CATCHMENT HYDROLOGICAL RESPONSE TO CHANGING LAND SYSTEM IN KASHMIR HIMALAYA
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1 IGC, August, 2012 CATCHMENT HYDROLOGICAL RESPONSE TO CHANGING LAND SYSTEM IN KASHMIR HIMALAYA Prof. SHAKIL A ROMSHOO HEAD, DEPT OF EARTH SCIENCES KASHMIR UNIVERSITY
2 PRESENTATION OVERVIEW KASHMIR HIMALAYAS UPPER INDUS SYSTEM DYNAMIC COMPONENTS STREAM FLOW CHANGES WETLANDS/LAKES LAND SYSTEM CHANGES AND PROJECTIONS CRYOSPHERE CLIMATE CHANGE SCENARIO HYDROLOGICAL CHANGES W.R.T. LULC BMPs/SCENARIO MAPPING CONCLUSIONS
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5 HYDROLOGIC SYSTEM LINKAGES
6 Discharge (Cusecs) Discharge (Cusecs) Discharge (Cusecs) Discharge (Cusecs) Observed Stream Flow Changes Yearly Discharge Winter Season Years Years SpringSeason SummerSeason Years Years
7 DACHIGAM (yearly) Name of the Test Test Static a=0.1 a=0.5 a=0.01 Result Mankendall S (0.01) Spearman s Rho S (0.01) Linear Regression S (0.01) Test Type DACHIGAM (Seasonal) Season Test statistic a=0.1 a=0.05 a=0.01 Result Winter S (0.01) Mann-Kendall Spearman's Rho Linear Regression Spring S (0.01) Summer S (0.01) Winter S (0.01) Spring S (0.01) Summer S (0.01) Winter S (0.01) Spring S (0.01)
8 J F M S N L D
9 Discharge (Cusecs) Discharge (Cusecs) Discharge (Cusecs) Discharge (Cusecs) Observed Stream Flow Changes Winter Season Years Years 100 Summer Season 100 Spring Season Years
10 Name of the Test DAKIL (yearly) Test Static a=0.1 a=0.5 a=0.1 Result Mankendall NS Spearman s Rho NS Linear Regression NS Test Type DAKIL (seasonal) Season Test statistic a=0.1 a=0.05 a=0.01 Result Winter NS Mann-Kendall Spring S(0.1) Summer S(0.05) Spearman's Rho Winter NS Spring S(0.05) Summer S(0.01) Linear Regression Winter NS Spring S(0.05)
11 Introduction Water Resource Map of UIB Various beneficial functions of wetlands include life sustaining processes like water storage (domestic, agriculture, industrial usage) protection from storm and floods, ground water recharge, storage for nutrients, erosion control and stabilisation of local climate (temperature, rainfall) and thus helps in maintaining ecological balance. During the recent past wetlands have been recklessly destroyed to create land for development and only in recent years their uses and values have begun to be understood and appreciated.
