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RECENT PROGRESS AND OPPORTUNITIES IN MACROSCALE HYDROLOGICAL MODELING

RECENT PROGRESS AND OPPORTUNITIES IN MACROSCALE HYDROLOGICAL MODELING. Dennis P. Lettenmaier Department of Civil and Environmental Engineering University of Washington presentation for Hydrological Sciences Review Royal Netherlands Academy of Arts and Sciences May 21, 2003.

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RECENT PROGRESS AND OPPORTUNITIES IN MACROSCALE HYDROLOGICAL MODELING

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  1. RECENT PROGRESS AND OPPORTUNITIES IN MACROSCALE HYDROLOGICAL MODELING Dennis P. Lettenmaier Department of Civil and Environmental Engineering University of Washington presentation for Hydrological Sciences Review Royal Netherlands Academy of Arts and Sciences May 21, 2003

  2. Macroscale modeling approach a) Strategy b) Testing and evaluation c) Implementation 2) Examples a) Derived data sets b) S/I streamflow forecasting c) Hydrologic effects of climate change 3) Weak links and research opportunities Outline of this talk

  3. 1. Macroscale modeling approach a) Strategy

  4. Traditional “bottom up” hydrologic modeling approach (subbasin by subbasin)

  5. Snoqualmie River at Carnation, WA Flood of record • Principal calibration locations were the Skykomish at Gold Bar and the Snoqualmie at Carnation

  6. Macroscale modeling approach (“top down”) 1 Northwest 5 Rio Grande 10 Upper Mississippi 2 California 6 Missouri 11 Lower Mississippi 3 Great Basin 7 Arkansas-Red 12 Ohio 4 Colorado 8 Gulf 13 East Coast 9 Great Lakes

  7. Macroscale modeling approach • b) Testing and evaluation

  8. Investigation of forest canopy effects on snow accumulation and melt Measurement of Canopy Processes via two 25 m2 weighing lysimeters (shown here) and additional lysimeters in an adjacent clear-cut. Direct measurement of snow interception

  9. Calibration of an energy balance model of canopy effects on snow accumulation and melt to the weighing lysimeter data. (Model was tested against two additional years of data)

  10. Summer 1994 - Mean Diurnal Cycle Rnet Rnet 300 Rnet 100 -100 250 H H 150 H 50 -50 120 LE LE LE 60 0 0 3 6 9 12 15 18 21 24 0 3 6 9 12 15 18 21 24 0 3 6 9 12 15 18 21 24 Observed Fluxes Simulated Fluxes Rnet Net Radiation H Sensible Heat Flux LE Latent Heat Flux Point Evaluation of a Surface Hydrology Model for BOREAS SSA Mature Black Spruce NSA Mature Black Spruce SSA Mature Jack Pine Flux (W/m2) Local time (hours)

  11. Eurasia North America ) 20 10 2 km 6 16 8 12 6 snow cover extent (10 8 4 4 2 0 0 J F M A M J J A S O N D J J F M A M J J A S O N D J Month Month Observed Simulated Range in Snow Cover Extent Observed and Simulated

  12. UPPER LAYER SOIL MOISTURE Illinois soil moisture comparison 0.40 TOPLATS regional ESTAR distributed X TOPLATS distributed 0.30 X SOIL MOISTURE (%) X X X X 0.20 X X X X X X X X X X 0.10 June 18th-July 20th, 1997 11:00 CST JUNE 20, 1997 11:00 CST JULY 12 1997 50 50 10 10 ESTAR TOPLATS ESTAR TOPLATS

  13. A B C D 200 Soil Moisture (mm) Normalized 100 0 J F M A M J J A S O N D J J F M A M J J A S O N D J J F M A M J J A S O N D J J F M A M J J A S O N D J E F G H 200 Soil Moisture (mm) Normalized 100 0 J F M A M J J A S O N D J J F M A M J J A S O N D J J F M A M J J A S O N D J J F M A M J J A S O N D J Observed Simulated 60°N 60°N E H A D G 50°N 50°N B C F 40°N 40°N 20°E 30°E 40°E 50°E 60°E 70°E 80°E 90°E 100°E 110°E 120°E 130°E 140°E Mean Normalized Observed and Simulated Soil Moisture Central Eurasia, 1980-1985

