WATER RESOURCES RESEARCH, VOL. 37, NO. 5, PAGES 1313–1326, MAY 2001
On evaluating the spatial-temporal variation of soil moisture in the Susquehanna River Basin Zhongbo Yu Department of Geoscience, University of Nevada, Las Vegas, Nevada
T. N. Carlson and E. J. Barron Earth System Science Center and Earth and Mineral Sciences Environment Institute Pennsylvania State University, University Park, Pennsylvania
F. W. Schwartz Department of Geological Sciences, The Ohio State University, Columbus, Ohio
Abstract. The Hydrologic Model System (HMS), a physically based distributed model, was used to simulate the soil moisture variation during a storm event in the Upper West Branch, a subbasin of the Susquehanna River Basin. The model was calibrated by comparing the simulated temporal daily variation in soil moisture with field data. Data from the Mahantango Watershed within the Susquehanna River Basin were used to drive the HMS for the temporal simulation of soil moisture as a function of soil texture, vegetation, and topography. Spatially distributed data sets with a resolution of 1 km were prepared for the simulations and used to examine the soil moisture variation as caused by the spatial variability in soil texture, vegetation type, and topography. The Pennsylvania State–National Center for Atmospheric Research Mesoscale Meteorological Model (MM5) coupled with the HMS was used to simulate the basin response of hydrologic processes to a storm event. The effect of infiltration on the soil moisture along the flow pathway in the river basin and the overall hydrologic response with observed precipitation compare well with observations. The simulated hydrologic response with the MM5simulated precipitation slightly underestimates the actual response.
1.
Introduction
Understanding the spatial and temporal variations of soil moisture is crucial to the parameterization of soil moisture characteristics for the land surface components in the atmospheric and hydrologic models. It is well known that soil moisture variations in time and space are controlled by many factors, such as soil texture, vegetation, and topography. Soil moisture affects the partitioning of incoming solar radiation into sensible heat flux and latent heat flux and the partitioning of incoming rainfall into surface runoff and subsurface infiltration. Thus soil moisture is one of the key parameters governing interactions among atmosphere, land surface, and groundwater. Unfortunately, the spatial and temporal variation in soil moisture cannot be easily observed at large scales. Soil moisture at large scales can be estimated by remote sensing and hydrologic modeling. Remote sensing methods provide spatial coverage of some variables useful for hydrologic models, although they do not provide a column-average soil moisture [Capehart and Carlson, 1997]. With appropriate input data, however, hydrologic models can provide a calculation of the spatial distribution of soil moisture for large-scale meteorological and hydrologic applications. The Hydrologic Model System (HMS) used in this study was extended to link to general circulation models (GCMs) with Copyright 2001 by the American Geophysical Union. Paper number 2000WR900369. 0043-1397/01/2000WR900369$09.00
mesoscale meteorological models (MMs). The resulting modeling system has the capability of simulating the large-scale hydrologic response (e.g., soil moisture, groundwater level, and streamflow) to atmospheric forcing in both the vertical and horizontal directions [Yu et al., 1999]. The development of HMS is part of the ongoing Susquehanna River Basin Experiment (SRBEX), which is an interdisciplinary science team project supported by the NASA Earth Observing System. Long-range goals of SRBEX include simulating the response of the Susquehanna River Basin to single-storm events and to sequences of storms, as well as to the annual cycle, to the El Nino–Southern Oscillation forcing, and to increased atmospheric greenhouse gas concentrations [Yu et al., 1999]. This particular study is concerned with elucidating factors and processes contributing to soil moisture variation in time and space and involves two subbasins of the Susquehanna River Basin, the Upper West Branch and the Mahantango. It involves three separate components. The entire watershed was treated as a whole unit, and HMS was first used to evaluate the influence of soil texture, vegetation type, and topography on the temporal variation of soil moisture in the vertical direction in the Mahantango. Second, a synthetic storm with soil data and vegetation information is generated in order to drive the hydrologic simulations in the Upper West Branch. From these results we examine how the spatial distribution of soil moisture is affected by the spatial variability of soil texture, vegetation type, and fractional vegetation cover. Third, the response of soil moisture and streamflow to a single-storm event simulated by a MM in the Upper West Branch is tested by linking HMS
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to the Pennsylvania State–National Center for Atmospheric Research Mesoscale Meteorological Model (also known as MM5 v1 and referred to as MM5 for brevity [Dudhia, 1993; Grell et al., 1995]).
2.
Hydrologic Model System (HMS)
The HMS [Yu and Schwartz, 1998; Yu et al., 1999] constitutes the main investigative tool in this study. This model system is a distributed mathematical representation of the surface and subsurface hydrologic processes that have a key role in determining the impacts of basin changes such as future climate and land cover changes. The HMS includes four modules, a Soil Hydrologic Model (SHM), a Terrestrial Hydrologic Model (THM), a Groundwater Hydrologic Model (GHM), and a Channel Groundwater Interaction Model (CGI) [Yu et al., 1999; Yu, 2000]. To the extent possible, the HMS directly incorporates spatial input derived from remotely sensed and archived digital information, such as from a digital elevation model (DEM), soil type and texture, vegetation fraction, and other hydrologic parameters. The hydrologic parameters that are required in HMS have a physical basis and can be related to specific hydrologic processes. The ultimate goal in performing simulations with HMS is to study the hydrologic response to environmental and land use/land cover changes, as described by historical records, and to anticipate these responses to likely future changes. 2.1.
