INTRODUCTION
General Background
The Burmese proverb states, “Rice offers survival for seven days; however, water gives life only a day,” emphasizing the essential role of water for all living beings While water is a renewable resource, effective management and strategic exploitation are crucial for maintaining a sustainable supply The United Nations’ Sustainable Development Goal No 6, particularly target indicator 6.4, underscores the importance of efficient water use and addressing water scarcity (UNHCR, 2017).
Vietnam is rich in water resources with dense river networks accompanied by
Vietnam is home to 2,360 rivers, which are categorized into three main types based on their basin area and administrative boundaries: those spanning multiple provinces, two provinces, and a single province (Taylor & Wright, 2001) The country features nine major river basins—Red River, Thai Binh, Bang Giang – Ky Cung, Ma, Ca La, Thu Bon, Ba, Dong Nai, and Mekong—that collectively cover approximately 80% of Vietnam's total land area (ADB).
Vietnam's water resource sustainability is significantly impacted by transboundary water management, with approximately 63% of its total water resources originating from neighboring countries such as China, Cambodia, Lao PDR, and Thailand The average annual surface water runoff in Vietnam is around 830 to 840 billion cubic meters, but only 43% of this volume is available for sustainable use Additionally, while the groundwater potential is estimated at 63 billion cubic meters, only 7% can be sustainably exploited.
The Mekong and Red-Thai Binh basins are significant regions in Vietnam, contributing approximately 42% to the nation's GDP To ensure sustainable economic growth, effective long-term water resource management strategies are essential for these basins, which face high trans-boundary water dependency—95% for the Mekong and 40% for the Red-Thai Binh Basin (2030WRG, 2017).
Vietnam's agricultural sector plays a crucial role in the economy, contributing 18% to the country's GDP while utilizing approximately 80% of its total water resources Agriculture occupies one-third of Vietnam's land area, with 20.6% designated for farming, 12% for permanent crops, and 2.1% for permanent pasture (2030WRG).
Vietnam's primary crops include paddy, maize, coffee, and sugarcane, with the Mekong Delta, Red River Delta, and Northern Highlands serving as the nation's key rice-producing regions According to the agricultural master plan for 2020, paddy fields will be maintained at a maximum of 3.8 million hectares, while production is aimed to reach 41 to 43 million tons per year by 2020 and 44 million tons by 2030 This ambitious production target underscores the significant water resources required to sustain both current and future agricultural outputs.
Annual water demand in Vietnam is increasing year by year; 80.2 bn m 3 /year in
2009, 80.6 bn m 3 /year in 2015 and projected to reach 95 bn m 3 /year by 2030 (ADB,
In 2016, the agricultural sector's water demand was 76 billion cubic meters, projected to rise to 91 billion cubic meters by 2030 Aquaculture required 10 billion cubic meters annually in 2016, with expectations to grow to 12 billion cubic meters by 2030 The industrial sector's water demand was 6 billion cubic meters per year in 2016, anticipated to increase to 15.6 billion cubic meters by 2030 Municipal water requirements stood at 3.1 billion cubic meters annually in 2016, expected to reach 5.7 billion cubic meters by 2030 Additionally, the hydropower sector's demand was 57 billion cubic meters in 2016, projected to rise to 63 billion cubic meters by 2030.
Vietnam's wastewater treatment system is still developing, currently managing to treat only 12-13% of municipal and 10% of industrial wastewater (Tien, 2015 & 2030WRG, 2017) The Cau, Nhue-Day, and Dong Nai River Basins are identified as the most polluted areas (MONRE, 2006) In 2015, the Ministry of Natural Resources and Environment (MONRE) amended technical guidelines for surface water quality (QCVN 08: 2008/BTNMT) to strengthen water resource management and quality control MONRE conducts quarterly water quality monitoring in the Cau River Basin, with results made public through an index score (Tran et al., 2017).
Climate change significantly impacts watershed hydrology, with projections indicating that by 2030, annual runoff could reach 15 billion cubic meters per year During the wet season, runoff may increase by 25 billion cubic meters, while a decrease of 10 billion cubic meters is expected in the dry season (2030WRG, 2017) These changes highlight the shifting rainfall patterns affecting seasonal water availability, as key climatic factors like precipitation and temperature play a crucial role in the sustainability of water resources.
Addressing water demand and resources is essential in light of projected population growth and the needs of industry and agriculture Adopting integrated approaches will enhance both the quantitative and qualitative management of water resources Effective management of existing water supplies is vital to guarantee a safe and reliable water source that supports multi-sectoral development.
Research Motivation
The Red-Thai Binh Basin is vital to Vietnam's socio-economic development, contributing approximately 15% to the country's GDP Its strategic geographical location, spanning from the northern highlands to the alluvial plains in the northeast, enhances business opportunities The basin accounts for 15% of Vietnam's total rice production, playing a crucial role in national food security and foreign income generation Additionally, it hosts numerous industrial clusters and craft villages, representing 65% of the nation's total To ensure sustainable water resources and improve the quality of life for residents, a comprehensive water resource exploitation plan has been called for by Mr Tran Hong Ha, Minister of the Ministry of Natural Resources and Environment.
Target Basin
The Upper Thai Binh River Basin (UPTBRB), located in Northern Vietnam, is part of the larger Red-Thai Binh River Basin, which spans almost the entire northeastern region of the country This transboundary river basin is shared by three countries: China (48%), Laos PDR (1%), and Vietnam (51%) Notably, the basin contributes 17% of Vietnam's total annual surface water runoff.
Mekong River Basin with 59% thus play an essential role to provide the sufficient water resources for the capital Hanoi (2030WRG, 2017)
Figure 1.1 Location of Target Basin (Source: TA-7629, VIE, 2012)
Problem Statements
The UPTBRB is facing a significant risk of water stress due to escalating demand from various sectors, with the 2030 Water Resources Group projecting a 42% increase in water demand in the Red-Thai Binh Basin by 2030 Previously classified under low water stress in 2016, the basin is expected to enter the water-stressed category by 2030, intensifying competition for reliable access to water resources in the near future.
The UPTBRB is significantly susceptible to climate change, with projections indicating an average surface temperature rise of 1.9 to 2.4°C by 2100 under RCP 4.5 scenarios Additionally, precipitation levels are anticipated to increase by 5 to 15%, leading to changes in both the intensity and patterns of rainfall.
The Cau River has been recognized as the most polluted watershed in Northern Vietnam (Tran et al., 2017) Evaluating water quality in the Cau River presents challenges due to variations in space and season To accurately assess pollution levels, comprehensive basin-wide water quality evaluations are essential, particularly between the main rivers and their tributaries Additionally, the absence of a water quality database for the Thuong and Luc Nam River Sub-basins significantly hinders effective water resource management.
Objectives
The main objectives of this study are as below;
- To evaluate basin water stress through integration of hydrological model and social statistics
- To evaluate basin river quality by multivariate statistical analysis and the Vietnamese Water Quality Index
- To contribute integrated water resource management through quantitative and qualitative approaches
Thesis Structure
This thesis adheres to the Master's thesis guidelines set forth by Vietnam Japan University, and provides a comprehensive summary of the general research information along with the relevant chapters.