  14. Cold Season Parameterization -- Frozen Soils Key Observed Simulated 5-100 cm layer 0-5 cm layer

  15. Macroscale modeling approach • c) Implementation

  16. 90 90 60 60 30 30 0 0 -30 -30 -60 -60 -150 -120 -90 -60 -30 0 30 60 90 120 150 Calibration at Global Scales • Only a limited number of model parameters were identified as calibration parameters. The remaining model parameters were determined independently and were not modified. • Calibration parameters: • Infiltration capacity shape parameter bi • Depth of second soil layer • Saturated hydraulic conductivity • Exponent for unsaturated hydraulic conductivity. • Calibration was performed for nine out of 26 river basins. • Simulated from 1980-1993 and compared to observed discharge. Selected 26 river basins, which were divided into calibration (red) and validation (green).

  17. Seasonal Evapotranspiration (1980-1993) Uncalibrated (base case) simulation

  18. Global Mean Annual Runoff Ratio (1980-1993) Uncalibrated (base case) simulation

  19. 2. Examples a) Derived data sets

  20. LDAS Long-Term Retrospective Data Set, 1950-2000 Ed Maurer Dennis Lettenmaier University of Washington Department of Civil and Environmental Engineering

  21. Baseline forcing data – for water and energy balance studies (e.g., GEWEX WEBS). Derived “Pseudo-Observations” – for variables not widely measured (e.g., soil moisture) – analogous to reanalysis. Climate variability and change - characterizing variability and change in variables not directly observed. Motivation

  22. Implementation Strategy • VIC model implemented for 15 sub-regions, with consistent forcings. • Surface forcing data: • Daily precipitation; maximum and minimum temperatures (from gauge measurements) • Radiation, humidity parameterized from Tmax and Tmin • Wind (from NCEP/NCAR reanalysis) • Soil parameters: derived from Penn State State STATSGO in the U.S., FAO global soil map elsewhere. • Vegetation coverage from the University of Maryland 1-km Global Land Cover product (derived from AVHRR)

  23. Temperature and Precipitation Data Precipitation and Temperature from gauge observations gridded to 1/8o Avg. Station density: • Within the U.S.: • Precipitation adjusted for time-of-observation • Precipitation re-scaled to match PRISM mean for 1961-90 (especially important in western U.S.

  24. Validation with Observed Runoff Hydrographs of routed runoff show good correspondence with observed and naturalized flows.

  25. Comparisons with Illinois Soil Moisture 19 observing stations are compared to the 17 1/8º modeled grid cells that contain the observation points. Moisture Level Moisture Flux Variability Persistence

  26. Evaluation of Energy Forcings Comparison with 4 SURFRAD Sites • 3-minute observations aggregated to 3-hour • Average Diurnal Cycle is for June, July, August 1996-99 • Peak underestimated 3-15% at each site (avg. 10% for all sites) • Daily average within 10%, (avg. 2%)

  27. Seasonal Soil Moisture Variation • Shown is seasonal variation of soil moisture. • Top plot is scaled by the total soil pore volume. • Bottom plot is scaled by its dynamic range for 50-years.

  28. Soil Moisture - Active Range 50-Year Soil Moisture Range Scaled by Annual Precipitation Scale indicates level of hydrologic interaction of soil column

  29. Soil Moisture - Persistence Persistence of soil moisture anomalies, based on the full 50+ year timeseries at each grid cell. Persistence is generally seen where soil moisture interaction is high.

