Evaluating the variability and budgets of global water cycle components V. Sridhar 1, G. Goteti 2, J. Sheffield 2, J. Adam 1 D.P. Lettenmaier 1, E.F. Wood 2 and C. Birkett 3 1 Department of Civil and Environmental Engineering Box , University of Washington, Seattle, WA Princeton University, Princeton, NJ 3 NASA/GSFC, Greenbelt, MD V. Sridhar 1, G. Goteti 2, J. Sheffield 2, J. Adam 1 D.P. Lettenmaier 1, E.F. Wood 2 and C. Birkett 3 1 Department of Civil and Environmental Engineering Box , University of Washington, Seattle, WA Princeton University, Princeton, NJ 3 NASA/GSFC, Greenbelt, MD

Global Water Cycle-Introduction Thermohaline circulation of the world ocean is due to the flux of continental freshwater. The surface water balance eqn.over land is dS/dt = P-E-Q Where P – Precipitation; E-Evapotranspiration; and Q is streamflow S is the sum of dominant terms ( soil moisture, snow water storage and lakes, wetlands and impoundments) Thermohaline circulation of the world ocean is due to the flux of continental freshwater. The surface water balance eqn.over land is dS/dt = P-E-Q Where P – Precipitation; E-Evapotranspiration; and Q is streamflow S is the sum of dominant terms ( soil moisture, snow water storage and lakes, wetlands and impoundments)

Global Water Cycle-Introduction (contd.) The surface energy budget eqn. is Rn = LH+SH+GH Where Rn-Net radiation; LH-latent heat flux; SH-ground heat flux and GH- Ground heat flux Changes in the water cycle due to natural variability and anthropogenic causes are linked as evaporation is common in both water and energy balance equations. Therefore, understanding each term and its variability becomes important to get the budgets to balance. The surface energy budget eqn. is Rn = LH+SH+GH Where Rn-Net radiation; LH-latent heat flux; SH-ground heat flux and GH- Ground heat flux Changes in the water cycle due to natural variability and anthropogenic causes are linked as evaporation is common in both water and energy balance equations. Therefore, understanding each term and its variability becomes important to get the budgets to balance.

Models considered in this study Parallel Climate Model (PCM): –It is a coupled climate model that executes on the Cray T3E computer –The components are interfaced by a flux coupler that passes the energy, moisture, and momentum fluxes between components. –Under numerous forcing scenarios model runs have been made by NCAR and simulations have reprocessed by the PCMDI. Historic run B06.27 is used here. Variable Infiltration Capacity (VIC) Parallel Climate Model (PCM): –It is a coupled climate model that executes on the Cray T3E computer –The components are interfaced by a flux coupler that passes the energy, moisture, and momentum fluxes between components. –Under numerous forcing scenarios model runs have been made by NCAR and simulations have reprocessed by the PCMDI. Historic run B06.27 is used here. Variable Infiltration Capacity (VIC) Atmospheric/landsurface component Sea ice component Ocean component

Hydrology Model -VIC Multiple vegetation classes in each cell and are specified by their leaf area index, root distribution and canopy resistances Sub-grid elevation band definition (for snow) 3 soil layers configuration Explicit 2-layer parameterization for ground heat flux Energy and water budget closure at each time step Subgrid infiltration/runoff variability Non-linear baseflow generation [Nijssen et al., 2001a (J.Clim); Nijssen et al., 2001b(J.Clim); Maurer et al., 2001(JGR)] Multiple vegetation classes in each cell and are specified by their leaf area index, root distribution and canopy resistances Sub-grid elevation band definition (for snow) 3 soil layers configuration Explicit 2-layer parameterization for ground heat flux Energy and water budget closure at each time step Subgrid infiltration/runoff variability Non-linear baseflow generation [Nijssen et al., 2001a (J.Clim); Nijssen et al., 2001b(J.Clim); Maurer et al., 2001(JGR)]

VIC Data Forcing variables (Tmax, Tmin, Precip, Wind), soil and vegetation at 0.5° resolution daily simulation for the period of Aggregated daily output to monthly to compute seasonal and interannual variabilities Forcing variables (Tmax, Tmin, Precip, Wind), soil and vegetation at 0.5° resolution daily simulation for the period of Aggregated daily output to monthly to compute seasonal and interannual variabilities