Chapter 1 provides an overview of Vietnam's water resources, detailing the current water demand across various sectors and the initiatives undertaken by relevant organizations for effective water resource management It highlights significant challenges related to water and outlines the research objectives driven by these issues Additionally, the chapter includes baseline data on the natural and hydro-climatic conditions of the target basin.
Chapter 2 focuses on a literature review that outlines the research scope and content, highlighting significant findings from previous studies in the basin It extensively discusses comparative studies that employ similar research methodologies in other basins, serving as valuable references for this research.
Chapter 3 describes about hydrological modelling in the basin Detailed step by step method to construct the watershed model, technical considerations and results are provided
Chapter 4 presents a comprehensive analysis of climate projections using a regional climate model to forecast future climate conditions It discusses historical, current, and future scenarios across various time frames, offering an in-depth understanding of climate change impacts in the basin.
Chapter 5 addresses water stress by estimating sectoral water demand and resources, utilizing findings from Chapters 3 and 4 This section integrates various assessment methods to evaluate both current and future water stress levels.
Chapter 6 provides river water quality evaluation Seasonal river water sampling and laboratory analysis are performed The results are driven by multivariate statistical analysis and the Vietnamese regulations
Chapter 7 is a conclusion and recommendation session in which all the key findings and necessary solutions are provided including limitations and future research direction.
Baseline Information about the Study Basin
The Upper Thai Binh River Basin (UPTBRB) comprises three major rivers: the Cau River, Thuong River, and Luc Nam River, which together form three distinct sub-basins The basin features seven tributaries, including the Cho Chu, Thuong Nghinh, Du, Cong, and Calo rivers in the Cau River Sub-basin, while the Luc Nam River Sub-basin has the Dinh Dem river, and the Thuong River Sub-basin includes the Rang tributary The Cau River, the longest at 288 km, originates in Bac Kan Province, followed by the Thuong River at 157 km, flowing from Lang Son to Bac Giang Province, and the Luc Nam River, which spans 200 km and serves as a vital waterway for Bac Giang Province All three rivers converge into the Thai-Binh River at Pha Lai, creating a rich and complex river network entirely within the country's boundaries.
The basin, located between 21°48'54.81"N and 106°27'44.98"E, spans an area of 12,720 km², representing approximately 4% of Vietnam's total land area Its upper region is bordered by dense high mountain ranges that stretch in a North-East direction, with the highest peak being Tam Dao Mountain at 1,592 m, while the lowest point is 3.8 m at Pha Lai, where the Thai Binh River originates The central and downstream areas feature fertile alluvial plains that provide ideal conditions for agriculture and settlement This basin encompasses several administrative provinces, including Bac Kan, Thai Nguyen, Hanoi, Vinh Phuc, Bac Ninh, Lang Son, and Bac Giang.
2015, the basin population is estimated at approximately 4.95 million The average basin population density is 389 persons per km 2 that is higher than the country’s 280 person per km 2
The basin is characterized by a tropical monsoon climate, with long-term annual temperatures ranging from a maximum of 28 °C to a minimum of 21 °C, resulting in a temperature gap of approximately 6 °C The average annual precipitation is around 1550 mm, with four distinct seasons: winter (December to February), spring (March to May), summer (June to August), and autumn (September to November) The peak rainfall occurs in July and August, exceeding 300 mm per month Notably, from 2005 to 2019, about 67% of the total runoff was recorded during the rainy season, particularly from June to September.
Figure 1.2 Long Term Annual Maximum Temperature Trend
Figure 1.3 Long Term Annual Minimum Temperature Trend
ANN UAL M AXI MU M TEMPER A TUR E (°C)
ANN UAL M INI MU M TEMPER A TUR E (°C)
Figure 1.4 Long Term Monthly Precipitation Trend
Figure 1.5 Average Monthly Discharge at the Gia Bay Hydrological Station
AN NUAL PRE C IPIT ATI ON (MM)
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Summary
The annual increase in water demand is significantly influenced by seasonal variations and climate change, while limited water treatment facilities pose a threat to river water quality and socio-economic growth To address these challenges, it is crucial to evaluate water resources from both quantitative and qualitative perspectives, enabling stakeholders to implement necessary practices and measures Figure 1.6 illustrates the approaches required to meet specific objectives in water resource management.
Figure 1.6 Work Flow of the Research n
LITERATURE REVIEW
Administrative Provinces
Bac Kan province, situated in the mountainous region of Northern Vietnam, has a population of approximately 303,100 and a population density of 64 people per square kilometer as of 2013 The province's economy is primarily driven by agriculture, while forests cover the majority of its area With a warm and temperate climate, Bac Kan experiences high annual precipitation exceeding 2,294 mm and an average annual temperature of 20.6 °C.
Thai Nguyen province, situated in the heart of the Northeast Region of Vietnam, spans an area of 3,526.64 km² and shares its northern border with Bac Kan Province As of 2019, the province boasts a population of approximately 1.29 million, resulting in a population density of 365 people per km² The region experiences an average temperature of 25 °C, with annual precipitation ranging from 2,000 mm.
2500 mm In 2010, industrial clusters were rapidly established thus the main economy of the province had shifted to industry-construction from agriculture (Thai Nguyen Overview, 2015)
Vinh Phuc Province covers an area of 1,237.52 km² and had a population of approximately 1.03 million as of 2013, resulting in a population density of 832 people per km² The region experiences an average temperature ranging from 24 to 25 °C and receives about 1,650 mm of precipitation annually Agriculture dominates the local economy, utilizing around 70% of the total land area.
Hanoi province, situated at the downstream of the Cau River and at the confluence of the Red River and Da River, is a bustling metropolitan area with a population of 4.7 million and a population density of 14,639 people per square kilometer across its 319.56 square kilometers The region experiences an average temperature of 29.2 °C and receives approximately 1800 mm of precipitation annually.
Bac Ninh, located just 30 km northeast of Hanoi, is Vietnam's smallest province, covering an area of 823 km² As of 2015, it has a population of approximately 1.54 million, resulting in a population density of 1,676 persons per km² The province experiences an average temperature of 23.6 °C and receives about 1,926 mm of precipitation annually Renowned as an industrial hub, Bac Ninh attracts significant foreign investment and trade, establishing itself as a high-tech development hotspot.
Lang Son is a mountainous province in Vietnam, bordered by Quang Ninh to the north, Bac Giang to the south, and Bac Kan to the west Covering an area of 8,310 km², it had a population of 831,887 in 2009, resulting in a population density of 102 people per km² The province's landscape is primarily characterized by forests and agricultural land, and it experiences a tropical monsoon climate with average temperatures ranging from 17 to 22 °C and annual precipitation between 1,200 and 1,600 mm, according to the Lang Son Statistics Office.
Bac Giang Province, covering an area of 3,895.59 km², is bordered by Lang Son Province to the north and Bac Ninh Province to the west As of 2019, it has a population of approximately 1.8 million, resulting in a population density of 483 people per km² The province features 72% mountainous districts, with an average annual temperature of 23.3°C and average annual rainfall of 1,915 mm The industry and construction sector is the largest contributor to the province's GDP, followed by agriculture (Wu A., 2021).
