Hydrologic Modeling on a 4km Grid over the Conterminous United States (CONUS)

1. INTRODUCTION1. INTRODUCTION

The Hydrology Laboratory (HL) of the NOAA/National Weather Service’s Office of Hydrologic Development (OHD) is developing advanced water resources products to meet the expanding needs of the public. Recently, the HL distributed modeling research program embarked on an exciting new development for large-scale, fine-resolution soil moisture modeling. We expect this work to provide important contributions toward meeting the Nation’s need for water resources information such as drought.

We present the first results of the new distributed SAC-SMA (Koren et al. 2005) and SNOW-17 (Anderson, 1996) models over the CONUS area. Distinctive characteristics of our CONUS runs are: (a) distributed SNOW-17 model parameters are estimated from physical factors (b) NWS-gridded monthly potential evaporation products are used, (c) the precipitation forcings are the Stage II and IV multi-sensor precipitation mosaicked NEXRAD products. To evaluate the results, we compare our CONUS soil moisture estimates to those from the 12 km products from the North American Land Data Assimilation System (NLDAS), and to the Oklahoma Mesonet soil moisture data for regional verification.

• The current work marks the first model run with snow-melt and water balance components on a CONUS scale at 4x4km resolution using gridded forcings and estimates of model parameters at the national scale.

• There is significant computational cost at 1-hourtime step. The current result is from 3hr time step run which takes about 4 minutes for each time step on Linux …machine.

• ( should be restated differently ??? Suggestion from all required ) The comparison of the HL-RMS to MOSAIC product suggest that both have similar trend with HL-RMS showing in cases of smaller basin superior results which we believe is attributed to finer scale modeling.

• The HL-RMS soil moisture products simulations compare well with the Oklahoma mesonet observations at the daily and basin scales. However the performance of HL-RMS in other areas should be verified with soil moisture dataFigure 2a. Vegetation cover (%),

Aspect and Forest type (not shown) are the major physiographic properties that influence snow melt factor

Figure 2b. Maximum and Minimum snow melt factors derived using the physiographic properties.

Fekadu Moreda, V. Koren, Z. Cui, S. Reed, Z. Zhang, M. SmithOffice of Hydrologic Development, National Weather Service NOAA, 1325 East-West Highway, Silver Spring, MD 20910, U.S.A.

www.nws.noaa.gov/oh/hl E-mail fekadu.moreda@noaa.gov

2. MODELS AND PARAMETERS2. MODELS AND PARAMETERS

The Sacramento Soil Moisture Accounting (SAC-SMA) model is reformulated to compute physically-based estimates of soil moisture using the equations of heat transfer and a soil column representation of SAC water storage and movement. This modified SAC-SMA now runs within the HL distributed model, and generates estimates of physically-based soil moisture content at the 4 km grid scale over CONUS. In addition, SNOW-17, the NWS’s operational temperature index-based snow model is implemented to run on a 4 km grid (Figure 1). For the CONUS scale run, we did not route the flow through hillslope and channels because the goal for this phase of the work get only soil moisture in the layers

REFERENCES

Rainfall-runoff models: 1. SAC-SMA2. CONT-API

Channel routing

SNOW -17

Grid inputs:PrecipitationTemperatureEvaporation

Surface runoff

Rain + melt

Grid outputs:flowState variables

Subsurface runoff

Hillslope routing

Rainfall-runoff models: 1. SAC-SMA2. CONT-API

Channel routing

SNOW -17

Grid inputs:PrecipitationTemperatureEvaporation

Surface runoff

Rain + melt

Grid outputs:flowState variables

Subsurface runoff

Hillslope routing

Rainfall-runoff models: 1. SAC-SMA2. CONT-API

Channel routing

SNOW -17

Grid inputs:PrecipitationTemperatureEvaporation

Surface runoff

Rain + melt

Grid outputs:flowState variables

Subsurface runoff

Hillslope routing

Rainfall-runoff models: 1. SAC-SMA2. CONT-API

Channel routing

SNOW -17

Grid inputs:PrecipitationTemperatureEvaporation

Surface runoff

Rain + melt

Grid outputs:flowState variables

Subsurface runoff

Hillslope routing

Rainfall-runoff models: 1. SAC-SMA2. CONT-API

Channel routing

SNOW -17

Grid inputs:PrecipitationTemperatureEvaporation

Surface runoff

Rain + melt

Grid outputs:flowState variables

Subsurface runoff

Hillslope routing

Rainfall-runoff models: 1. SAC-SMA2. CONT-API

Channel routing

SNOW -17

Grid inputs:PrecipitationTemperatureEvaporation

Surface runoff

Rain + melt

Grid outputs:flowState variables

Subsurface runoff

Hillslope routing

Figure 1. Grid based modular structure and input/outputs of the NWS HL-RMS.

