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Integrated Biophysical and Socioeconomic Model for Adaptation to Climate Change for Agriculture and Water in the Koshi Basin

  • Nilhari NeupaneEmail author
  • Manchiraju Sri Ramachandra Murthy
  • Golam Rasul
  • Shahriar Wahid
  • Arun B. Shrestha
  • Kabir Uddin
Living reference work entry

Abstract

Water vulnerability is one of the major challenges facing people in the Himalayan river basins and is expected to increase with climate and other change. In order to develop appropriate and effective adaptation strategies, it is necessary to understand the level and spatial distribution of water vulnerability and the underlying factors contributing to it, both biophysical and socioeconomic. The development and application of a water vulnerability assessment model at district level, and its use in adaptation planning, is described using the transboundary Koshi River basin as an example. The whole basin showed a relatively high degree of water vulnerability, with mountain districts the most vulnerable followed by the mid-hills and the plains. The mountain and mid-hill areas were more vulnerable in terms of resource stress and ecological security, whereas the plains areas were more vulnerable in terms of development pressure; all parts of the basin were vulnerable in terms of management capacity. Significant correlation among the four components indicated that improvements in resource availability, ecological security, and management capacity would reduce development pressure and overall vulnerability. Adaptation plans need to be based on district-specific vulnerability characteristics; some suggestions and recommendations for adaptation plans are made.

Keywords

Koshi basin Integrated model Climate change Adaptation GIS Socioeconomics 

Introduction

Water is a multidimensional resource used for industrial, domestic, agricultural, and recreational purposes. In the majority of developing countries, a significant proportion of the total water withdrawal is for agricultural purposes, and there is a very strong linkage between water availability and peoples’ livelihoods, as most livelihoods depend on agriculture. Water availability is mainly influenced by climatic parameters, such as rainfall, temperature, and snowfall, and anthropogenic changes, such as changes in the human and livestock populations, economic development, and urbanization (Kundzewicz and Somlyody 1997). The anthropogenic changes can in turn affect the climatic parameters leading to further change. Many drivers of change impact on current and future water availability, and water is considered to be a vulnerable resource to different degrees globally (Rosenzweig et al. 2004; Bates et al. 2008) increasing the challenges to water resource management (Kojiri 2008). The vulnerability of water resources directly affects peoples’ livelihoods leading to increased poverty and problems of food and energy insecurity (Rasul 2012, 2014). Normally, poorer people in developing countries are more, and more rapidly, affected (Sullivan and Meigh 2005) because they are less resilient and have limited options for coping.

A water vulnerability assessment is a key entry point for suggesting suitable policy options for the sustainable utilization of water resources and plans for adaptation (Kelly and Adger 2000). The concept of vulnerability is complex and context specific, and different assessment tools have been developed for different purposes at different scales. The study described here focused on developing a water-based vulnerability assessment that could be used to develop adaptation strategies based directly on the parameter and linked indirectly with water resources specific to the study area. The study used UNEP water vulnerability assessment tools (Babel and Wahid 2009a, b; UNEP 2011) as a framework, with some modifications and additional context-specific water-related variables so that it can be replicated in transboundary river basins of the Hindu Kush Himalayas. The strength of the approach is that it treats the biophysical and socioeconomic aspects of the river basin equally, in contrast to other studies which have tended to ignore the socioeconomic component. The water issues of the Koshi basin were used as a case study for transboundary river basins of the Hindu Kush Himalayan region; districts were taken as the smallest unit to allow empirical estimates of the current vulnerability scenarios of the basin at district level, identify basin specific vulnerability factors, and suggest suitable policy options. The study contributes to the conceptualization and methodological advance of water vulnerability assessment tools and demonstrates the applicability of the model and its contribution to adaptation planning using the Koshi basin as an example.

Notable studies on water vulnerability assessment in South Asian river basins include one for the lower Brahmaputra river basin (Gain et al. 2012) and one for the Ganges-Brahmaputra-Meghna river basin (Babel and Wahid 2011), both at macro-levels. Microlevel water vulnerability assessments have been conducted for the Bagmati River basin (Babel et al. 2011; Pandey et al. 2011) and medium-sized river basins in Nepal (Pandey et al. 2010, 2012), and a water poverty analysis for the Kali Gandaki river basin (Manandhar et al. 2012). Until now, no vulnerability assessments have been carried out at a transnational scale using district-level data (third-level administrative unit) to bridge the scales. The Koshi River is one of the major tributaries of the Ganges, and the river basin is characterized by a high level of poverty and food insecurity and thus offers a useful example for developing and testing the assessment approach.

The development of the water vulnerability assessment model, and application in planning for adaptation to climate change, is described in the following: the framework is presented in the first section; the second section describes the application of the model in a vulnerability assessment of a transboundary river basin at district level and its use in adaptation planning using the Koshi River basin as an example; and finally the approach and results are discussed and conclusions drawn.

