Introduction SummaryResults

Approaches

Exploring impact of forcing data on hydrologic simulations and climate sensitivity

When assessing climate impact on hydrologic processes, we face a number of different modeling approaches, including forcing dataset, downscaling of atmospheric model outputs, spatial scales, hydrologic model. It’s been shown that different modeling approaches result in different hydrologic simulations (e.g. Vano et al. 2012). The study objective is to examine how different forcing datasets affect hydrologic simulations. Ultimately we will evaluate between-dataset difference in sensitivity to climate variability

Figure 2. Upper Colorado River Basin. Three elevation bands of the UCRB.

Naoki Mizukami1, Martyn Clark1, and Pablo Mendoza1,2

1. National Center for Atmospheric Research2. University of Colorado, Boulder

• We illustrated how differences in climate forcing were propagated into hydrologic simulation using CLM simulation with NLDAS and VIC climate forcing. Ultimate objective of this study is to examine how hydrologic sensitivity to climate variability differ between the two data sets.

• In a comparison between two forcing datasets and CLM simulations, difference in SW radiation affects hydrologic simulations among the forcing variables. NLDAS has less precipitation and higher temperature and higher humidity and higher wind speed than VIC data, but more snow accumulation and less ET, as a result more runoff. Therefore SW radiation and humidity impact on model simulation difference. In VIC forcing dataset, this SW radiation difference appears to be caused by difference in diurnal temperature range between two dataset. Diurnal temperature range is used to estimate SW radiation and humidity in VIC forcing data.

• An analysis of sensitivity difference to climate variables is still working in progress. Based on a preliminary analysis, both datasets appear to exhibit similar hydrologic sensitivity to temperature and precipitation, though hydrologic simulations with two datasets produced different magnitudes of the hydrologic states. Different elevation bands exhibit different impacts of climate variables on the processes. Sensitivity of snow accumulation to temperature/precipitation still needs to be examined.

Community Land Model (CLM; Olsen et al. 2010; Figure 1) was run over the upper Colorado River Basin (Figure 2) at 1/8 degree from 10/1/1980 to 9/30/2008. CLM was forced by two historical climate datasets 1) North American Land Data Assimilation System (NLDAS; Xia et al. 2012) and University of Washington dataset (VIC; Maurer et al. 2003) to examine the impact of different forcing on hydrologic simulation and sensitivity to climate variability. Major difference in data source are summarized in Table below.

Figure 1. Schematics of land surface processes simulated in Community Land Model

Variables NLDAS VIC

Precipitation CPC gage + elev adjust Co-op Gage

Temperature NARR + elev adjust Co-op Gage

Specific humidity NARR + elev adjust MT-CLIM algorithm1

SW radiation NARR + GOES adjust MT-CLIM algorithm1

LW radiation NARR+ elev adjust TVA Algorithm (1972)

Pressure NARR+ elev adjust Lapse rate from Sea level pressure

Wind Speed NARR NCEP-NCAR

Reference

Figure 3 shows overall comparison of two datasets.• The most pronounced differences are seen in the highest

elevation band (e.g., Precipitation, humidity, SW radiation)• The variable that exhibit the most difference is SW (i.e., SW in

NLDA is much lower than VIC SW radiation).

It appear that SW radiation difference is translated into difference in CLM simulations of SWE during winter time and also evapotranspiration during summer. As a results, VIC produces less runoff. This radiation effect is more significant in higher elevation.

Figure3. Comparison of monthly values for 23 water years for 3 elevation bands (left panel) and spatial pattern of annual means (right panel) between NLDAS and VIC.

Between-dataset difference in climate variables Between-dataset difference in CLIM simulations

Between-dataset difference in model sensitivity to climate variables

Figure 6. Comparison of climatological annual cycles of elevation-band average SWE, ET, and Runoff between NLDAS and VIC.

