Analysis of climatic variability along with snow cover area and forecasting snow cover area in higher Himalayas in Nepal using ANN by Bhogendra Mishra A study for the partial fulfillment of the requirements for the degree of Master of Science in Remote Sensing and Geographic Information Systems Committee: Dr. Nitin K. Tripathi (Chairperson) Dr. Mukand S. Babel (Co-chairperson) Dr. Taravudh Tipdecho By: Mr. Bhogendra Mishra 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 o c in last 100 years 0.44 o c 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 increased Cumulative 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 Used Temperature –Observed data Three stations 1980 to 2008 –Remote sensing –CRU 3.0 ( ), (max and min) Spatial resolution: 0.25 o x 0.25 o Temporal resolution: one month –MOD11A2 ( , 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 ( ), (max and min) Spatial resolution: 0.25 o x 0.25 o Temporal resolution: one month Snow cover (maximum snow extent ) –MOD10A2 ( ) Spatial resolution: 500 x 500 m Temporal resolution: 8 day –MOD10A1 ( ) 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

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 Work flow block diagram

SeasonR2R2 RMSE Before correction After correction Winter Spring Summer Autumn Where, G t is the ground temperature, C t 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 o C/yr) Summer and spring has random trend in most of the zones 0.6 o C increased in last 30 years

Season R2R2 RMSE Before correction After Correction Winter Spring Summer Autumn Where, G t is the ground temperature, C t 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 o C/yr) Spring has random trend 0.9 o C 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 = The root mean square error = Precipitation Pattern Analysis

Seasonal Precipitation Trend ( ) The up arrow represents increasing trend, down arrow represents decreasing and cross represents no trend

Precipitation trend summary The up arrow represents increasing trend and cross represents no trend

MODIS high temporal and low spatial resolution Freely available Easy for large spatial extend Comparison of ASTER and MODIS snow cover Relate 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 cover Relate 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 1B Granule (*.hdf) MOD10A1 Granule (*.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 Correlation coefficient % 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 AST area = − ×MOD area AST area - Aster snow cover area, MOD are a- MODIS snow cover area

Zone IZone II Zone IIIZone IV Seasonal Maximum Snow Cover Extent

Seasonal maximum snow cover based on MOD10A2 in elevation more than 2000m Seasonal Maximum Snow Cover Extent

The annual snow covered based on MOD10A2 from February 2000 to October 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 forecasting  Flood forecasting  Water demand forecasting  Typhoon 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/snow fall Snow cover Min. temp. Elv.Max. temp. Avg. temp Ppt/ snowfall Snow cover Min. temp Elv Max. temp Selected input parameters: Average temperature, precipitation, Avg. temp. – Average temperature, ppt – precipitation, Min. temp. – Minimum temperature, Max. temp. Maximum temperature, Elv. - Elevation

Selected Models Model 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, y k is the lag value and b k is the corresponding weigtht, and ε is the biased., D is Gaussian function, m number of element of an input vector, x i and x ik, i is the weight for connection between i th 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) NARXGRNN Lead time 1 monthLead time 6 monthsLead time 12 months R2R2 RMSEEIR2R2 RMSEEIR2R2 RMSEEI R2R2 RMSEEI

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.

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