Framework for a Vulnerability Assessment

Vulnerability is a complex concept; it differs from one discipline to another and is highly context specific (Young et al. 2010). The most widely used definition of vulnerability is that given by the IPCC (Intergovernmental Panel on Climate Change): “Vulnerability is the degree to which a system is susceptible to, or unable to cope with, adverse effects of climate change , including climate variability and extremes” (McCarthy et al. 2001). Vulnerability (V) can be expressed as
$$ \mathrm{V}=\mathrm{f}\ \left(\mathrm{exposure},\mathrm{sensitivity},\mathrm{adaptive}\ \mathrm{capacity}\right) $$

The exposure and sensitivity of a system are related to hazards and cause the vulnerability, while the adaptive capacity is a “shock absorber” and has an inverse relationship to exposure and sensitivity (Brooks et al. 2005; Hamouda et al. 2009). Biophysical forces are responsible for the exposure and sensitivity of the system, while sociocultural, political, and economic forces contribute to the adaptive capacity (Adger 2006; Smit and Wandel 2006).

Why Use an Integrated Modeling Approach for Water Vulnerability Assessment?

Most vulnerability assessments have been based on physical or biological disciplines (Sullivan and Meigh 2005) which view vulnerability in terms of the likelihood of occurrence of climate-related events (Adger 2006). This conventional approach is a top-down or end-point approach and does not look at adaptation options to cope with the vulnerability (Young et al. 2010). In contrast, social scientists view vulnerability as a set of socioeconomic and institutional factors that determine people’s ability to cope with stress or change (Kelly and Adger 2000; Brooks 2003). According to Sen, socioeconomic parameters such as access to essential resources like forest, land, and water should also be reflected in the vulnerability analysis (Sen 1981, 1990). Similarly, Adger et al. (2005) strongly emphasized the importance of incorporating socioeconomic systems with biophysical systems at varied spatial and social scales in the vulnerability assessment. O’ Brien et al. defined vulnerability to climate change as a function of a range of biophysical and socioeconomic factors (O’ Brien et al. 2004). Incorporating socioeconomic parameters into a vulnerability assessment is called a bottom-up or starting-point approach and has direct application in adaptation planning (Young et al. 2010). In the past, the vulnerability created by socioeconomic factors was largely ignored (Adger and Kelly 1999), in part because of the quantification and indexing problems for qualitative and quantitative data (Cutter et al. 2003; Plummer et al. 2012).

Water-based vulnerability assessments should also incorporate socioeconomic factors (Sullivan 2011) because people constantly interact with water resources for their livelihoods, and social factors make a significant contribution to water vulnerability (Sullivan and Meigh 2005). Incorporating socioeconomic factors in water vulnerability assessments will enable more effective adaptation strategies to be developed (Vorosmarty et al. 2000; Sullivan and Meigh 2005) and provide a holistic approach along the lines of integrated water resources management (IWRM) (Vorosmarty et al. 2000; Babel and Wahid 2011; Plummer et al. 2012).

Two of the earliest studies using biophysical and socioeconomic parameters in water vulnerability assessments are those by Boruff et al. (2005), who combined physical and socioeconomic factors in a model used to determine county-level vulnerability caused by coastal erosion in the USA, and by Sullivan and Meigh (2005), who developed a climate vulnerability index (CVI) focused on water-related variables which considered a wide range of socioeconomic and physical parameters at different spatial scales. In the UNEP methodologies guideline for vulnerability assessment of freshwater resources (Huang and Cai 2009), water resource vulnerability is decomposed into resources stress (RS), development pressure (DP), ecological security (ES), and management challenges (MC). The first three are linked to the parameters of exposure and sensitivity in the conventional definition of vulnerability, while the last is linked to adaptive capacity. Sullivan (2011) considered the resource stress component to be the supply-driven aspect of water vulnerability, and the other three components to be the demand-driven aspect of water vulnerability.

Steps in Vulnerability Assessment

Figure 1 shows a schematic outline of the water vulnerability assessment framework (modified from Hamouda et al. 2009; Gain et al. 2012).
Fig. 1

Framework for water vulnerability assessment

The first step is to define the water resource system that defines the biophysical and socioeconomic components. The second step is to define the problems in the system and choose the scale. Major water-related problems include the degree of water scarcity for different sectors, water pollution, erosion, upstream deforestation, land degradation, upstream and downstream conflicts, food insecurity, and incidence of poverty. Spatial scales can range from the whole basin or sub-basin to watershed, community, or individual levels; temporal scales decide whether the study is a monthly, seasonal, annual, or long-term assessment. The selection of spatial and temporal scales depends on the nature and scope of the work.

