Master Thesis, Department of Geosciences
Modelled runoff sensitivity to snow parameterization
-a case study for the Upper Beas basin in Himachal Pradesh, India
Trine Jahr Hegdahl
Modelled runoff sensitivity to snow parameterization
- a case study for the Upper Beas basin in Himachal Pradesh, India
Trine Jahr Hegdahl
Master Thesis in Geosciences Discipline: Hydrology Department of Geosciences
Faculty of Mathematics and Natural Sciences
University of Oslo
02. June 2014
© Trine Jahr Hegdahl, 2014
Tutors: Lena M. Tallaksen (UiO), Kolbjørn Engeland (UiO) and Chong-Yu Xu (UiO) This work is published digitally through DUO – Digitale Utgivelser ved UiO
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1
Abstract
For rivers with headwaters in the high mountains of Himalaya, snow and glacier melt are large contributors to river discharge. The timing and size of melt flow is important for the availability of water in these basins. For hydrological modelling there are different methods used to calculate snowmelt. The aim of this study is to evaluate the sensitivity in the water balance components, runoff in particular, for hydrological models with different complexity in the snowmelt routine.
For this purpose three specific tasks were defined. 1) Establish a gridded hydrological model for the Upper Beas basin including subbasins, based on local and global datasets, 2) Preform a systematic analysis of model sensitivity in terms of runoff and snow/glacier covered area, using three models with varying complexity in the snowmelt routine and 3) Analyze the variability in the seasonal runoff pattern across the Upper Beas basin.
Three models; a temperature-index model, an enhanced temperature-index model including a radiation term, and an energy balance model, were set up and used for the analysis. Input data are observed discharge, precipitation, relative humidity and temperature. Global datasets was used as input for wind and short wave radiation.
It showed to be difficult to distinguish a pattern between the models. High correlation coefficients for all subbasins were obtained, high Nash-Sutcliffe efficiencies for calibration while lower for validation. There were large volume errors, especially in north for Manali subbasin, causing large errors also downstream. All models had problems predicting the negative trend in discharge, originating from the high elevation subbasins Sainj and Parvati in the later part of period.
From spatial distribution analysis it was clear that the models predict large amounts of precipitation at high elevation, Sainj and Parvati subbasins, and north in Manali. Very high runoff occurred close to the snow accumulation areas. Storage reduction up to 6000 mm/year, indicate that overestimation in runoff can be explained by this.
There were some differences between model predictions. An overall tendency was that for annual evaluation the enhanced temperature-index model had a higher runoff prediction for most basins except for Tirthan where the full energy model had the highest bias.
2
Acknowledgments
I would like to start by thanking my main supervisor Lena M. Tallaksen for support and finding the HyCamp project file from the shelves when discussing the theme for my thesis. The HyCamp project was not yet initiated, and this study could contribute.
Supervisor Kolbjørn Engeland has done a great job in guiding me through the ENKI model setup.
With inside knowledge of all the tricks and the nice to knows, he made the task easier. Thanks also to supervisor Chong-Yu Xu who was supportive in the start-up of the project, and to the initiator of HyCamp, John F. Burkhart.
Help came all the way from India, and I would like to thank Dr. Sharad Jain for solving my coordinate problems, they were causing a lot of problems.
Thanks to: Lu Li and Hong Li for shearing information and knowledge about the Beas data, and not least Sjur Kolberg, for great feedback on ENKI and the snow routine GammaSnow.
To my supporting family for your patience and understanding, thank you.
Trine Jahr Hegdahl Oslo, 02.06.2014
This study was jointly supported by the Research Council of Norway projects JOINTINDNOR (203867), and INDNOR (222195): Hydrologic sensitivity to Cryosphere-Aerosol interaction in Mountain Processes (HyCAMP).
3
Table of contents
