Spatial Analysis

of Mugu Karnali

Watershed Conduct a climate change

risk and vulnerability

assessment of agro-

ecological zones of Nepal

and appraising climate

change adaptation

measures in agriculture

November 2017

1

Table of Contents

1. Methodology 2

2. Hazard Analysis 2

2.1 Availability of Data 2

2.2 Climate Change Hazard Maps 2

2.3 Climate Change Hazard Indexing 2

2.4 Climate change scenario in Mugu Karnali Watershed: 3

3. Exposure Analysis 4

4. Vulnerability 5

4.1 System of Concern 5

4.2 Sensitivity Analysis 5

4.3 Adaptive Capacity Analysis 6

4.4 Vulnerability 8

5. Risk Analysis 9

5.1 Drought Risk: 9

5.2 Extreme rain impact: 11

5.3 Gradual Impact from CC 14

2

1. Methodology

The spatial analysis has been carried out for Mugu Karnali Watershed as per the framework, indicators and data

sources defined in the “Climate Change Risk and Vulnerability Assessment Framework Report” dated 19 October

2017.

In the following sections, the findings of the spatial analysis for Hazard, Exposure, Sensitivity, Adaptive Capacity,

Climate Change Vulnerability and Climate Change Risks are presented with the maps. The climate change risk

map indicates the hotspots in the Mugu Karnali Watershed.

2. Hazard Analysis

Hazard analysis is aimed at characterizing the disaster patterns by means of relevant metrics (e.g. temperature

and rainfall) coming from trend analysis and investigating different climate change scenarios. Threshold of these

climatic parameters is key to determining the occurrence of extreme events.

2.1 Availability of Data

This analysis combines spatial and non-spatial data (socio-economic and demographic). We excluded all the low

resolution data (district level) so as to concentrate on village unit. Most of the data used are recent ones collected

from Central Bureau of Statistics (CBS). Some data are also collected from Department of Irrigation (DoI),

ICIMOD, Department of Survey (DoS) and Department of Hydrology and Meteorology (DHM). All data sets are

interpreted at VDC level and converted to raster grid in ArcGIS.

2.2 Climate Change Hazard Maps

The Hazard maps are based on climate change scenarios (2040-2059) and historical climate trends. The Climate

Change (CC) hazard maps are prepared in 5x5 km grid size from 1 degree projected values (from World Bank

climate portal). The 1 degree data from 11 grids were interpolated to 5x5 km using IDW interpolation technique

in ArcGIS. All together 13 climate projection parameters are interpolated to 5x5 km grid. Most of the parameters

worked out are thresholds based on climate change parameters (table 1.1). The climate trends are acquired from

DHM 2017 (series 1971-2014) and scenarios are derived for the period 2040-2049 from IPCC AR5 assessment,

RCP 4.5 scenario and Miros EMS model. All Climate Change parameters do not represent similar types of

hazards, and therefore, the parameters are grouped and developed into 3 sets of hazard maps. The item No 2, 5

and 7 from Annex 1.1 are merged together to develop gradual impact of climate change. Likewise, items 9, 10 and

11 are merged to develop hazard maps which cause disaster such as flood and landslide and item 12 is used as

drought hazard likelihood map.

2.3 Climate Change Hazard Indexing

The units of climate parameters are not same. While some parameters represent changes others represnt absolute

values. Therefore CC indicators are normalized in the 0-1 range using the threshold normalization method from

maximum value of the region. The method used for indexing the climate change parameters is: Index = actual

changes in a grid/maximum changes in a grid. This method will rank the indicators logically and will

avoid ‘0’ values. In AR5 framework, values with ‘0’ to vulnerability, exposure and hazard will result in zero risk

which is not reflective of the reality. The developed CC hazard index maps are depicted from figures 1.1 through

1.3. The map index is with respect to maximum value of the interpolated grids from 11 grids. For the purpose of

the analysis with ArcGIS, each map is rescaled from 1-3 as described in the methodology.

Concept: Climate change impacts agro ecosystem. If the CC parameters deviate from normal on long term basis,

it will change the local hydrological cycle and create impacts on the stability of the agro ecological system.

3

2.4 Climate change scenario in Mugu Karnali Watershed:

The scenario of changes in general parameter or normal parameters such as rainfall, temperature and moist and

dry days reveals gradual climate impact on agro ecological systems. The gradual change in climate change will be

relatively higher in upper watershed than that in the lower part (figure 1.1). The Mugu Karnali watershed is

located at dry and high land area with low population and facilities.