The third step is to select an appropriate analytical tool or model. There are more than 50 models available for water vulnerability analysis (Plummer et al. 2012). The most suitable model for the study should be selected and modified based on the nature of the problem to be addressed. The next step – identifying and defining the indicators of vulnerability – is a very important part of the analysis. Vulnerability is a complex concept and cannot be measured directly. It has to be represented using a combination of proxy indicators and variables, even though this does not represent it fully (Brenkert and Malone 2005). Sullivan and Meigh (2005) defined an indicator as a statistical concept that provides an indirect way of measuring a given quantity and state and allows for current assessment and comparison over time. According to Vorosmarty et al. (2000), the indicators should be relevant to the issues, policy relevant, analytically and statistically sound, understandable, and easy to interpret. There are two approaches to indicator selection: firstly, from the literature, using inductive or deductive approaches, and secondly, from expert judgment (Hinkel 2011).

After the list of indicators and subindicators has been developed, the data collection can proceed. Stakeholder’s participation and field visits are important for triangulation. After the data has been compiled, the indicators should be redefined based on the data availability. The indicators are then normalized and entered into the vulnerability model. Finally, the model is run, the results are interpreted, and policy recommendations are made.

Application of the Vulnerability Framework to the Koshi River Basin

The water vulnerability assessment framework (Fig. 1) was applied to the Koshi River basin at district scale. The aim was to identify the vulnerable districts and the biophysical and socioeconomic factors contributing to basin vulnerability and to use this to recommend location-specific adaptation strategies that can help policy makers to evaluate or modify existing policy options for adaptation management strategies for water resources (Babel et al. 2011). The individual steps in the assessment are described in the following.

Defining the Koshi System and the Problem

The Koshi River basin is a transboundary basin which originates in the southern area of the Tibetan Plateau in China, passes through Nepal from north to south, and then crosses the northern part of Bihar in India before joining the Ganges. The basin can be broadly divided into four geographical regions (Fig. 2): the Transhimalayan region, which consists of six counties in Tibet Autonomous Region of China (>3,500 masl); the mountains, comprising five mountain districts in eastern Nepal (3,500–8,000 masl); the mid-hills, comprising 11 mid-hill districts in Nepal (1,000–3,500 masl); and the Terai (plains area), comprising 11 Terai districts in Nepal and 16 districts in Bihar state in India (<1,000 masl). For the purposes of the study, the first three were taken as the upstream and the Terai districts as the downstream.
Fig. 2

The Koshi basin study area

The Koshi is a major tributary of the Ganges and the third largest river in the Himalayas after the Brahmaputra and Indus (Sharma et al. 2000). The river basin covers an area of 90,404 km2, 22 % in India, 33 % in China, and 45 % in Nepal. It is home to more than 50 million people, most with livelihoods based on agriculture. Both the socioeconomic and the biophysical gradients of the basin change with elevation, resulting in differences in water availability and accessibility in different parts of the basin and thus differences in water vulnerability. The upper part of the basin has problems associated with snowmelt, water runoff, soil erosion, and land degradation, while the lower part has problems associated with waterlogging, population growth, expansion of agricultural land, and urbanization.

Rainfall in the basin is highly variable in time and space. The annual rainfall ranges from 207 mm in the Transhimalaya to more than 3,000 mm in the eastern mountains and mid-hills of Nepal (Fig. 3). More than 80 % of the rainfall is from the downstream monsoon, which occurs from July to September. Climate change is expected to increase water scarcity, and this together with socioeconomic changes is expected to decrease the per capita water availability for domestic and agricultural use, which will ultimately influence the agro-based livelihoods of the people in the basin and threaten food security. The per capita water availability per annum in the countries that share the basin (2,060, 1,784, and 6,895 m3 per capita for China, India, and Nepal, respectively) is higher than the water poverty threshold of 1,700 m3 (Falkenmark and Widstrand 1992) but lower than the world average of 7,243 m3; and actual water withdrawal is considerably lower than the availability (584, 805, and 966 m3 per capita per annum for China, India, and Nepal, respectively) (Rosegrant et al. 2002; Babel and Wahid 2011; FAO AQUASTAT 2013).
Fig. 3

Average annual rainfall in the Koshi basin

Selection of the Vulnerability Assessment Model

The UNEP water vulnerability assessment model (Babel and Wahid 2009a, b; UNEP 2011) was selected for the Koshi basin assessment. The advantages of this method are first that it offers a flexible and holistic approach that combines biophysical and socioeconomic indicators and second that it has been widely used for determining water vulnerability at basin level and is simple to calculate and the results easy to interpret. In this model, vulnerability is decomposed into resource stress (RS), development pressure (DP), ecological security (ES), and management challenges (MC). Resource stress is defined as the pressure on the water resources resulting from socioeconomic and environmental change (Babel et al. 2011); development pressure refers to the main socioeconomic development activities in the basin; ecological security refers to the ecological health of the basin, because the quality of the environment is an essential factor for sustainable use of water resources and coping with water stress (Manandhar et al. 2012); and management capacity (also called social vulnerability) refers to people’s ability to manage water and is directly linked with the adaptive capacity of the basin. Mathematically, water vulnerability can be expressed as water vulnerability = f [resource stress (RS), development pressure (DP), ecological security (ES), management challenges (MC)].