Abstract ... 1
Acknowledgments ... 2
Table of contents ... 3
List of Figure and Tables ... 7
1 Introduction ... 13
1.1 Background ... 13
1.2 Objectives ... 16
2 Theory Snow ... 17
2.1 Snow accumulation ... 17
2.1.1 Snowfall ... 17
2.1.2 Snow distribution ... 18
2.1.3 Glacier ... 18
2.2 Snow Melt ... 19
2.2.1 Surface energy-balance ... 20
2.2.2 Temperature-index approach ... 26
2.3 Snowpack evolution ... 26
3 Study area and data... 28
3.1 Upper Beas basin ... 28
3.1.1 Geology and topography and vegetation ... 28
3.1.2 Climate ... 30
3.1.3 Hydrology ... 33
3.2 Data ... 37
3.2.1 Hydro-meteorological data ... 37
3.2.2 Data control and correction ... 37
3.2.3 Watch forcing ERA 40. Wind and Shortwave radiation ... 39
3.3 Raster data ... 39
3.3.1 Landuse, vegetation cover and vegetation heights ... 39
3.3.2 Elevation and basin areas ... 41
3.3.3 Glaciers ... 42
3.4 Characteristics of the subbasins ... 42
4
4 Methods Model Setup ... 45
4.1 The ENKI modelling framework ... 45
4.2 Model setup ... 45
4.3 Subroutines ... 46
4.3.1 IDWPrec, IDWHum, IDWrad and IDWwind ... 47
4.3.2 BayesTkrig ... 48
4.3.3 PcorrMap2 ... 48
4.3.4 HydraEP ... 48
4.3.5 HydraCanopy ... 49
4.3.6 GamSnow, GamSDF and GamDDF ... 49
4.3.7 HydraSoil... 49
4.3.8 HBVresponse ... 49
4.3.9 QsubCat ... 50
4.3.10 SumCatchments... 50
4.4 Snow routines ... 52
4.4.1 GamSnow ... 52
4.4.2 GamDDF (Degree day Factor) ... 53
4.4.3 GamSRF (Short wave Radiation and degree day Factor) ... 53
4.4.4 Gamma snow distribution ... 53
5 Model performance and Simulations ... 56
5.1 Model calibration and validation ... 56
5.1.1 Calibration and validation strategy ... 56
5.1.2 Automatic calibration method ... 57
5.2 Model performance metrics... 57
5.2.1 Nash-Sutcliffe efficiency (NSE) ... 57
5.2.2 Pearsons correlation coefficient, r ... 58
5.2.3 Percent bias (Pbias) ... 58
5.2.4 Absolute Percent bias (APB) ... 59
5.2.5 Rank test ... 59
5.2.6 Hydrograph comparison ... 59
5.2.7 Flow duration curves ... 60
5.3 Water balance components ... 60
5
5.3.1 Seasonal spatial distribution ... 61
5.3.2 Annual spatial distribution ... 61
5.3.3 Annual water balance basin wise ... 61
5.4 Spatial variability in Seasonal runoff ... 61
5.5 Model sensitivity to snowmelt routine ... 61
5.5.1 Monthly deviation in runoff at high flow ... 61
5.5.2 Annual cumulative deviation in runoff ... 62
5.5.3 Deviation in simulated runoff for a high glaciered subbasin ... 62
5.6 Trend in input data ... 62
5.6.1 Linear regression ... 62
5.6.2 Mann-Kendall trend test ... 63
5.7 Software ... 63
6 Results ... 64
6.1 Model setup ... 64
6.1.1 Preliminary calibration ... 64
6.1.2 Calibration and validation ... 65
6.2 Model Performance... 67
6.2.1 Pearsons r ... 67
6.2.2 Percent Bias (Pbias) ... 67
6.2.3 Absolute percent bias (APB) ... 68
6.2.4 Rank test ... 68
6.3 Hydrographs and flow frequency curves ... 70
6.3.1 Daily hydrographs ... 70
6.3.2 Monthly time series ... 70
6.3.3 Annual time series ... 73
6.3.4 Flow duration curves ... 76
6.4 Water balance components ... 77
6.4.1 Seasonal spatial distribution ... 77
6.4.2 Spatial distribution of annual water balance components ... 82
6.4.3 Annual water balance basin wise ... 87
6.5 Spatial variability in seasonal runoff ... 89
6.6 Model sensitivity to snowmelt routine ... 92
6
6.6.1 Monthly deviation in runoff at high flow ... 92
6.6.2 Annual cumulative deviation in runoff ... 94
6.6.3 Deviation in simulated runoff for a high glaciered subbasin ... 97
6.7 Trend in input data ... 99
6.7.1 Linear regression ... 99
6.7.2 Mann-Kendall trend test ... 102
6.7.3 Data variability in the calibration and validation period ... 105
7 Discussion ... 108
7.1 Model performance ... 108
7.1.1 Calibration and validation ... 108
7.1.2 Model performance... 109
7.1.3 Hydrographs and flow duration curves ... 109
7.2 Distribution of the water balance components ... 109
7.2.1 Spatial distribution ... 109
7.2.2 Annual water balance ... 111
7.3 Spatial variability in seasonal runoff ... 111
7.4 Runoff sensitivity for the three models GamSnow, GamSRF and GamDDF ... 111
7.4.1 Monthly deviation in runoff at high flow ... 111
7.4.2 Annual cumulative deviation in runoff for all subbasins ... 112
7.4.3 Deviation in simulated runoff for a high glaciered subbasin ... 112
7.5 Analysis of the input data ... 113