As per the World Bank Climate Change Portal, the changes in extreme parameters such as change in 5 days and

one day rainfall (threshold values) is high over lower part of the watershed (figure 1.2).

Likewise, the projection of annual drought likelihood in watershed ranges from 0.3 to 0.4. It is high at lee ward

side of the upper watershed. The lower part is a relatively less impact area (figure 1.3).

Figure 1.1 Climate Change Hazard 1: Gradual probable impact to agro ecology from Production

Figure 1.2 Climate Change Hazard 2: Direct probable impact to agro ecology from Flood and Landslide

Figure 1.3 Climate Change Hazard 3: Wide spread impact to agro ecology from Drought

4

3. Exposure Analysis

Exposure assessment is aimed at identifying the elements at risk. In this step primarily land use (agriculture area)

and land cover (forest area) data set is analyzed for the localization of people, ecosystem resources, and social,

economic and cultural assets that could be adversely affected. Since agricultural production is closely related to

availability of irrigation structures and water bodies, determination of exposure of these assets becomes

significant towards assessment of overall exposure. Livestock population significantly depends on the land use

pattern of an agro-ecological zone hence constitutes an important part of exposure analysis.

In agro ecological system of concern, indicators of Agriculture and Ecosystem based Exposures are selected. The

selected Parameters are shown in Table 1.1. Four parameters are selected for exposure analysis:

a. Population of Livestock: At the VDC scale livestock data are collected from CBS. The data is from

2011 census of CBS. The data collected are of number of livestock in each VDC (cow, buffalo, Chicken,

Pig, Duck sheep and goat). The livestock are standardised by dividing the livestock population of each

VDC by the maximum livestock population among VDCs within the watershed. Higher the number

arrived at from such an exercise more is the exposure to climate change. . The Index value will lie between

0-1.

b. Agriculture area: The data are acquired from ICIMOD digital map of 2010. The percent agriculture

area in a VDC is divided by maximum percentage of land under agriculture from among VDCs to develop

to arrive at agriculture exposure. The Index value obtained will lie between 0-1. The Index value obtained

between 0-1 is rescaled from 1-3 for the ARC GIS tool. The indexing is straightforward in interpretation

as greater the agriculture area in a VDC the higher is its exposure.

c. Forest area: The data are acquired from ICIMOD digital map of 2010. The percent agriculture forest

area in a VDC total area is divided by maximum percentage of agriculture forest area from among VDCs

to develop to arrive at forest exposure. The Index value obtained will lie between 0-1. The Index value

obtained between 0-1 is rescaled from 1-3 for theARC GIS tool. The indexing is straightforward in

interpretation as greater the forest area in a VDC the higher is the exposure.

d. Water bodies’ area: The data are acquired from ICIMOD digital map of 2010. The percent water

bodies’ area in a VDC total area is divided by maximum percentage water bodies’ area from among VDCs

to develop to arrive at water exposure. The Index value now will obtained between 0-1. The Index value

obtained between 0-1 is rescaled from 1-3 for ARC GIS tool. The indexing is straightforward in

interpretation as greater the water area in a VDC higher is the Exposure.

Combined exposure: The combined exposure is calculated by average method i.e. (Livestock

Exposure+ Forest Exposure + Water Exposure + agriculture Exposure)/4

Table 1.1: Indicators of Exposure

Indicators Data type Scale Source

Population of livestock No/ VDC VDC scale CBS 2011

Agricultural area Total area/ VDC area VDC scale ICIMOD, 2010/DoI 2016

Forest area + Rangeland

Area

Forest area/vdc area VDC Scale ICIMOD 2010DoI, 2016

Water bodies/Ponds Stream density VDC Scale DoS 1995

5

4. Vulnerability

Vulnerability assessment is aimed at evaluating the degree to which ecosystem services and their users (system

of concern) could be effected by climate change based on site-specific information.

4.1 System of Concern

For the purpose of this study the ecosystem services in the selected watersheds of the agro-ecological zones is

mostly derived from agriculture, water and forest with agriculture being most prominent. So the focus of this

assessment will be the provisioning ecosystem services derived from agriculture first, then water and forest and

its interaction with people and livelihood.

4.2 Sensitivity Analysis

As per the system of concern defined above, since agricultural production is closely related to availability of water

resources, dependency on water sources has been considered for determination of sensitivity. Forest being the

other important source of ecosystem services, livelihood dependency on forest is considered significant to assess

sensitivity.

