Evaluation and Improvement of a Macro-Scale Land Surface Hydrology Model for a Stream Flow Trend Attribution Study in the Northern High Latitudes Jennifer C. Adam1, Fengge Su1, Laura C. Bowling2, and Dennis P. Lettenmaier1 Department of Civil and Environmental Engineering, Box 352700, University of Washington, Seattle, WA 98195 2. Department of Agronomy, Purdue University, West Lafayette, IN 47907 FWI All-Hands Meeting, Estes Park, Colorado, May 31 – June 2, 2006 Photo: http://www.globalcarbonproject.org/ Improvement of Frozen Soil Simulations Evaluation of Simulated Streamflow Trends ABSTRACT Combined annual streamflow volumes from the six largest Eurasian basins have increased by approximately 7% between 1936 and 1999. The export of freshwater to the Arctic Ocean plays a key role in both regional and global climates (e.g. via effects on the strength of the North Atlantic Deep Water (NADW) formation that drives the thermohaline circulation), therefore it is important to understand how river discharge to the Arctic Ocean will respond to predicted climatic changes. Incorporation and validation of the key physical processes into the modeling framework is crucial for the accurate prediction of future river discharge rates into the Arctic Ocean (and feedbacks to the climate system) because these processes respond differently to climatic changes. For example, increased precipitation due to an accelerated hydrologic cycle will likely continue to provide additional freshwater to the system, whereas warming-induced melting of permafrost provides freshwater only until the excess ground ice in Arctic permafrost is melted. We report a 70-year (1930-2000) run of the Variable Infiltration Capacity (VIC) macroscale hydrology model over the Eurasian Arctic land domain. We evaluate the ability of the model to reproduce the climatologies and trends of observed and reconstructed streamflow. Whereas the model reasonably captures streamflow trends in regions of seasonally frozen soil, the model underestimates trends in permafrost regions, and we owe this partially to simulated soil temperatures that are too cold. We focus initial improvement of the streamflow simulations on the parameterization of the finite-difference frozen soils model. In particular, we use a zero-flux bottom boundary instead of a constant temperature bottom boundary, which allows the model to solve for the bottom boundary temperature; we provide an observation-based initialization of the bottom boundary temperature; and we apply differing bottom boundary depths and node distributions. Annual Soil Temperature @ 3.2 m, C Cherkauer et al. (1999) finite difference algorithm solving of thermal fluxes through soil column infiltration/runoff response adjusted to account for effects of soil ice content parameterization for frost spatial distribution tracks multiple freeze/thaw layers can use either “no flux” or “constant temperature” bottom boundary Development #1: Bottom Boundary Initialization (Soil Temperature) Calculate long-term annual average soil temperature at 3.2m depth (near annual damping depth) from Frauenfield et al. (2004) station data – fig. A Interpolate to the 100 km EASE grid (with lapsing of temperature with elevation) – fig. B Spin-up model for 60 years with forcings that are held at 1930’s climatology Check for drift Spatial Resolution: 100km by 100km EASE (Brodzik 1997) Temporal Resolution: 3-hourly 1930 to 2000 VIC Features: Two-layer energy balance snow model Frozen soil/permafrost algorithm Lakes and wetlands model Blowing snow algorithm A) Frauenfield et al. 2004 station data Calibration: Su et al. (2005) Trends Evaluation: (see below) permafrost regions: underestimated seasonally frozen soil: ~captured Current Set-Up: (Su et al. 2005) constant T bottom boundary – damping depth of 4m, Tb defined as annual average air temperature, 15 nodes utilized spatial frost turned on B) Interpolated station data Lena Yenisey Ob’ Soil Temperature, °C 1930 1931 1932 A B C Development #2: NOFLUX Bottom Boundary Use NOFLUX bottom boundary (fig. B) versus constant temperature bottom boundary (fig. A). Model solves for Tb. Use boundary depth equal to 3 x annual damping depth (15m) and initialize at more realistic soil temperature (-3 °C)– fig C. Constant T BB Dp = 4m Tb_init = -12 °C NOFLUX BB Dp = 