The vulnerability index (VI) is calculated by aggregating and assigning weights to the four vulnerability components. The water vulnerability equation can be presented as
$$ VI=\left[{\displaystyle {\sum}_{i=1}^n}\left({\displaystyle {\sum}_{j=1}^{m_i}}{X}_{ij}\times {w}_{ij}\right)\times {W}_{ij}\right] $$
(1)
where

VI is the vulnerability index

n is the number of vulnerability components

m i is the number of parameters in the ith component

X ij is the jth parameter in the ith component

w ij is the weight to the jth parameter in the ith component

W ij is the weight given to the ith component

VI has a value between 0 and 1, with higher values representing higher vulnerability.

Preliminary and Final Identification of Indicators, Subindicators, and Variables

A list of potential socioeconomic and biophysical variables closely linked with water vulnerability was prepared using an intensive literature survey and intuitive understanding of society-water interactions (Sullivan 2002; Lawrence et al. 2002; Brooks et al. 2005; Pandey et al. 2012). Expert opinion was also solicited from an interdisciplinary team. The variables were then grouped broadly under the four components of water vulnerability proposed by UNEP. In all 88 biophysical and socioeconomic variables were identified as potential variables in the preliminary list, 19 related to resource stress, 24 to development pressure, 16 to ecological security, and 29 to management capacity.

After listing potential indicators and variables, data were collected from secondary sources including the Central Bureau of Statistics (2001, 2003, 2004, 2011), Census of India (2001, 2011), Central Bureau of Statistics and International Centre for Integrated Mountain Development (2003), Central Bureau of Statistics, World Food Programme, and World Bank (2006), Chaudhuri and Gupta (2009), Nepal Living Standards Survey (NLSS 2011), and Ministry of Agricultural Development/Government of Nepal (2012). Where possible, long-term data were collected for the individual variables; where these were unavailable, multiple year data were collected, or data from a single year was used as a proxy for long-term data as suggested by Brooks et al. (2005). The list of potential indicators and variables was then evaluated. Some variables were removed, either because a high level of colinearity was identified between two variables within the same component or because the data could not be accessed through secondary sources for all the basin districts. For example, the number of households that belong to an irrigation management group, an important indicator of management capacity, was available for Nepal but not for India. The final list contained 34 variables, five for resource stress, six for development pressure, seven for ecological security, and 16 for management challenges. Table 1 shows the list of variables and their functional relationship with vulnerability.
Table 1

Indicators and variables used in the vulnerability assessment of the Koshi River basin and their functional relationship with vulnerability

Variable (year)

Unit

Functional relationship

Resource stress

Rainfall variability (1950–2000)

Coefficient of variation

Increase

Temperature variability (1950–2000)

Coefficient of variation

Increase

Water availability

km3

Decrease

Water withdrawal

km3

Increase

Distance to water source

km

Increase

Development pressure

Population density

Persons/km2

Increase

Population growth

%

Increase

Arable land

%

Increase

Irrigated area

%

Increase

Livestock density/ha

Livestock units/ha

Increase

GDP per capita

USD

Increase

Ecological security

Fertilizer use

%

Increase

Crop intensity

%

Increase

Households that use conventional energy for cooking

%

Increase

Households that use conventional energy for lighting

%

Increase

Forest area

%

Decrease

Slope

%

Increase

Erosion

%

Increase

Management capacity

Literacy rate

%

Decrease

Permanent house

%

Decrease

Family size

Number/hh

Increase

Farm size

ha/hh

Decrease

Herd size

Number/hh

Decrease

Economically active population

% (15–59 years)

Decrease

Yield of food grain (rice yield as a proxy)

kg/ha

Decrease

Milk production

Metric t

Decrease

Irrigation coverage

%

Decrease

Drinking water coverage

%

Decrease

Sanitation (toilet facilities)