7.6 Model setup ... 114
7.6.1 Model structure ... 114
7.6.2 Input variables ... 114
7.6.3 Snow routine ... 116
8 Conclusion ... 118
9 References ... 120
Appendix I Calibration ... 124
Calibration range ... 124
Calibration step 1. ... 125
Calibration step 2. ... 126
Appendix II Wind and Radiation ... 128
7
List of Figure and Tables
FIGURE 1.1. CONCEPTUAL CLIMATIC AND HYDROLOGIC MODEL FOR THE HIMALAYA.LIGHT GRAY SHADING OF HIMALAYAN CATCHMENTS PRIMARILY IN THE CENTRAL AND EASTERN HIMALAYA INDICATE SNOWMELT CONTRIBUTION OF <25% TO ANNUAL DISCHARGE. MODERATE TO HIGH SNOWMELT CONTRIBUTION OCCUR IN CATCHMENTS WITH SIGNIFICANT WINTER SNOW COVER (E.G.,INDUS, SUTLEJ,ARUN AND TSANGPO).BLACK ARROWS INDICATE CATCHMENT AND THEIR CORRESPONDING SUMMER (MAY TO OCTOBER) RAINFALL AMOUNTS: WESTERN BASIN RECEIVE <65% OF THEIR ANNUAL DISCHARGE FROM RAINFALL (INDUS:40%), WHEREAS BASINS TO THE EAST OF THE SUTLEJ RECEIVE >70% OF THEIR ANNUAL DISCHARGE FROM RAINFALL.GRAY ARROWS INDICATE MAJOR
MOISTURE-SOURCE DIRECTIONS.(BOOKHAGEN AND BURBANK,2010) ... 14
FIGURE 1.2 THE GLACIER RETREAT SINCE THE MID-19TH CENTURY IS OBVIOUS IN THE HIMALAYA, WITH THE EXCEPTION OF THE GLACIERS AT NANGA PARBAT IN THE NORTHWEST (RA,CL).GLACIERS IN THE KARAKORAM SHOW COMPLEX BEHAVIOR (BOLCH ET AL.,2012). 16 FIGURE 2.1THE PROCESS OF OROGRAPHIC LIFTING ... 17
FIGURE 2.2SCHEMATIC REPRESENTATION OF THE LONG-TERM EFFECT OF NEGATIVE GLACIER NET MASS BALANCE (A) ON GLACIER VOLUME (B) AND ANNUAL GLACIER RUNOFF (C).VOLUME RESPONSE LAGS FORCING DUE TO THE TIME REQUIRED TO REMOVE ICE BY MELTING. NOTE THAT DISCHARGE IS LARGER DURING THE FIRST PORTION OF THE RESPONSE PERIOD UNTIL THE GLACIER IS SMALL ENOUGH TO REDUCE EXCESS RUNOFF (JANSSON ET AL.,2003) ... 19
FIGURE 2.3THE ENERGY FLOW THROUGH EARTH’S ATMOSPHERE AND EXCHANGE WITH THE SURFACE – AVERAGED FOR THE GLOBE AND A YEAR.ALL UNITS ARE WM−2.THE NUMBERS ARE CONSTRAINED BY SATELLITE OBSERVATIONS AT THE TOP OF THE ATMOSPHERE. PARTITIONING WITHIN THE ATMOSPHERE IS ESTIMATED FROM RADIATION MODELS.REDRAWN FROM KIEHL AND TRENBERTH (1997).©AMERICAN METEOROLOGICAL SOCIETY. ... 20
FIGURE 2.4INCOMING SOLAR RADIATION AT NOON AT DIFFERENT LATITUDES AND DURING A YEAR (CUFFEY AND PATERSON,2010). ... 21
FIGURE 2.5 RELATIONSHIP OF ALTITUDE VERSUS DENSITY, PRESSURE, TEMPERATURE ... 25
FIGURE 3.1LEFT:THE INDIAN SUBCONTINENT WITH THE HIMALAYAN MOUNTAIN CHAIN AND THE SITUATION OF UPPER BEAS BASIN IN THE STATE HIMACHAL PRADESH,INDIA.RIGHT:UPPER BEAS BASIN AND THE LOCATION OF ALL HYDRO-METEOROLOGICAL STATIONS. (BASEMAP:ESRI-NATIONAL GEOGRAPHIC WORLD MAP). ... 29
FIGURE 3.2PRECIPITATION DISTRIBUTION IN 1997(LEFT) AND 2004(RIGHT).KUMAR (2007) FOR UPPER BEAS BASIN. ... 31
FIGURE 3.3SESONAL AVERAGE MONTHLY PRECIPITATION (MM/MONTH) FOR ALL OBSERVATIONS IN THE UPPER BEAS BASIN. ... 32
FIGURE 3.4SEASONAL AVERAGE DAILY TEMPERATURE (˚C) FOR FOUR STATIONS IN THE UPPER BEAS BASIN. ... 33
FIGURE 3.5SEASONAL AVERAGE DAILY DISCHARGE (M3/S*DAY) FOR THE PERIOD 1990-2001. ... 34
FIGURE 3.6SNOW LINE ALTITUDE AT THE END OF THE ABLATION SEASON FOR THE CHHOTA SHIGRI GLACIER,HIMACHAL PRADECH (KULKARNI ET AL.,2011)... 35
FIGURE 3.7AVERAGE SEASONAL CYCLE (MONTHLY MEANS JANUAR –DECEMBER) OF DIFFERENT ENERGY FLUXES IN THE ABLATION ZONE AT 4550M FOR THE CHHOTA SHIGIR GLACIER (PITHAN,2011). ... 36
8
FIGURE 3.8SNOW COVER DEPLETION CURVE FOR BEAS BASIN AT 3000-3600M.(KULKARNI ET AL.,2011) ... 36
FIGURE 3.9LANDCOVER DISTRIBUTION ANALYZED FROM THE IRSLISSIII DATA. ... 40
FIGURE 3.10SHOWING THE ELEVATION BY INTERVALS OF THE UPPER BEAS BASIN.THE MODEL USES THE DISTRIBUTED 1X1 KM GRID CELL RESOLUTION. ... 40
FIGURE 3.11.FLOW DURATION CURVE OF THE DISCHARGE FREQUENCY OCCURRENCE FOR THE FOUR CALIBRATED DISCHARGE STATIONS IN THE UPPER BEAS BASIN.SAME DATA WITH DIFFERENT SCALING CLARIFIES THE DIFFERENCES IN LOW AND HIGH FLOW. TO THE RIGHT SHOWS A LOG LOG PLOT, CLEARIFYING THE DIFFERENCES AT HIGH FLOWS. ... 43
FIGURE 3.12LEFT:THE MAIN SUBBASINS USED DURING CALIBRATION.GLACIERED AREA IN DARK BLUE COLOR.RIGHT:PARVATI IS A SUBBASIN OF BHUNTAR AND PULGA A SUBBASIN OF PARVATI. ... 44
FIGURE 4.1EXAMPLE OF THE SETUP OF A MODEL FROM A LIBRARY OF SUBROUTINES. ... 46
FIGURE 4.2SCHEMATIC STRUCTURE OF THE HYDROLOGICAL MODEL, IN NORWEGIAN (TØFTE ET AL.,2008) ... 47