12 WATER RESOURCES
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14 Land System Changes in Kashmir CLASS NAME 1972 AREA (HA) 1992 AREA (HA) 2008AREA (HA) FOREST AGRICULTURE PLANTATION HORTICULTURE WATER RIVER BED AQUA_VEGETATION BUILT-UP
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16 Land System Change Projections ( )
17 Changes in Kashmir Cryosphere Total Glacier area in Total Glacier area Total Glacier area Total Glacier area 1969 (sq. Km) 1992 (Sq. Km) 2001(sq. Km) 2010 (Sq. Km)
18 Water Tower of Asia
19 Rainfall (mm) Rainfall (mm) Average_Temperature ( 0 C) Temperature (0C) Average Temperature (Dec. & Jan.) ( 0 C) Average Temperature (Dec &Jan) 8,00 7,00 6,00 5,00 4,00 3,00 2,00 1,00 0, , Total Precipitation Dec-Jan Total Precipitation (Dec. &Jan. )
20 HYDROLOGIC STYTEM LINKAGES
21 CATCHMENT SCALE STUDIES The study area is the Dal Lake Catchment highly urbanized and situated in Srinagar city (KASHMIR HIMALAYAS) having an area of 337 km². Location of Dal Lake Catchment
22 LU/LC MAP OF DAL LAKE CATCHMENT: 1992
23 LU/LC MAP OF DAL LAKE CATCHMENT: 2005
24 Area change in LULC from 1992 to 2005 S. No. Class Name Area (Km 2 ) Area change % change 1 Built up Agriculture Fallow Horticulture Coniferous Forest Deciduous Forest Sparse Forest Grasslands Scrubland Plantation Aquatic Vegetation Bare Land Bare Exposed Rocks Water Bodies Water Channel Area Snow Golf course/turf
25 c Reference Totals Classified Totals Number Correct Producers Accuracy (%) Users Accuracy (%) Built up Agriculture Agriculture Fallow Horticulture Coniferous Forest Deciduous Forest Sparse Forest Grasslands Scrubland Plantation Aquatic Vegetation Barren Bare Exposed Rocks Water Water Channel Area Snow Totals Overall Accuracy = 89.67% %
26 KAPPA (K^) STATISTICS OVERALL KAPPA STATISTICS = KAPPA FOR EACH CATEGORY Class Name Kappa Built up 1 Agriculture Agric. Fallow Horticulture 1 Coniferous Forest Deciduous Forest Sparse Forest Grasslands Scrubland Plantation Aquatic Vegetation Barren Bare Exp. Rocks Water 1 Water Channel Snow OVERALL KAPPA STATISTICS= KAPPA (K^) STATISTICS KAPPA FOR EACH CATEGOR Class Name Kappa Built up Agriculture Agricultural Fallow Horticulture 1 Coniferous Forest Deciduous Forest Sparse Forest Grasslands 1 Scrubland Plantation Aquatic Vegetation Barren.9126 Bare Exposed Rocks.6610 Water 1 Water Channel Area Snow.6644
27 DW1 DW2 DW 3 D W 7 DW4 DW5 D W6 DW 11 D W 8 DW1 3 DW 12 D W 9
28 DAL LAKE ENVIRONS
29 LULC CHANGES AROUND DAL LAKE ( ) CLASS NAME Area (hectares) Area change ( ) Agriculture Aquatic veg Bare Land Builtup Fallow Land Forest Grasslands Horticulture Plantation Stream Bed Water
30 ETM, 1992 LISSIII, 2005 SOIL ERODIBILITY (K) FACTOR CURVE NO MULTIDATE LU/LC MAPS SLOPE LENGTH (LS) FACTOR C & P PRACTICES WEATHER DATA MULTIDATE CSA MAPS TRANSPORT DATA DAILY TEMP & PCPT GWLF MODEL SIMULATION OF RUNOFF, EROSION & SEDIMENT/NUTRIENT LOADS QUANTIFING THE HYDROLOGICAL PROCESSES
31 Hydrological Model Q kt ( R t R t M t M t 0.2DS kt 0.8DS kt ) 2 DS kt CN kt X kt 0.132* RE t * K k *( LS) k * C k * P k * AR k SR 0.001* C * Y m s m
32 DATA SETS USED SATELLITE DATA/GEOSPATIAL DATA: LANDSAT- ETM (1992) IRS: LISS III (2005) Topographic data DEM (30m) Time series of Hydrometeorological data II) FIELD DATA/ LAB INVESTIGATION Physicochemical data generated for soil Ground truth data III) ANCILLARY DATA Published reports and journals
33 HYDROLOGOICAL PARAMETERS(CM) HYDROLOGOICAL PARAMETERS(CM) Geospatial Analysis-Parameters
34 Input parameters used in the GWLF Model Source Areas Hydro Conditions LS C P K WCN Agriculture Fair Horticulture Fair Forest Fair Hay/Pasture Fair Low int Devel N/A High int Devel N/A Bare land Poor
35 CHANGES IN NUTRIENT LOADING OF DAL LAKE ( ) Hydrological Process Runoff (mm) Year Change (Tons/Yr) (mm) Erosion Sediment NUTRIENT LOADS Total Nitrogen (TN) Dissolved Nitrogen (DN) Total Phosphorus (TP) Dissolved Phosphorus (DP)
36 Source area contribution to sediment & erosion yields SOURCE 1992 LULC Erosion Tons/yr 2005 LULC Erosion Tons/yr 1992 LULC Sediment Tons/yr 2005 LULC Sediment Tons/yr pastures Cropland Forest Horticulture Bare 1, , Lo_Int_Dev Hi_Int_Dev Stream bank Total
37 Sediment (mg/l) VALIDATION STUDY RESULTS NATSH-SUTCLIFFE (R2) COEFFICIENT : 0.85
38 Source area contribution to avg. sediment & erosion yields
39 SCENARIO MAPPING
40 PREDICT POLLUTANT REDUCTION IMPACT COMPARISON TOOL developed for use in evaluating the implementation of both rural and urban pollution reduction strategies at the watershed level. allows the user to create various scenarios in which current landscape conditions can be compared against future conditions that reflect the use of different pollution reduction strategies.