%

Decrease

Access to electricity

%

Decrease

Access to bank

%

Decrease

Households with TV

%

Decrease

Access to forest

%

Decrease

Poverty incidence

%

Increase

Operationalizing the Model

This integrated approach to vulnerability assessment uses a wide range of biophysical and socioeconomic variables at different scales and with different units. The indicators were normalized to a value between 0 and 1 using UNDP’s normalization approach, as used, for example, in calculating the Human Development Index (HDI) (UNDP 2006). The functional relationship was taken into account while normalizing. The equations used are shown below, Eq. 2 was used for positive functional relationships and Eq. 3 for inverse relationships (modified from Turvey 2007; Hamouda et al. 2009; Islam et al. 2013):
$$ {X}_{ij\left(\mathrm{normalized}\right)}=\left[\frac{X_{ij}- \min \left({X}_{ij}\right)}{ \max \left({X}_{ij}\right)- \min \left({X}_{ij}\right)}\right] $$
(2)
$$ {X}_{ij\left(\mathrm{normalized}\right)}=\left[\frac{ \max {X}_{ij}-{X}_{ij}}{ \max \left({X}_{ij}\right)- \min \left({X}_{ij}\right)}\right] $$
(3)
where

Index X ij (normalized) is the normalized value of an indicator

X ij is the actual value of the indicator

max(X ij ) and min(X ij ) are the maximum and minimum values of the indicator, respectively

After normalization, the normalized values were entered into Eq. 1 to determine the vulnerability index per component and overall. According to UNEP (2011), assigning relative weights to individual indicators can be biased and will lead to problems with comparisons; thus, parameters should be assigned equal weights. For this reason, a balanced weighted average approach was used (Schulze 2007; Pandey et al. 2012; Manandhar et al. 2012) in which each of the four components contributed equally to the total vulnerability. The resulting composite score described the water vulnerability of the Koshi basin . The UNEP reference sheet shown in Table 2 was used to interpret the results.
Table 2

Reference sheet for interpretation of the vulnerability index

Vulnerability index

Interpretation

Low (<0.2)

Healthy basin

Moderate (0.2–0.4)

Basin generally in good condition but facing issues in management capacity, needs strong policy intervention to improve the situation

Relatively high (0.4–0.5)

High stresses in the basin, needs long-term river basin plan to improve the situation

High (0.5–0.6)

Very high (0.6–0.7)

Severe (0.7–1.0)

Highly degraded basin, needs long-term integrated plan to improve the situation

Modified from Huang and Cai (2009)

Results

The results of the water vulnerability assessment were limited to the part of the basin in India and Nepal; this consists of 43 districts and three broad geographical zones.

Basin-Scale Vulnerability

The overall vulnerability index for the whole Koshi basin was 0.401, with a range of 0.327–0.482, which indicates a relatively high level of water vulnerability in the basin (Table 3). Of the basin districts analyzed, 51 % were moderately vulnerable and 49 % highly vulnerable (Table 4). None of the basin districts was either healthy or highly degraded. The low SD value indicates that there is little variation in the degree of vulnerability within the basin, but the sources of variation within the basin may differ.
Table 3

Vulnerability of the Koshi basin

Vulnerability component

Minimum

Maximum

Mean

aSD

Resource stress

0.220

0.731

0.350

0.110

Development pressure

0.164

0.709

0.300

0.101

Ecological security

0.335

0.594

0.460

0.060

Management capacity

0.305

0.632

0.485

0.071

Overall index

0.327

0.482

0.401

0.036

aSD standard deviation of the mean

Table 4

No. of basin districts in different vulnerability categories

Vulnerability category

No. of districts (n = 43)

Resource stress

Development pressure

Ecological security

Management capacity

Overall index

Low

0

8(19 %)

0

0

0

Moderate

37(86 %)

31(72 %)

7(16 %)

5(12 %)

22(51 %)

High

5(12 %)

3(7 %)

36(84 %)

38(88 %)

21(49 %)

Severe

1(2 %)

1(2 %)

0

0

0

The whole basin is moderately vulnerable in terms of resource stress and development pressure (0.35 and 0.30), but the relatively higher standard deviations of these components indicate that the degree of vulnerability varies significantly within the basin (Table 3). In both cases, more than 70 % of the districts were moderately vulnerable and only a few highly or severely vulnerable (Table 4). The index values for ecological security and management capacity for the whole basin were higher (0.46 and 0.48) and with a relatively small SD, showing a higher degree of vulnerability with smaller variation within the basin (Table 3); more than 80 % of districts were highly vulnerable in terms of ecological security and management capacity (Table 4).

Mapping Vulnerability Across Geographical and District Scales

Table 5 shows the 12 most vulnerable and 12 least vulnerable districts based on the aggregate vulnerability index. Comparison of the most and least vulnerable districts within a basin can help in designing policy options for the improvement of problematic districts by learning from less vulnerable districts. Figure 4 shows the geographical distribution of different levels of aggregate vulnerability. Overall, vulnerability tends to be higher in the mountains and mid-hills and moderate in the plains areas, with some exceptions. All five mountain districts were in the top 12 vulnerable districts (Table 5), as were 4 of 11 mid-hill districts, but only 3 of 27 plains districts. Nine plains districts and 3 mid-hill districts were among the 12 least vulnerable districts.
Table 5