Precipitation Data Sources: Hourly gridded national muti-sensor archives (hhtp://www/..) STAGE II (Oct 1998 – Sept 1999) http://wwwt.emc.ncep.noaa.gov/mmb/ylin/pcpanl/stage2/

STAGE II + STAGE IV – (Oct 2001 – Sept 2002) http://wwwt.emc.ncep.noaa.gov/mmb/ylin/pcpanl/stage4/

Temperature data sources: Hourly gridded temperature for (Oct 2001 – Sept 2002) is obtained by extracting and interpolating the RUC p3 hourly 13 km grid in to HRAP (4kmX4km) grid. http://maps.fsl.noaa.gov/pig.cgi?20km_RUC

Potential Evaporation: Gridded monthly NOAA potential evaporation is used

Anderson, E. (1996) Initial parameter values for the snow Accumulation and ablation model. NWSRFS user manual IV.2.2Mitchell, K.E., et al. (2004), The multi-institution North American Land Data Assimilation System (NLDAS): J.Geophys. Res., 109.Koren, V.I., M. Smith, D. Wang, Z. Zhang. 2000. Use of soil property data in the derivation of conceptual rainfall-runoff model parameters. Proceedings of the 15th

Conference on Hydrology, AMS, Long Beach, CA, pp. 103 – 106 .

Upper 10cm thick layer

Correlation of basin-averaged soil moisture

There is high correlation between the Oklahoma mesonet soil moisture observations and model simulated soil moisture in the upper and lower layers. Higher correlation is observed for the HL-RMS simulation than the MOSAIC, particularly, for small size basins

Root Mean Square Error

Smaller root mean square error is observed for HL-RMS. Errors are larger for smaller basins than for large basins.

Basin average Soil Moisture

Both MOSAIC and HL-RMS tends to over estimate the upper 10 cm soil moisture. For the lower 30 cm layer HL-RMS over estimates while MOSAIC again underestimate. In the case of HL-RMS , this might be associated to the division of the upper and lower layer one compensating the other. Over all root zone (40cm), model soil moisture from HL_RMS agree better with observations than that from MOSAIC.

Lower 30cm thick layer

H13H-1406 AGU 2005 Fall Meeting

December 5 - 9, San Francisco

Vegetation Cover %

Forest Cover MFMAX MFMIN

Coniferous forest quite dense 0.5 -0.8 0.2 - 0.3

Mixed forest Coniferous plus open and/or deciduous

0.8 – 1.0 0.25 - 0.4

Predominantly Deciduous 1.0-1.3 0.35-0.5

Open Areas 1.30-2.0 0.5-0.9.

•For SAC-SMA gridded a priori parameters derived from soil property maps (Koren et al, 2001) maps are used

•For Snow-17, a priori parameters of MFMAX and MFMIN derived from dominant forest type, forest per cent coverage and dominant aspect are used. (Table 1 and Figure 2.). Other parameters are set to typical values. Because of the gridded modelling, the depletion curves are not necessary. The elevation and latitude for the grid is obtained directly from DEM at the center of the grid

Aspect

Routing not included

Run reformulated Sacramento Model and Snow-17Run reformulated Sacramento Model and Snow-17 models continuously over CONUS.continuously over CONUS.- Generate simulated soil moisture grids for two layers (10 cm and 30 cm, see - Generate simulated soil moisture grids for two layers (10 cm and 30 cm, see Figure 3bFigure 3b. ) . ) - Convert the soil moisture content into saturation index by using wilting point and saturation property at each Convert the soil moisture content into saturation index by using wilting point and saturation property at each gridgrid