Hydrologic Processes
Runoff in HMS is simulated as infiltration– excess runoff [Horton, 1933] and saturation– excess runoff [Dunne and Black, 1970]. Before the incoming precipitation is available for calculating infiltration and runoff, part of it is partitioned into the vegetation leaf or canopy interception which is directly proportional to the fractional vegetation cover, leaf water-holding capacity, and leaf area index. The actual rainfall-runoff partitioning in HMS incorporates a Green and Ampt infiltration model to simulate the generation of infiltration and excess runoff. Infiltration and evaporation are treated as sources and sinks in the Richards’ equation rather than incorporated into the upper boundary condition. The soil hydrology model component (SHM) distributes infiltration over the soil layers (top 10 cm) through a weighting function. The model involves a numerical solution of the one-dimensional (vertical) Richards’ equation, which describes the vertical flow of soil moisture in the soil matrix [Capehart and Carlson, 1994]. In SHM the Penman-Monteith equation [Monteith, 1981] is used to calculate evaporation over the bare soil (extracting from the top 10 cm) and evapotranspiration over the vegetation canopy in terms of a soil moisture availability parameter (expressed in terms of volumetric water content) weighted according to the fractional vegetation cover [Kustas et al., 1993]. Evaporation from the near-surface soil layers is determined using a weighting function, whose value decreases with distance from the surface. Transpiration is assumed to originate with water in a root zone, whose depth depends on the plant height. Transpiration from each soil layer is an inversely weighted function of the distance from the surface. A small fraction of the infiltration is considered as the macropore flow into the groundwater flow system. Adding this flow component was necessary to provide a “fast flow” component, which appears to be evident in observed hydraulic head and groundwater base flow [Pionke et al., 1988; Yu et al.,
1999]. We found that analytical modeling schemes that did not include this component [e.g., Abramopoulos et al., 1988] inadequately accounted for the rise and fall of the water table. The groundwater hydrology model component (GHM) receives the spatially distributed groundwater recharge and simulates the spatial and temporal changes in the water table configuration. The terrestrial hydrology model component (THM) uses an algorithm that accounts for the downslope flow direction, the overland flow, and the channel flow. On the basis of the DEM each grid cell is classified as either an overland cell or a stream cell. Within each stream cell the channel width is linearly interpolated from widths at the watershed outlet and upstream tributary, and the rest of area within a stream cell is treated as overland area. Assuming a linear flow surface across a grid cell, the THM is formulated with the kinematic wave equation to simulate the inflow, outflow, and storage for each grid cell. A channel routing algorithm is used in stream grid cells to route water through the DEM-derived channel network to the basin outlet. By applying overland flow and channel flow routings to each grid cell within the simulated domain for each time step and by coupling these flows with the rainfallrunoff generation schemes, infiltration can be determined along the flow path. The groundwater level in GHM and the river stage in THM for each stream cell are used in the CGI to compute the flow between the channel and groundwater in the stream cells. Output from the model includes the spatially distributed soil moisture, runoff depth, infiltration, subsurface drainage, groundwater level, and channel groundwater flux at intervals during the storm period, as well as the composite hydrograph at the basin outlet. The composite hydrograph consists of surface runoff, saturated return flow, and base flow. 2.2.
Subgrid Spatial Variability
Theoretical, numerical, and observational studies [Andre et al., 1986; Entekhabi and Eagleson, 1989; Pitman et al., 1990] indicate a need to incorporate subgrid-scale spatial variability in climate models and distributed hydrologic models of terrestrial hydrologic processes [Beven, 1989]. Hydrologic processes depend on the spatial variability in precipitation and soil hydraulic conditions. The simulated precipitation in climate models, however, represents an average over the grid cell. This spatial averaging over dimensions of the order of 10 –100 km (even larger in a GCM) results in average rainfall intensities that are significantly lower than the actual peak intensities. This is one cause of the underestimation of surface runoff and other variables [Yu et al., 1999]. This problem has been addressed by using “effective values” of soil hydraulic parameters [Rawls and Brakensiek, 1985; Grayson et al., 1992]. However, Beven [1989] questioned the physical basis for such practice. An additional cause of underestimates in runoff is the gridscale homogeneity in soil hydraulic parameters (i.e., saturated hydraulic conductivity). Simulations capable of incorporating heterogeneity in hydraulic conductivity would improve the fit between the simulated results and observed data. Wood et al. [1988] defined a “representative elementary area” (REA) for the large-scale distributed hydrologic modeling. A statistical distribution can be used to represent the spatial heterogeneity because the heterogeneity becomes less important when the scale is greater than the REA. As a first step in representing variability in precipitation and hydraulic parameters, the following probability distribution is implemented in HMS to distribute the average value among subgrid cells within a large grid cell [Yu, 2000]:
YU ET AL.: EVALUATING THE SPATIAL-TEMPORAL VARIATION OF SOIL MOISTURE
f共 p t兲 ⫽
冉 冊 冕
c cpi , exp ⫺ P P
3.1.
