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DHSVM –
Annotated Bibliography By Jeff Phillippe (Geo 565) The bibliography below is a comprehensive listing of recent articles associated with the DHSVM (Distributed Hydrology Soils Vegetation Model) – a GIS based hydrologic model. A review of each article summarizes the main points and contains a brief discussion of the appropriateness of using DHSVM in the Hood River Basin, OR.
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Wigmosta, M.S., Vail, L. W., and Lettenmaier, D.P. A Distributed
Hydrology- Vegetation Model for Complex Terrain. Water
Resources Res., 30, 1665-1679,
1994.
Waichler et al. present the findings of their own DHSVM, established in the early 1990’s. The model is unique in that they developed it for mountainous areas and on a catchement scale. Furthermore, unlike all other models of the time, it incorporates and outputs data on a pixel by pixel basis, with a user-defined resolution and time scale. The DHSVM structure integrates a 2-layer canopy model for evapotranspiration, a doubled rooting zone model, a saturated groundwater flow model, and an energy balance model for snow accumulation and ablation. Meteorologic values of precipitation, air temperature, solar radiation, wind speed, and vapor pressure are assigned to each pixel at a specified height above the overstory.
They ran the model in the Middle Fork Flathead River Basin of NW Montana, calibrating it with 2 years of recorded precipitation and discharge, and verified it using snow coverage and discharge data the following two years. Calibration was met through adjusting the lapse rate, soil thickness, rooting zone, thickness, and saturated hydraulic conductivity. The simulated flows had an r-squared value of 0.95 with measured values. The simulated spatial extent of snow was accurate but had a time lag.
The confirmation and applicability
of DHSVM in this study area has good indications for similar use in the
Bhanumurthy, V., Simhadrira, B., Srinivasarao,
G., Rao, V. V., Raju, P.
V., Siva
Sankar, E., Application of Remote Sensing and GIS in
Water Resources Development, Water and Energy International,60, 38-50, 2003.
Bhanumurthy et al. describe the various applications of GIS and remote sensing (other than DHSVM) to water resources development – in other words, how to use these tools to get the “optimal management” of water supply. Remote sensing/GIS are now being applied to water supply assessment, hydrologic/hydraulic modeling, flood management, irrigation management, sedimentation assessment of reservoirs, and seasonal snowmelt forecasting.
Although not the same as DHSVM,
the team describes a similar GIS-based model SLURP, which was developed at NHRI
in
One observable flaw in the article
is that the authors make no distinction between glacier and snowmelt
periods. It should be noted that glacier
meltwater runoff has a significant lag time after melting whereas snowmelt
runoff does not. This fact has major
consequences for and needs to be considered in the hydrologic modeling of the
Chennault,
J. W., Modeling the contributions of glacial meltwater to streamflow in Thunder
Creek, North Cascades National Park,
Chennault presents an excellent DHSVM analysis of glaciermelt in the North Cascades, WA. All other runs of the model address meteorological and land type issues but this is the first one to incorporate the effects of glaciation on basin runoff. He did so by inputting glacier cells into the vegetation grid. By adjusting the number of glacier cells, Chennault modeled the effect of glacier retreat on total runoff.
Chennault found that in the Thunder Creek Watershed,
glaciermelt contributions varied from 0.6% to 56.6% and the onset of glacier
melt ranged from June 13 to July 26.
Greatest contributions were provided during warm, dry years, when water
stresses were most likely the highest.
This is likely the case for the
Kenward, T., Lettenmaier, D., Wood, E., Fielding,
E. Effects of Digital
Elevation Model Accuracy on Hydrologoic Predictions, Remote Sensing and the
Environment,
74, 432-444, 2000.
In this study, Kenward et al. quantifies the deviation of both spatial accuracy and hydrological modeling output of two DEMs to a reference DEM. They compared a standard USGS 7.5’ DEM and the remotely sensed SIR-C 30 meter DEM to a 5 m-resolution product of low-altitude aerial photography, all from Mahantango Creek. The USGS DEM-generated water basin area matched the reference area and reference elevation points deviated by a mean of only 4.03 m, whereas the SIR-C DEM was 3.6% larger and reference points deviated by 23.9 m.