Hydrological Modelling
Hydrological modelling, as defined by Allaby & Allaby (1999), involves characterizing real hydrologic features and systems through the integration of physical models, mathematical equations, and computer simulations This process aids in understanding watershed hydrology by analyzing runoff over time, both on land and underground, while also considering the amount of water stored in the soil (Digman).
Hydrological models are primarily classified into lumped and distributed types, with lumped models ignoring spatial variability, while distributed models break the catchment into smaller units Additionally, models can be categorized as static or dynamic based on time factors Notable examples in hydrological studies include empirical models like Artificial Neural Networks (ANN), conceptual models such as the HBV model and TOPMODEL, and physically based models like MIKESHE and SWAT (Devia et al., 2015).
The Soil and Water Assessment Tool (SWAT) is widely recognized for its effectiveness in studying watershed hydrology in the context of climate change, with 36 validated hydrological studies conducted in Vietnam (Tan et al., 2019) Research by Tran et al (2017) highlighted a significant correlation between rainfall and daily total nitrogen load in the Cau River Basin using the SWAT model Additionally, Thai et al (2017) employed SWAT to analyze erosion and stream flow in the Cau watershed, predicting varying patterns of soil loss and increased river discharge due to climate change To address data scarcity in the Cau watershed, Bui et al (2019) integrated SWAT with QUAL2K for enhanced water quality modeling Furthermore, Chuong et al (2014) assessed water quality in the Ta Trach watershed using the SWAT model In Thailand's Songkhram River Basin, S Shrestha et al (2018) combined SWAT with climate models to examine the effects of climate and land use on hydrology and water quality, revealing a connection between stream flow and future climate, as well as a strong relationship between nitrate-nitrogen levels and land cover Lastly, S Shrestha et al (2016) explored water resource potential in Nepal's Indrawati River Basin, finding that river discharge patterns and intensity varied consistently with future climate scenarios.
Climate Change
Vietnam has experienced notable climate change, with an annual temperature rise of 0.62 °C recorded from 1958 to 2014, translating to an average increase of 0.1 °C per decade Additionally, a significant increase of 0.42 °C was noted between 1985 and 2014.
– 2014 In the North-East region, the increased days of maximum temperature greater than 35 °C is observed during 1960 – 2014 (Ngu et al., 2016) The annual changes of temperature were provided in figure 2.1
Figure 2.1 Annual Changes of Temperature (Ngu et al., 2016)
Over the past 57 years (1958-2014), Northern Vietnam has experienced a significant decline in annual precipitation, ranging from 5.8% to 12.5%, despite a slight overall increase in precipitation across the country In contrast, the South Central and Southern regions have seen increases of 19.8%, 6.9%, and 18.8% in precipitation, respectively Seasonal variations indicate a decrease in autumn rainfall in the North, while spring sees significant rainfall Meanwhile, Southern Vietnam has experienced increased precipitation during both winter and spring (Ngu et al., 2016).
Table 2.1 Variation of Precipitation (%) during 1958 – 2014
Climate change projections for Vietnam utilize five regional climate models, including Weather Research and Forecast (clWRF) and Conformal Cubic Atmospheric Model (CCAM), focusing on a long-term baseline from 1986 to 2005 Future climate scenarios are assessed for three periods: early (2016-2035), mid (2046-2065), and late (2080-2099) in the 21st century Under the RCP 4.5 scenario, surface temperatures are expected to rise by 1.9 to 2.4 °C, with precipitation in the North increasing by over 20% By mid-century, annual temperatures may rise by 1.6 to 1.7 °C, with seasonal increases of 1.2 to 2.0 °C Maximum and minimum temperatures are projected to increase by 1.7 to 2.7 °C and 1.4 to 1.6 °C, respectively Additionally, winter rainfall may decrease by up to 10%, while spring precipitation could rise by 10%, summer rainfall by 5 to 15%, and autumn rainfall by 15 to 35%.
The latest challenge highlighted by the IPCC AR5 is the climate change impact projections Given the current development of the basin as both an agricultural area and a modern industrial zone, it is crucial to propose a sustainable water resources policy and strategy.
Water Stress Assessment
Various methods exist to measure basin vulnerability regarding water stress, including the Falkenmark indicator, Green-Blue Water Scarcity Index, Water Stress Index, Smakhin Water Supply Stress Index, Stream Flow-Based Index, Water Scarcity Index, Water Supply Sustainability Risk Index, Watershed Sustainability Index, Green Water Stress Index, and Green Water Scarcity Index A comprehensive technical review of these indicators is provided in the report by Xu and Hu (2017).
Water stress occurs when individuals lack access to a reliable and adequate water supply for their daily needs It can be characterized as a situation where a community experiences insufficient water availability over time (Rijsberman et al., 2006) A commonly used index measures the ratio of water withdrawal to available resources, categorizing water stress levels as low (0.4) (Vorosmarty et al., 2005; Xu and Hu, 2017).
The IPCC AR4 projections indicate a rise in global average temperatures coupled with decreased precipitation, which could disrupt the water supply-to-consumption ratio (Solomon et al., 2007) Bromand (2015) identified potential water stress in the Kabul River Basin using a coupled SWAT model and climate projections Koiso et al (2019) assessed water supply and demand in the Mekong River Basin under the RCP 8.5 scenario, revealing that climate change would exacerbate water stress Research by Yamamua et al (2018) highlighted that downstream regions of the Mekong River Basin face significant water stress, with over 80% of the Murray-Darling Basin already experiencing high stress levels since the 2020s Indicators suggest that water stress will intensify in the future Additionally, Sun et al (2008) found that in the Southeastern United States, rising populations have increased water stress intensity, while changes in land use have had minimal effects on water resource availability.
A comprehensive assessment of water stress in Vietnam was conducted by the 2030 Water Resource Group across 16 major river basins, utilizing the water exploitation index (WEI) in 2016 The WEI, which measures the ratio of annual freshwater withdrawal to long-term water resource potential, indicates the level of pressure on water availability from municipal, agricultural, and industrial sectors The index categorizes water stress levels as follows: no stress is below 10%, low stress ranges from 10% to 20%, stressed is between 20% and 40%, and severe stress is indicated by values exceeding 40% Specifically, the dry season WEI for the Red-Thai Binh basin demonstrated low stress at 19% in 2016, with projections suggesting it may reach a stressed level of 27% by 2030.
Figure 2.2 Water Stress Levels in Vietnam (2030WRG, 2017)
Water Quality Evaluation
Vietnam is divided into seven major regions: Northwest, Northeast, Red River Delta, North Central Coast, South Central Coast, Central Highlands, and the Mekong River Delta While the upstream water quality in these areas is generally good, it deteriorates downstream due to the direct discharge of untreated wastewater from residential areas, industrial clusters, craft villages, and agricultural zones, along with the improper use of pesticides and fertilizers.
