Identifying physio-climatic controls on watershed vulnerability to climate and land use change

Transcription

1 Identifying physio-climatic controls on watershed vulnerability to climate and land use change SWAT 2018 IIT Madras, India Ankit Deshmukh* 1,Riddhi Singh 2 January 12, Department of Civil Engineering, Indian Institute of Technology Hyderabad, Kandi, Telangana, India Department of Civil Engineering, Indian Institute of Technology Bombay, Powai, Maharashtra, India

2 We need reliable projections of streamflow to manage freshwater resources in the future Figure 1: Global extent of water scarce regions in the last decade. (UNEP, 2008) However, obtaining future projections are challenging! 1

3 Changing climate will lead to significant and uncertain changes in future Historical GCM uncertainty 7 Observations (GPCP) 6 Internal variability 5 Model uncertainty Scenario uncertainty Projected change in global mean precipitation [%] Projected change in global mean temperature [K] 0 0 Adler et al., 2003 Brohan et al., Decadal mean projections for the 21 st century (the total uncertainty in CMIP-3 global mean) Hawkins and Sutton [2011] 2

4 Uncertainties are present in all the steps of obtaining a hydrologic projection Emission scenarios (future CO 2 concentration) uncertainty about future economy Climate change General circulation models Initial conditions Parametric uncertainty Land use change Downscaling Hydrologic model Indicators of hydrologic response Downscaling uncertainty Model structural uncertainty Parametric uncertainty Uncertainty about critical thresholds Determine if these ranges fall in the vulnerable region Thus we need a way to provide useful information to stakeholders in the presence of large uncertainties. 3

5 We provide an alternative approach to identify vulnerability to climate change which is independent of future projections of climate and land use change Determine plausibility of vulnerability sy Identify regions in framework space that lead to vulnerability u p analysis Bottom Determine vulnerability ranges for the hydrologic indicator Figure 2: Bottom up: A vulnerability based approach Our vulnerability based approach is useful for decision makers and water policy makers. Singh et al. [2014] 4

6 We develop a framework which combines strength of bottom-up approach and comparative hydrology User should defined vulnerability: One can defined vulnerability as streamflow reduced by >50% in the watershed Bottom-Up Framework Thresholds Correlation The framework provides a quantitative measurement (critical thresholds) of watershed vulnerability to climate and land use change. 5

7 Qsurface We implement the bottom-up approach using exploratory modelling framework Climate and land use combinations 12 o C ET %Veg GW store Q subsurface Q baseflow Hydrologic model Streamflow Simulations Exploring the temperature space ΔT 0 o C -40% ΔP +60% Exploring the precipitation space 0 1 Deep-rooted Vegetation Exploring the land use space Screening (NSE & %Bias) Parameters θ Random sampling Indicators C0 C1 C2 C3 Indicator classes 12 o C Mapping of classes to the sample space ΔT 0 o C -40% ΔP +60% 0 1 Deep-rooted Vegetation CART Deshmukh and Singh [2016] 6

8 We use a spatial lumped model to simulate runoff which account land-use as fraction of deep rooted vegetation in the watershed %veg: Fraction of deep rooted vegetation in watershed Precipitation T Cei LAI min/max ET Degree day factor Threshold temperature Rain/Snow Legend Maximum storage Fluxes Parameter Field Capacity Shape parameter Recharge coefficient Ground water store Surface flow Subsurface flow A SS Baseflow A BF Simulated runoff used to calculate indicator of vulnerability (i.e. mean annual runoff). Singh et al. [2014] 7

9 We classify an indicators based on class definitions Indicator mean annual runoff (for water availability in the stream) classified into 4 classes. C0 C1 C2 C3 Class: Change in streamflow C0: > 0% C1: 0% to -25% C2: -25% to -50 C3: < -50% We use a classification method, CART to identify the combinations of climate and parameters which leads to a specific class. 8

10 We select 77 United states watersheds for this study North km Watersheds Removed watersheds 134 km km 2 We remove 8 watersheds based on model performance criteria and low runoff ratio. Deshmukh and Singh [2016] 9

11 We use CART to relate climate, land use and parameters samples into vulnerability classes ΔP 0.75 Node:2 Samples: if true ΔP< 0.85 Node:1 if false ΔP< 1.05 C0 C1 C2 C3 Class: Change in streamflow C4: > 0% C1: 0% to -25% C2: -25% to -50 C3: < -50% Samples: 2631 M< 0.55 Node:3 Samples: Node:4 M 0.35 Node:5 Samples: 2275 C3 ΔP 0.95 C0 C2 C3 C0 Precipitation threshold = 24.3% (Reduction) C1 C2 Landuse threshold = Critical thresholds are computed by weighted averaging of thresholds for all watersheds (for precipitation and land use change) Deshmukh and Singh [2016] 10

12 We identity relationship between critical threshold and watershed physio-climatic characteristics a. Precipitation change threshold b. Landuse change threshold M.A. runoff Flood IQ dsc Drought M.A. runoff Flood IQ dsc Drought Drainage Topography Geology Landuse Climate TWI 1 st order stream 4 th order stream 2 nd order stream Stream density Elevation Aspect East Area Aspect North Avg. permeability HG-B Avg. silt Organic matter Avg. WT depth Urban Forest Agriculture M. A. precipitation Aridity Index Indicators Indicators M. A. PET -1 Correlation 1 Significant corrrelation (P-value < 0.05) Insignificant correlation The correlation explain generalized relationship of watershed physio climatic characteristic with watershed vulnerability. Deshmukh and Singh [2016] 11

13 Thank you Questions?

14 References A. Deshmukh and R. Singh. Physio-climatic controls on vulnerability of watersheds to climate and land use change across the united states. Water Resources Research, E. Hawkins and R. Sutton. The potential to narrow uncertainty in projections of regional precipitation change. Climate Dynamics, 37(1-2): , R. Singh, T. Wagener, R. Crane, M. Mann, and L. Ning. A vulnerability driven approach to identify adverse climate and land use change combinations for critical hydrologic indicator thresholds: Application to a watershed in pennsylvania, usa. Water Resources Research, 50(4): , 2014.