FIGURE 4.3PRINCIPLE OF THE SNOW DEPLETION CURVE SDC,SNOW COVERED AREA SCA,SNOW STORAGE X, AVERAGE STORAGE M, THE COEFFICIENT OF VARIATION CV, INITIAL BARE GROUND FRACTION Y0, MELTED SNOW Q, SNOW WATER EQUIVALENTS OF REMAINING SNOW SWE, ACCUMULATED MELT DEPTH Λ(T), TIMESTEP T (KOLBERG AND GOTTSCHALK,2006) ... 55
FIGURE 6.1BAR PLOT OF SPECIFIC ANNUAL OBSERVED AND SIMULATED RUNOFF (MM/YEAR) FOR THALOUT,BHUNTAR,SAINJ AND TIRTHAN.LEFT: INCORRECT COORDINATES FOR SAINJ AND TIRTHAN DISCHARGE STATIONS.RIGHT: CORRECT COORDINATES FOR SAINJ AND TIRTHAN. ... 64
FIGURE 6.2HYDROGRAPH SHOWING DAILY OBSERVED DISCHARGE AND SIMULATED RUNOFF FOR THREE MODELS MAIN AXIS, PRECIPITATION ON THE SECONDARY AXIS. ... 70
FIGURE 6.3MONTHLY TIMESERIES .MEAN DAILY RUNOFF (M3/S AND DAY) ... 71
FIGURE 6.4MONTHLY TIME SERIES TIRTHAN PARVATI AND MANALI.MEAN DAILY RUNOFF (M3/S ASD DAY) ... 72
FIGURE 6.5THE MONTHLY SIMULATED RUNOFF FOR THE PARVATI,MANALI AND PULGA.THE VALUES ARE FROM GRIDRUNOFF, AND SUM OF ALL GRIDCELLS IN THE GIVEN CATCHMENT AREA (~1173KM2).TIME PERIOD: MONTHLY 1991-2001.MEAN DAILY (M3/S). .... 73
FIGURE 6.6ANNUAL TIMER SERIES FOR THALOUT,BHUNTAR AND PARVATI.MEAN DAILY RUNOFF (M3/S AND DAY) ... 74
FIGURE 6.7 ANNUAL TIME SERIES OF THE UPPER BEAS BASIN.MEAN DAILY RUNOFF (M3/S) ... 75
FIGURE 6.8FLOW DURATION CURVES FOR OBSERVED AND SIMULATED DAILY RUNOFF . ... 76
FIGURE 6.9 SEASONAL DISTRIBUTION OF MEAN DAILY PRECIPITATION (MM/DAY) FOR THE UPPER BEAS BASIN,1991-2001. ... 78
FIGURE 6.10 SEASONAL DISTRIBUTION OF MEAN DAILY RUNOFF (MM/DAY) SIMULATED USING THE GAMSNOW MODEL, FOR THE UPPER BEAS BASIN,1991-2001. ... 79
FIGURE 6.11 SEASONAL DISTRIBUTION OF MEAN DAILY EVAPORATION (MM/DAY) FOR THE UPPER BEAS BASIN,1991-2001. ... 80
FIGURE 6.12 SEASONAL DISTRIBUTION OF CHANGE IN STORAGE (MM/DAY) FOR THE UPPER BEAS BASIN,1991-2001. ... 81
FIGURE 6.13 SPATIAL VARIABILITY IN ANNUAL PRECIPITATION (MM/YEAR) FOR THE UPPER BEAS BASIN. ... 83
FIGURE 6.14 SPATIAL VARIABILITY IN ANNUAL RUNOFF (MM/YEAR) FOR THE UPPER BEAS BASIN ... 84
FIGURE 6.15 SPATIAL VARIABILITY IN ANNUAL EVAPORATION (MM/YEAR) FOR THE UPPER BEAS BASIN ... 85
FIGURE 6.16 SPATIAL VARIABILITY IN ANNUAL CHANGE IN STORAGE (MM/YEAR) FOR THE UPPER BEAS BASIN ... 86
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FIGURE 6.17 CORRELATION PLOT OF MEAN STORAGE CHANGE (MM/YEAR) FOR THE GAMSNOW MODEL OUTPUT FOR ALL BASIN, TO MEAN BASIN ELEVATION, AND TO GLACIER COVERED AREA.PERIOD 1991-2001. ... 87 FIGURE 6.18 ANNUAL WATER BALANCE.TOP: WHOLE UPPER BEAS REPRESENTED BY THE THALOUT DISCHARGE STATION.BOTTOM LEFT:
PARVATI SUBBASIN, BOTTOM RIGHT:TIRTHAN SUBBASIN.VALUES GIVEN IN MM/YEAR FOR PRECIPITATION (RED),RUNOFF (BLUE), EVAPORATION (GREEN) AND CHANGE IN STORAGE Δ(BLACK). ... 88 FIGURE 6.19 BOXPLOTS OF APRIL SPATIAL RUNOFF FOR ALL YEARS 1991-2001.FROM TOP LEFT:PULGA,BHUNTAR,SAINJ AND TIRTHAN. ... 90 FIGURE 6.20 BOXPLOTS OF AUGUST SPATIAL RUNOFF FOR ALL YEARS 1991-2001.FROM TOP LEFT:PULGA,BHUNTAR,SAINJ AND
TIRTHAN ... 91 FIGURE 6.21MONTHLY DEVIATION IN RUNOFF AT HIGH FLOW FOR THE INDIVIDUAL YEARS FOR THALOUT AND BHUNTAR PRESENTED AS
PERCENT OF OBSERVED VALUES. THE MODELS ARE SNOW:GAMSNOW,SRF:GAMSRF,DDF:GAMDDF, AND OBS: OBSERVATION. ... 92 FIGURE 6.22 MONTHLY DEVIATION IN RUNOFF AT HIGH FLOW FOR THE INDIVIDUAL YEARS,PARVATI,SIANJ,TIRTHAN AND MANALI.
PRESENTED AS PERCENT OF OBSERVED VALUES. THE MODELS ARE SNOW:GAMSNOW,SRF:GAMSRF,DDF:GAMDDF, AND OBS: OBSERVATION. ... 93 FIGURE 6.23 ANNUAL CUMULATIVE DEVIATION IN RUNOFF, IN PERCENT OF ANNUAL OBSERVATION. FOR THALOUT,TIRTHAN AND SAINJ.95 FIGURE 6.24 ANNUAL CUMULATIVE DEVIATION IN RUNOFF, IN PERCENT OF ANNUAL OBSERVATION. FOR THALOUT,TIRTHAN AND SAINJ.96 FIGURE 6.25PULGA BASIN MODELLED RUNOFF COMPARISON.THE GRAPH ON THE TOP SHOWS THE MONTHLY DIFFERENCES BETWEEN THE
MODELS.THE BOTTOM GRAPH REPRESENT THE PERCENT DIFFERENCE IN DISCHARGE FOR THE MONTHLY HIGH FLOW EACH YEAR, 1991-2001. ... 98 FIGURE 6.26 ANNUAL MEAN DAILY DISCHARGE (M3/S) FOR UPPER BEAS BASIN.FROM UPPER LEFT:THALOUT,BHUNTAR,SAINJ AND
TIRTHAN.LINEAR REGRESSION TREND LINE... 99 FIGURE 6.27 ANNUAL PRECIPITATION (MM/YEAR)UPPER BEAS BASIN.FROM UPPER LEFT:BHUNTAR,LARJI,MANALI,PANDOH,SAINJ AND
BANJAR.LINEAR REGRESSION TRENDLINE... 100 FIGURE 6.28ANNUAL MEAN TEMPERATURE (˚C) IN UPPER BEAS BASIN.FROM UPPER LEFT:BHUNTAR,LARJI,MANALI AND PANDOH.