The DHSVM was applied using all
three DEMs.
The USGS- and SIR-C DEM- simulated annual runoffs were 0.3% and 7%
larger than that of the reference DEM respectively. Additionally, peak runoff was generally lower
for the two test DEMs. In conclusion, Kenwood et al. show that the
accuracy of the input DEM is essential to hydrologic modeling,
and for the DHSVM in particular. Most
likely, it would be more appropriate to use the USGS DEMs
over SIR-C for the use of DHSVM in the
Stork, P., Bowling, L., Wetherbee, P., Lettenmaier, D.
Application of a GIS-based distributed hydrology model for prediction of
forest harvest effects on peak stream flow in the
Stork et al. use the DHSVM in a practical application; they investigate the impact of clear-cutting and road installation on local peak discharges. They provide a thorough summary of the evolution of hydrologic modeling in the second half of the last century and present excellent graphics and explanations of how the DHSVM works.
They credit the availability of DEMs and land data and speedier processing (for the use of GIS) to the development of sophisticated models. Furthermore, the paper suggests useful tips on the use of GIS in the modeling. Basin boundaries are delineated with GIS utilities that track flow directions through a watershed (ie. ARC/INFO FLOW DIRECTION and WATERSHED routines). DHSVM has the power to automatically adjust temperature and humidity values for different elevations. DHSVM automatically assigns porosity, field capacity, wilting point, and vertical hydraulic conductivity to each pixel based on soil type input. The same goes for vegetation type.
A new add-on is road networks,
which will require attributes, and is applicable to the
VanShaar, J.R., I. Haddeland, and D.P. Lettenmaier, Effects of land cover changes on the
hydrologic response of interior Columbia River Basin forested catchments, Hydrol.
Proc., 16, 2499-2520, 2002
VanShaar et al. use the DHSVM to
determine the hydrological effects of different land coverage for several
catchments within the
This study emphasizes the point that setting
up initial variables of interception storage, soil moisture, snow water
equivalent content, and saturation extent requires one year of model run
through. This is a problem for places
such as the
Waichler, S.R., Wemple,
B.C., Wigmosta, M.S. Simulation of water balance and
forest treatment effects at the
Proces., 19, 3177-3199, 2005.
Waichler
et al. assess the effectiveness of the DHSVM in reproducing observed water
balances in several catchments of the H.J.
In general though, the study
confirms the effectiveness of DHSVM and bodes well for the modeling of the
Waichler, S.R., Wigmosta, M.S. Development of Hourly Meteorlogical
Values from Daily Data and Significance to Hydrological Modelling
at H.J. Andrews
Experimental
Since the DHSVM is heavily reliant on an extensive amount of meteorological and hydrologic data (both spatially and temporally), it is often necessary to interpolate missing figures. For instance, one might be running the model on a 2-hour timescale but might only have temperature data for daily minimums and maximums. In this paper, Waichler and Wigmosta outline tested methods for interpolating hourly data, based on model runs in the H.J. Andrews Experimental Forest, OR. Given a data set of daily minimum and maximum air temperatures, precipitation, minimum and maximum relative humidity, and wind speed, one can generate hourly values of air temperature, precipitation, relative humidity, wind speed, and shortwave radiation. Therefore to run the DHSVM one needs a minimum of the aforementioned data. With the exception of wind speed, all sets were found to interpolate (with a selected equation) with a high degree of efficiency.
With the lack of extensive
historical meteorological data in the
Wang, S., Huang, R., Ding, Y., Leung, L.R., Wigmosta,
M. S., and Vail, L.W. Improvements of a
Distributed Hydrology Model DHSVM and its
Climatological-Hydrological
Off-Line Simulational Experiments, Acta Meteorologica Sinica, 16, 374-387.
Written by Chinese climatologists
and published in a European journal, this research represents the international
importance of the DHSVM in the science community. Recognizing the differences in climate,
hydrology, soils, and vegetation characteristics between the Haile-Loache China River basins and the catchments of the
The paper reproduces most of the
methods discussed in Wigmosta et al. (1994). It uses the improved Penman-Monteith (IPM) which is more appropriate to the evaporation
variability in