Twelve most vulnerable and least vulnerable districts in the Koshi basin

Most vulnerable districts

Geographical area

Index

Least vulnerable districts

Geographical area

Index

Taplejung

M

0.482

Sunsari

T(N)

0.327

Solukhumbu

M

0.474

Dhanusa

T(N)

0.332

Sankhuwasabha

M

0.467

Lalitpur

MH

0.343

Ramechhap

MH

0.456

Siraha

T(N)

0.346

Araria

T(B)

0.449

Sarlahi

T(N)

0.355

Purnea

T(B)

0.446

Sheohar

T(B)

0.365

Katihar

T(B)

0.439

Saptari

T(N)

0.366

Sindhuli

MH

0.432

Bhaktapur

MH

0.367

Panchthar

MH

0.432

Mahottari

T(N)

0.369

Dolakha

M

0.427

Makwanpur

MH

0.370

Sindhupalchowk

M

0.422

Bhagalpur

T(B)

0.375

Bhojpur

MH

0.419

Morang

T(N)

0.376

M mountains, MH mid-hills, T(B) Terai (Bihar), T(N) Terai (Nepal)

Fig. 4

Aggregate vulnerability at district level in the Koshi basin

The aggregate vulnerability score provides a general indication of water vulnerability but does not explain the underlying causes. The component-wise values provide more indication of the causes and thus of areas of focus in developing relevant adaptation policies and strategies. Figure 5 shows the component-wise vulnerability of the Koshi basin districts from upstream to downstream; Table 6 shows the 12 most vulnerable districts for each component; and Figs. 6, 7, 8, and 9 show the geographical distribution of levels of vulnerability for the individual components.
Fig. 5

Component-wise vulnerability of districts in the Koshi basin from upstream to downstream

Table 6

Twelve most vulnerable districts for each vulnerability component

District

Geographical area

Value

District

Geographical area

Value

Resource stress

Development pressure

Solukhumbu

M

0.731

Kathmandu

MH

0.709

Taplejung

M

0.684

Bhaktapur

MH

0.486

Dolakha

M

0.577

Purnea

T(B)

0.423

Sankhuwasabha

M

0.556

Katihar

T(B)

0.412

Sindhupalchowk

M

0.524

East Champaran

T(B)

0.380

Ramechhap

MH

0.462

Muzaffarpur

T(B)

0.378

Panchthar

MH

0.372

Araria

T(B)

0.373

East Champaran

T(B)

0.366

Supaul

T(B)

0.372

Madhubani

T(B)

0.362

Lalitpur

MH

0.368

Bhojpur

MH

0.361

Begusarai

T(B)

0.367

Purnea

T(B)

0.360

Bhagalpur

T(B)

0.362

Muzaffarpur

T(B)

0.358

Madhubani

T(B)

0.350

Ecological security

Management capacity

Ramechhap

MH

0.594

Sindhuli

MH

0.632

Okhaldhunga

MH

0.567

Araria

T(N)

0.597

Dhankuta

MH

0.557

Bhojpur

MH

0.595

Kavre

MH

0.550

Rautahat

T(N)

0.576

Panchthar

MH

0.547

Khotang

MH

0.570

Khotang

MH

0.542

Mahottari

T(N)

0.562

Tehrathum

MH

0.538

Panchthar

MH

0.546

Bhojpur

MH

0.534

Purnea

T(I)

0.545

Taplejung

M

0.523

Ramechhap

MH

0.544

Sindhuli

MH

0.516

Okhaldhunga

MH

0.527

Dolakha

M

0.502

Udaipur

MH

0.527

Sindhupalchowk

M

0.501

Taplejung

M

0.524

M mountains, MH mid-hills, T(B) Terai (Bihar), T(N) Terai (Nepal)

Fig. 6

Resource stress vulnerability at district level in the Koshi basin

Fig. 7

Development pressure vulnerability at district level in the Koshi basin

Fig. 8

Ecological vulnerability at district level in the Koshi basin

Fig. 9

Management capacity vulnerability at district level in the Koshi basin

Resource stress was generally higher in the mountains and some of the mid-hills and much lower in the plains districts (Figs. 5 and 6). The 12 most vulnerable districts in terms of resource stress included all five mountain districts, two districts from the mid-hills, and five in the plains (Table 6).

Development pressure vulnerability was generally higher in the mid-hills and plains areas (Figs. 5 and 7). The 12 most vulnerable districts included three from the mid-hills (Kathmandu, Bhaktapur, and Lalitpur) and nine from the densely populated plains area in India (Table 6).

Ecological vulnerability extended over the whole basin, but was more pronounced in the hills and mountains than in the plains (Figs. 5 and 8). All 12 of the most vulnerable districts from an ecological perspective were from mountain and mid-hill areas, and all had a relatively high index value (>0.5) (Table 6).

Vulnerability due to low management capacity was high in most of the basin (Figs. 5 and 9), and the 12 most vulnerable districts included districts in all three geographical regions.