  30. Variables

  31. Data Availability • Monthly Average Variables • Currently downloadable from www.hydro.washington.edu • Each file contains monthly data for 1950-2000 • Avg. File Size (compressed netCDF): 120 MB • 3-Hourly Variables • Archived at San Diego Supercomputer Center (link from www.hydro.washington.edu) • Each file contains 3-hourly data for one year • Avg. File Size (compressed netCDF): 200-450 MB • Daily Variables • Archived at San Diego Supercomputer center (link from www.hydro.washington.edu) • Each file contains daily average data for one year • Avg. File Size (compressed netCDF): 20-100 MB

  32. Example Application 1: Hydrologic predictability over the Missouri River basin

  33. Lead-4 Lead-3 Lead -2 Lead 1 Lead-0 Forecast Season DJF M A N F M A J J S O D J Dec 1 Mar 1 Jun 1 Sep 1 Dec 1 Initialization Dates for DJF Forecast Methods for Determining Runoff Predictability • Indices Characterizing Sources of Predictability: • SOI – An index identifying ENSO phase • AO – An index of phase of the Arctic Oscillation • SM – Soil moisture • SWE – Snow water equivalent • Varying Lead Times between Initial Conditions (IC) and Forecast Runoff • Only Use Indices in Persistence Mode Climate Land

  34. Runoff r2SWE r2SOI/AO SOI/AO SWE Methods 2 • Multiple linear regression used between IC and runoff • Variance explained (r2) indicates level of predictability • Variables introduced in order • of how well indices represent • current knowledge of state: • SOI/AO • SWE • SM • Incremental predictability

  35. Methods 3 Test for Significant Predictability (r2) in 2 steps • Local Significance: • Tested at each grid cell • Accounts for temporal autocorrelation • 95% confidence level estimated • Field Significance (Livezey and Chen, 1983): • Tests area showing local significance over entire basin • Accounts for limited sample size, spatial correlation in both predictors and predictand • 95% confidence for field significance

  36. Total Runoff Predictability Lead, months 1.5 4.5 7.5 10.5 13.5 • Uses all 4 indices to predict runoff • “X”  no field significance • Field significance is domain-wide measure

  37. Predictability due to Climate Signals • Predictors currently available • Moderate levels of r2 • Greater influence in winter, in area and lead time • Difficulty in long-lead persistence prediction with climate signals

  38. Predictability due to Soil Moisture • Widespread predictability at 0 lead (1½ month) • Winter Runoff: little predictability where runoff is high • Summer Runoff: limited predictability to 3 seasons

  39. Example Application 2: Hydrologic predictability over the North American Monsoon (NAMS) region

  40. Exploratory Work on Teleconnections between SST and Soil Moisture Study Domain and Datasets Sea surface temperature:Extended Reconstruction of Global Sea Surface Temperature data set based on COADS data. (1847-1997) developed by T.M. Smith and R.W. Reynolds, NCDC. The original data resolution is 2ºlongitude, 2 º latitude. It was interpolated into 0.5 ºresolution (The ocean domain is chosen according to the Bin Yu and J.M. Wallace’s paper, 2000, J. Climate, 13, 2794-2800) Soil Moisture:VIC retrospective land surface dataset (1950-1997). The original data with 1/8 degree resolution is aggregated into 0.5 º resolution.

  41. SST has significant positive correlation coefficient with soil moisture in most areas even with lead time more than 9 months. Southwestern United States shows higher correlation Coefficient (greater than 0.6) with SST than Mexico region. June shows larger area with higher coefficient than other months. Maximum Positive Correlation Coefficient

  42. Southwestern US area shows highest predictability (the highest variance explained is about 0.45) Predictability of Soil Moisture by SST First and Second PC

  43. Soil moisture shows significant persistence even in at 6-month lead time especially for June soil moisture. Mexican part of the domain also shows high persistence for June soil moisture Predictability of Soil Moisture by Persistence

  44. The highest variance explained is more than 90%. For June, over 40% of the variance is explained over most of the study domain, including Mexico. June Soil Moisture Predictability by Persistence and SST PCs

  45. Last December can explain 66.7% June soil moisture LDAS Data Predicted June soil moisture predicted by Persistence and SST PCs

  46. Introducing SST PCs benefits long-time lead predictability (of June soil moisture), but no significant benefits for less than 6-month lead time predictability. SST and Persistence Persistence

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