average 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 an increase 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) h

3.3.2 The average flow in the rainy season

The Srepok river basin experiences its rainy season from July to December Research has analyzed the average flow rates for 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, there is a significant increase in average discharge across all basins compared to the baseline period of 1986 to 2005 The most notable rise is observed in the EaSupThuong sub-basin, with an average increase of approximately 0.7 m³/s Other sub-basins exhibit average increases ranging from 0.05 to 0.25 m³/s, with the smallest increase recorded at 0.02 m³/s These findings indicate that climate change is expected to elevate flood season flows in the future compared to previous periods.

20 The average annual flow in rainy season (m 3 /s)

0.8 The average annual flow change in rainy season (m 3 /s) h

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 showing the most significant decline of approximately 0.25 m³/s Other sub-basins are expected to see an average reduction of about 0.05 m³/s Conversely, a few sub-basins, including Eakao, EaUy, and Srepok4, may witness slight increases of 0.02 m³/s Overall, climate change is anticipated to negatively impact dry season flow in many regions, leading to reduced average discharge values over the years when 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) h

Assessing the capacity of sustainable exploitation and using water resources

Water demand in the provinces of Gia Lai, Dak Lak, Dak Nong, and Lam Dong is based on comprehensive socio-economic development planning and local water supply strategies This demand was further refined to align with the water resources planning of the Srepok River basin, as detailed in the DWRPIS report from 2015 The recalibration of water demand specifically addresses the 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 Conversely, the Ia Drang area experiences the lowest water demand during this period.

By 2030, water demand is projected to increase by approximately 1.3 times compared to 2017, with the Ia Drang river basin experiencing the highest growth at 1.6 times In contrast, the Krong No area will see a modest increase of about 1.07 times due to its low population density and less developed agriculture influenced by its topography Additionally, the Ia Lop, Ea H'leo, Krong Ana, and Srepok river basins are expected to experience an average increase of 1.24 times in water demand compared to 2017.

By 2030, domestic water demand is projected to increase significantly, reaching 1.5 times the levels seen in 2017 The 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 water user across all sectors 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 h

Figure 3.16: The water demand by sector in 2030

By 2030, water demand in the Srepok river basin is projected to rise across all sectors, with significant increases anticipated in industry, aquaculture, and agriculture Additionally, a decrease in rainfall during the dry season is expected to further heighten water demand 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 Ea KrongAna facing the most severe shortage, 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 h

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 shortages, with deficits of 124.23 million m³ and 0.66 million m³, respectively The Ea Krong Ana and Srepok basins are identified as the most affected areas in terms of water scarcity across the sector.

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, marking an increase of approximately 58.55 million m³ since 2017 This shortage is most pronounced during the dry season and gradually lessens in the rainy season Contributing factors include climate change, characterized by heightened evaporation and reduced rainfall, alongside a significant 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, despite a reduction in shortages compared to previous years The primary impact of this water scarcity will be felt in the agricultural sector, driven by the rising demand for irrigation.

The Srepok River basin is expected to experience ongoing water shortages, particularly during dry years, exacerbated by the impacts of climate change Prioritizing water supply for industry remains crucial in addressing these challenges.

By 2030, water demand is projected to increase by 35%-40% compared to 2017, leading to significant water shortages, particularly during the dry season from January to June, with the peak deficits occurring in February and March The most affected areas include the Ea KrongAna and Srepok basins, where agriculture is the primary consumer, accounting for approximately 90% of the total water shortage The Srepok river basin, characterized by its rural and mountainous landscape and low population density, heavily relies on agriculture, which drives nearly 90% of the region's overall water demand.

The economic value of water usage in the primary sector indicates 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 industrial use, while ensuring that livestock and aquaculture receive the minimum water necessary for production Any remaining water can then be allocated for irrigation, and if shortages occur, alternative measures must be implemented to ensure sustainable water management and usage.

According to Do and Nguyen (2019) and Luyen (2013), a balanced approach to water allocation in the Srepok River basin involves ensuring a full water supply for daily life, prioritizing industrial needs, and providing 85% of water resources for agriculture To achieve optimal economic benefits, it is essential to reduce the percentage of water demand in the agricultural sector, 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 purposes in the Srepok River basin can lead to a significant reduction in water demand, decreasing it by approximately 15-27% in each sub-basin.