The water quality in the Northeast region, Red River Delta, South Central Coast, and Northeast of the Mekong region has significantly deteriorated Figure 2.3 illustrates the distribution of seven regions, highlighting the current status of water quality and the corresponding pollution sources (2030WRG, 2017).
The Water Quality Index (WQI) is a mathematical formula that consolidates various pollution factors into a single score, assigning subjective weights to each parameter for evaluation (Garcia, 2017) This index is utilized in both developed and developing nations to aid decision-makers by enhancing public awareness and encouraging community involvement.
The Water Quality Index (WQI) has proven to be an effective tool for sound water resource management, with widespread applications in countries such as India, Chile, England, Wales, Taiwan, Australia, and Malaysia (Phu, 2019) Vietnam introduced its first WQI application in 2011, and an improved version has been publicly implemented since 2019.
On November 12, 2019, the Vietnamese Government introduced a new calculation method for the Vietnamese National Water Quality Index (VN_WQI) through Decision No 1460/QD-TCMT, aimed at promoting the assessment of surface water quality.
2019) This new VN_WQI is an improved version of the former WQI stipulated on July
The updated VN_WQI now incorporates subjective purposes, such as pesticide or heavy metal modules, enhancing the calculation method This new version includes a broader range of water quality parameters compared to the previous WQI from 2011, which was limited to just nine parameters: water temperature (WT), pH, biochemical oxygen demand (BOD5), chemical oxygen demand (COD), turbidity, and total suspended solids (TSS).
The new VN_WQI incorporates key parameters such as NH4+-N, PO4 P, and coliform (Pham et al., 2017 & Son et al., 2020), while excluding physical factors like turbidity and total suspended solids This updated index offers a more detailed classification system, featuring six categories compared to the previous five, although the overall index value still ranges from 0 to 100 Generally, a higher Water Quality Index (WQI) signifies better water quality.
Water Quality Index (WQI) is extensively utilized in Vietnam, as demonstrated by Son et al (2020), who assessed the water quality of the Cau River Their study employed a combination of various metrics, including the water quality index, comprehensive pollution index, organic pollution index, and trace metal pollution index, to provide a thorough evaluation of the river's water quality.
Seasonal and spatial distribution significantly impact river water quality, with organic pollution posing a higher risk of eutrophication, particularly in downstream areas Phu (2019) utilized the Water Quality Index (WQI) to evaluate the water quality status of the Luy River in Binh Thuan Province, demonstrating the effectiveness of WQI in analyzing seasonal variations and spatial distribution within the watershed Similarly, Hong (2018) conducted a WQI-based assessment on the Tien River, highlighting the importance of monitoring water quality across different regions and seasons.
River quality in Southern Vietnam to find out long term annual and seasonal changes of water quality
Statistical tools and analysis are effective to treat water quality data Zeinalzadeh
Rezaei (2017) utilized principal component analysis (PCA) and the Water Quality Index (WQI) from the National Sanitation Foundation to assess the Shahr Chai agricultural watershed in Iran, revealing that reduced stream flow, increased pollution load, and agricultural discharge negatively impacted downstream water quality Similarly, Yamazaki et al (2017) conducted a multivariate statistical analysis, including PCA and cluster analysis, to evaluate the water quality of the Rekifune River and Satsunai River watersheds in Japan, finding that significant pollution from tributaries influenced the major river's overall quality.
Ali (2016) conducted a comprehensive evaluation of the Tigris River's water quality in Iraq by employing a combined approach that included Principal Component Analysis (PCA), Cluster Analysis, and Water Quality Index (WQI) This study revealed critical insights into point sources of pollution, the spatial similarities in pollutant characteristics, and the essential treatment requirements for improving water quality.
In a study conducted in 2010, PCA was utilized to identify key water quality parameters and discharge sources of the River Ganges in India Additionally, Gajbhiye et al (2015) analyzed surface water quality in Jabalpur City, highlighting that PCA effectively revealed the primary sources of variability in water quality.
In 2019, PCA was utilized to identify key water quality parameters for calculating the Water Quality Index (WQI) in India's River Gang Kamble and Vijay (2011) conducted an analysis of water quality in Mumbai's coastal region, revealing seasonal variations in pollution levels through cluster analysis Additionally, Garcia (2017) employed various WQIs and PCA to assess nutrient and organic loads in Brazil's Acude Macela Reservoir.
Recent studies in the study basin revealed that all Water Quality Index (WQI) assessments utilized an outdated calculation method The updated version incorporates a broader range of water quality parameters, enhancing the evaluation of water quality status Additionally, multivariate statistical analyses, including Principal Component Analysis (PCA) and Cluster Analysis (CA), have demonstrated effectiveness in assessing the spatial and temporal variations in river water quality across three sub-basins.
Figure 2.3 River Water Quality in Vietnam (2030WRG, 2017)
Summary
Research on water stress assessment in the UPTBRB using hydrological models is lacking, with previous studies primarily focused on stream flow and erosion The reliance on outdated spatial and time series data poses challenges in accurately reflecting recent changes in watershed hydrology and climate change To assess the current water quality of the Cau River and develop a comprehensive water quality database for the Thuong and Luc Nam River Basins, it is essential to apply updated Water Quality Index (WQI) methods alongside multivariate statistical analyses for a more in-depth evaluation of basin water quality.
HYDROLOGICAL SIMULATION
SWAT Hydrological Model
The Soil and Water Assessment Tool (SWAT) is utilized for modeling the Upper Pease-Texas Basin River Basin (UPTBRB), particularly due to agriculture being the dominant land cover SWAT is an effective hydrological model for simulating both water quantity and quality, and is supported by the United States Department of Agriculture (USDA) This continuous, physically-based, semi-distributed model assesses the effects of land management practices on surface flow, sedimentation, and chemical yields For comprehensive insights into SWAT modeling, refer to the SWAT Theoretical Documentation and User’s Manual available at http://swat.tamu.edu.
The model begins by dividing the targeted basin into multiple sub-basins and generating hydrological response units (HRUs) based on similar geomorphological and hydrological features It requires input data including topography, land cover, soil layers, and climatic conditions SWAT then simulates physical processes such as runoff, nutrient, and sediment transport over continuous time This tool is particularly effective in data-scarce basins and can assess the impact of various factors, including land use and climate change, on water resources This study utilized the Arc interface of SWAT 2012 as an extension within ArcGIS.
In SWAT, the water balance of hydrological cycle is calculated using the following equation 3.1 with regard to the hydrological cycle in the basin
SW t = SW 0 + ∑ R day – Q uaff – E a – w sweep – Q gw ) (3.1)
Where SW t is the final calculated soil water content (mm H2O) in a given time (days)
SW 0 is the initial amount (mm H2O) of soil water content, t is the time (days), n
R day represents the daily precipitation measured in millimeters of water (mm H2O), while Q suf indicates the surface runoff for that day Additionally, E a denotes the evapotranspiration amount, also in mm H2O, and w sweep refers to the water percolating through the soil profile on the same day Lastly, Q gw signifies the return flow amount occurring on that day.