Five districts (Taplejung, Ramechhap, Panchthar, Bhojpur, and Purnea) were among the 12 most vulnerable districts for three of the four components (Table 6).

The Relationship Among the Vulnerability Components

The results of correlation analysis among the four components of water vulnerability are shown in Table 7. Resource stress, ecological security, and management capacity have a positive and linear relationship with each other and a negative relationship with development pressure. In other words, improvement in any of the first three components will improve the others and reduce development pressure and the overall vulnerability index; conversely, any deterioration in these components will generate adverse effects.
Table 7

Correlation analysis among the components

Component

Resource availability

Development pressure

Ecological security

Management capacity

Resource availability

X

Significant (at 5 %) and negative relationship

Significant (at 1 %) and positive relationship

Not significant but coefficient is positive

Development pressure

 

X

Significant (at 1 %) and negative relationship

Significant (at 1 %) and negative relationship

Ecological security

  

X

Significant (at 1 %) and positive relationship

Management capacity

   

X

Discussion

There was a higher incidence of overall vulnerability in the mountain and mid-hill areas. This can be linked to the mountain specificities (Jodha 1990; Rasul and Karki 2007) and the policy bias affecting mountain areas (Rasul and Karki 2007). The variation in vulnerability among districts within each geographical region is a result of the district-specific socioeconomic and biophysical characteristics, which can be looked at using the component-wise vulnerability results and their interrelationships.

Resource stress was not a major issue in most of the districts in the plains; water in this area is generally abundant and underutilized (Shah et al. 2006). However, it was critical in the mountain districts and some mid-hills districts, such as Bhojpur and Dhankuta, where most people have to rely on rainfall as their main or only source of water. The high resource stress in these areas may be related to the problems of higher slope gradient, high runoff, and greater variability in rainfall (ICIMOD 2009). Resource stress is expected to intensify with climate change . Resource stress was negligible in the plains area of Nepal and most of Bihar. Many reports suggest that the major problems in Bihar relate to waterlogging and flooding rather than water stress (Government of Bihar 2012). The somewhat higher incidence of resource vulnerability in some Bihar districts (Table 6) is mainly due to lower and variable rainfall, drought is prominent in these districts (Government of Bihar 2012).

In contrast to resource stress, development pressure had a higher incidence in the plains areas as well as in a few urban hubs in the mid-hills. This results from higher population growth and more economic activities in comparison to the mountains and most of the mid-hills.

Ecological vulnerability was higher in the mountains and mid-hills as a result of higher rates of deforestation, higher livestock pressure, and slope farming, consistent with the findings of Jodha (1990). Babel and Wahid (2011) also observed more ecological problems in the (higher) Nepal part of the Ganges-Brahmaputra-Meghna basin than in the (lower) Indian part. However, the ecological problems upstream have a negative impact downstream. The lower ecological vulnerability in a few mid-hills districts may be due to the recent increase in community forestry in those areas in comparison with other parts of the Koshi basin (Sharma et al. 2000). The higher vulnerability in a few districts in the plains area might be due to relatively higher fertilizer use, lower forest coverage, and more use of conventional sources of energy.

Vulnerability due to a lack of management capacity was widespread in the basin irrespective of geographical zone and was particularly high in districts with higher poverty and illiteracy rates and poor performance of other socioeconomic variables. Here the problem is not so much lack of the resource but more lack of capacity to manage what is available. The low index values in the three mid-hill districts of Kathmandu, Lalitpur, and Bhaktapur (which together form Kathmandu Metropolitan City) are due to the higher socioeconomic indicators in those districts and are consistent with the findings of Pandey et al. (2012) on the Bagmati River basin.

Five districts – Taplejung (mountains), Ramechhap, Panchthar, and Bhojpur (mid-hills), and Purnea (plains) – were among the 12 most vulnerable districts for three of the four vulnerability components and can thus be considered as hot spots of vulnerability. The nature of vulnerability in these districts is more multidimensional and widespread than elsewhere and thus requires immediate intervention for recovery.

The strong positive relationship between resource availability and ecological security indicates that an increase in resource availability will also increase the ecological security, for example, an increase in moisture in the Koshi basin will result in an increase in biomass. Equally, improving the ecological security will enhance resource availability, for example, by improving the water retention capacity of the watershed. The reverse is also true, a decrease in resource availability will reduce ecological security and vice versa. The positive, but not significant, relationship between resource availability and management capacity indicates that improved management capacity cannot significantly increase the resource availability, this may be the result of the high levels of poverty. Unlike other basins, the Koshi has abundant water but people cannot use it due to the lack of economic investment.