85% water supply The detailed measures to sustainable exploitation and use will be presented in the next chapter h

SOLUTION AND RECOMMENDATION

Problems exist

The growing disparity between water demand and supply is expected to worsen, as water needs continue to rise without a corresponding increase in available resources Additionally, rising temperatures contribute to the deterioration of water quality and quantity through heightened evaporation Compounding this issue, climate change has led to unpredictable rainfall patterns, further straining water resources.

In addition, some of the main phenomena causing waste and loss of irrigation water for agricultural production include:

- Water is lost while flowing through the leading works in the field due to infiltration resulting in obstruction of water flow, lack of water regulation for each irrigation area

- There are no positive measures to limit evaporation of the surface water

- Irrigation overuse, exceeding the area of plants capable of using irrigation water

Water loss is caused by the lack of investment in construction and upgrading of facilities and equipment and by limited management Such as:

The lack of clear ownership over the project is leading to significant violations of its protective scope, which adversely impacts the efficiency of water supply operations Despite decentralization efforts, the ambiguity surrounding rights and responsibilities continues to exacerbate these issues.

- For works managed by enterprises: Due to lack of funds for management, upgrading and repair

The Central Highlands features a dense network of rivers and streams, making it suitable for reservoir construction due to its uncomplicated geology and topography The steep riverbed slopes in the upstream areas lead to a short concentration time for floodwater, necessitating carefully designed reservoirs on each tributary.

To ensure the safety and synchronization of the river system, careful planning is essential Unplanned construction of reservoirs poses significant risks, including flooding during the rainy season and water shortages in certain areas during dry periods.

Legal documents governing the management and operation of irrigation works are often inadequate and inconsistent, particularly regarding organizational guidance and financial regulations Furthermore, many regions have not effectively utilized the existing documentation, leading to gaps in implementation.

Many farmers lack awareness about efficient water usage, resulting in insufficient understanding of the irrigation needs for different crop growth stages This often leads to excessive and unnecessary water consumption, highlighting the need for improved education on water management in agriculture.

Solution and recommendation

4.2.1 Innovation of policy and management: IWRM (Solutions for water resource management)

Integrated Water Resources Management (IWRM) is increasingly acknowledged as the sole sustainable solution to address water scarcity Effective water development and management must adopt a participatory approach that engages users, decision-makers, and policymakers at all levels.

To ensure sustainable water management in the Srepok basin, it is essential to implement legal documents that adhere to provincial jurisdiction Emphasizing efficient and responsible water usage, policies should prioritize water resources for essential daily needs and key production areas while also integrating environmental protection into water exploitation strategies.

Optimizing the approval process for water operations, particularly in reservoir and hydropower plants, is essential to enhance water supply efficiency during droughts and water shortages This adjustment aims to mitigate community damage during the rainy season.

Reviewing and adjusting specific regulations on exploitation and use to protect water resources in each province in the Srepok basin h

Strengthening measures to manage and prevent loss of water resources from exploitation and use of water, especially irrigation and water supply works

In March 2021, Decision No 417/QD-BTNMT by the Minister of Natural Resources and Environment approved a program aimed at the digital transformation of natural resources and environment by 2025, with a vision towards 2030 This initiative emphasizes the importance of enhancing the database for resources and environment in cyberspace to support the development of digital government and smart cities A key requirement is the implementation of artificial intelligence technology for real-time monitoring, forecasting, and warning of water resources, which will facilitate accurate and timely decision-making To achieve this, it is essential to upgrade the quality and quantity of water resources monitoring networks for early detection of issues, particularly during dry seasons Additionally, developing system software for swift data updates will enable stakeholders to promptly assess water shortage areas and adjust supply and usage plans, thereby preventing long-term water shortages that could adversely impact socio-economic development and community well-being.