The surface runoff of the basin is estimated using the following equation 3.2
The initial abstractions, including surface storage, interception, and infiltration before surface runoff, are represented by 'I_a' (mm H2O) The retention parameter 'S' (mm H2O) is determined using equation 3.3, as it is influenced by factors such as soil changes, land management practices, topography, and the amount of stormwater (SW_t).
The curve number (CN) for each hydrological response unit (HRU) is influenced by soil permeability, land cover, and soil moisture content Changes in CN are closely linked to moisture conditions, categorized as 1) dry (wilting point), 2) average moisture, and 3) wet (field capacity) Below, we provide the detailed equations for calculating CN for each condition.
In the context of hydrology, CN1, CN2, and CN3 represent varying moisture conditions, with CN2 specifically applicable to slopes less than 5% The slope of the terrain is a crucial topographic factor that influences the Curve Number (CN) Therefore, for areas with steeper slopes, Equation 3.6 is employed to accurately assess the CN.
The CN 2s represents the adjusted curve number influenced by slope, while CN 2 and CN 3 are also modified based on a default slope of 5% In this context, 'slp' refers to the average slope of the basin.
3.1.1 Preparation of In-put Data for Model Set-up
The SWAT model distinguishes itself from other hydrological models by necessitating various input parameters, including a Digital Elevation Model (DEM), Land Cover (LC), soil classification maps, and comprehensive weather data such as temperature, precipitation, solar radiation, relative humidity, and wind speed This study focused on gathering the highest quality data available within the basin to ensure accurate modeling outcomes.
Digital Elevation Models (DEMs) are crucial for hydrologic modeling, as they help in defining basin boundaries and classifying elevation levels and sub-basins This research utilized the SRTM3 DEM (version 3), which has a spatial resolution of 30-meter grid cells, created by the United States Geological Survey (USGS) and the National Geospatial-Intelligence Agency (NGA) The DEM data was obtained from the CGIAR-CSI website at http://srtm.csi.cgiar.org/srtmdata/ The illustration of the DEM for UPTBRB is shown in Figure 3.1.
Figure 3.1 DEM of the UPTBRB n ii High Resolution Land Use and Land Cover
High Resolution Land Use and Land Cover (HRLULC) data, developed by the Earth Observation Research Center/Japan Aerospace Exploration Agency (EORC/JAXA) for mainland Vietnam in 2015, was utilized to classify land use patterns in the basin With a high spatial resolution of 15 meters and detailed land cover classifications, HRLULC is particularly effective for SWAT hydrologic modeling, allowing for precise assignment of specific land use patterns (EORC/JAXA, 2016).
The HRLULC dataset was obtained from www.eroc.jaxa.jp and subsequently extracted at the basin scale It was analyzed across six primary categories—barren land, agricultural land, forest, urban/built-up areas, water bodies, and others—to facilitate a comprehensive understanding of the overall land cover status (refer to Figure 3.2).
The upstream region of the basin is primarily covered by forest, while agricultural land dominates most areas throughout the basin Although residential land is minimal in the upstream region, small clusters begin to appear as one moves downstream; however, this residential land remains significantly lower in proportion compared to forest and cultivated areas.
A few proportions of water body and barren land were also observed The share of others type like grass/shrub land was also found
Statistics indicate that agriculture is the predominant land cover, accounting for 42.33%, followed closely by mixed forest cover at 40.14% Additionally, range areas occupy 7.61%, while residential land, water bodies, and barren land comprise 4.12%, 2.69%, and 3.11%, respectively.
Figure 3.2 High Resolution Land Use and Land Cover Map of the UPTBRB (2015)
Figure 3.3 Major Land Cover Statistics in the UPTBRB
The land cover classification layers were processed according to SWAT land cover codes With high resolution and accuracy, the dataset provided a detailed classification, allowing for careful assignment of the corresponding SWAT land use and land cover codes, as illustrated in Table 3.1.
Table 3.1 SWAT Land Use Code and Statistics (2015)
Cover Code Land Use Area (ha) Area (%)
AGRR Agricultural Land-Row Crops 242185.8499 19.04
The soil database necessary for the UPTBRB was obtained from the FAO GeoNetwork website The soil map was extracted in accordance with the basin boundary, and an analysis of the spatial distribution of soil classes was conducted, as illustrated in Figure 3.4.
The detailed results of analysis were illustrated in the figure 3.5
Figure 3.4 Soil Map of the UPTBRB n
Figure 3.5 The Proportion of Soil Classes observed in the UPTBRB iii Weather Data
SWAT Model Set-up
The SWAT model effectively extracted hydrological features, including flow direction and flow accumulation, after defining the outlet of the entire basin It also delineated the basin boundary and produced a detailed report of the topographic profile, encompassing 27 sub-basins.
Following the subdivision of sub-basins, a detailed analysis of Hydrological Response Units (HRUs) was conducted Spatial data, including land cover, soil maps, and slope classifications, were incorporated, resulting in the SWAT model generating a total of 347 HRUs (refer to Figure 3.7).
The SWAT weather database was developed using five key parameters: maximum temperature, minimum temperature, precipitation, relative humidity, and wind speed, sourced from 11 stations Additionally, solar radiation data was calculated for one station and incorporated into the SWAT model.
SWAT model for the UPTBRB was successfully simulated for the period of
(2005 – 2019) whereas the first three years (2005 – 2007) was used as a warming period n to maintain model performance SWAT Error Check Tool was used to examine the simulated hydrological parameter value.
SWAT Model Calibration and Validation
The SWAT model's output was calibrated and validated using the Soil and Water Assessment Tool Calibration and Uncertainty Procedure (SWAT-CUP) with the Sequential Uncertainty Fitting (SUFI-2) algorithm Calibration involves adjusting input parameters to achieve the best match between model simulations and observed data, optimizing the objective function in the process Validation uses an independent dataset to assess the performance of the calibrated parameters without further adjustments Additionally, sensitivity analysis identifies the most influential parameters on the basin's hydrological processes, impacting model outputs For more detailed information, refer to the SWAT User’s Manual (Abbaspour, 2012).
Model Performance Evaluation
The SWAT model's performance was assessed using various statistical parameters, with the Nash Sutcliff Efficiency (NSE) serving as the primary objective function due to its widespread use in hydrology Additionally, other metrics such as Percentage Bias (PBIAS) and the ratio of root mean square error (RSR) were also taken into account.
The NSE (Nash-Sutcliffe Efficiency) assesses the fitness of a model by comparing simulated and observed data, with values ranging from -∞ to 1, where a higher value indicates better model performance PBIAS (Percent Bias) reveals the tendency of simulated values to overestimate or underestimate observed values, with an optimal value of zero; positive values indicate underestimation, while negative values indicate overestimation RSR (Root Mean Square Error to Standard Deviation Ratio) evaluates model performance, with a higher value correlating to better performance and an optimal value of zero (Singh et al., 2004; S Shrestha et al., 2016).
The observed flow quantities (Qm) and simulated flow quantities (Q) are analyzed, with Q representing the average discharge during the observed period and the average discharge during the simulated period The total number of flow quantities is denoted by n, and i corresponds to the specific time-step The model's fitness criteria, as outlined by Moriasi et al (2007), are detailed in Table 3.3.