Saturation index = (soil moisture –wilting point)/(saturation-wilting point)Saturation index = (soil moisture –wilting point)/(saturation-wilting point)

-Extract from archive simulation of soil moisture from the MOSAIC Model. Mosaic simulation is performed by Extract from archive simulation of soil moisture from the MOSAIC Model. Mosaic simulation is performed by the North American Land Data Assimilation System (NLDAS, the North American Land Data Assimilation System (NLDAS, http://ldas.gsfc.nasa.gov)http://ldas.gsfc.nasa.gov)

-These data, among many variables, consist of 10cm and 30 cm thickness soil moisture at about 12 km grid These data, among many variables, consist of 10cm and 30 cm thickness soil moisture at about 12 km grid scale over CONUS. For comparison purpose the soil moisture content of MOSAIC is also converted to scale over CONUS. For comparison purpose the soil moisture content of MOSAIC is also converted to saturation index.saturation index.

Two sets of analyses were performedTwo sets of analyses were performed1) CONUS-wide comparison of soil moisture between HL-RMS and MOSAIC1) CONUS-wide comparison of soil moisture between HL-RMS and MOSAIC2) Detailed comparison of soil moisture from HL-RMS and MOSAIC with Oklahoma Mesonet observations 2) Detailed comparison of soil moisture from HL-RMS and MOSAIC with Oklahoma Mesonet observations

For detailed comaprison:For detailed comaprison:- Generate saturation index grids for Oklahoma Mesonet for 5cm and 25 cm depth from 100 point - Generate saturation index grids for Oklahoma Mesonet for 5cm and 25 cm depth from 100 point measurements (measurements (Figure 3aFigure 3a). An inverse distance method is used to extrapolate point soil moisture data to ). An inverse distance method is used to extrapolate point soil moisture data to grids. The 5 cm depth is compared to the upper 10 cm model simulations and the 25 cm depth is compared grids. The 5 cm depth is compared to the upper 10 cm model simulations and the 25 cm depth is compared to the lower 30 cm layer of model simulations (to the lower 30 cm layer of model simulations (Figure 3b.)Figure 3b.)- Select 75 ((areas 10 -15,000 sq. km.)- Select 75 ((areas 10 -15,000 sq. km.) which are not affected significantly by flow regulation in the Arkansas which are not affected significantly by flow regulation in the Arkansas River basin. Compared correlation, bias and time series plot of daily soil moisture basin average of River basin. Compared correlation, bias and time series plot of daily soil moisture basin average of (observed , HL-RMS and MOSAIC)(observed , HL-RMS and MOSAIC)

Table 1. Recommended values of MFMAX and MFMN, Anderson (1996).

4.4. METHODSMETHODS

3. DATA3. DATA

Mesonet Sites:USGS Gauges 75 cm 25 cm

5. Results 5. Results Example of CONUS top 10cm soil moistureExample of CONUS top 10cm soil moisture

Detailed Comparison of Two Layers Soil Moisture for 75 basins (for the period of Oct 1, 1998 – Sept 31, 1999)Detailed Comparison of Two Layers Soil Moisture for 75 basins (for the period of Oct 1, 1998 – Sept 31, 1999)

Model SimulationMesonet Measures

at depths

25cm

5cm 10 cm

30 cm

Figure 3a. Location of mesonet and USGS river gage stations

Figure 3b. Measurement point and Model simulation layers

Minimum Melt Factor, MNMF

Maximum Melt Factor, MXMF

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Soil Moisture Time series Good simulation is observed during dry season.

6. SUMMARY6. SUMMARY

Model Parameters

Models

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Oct Dec Jan Mar May Jul Sep

Obs

HL-RMS

MOSAIC

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2.55

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Oct Dec Jan Mar May Jul Sep

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HL-RMS

MOSAIC

0

2.5

5

MOSAIC Soil Moisture for April 05 2002 12z

HL-RMS Soil Moisture for April 05 2002 12z

Soil moisture (m3/m3))

Figure 4. The MOSAIC and HL-RMS has produced a similar trend of soil moisture over the CONUS. However, the model the soil moisture is more drier for MOSAIC in the South West, for example. The spatial detail from the HL-RMS fine scale simulation can be seen from the expanded soil moisture maps.

Precip (mm)Precip (mm)