41 METHODOLOGY ADOPTED FOR PREDICT MODEL BMPS STRUCTURAL/ NON-STRUCTURAL URBAN Constructed wetlands Bio-retention areas Detention basins AGRICULTURAL Crop rotation Cover crops Contour farming Agr. to wetland conversion Agr. to forest conversion Grazing land management Terraces & diversions STREAMS Vegetative buffers Stream bank fencing Stream bank stabilization SCENARIOS PREDICT MODEL BEST CASE SCENARIO
42 SOME OF THE BMPs
43 SOURCE Nutrient load reduction in response to BMP Baseline Scenario MSCN1 MSCN2 Nitrogen (Tons/Yr) Baseline Scenario MSCN1 Phosphorus (Tons/Yr) MSCN2 Hay/Pasture Agriculture Forest Horticulture Turf /Golf course Bare Land Low Int. Dev High Int. Dev Stream Bank Ground water Totals % Reductions 20.0% 37.0% 26.6% 40.0%
44 Reduction in sediment loads in response to different scenarios SOURCE Baseline Scenario MSCN1 Sediment (Tons/Yr) MSCN2 Hay/Pasture Agriculture Forest Bare Land Low Int Dev Hi Int Dev Stream Bank Total Sediment
45 Conclusions The study demonstrated the effectiveness of the GIS based Modeling system in quantifying the hydrological processes and nutrient loads from the critical source areas at the catchment scale. The change in the LULC of the catchment has contributed significantly towards increased nutrient loads at the catchment scale. The implementation of different BMPs can prove quite effective in controlling the nutrient pollution of water body and thus improve the ecological condition of the lake including the water quality
46 Thank you 46
47 50 km 2 RCM data downscaled to 10 km 2 grids
48 ANNUAL AVERAGE MAXIMUM TEMPERATURE Temperature ( 0C) Years Temperature ( 0C) Years Temperature (0C) Years Temperature (0C) Years
49 ANNUAL AVERAGE MINIMUM TEMPERATURE Temperature (0C) Years Temperature (0C) Years Temperature (0C) Years Temperature (0C) Years
50 Temperature (0C) Years Baramulla WMO RCM Temperature (0C) Years Budgam WMO RCM Validation of mean maximum temp Temperature (0C) Years Anantanag WMO RCM Temperature (0C) Years Kupwara WMO RCM
51 ANNUAL AVERAGE PRECIPITATION Rainfall (mm) Years Rainfall (mm) Years Rainfall (mm) Years Rainfall (mm) Years
52 The maximum mean annual temperature is projected to increase by 2 0 C (± 0.9) from 2011 to 2040 and C (±1.17) from 2040 to The minimum mean annual temperature is projected to increase by C (± 0.61) from 2011 to 2040 and C (± 0.88) in 2040 to The annual precipitation is likely to decrease by about 13.1 % from and 4.07 % from respectively.
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