⬁
f共 pi 兲 dpi ⫽ 1,
(1)
0
where f( p i ) is the fraction of a grid cell with precipitation p i , P is the grid cell average value of simulated precipitation or hydraulic parameter, and c is conversion coefficient. Coefficient c may range from 0.3 for convective precipitation to 1.0 for large-scale precipitation [Entekhabi and Eagleson, 1989; Thomas and Henderson-Sellers, 1991]. A value of 1.0 is used for coefficient c in this study for precipitation and hydraulic conductivity. This approach for representing the distribution of hydraulic conductivity may affect runoff timing because the typical saturated hydraulic conductivity is distributed lognormally. 2.3.
Soil Moisture
The schemes of Clapp and Hornberger [1978], van Genuchten [1980], and Cosby et al. [1984] for evaluating hydraulic properties in the soil zone are implemented in the SHM. The hydraulic conductivity and soil water matric potential in all three schemes are expressed as functions of the normalized volumetric water content (S e ), which is defined as Se ⫽
⫺ r , s ⫺ r
(2)
where is the volumetric water content, s is the soil water content at saturation, and r is the residual soil water content, which is often taken to be zero.
HMS Data and Parameters
This study involves two subbasins of the Susquehanna River Basin, the Upper West Branch and the Mahantango (Plate 1). The Upper West Branch has an area of 14,710 km2, and Mahantango has an area of 440 km2. Most of the Upper West Branch lies within the Appalachian Plateau physiographic province, which is topographically characterized by flat upland areas that are deeply dissected by numerous steep-walled, narrow valleys. The southern portion of the Upper West Branch and the Mahantango lies within the Valley Ridge province of the Appalachian Plateau, which consists of long, low, evencrested ridges averaging approximate 400 –500 m in elevation. The topography is controlled by a succession of narrow, steepsided ridges and valleys, trending northeast-southwest. The Mahantango is an experimental watershed managed by the US Department of Agriculture (USDA) Agricultural Research Service. A major part of the work in the distributed hydrologic modeling involves image processing and integrating its products into the HMS. Our intent was to make full use of both groundbased and remotely sensed information. Routinely available several times a year over all points in the continental United States, multispectral satellite imagery has the capacity to detect changes in land use and land cover at a scale that can be used for both spatial and temporal comparisons and for regional hydrologic modeling of important hydrologic processes. Two sources of remotely sensed data were used, those from the National Oceanic and Atmospheric Administration (NOAA) advanced very high resolution radiometer (AVHRR) and from Landsat.
AVHRR
Three independent methods [Choudhury et al., 1994; Gillies and Carlson, 1995; Carlson and Ripley, 1997] have substantiated a relationship between normalized difference vegetation index (NDVI) and fractional vegetation cover (Fr), which can be expressed as Fr ⫽ N*2 ,
(3)
where N* is a scaled NDVI defined as N* ⫽
共NDVI ⫺ NDVIo 兲 , 共NDVIs ⫺ NDVIo 兲
(4)
where the subscripts s and o denote the values for dense vegetation and bare soil, respectively. Thus, in an image with a full scope of vegetation cover, N* and Fr will range from zero to one. The U.S. Geological Survey (USGS) provides a biweekly composite of NDVI products in near real time at 1-km resolution [Eidenshink, 1992]. The NDVI sets have been used to create fields of fractional vegetation cover using the method described by Gillies et al. [1997]. Six NOAA AVHRR (1-km resolution) images taken during several months in 1990 over the study area were used to derive the fractional vegetation cover for the temporal simulation over the Mahantango (section 5.1). An image from the second week of April was processed to derive the fractional vegetation cover for the distributed modeling in the Upper West Branch (sections 5.2, 5.3, and 5.4). Values of fractional vegetation cover over the basin range from 0.0 to 0.7 and were kept constant during 8-day storm simulation (Plate 2). 3.2.
3.
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Landsat Transverse Magnetic (TM) Images
Landsat TM images covering the study area were corrected and georeferenced to a common universal transverse Mercator map base. TM images have been classified according to several basic land surface types [Anderson et al., 1976]. Land use–land cover images were classified [Carlson and Arthur, 2000] and compiled in a 1-km resolution for use in HMS. Deciduous forest and agricultural land uses are two major vegetation types over two subbasins (Plate 2). Deciduous forest is the major land use in the Upper West Branch, while the Mahantango Watershed is mostly covered by corn and meadow. 3.3.
