Sampling Network in Illinois

1. Impact of Water Table Dynamics on Hydrological Simulation of the NCAR CLMMin Hui Lo, Pat J.-F. Yeh, and James S. FamigliettiDepartment of Earth System Science, University of California, Irvine Abstract In this study we investigate the effect of groundwater on hydrological simulations using the NCAR CLM3.0 land surface model. First, we incorporate a lumped unconfined aquifer model into the CLM, in which the water table is interactively coupled to the soil column through the groundwater recharge flux. The coupled model (CLM+GW) is first calibrated in Illinois using observed groundwater well data as well as the river flow data. Simulated water storage change is compared to GRACE (Gravity Recovery and Climate Experiment) remote sensing data. In comparison to the CLM, the CLM+GW does a better job simulating baseflow and deeper soil moisture. Introduction Land hydrological processes can record previous atmospheric forcing anomalies, resulting in impacts and interactions in the following season or year. Some previous studies have indicated the importance of surface soil moisture for atmospheric processes [Dirmeyer, 2001; Koster and Suarez, 2001 and 2003; Koster et al., 2004]; however, the effect of progressively deeper soil moisture (PDSM) and groundwater is relatively unknown. Only a few studies have paid attention to deeper soil moisture [Wu et al. 2002; Wu and Dickinson, 2004]. They noted an increase of soil moisture persistence with depth and that deeper soil layers have longer memory in comparison with the surface layer. Therefore, not only can surface soil moisture affect the atmosphere, but PDSM and groundwater might play a more important role due to their longer time scale in comparison with the fast interaction between the atmosphere and surface soil moisture. Hence, in this study we will discuss the effect of GW dynamics and PDSM on hydrological processes via NCAR CLM simulations. First we will focus on the interaction between PDSM and GW. To understand their interactions, we will develop the CLM with GW component (CLM+GW) based on the work by Yeh and Eltahir [2005]. The developed model incorporates a lumped unconfined aquifer model which is interactively coupled to CLM, and has successfully reproduced the observed 22-year (1984-2005) monthly regional hydroclimatology in Illinois. 1 4 Results (1) Total runoff comparison (2) Runoff partitioning (3) Deeper SM and GW recharge FIG. 1. simulated total runoff in comparison with observations, (a) without GW model, (b) with GW. FIG. 2. simulated surface runoff and baseflow, (a) without GW model, (b) with GW. FIG. 3. (a) simulated deeper soil moisture in comparison with observations, (b) GW recharge. Simulation results in Fig. 1 show that the total runoff is overestimated in case A, which is due to excessive baseflow. Case B reproduces the total runoff reasonably well except for some flow peaks. Despite the small difference in the R2 of runoff simulation(case A: 0.64; case B: 0.66), its partitioning into surface and baseflow shows significant differences (Fig. 2). Also the deeper soil moisture simulation improves in case B due to the ability of the CLM+GW to simulate diffusion fluxes from groundwater to soil moisture (Fig. 3). Soil Moisture (SM) and Water Table Depth (WTD) Dataset The data on SM was collected by the Illinois State Water Survey (ISWS). Weekly (March ~ October) and biweekly (November ~ February) measurements were taken at 11 different soil layers with a resolution of about 20 centimeters down to 2 meters below the surface. The WTD data consists of monthly groundwater depth at 18 wells scattered throughout Illinois for monitoring unconfined aquifers. These wells are relatively shallow and the average depth to the water table ranges between 1 to 10 meters below the surface. Atmospheric Forcing Dataset To drive the CLM in an offline simulation, six input atmospheric forcings are required (precipitation, solar radiation, near surface air temperature, air humidity, air pressure, wind speed). The time resolution is 3 hours. Precipitation and temperature are taken from the National Climate Data Center (NCDC) Integrated Surface Hourly dataset. Seventeen NCDC stations located in Illinois are used to derive state-average values by simple averaging. Air humidity, pressure, wind speed, and solar radiation are taken from the 6 hourly NCEP-NCAR R2 to the 3 hourly resolution. 