Zong-Liang Yang, Guo-Yue Niu and Robert E. Dickinson* The University of Texas at Austin

Modeling Runoff in CLM Zong-Liang Yang, Guo-Yue Niu and Robert E. Dickinson* The University of Texas at Austin * Georgia Institute of Technology Prepared for Hydrology Project, CCSM Land Model Working Group Meeting March 27, 2006 www.geo.utexas.edu/climate Introduction| SIMTOP | Validation | SIMGM | Validation | Conclusions

Outline • Introduction • Design of a Simple TOPMODEL-Based runoff Scheme (SIMTOP) • Validate SIMTOP against GRACE ΔS • Development of a Simple Groundwater Model • Assess Model against GRACE ΔS • Conclusions Introduction| SIMTOP | Validation | SIMGM | Validation | Conclusions

Design of the Simple TOPMODEL-Based Runoff Scheme (SIMTOP) Surface Runoff : Rs = P Fmax e – C f zwt p = precipitation zwt = the depth to water table f = the runoff decay parameter which determines recession curve Fmax and C = topographic parameters Subsurface Runoff : Rsb= Rsb,maxe –f zwt Rsb,max = the maximum subsurface runoff when the grid-mean water table is zero. It should be related to lateral hydraulic conductivity of an aquifer and local slopes. Rsb,max=1.0x10-4 mm/s through sensitivity experiments. SIMTOP parameters: Two calibration parameters Rsb,max and f Two topographic parameters Fmax and C Introduction| SIMTOP | Validation | SIMGM | Validation | Conclusions

fsat Justification of Surface Runoff Formulation and Derivation of Topographic parameters Relationship Between the Saturated Area and Water Table Depth Map of saturated areas showing expansion during a single rainstorm.[Dunne and Leopold, 1978] zwt fsat = Fmax(λ) e –C f zwt fsat λ – topographic wetness index derived from DEM Introduction| SIMTOP | Validation | SIMGM | Validation | Conclusions

DEM –Digital Elevation Model ln(a) – contribution area ln(S) – local slope 1˚ x 1˚ Justification of Surface Runoff Formulation and Derivation of Topographic parameters Topographic Wetness Index:λ = ln(a/tanβ) = ln(a) – ln(S) The higher topographic wetness index, the wetter the pixel Introduction| SIMTOP | Validation | SIMGM | Validation | Conclusions

1˚ 1˚ 1.0 Fmax CDF 0.2 0.5 PDF 0.1 λ λ λm λm Justification of Surface Runoff Formulation and Derivation of Topographic parameters TOPMODEL(Beven and Kirkby, 1979; Sivapalan et al., 1987) : zi – zm = – (λi – λm) / f where zi and λi are water table depth and topographic index at a pixel; while zm and λm are their grid-cell (catchment) mean values. The Saturated Fraction the Grid-Cell: Fsat = Prob { zi < 0 } or Prob { λi > λm – fzm} zm λm zi, λi Lowland highland Introduction| SIMTOP | Validation | SIMGM | Validation | Conclusions

Justification of Surface Runoff Formulation and Derivation of Topographic parameters A 1 ˚x 1˚ arid-cell in the Amazon River basin Both Gamma and exponential functions fit for λi > λm Fmax = 0.45; C = 0.6 Introduction| SIMTOP | Validation | SIMGM | Validation | Conclusions

Justification of Surface Runoff Formulation and Derivation of Topographic parameters A 1 ˚x 1˚ arid-cell in Northern Rocky Mountain Gamma function fails, while exponential function works. Fmax = 0.30; C = 0.5 Introduction| SIMTOP | Validation | SIMGM | Validation | Conclusions

Justification of Surface Runoff Formulation and Derivation of Topographic parameters Fmax=0.35; C = 0.51 to 1.10 Woods and Sivapalan (2003) Exponential function fits very well in well-developed catchments. The larger the catchment, the better the fitting. C ~ 0.6 Introduction| SIMTOP | Validation | SIMGM | Validation | Conclusions

Justification of Subsurface Runoff Formulation TOPMODEL (Beven and Kirkby, 1979) Rsb= Rsb,max e fS where S is the deficit of the subsurface water storage Sivapalan et al., (1987) and Stieglitz et al. (1997 ) Rsb= K0/f e –λ e –f zwt It needs very large K0, which is justified by soil surface macropore (1000 times lager than in LSM); Chen and Kumar (2001): Rsb =αK0/f e –λ e –f zwt (where αK0 is the lateral K) Difficulties in determining α globally; λ needs very high resolution DEM (30 m or finer) to determine slopes. Niu et al. (2005): Rsb = Rsb,max e –f zwt (Rsb,max= 1.0x10-4 mm/s) Introduction| SIMTOP | Validation | SIMGM | Validation | Conclusions

The Exponential Relationship between Streaflow and Water Table Depth Groundwater level is highly correlated with streamflow in a strong nonlinear manner and explains 2/3 of the streamflow (Yeh and Eltahir, 2005) Introduction| SIMTOP | Validation | SIMGM | Validation | Conclusions