4m Tb_init = -12 °C NOFLUX BB Dp = 15m Tb_init = -3 °C Stream Flow (103 m3/s) Lena Yenisey Ob’ Observed (R-ArcticNet v3.0) STATEMENT OF PURPOSE: To evaluate and improve the performance of a land surface hydrology model to predict long-term trends in streamflow from the Eurasian Arctic land areas. Naturalized (McClelland et al. 2004) Simulated Study Domain Month Soil Surface (Top Boundary) Soil Bottom Boundary We focus on Northern Eurasian basins (stream flow has been shown to be increasing and longer records exist for these basins). We chose three primary and nine secondary smaller and sub-basins with varying extents of permafrost. ~400 periods between 1936 and 2000 were tested for 99% significance using the seasonal Mann-Kendall test (Hirsch et al. 1982) for gauged and reconstructed streamflow. Trend slopes were calculated for the periods for which observed streamflow trends passed 99% significance: observed (gauged and reconstructed) (left panel) and simulated (right panel). Development #3: Node Distribution Increase number of nodes (e.g. 18 in this example) Use exponential node distribution to capture greater variability in surface layers (fig. B), versus linear node distribution (fig. A). For a cell in discontinuous permafrost, linear distribution predicts seasonally frozen soil, while exponential distribution predicts permafrost. 1 Observed Trends Simulated Trends 2 3 Lena 4 5 6 Lena Depth, m A) Linear Node Distribution 7 Yenisey 1 2 3 Q-Observed Trend 4 Ob’ 5 6 B) Exponential Node Distribution 7 1930 1931 1932 1933 1934 1935 1936 1937 Continuous Permafrost Discontinuous Permafrost Sporadic Permafrost Isolated Permafrost Seasonally Frozen Soil Temperature, °C Yenisey CONCLUDING REMARKS We are able to simulate streamflow climatology fairly well for all primary basins, but streamflow trend is only reasonably captured for the basins outside permafrost regions (e.g. Ob’). In permafrost regions, whereas observed trends show a large spread in trends with period, simulated trends are nearly always negligible for all periods. This indicates that soil moisture dynamics are not simulated reasonably in permafrost regions, partially due to simulated soil temperatures that are too cold. We focus improvements on the frozen soils model, adjusting the bottom boundary parameterization, initialization, and depth; as well as number and distribution of nodes. We plan to diagnose further problems using observed ground data, such as soil temperature and soil moisture. Primary Study Basins Secondary Study Basins Primary Basins Permafrost Extent (Brown et al. 1998) Area (106 km2) All Types Cont. Discont. Sporadic Isolated Lena 2.43 100% 80% 11% 6% 3% Yenisey 2.44 89% 33% 12% 18% 26% Ob’ 2.95 27% 2% 4% 9% Ob’ 1940 1960 1980 2000 1940 1960 1980 2000 G = Gauged (R-ArcticNET v3.0) Brodzik, M.J. 1997. EASE-Grid: A versatile set of equal-area projections and grids. Boulder, CO, USA: National Snow and Ice Data Center. Brown, J., O.J. Ferrians Jr., J.A. Heginbottom, and E.S. Melnikov. 1998. Circum-Arctic Map of Permafrost and Ground-Ice Conditions. Boulder, CO: National Snow and Ice Data Center/World Data Center for Glaciology. Digital Media. Cherkauer, K. A. and D. P. Lettenmaier, Hydrologic effects of frozen soils in the upper Mississippi River basin, J. Geophys. Res., 104(D16), 19599-19610, 10.1029/1999JD900337, 1999. Hirsch, R.M., J.R. Slack, and R.A. Smith, 1982, Techniques of trend analysis for monthly water quality data, Water Resources Research 18, 107-121. Frauenfeld, O. W., T. Zhang, R. G. Barry, and D. Gilichinsky, 2004: Interdecadal changes in seasonal freeze and thaw depths in Russia. J. Geophys. Res., 109, D05101, doi:10.1029/2003JD004245. Liang, X., D. P. Lettenmaier, E. F. Wood, and S. J. Burges, A Simple hydrologically Based Model of Land Surface Water and Energy Fluxes for GSMs, J. Geophys. Res., 99(D7), 14,415-14,428, 1994. McClelland, J.W., R. M. Holmes, and B. J. Peterson, 2004, Increasing river discharge in the Eurasian Arctic: Consideration of dams, permafrost thaw, and fires as potential agents of change, J. Geophys. Res., 109. Su, F., J.C. Adam, L.C. Bowling, and D.P. Lettenmaier, 2005, Streamflow Simulations of the Terrestrial Arctic Domain , J. Geophys. Res., 110. M = Naturalized (McClelland et al. 2004) Streamflow Trend, mm year-2 Q-Simulated Trend