Geographic Information System
Long-term historic meteorological records (i.e., precipitation, temperature, and solar radiation) for various timescales (ranging from an hour to 1 day) within the basin have been compiled from USGS, NOAA, and the National Climate Data Center (NCDC). DEM data for the Susquehanna River Basin have been set in a mosaic from the USGS 3-arc-sec data set (Plate 1). DEMs have been used in hydrologic simulation [Beven and Kirkby, 1979; Moore and Grayson, 1991; Yu and Schwartz, 1998] and delineation of stream patterns in a watershed [Band, 1986; Jenson, 1993]. For this study the DEM was generalized to the same grid system as the precipitation and the other data sets (a 1-km grid spacing). The DEM constitutes the basis in HMS for determining the drainage area, the surface water flow directions, the aspect ratio, the basin boundary, and the stream network at this scale. The DEM is adequate to describe the catchment features and to delineate the perennial channel network which play a significant role in determining flow paths and surface runoff production and simulating interaction between the surface water and the groundwater [Yu et
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Plate 1. Location map of study areas.
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al., 1999]. Quinn et al. [1991], Zhang and Montgomery [1994], and Wolock and Price [1994] indicated that the DEM grid spacing affects the calculations of DEM-derived attributes and hydrologic simulations. Rainfall-runoff partitioning in HMS requires spatially distributed information on land use–land cover, soil type, topography, and hydraulic properties in order to compute the spatially averaged actual infiltration for each grid cell. We have compiled the State Soil Geographic (STATSGO) soils data (compiled by the Natural Resources Conservation Service, USDA) with land cover data (derived by the EROS Data Center from AVHRR satellite imagery) (Plate 2). The hydraulic parameters (i.e., the average saturated hydraulic conductivity and the average capillary suction) maps were derived by assigning an average value for each 1-km grid cell based on the parameterization scheme of hydraulic variables and the soil texture from STATSGO soil data.
4.
Plate 2. Spatial distribution of (a) soil types silty loam (brown (4)), sandy loam (red (7)), and clay or clay loam (green (13)); (b) vegetation types agricultural (purple (1)), deciduous (blue (4)), and evergreen (red (19)); and (c) fractional vegetation cover (percent).
Model Calibration
A rigorous calibration of various hydrologic components was undertaken within HMS. Field observations of soil moisture and streamflow were used as calibration targets. The streamflow was separated into components of surface runoff and base flow through an empirical procedure of hydrograph separation [Yu and Schwartz, 1999]. Objective functions for a transient simulation are used to minimize the difference between the simulated results and observed or estimated data (e.g., soil moisture, base flow, and streamflow). The model calibration consists of two stages, a balancing period and a transient period. The balancing period allows various hydrologic models to “forget” the initial conditions, because they are not known, and avoids that any arbitrary initial conditions affect the early part of simulated results. This stage allows various hydrologic components to reach an equilibrium driven by the average meteorological forcing (e.g., precipitation and temperature) and serves as a model calibration in a steady state. In the transient period the actual observed meteorological data are used to drive the model. The estimated parameters obtained from the balancing period is further optimized by minimizing the difference between the simulated results and observed data. The simulation of soil moisture in the upper 40 cm compares well with measurements from a corn field using gypsum blocks located in the southern part of the Upper West Branch [Capehart and Carlson, 1994; Yu et al., 1998]. A short-term calibration of soil moisture across a layer 15 cm thick over a corn field for a period from July 8 to July 17, 1990, in the Mahantango is also presented as a function of three schemes of calculating hydraulic conductivity and matric potential from saturated hydraulic conductivity and soil moisture content (Figure 1). There is little difference in simulated soil moisture among three schemes. The scheme of Cosby et al. [1984] for calculating hydraulic parameters is used for the simulation. Smith et al. [1994] describe a data set that includes humidity, wind speed, cloud cover, temperature, and precipitation. Measured plant height, leaf area index, and wind speed were used in the evapotranspiration simulation, while the seasonal fractional vegetation cover was inferred from discontinuous measurements for evapotranspiration calculation. The measured incoming radiation was used to calibrate the model calculation but was not used in the simulation because of the discontinuity of the field measurement. Carlson et al. [1995] describe soil and vegetation data in detail for this period. In general, all three schemes
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Plate 3. The simulated spatial distribution of S e over the top 10-cm soil layer at time (a) 6, (b) 12, and (c) 60 hours with uniform soil texture and vegetation type.
Plate 4. The simulated spatial distribution of S e with real soil texture and vegetation type data sets over the top 10-cm soil layer at time (a) 6, (b) 12, and (c) 82 hours.
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Branch to a storm, described by the observed and MM5simulated precipitation fields, is presented in section 5.3. 5.1.
