A FRAMEWORK FOR THE PREDICTION OF SOIL MOISTURE

A. N. Flores, E. Istanbulluoglu, R1 L. Bras*, and D. EntekhabiDepartment of Civil and Environmental Engineering, Massachusetts Institute of Technology

Cambridge, Massachusetts, 02139

ABSTRACT soil moisture is also important to predict river stage andreservoir volume, as well as forecast flood crests and low

Through its influence on the mobility of troops and flows, flash-flood risk in small catchments, and low-levelmateriel, the interaction between weather and landscape is fog formation. From the warfighter's perspective,of primary importance to the effectiveness and timeliness characterization of the surface soil moisture is also criticalof Army operations. More specifically, knowledge of the to electro-optical weapon systems navigation, and minespatial and temporal variability in soil moisture over large placement and detection.areas, at the scale of tactical operations (-100 in), has thepotential to dramatically improve trafficability Presently, assessment of trafficability is often doneassessments. The majority of Army operations are through analysis of field-based soil moistureconducted in regions where field observations of soil measurements at points within a region. However,moisture are sparse in space and/or time or completely Entekhabi et al. (1996) concluded that these point scaleunavailable. However, remotely sensed information about data represent the soil moisture only in very localizedthe factors that affect the spatial variability in soil areas. The influence of local microtopography, soils andmoisture over a range of spatial scales are available. We vegetation, all of which demonstrate significant spatialpresent here a framework by which we can fuse these heterogeneity, make interpretation of these localizedremotely sensed data representing the various factors measurements difficult from the perspective ofaffecting soil moisture through the existing tRIBS trafficability over broader regions. Near surface soilhydrologic model to produce forecasts of the spatial moisture is remotely sensed by a number of operationaldistribution of soil moisture. Using data assimilation satellites, but at coarse spatial scales and long temporaltechniques these forecasts can be dynamically updated intervals from the perspective of Army operations.when remotely sensed observations of soil moisture usingbecome available. When used in conjunction with tactical Using models to predict the spatial distribution of soildecision aids, such as IWEDA, the proposed fusion of moisture at operational spatial scales (-100 in), and howdata through tRIBS has the potential to improve it changes over operational time scales can improvetrafficability assessments and other soil moisture trafficability assessments. Soil-vegetation-atmospheredependent Army operations. transfer (SVAT) models (e.g., Peters-Lidard et al., 1997)

can predict soil moisture in a dynamic and spatially1. INTRODUCTION distributed fashion over large regions, but at resolutions

that are too coarse for detailed trafficability assessmentThe interaction between weather and the land surface (e.g., 1 kin). Furthermore, these SVAT models are unable

is a limiting factor in the effectiveness and timeliness of to capture the role of topography in redistributing soilArmy operations. In particular, soil moisture is a dynamic moisture in the saturated and unsaturated zones.land surface state variable that reflects the interaction Distributed watershed hydrology models have physicallybetween precipitation and the hydrologic response of a based parameterizations to model the redistribution of soilbasin and influences the ease of troop and vehicle moisture in the subsurface, but need to be constrained tomovement. observations to reduce the uncertainty in the forecast.

Army applications require information on soil We present here a framework to forecast soilmoisture to forecast trafficability for tactical vehicles, moisture at operational scales by assimilatingground force deployment, and mission logistical observations of remotely sensed soil moisture data with aapproach. Tactical decision makers can more readily distributed watershed hydrology model. When combineddetermine possible enemy or friendly lines of approach with empirical models to predict remold cone index (RCI)with the aid of detailed and thorough trafficability as a function of soil moisture and properties, a map ofassessment. For example, moisture from the surface to 5 RCI can be produced which can then be consideredcm in depth is important for surface traction, while against the vehicle cone index (VCI) for a particularmoisture from 5-10 cm in depth impacts light armor vehicle type. In similar fashion to the Integrated Weathervehicle speed, and moisture from 10-30 cm can impact Effects Decision Aid (IWEDA) program, the end productmultiple pass trafficability for tanks. Moisture from 30-80 of this framework is a time-evolving map ofcm in depth affects large scale operations. Furthermore, Green/Amber/Red conditions for a particular vehicle type,

1

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Standard Form 298 (Rev. 8-98)Pirscribed by ANSI Std Z39-18