Finally, instruct the people on what to plant and what to raise to suit their production level and natural resources, in which special attention is paid to water resources

The increasing global demand for water has heightened the need for pumping, washing, transporting, and treating water, which in turn escalates energy consumption and contributes to climate change Conversely, certain climate change mitigation strategies, such as the expanded use of biofuels, can worsen water scarcity To achieve sustainable development goals, particularly SDG 6—ensuring the availability and sustainable management of water and sanitation for all—and SDG 13—taking urgent action to combat climate change and its impacts—it is essential to implement effective measures that balance water resource management with climate action.

Develop climate policies and plans, country's water resources and climate must be an integrated approach to climate change and water management It also requires h

70 difficulty to allocate water resources among water uses, including climate change mitigation and adaptation activities

Implementing effective measures like reusing wastewater for irrigation and utilizing clean energy sources, such as solar and wind energy, to pump water in agriculture can significantly enhance efficiency.

Agriculture is the most significant sector impacted by water scarcity, responsible for 70% of global freshwater withdrawals and over 90% of consumptive water use (FAO, 2017).

Many on-site irrigation systems fail to utilize water efficiently, leading to either excessive or insufficient watering for healthy plant growth Enhancing these irrigation systems is essential to bridge the gap between water supply and demand Unfortunately, outdated irrigation practices have hindered farmers' capacity to produce food and fiber in developing regions.

To optimize water usage in agriculture, it is essential to reassess the crop structure, particularly focusing on coffee and other water-intensive crops, to alleviate pressure on irrigation during the dry season Developing economically viable crops that require less water is crucial for sustainable farming practices Additionally, the implementation of efficient water-saving irrigation models, including the reuse of water for irrigation, should be prioritized to enhance resource management (Nguyen, 2012).

High-tech agriculture is revolutionizing farming practices through digitization, exemplified by the Italian company Bluetantacles By providing farmers with precise data on weather and environmental conditions, they enable tailored irrigation strategies that align with the actual needs of crops This innovative approach has led to a remarkable reduction in water consumption, achieving savings of up to 30% through the use of advanced soil moisture sensors (Products Bluetantacles, 2020).

Spanish startup BioAgro has developed an innovative irrigation platform that utilizes sensors to monitor soil moisture levels This technology automatically waters and fertilizes plants when necessary, optimizing resource use Additionally, the platform offers farmers forecasts and alerts regarding potential threats to crops, enabling timely and effective responses to safeguard their harvests.

Efficient water-saving solutions, such as drip irrigation, sprinkler irrigation, and underground irrigation, are essential for impoverished areas, providing cost-effective and practical methods to conserve water (Nguyen, 2012) These systems enable timely and convenient water management, significantly benefiting agricultural practices (EIT Food, 2020).

Aquaponics, an innovative agricultural technology, presents an effective solution to water scarcity by integrating aquaculture and hydroponics This system significantly reduces water usage, achieving up to 90% less consumption compared to conventional farming practices (Samanta, 2020).

4.2.4 Structural measures in the Srepok river basin

The water shortage area is mainly concentrated in the Ea KrongAna and Srepok river basins; therefore, some structural measures can be implemented here:

The government should advocate for the implementation of rainwater harvesting and water storage systems at the household level to ensure access to clean water for domestic use and irrigation, while also contributing to the replenishment of groundwater levels These systems are best constructed during the dry season for optimal effectiveness.

Next, in the dry season (Jun to November), it is necessary to increase groundwater exploitation for irrigation purposes

To enhance water resource management, comprehensive monitoring systems for water use, quality, and wastewater should be established at key supply and discharge points within the Srepok river basin Currently, the basin is equipped with five surface water hydrological monitoring stations, 64 groundwater monitoring stations, and one environmental monitoring station In 2016, the Prime Minister approved Decision No 90/20016/QD-TTG, which outlines the national monitoring network plan, including the addition of 21 surface water monitoring stations to the Srepok river basin.

CONCLUSIONS AND RECOMMENDATIONS

Conclusion

In recent years, Vietnam has experienced significant changes in extreme climate phenomena due to global climate change, leading to a rise in unpredictable natural disasters As a result, it is increasingly crucial to implement effective strategies to address climate change and its impacts on water resources, particularly concerning the anticipated future water scarcity.