Figure 1. The simulated temporal variation in soil moisture over the top 15-cm soil layer for the three parameterization schemes (solid circles indicating neutron probe measurements for an average depth of 15 cm). perform well in terms of comparisons between simulations and neutron probe measurements. The simulation of soil moisture by Smith et al. [1994] over this period pertains to the same period covered by the NASA Mutisensor Airborne Campaign (MAC-HYDRO) in the Mahantango, which lies within the Susquehanna River Basin. The vegetation types used in these simulations include a mix of cropland, deciduous broadleaf, and mixed forest. Characteristics of the three vegetation types such as height, albedo, roughness, stomatal resistance, and leaf area index for the simulation period are provided by Capehart and Carlson [1994]. Simulated soil moisture over the 4-, 12-, 36-km grid cells for the top 15-cm and 30-cm soil layer compares well with the average of all the field measurements taken with neutron probe and electromagnetic devices within grid cells during the MAC-HYDRO experiment. The values of hydraulic conductivity and storativity in the groundwater system were optimized by comparing the overall simulated base flow with estimated base flow through a hydrograph separation of observed streamflow in the Upper West Branch. The simulated base flow in the calibration process compares well with that estimated for the entire basin [Yu et al., 1998]. The simulated streamflow also reproduces the observed streamflow at the basin outlet for various storms in terms of peak flow values and timing [Yu et al., 1999]. The simulated water balance matches well with the actual balance, although discrepancies between the simulated and observed streamflows exist (probably because of an error in observed precipitation). The surface water timing parameters used in the overland flow simulation (such as surface roughness) and in the simulation of channel flow (such as the geometry weighting factor) were calibrated in a small watershed within the Mahantango and were kept unchanged in this study. The model calibration is described in detail by Yu et al. [1999].
5.
Temporal Variation in Soil Moisture
In order to evaluate the variation in soil moisture affected by soil texture, vegetation, and topography, various artificial environmental conditions were imposed on the soil moisture simulation with the data collected for the period of March 1 to July 18, 1990, in the Mahantango Watershed. The Mahantango Watershed was treated as a whole unit. The one-dimensional simulations were begun assuming an initial constant distribution of soil moisture. Each simulation used an hourly time step and 2-cm vertical discretization in the unsaturated zone. The groundwater table was set at 2 m below the surface. When the water table rises, the discretized soil layers under the water level are forced to become saturated. A simulation with three generic soil textures was driven by the observed meteorological data. The temporal variation in soil moisture within the uppermost 10 and 30 cm of the soil is shown in Figure 2 for three soil textures. In general, the calculated soil moistures for the various soil textures closely resemble each other and appear controlled by the meteorological forcing (e.g., temperature and precipitation). The largest fluctuation in S e within the top 10-cm soil layer occurred in the loamy sand, and the smallest occurred in the silty clay loam (Figure 2a). As expected, the highest S e in the top 30-cm soil layer was evident with the silty clay loam. The loamy sand had the lowest S e (Figure 2b). Differences in soil moisture over the
Results
This section on results begins with a detailed look at the temporal variation of soil moisture in the Mahantango as influenced by soil texture, vegetation type, and topography. The spatial variation of soil moisture in the Upper West Branch is examined by the simulations with uniform soil texture and vegetation and actual distribution of soil texture and vegetation. The simulated hydrologic response in the Upper West
Figure 2. The simulated temporal variation of S e over the (a) top 10-cm and (b) 30-cm soil layers for three types of soil texture.
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top 10-cm soil layer for three soil types are small and increase with depth. This result indicates that the influence of meteorological forcing is smoothed gradually with depth, and the influence of soil texture increases with depth. The observed meteorological record for the period of March 1 to July 18, 1990, was used to drive simulations of four generic vegetation types: agricultural, deciduous, evergreen, and grassland in a silty loam (K s ⫽ 7.0 ⫻ 10 ⫺6 m/s with an order of magnitude variance). The seasonal fluctuations in S e , which are imposed by the rainfall events, are similar but with different amplitudes (Figure 3). The differences among four vegetation types are significant within the top 10-cm soil layer and are magnified in the summer. Again, the differences become less masked with depth (not shown). The mixed agricultural land use provides the lowest soil moisture among four vegetation types, while the evergreen land use provides the highest. The reason for this is that the evergreen land use keeps more water in soil and vegetation than the agricultural land use does, and the agricultural land use produces more runoff and tends to lose infiltrated water to the groundwater system and evapotranspiration quickly. The lowest soil moistures are simulated in June with the agricultural land use category. Seasonal variations in soil moisture for the agricultural and deciduous land uses are similar during the winter and early spring. In contrast, temporal trends of soil moisture for the deciduous and evergreen land uses, though almost identical during the period of mid May to later July, differ significantly during the winter and early spring. The next set of simulations evaluated how the topography affects soil moisture variation; slope angles are varied in a series of simulations over the same period. The simulated S e with various slopes was compared to the simulation with zero slope. Shown in Figure 4 are the differences in S e between the simulation with zero slope and the simulation with various slopes for slopes of 5⬚, 15⬚, 25⬚, and 35⬚. The differences increase as the slope increases, although they are small. The topography also exerts other controls on the soil moisture such as on the overland flow path and infiltration along the flow path (discussed in section 5.2). 5.2.
Spatial Variation in Soil Moisture
This section examines the effect of topography, soil texture, and vegetation type on the spatial distribution of soil moisture.
Figure 3. The simulated temporal variation of S e over the top 10-cm soil layer for four vegetation types.
Figure 4. The simulated temporal variation of S e over the top 10-cm soil layer for four different land surface slopes.
