Analysis of climatic variability along with snow cover area and forecasting snow cover area in higher Himalayas

in Nepal using ANN

byBhogendra Mishra

A study for the partial fulfillment of the requirements for the degree of Master of Science in

Remote Sensing and Geographic Information SystemsCommittee: Dr. Nitin K. Tripathi (Chairperson)

Dr. Mukand S. Babel (Co-chairperson)Dr. Taravudh Tipdecho

By: Mr. Bhogendra Mishra 109716

Committee: Dr. Nitin K. Tripathi (Chairperson) Dr. Mukand S. Babel (Co-chairperson)

Dr. Taravudh Tipdecho

Analysis and Forecasting of Snow Cover using ANN in Kaligandaki Basin, Nepal

RS & GIS SET, AIT

May , 2011

Climate change and Glaciers..

0.74oc in last 100 years

0.44oc in last 25 years

Every year after 2000 are the warmest

Year in the history since 1850

Mountain regions are more vulnerable because the warming

trend is higher

Heavy rainfall and severe storms appears to have

increasedCumulative mass balance of selected glacier

systems (Dyurgerov and Meier, 2005)

Villagers have begun farming vegetable and fruits in higher Himalayas

Untimely and unpredictably heavy rainfall

GLOF and avalanche frequency increased

Experiencing longer winter drought, post winter snowfall and hailstorm Moraine dam lake formations.

Migration from flat mud roof to slope metal sheet

Impact of CC

Climbing the vegetation lie and appearances of new species in

higher altitude

Study area

• 1.2 million population• Big snow cover area • More rainfall stations• Sensitivity for national power

supply• Visible climate change issues

• Can we use the remote sensing data in higher Himalayas or monitoring Glaciers?

• What is the relationship between climate variability and snow cover in higher Himalayas?

• Can we forecast the snow cover area ?

Research Question

To develop and test simple, robust methodologies to quantify the impact of climate change in the hydrologic regime of higher Himalayas, Nepal.

Objectives

Reduce the uncertainty on the remote sensing (CRU and TRMM temperature and precipitation) data in the Kaligandaki basin.

Relate the MODIS snow product to ASTER data.

Analyze the trend of temperature, precipitation and snow cover area.

Test the usability of ANN to predict the snow cover area and forecasting the snow cover area using climate parameters from selected GCM scenario.

7

Data UsedTemperature

– Observed data • Three stations 1980 to 2008

– Remote sensing– CRU 3.0 (1901- 2006), (max and min)

• Spatial resolution: 0.25o x 0.25o • Temporal resolution: one month

– MOD11A2 (2006 -2010, night and day)• Spatial resolution: 1 x 1 km • Temporal resolution: 8 day

Precipitation– Observed data

• Two stations 1980 to 2008• Temporal resolution: one month

– Remote sensing– CRU 3.0 (1901- 2006), (max and min)

• Spatial resolution: 0.25o x 0.25o • Temporal resolution: one month

Snow cover (maximum snow extent 2000-2010)– MOD10A2 (2000-2010)

• Spatial resolution: 500 x 500 m• Temporal resolution: 8 day

– MOD10A1 (2000-2010)• Spatial resolution: 500 x 500 m• Temporal resolution: daily (retrieve corresponding to