Forcing data preprocessing Gridded daily time-series ( ) of Precip, Tmax, Tmin and Wind speed at 0.5° is used over all global land areas (excluding Antarctica). Adam and Lettenmaier (2003) adjusted the monthly precipitation time-series of Willmott and Matsuura (2001) for gauge undercatch. Monthly time-series of mean, maximum,minimum temperatures and diurnal range were created mainly from the dataset of New et al. (2000) and Willmott and Matsuura (2001). Daily time-series of 2° grids of Nijssen et al. (2001), were used to estimate the daily variability of precipitation, maximum temperature, and minimum temperature and disaggregated to 0.5°. These daily time-series values were then scaled to have the desired monthly precipitation and temperature means. Daily 10-m wind fields were obtained from the NCEP-NCAR reanalysis (Kalnay et al. 1996) and regridded to 0.5° resolution by linear interpolation. Gridded daily time-series ( ) of Precip, Tmax, Tmin and Wind speed at 0.5° is used over all global land areas (excluding Antarctica). Adam and Lettenmaier (2003) adjusted the monthly precipitation time-series of Willmott and Matsuura (2001) for gauge undercatch. Monthly time-series of mean, maximum,minimum temperatures and diurnal range were created mainly from the dataset of New et al. (2000) and Willmott and Matsuura (2001). Daily time-series of 2° grids of Nijssen et al. (2001), were used to estimate the daily variability of precipitation, maximum temperature, and minimum temperature and disaggregated to 0.5°. These daily time-series values were then scaled to have the desired monthly precipitation and temperature means. Daily 10-m wind fields were obtained from the NCEP-NCAR reanalysis (Kalnay et al. 1996) and regridded to 0.5° resolution by linear interpolation.

Seasonal and Interannual variability Quantification of variability in water budget components * MSV---the mean of the monthly range (maximum minus minimum) in the 21-year global simulations * MIAV---the mean absolute difference in annual totals for each of the variables Quantification of variability in water budget components * MSV---the mean of the monthly range (maximum minus minimum) in the 21-year global simulations * MIAV---the mean absolute difference in annual totals for each of the variables

MSV-Precipitation Both PCM and VIC model, show a predominant seasonal change in precipitation along the equatorial low (where precipitation is abundant in all seasons). Subtropical high (North and southern Hemisphere) exhibit relatively moderate change (dry in all seasons). Both PCM and VIC model, show a predominant seasonal change in precipitation along the equatorial low (where precipitation is abundant in all seasons). Subtropical high (North and southern Hemisphere) exhibit relatively moderate change (dry in all seasons). OBS. PCM

MIAV-Precipitation MIAV is quite distinct in the equatorial low (over Amazon basin) and mid-latitudes (of USA) and in South Asia. Subpolar low regions (Alaska, Canada) shows some variability (where precipitation is abundant) A high variability in the “source” term is expected to have strong impact on other water budget components. MIAV is quite distinct in the equatorial low (over Amazon basin) and mid-latitudes (of USA) and in South Asia. Subpolar low regions (Alaska, Canada) shows some variability (where precipitation is abundant) A high variability in the “source” term is expected to have strong impact on other water budget components. OBS. PCM

MSV-Evaporation MSV in Evaporation is quite high both in equatorial and subtropical high regions. The only region that showed less changes are sub-Sahara Africa and Australian desert. Evaporation variability is partly driven by variabilities in precipitation. MSV in Evaporation is quite high both in equatorial and subtropical high regions. The only region that showed less changes are sub-Sahara Africa and Australian desert. Evaporation variability is partly driven by variabilities in precipitation. VICPCM

MIAV-Evaporation MIAV is relatively less from VIC simulations across the continents, except Australia. PCM displays higher variability over much of Africa, Australasia and S. America. MIAV is relatively less from VIC simulations across the continents, except Australia. PCM displays higher variability over much of Africa, Australasia and S. America. VICPCM

MSV-Change in SWQ Greater change in snow water equivalent is obvious over high latitudes. Obviously absence of snow in the lower regions and thereby no variabilities, except over Himalayas. Greater change in snow water equivalent is obvious over high latitudes. Obviously absence of snow in the lower regions and thereby no variabilities, except over Himalayas. VIC PCM

Higher MSV in precipitation (~270 mm) results in high variability in runoff (~50 mm) over S. America by VIC obs. PCM runoff variability is low. Europe and S. America exhibit high variability (~80 mm) in evaporation that is equal in magnitude. PCM predicts a high variability. Out of Asia, Europe and N. America— more variability in snow water equivalent is in N.America. MSV in soil moisture is about mm from VIC and about 110 mm from PCM across all continents. Higher MSV in precipitation (~270 mm) results in high variability in runoff (~50 mm) over S. America by VIC obs. PCM runoff variability is low. Europe and S. America exhibit high variability (~80 mm) in evaporation that is equal in magnitude. PCM predicts a high variability. Out of Asia, Europe and N. America— more variability in snow water equivalent is in N.America. MSV in soil moisture is about mm from VIC and about 110 mm from PCM across all continents.