Results of Simulation
3.5.1 SWAT Model Calibration and Validation Result
The SWAT model was effectively calibrated and validated at the Gia Bay hydrological station using observed river flow data The calibration achieved a Nash-Sutcliffe Efficiency (NSE) of 0.77, a Percent Bias (PBIAS) of -9.6, and a Relative Standard Deviation (RSR) of 0.48, while the validation yielded improved results with an NSE of 0.84, a PBIAS of -7.4, and an RSR of 0.39 These statistical indicators confirm the SWAT model's excellent overall performance, with the entire basin simulated using calibrated parameters through the ArcSWAT manual calibration tool Detailed calibrated parameter information can be found in Table 3.5.
Table 3.4 Goodness-of-fit Statistics for Discharge Simulation
Process Period NSE PBIAS RSR Performance
Table 3.5 Calibrated Parameters and Fitted Values
Fitted Value A_GWQMN.gw Threshold depth of water in the shallow aquifer required for return flow to occur (mmH2O)
A_GW REVAP Groundwater revap coefficient
Groundwater delay from soil to channel (days)
V_CH K2.rte Hydraulic conductivity of the main channel (mm/h)
Available water capacity of soil layer (mmH2O/mm soil)
Base flow alpha factor (1/days)
R_CN2.mgt Initial SCS runoff curve number II
V_ESCO.bsn Soil evaporation compensation factor
Method R involves multiplying the existing parameter value by (1 + a specified value), while Method V entails replacing the existing parameter value with a new specified value In contrast, Method A adds a given value to the existing parameter value.
Figure 3.8 Calibration Result at the Gia Bay Hydrological Station
Figure 3.9 Validation Result at the Gia Bay Hydrological Station
Ja n- 08 Ma y- 08 S ep- 08 Ja n- 09 Ma y- 09 S ep- 09 Ja n- 10 Ma y- 10 S ep- 10 Ja n- 11 Ma y- 11 S ep- 11 Ja n- 12 Ma y- 12 S ep- 12 Ja n- 13 Ma y- 13 S ep- 13 Ja n- 14 Ma y- 14 S ep- 14 Ja n- 15 Ma y- 15 S ep- 15 Monthly Precipitation (mm)
M ea n Mo nth ly Flow (m 3 /s )
L95PPU (Lower Limit) U95PPU (Upper Limit) Monthly Precipitation
Ja n- 16 Ma r- 16 Ma y- 16 Jul- 16 S ep- 16 No v- 16 Ja n- 17 Ma r- 17 Ma y- 17 Jul- 17 S ep- 17 No v- 17 Ja n- 18 Ma r- 18 Ma y- 18 Jul- 18 S ep- 18 No v- 18 Ja n- 19 Ma r- 19 Ma y- 19 Jul- 19 S ep- 19 No v- 19 Monthly Precipitation (mm)
M ean Mo nth ly Flow (m 3 /s)
L95PPU (Lower Limit) U95PPU (Upper Limit) Monthly Precipitation
After SWAT model calibration and validation, the simulated basin’s hydrological components were further studied and the results were described in the table 3.6 and 3.7
Table 3.6 Average Monthly Hydrological Components
Table 3.7 Annual Water Balance Statistics
Note: Surf Q= Surface runoff, LAT Q= lateral flow, GWQ = groundwater contribution to stream, SW = soil water content, ET = actual evapotranspiration, PET= potential evapotranspiration n
Figure 3.10 Annual Water Balance of the UPTBRB (2008 – 2019)
The SWAT model indicates that the basin experiences an annual precipitation of 1,788.3 mm, with actual evapotranspiration measuring 661.05 mm This yields an annual water resource potential of 1,126.98 mm, which will be considered the current annual water resource potential for the long-term period from 2005 to 2019.
Summary
The SWAT model was developed using the latest spatial data, including a 30 m resolution Digital Elevation Model (DEM) and high-resolution land use and land cover data at 15 m resolution, alongside recent climatic data from 11 observation stations The model calibration and validation demonstrated excellent performance with Nash-Sutcliffe Efficiency (NSE) values of 0.77 and 0.84, marking the best simulation results for the basin from 2000 to 2021 According to the SWAT model, the basin experiences an annual precipitation of 1788.3 mm and an annual actual evapotranspiration of 661.05 mm.
Year (mm) Rainfall Surf Q LAT Q Percolate SW ET GWQ n
CLIMATE PROJECTION
Future Climate Scenario
The study investigated the effects of future climate change on the basin's water resources It utilized the MPI-ESM-LR Regional Climate Model to project climate trends, focusing on maximum and minimum temperatures as well as precipitation The analysis applied the RCP 4.5 carbon emission scenario for the years 2046 to 2060, using a baseline period from 1991 to 2004 Data for the Regional Climate Model was sourced from the ESGF data portal, with detailed information outlined in Table 4.1.
The MPI-ESM-LR model, with a resolution of 0.5˚ × 0.5˚, was utilized for daily simulations under the RCP 4.5 scenario for the period from 2046 to 2060 To address systematic errors inherent in the model, bias correction was applied using the linear scaling method, which proved to be effective and comparable to more complex techniques Precipitation was adjusted using a multiplicative approach, while temperature corrections were made with an additive method, as noted by Shrestha et al (2017).
& Okwala et al., 2020) Table 4.2 provided period of study and projected climatic variables
Table 4.2 Period of Study and Projected Climatic Variables
Historical/baseline 1991 - 2004 5 pr, tasmax, tasmin Observed data Future (MPI) 2046 - 2060 5 pr, tasmax, Corrected RCM n
Note: pr means precipitation, tasmax indicates maximum temperature and tasmin is for minimum temperature
In this study, precipitation (P) is measured in millimeters (mm) and temperature (T) is expressed in degrees Celsius (°C) The mean value of the time series data is denoted as à m, while historical data from the Regional Climate Model (RCM) output is indicated by his Simulated data is represented by fut, with m referring to monthly datasets and d to daily datasets Observed data is abbreviated as obs, and bias-corrected data is marked with an asterisk (*).
The equations 4.5 and 4.6 were utilized to assess the variations in climatic variables, specifically temperature, in relation to historical periods across different time frames Equation 4.5 calculates the change in precipitation for current and future scenarios, while equation 4.6 determines the temperature change by comparing current and future temperatures to historical data.
Where ΔPrecip current/future and Δ𝑇 / are the changes in precipitation (%) and temperature (°C) with respect to historical period of study
This study conducted climate projections at five strategically located meteorological stations across the basin: Bac Kan, Thai Nguyen, Bac Gaing, Huu Long, and Luc Ngan Utilizing projected RCM data, future climate trends were analyzed, and the spatial distribution of these stations effectively represents the basin's overall climate conditions.
Performance Analysis of Bias Correction Method
Before proceeding with further applications, it is crucial to assess the performance of the dataset after bias correction Performance evaluation was conducted by comparing the grid time series of raw data with bias-corrected values, referencing the baseline period of 1991 to 2004 The Root Mean Square Error (RMSE) was utilized to determine prediction accuracy between observed and simulated values, with RMSE values ranging from 0 to +∞; a lower RMSE indicates better model prediction accuracy.