2 Sampling Network in Illinois (4) Water Table (5) Total water storage change The dynamics of the water table can be simulated well although the simulated water table is higher than the observation (Fig. 4). In comparison to the in-situ data and GRACE data, the seasonal pattern and amplitude of both groundwater and soil moisture storage changes can be well reproduced by the CLM+GW (Fig. 5). FIG. 4. (a) simulated water table in comparison to observations; (b) water table dynamics. FIG. 5. (a) simulated total water storage change compared to obs; (b) in comparison to GRACE. Conclusion CLM (as well as most land surface models) tends to overestimate total runoff due to unrealistic baseflow simulation (i.e., assuming drainage equals baseflow). After including the GW model in the CLM, the 22-year (1984-2005) simulation results indicate that the hydrologic fluxes (e.g., total runoff, evaporation) and states (e.g., deeper soil moisture, water table depth, TWSC) agree significantly better with the observations in Illinois. 5 Model description a. Community Land Model In this study, we incorporate the unconfined aquifer model from Yeh and Eltahir [2005] into the NCAR CLM3.0 with a simple TOPMODEL-based surface runoff scheme [Niu et al., 2005] and a modified frozen soil scheme [Niu and Yang, 2006].There are 10 soil layers and the depth is 3.43m in the default CLM. To accurately locate the water table position and capture the sharp moisture gradient near the water table, we extend the soil layers to 50 layers, and after 6th layer, the resolution is 20cm from 0.21m to 9.21m deep. The number of unsaturated layers is dynamic and corresponds to water table depth fluctuations (see schematic). If the water table becomes shallow (deep), the number of unsaturated layers decreases (increases). b. Groundwater Model The unconfined aquifer is represented as a lumped reservoir: where Sy is the specific yield, H is the WTD, Igw is the groundwater recharge which is the flux at the interface between the unsaturated and saturated zone, and Qgw is the groundwater discharge. For each time step (30 min) the groundwater model receives the drainage flux from the overlying soil layer and discharges baseflow into streams based on water table position, which is then updated. The recharge flux can be positive or negative depending on the relative magnitude of the gravity drainage and diffusion fluxes. In the beginning of next time step, the soil moisture profile is updated according to the new water table position. By doing so, the dynamic interactions between the unsaturated and saturated zones can be explicitly represented. 3 Table1: simulated and observed hydrological components Future Work The CLM+GW will be applied to the entire Mississippi River basin to study the importance of GW and soil moisture interactions and to explore the potential of using GRACE-measured water storage to constrain model performance. Groundwater-supported evapotranspiration (ET) may account for a significant portion of total ET in shallow water table areas. The developed CLM+GW model can be coupled to climate models to study these potential groundwater-atmosphere interactions, including the role of PDSM in land memory. Acknowledgments We would like to express our gratitude to Dr. Z. L. Yang and Dr. Niu at the University of Texas at Austin for providing the SIMTOP scheme . This research was supported by NOAA CPPA funding. The authors are grateful to Illinois State Water Survey for providing the hydrological data used here. References Dirmeyer, P. A., 2001: An evaluation of the strength of land–atmosphere coupling. J. Hydrometeor.,2, 329–344. Koster,R. D., and M. J. Suarez, 2001: Soil Moisture Memory in Climate Models, J. Hydrometeor., 2, 558-570. ——, and ——, 2003: Impact of Land Surface Initialization on Seasonal Precipitation and Temperature Prediction, J. Hydrometeor., 4, 408-423. Niu, G.-Y., Z.-L. Yang, R. E. Dickinson, and L. E. Gulden, 2005: A simple TOPMODEL-based runoff parameterization (SIMTOP) for use in GCMs, J. Geophys.Res., 110, D21106, doi: 10.1029/2005JD006111. Niu, G.-Y. and Z.-L. Yang, 2006: Effects of frozen soil on snowmelt runoff and soil water storage at a continental scale, J. Hydrometeor., 7 (5), 937–952. Wu, W., M. A. Geller and R. E. Dickinson, 2002: The Response of Soil Moisture to Long-Term Variability of Precipitation, J. Hydrometeor.,3, 604-613. Wu, W., and R. E. Dickinson, 2004: Time Scales of layered Soil Moisture Memory in the Contest of Land-Atmosphere Interaction. J. Climate, 17, 2752-2764. Yeh, P. J.-F., and E. A. B. Eltahir, 2005: Representation of water table dynamics in a land surface scheme. Part I: Model development. J. Climate.,18, 1861–1880.