To do this, we first present the simulation with a hypothetical set of conditions. In this experiment a single soil texture (silty loam) and a single vegetation type (deciduous) along with the actual topography are applied to the entire Upper West Branch to examine how the topography affects the overland flow and the spatial distribution of soil moisture. Details of surface and subsurface parameters are given by Yu et al. [1999]. The Upper West Branch is discretized by a grid of 190 ⫻ 170 grid blocks, with a grid spacing of 1 km. The hourly time steps are used for this simulation. The reasons for selecting 1-km2 grid blocks are that most of the data sets are available at this resolution and 1-km resolution is of the same order of REA described by Wood et al. [1988]. Our study indicates that a 1-km DEM is adequate to delineate catchment features and perennial channel network in this area. This simulation started with an initial constant vertical profile of soil moisture, and a precipitation rate of 8 mm/h was uniformly distributed over the basin for the first 6 hours. Plate 3 shows the distribution of S e at 6, 12, and 60 hours after the simulation with deciduous vegetation and silty loam soil starts over the top 10-cm soil layer. At the end of the rainfall event the distribution of soil moisture is quite uniform over the entire basin (Plate 3a). Overland flow is routed to streams during and hours after the rainfall event based on the flow direction determined from the DEM. Most of the cells associated with valleys have higher soil moisture contents that are reflected in the spatial distribution of S e (Plate 3b). As the simulation progressed, reinfiltration along the overland flow pathway increasingly differentiated the soil moisture between valley and ridge cells (Plate 3c). Simulated soil moistures at 24 hours on grid cells, which have different contributing areas, along a section across the basin are summarized in Figure 5 as a function of contributing area. The high soil moisture content in the valley cell is not a linear function of the contributing area in a large watershed other than a small catchment. When the contributing area in a valley cell is beyond a critical (or threshold) value, such a valley cell with larger contributing area may not necessarily have a higher soil moisture content than the valley cell with a critical contributing area (Figure 5). Thus a high soil moisture content in a valley cell is more dependent on
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basin (Plate 4c). These may be due to the effect of vegetation type (the mixed agricultural land use). 5.3.
Figure 5. Simulated soil moisture versus contributing area.
the length of the hillslope and the flow path rather than the contributing area itself. The simulation with the actual soil texture, vegetation type, and fractional vegetation data (Plate 2) was conducted to evaluate the effect of soil texture and vegetation type on the spatial distribution of soil moisture for this basin. Fractional vegetation data were derived from USGS biweekly composites of NDVI for the second half of April. Other parameters, including the rainfall rate, are same as the previous experiment. The spatial distribution of S e over the top 10-cm soil layer at times of 6, 12, and 82 hours is presented in Plate 4. The effect of soil texture on the spatial distribution of S e is evident just at the end of the rainfall event (Plate 4a). The grid cells associated with soil textures of loam and sandy loam have relatively lower soil moisture contents compared to silty loam and clay soils. The combined effect of topography and soil texture can be observed in the spatial distribution of S e 12 hours after the simulation starts (Plate 4b). Most of valley cells have relatively high soil moisture contents except those cells associated with loam and sandy loam soils. Most of the stream networks correspond to cells with a high soil moisture content except areas with variation in the soil type. The grid cells associated with clay and clay loam soils are located in two elongated areas in the northwest part of the basin. These soils show relatively high soil moisture contents compared to the other cells. The distribution of S e in space strongly correlates with the distribution of fractional vegetation data at 82 hours, suggesting the great importance of vegetation fraction. The mixed effect of topography, soil, and vegetation on simulated soil moisture can be seen in Plate 4c. The results indicate that the evapotranspiration calculation and infiltration are highly sensitive to the fractional vegetation cover for each grid cell because the rainwater intercepted by vegetation is a function of the fractional vegetation cover. The higher fractional vegetation cover indicates that its roots will be able to extract more moisture from deeper zones. More vegetation cover should reduce the amount of bare soil evaporation directly from the top 10-cm soil layer and distribute the transpiration uptake of soil moisture over a greater depth of soil. The result is that the upper 10-cm soil layer is not preferentially depleted. Because of this relationship the simulations produced a relatively high soil moisture in the lower (or east) part of the basin, which corresponds with an area of high fractional vegetation cover (lower overall runoff). The relatively lower soil moisture in the upper (or west) part of the basin corresponds to a low fractional vegetation cover. A few strips of lower soil moisture are found in the lower part of the
Hydrologic Response to a Storm Event