Effective water resource planning and management are crucial for addressing climate change impacts on water resources, as all solutions to these challenges are interconnected.

The study "Assessing Climate Change Impacts on Surface Water Flow towards Sustainable Exploitation and Utilization of Water Resources in the Srepok River Basin in Vietnam" aims to enhance water resource management in the Srepok River Basin It presents novel findings that differentiate it from previous research, highlighting the significant effects of climate change on surface water flow and emphasizing sustainable practices for water resource exploitation and utilization.

1 The thesis analysed and evaluated the natural features and water resources characteristics in the Srepok river basin The Srepok river basin was delineated into six sub-basins (Ia Drang, Ia Lop, EaH’leo, Srepok, Krong Ana and Krong No) that are suitable for natural conditions and water resources exploitation and use

2 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

3 Assessment and forecast of water resources in the basin, surface flows, shortage water area under the impact of climate change

The thesis utilizes the 2016 MONRE report on climate change scenarios and sea-level rise in Vietnam to evaluate changes in water resources within the Srepok River basin It examines the impact of climate change on river flows, highlighting significant alterations in the region's hydrology.

The total annual flow in the basin experiences an increase during the flood season and a decrease in the dry season Although the variation is minimal, these flow trends are crucial for evaluating the impacts of climate change on water balance assessments.

The ongoing water shortage is significantly influenced by the role of reservoirs and hydropower plants within the overall system As climate change progresses, the deficit is expected to increase steadily from its current levels to future stages.

By 2030, the water deficit is projected to increase by approximately 58.55 million m³ compared to 2017, leading to significant water shortages in various regions, particularly during the dry season from January to June, with peak deficits occurring in February and March The most affected areas are the Ea KrongAna and Srepok basins, characterized by high population density and economic activity Agriculture is the primary sector impacted, accounting for about 95% of the total water shortage In the Srepok river basin, where the economy relies heavily on agriculture, water demand from this sector constitutes around 95% of total water needs To mitigate shortages, it is estimated that agricultural water demand could be reduced by 14%-27% to ensure an 85% supply in this critical sector.

4 Based on the water shortage identification in each sub-basin and water-using sector Proposing sustainable water use solutions to sustainable water use in the Srepok river basin

5 With all the above results, this thesis will help manage the exploitation and use of water sustainably in the basin, especially in the context of climate change Besides, the thesis can also be considered a document for the planning and socio- economic development of the province.

Recommendation

This research faces limitations, particularly in accessing local data on irrigation constructions and water usage, which hinders a comprehensive assessment of the water balance in the basin Additionally, the thesis primarily focuses on evaluating specific aspects without a broader analysis.

The article highlights the significant impact of climate change on surface water resources while neglecting the effects on groundwater resources in the basin's water balance Additionally, it does not address the climate change scenarios RCP 2.6 and RCP 4.5, as outlined in the climate change and sea-level rise scenario report for Vietnam published by MONRE.

In 2016, it was recommended that the RCP 8.5 scenario, representing the highest greenhouse gas concentration, be utilized for permanent works and long-term planning to highlight the severe impacts of climate change on water resources from 2016 to 2035 The current focus on investing in sustainable solutions aims to enhance resilience and adaptive capacity to climate-related hazards, aligning with Sustainable Development Goal 13 However, the thesis lacks updated rainfall and evaporation data from 1980 to 2018 or 2019, which is essential for accurately simulating flow and predicting rainfall projections for 2019 and beyond Additionally, the author has not conducted fieldwork to assess water resource characteristics and usage, relying solely on existing reports and data collection.

Future research should focus on addressing the limitations of this study by incorporating updated data, including rainfall and evaporation records from 1980 to 2019, as well as groundwater information Additionally, it is essential to include various climate change scenarios, such as RCP 2.6 and RCP 4.5, to thoroughly evaluate the effects of climate change on water resources in the Srepok River basin Furthermore, the study will gather more data on water usage and irrigation infrastructure from local government sources and the Ministry of Natural Resources and Environment (MONRE) to optimize water balance simulations within the river basin.

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