MIAV in precipitation is the highest for S.America followed by Australia and Europe and Asia show the least from both and VIC obs. Australia has the highest MIAV in evaporation. Runoff variability is quite significant for S. America and Oceania that reflects the variability in precipitation as well. PCM runoff is very low. Variability in snow water equivalent is the highest for N. America followed by Europe. PCMs are relatively low. Australia has the highest variability in soil moisture and average is about 50 mm, equal in magnitude as that MSV. PCM has twice the magnitude in MIAV. Estimates of MSV and MIAV of runoff, snow water eq., are considerably smaller for PCM. PCM soil moisture obtained as the remainder of water budget results in high MSV and MIAV. MIAV in precipitation is the highest for S.America followed by Australia and Europe and Asia show the least from both and VIC obs. Australia has the highest MIAV in evaporation. Runoff variability is quite significant for S. America and Oceania that reflects the variability in precipitation as well. PCM runoff is very low. Variability in snow water equivalent is the highest for N. America followed by Europe. PCMs are relatively low. Australia has the highest variability in soil moisture and average is about 50 mm, equal in magnitude as that MSV. PCM has twice the magnitude in MIAV. Estimates of MSV and MIAV of runoff, snow water eq., are considerably smaller for PCM. PCM soil moisture obtained as the remainder of water budget results in high MSV and MIAV.

Lakes, Wetlands and Impoundments Lakes and wetlands are good indicators of climate change. They play a major part in global water budget computations. Measurements of levels of lakes and wetlands are difficult and observations are sparsely available. Remote sensing of lake and wetland levels becomes crucial. A few major African lakes and wetlands data from TOPEX/POSEIDON satellite was made available to us by Charon Birkett, GSFC/NASA They constitute 8.4 % of total lakes and 5% of the total land area. Lakes and wetlands are good indicators of climate change. They play a major part in global water budget computations. Measurements of levels of lakes and wetlands are difficult and observations are sparsely available. Remote sensing of lake and wetland levels becomes crucial. A few major African lakes and wetlands data from TOPEX/POSEIDON satellite was made available to us by Charon Birkett, GSFC/NASA They constitute 8.4 % of total lakes and 5% of the total land area.

Lake Level Changes LakeArea(km2)LakeArea(km2) Nyasa6400Turkana6750 Tana3600 Victoria68800 Tanganyika32000 Sudd Marshes~10000 LakeArea(km2)LakeArea(km2) Nyasa6400Turkana6750 Tana3600 Victoria68800 Tanganyika32000 Sudd Marshes~10000

MSV in change in lake level is about 14 mm (for 5% of Continental Africa) and MIAV is approx. 2.3 mm. MSV in soil moisture for Africa is about 50 mm (VIC) and the by including effect of lakes of remaining 92%, lake storage change could be about 150 mm. Hence, exclusion of change in lake storage in the water budget equations is therefore expected to have far reaching implications. MSV in change in lake level is about 14 mm (for 5% of Continental Africa) and MIAV is approx. 2.3 mm. MSV in soil moisture for Africa is about 50 mm (VIC) and the by including effect of lakes of remaining 92%, lake storage change could be about 150 mm. Hence, exclusion of change in lake storage in the water budget equations is therefore expected to have far reaching implications.

Conclusion PCM showed a relatively low variability in runoff and snow water equivalent and high in evaporation and soil moisture, but mostly in agreement with VIC variability spatially. Resolution of PCM (~2.8°) is fine but VIC results could be successfully interpreted where data is sparse. Variabilities are high in S. America, Australia and Africa. Soil moisture did not include change in storage in wetlands, lakes and impoundments. TOPEX/POSEIDON lake data throw some light on African lake levels. Our ongoing work includes simulating the effect of lakes and wetlands over Africa as it assumes great significance in getting the water budget right. PCM showed a relatively low variability in runoff and snow water equivalent and high in evaporation and soil moisture, but mostly in agreement with VIC variability spatially. Resolution of PCM (~2.8°) is fine but VIC results could be successfully interpreted where data is sparse. Variabilities are high in S. America, Australia and Africa. Soil moisture did not include change in storage in wetlands, lakes and impoundments. TOPEX/POSEIDON lake data throw some light on African lake levels. Our ongoing work includes simulating the effect of lakes and wetlands over Africa as it assumes great significance in getting the water budget right.