Four sensitive parameters are worked out for sensitivity analysis. The most sensitive indicators from Climate

Change risk on exposed agro ecosystem are total population, livelihood dependency on forest, livelihood

dependency on water and women population to total population.

a. Population: The VDC population data are collected from CBS. The data is from 2011 census. When the

sensitivity index of a region is high, the region in more sensitive to climate change. The index of

population is developed by dividing the population density of the VDC by the maximum population

density among the VDCs of the watershed. The Index value now will obtained between 0-1.

b. Livelihood dependency on forest: The data for households that are dependent on forests for energy

is available in the 2011 census of CBS. The Index is developed by dividing the dependency of a VDC on

forests by the maximum dependency on forests the VDCs of the watershed. The Index value now will lie

in the range 0-1.

c. Female population: The data for female population is acquired from 2011 census of CDS. The index is

developed by dividing total number of female population in VDC by the total maximum female

population among VDCs within the watershed. The Index value now will obtained between 0-1.

6

d. Livelihood dependency on water source system: The data is available in the 2011 census of CBS.

The Index is developed as dependency in water distribution system divided by maximum dependency on

water distribution system among VDCs. By Dependency we are referring to the number of people using

the water source system. The Index value now will obtained between 0-1.

Combined Sensitivity: The combined sensitivity is an average of all these 4 parameters and the Index

value obtained between 0-1 is rescaled from 1-3 for ARC GIS tool. The combined sensitivity map is shown

in Figure 1.4.

The combined sensitivity is high in lower part of the watershed and low at upper part of the watershed.

Figure 1.4 Combined Sensitivity

Table 1.2: Sensitivity indicators

Indicators Data type Scale Source

Population Density Nos/area VDC scale CBS 2011

Livelihood dependency on

forest

Fire wood Dependent and

housing material depending HH

Nos /area

VDC Scale CBS2011

Woman population Women population to total

population

VDC scale CBS2011

Livelihoods dependent on

Water sources system

HH No/Area VDC Scale CBS2011

4.3 Adaptive Capacity Analysis

Again, going by the system of concern defined, the key indicators that will be used for assessing the adaptive

capacity are coverage of irrigation, food sufficiency/security etc. Access to road/ market place is also important

to assess the overall capacity of the community to adapt to climate variabilities. In addition, literacy rate and

presence of effective o/working age group are basic indicators that develop the inherent capacity of a community

towards any climate adversities. The 4 indicators used for this analysis is shown in Table 1.3.

a. Irrigation Coverage: It is the land area covered by irrigation in a VDC. The larger the coverage of

irrigation, greater will be the adaptive capavity in a VDC. This data is collected from Department of

7

Irrigation. Irrigation Coverage for a VDC is normalized by dividing the irrigation coverage of a VDC by

the maximum irrigation coverage among VDCs. The Index obtained will lie between 0-1.

b. Literacy rate: Literacy rate is a key indicator in determining a community’s inherent adaptive capacity.

It is normalized by dividing the literacy rate of a VDC by the maximum literacy rate among the VDCs.

Higher the literacy rate the higher will be the adaptive capacity. The Index obtained will lie between 0-1.

c. Road accessibility: It is estimated as higher the distance of motorable road lower is the adaptive

capacity. Therefore, the distance of road from VDC has been inversed to get road accessibility. The

normalization is done through: 1-(Distance from VDC/Maximum Distance). The Index value now will

obtained between 0-1.

d. Effective age group: Population composition by age is important for several reasons. The age structure

of Nepal’s population has changed significantly over the last few decades. Generally, the population of

the age group between 15 to 59 years is known as economically active, and the people below these age

group (0-14 years and 60 years and above) are considered as economically dependent (CBS 2001). This

age group, 15-59 population, is normalized by dividing the effective age group of a VDC with the

maximum affective age group population among VDCs. The Index value now will obtained between 0-1.

e. Food Security: The data for Food Security at the VDC level is acquired from food security map

developed by NeKSAP. Better food security implies more adaptive capacity of a community.

Combined adaptive Capacity: All adaptive capacity parameters are averaged and the Index value

obtained between 0-1 is rescaled from 1-3 for ARC GIS tool. The combined sensitivity map is shown in

Figure below

Figure 1.5: Adaptive Capacity

8

The map shown above is inversely calculated for vulnerability analysis. The high risk area indicated in the above

map signifies low adaptive capacity area of the watershed.