LINEAR REGRESSION TREND LINE. ... 101 FIGURE 6.29 ANNUAL MEAN RELATIEVHUMIDITY (%) FOR UPPER BEAS BASIN.FROM UPPER LEFT:BHUNTAR,LARJI,MANALI AND
PANDOH.LINEAR REGRESSION TREND LINE. ... 102 FIGURE 6.30.BOXPLOTS OF MEAN ANNUAL DISCHARGE FOR THE UPPER BEAS BASIN;CALIBRATION (LEFT) AND VALIDATION (RIGHT) PERIOD ... 106 FIGURE 6.31BOXPLOTS OF MEAN ANNUAL PRECIPITATION FOR THE UPPER BEAS BASIN;CALIBRATION (LEFT) AND VALIDATION (RIGHT)
PERIOD ... 106 FIGURE 6.32BOXPLOTS OF MEAN ANNUAL TEMPERATURE FOR THE UPPER BEAS BASIN;CALIBRATION (LEFT) AND VALIDATION (RIGHT)
PERIOD ... 107
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FIGURE 6.33 BOXPLOTS OF MEAN ANNUAL RELATIVE HUMIDITY FOR THE UPPER BEAS BASIN;CALIBRATION (LEFT) AND VALIDATION (RIGHT) PERIOD. ... 107 FIGURE 1LOCATION OF THE WIND AND RADIATION DATA.V1: TOP LEFT,V2: BOTTOM LEFT,V3: TOP MIDDLE,V4: BOTTOM MIDDLE,
V5:TOP RIGHT,V6: BOTTOM RIGHT ... 128
TABLE 2.1ALBEDO RANGE DEPENDENT ON SURFACE PROPERTIES OF SNOW AND ICE (PATERSON,1994). ... 22 TABLE 3.1SEASONAL DISTRIBUTION OF AVERAGE RAINFALL IN DIFFERENT RANGES OF THE HIMALAYAS IN THE BEAS BASIN.SEASONAL
CONTRIBUTION AS PERCENT OF ANNUAL PRECIPITATION INDICATED IN THE PARENTHESIS (SINGH AND KUMAR,1997) ... 31 TABLE 3.2.OBSERVED DATA FOR THE DIFFERENT STATIONS IN THE UPPER BEAS BASIN.ALL INPUT DATA FILES, ARE PREPARED FOR 1979-
2005.STATION ELEVATION IN METER ABOVE SEA LEVEL (MASL). ... 38 TABLE 3.3WATCH FORCING DATA FOR SHORT WAVE INCOMING RADIATION AND 10M WIND. ... 38 TABLE 3.4.REMOTE SENSING DATA. ... 38 TABLE 3.5BASIN AREA, GLACIER COVERAGE AND MEAN ELEVATION FOR UPPER BEAS REPRESENTED BY THE THALOUT DISCHARGE STATION,
AND THE INDIVIDUAL SUBBASINS. ... 42 TABLE 4.1DESCRIPTION OF PARAMETERS AND THEIR RELATION TO THE DIFFERENT SUBROUTINES... 50 TABLE 4.2MAPS USED IN THE MODEL SETUP, ROUTINE CONNECTION AND SHORT DESCRIPTION. ... 51 TABLE 6.1 NSE RESULTS FOR CALIBRATION (01.08.1991-31.07.1996) AND VALIDATION (01.08.1996-31.07.2001) FOR ALL THREE
MODELS WITH FREE PARAMETERS FOR ALL SUBROUTINES. TH:THALOUT,BH:BHUNTAR,SA:SAINJ,TIR:TIRTHAN.MEAN: AVERAGE NSE ALL FOUR SUBBASINS.GAMSNOW1:BEST MEAN NSE,GAMSNOW2:BEST THALOUT NSE. ... 65 TABLE 6.2 NSE RESULTS FOR CALIBRATION (01.08.1991-31.07.1996) AND VALIDATION (01.08.1996-31.07.2001) FOR ALL THREE
MODELS WITH FREE PARAMETERS ONLY FOR THE SNOWMELT SUBROUTINES. TH:THALOUT,BH:BHUNTAR,SA:SAINJ,TIR:TIRTHAN. MEAN: AVERAGE NSE ALL FOUR SUBBASINS. ... 65 TABLE 6.3OPTIMIZED VALUES FOR ALL THREE MODELS FROM CALIBRATION STEP 1.GAMSNOW 1 IS SHOWS THE OPTIMIZED PARAMETERS
FOR THALOUT BASIN HIGHEST NSE.GAMSNOW2 IS THE HIGHEST AVERAGE NSE, ALL SUBASINS.GAMSRF AND GAMDDF ARE VALUES FOR HIGHEST AVERAGE NSE. ... 66 TABLE 6.4SNOWMELT SPECIFIC OPTIMIZED PARAMETERS FOR THREE MODELS. ... 66 TABLE 6.5PEARSONS R FOR CALIBRATION (08.1991-07.1996), VALIDATION (08.1996-07.2001) AND THE WHOLE PERIOD
(01.01.1991-31.12.2001) ... 67 TABLE 6.6PERCENT BIAS FOR CALIBRATION (08.1991-07.1996), VALIDATION (08.1996-07.2001) AND THE WHOLE PERIOD
(01.01.1991-31.12.2001) ... 67 TABLE 6.7ABSOLUTE PERCENT BIAS FOR CALIBRATION (08.1991-07.1996), VALIDATION (08.1996-07.2001) AND THE WHOLE PERIOD
(01.01.1991-31.12.2001). ... 68 TABLE 6.8RANK OF ALL PERFORMANCE MEASURES (PM) BASED ON TABLE 6.2 TO TABLE 6.7.BEST RANK 1, LOWEST 3. CAL: CALIBRATION
PERIOD,VAL: VALIDATION PERIOD,SIM: TOTAL SIMULATED PERIOD,NSE:NASH-SUTCLIFFE EFFICIENCY, R:PERSONAS R,PBIAS: PERCENT BIAS,APS: ABSOLUTE PERCENT BIAS. ... 69
11
TABLE 6.9SHOWS THE MANNKENDALL TEST RESULTS FOR ANNUAL DISCHARGE MEASUREMENTS, FOR 95% CONFIDENCE INTERVAL A AND B IS THE LOWER AND UPPER BOUNDARIES, Z IS THE TEST STATISTICS AND N IS NUMBER OF SAMPLES. ... 103 TABLE 6.10SHOWS THE MANNKENDALL TEST RESULTS FOR ANNUAL PRECIPITATION MEASUREMENTS, FOR 95% CONFIDENCE INTERVAL A
AND B IS THE LOWER AND UPPER BOUNDARIES, Z IS THE TEST STATISTICS AND N IS NUMBER OF SAMPLES. ... 103 TABLE 6.11SHOWS THE MANNKENDALL TEST RESULTS FOR ANNUAL MEAN TEMPERATURE MEASUREMENTS, FOR 95% CONFIDENCE
INTERVAL A AND B IS THE LOWER AND UPPER BOUNDARIES, Z IS THE TEST STATISTICS AND N IS NUMBER OF YEARS. PANDOH (LONG) IS THE ANALYZING OF TEMPERATURE AT PANDOH FROM 1979-2006, THE OTHER STATIONS DOES NOT HAVE SUFFICIENT DATA IN THIS PERIOD. ... 104 TABLE 6.12MANN-KENDALL TREND TEST FOR RELATIVE HUMIDITY.95% CONFIDENCE INTERVAL A AND B IS THE LOWER AND UPPER
BOUNDARIES, Z IS THE TEST STATISTICS AND N IS NUMBER OF YEARS. ... 104 TABLE 6.13MEAN ANNUAL PRECIPITATION, TEMPERATURE, RELATIVE HUMIDITY AND DISCHARGE FOR THE OBSERVATIONS IN UPPER BEAS
