1. 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 , University of Washington, Seattle, WA
2. Department of Agronomy, Purdue University, West Lafayette, IN
FWI All-Hands Meeting, Estes Park, Colorado, May 31 – June 2, 2006
Photo:
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 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 ( ) 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 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 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 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
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’
G = Gauged
(R-ArcticNET v3.0)
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Center.
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110.
M = Naturalized
(McClelland et al. 2004)
Streamflow Trend, mm year-2
Q-Simulated Trend