Microwae Soil AMoisture rensing MM

Visl VegetationS i=i Sesi~ng Radar & GPM oL. Precipitation R"

t .*:- Variability

C')> S,=0Lad ove0r- ,/ "i • ~~~ ~~Induced Precliauin

1bin lOOnm 1ikm 10km 100l km

Scale

Fig. 1: Soil moisture variability is effected by several factors which act overvarying spatial scales, but each of which have corresponding sources of remotelysensed data.

but at spatial scales of tactical decision making. In the understanding how these data affect soil moisturefollowing we discuss the role of topography, vegetation observation and modeling are critical developments toand precipitation in the spatial distribution of soil this work.moisture. This is followed by an outline of the distributedhydrologic model we employ to simulate soil moisture Fundamentally, precipitation delivers moisture to athrough space and time. The fusion of remotely sensed basin. When viewed as spatial fields over extensivesoil moisture data with the hydrologic model through a domains, precipitation events may drastically range indata assimilation framework is subsequently addressed. size or exhibit two orders of magnitude of internalFinally, we present potential applications of this work to variability. Soils, landuse and vegetative cover impact soilproduce trafficability assessments at tactical operation moisture through their influence on the redistribution ofscales. water in the subsurface and the depletion of water from

storage through evapotransipration. Topography also2. FACTORS AFFECTING SOIL MOISTURE serves as a control on the lateral redistribution of water in

the subsurface by providing gravitational potential forSoil moisture is a land surface variable which links flow through porous media. Vegetation, landuse and

global water, energy, and biogeochemical balance. As macroscale variation in soil characteristics affect soilsuch, the distribution of soil moisture at a point changes moisture variability over domains ranging in size fromthrough time is a function of several factors acting across hundreds of meters to tens of kilometers. However, locala range of spatial and temporal scales (Rodriguez-Iturbe topography controls soil moisture variability at spatialet al., 1995; Schmugge and Jackson, 1996). Among the scales critical to detailed trafficability assessments.more important factors affecting the spatial variability ofsoil moisture are topography, vegetation, and The redistribution of water in the subsurface has beenprecipitation. Fig. 1 demonstrates the influence these previously shown to occur preferentially in a downslopefactors have on the distribution of soil moisture at their direction, underscoring the importance of adequatecharacteristic scales. While presumably, there exists some representation of topography in soil moisture modeling. Inscales over which the effects of multiple factors can particular, the downslope movement of water in soils withoverlap, remotely sensed data corresponding to each sloping layers will be proportional to the slope itself andsource of variability can provide information in the vertical flux of water in the subsurface (Zaslavsky andinaccessible locations and across extensive domains. Sinai, 1981). Furthermore, as shown by Yeh et al.Merging these data to develop value-added products (1985ab,c) for randomly interbeded layers of soil withwhich aid in trafficability assessments at tactical different textures, the horizontal hydraulic conductivityoperation scales is the primary focus of this work. Hence, can be up to six orders of magnitude greater than the

2

Cnpy _ Evapo-

SIterception transpiration

Infiltration

Flow

ChannelFlow

- ~) -_ ---- --

Fig. 2: A conceptual diagram of the tR[BS model framework, and thehydrologic processes it encompasses.

vertical hydraulic conductivity. When considered The TIN framework allows the user to preserve importanttogether, these factors demonstrate that moisture in the topographic characteristics such as individual ridges andsubsurface moves preferentially away from ridges toward valleys at fine resolutions while allowing planar regionsvalley bottoms (Famiglietti et al., 1998; Western et al., to be represented at coarser resolutions. The advantage of1999). Topographic position, hydraulic properties of the such a computational surface is that fine-scale features aresoil, and its wetting-drying history thus control the spatial preserved without spending equal computer processorpatterns in soil moisture, resources on regions with relatively homogeneous

topography. Intersections of the TIN mesh, which3. HYDROLOGIC MODEL OUTLINE correspond to the centroid of each Voronoi polygon,

comprise the computational nodes. The region boundedTo simulate the response of the land surface to by the Voronoi polygon and the soil column represents a