BASIN, FOR THE CALIBRATION (1991-1996) AND VALIDATION (1997-2001) PERIOD. ... 105
12
13
1 Introduction
1.1 Background
Himalaya, from Sanskrit translating to the abode of snow1, is the largest glaciered area outside Antarctica and Greenland and literally the water tower of the world. The Himalayan mountain range with its high mountains, glaciers and snow covered areas, is of great importance to the availability of water in the densely populated areas of the Indian subcontinent. About 1.3 billion people live in the basins of the great Himalayan rivers and are relying on the water from the monsoon rain and snow melt for drinking, irrigation, sanitation and industry (Eriksson et al., 2009). Indus is one of the rivers originate in the Himalaya with the highest influence of snow and glacier melt on river discharge, estimated to about 50% of total annual runoff (Winiger et al., 2005). The melt of snow and glacier enables low flow of rivers in periods with little or no precipitation. Declining water availability in the Indus basin alone can threaten the food supply to about 25 million people by 2050 (Immerzeel et al., 2010).
The land-ocean atmospheric interaction is of great importance for the hydrological budget in the region (Xu et al., 2008). The major part of the Indian subcontinent receives most precipitation during the Summer Monsoon. The intensity and duration of the Monsoon is reduced from the east to the west. Northwest Himalaya gets additional winter precipitation caused by the Western Disturbance (Lall and Moddie, 1981). The main precipitation sources and the snow and glacier contribution to river flow for the different regions of the Himalaya are described by Bookhagen and Burbank (2010) in Figure 1.1
Most of the glaciers in the Himalaya are reported to retreat (Cogley, 2011). Some glaciers in Western Himalaya do not show any clear trend and are often referred to as the Korakoam anomaly (Bolch et al., 2012, Kääb et al., 2012a), Figure 1.2. Glaciers in this varied environment will have different behavior. They belong to different climatic systems; alpine, subtropic and Himalayan, and they differ greatly in shape and size; valley, hanging, cirque and plataue glaciers (Schmidt and Nuesser, 2012). The glaciers of Himalaya, as indicators of the climatic change, have received extensive attention recently. Remote sensing techniques can prove to be essential
1 http://en.wikipedia.org/wiki/Himalayas
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in monitoring the glacier and snow cover. Kulkarni et al. (2011) showed that the basin-wise loss of glacier area from 1962 to 2001/2004 in Western Himalayan basins was between 8 and 20 %.
Glacier smaller than 1 km2 were retreating at double rate compared to larger glaciers. In addition, the seasonal snow line shifted to higher elevation, this can be a signal of increased temperature, and/or less winter precipitation.
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Figure 1.1. Conceptual climatic and hydrologic model for the Himalaya. Light gray shading of Himalayan catchments primarily in the central and eastern Himalaya indicate snowmelt contribution of <25% to annual discharge. Moderate to high snowmelt contribution occur in catchments with significant winter snow cover (e.g., Indus, Sutlej, Arun and Tsangpo). Black arrows indicate catchment and their corresponding summer (May to October) rainfall amounts: western basin receive <65% of their annual discharge from rainfall (Indus: 40%), whereas basins to the east of the Sutlej receive
>70% of their annual discharge from rainfall. Gray arrows indicate major moisture-source directions. (Bookhagen and Burbank, 2010)
The understanding of, and ability to describe the processes and interactions of melt from high altitude snow and glaciers, is essential to make good predictions for river flow. A hydrological model is an abstraction of the physically complex natural system. Lamadrid and MacClune (2010) made an inventory of climate and hydrological modelling in the Hindu-Kush-Himalayan region finding 29 hydrological models being used in this region. The hydrological models varied in complexity, resolution and extent, not all having a glacier module, and only few attempting to do dynamic glacier calculations. The choice of model complexity is influenced by the available data, the objective of the study and the nature of the environment the model is applied (Haan et al., 1982).