0 The Mean Absolute Error (MAE) value indicates the status of unbiased simulation whereas the optimum value is 0 Both RMSE and MAE have been serving as the profound indicators in the evaluation of climate change and hydrological modelling fields (Mendez et al., 2020)
Where: Mi and Oi are the simulated and observed values n
Results of Climate Projection
The projected climate variables, including maximum temperature, minimum temperature, and precipitation, were derived from the MPI-ESM-LR model under the RCP 4.5 scenario, with future trends assessed against a baseline period from 1991 to 2004 Prior to analyzing future changes, the effectiveness of the linear scaling bias correction method was evaluated using RMSE and MAE statistical parameters Results, as shown in Table 4.3, indicated that the linear scaling method demonstrated high applicability, significantly enhancing the performance of the corrected dataset, although some parameters showed limited improvement.
Station BK BG TN HL LN
RAW ABC RAW ABC RAW ABC RAW ABC RAW ABC
Note: BK: Bac Kan, BG: Bac Giang, TN: Thai Nguyen, HL: Huu Long, LN: Luc
Ngan, RAW: Raw, ABC: After Bias Correction
The analysis utilizing bias-corrected RCM data (referred to as MPI/MPI-Corrected) examined monthly changes across three periods: historical (1991-2004), current (2005-2019), and future (2046-2060) Findings indicate a consistent rainy season from May to August, characterized by significant precipitation However, a notable shift in peak rainfall patterns was observed, with the highest precipitation historically occurring in July, followed by a steady decline after August.
However, in the 21 st century, August has become the highest precipitation received month The future status of precipitation also agreed that peak rainfall would be occurred in August
A comprehensive analysis of peak precipitation events reveals that Thai Nguyen station will experience the highest levels of rainfall among the evaluated meteorological stations Historical data from 1991 to 2004 indicates a maximum daily precipitation of 287.4 mm at Thai Nguyen station, while the period from 2005 to 2019 recorded a peak of 262.1 mm Projections from the RCM suggest that between 2046 and 2060, Thai Nguyen station will continue to record the highest daily precipitation levels.
Table 4.4 illustrates seasonal precipitation trends, showing a current winter increase of 2.68%, while future projections indicate a decrease of 13.79% In spring, current precipitation has decreased by 12.93%, but is expected to rise by 0.9% in the future Conversely, summer and autumn precipitation have seen increases of 0.56% and 26.85%, respectively, yet future forecasts suggest declines of 3.04% and 4.60%.
Table 4.4 Changes in Seasonal Precipitation
Season Historical Current Current-Changes MPI MPI-Changes
Recent findings indicate a slight increase in current annual precipitation, rising to 1551 mm from a historical average of 1537 mm However, future projections suggest a potential decrease of 2%, bringing annual precipitation down to 1512 mm in the basin The corrected RCM-MPI model predicts changes in early monsoon characteristics, with irregular extreme rainfall patterns and a significant reduction in autumn rainfall, both of which are closely linked to increased flood risks Additionally, winter months are expected to be drier than any recorded in history, raising concerns about potential drought risks.
Figure 4.2 Average Monthly Precipitation Trends (5-stations average)
Figure 4.3 Annual Changes of Precipitation Trends (5-stations average)
4.3.3 Projected Maximum and Minimum Temperature
Future temperature projections indicate an increase in both maximum and minimum temperatures across all months compared to baseline and current conditions Temperatures are expected to rise consistently from January, peaking in June before stabilizing for about three to four months, followed by a gradual decline.
JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC
Time (Month) Historical Current MPI-Corrected
ANNUAL PRECIPI T ATI ON ( MM)
The historical maximum recorded temperature in the Thai Nguyen station reached 40.7 °C, while the current maximum in Huu Long station is 41.30 °C Conversely, the minimum daily temperature recorded historically was 30.70 °C at Huu Long station, compared to 30.20 °C at Thai Nguyen station under current conditions.
2060, the record-breaking maximum temperature is likely to occur in Huu Long station and the record-breaking minimum temperature will be in the Bac Kan station
Seasonal temperature analysis indicates a current decrease in maximum winter temperatures by -0.32 °C, with projections showing an increase of +1.46 °C by 2060 Spring temperatures are expected to rise by +6.99 °C to +7.29 °C, summer by +8.24 °C to +9.64 °C, and autumn by +8.44 °C to +9.74 °C For minimum temperatures, winter shows a slight decrease of -0.01 °C, but is likely to increase by +1.26 °C in the future Spring minimum temperatures may rise by +0.24 °C to +0.98 °C, summer by +0.68 °C to +2.01 °C, and autumn by +0.44 °C to +1.28 °C Overall, both maximum and minimum temperatures are projected to increase across all seasons.
Table 4.5 Changes in Seasonal Maximum Temperature
Season Historical Current Current-Changes MPI MPI-Changes
Table 4.6 Changes in Seasonal Minimum Temperature
Season Historical Current Current-Changes MPI MPI-Changes
Table 4.7 indicates that the current annual maximum temperature has risen by +0.14 °C, with projections suggesting an increase of approximately +1.33 °C in the future Additionally, a historical increase of +0.34 °C has been observed, with future estimates indicating a potential rise of +1.39 °C Figures 4.4 to 4.7 visually represent the average changes on both monthly and annual scales.
Table 4.7 Changes in Long Term Annual Temperature
Parameter Historical Current Current-Changes MPI MPI-Changes Maximum
Figure 4.4 Average Monthly Changes of Maximum Temperature (5-stations average)
JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC
MAXI MUM TE MPERAT URE ( °C)
TIME (MONTH) Historical Current MPI-Corrected n
Figure 4.5 Average Monthly Changes of Minimum Temperature (5-stations average)
Figure 4.6 Annual Changes of Maximum Temperature (5-stations average)
JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC
M INI MUM TE MPERAT URE ( °C)
TIME (MONTH) Historical Current MPI-Corrected
AN NUAL M AXI MU M TEMPER A TUR E (°C)
TIME SERIES Historical Current MPI-Corrected n
Summary
Climate projection was successfully carried out at five meteorological stations Climate change analysis were done for three decadal time frames, historical (1991 –
The performance of extracted RCM data improved with the linear scaling bias correction method, indicating a significant decrease in winter precipitation and a slight reduction in summer, while spring and autumn are expected to experience increased precipitation Additionally, both maximum and minimum temperatures are projected to rise across all seasons The bias-corrected future climate data will be utilized in the SWAT model to assess future water resource potential.
ANNUA L MI NIM U M TEM PERATU RE (°C)
TIME SERIES Historical Current MPI-Corrected n
WATER STRESS ASSESSMENT
Water Demand
Water demand was assessed by integrating various factors, including municipal, agricultural, and industrial needs Estimates of water usage by each sector were primarily derived from national reports and statistics The overall water demand in the basin was calculated using a specific equation.
