Assessing methods to disaggregate daily precipitation for hydrological simulation

Transcription

1 Assessing methods to disaggregate daily precipitation for hydrological simulation Peng Gao, Gregory Carbone, Daniel Tufford, Aashka Patel, and Lauren Rouen Department of Geography University of South Carolina

2 Background CISA (Carolinas Integrated Sciences and Assessments) incorporates climate information into water and coastal management Hydrological modeling: how climate affects water supply and quality

3 Background Continuous simulation modeling (e.g. Hydrologic Simulation Program-Fortran (HSPF)) an important tool to investigate the impacts of climate change on water resources high spatial and temporal resolution (e.g. hourly or subdaily) rainfall data

4 Challenges - the constraint of data availability Precipitation data are often available only at coarser levels (i.e., daily) (25,000 daily recording stations, 8,000 hourly stations in U.S.) (Booner, 1998)

5 Challenges - the constraint of data availability Meteorological variables from the GCMs (General Circulation Models ) needed for hydrological simulation - typically at monthly or daily scales

6 Rainfall Depth (in) Solutions Disaggregate the daily rainfall to hourly time series Date Jun 16 0 Prec. (inch) Jun Jun et al Hours

7 Background Many disaggregation methods Few tests to assess the performance of these methods on hydrological simulations

8 Overview of the Study Examine three different disaggregation methods to construct hourly precipitation time series from daily precipitation Use those time series as input and compare simulated flows against observed flows

9 Three Disaggregation Methods

10 Method1 Triangular by HSPF Daily rainfall needs to be disaggregated: 0.10 daily total ratio for each hour Find the daily total closest to but larger than the daily rainfall that needs to be disaggregated Distribute the daily rainfall proportionally to ratio for each hour

11 Method 2 and 3 It iteratively searches the rainfall events from the existing rainfall event database until the remaining amount is lower than an assigned minimum threshold Advantage:reserving the probability distribution

12 Rainfall Depth (in) To disaggregate a inch daily total with the assigned minimum threshold: inch Rainfall Depth (in) Remaining = = Remaining = = Remaining = = < Hours Rainfall events Total: 0.04 inch (0.01 inch at 3 o clock and 0.03 inch at 4 0 clock) Total: 0.02 inch (0.02 inch at 8 o clock) Total: 0.01 inch (0.01 inch at 9 o clock) Total: 0.01 inch (0.01 Event3 inch at 6 o clock) Total: Event inch (0.01 inch at 17 o Event1 clock) et al. How to deal with the remaining amount of inch?

13 Rainfall Depth (in) Method2: Socolofsky method The remaining amount follows exponential distribution (e.g. Modeled remaining amount = - Actual remaining amount (i.e., )*log (random seed) at random hour from (0 to23) ) Modeled Daily total: inch inch Remaining amount Event3 Event2 Event Hours Actual Daily total: inch

14 Rainfall Depth (in) Method3: Adjusted Socolofsky method The remaining amount is directly placed into a onehour storm event Modeled Daily total: inch Remaining amount Event3 Event2 Event Hours Actual Daily total: inch

15 Rainfall Depth (in) Rainfall Depth (in) Rainfall Depth (in) Hours Event3 Event2 Event1 Remaining Amount Event3 Daily total: inch Triangular Distribution Socolofsky method 0.01 Event Event Hours Remaining Amount Event3 Adjusted Socolofsky method 0.01 Event Event Hours

16 Characteristics Comparison Triangular Distribution Socolofsky Method Adjusted Socolofsky Method Stochastic N Y Y Using existing hourly data Conserve daily total Conserve PDF N Y Y Y N Y N Y Y Multi events N Possible Possible

17 Observed Hourly Precipitation Design Sum to Daily Precipitation HSPF Simulated Stream Flow Disaggregate Disaggregated Precipitation Observed Stream Flow

18 Observed Hourly Precipitation Virtual station: area weighted mean of stations whose Thiessen polygons fall into the watershed

19 Rainfall Depth (in) Rainfall Depth (in) Disaggregated Precipitation Triangular Distribution Ten simulations for each Socolofsky method and Adjusted Socolofsky method using precipitation from the virtual station as the existing rainfall event database Simulation Rest Amount Event3 Simulation Event2 Event1 Rest Amount Hours 0.01 Event3 Event Event1 Hours

20 Comparisons Four types of hourly precipitation time series Precipitation from the virtual station ( a combination of observed hourly precipitation) Triangular distribution Socolofsky Method (ten simulations) Adjusted Socolofsky Method (ten simulations) The simulated stream flows VS. the observed stream flows in the verification time period

21 Study Area

22 Model performance evaluation How close the simulated flows to observed flows index of agreement (d) (higher, better) mean absolute error (MAE) (lower, better) Nash-Sutcliff efficiency (NS) (closer to 1, better) percent bias (p-bias) (lower, better) root mean squared error (RMSE) (lower, better) Weather simulated flows follow the same distribution Mann-Whitney-Wilcoxon Test

23 Results Socolofsky (S) and Adjusted Socolofsky (AS) VS. Virtual station (V)

24 Statistic of daily stream flow in watershed South Yadkin, NC Results d MAE NS p-bias RMSE V S S S S S S S S S S d MAE NS p-bias RMSE V AS AS AS AS AS AS AS AS AS AS Socolofsky (S) and Adjusted Socolofsky (AS) produced similar statistics to precipitation from the virtual station (V)

25 Statistic of daily stream flow in watershed South Yadkin, NC Results d MAE NS p-bias RMSE V S S S S S S S S S S d MAE NS p-bias RMSE V AS AS AS AS AS AS AS AS AS AS Socolofsky (S) varied more than Adjusted Socolofsky (AS) because it does not conserve the depth of daily rainfall

26 Triangular (T) VS. Virtual station (V) Results South Yadkin Upper Broad South Fork Tyger Black d MAE NS p-bias RMSE V T V T V T V T V T The performance of Triangular (T) is not consistent

27 Results Distribution of Simulated Flows Mann-Whitney-Wilcoxon Test Socolofsky (S) and Adjusted Socolofsky (AS) VS. Virtual station (V) no difference Triangular (T) VS. Virtual station (V) significant difference

28 Conclusion The adjusted Socolofsky method most robust in terms of performance when compared to the model verification run a useful means of disaggregating the daily precipitation from GCMs under different scenarios

29 Acknowledges Department of Geography, CISA, Geography Graduate Student Association provides travel fundings