Spatial and Temporal Variability of Soil Moisture in North America American Geophysical Union- European Geophysical Society Joint Meeting April 8, 2003 D.P. Lettenmaier, E.P. Maurer and C. Zhu Univ. of Washington, Seattle, WA USA Source: NASA

Societal Interest in Hydrologic Variability Droughts Floods Extreme Events Source: NOAA, Hydrologic Info. Ctr. $6-8 billion/yr $5 billion/yr

Presentation Outline 1)Background 2)Construction of a retrospective gridded soil moisture data set 3)Some examples

The Land-Surface Water Budget EP W Q How well can can variability in the water budget components be determined with observations? We need long records of observations to define variability and predictability

Runoff (Streamflow) Observations Streamflow in the U.S. measured at roughly 7,000 active gauging stations. Stations can represent regulated flow conditions Streamflow is a spatially integrated quantity Source: U.S.G.S.

Spatial Density of Observations Precipitation appears well defined, generally since 1948 Ameriflux (flux towers) provides measurements of E, since mid 1990’s 1 per 130,000 km 2 Source: A. Robock, Rutgers U. 1 per 700 km 2 Spatial coverage of Soil moisture observations is poor at continental scale

2) Development of the data set

Hydrologic Model VIC Model Features: Developed over 10 years Energy and water budget closure at each time step Multiple vegetation classes in each cell Sub-grid elevation band definition (for snow) Subgrid infiltration/runoff variability Drive a Hydrologic Model with well-known P, T, reproduce Q, derive snow, soil moisture, ET

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 T max and T min  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)

Temperature and Precipitation Data Within the U.S.: Precipitation adjusted for time-of- observation Precipitation re-scaled to match PRISM mean for (especially important in western U.S. Precipitation and Temperature from gauge observations gridded to 1/8 o Avg. Station density: AreaKm 2 /station U.S Canada2500 Mexico6000

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

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

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.

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

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.

6 Sample Hydrographs Good agreement of Seasonal cycle Low Flows Peak Flows Model Obs.

Locations of users of the data through early 2003 for details: Maurer et al, J Clim, 2002, for data set availability:

3) Some examples

Estimation of Long-Lead Runoff Predictability Mississippi River Basin Predictability derives from: Climate Soil moisture Snow Winter runoff concentrated in SE High snowmelt runoff in summer

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 Forecast Season DJF Initialization Dates for DJF Forecast Dec 1 Mar 1Jun 1Sep 1 Lead-0Lead-4Lead-3Lead -2Lead 1 DJ FMA M JJ A SO N Climate Land

Methods 2 Multiple linear regression used between IC and runoff Variance explained (r 2 ) indicates level of predictability Variables introduced in order of how well indices represent current knowledge of state: 1.SOI/AO 2.SWE 3.SM Incremental predictability Test for both local and field significance r 2 SOI/AO r 2 SWE Runoff SOI/AO SWE

Total Runoff Predictability Lead, months Uses all 4 indices to predict runoff “X”  no field significance Predictability deteriorates with time

Predictability due to Soil Moisture Soil Moisture provides the dominant runoff predictability at 0 season lead (1½ month). Winter Runoff: predictability is where runoff is low -- little predictability where runoff is high. Summer Runoff: Very modest predictability beyond lead 0. Snow provides summer runoff predictability in west.

Exploratory Work on Teleconnections between SST and Soil Moisture Sea surface temperature: Extended Reconstruction of Global Sea Surface Temperature data set based on COADS data ( ). Original data resolution of 2º interpolated to 0.5º Soil Moisture: VIC retrospective land surface dataset ( ). Original data at 1/8º resolution is aggregated to 0.5º Study Domain and Datasets

Investigating Sea Surface Temperature/Soil Moisture Teleconnections Teleconnection between SST and Precipitation The eastern Pacific winter SST linked to summer monsoon rainfall in the southwestern United States and northwestern Mexico. Feedback between Soil Moisture and Precipitation Soil moisture responds to Asian and Africa Monsoon precipitation and affects the precipitation. Correlation between SST and Soil Moisture Late spring/early summer North Pacific SST correlated with Canadian soil moisture. Historic Studies Indicate Predictive Connection

Predictability of Soil Moisture by SST First and Second PC SST 1 st and 2 nd PCs together explain about 80% of SST variance, so most of SST signal is captured Southwestern US shows significant soil moisture variance explained by SST PCs 1 and 2 (up to 45%) Mexico sees lower soil moisture variance explained by SST

June soil moisture predicted by SST First and Second PCs Previous December SST 1 st and 2 nd PCs can explain 44.1% variance of June soil moisture at this point VIC Retrospective Data Predicted with PCs

Predictability of Soil Moisture by Persistence Soil moisture shows high persistence up to 6-month lead time. Highest is for for June soil moisture in Southwestern US. Mexico region also shows high persistence for June soil moisture Month of SM being predicted Previous month of predictor SM

June-September Soil Moisture Predictability using Persistence and SST PCs The highest variance explained is more than 90%. 40% of variance can be explained in most of the study domain for June including Mexico.

SST and Persistence Persistence June Soil Moisture Predictability using Persistence and SST PCs The incremental benefit of SST knowledge at lead times shorter than 6 months is negligible Introducing SST PCs at longer lead time adds greater predictability beyond that achievable with soil moisture persistence alone. June Soil Moisture Being Predicted

CONCLUSIONS Derived soil moisture data sets offer best hope for incorporating long-term information about land surface boundary condition in teleconnection studies (also many other applications, e.g. long- range forecasting) Many issues about particulars – e.g., model dependence, calibration, etc. Nonetheless, derived land surface conditions from this approach are much more realistic than from reanalysis (see e.g. Maurer et al, J Clim, 2002) Planned extensions of data set back to 1916, and domain extension to include North American Monsoon Experiment Tier 1 (much of Mexico)