= (Domestic× Basin Population) + (Industrial × Area) + (Agricultural × Area) (m 3 /year/person) (m 3 /ha) (m 3 /ha) (5.1)
Water Resources
The water resource in the basin was calculated using Equation 5.2, incorporating annual precipitation and actual evapotranspiration (AET) data generated by the SWAT hydrological model, as detailed in Section 3.53.
Water Resources = (Precipitation ˗ AET) × Basin Area × 10 3 (5.2) (m 3 /year) (mm/year) (km 2 )
Water Stress
The water stress assessment utilized equation 5.3 to evaluate both current and future conditions, offering a detailed analysis of water demand and resource potential within the basin.
Water stress levels in sub-basins were assessed using specific indicators, categorized as follows: no stress (value < 0.1), low stress (0.1 to 0.2), high stress (0.2 to 0.4), and severe stress (≥ 0.4), according to studies by Vorosmarty et al (2005) and Xu and Hu (2017).
Future Water Stress
5.4.1 Future Water Demand a) Population Projection
The population in the basin was projected in order to calculate future water demand for municipal sector Geometrical increase method with the baseline year of
(2010 – 2020) was collected from https://www.worldpop.org and applied to forecast future basin population This method considered decadal population growth percentage as a constant for decadal projection
Where, P rate is present population growth rate, P present is number of present populations,
Where; 𝐼 is decadal population growth percentage, P rate1,2,etc present population growth rate, N is number of P rate
The future population growth in the basin is determined using a geometrical increase method formula
Where, P future is estimated future population, P present is number of present populations, and n is the projected year
The availability of water resources is closely linked to the hydrological cycle and climate trends By utilizing bias-corrected Regional Climate Model (RCM) data, particularly focusing on maximum temperature, minimum temperature, and precipitation, the SWAT model was employed to assess the future potential of water resources in the basin.
Result of Water Stress Assessment
The estimation of sectoral water demand in the basin involved integrating data from municipal, industrial, and agricultural sectors For municipal water withdrawal, capita-based consumption was utilized, while spatial-based exploitation was applied to assess the economic implications of the industrial and agricultural sectors.
Water consumption statistics indicate that the average daily water usage varies by region, ranging from 90 to 120 liters per person In urban areas, this demand is projected to exceed 120 liters per capita Consequently, the reference annual water demand per person is estimated at 43.8 cubic meters.
As of 2015, the total basin population was approximately 4.95 million thus the annual domestic water demand was mounted to approximately 216 mn m 3 /year b) Industrial Water Demand
The industrial clusters in the basin exhibit a unique pattern of industrial water demand compared to other regions in Vietnam To enhance the management of industrial parks and economic zones, the Ministry of Finance has implemented Circular No 43/2019/TT-BTC, aimed at promoting trade and attracting foreign investment.
According to Circular No.33 TCXD VN33/2006, the average water demand for 1 hectare of industrial area is 45 m³ per day In the basin, there are 10 major industrial parks covering around 3,300 hectares However, the current water demand reflects a 50% reduction, equating to 1,650 hectares, as many areas are still under development Consequently, the overall annual water demand for the industrial sector is estimated to be approximately 27 million m³ per year.
Agriculture is the primary land use, accounting for 42.33% of land, with 47% dedicated to row crops, 35% to orchards, and 18% to paddy fields The irrigation water requirement for rice fields ranges from 10,000 to 12,000 m³ per hectare annually, with a minimum of 5,000 m³ per hectare during the rainy season Consequently, a uniform water requirement of 5,000 m³ per hectare per year is applied across all agricultural lands Overall, the agricultural sector's total water demand is estimated to be around 2.7 billion m³ annually.
After estimating the sectoral water demand, the proportion of water demand was depicted in figure 5.1
Figure 5.1 Proportion of Sectoral Water Demand
The basin experiences an annual precipitation of 1788.27 mm, with an actual evapotranspiration of 674.94 mm Utilizing equation 5.3, the annual available water resource potential for the entire basin is estimated to be 14 billion cubic meters per year.
Water stress assessment using equation 5.3 revealed varying levels of stress across individual sub-basins: 15% (4 sub-basins) exhibited low water stress, 33% (9 sub-basins) experienced moderate water stress, 48% (13 sub-basins) were categorized as having medium water stress, and 4% (1 sub-basin) fell into the stressed category Notably, the only outlet basin displayed high water stress, although its small area made this less apparent For more detailed statistics on current water stress, refer to Appendix 2.A.
Figure 5.2 Distribution of Current Water Stress Levels
With the purpose to adopt necessary response plans and mitigation measures, future water stress was also predicted through multi-projection methods a) Municipal Water Demand
The projected population of the basin is expected to increase by approximately 6.51 million by the year 2060, based on the geometrical increase method for the period of 2046 to 2060 According to the 2030 Water Resources Group (2030WRG), the per capita water demand is anticipated to reach 150 liters per day by 2030 Utilizing the geometric population data outlined in Appendix 1 and equation 5.1, the annual municipal water demand is estimated to reach around 354 million cubic meters per year.
By 2060, all industrial clusters are projected to operate at full capacity across a total area of 3,300 hectares, leading to an estimated increase in future water demand of approximately 54 million cubic meters per year.
The Agricultural Master Plan of 2020 aims to boost rice production to 41 to 43 million tons per year by 2020, reaching 44 million tons by 2030 As domestic consumption and agricultural exports are also projected to rise, a 10% increase in agricultural land is necessary to meet future production demands Consequently, it has been determined that approximately 2.99 billion cubic meters of water per year will be required to support the irrigation of these future cultivation areas.
The future potential of water resources was assessed using the SWAT hydrological model alongside climate projections By integrating the SWAT model with bias-corrected Regional Climate Model (RCM) data, future annual precipitation and actual evapotranspiration rates were projected.
1596 mm/year and 685 mm/year Future basin water resource potential was estimated using equation 5.2 and determined as approximately 11.6 bn m 3 /year
A projection of future water stress was conducted using sector-wide water demand, regional climate models under the 4.5 scenario, and hydrological models The findings indicate that by 2060, only 4% of the sub-basins will experience low water stress, while 30% will face moderate stress Additionally, 41% are predicted to encounter medium water stress, and the remaining 26% are expected to undergo significant water stress (refer to figure 5.3) Comprehensive statistics on future water stress can be found in Appendix 2.B.
Figure 5.3 Distribution of Predicted Water Stress Levels
Figure 5.4 Comparison of Current and Future Water Statistics (Annual) n
Figure 5.5 Changes in Water Demand by Each Sector
Figures 5.4 and 5.5 illustrate the projected increase in water demand alongside a decline in available water resources This trend highlights the urgent need to focus on alternative water resources and the performance of ecological parameters to address future water challenges.
Summary
The SWAT hydrology model and climate projections were utilized to assess current and future water stress at the sub-basin level Currently, the basin experiences medium water stress (less than 0.4), indicating that natural processes can replenish water levels However, projections indicate that by 2060, water stress could escalate to high levels (greater than 0.4) if effective water management strategies are not implemented This situation will intensify competition for water resources, particularly among users in the middle and downstream regions.