The simplicity and good performance of temperature-index method for snowmelt modelling explains the extensive use (WMO, 1986, Hock, 2003). Different temperature-index models have
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been used in the Beas basin, Indian Himalaya. The surface-runoff model (SRM) (Martinec et al., 1994) is divided into elevation zones, with individual snow depletion curve and melt index for each zone (Prasad and Roy, 2005, Kumar et al., 1992). Li et al. (2013a) used a distributed HBV- model (Bergstrom, 1976) determining the seasonal snow and glacier melt contribution to the Beas river. The Water balancing model (Wasmod) (Xu et al., 1996) was implemented a glacier snow melt module, to investigate the performance of global gridded precipitation datasets compared to observed data, on runoff (Li et al., 2013b).
The findings of Sicart et al. (2008) however, suggests that temperature and melt energy is not well correlated for tropical glaciers at low latitude and high altitude. Direct solar radiation is responsible for 80-85% of the ablation in the western Himalaya (Hewitt, 2005). Some melt equations are settling towards a semi physical approach, using a radiation part in the melt equation. Pellicciotti et al. (2012) used the distributed TOPKAPI model (Todini and Ciarapica, 2001) with an enhanced melt equation for modeling in the Hunza River basin, Karakoram. Short term measurements to determine the surface energy fluxes, showed crucial for correct parameterization of surface melt processes. The model showed sensitivity to the temperature lapse rate that was used to extrapolate temperature from measure point, and the temperature- index and short wave radiation index. Thayyen et al. (2005) found a non-linearity for temperature lapse rates ranging between 2540 and 3763 masl., Din-Gad, Indian Himalaya. Valley lapse rates are more influenced by monsoon, whereas alpine lapse-rates were more stable. Results from a model study at the Chhota Shigri glacier confirmed radiation to be the most important source for melt (Pithan, 2011). In addition, it showed that the mass balance of the glacier was most sensitive to atmospheric humidity.
16 1.2 Objectives
The aim of this study is to evaluate the sensitivity in the water balance components, runoff in particular, for hydrological models with different complexity in the snowmelt routine.
For this purpose the specific tasks are:
1. Establish a gridded hydrological model for the Upper Beas basin including subbasins, based on local and global datasets.
2. Preform a systematic analysis of model sensitivity in terms of runoff and snow/glacier covered area, for three models with a varying complexity in the snowmelt routine.
3. Analyze the variability in the seasonal runoff pattern across the Upper Beas basin
Figure 1.2 The glacier retreat since the mid-19th century is obvious in the Himalaya, with the exception of the glaciers at Nanga Parbat in the northwest (RA, CL). Glaciers in the Karakoram show complex behavior (Bolch et al., 2012)
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2 Theory Snow
The fundament to understand the role of snow in the hydrological cycle is to understand the basic of precipitation as snowfall, the distribution and redistribution of the snow, the melt and transformation of snowpack, and for some areas the transformation from snow to glacier. Snow and glaciers are frozen water, a storage that can be short termed in seasonal snow packs, or long termed as glacier ice. The melt and release of liquid water is important to the hydrology of river basins and the timing of river flows.
2.1 Snow accumulation
2.1.1 Snowfall
Snowfall is precipitation in solid form, and is dependent on temperature. Altitude and latitude determines the temperature and by this influencing the distribution of snow (DeWalle and Rango, 2008). Large atmospheric circulations are transporting water vapor and determining the regional and local climatology. In rugged terrain air masses are pressed over mountain and cooled, this orographic lifting is responsible for precipitation and snowfall in many mountain ranges, as shown in Figure 2.1 The process of orographic lifting. Addition processes of lifting and cooling and hence responsible for precipitation is frontal activity, where cold and warm fronts meeting, and convergence, where air is moving towards low pressure (DeWalle and Rango, 2008, Aguado and Burt, 2010).
Figure 2.1 The process of orographic lifting2
2 http://snowbrains.com/pacific-northwest-get-much-snow/
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The measuring of precipitation and especially snow is difficult, and a source for error (Dingman, 2008). The density of snow varies from 20 kg/m3 to 250 kg/m3 depending on the temperature and wind, usually the density of 100 kg/m3 is used for new snow. Snowfall and snowpacks are measured in snow water equivalents. The relationship between snow and water depth is:
( 1 )
where hm is the height of water resulting from the melt of snow height, hs. ρs is the snow density and ρw is the water density.
Precipitation is mainly point measurements that can be transferred to distributed values for the calculation of areal average precipitation, by a variety of methods. Direct methods included Arithmetic average, Theisson polygons and two-axis method (Dingman, 2008). Surface fitting methods, where the surface represents the precipitation at any locations, include optimal Interpolation/Kriging and inverse distance interpolation (Tabios and Salas, 1985).
2.1.2 Snow distribution
In addition to the regional variation of snowfall, snow, unlike rain is the subject to redistribution after snowfall. Transportation of snow can reach kilometers before settling, and the snow cover distribution is effected by wind, topography, surface roughness and canopy interception (DeWalle and Rango, 2008).
Canopy interception is the accumulation of precipitation on leaves and branches of vegetation.
The intercepted precipitation will evaporate or fall to the ground. Throughfall is the term used for snow that fall through the canopy or drips from the vegetation to the ground (DeWalle and Rango, 2008). The interception loss for snow has showed to have less hydrological importance because of low rates of evaporation/sublimation during winter. This effect can vary for different climate, where cold dry climate will intercept snow longer than humid environments (Storck et al., 2002).