Diagnostic Water Table Depth from Soil Moisture Profile Ψi – zi Capillary Ψsat – zwt Gravity Gravity Soil water profile when gravity equals to capillary force Water profile under gravity Koster et al. (2000); Chen and Kumar (2001) Introduction| SIMTOP | Validation | SIMGM | Validation | Conclusions

Model Design: A Simple Groundwater Model (SIMGM) Groundwater Discharge (Baseflow or Subsurface Runoff) SIMTOP (Niu et al., 2005) Properties of the Aquifer 1. Hydraulic Conductivity: 2. Specific Yield: Introduction| SIMTOP | Validation | SIMGM | Validation | Conclusions

Validate the Model against the Valdai Data The model reproduces SWE, ET, runoff, and water table depth. The water table depth has two peaks and two valleys in one annual cycle WTD is very sensitive to soil permeability (sand percentage, frozen soil), Runoff, and ET parameters. Introduction| SIMTOP | Validation | SIMGM | Validation | Conclusions

Validate the Model against GRDC Runoff Good agreements between the modeled runoff and GRDC Runoff; Runoff ~ exp(- f *wtd) The modeled water table depth ranges from 2.5m in wet regions to 30m in arid regions. Introduction| SIMTOP | Validation | SIMGM | Validation | Conclusions

Regional Averaged Runoff Agreement between the modeled runoff and GRDC runoff in most regions except for mid-latitudes; Surface runoff accounts for about 20%; Groundwater discharge accounts for about 80% of the total runoff. Introduction| SIMTOP | Validation | SIMGM | Validation | Conclusions

Validate the Model Against GRACE ΔS Anomaly The modeled ΔS anomaly agrees very well with GRACE data in river basins where ΔS is not affected by frozen soil; Groundwater ΔS anomaly accounts for about 60-80% of the total ΔS anomaly; The model capture the inter-annual and inter-basin variability of the ΔS anomaly. Introduction| SIMTOP | Validation | SIMGM | Validation | Conclusions

Validate the model Against GRACE WTD Anomaly GRACE ΔS / 0.2 The modeled water table depth agrees very well with GRACE data in terms of inter-annual and inter-basin variability in river basins where ΔS is not affected by frozen soil; The uncertainty in GRACE data is mainly the attenuation effects induced by smoothing. Introduction| SIMTOP | Validation | SIMGM | Validation | Conclusions

P – E, Groundwater Recharge, and Discharge Phase lags in P – E, groundwater recharge and discharge; Negative recharge in dry seasons when P – E is negative Variations of P – E, groundwater recharge, discharge are consistent with the groundwater storage anomalies and WTD in terms of the inter-annual and inter-basin variability. Introduction| SIMTOP | Validation | SIMGM | Validation | Conclusions

The Impacts of Groundwater Model on SM and ET It has a large impact on bottom-layer soil moisture, most obviously in cold regions (30% globally); It has a smaller impacts on surface-layer soil moisture (5% globally); The impacts on ET are mostly in arid-to-wet transition zones, i.e., the “hot spots” (20% in sensitive zones). Introduction| SIMTOP | Validation | SIMGM | Validation | Conclusions

Soil Moisture Profiles in Selected Regions It has a large impacts on the soil moisture profile in most regions; It has a relative small impacts in arid regions because the WTD is very deep and thus the capillary forces are weak. Introduction| SIMTOP | Validation | SIMGM | Validation | Conclusions

Transpiration vs. Soil Surface Evaporation Groundwater has a negligible impacts on transpiration, although it greatly increases deep soil moisture; It enhanced the ground-surface evaporation in dry seasons corresponding to the increases in the surface-layer soil moisture. Introduction| SIMTOP | Validation | SIMGM | Validation | Conclusions

Conclusions • We developed a simple groundwater model (SIMGM) for use in GCMs by representing the recharge and discharge processes in an unconfined aquifer, which is added as a single integration element. • It is first validated against the observed water table depth in a small cold-region watershed. It captures not only the summer valley also the winter valley of the observed water table. • On the global scale, it reproduces the GRDC runoff; Groundwater discharge accounts for about 80% of the total runoff. • The modeled ΔS anomaly agrees very well with GRACE data in terms of inter-annual and inter-basin variability in most river basins. • Groundwater ΔS anomaly accounts for about 60-80% of the total ΔS anomaly; The modeled water table depth agrees very well with that converted from GRACE. The groundwater storage and WTD anomalies are mainly controlled by P – E, or climate. • It produces a much wetter soil globally except for arid regions; It produces about 4 – 20% more annual ET mainly through the enhanced ground surface evaporation instead of transpiration in humid-arid regions. Introduction| SIMTOP | Validation | SIMGM | Validation | Conclusions

Zong-Liang Yang, Guo-Yue Niu and Robert E. Dickinson* The University of Texas at Austin