ASTLB1)– ASTLB1

• Spatial resolution: 15 x 15 m• Random scene

• HadCM3 – A1 scenario

26 N

28 N

30 N

81 E 85 E 89 E

26 N

28 N

30 N

81 E 85 E 89 E81 E 85 E 89 E

Downscaling, Validation and Trend Analysis

Climate variable selection

Downscaling

Non-parametric test

Trend quantification

Trend detection

Avg. temperature, min. temperature, max

temperature, precipitation, cloud cover days, frost days,

humidity, etc

Literature, data availability, dependency to each other

Low spatial resolution data, heterogeneous terrain

Literature, Can consider land topography, observed parameters, result is very

good, validate with RMSE, easier to work

To check the normality distribution

Shapiro wilk test (W)m- from literature

Mann Kendall test, from literature

Sen’s slope, from literature

Min/max temp, precipitation, snow cover

Min/max temp, precipitation, snow cover

Work flow block diagram

Season R2 RMSE

Before correction

After correction

Winter 0.948 6.65 3.54

Spring 0.982 5.489 0.875

Summer 0.993 8.043 0.858

Autumn 0.987 5.956 1.209

Where, Gt is the ground temperature, Ct is the CRU temperature, and E is the weighted average elevation of the zone in km. whose value is 1.47, 3.48, 4.95, 5.71 fore zone1, zone2, zone3 and zone4 respectively, Winter-: December- February; Spring :– March- May; Summer:- June- August; Autumn-September-November

CRU (Maximum) Temperature Analysis

Maximum trend – Winter (0.027 oC/yr)

Summer and spring has random trend in most of the zones

0.6oC increased in last 30 years

Season R2 RMSE

Before correction

After Correction

Winter 0.871 7.65 3.14

Spring 0.918 4.948 1.274

Summer 0.953 8.743 0.938

Autumn 0.978 5.956 1.902

Where, Gt is the ground temperature, Ct is the CRU temperature, and E is the weighted average elevation of the zone in km. whose value is 1.47, 3.48, 4.95, 5.71 fore zone1, zone2, zone3 and zone4 respectively, Winter-: December- February; Spring :– March- May; Summer:- June- August; Autumn-September-November

CRU (Minimum) Temperature Downscaling

Maximum trend – Autumn (0.054 oC/yr)

Spring has random trend

0.9oC increased in last 30 years

While trying to relate the ground based and CRU/TRMM precipitation, following result has been obtained for annual precipitation

The reason is that the lower part of the study area is the region where the maximum rainfall in Nepal i.e. approximately 5000mm/y and upper part has the lowest rainfall i.e. approximately 150mm/y therefore this region can be considered as an anomaly for the CRU/TRMM precipitation.

The standard error before correction = 650.7The root mean square error =748.964

Precipitation Pattern Analysis

Seasonal Precipitation Trend (1980-2008)

The up arrow represents increasing trend, down arrow represents decreasing and cross represents no trend

-1.08

-2.52

-1.41

1.4

14.26 4.85

4.74 6.55

5.88

1.41

14.36

Precipitation trend summary

The up arrow represents increasing trend and cross represents no trend

8.57

32.44

35.26

MODIS high temporal and low spatial resolution Freely available Easy for large spatial extend

Comparison of ASTER and MODIS snow coverRelate MODIS to ASTER

High spatial resolution and low temporal resolution Very expensive Difficult to study large spatial extend

MODIS

ASTER

Why to compare ???

Achieve the data freely and easily for larger spatial extent with better quality

Comparison of ASTER and MODIS snow coverRelate MODIS to ASTER

Obtained snow cover area

Obtained the snow map

Resample to 25 m false color image

Mask study area for snow cover map

Aster Level 1BGranule (*.hdf)

MOD10A1Granule (*.hdf)

Mask Study Area

Obtained snow cover area

TERRA (Over Kaligandaki)

Accuracy assessment for fractional and absolute snow cover area

Mean absolute error (%)

Root mean square error (%)

Kappa index

15.17

19.16

0.676

Correlation coefficient 0.796

80.84% accuracy in MOD10A1 with comparison to ASTER for snow cover area

Relate MODIS to ASTER

It is substantially in agreement with the ASTER but not the perfect.

With 32 sample points

ASTarea = −1.59 + 0.91 ×MODarea

ASTarea- Aster snow cover area, MODarea- MODIS snow cover area

Zone I Zone II

Zone III Zone IV

Seasonal Maximum Snow Cover Extent

Seasonal maximum snow cover based on MOD10A2 in elevation more than 2000m

Seasonal Maximum Snow Cover Extent

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 20100

10

20

30

40

50

60

70

80

90

100

Winter

Spring

Summer

Autumn

Year

Se

as

on

al s

no

w c

ov

er(

%)

The annual snow covered based on MOD10A2 from February 2000 to October 2010. The % represents the percentage of time, that pixel was snow covered during the considered time frame

Snow cover area variation in % of time

Artificial Neural Network

Has already proved to be very useful for Rainfall forecastingFlood forecastingWater demand forecastingTyphoon forecasting etc.