Table 1.3: Adaptive capacity indicators

Indicators Data type Scale Source

Irrigation Coverage Irrigated area in VDC VDC Scale DoI, 2016

Literacy rate % in VDC VDC Scale CBS 2011

Food sufficiency Suffency/not suffency in

VDC

VDC Scale CBS 2011

Road accessibility Road density VDC Scale Dolidar, DoI

Effective age

group/Economical active

Age distribution VDC Scale CBS 2011

4.4 Vulnerability

This is a calculated figure based on Sensitivity and Adaptive Capacity.

Figure 1.6: Vulnerability

9

5. Risk Analysis

For the three hazard maps developed and discussed above, their probable risks in exposed agro-ecological zone

is worked out as per the below matrix for the spatial analysis of Risk. Therefore, 3 sets of risks maps are prepared

in response to CC hazards – drought impact, extreme precipitation impact and gradual impact of CC.

Exposure

CC Hazard Nature

Drought Impact Extreme precipitation impact Gradual impact

Population of livestock less Yes Yes

Agricultural area Yes Yes Yes

Forest density less Yes less

Water bodies/Ponds Yes Yes less

5.1 Drought Risk:

Drought risk could impact all exposed agro ecological parameters. Therefor 4 sets of risk maps are produced

under annual drought likelihood. The results are shown in figures 1.7 to 1.9.

Agriculture risk is high over the lower part of the watershed. About 75 % of total area in the upper part has low

risk.

Figure: 1.7: Risk to Agriculture from Drought

10

High risk is posed to livestock in the middle part of the watershed and low risk is posed to the upper part of

watershed. The lower part of watershed is in medium-high risk category.

Figure 1.8: Risk to Livestock from Drought

The lower risk to water bodies is prevalent in the lower part of the watershed. The middle part is medium to high

risk and most of the upper part and the lowest parts of the watershed are low risk areas.

11

Figure 1.9: Risk to Water from Drought

5.2 Extreme rain impact:

Mugu and Dolpa receive lower rainfall compared to other parts of Nepal but minor changes in the incidence of

extreme rainfall could lead to disasters.

High rainfall in short periods impact agriculture in the lower part of the basin, while nearly 75%of the area ranging

from the middle to upper parts of the watershed have low risk to agriculture.

12

Figure 1.10: Agriculture Risk

Medium to high risk of livestock is seen at lower part of basin. Likewise most of the upper part is also medium to

high risk. The lowest is seen at middle part of watershed.

Figure 1.11: Risk to Livestock

13

Risk to forest is low at upper part and high risk at middle part. The lower part of the watershed lies under medium

to high risk.

Figure 1.12: Risk to Forest

The middle part of watershed is high risk area and lower part comparatively low risk. The upper part of watershed

is medium- high risk for the water bodies.

Figure 1.13: Risk to Water Bodies

14

5.3 Gradual Impact

The lower part of watershed is under medium to high risk and upper part of watershed is under high risk.

Figure 1.14: Risk to Agriculture from Productivity Loss

15

Annex 1.1: Hazard Indicators

Item No Indicators Scenerio /source

1 Number of Hot Days (Tmax > 35°C) CMIP 5-RCP 4.5-Period 2040-2059

WB

2 Number of Frost Days (Tmin < 0°C) CMIP 5-RCP 4.5-Period 2040-2059

WB

3 Maximum of Daily Max-Temperature CMIP 5-RCP 4.5-Period 2040-2059

WB

4 Maximum of Daily Min-Temperature CMIP 5-RCP 4.5-Period 2040-2059

WB

5 Monthly Temperature CMIP 5-RCP 4.5-Period 2040-2059

WB

6 Monthly Precipitation CMIP 5-RCP 4.5-Period 2040-2059

WB

7 Maximum Length of Consecutive Dry Spell CMIP 5-RCP 4.5-Period 2040-2059

WB

8 Maximum Length of Consecutive Wet Spell CMIP 5-RCP 4.5-Period 2040-2059

WB

9 Expected Daily Rainfall Maximum in 25 Years (25-yr

Return Level)

CMIP 5-RCP 4.5-Period 2040-2059

WB

10 Maximum 5-day Rainfall CMIP 5-RCP 4.5-Period 2040-2059

WB

11 Largest Single Day Rainfall CMIP 5-RCP 4.5-Period 2040-2059

WB

12 Annual Severe Drought Likelihood CMIP 5-RCP 4.5-Period 2040-2059

WB

13 Expected Largest Monthly Rainfall Amount in 25 Years

(25-yr Return Level)

CMIP 5-RCP 4.5-Period 2040-2059

WB

Trend

14 Maximum temperature Trend DHM Data 1971-2014

15 Minimum Temperature trend DHM Data 1971-2014

16 Seasonal Precipitation Trend DHM Data 1971-2014