Figure 7 shows how hydrologic states are related to climate variables at an annual scale. A slope for each group (i.e. elev. band and product) indicates sensitivity of a state variable to a climate variable.

Figure 5. Comparison annual cycle of Tmax, Tmin, and DTR between NLDAS and VIC.

Figure 4 shows difference in seasonality of SW radiation and relative humidity. Difference in SW is greater during summer. NLDAS exhibit higher RH during winter.

Hungerford, R.D., Nemani, R.R., Running, S.W., Coughlan, J.C., 1989. MT-CLIM: a mountain microclimate simulation model. US Forest Service Research Paper, INT-414.

Kimball, J. S., S. W. Running, and R. Nemani, 1997: An improved method for estimating surface humidity from daily minimum temperature. Agricultural and Forest Meteorology, 85, 87-98.

Maurer, E.P., Wood, A.W., Adam, J.C., Lettenmaier, D.P., Nijssen, B., 2002. A Long-Term Hydrologically Based Dataset of Land Surface Fluxes and States for the Conterminous United States*. Journal of Climate, 15(22): 3237-3251.

Oleson, K. W., D. M. Lawrence, G. B. Bonan, M. G. Flanner, E. Kluzek, P. J. Lawrence, S. Levis, S.C. Swenson, P.E. Thornton, A. Dai, M. Decker, R. Dickinson, J. Feddema, C.L. Heald, F. Hoffman, J.-F. Lamarque, N. Mahowald, G.-Y. Niu, T. Qian, J. Randerson, S. Running, K. Sakaguchi, A. Slater, R. Stockli, A. Wang, Z.-L. Yang, Xi. Zeng, and X. Zeng, 2010: Technical Description of version 4.0 of the Community Land Model (CLM), 257 pp.

Thornton, P. E., and S. W. Running, 1999: An improved algorithm for estimating incident daily solar radiation from measurements of temperature, humidity, and precipitation. Agricultural and Forest Meteorology, 93, 211-228.

Vano, J. A., T. Das, and D. P. Lettenmaier, 2012: Hydrologic sensitivities of Colorado River runoff to changes in precipitation and temperature. Journal of Hydrometeorology.

Xia, Y. et al., 2012. Continental-scale water and energy flux analysis and validation for the North American Land Data Assimilation System project phase 2 (NLDAS-2): 1. Intercomparison and application of model products. J. Geophys. Res., 117(D3): D03109.

AcknowledgementThis was funded by Bureau of Reclamation and U.S. Army Corps of Engineers. Authors acknowledge many NCAR scientists as well as Levi Brekke (BoR) and Jeff Arnold (USACE) for scientific discussions and technical supports.

Since VIC uses a set of empirical algorithm with daily Tmax, Tmin, daily precipitation to estimate radiation and humidity, annual cycle of Tmax, Tmin, DTR, and wet-day are further examined (Figure 5). • Both have similar climatological wet day• Both have similar Tmax, but VIC has lower Tmin making greater

DTR than NLDAS. • The large DTR in VIC may cause larger VIC SW radiation.

Figure4. Comparison of annual cycle of SW radiation and RH between NLDAS and VIC.

1Hungerford et al. 1989; Kimball et al. 1997; Thornton and Running 1999

Figure 7. Scatter plots of simulated hydrologic states vs. climate variables (T & P) for 3 elevation-bands.

Diurnal Temperature Range (DTR) Wet days

TmaxTmin

AGU 2012 Fall MeetingH41-A1158

Contact: mizukami@ucar.edu

NLDAS

VIC

Ele. band 1 (< 2 km)

Ele. band 2 (2km - 3 km)

Ele. band 3 (> 3 km)

Figure 8 shows composite daily time series based on annual precipitation and temperature over the highest elevation band.

Figure 8. Daily time series averaged over the top wettest and driest years (left two columns) and top 5 warmest and coldest years (right two columns)

Mean over top 5 warmest yrs

Mean over top 5 coldest yrs

SW radiation Relative humidity