The strong negative correlation between development pressure and ecological security indicates that an increase in development pressure will lead to a decrease in ecological security. For example, an increase in agricultural land will decrease forest cover, and cultivation of cereals on sloping land will increase soil erosion. The strong negative correlation between development pressure and management capacity indicates that increasing the management capacity will decrease the development pressure. For example, if people’s management capacity is strengthened, they will switch to more efficient methods of farming, use water in more profitable sectors, and use more efficient methods of irrigation, all of which decrease the development pressure.

Finally, the strong positive relationship between ecological security and management capacity indicates that when people’s management capacity is strengthened, they will switch to more ecologically appropriate practices, for example, agroforestry in place of cereal cultivation, which will increase ecological security by reducing erosion and land degradation.

The extent and causes of vulnerability within the Koshi basin differ from one geographical zone to another and from one district to another. Some districts are stronger in one aspect and weaker in others. Therefore, any adaptation plan must be based on the district-specific vulnerability characteristics and the relationship among the vulnerability components.

Conclusions and Policy Implications

The results describe the development of a water vulnerability assessment tool based on a modification of the UNEP model and its use to assess water vulnerability in the Koshi basin at district level. The overall vulnerability of the Koshi basin was relatively high, with mountain districts the most vulnerable followed by the mid-hills and the plains. The 43 districts were mapped using a biophysical and socioeconomic modeling approach based on four components of vulnerability, and the results converted into GIS interfaces. The component-wise vulnerability assessment showed that mountain and mid-hill areas (upstream) are more vulnerable in terms of resource stress and ecological security, whereas the plains areas are more vulnerable in terms of development pressure, while all parts of the basin were vulnerable in terms of management capacity. Five districts were identified as hot spots of water vulnerability that need immediate attention to improve the situation.

Significant correlation among the four components indicated that improvements in resource availability, ecological security, and management capacity would reduce development pressure and overall vulnerability, while deterioration in any of the three components would have the reverse effect.

The validity of the model could be increased in further studies by adding important parameters like water quality and using field surveys to obtain data. Assessment of smaller areas, for example, at VDC or ward level, would also improve the assessment of vulnerability.

This study shows clearly that adaptation plans should be based on location-specific vulnerability characteristics rather than using a blanket approach. The findings will help policy makers to identify the water vulnerable geographical zones and districts and to frame policy plans for these particular locations.

Recommended adaptation options to reduce the resource stress upstream include construction of storage structures like rainwater storage tanks and reservoirs and rehabilitation of old infrastructure (ICIMOD 2009; Bartlett et al. 2010). These measures would also help provide a continuous water supply to the downstream during the dry season.

Recommended adaptation strategies to reduce development pressure without compromising the net social benefit include incorporating improved crop varieties and reducing cropping intensity; introducing water demand management strategies such as increasing water productivity and water use efficiency by diverting water to more profitable crops, and using more efficient irrigation methods; and replacing low yielding cattle with higher yielding cattle.

Recommended adaptation strategies to reduce ecological vulnerability include afforestation of barren land, replacement of cereal crops on sloping land by agroforestry practices and hedgerows (White and Bhuchar 2006), increasing access to electricity in the basin from the present low rates to help lower the deforestation rate, and using balanced amounts of fertilizer (downstream) and/or green manure to maintain soil fertility. Introduction of adaptation strategies upstream will also help improve ecological vulnerability downstream.

Finally, it is essential to increase management capacity, which is mostly governed by socioeconomic factors, across the basin. Management capacity should be improved through government capacity building programs in such areas as food security, literacy, and rural electrification.

The significant interrelationships among the vulnerability components suggest that adaptation plans designed to improve one component will automatically result in improvements in the other components. Similarly, the Koshi basin has strong upstream downstream linkages. If suitable adaptation plans are adopted upstream, this will bring positive benefits downstream, while maladaptations upstream will have a negative effect downstream. This study recommends the need for a mountain-specific policy focus as suggested in Rasul and Karki (2007).

Finally, the study recommends carrying out a comprehensive vulnerability assessment at community or household level using field data in order to clearly identify the vulnerable sections of society, since it is people, not administrative units, who are vulnerable (Brooks et al. 2005). This study acts as an entry point for such studies by identifying the vulnerable locations within the Koshi basin.

Notes

Acknowledgements

This study was undertaken as a part of the Koshi Basin Programme (KBP) of ICIMOD funded by Department of Foreign Affairs and Trade (DFAT) of Australia. We acknowledge DFAT for financial support. The views expressed are those of the authors and do not necessarily reflect those of ICIMOD or the other organizations mentioned above.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Nilhari Neupane
    • 1
    Email author
  • Manchiraju Sri Ramachandra Murthy
    • 1
  • Golam Rasul
    • 1
  • Shahriar Wahid
    • 1
  • Arun B. Shrestha
    • 1
  • Kabir Uddin
    • 1
  1. 1.International Centre for Integrated Mountain Development (ICIMOD)KathmanduNepal

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