This section describes the simulated hydrologic response of the Upper West Branch Watershed to a storm event in April 1986. The simulation is run with a global time step of 1 hour, which corresponds to the available hourly precipitation collected by NCDC. A 10-min time step is used in the submodels for the overland and channel flow routing. The storm description and analyzed observed precipitation are provided by Lakhtakia et al. [1998] and Yu et al. [1999]. Except for the precipitation data used to drive the simulation, the values of all the variables and parameters (e.g., vegetation and soil) at each grid cell are assumed to be constant in time. The 200-hour simulation was begun at 1200 UTC on April 14, 1986. A balancing period and a transient period are provided before the actual time period of interest to allow various submodels to “forget” the arbitrary initial conditions (e.g., soil moisture and groundwater level) and to reach a balanced equilibrium condition among various surface and subsurface hydrologic components [Yu and Schwartz, 1999; Yu et al., 1999]. In the 150-day balancing period an average external forcing (e.g., precipitation and groundwater recharge) is used to drive the HMS. The simulation in the transient period is driven by the observed meteorological record and provides the initial conditions for the simulation of hydrologic response to a storm event. The simulated and observed streamflows along with observed precipitation data are shown in Figure 6. In general, the simulated streamflow compares well with that observed at the basin outlet. The simulated results are superior to the previous simulations reported by Yu et al. [1999] chiefly because subgrid-scale spatial variability is implemented in the model. A threshold in the Green and Ampt infiltration model originally implemented in HMS limits the runoff calculation when the rainfall intensity is less than the saturated hydraulic conductivity in a grid block. The saturated hydraulic conductivity in most of the grid blocks within the Upper West Branch is greater than the rainfall intensity in most periods during the storm event. The ascending limb of the hydrograph was well reproduced by the simulation. The peak flow in the simulated streamflow is higher than the observed peak value although timing and total water balance are quite comparable. The underestimation of streamflow in the descending limb of the hydrograph may be due to the operation of reservoirs and other man-made factors that were not considered. Another reason for the difference in observed and simulated recessions could be the grid resolution used in the model. The 1-km grid blocks could not account for the expansion of saturated zones near the streams in the stream cells during the storm even though the model did implicitly account for some of saturation flow. The expansion of the saturated zone is generally considered as one of the major mechanisms producing surface runoff during the storm event. The saturated zones near the streams were generated quickly, within the timescale of the storm event [Pionke et al., 1988]. The high soil moisture and groundwater level would accelerate the expansion of the near-stream saturated zone at this time of the year. The graph in Figure 7 shows the spatially averaged change in soil moisture in the top 10-cm and 40-cm of the soil during the storm. Fluctuations in soil moisture due to the precipitation pulses are evident in the top 10-cm soil layer and are much less
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Figure 6. The simulated and observed streamflow hydrograph at the basin outlet with observed precipitation. so in the top-40 soil layer. Clearly, the amplitude of soil moisture fluctuation responding to the meteorological forcing (precipitation and temperature) decreases with depth during the simulation period. The spatial distribution of S e in the top 10-cm layer at the end of the simulation is shown in Plate 5. The fields of relatively high soil moisture are located in the lower (or eastern) part of the basin. This is attributed to two major factors. One factor is the relatively dense vegetation (high fractional vegetation cover) at the lower part of the basin. The other factor is the large amounts of precipitation which occur in this area. The influence due to soil texture, vegetation cover (strip farming land), and stream network can also be observed in the spatial distribution of S e . 5.4. Hydrologic Response to a Storm Event Simulated by MM5 A logical step forward in the hydrologic modeling is the coupling of climate models with terrestrial hydrologic modeling. Here we demonstrate how such a coupled system ulti-
mately may provide a tool not only to study basin response but to predict basin behavior. The linkage between MM5 and HMS, as well as the databases and data sets required for a storm simulation, are summarized by Yu et al. [1999]. The one-way linkage allows MM5 to drive HMS without feedbacks from the hydrologic system. Ongoing model development incorporates two-way interactions. For this demonstration the storm of April 1986 was simulated using MM5, a widely used atmospheric mesoscale model [Lakhtakia et al., 1998; Yu et al., 1999]. MM5 is a threedimensional, primitive-equation mesoscale meteorological model with the capability of simulating and predicting a large variety of atmospheric phenomena. In this case, three nested horizontal domains were used with a two-way nesting procedure [Yu et al., 1999]. The innermost of the triple domains is the smallest one, encompassing the entire Susquehanna River Basin, producing hourly precipitation fields at a 4-km resolution over the study area. To account for the subgrid-scale spatial variability, the MM5-simulated precipitation at each
Figure 7. Time series of simulated soil moisture over the top 10-cm and 40-cm soil layers with observed precipitation.
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Plate 5. The spatial distribution of simulated values of S e over the top 10-cm soil layer at the end of 200-hour hydrologic simulation. grid cell (4 km) is randomly distributed among the 1-km hydrologic grid cells falling within the MM5 grid cell [Yu, 2000]. The hourly downscaled precipitation is used to drive HMS in the hydrologic simulation. The simulated and observed streamflow hydrographs using
the precipitation generated by MM5 are shown in Figure 8 along with the spatially averaged precipitation. Although the general structure of the storm system was reasonably well captured with the MM5 storm simulation [Lakhtakia et al., 1998], discrepancies exist between the temporal trends in simulated
Figure 8. The simulated and observed streamflow hydrograph at the basin outlet with MM5-simulated precipitation.