It has not yet tested for snow cover forecasting

Aims to test the usability of ANN in for snow cover forecasting

No other snow cover forecasting methodology available for long lead time

Possible input parameters: snow cover area, average temperature, minimum temperature, maximum temperature, rainfall/snowfall, elevation, cloud cover days, frost days, and Julian day of year and snow cover area itself in the last month.

Input Selection

Avg . temp.

Ppt/snowfall

Snow cover

Min. temp.

Elv. Max. temp.

Avg. temp. 1 0.34 0.77 0.98 0.81 0.99

Ppt/ snowfall 0.34 1 0.26 0.36 0.11 0.31

Snow cover 0.77 0.26 1 0.78 0.62 0.74

Min. temp. 0.98 0.36 0.78 1 0.82 0.96

Elv. 0.81 0.11 0.62 0.82 1 0.76

Max. temp. 0.99 0.31 0.74 0.96 0.76 1

Selected input parameters: Average temperature, precipitation,

Avg. temp. – Average temperature, ppt – precipitation, Min. temp. – Minimum temperature, Max. temp. Maximum temperature, Elv. - Elevation

Selected ModelsModel development

where n is the total number of hidden layers, m is the total number of input variables and p is lag time, a ij is corresponding weight, yk is the lag value and bk is the corresponding weigtht, and ε is the biased., D is Gaussian function, m number of element of an input vector, x i and xik, i is the weight for connection between ith neuron in the pattern layer and the summation neuron.

85 % training testing and validation (70%+15%+15%) 15% cross validation

Average temperature, precipitation Average temperature, precipitation, snow cover (previous month)

NARX GRNN

Lead time 1 month Lead time 6 months

Lead time 12 months

R2 RMSE EI R2 RMSE EI R2 RMSE EI

0.56 10.5 0.95 0.45 14.9 0.84 0.28 15.6 0.93

R2 RMSE EI

0.65 12.7 0.92

Simulation

GRNN: it’s consistent performance which is independent to time interval .

HadCM3: It can model the complex topographic topography, literature, many research has done based on this GCM in Himalayas.

A2 scenario: moderate economic growth, which represent the moderate scenario rather than any extremities, which likely to occur then other in near future.

Snow Cover Area Prediction

Snow cover area forecasted using GRNN

CRU temperature can be used in the study of higher Himalayas.

CRU precipitation cannot be used for higher Himalayas.

MOD10A1 has approximately 81% accuracy as compared to ASTER data. Temperature is in increasing trend, trend is higher in higher altitude and

highest in autumn and no trend is found in spring.

Precipitation exhibits increasing trend in summer and spring whereas decreasing in winter in some places.

Snow cover shows decreasing trend in spring.

Snow line is at 5400m altitude which were previously reported to 5200m.

ANN can be used to forecast snow cover area. NARX is recommended for short lead time and GRNN for long lead time.

The glacier area will not be completely disappear in by 2035 in Kaligandaki basin (Himalayas) as come in several reports in last decade

Conclusions

Recommendations Would like to recommend to study for whole Himalayas range which will have greater insight on impact

If we can increase the number of sample as well as spatial distribution for all over the Himalayas for the MODIS and ASTER snow cover the results would be more reliable and accurate.

High spatial as well as temporal resolution data is required for trend analysis, similarly recommend to consider more climate parameters also.

The most important recommendation is that, this study can bring further to use other forecasting methods for the snow cover forecasting and compare the result with ANN. Similarly, we can forecast the snow melt runoff using ANN, which can be validated with SRM.

Thank you

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