2.1.3 Glacier
The glacier develops in areas where annual snow accumulates over many years. The glacier is defined by an accumulation zone and an ablation zone, divided by the equilibrium line altitude (ELA). ELA is often close to the snowline, the lowest elevation for the perennial snow cover.
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Glaciers are very slow flowing. When the mass from the accumulation zone to the ablation zone is equal the net balance, the glaciers are in steady state. Surging type glaciers can be stagnant over years, during which the lower part thins and the upper parts thickens, followed by strong advancing rates of the terminus (Cuffey and Paterson, 2010). Retreating glaciers indicates a net contribution to the runoff, but the reduced glacier volume will at some stage reach a turning point. The shift from an increase to a decrease in runoff due to glacier retreat and volume change is shown as the peak in runoff in Figure 2.2 c). Reduced melt can also be experienced when debris cover gives an insulating effect at the tongue of the glacier.
Figure 2.2 Schematic representation of the long-term effect of negative glacier net mass balance (a) on glacier volume (b) and annual glacier runoff (c). Volume response lags forcing due to the time required to remove ice by melting. Note that discharge is larger during the first portion of the response period until the glacier is small enough to reduce excess runoff (Jansson et al., 2003)
The areal cover of the glacier will not always be sufficient to determine whether a glacier is retreating or advancing on a permanently basis, or if this change is due the glacier dynamics. The total mass of the glacier is the most important factor, which unfortunately can be difficult to establish.
The glacier melt is subject to the same energy input as snow. The main difference is that glaciers are not porous media, and the response from melt is more direct by surface, englacial or subglacial flow (Cuffey and Paterson, 2010).
2.2 Snow Melt
Snow melt depends on the net energy input to a snowpack. The processes influencing the melt of snow are generally well understood. The calculations can however prove difficult because of the
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lack of onsite information (DeWalle and Rango, 2008). To determine the melt energy there is a range of methods from detailed evaluation of the energy budget, to simplified relationships using only temperature as the index of melt energy. The surface energy-balance and temperature-index approaches are described in the following subsections.
2.2.1 Surface energy-balance
The energy received per unit area (W/m2) surface is defined as the energy flux. In Figure 2.3 (Kiehl and Trenberth, 1997) the energy fluxes through the Earth’s atmosphere are presented, and can be illustrative for net energy fluxes at the surface of snow and ice, calculated by the equation:
( 2 )
ΔQ is the net energy flux at the surface, Sin and Sout is incoming and outgoing shortwave radiation, Lin and Lout are long wave radiation, HSE and HL are turbulent sensible and latent energy fluxes, EG is the subsurface energy flux. The energy contribution from rain and the ground is often considered small and will not be included in the energy budget. It should however be noted that during intense rainstorms the contribution from rain can be important; accounting for both sensible and latent heat exchange (DeWalle and Rango, 2008).
Figure 2.3 The energy flow through Earth’s atmosphere and exchange with the surface – averaged for the globe and a year. All units are Wm−2. The numbers are constrained by satellite observations at the top of the atmosphere.
Partitioning within the atmosphere is estimated from radiation models. Redrawn from Kiehl and Trenberth (1997). © American Meteorological Society.
21 Short wave radiation
The incoming solar radiation, the irradiance, is shown in Figure 2.4 Incoming solar radiation at noon at different latitudes and during a year (Cuffey and Paterson, 2010).at noon for different latitudes and during a year. Irradiance is the amount of energy per m2 before the atmospheric loss is accounted for, some processes of reflection and absorption can be seen in Figure 2.3.
The energy contribution from short wave radiation is dependent on the albedo at the snow or glacier surface by the following equation:
( 3 )
S is the net energy contribution to the surface, α is the albedo, describing the reflectiveness of the surface.
The incoming radiation is effected by the slope and the aspect of the terrain. Shadow will reduce radiation and effective area exposed to the shortwave radiation is dependent on the slope, steepness of the terrain.
Figure 2.4 Incoming solar radiation at noon at different latitudes and during a year (Cuffey and Paterson, 2010).
Albedo
The albedo is the reflective properties of a surface, and an important parameter controlling the amount of energy absorbed by the surface. The albedo for newly fallen snow can be as high as 0.9, meaning that 90% of the incoming solar radiation is reflected and only 10% absorbed as energy in the snow. The metamorphosis, snow crystals growth and the wetness in the snow are
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some of the contributors to decreasing the snow albedo (DeWalle and Rango, 2008). An overview of albedo for different snow and ice surfaces are presented in Table 2.1.
Table 2.1Albedo range dependent on surface properties of snow and ice (Paterson, 1994).
Snow albedo can be determined by a two-way measurement of incoming and outgoing radiation from a snow surface. Measurements of albedo is however not readily available. The reduction of albedo can be calculated as a function of time after snowfall or by temperature. Brock et al.
(2000) found that the decay of albedo, α, could be expressed as a logarithmic function dependent on the cumulative maximum positive air temperature Tacc i , as expressed in equation ( 4 ), α1 initial albedo and α2 albedo reduction factor.
( 4 )
( 5 )
Equation ( 5 ) developed by the Corps of Engineers (1956) is a different approach where the daily albedo, α, is inversely related to the number of days since last snowfall, nd, α0 is the minimum albedo, b and k are recession coefficient. The high albedo of new snow is an important factor during melt, when increased albedo after snowfall slows down the melting in magnitudes.
Airborn pollutants like mineral particles and black carbon can alter the albedo when deposited on the glacier or snow surface. Cuffey (2010) found that for the black carbon concentrations as low Description of Snow and ice Albedo range
Dry snow 0,80-0,97
Melting snow 0,66-0,88
Firn 0,43-0,69
Clean ice 0,34-0,51
Partly dirty ice 0,26-0,33
Dirty ice 0,15-0,25
Debris covered ice 0,10-0,15