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Figure 9. Time series of simulated soil moisture of basin average over the top 10-cm and 40-cm soil layers with MM5-simulated precipitation.
and observed precipitation. The overall precipitation peaks in the MM5 simulation arrived 6 hours earlier than the observed peaks, resulting in an early arrival of the peak streamflow. The simulated precipitation was generally lower than the observed, which, along with averaged hydraulic parameters used, caused a significant underestimation in the surface runoff [Yu et al., 1999]. With the implementation of the subgrid-scale spatial variability in precipitation and therefore in the hydraulic parameters, agreement between the simulated and observed streamflows is improved compared to the simulated results of Yu et al. [1999]. Shown in Figure 9 are the simulated soil moisture produced by the MM5-simulated storm in the top 10-cm and 40-cm soil layers. The early arrival of the simulated soil moisture peak (6 hours ahead of the observed peak) is clearly seen; this behavior is a consequence of the early arrival of the simulated storm in MM5. A slight difference was noted in soil moisture between the simulations with MM5 and that using the observed precipitation. Such differences decrease with depth, however. In general, the temporal variations in soil moisture are consistent for both cases.
6.
Summary and Conclusions
This paper describes the use of a distributed hydrologic model (HMS) to simulate soil moisture response to atmospheric forcing and soil properties. In general, the temporal variation of soil moisture is a function of soil texture, vegetation type, and topography. The daily fluctuation of soil moisture in the surface soil layer (e.g., 10 cm) depends on soil hydraulic conductivity; the higher the saturation conductivity is, the greater the spatial and temporal fluctuations are. These fluctuations dampen with the depth. Soil moisture also varies as a function of vegetation. The agricultural land use appears to provide a higher amplitude of fluctuation in response to rainfall events. This land use also yields the lowest average soil moisture of all vegetation types available in the study area. Topography has relatively less influence on rainfall-runoff partitioning. It has a significant effect on the overland flow routing and infiltration along the flow
pathways. Soil moisture is directly related to the area contributing to water flow into that locality (e.g., catchment), especially for a small contributing area. As the scale increases to a critical (or threshold) point, soil moisture is controlled by the length of the hillslope rather than the contributing area. Soil texture has an immediate impact on rainfall-runoff partitioning at the start of a storm event. Because of the topography, direct surface runoff only requires hours to reach the stream valley. In general, cells in the stream valley have a higher soil moisture than cells located on the valley walls and upland. Vegetation type and fractional vegetation cover play a very important role not only in the infiltration obtained during the storm but also in the evapotranspiration during and after the storm. The spatial distribution of precipitation and the vegetation cover exert the most significant control on the spatial distribution of soil moisture within the basin. In general, however, the spatial distribution in soil moisture is controlled by the combined influence of precipitation, soil texture, vegetation type, fractional vegetation cover, and topography. With the implementation of subgrid-scale spatial variability in precipitation and hydraulic conductivity the simulated streamflow, which includes the direct surface runoff and groundwater base flow, compares well with the observed measurements at the watershed outlet. The peak in the simulated streamflow is slightly larger than that observed, even though the total simulated water balance compares well with that observed. The simulated soil moisture also shows the influence of the diurnal meteorological forcing, which decreases with depth. The streamflow simulation with the MM5-simulated precipitation reproduces the general trend of the observed streamflow. The peak of simulated streamflow arrives 6 hours earlier than the observed peak because of the early arrival of the MM5-simulated storm. The time series of the simulated soil moisture with the MM5-simulated precipitation also peaks earlier and has relatively lower values than the time series with the observed precipitation. Field data of dynamic response of surface runoff, groundwater level, soil moisture, and streamflow has been collected within the Mahantanga watershed to study the expansion of near-stream saturated zones. This
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mechanism will be incorporated as one of subgrid-scale spatial variabilities in the model based on the field data; more research is needed to develop parameterization schemes of aggregating and disaggregating hydrologic variables between small-scale hydrologic processes and large-scale hydrologic response. The results presented here provide a useful framework for two-way coupling between regional climate and hydrology models. Acknowledgments. This study was funded by NASA (1779-UNLVNASA-4553 through Global Water Cycle: Extension Across the Earth Sciences: NASA EOS, NAGW-2686). All computations were performed on the Earth System Science Center Computer System, which includes a SUN workstation network and a CRAY J-series computer, and on the hydrocomputing system in the Department of Geoscience at University of Nevada, Las Vegas. This manuscript was substantially improved by the thoughtful reviews of L. E. Band and an anonymous reviewer. The authors would like to acknowledge Mercedes Lakhtakia for conducting the MM5 storm simulation, Dick White and Doug Miller for processing some of the hydrologic data, and Joseph Santanello for producing the fractional vegetation cover maps.
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E. J. Barron and T. N. Carlson, Earth System Science Center and EMS Environment Institute, Pennsylvania State University, University Park, PA 16802. (
[email protected];
[email protected]) F. W. Schwartz, Department of Geological Sciences, The Ohio State University, Columbus, OH 43210. (
[email protected]) Z. Yu, Department of Geoscience, University of Nevada, Las Vegas, NV 89154. (
[email protected])
(Received March 11, 1999; revised October 24, 2000; accepted November 14, 2000.)