spatially distributed high-resolution precipitation data we finite-volume element, with interfaces between adjacentemploy a distributed parameter hydrologic computer Voronoi cells serving as control surfaces through whichmodel which uses a geographic information systems fluxes are calculated. tRIBS includes physically-based(GIS) representation of a watershed and its physical parameterizations describing the processes of canopyattributes as a computational surface. The computational interception, evapotranspiration, infiltration, lateralsurface used by the model is a triangular irregular redistribution of soil moisture, and runoff routing in anetwork (TIN), which allows for representation of basin spatially explicit fashion.topography at multiple scale resolutions. The TIN-basedReal-time Integrated Basin Simulator (tRIBS) is a fully Canopy interception is accomplished using a modeldistributed, physically-based, dynamic basin hydrology that relies on species dependent retention capacitiescomputer model that takes as input readily available (Rutter et al., 1971, 1975). Drainage from the canopy isgeospatial data. A full description of the tRIBS model and accounted for following Shuttleworth (1979).its application to several test basins is discussed in detail Evapotranspiration, as well as latent, sensible, and groundby Ivanov et al. (2004), but a brief introduction to the heat fluxes are modeled using energy budget methods.model is provided here. A schematic diagram showsprocesses incorporated into the tRIBS model as well as a Evapotraspiration from bare soil and vegetatedrepresentation of the TIN-based computational surface surfaces is limited by soil water content in the top soil(Fig. 2). layer and root zone. A kinematic approximation for

unsaturated flow (Cabral et al., 1992, Garrote and Bras,As previously mentioned tRIBS relies on a 1995) underlies the infiltration method of tRIBS, and

triangulated irregular network and its associated Voronoi different parameterizations are used for ponded and(or Thiessen) polygon mesh as its computational surface, unsaturated infiltration.

3

5S 0 5 KA'rakwom~

Root MOL*DA 92 -0 422•0.422 -0:.4790.479 -0.61S0.51 -0.574D0.574 - 0.713D 713 -0 7710:771 -0:8U0m .854-1

Fig. 3: Mean soil moisture content in the top 1 m of soil over a 7-yearsimulation for the Blue River basin, OK.

STATSGO, etc.), land use/land cover (e.g., NLCD, etc.),Lateral redistribution of soil moisture in the initial water table elevations (e.g., following Sivapalan et

unsaturated zone occurs during both storm events and al., 1987), precipitation data (e.g., NEXRAD, etc.), andinterstorm intervals. Lateral fluxes in the saturated zone meteorological data (e.g., temperature, etc.).are computed using an approach that explicitly routesflow in a cell-to-cell fashion. Within a cell saturation The output of a tRIBS simulation for soil moistureexcess runoff and groundwater return flow are generated applications is a map showing the soil moisture in eachby considering the difference between influx and outflux Voronoi cell at a particular timestep in the simulationof water within each finite volume cell at each timestep. (Fig. 3). In a later section we present a framework for

translating this map of soil moisture into a map ofInfiltration excess runoff is generated when the Green/Amber/Red zones for a particular vehicle.

throughfall exceeds infiltration capacity, and perchedsubsurface storm flow occurs when water from the Ivanov, et al. (2004) demonstrated tRIBS capabilityunsaturated zone of a cell is discharged onto the surface of discerning the role the relationship between topographyof the downslope neighbor. Reinfiltration of runoff is not and the frequency of occurrence of different runoffconsidered. Overland flow at a computational element is generation mechanisms for regions within the Blue Riverpartitioned into hillslope runoff and channel runoff, basin characterized by different groundwater dynamics.Travel times of hillslope runoff are computed using a Each panel in Fig. 4 shows the frequency of occurrence ofvelocity parameter that must be calibrated for individual a particular runoff generation mechanism versus the so-watersheds. Channel runoff is routed using the kinematic called topographic index, ln(A/tan/5), where A is thewave equation, assuming channels have approximately contributing drainage area to a point, and tanf# is the localrectangular cross-sectional geometries and that the ground surface slope. Larger values of ln(A/tan/3) areManning equation is valid. associated with a tendency for greater soil moisture. The

different markers in the plots of Fig. 4 group dominantAs input, tRIBS requires digital elevation models groundwater dynamics regions. All four panels in Fig. 4

(e.g., USGS NED, SRTM, etc.), soil data (e.g., show that frequency of occurrence of the different runoff

4

(a) Infiltration excess (b) Saturation excess0U5 14

Recharge reg. Recharge reg.0. E-- Midline reg. 12 Midline reg.

0 .3 . . . . . . . . . . . - E - D is c h a rg e re g . 1 2 -9 - D is c h a rg e re g . . . . . . . . . . . . . . . . .

Sign. discharge -A- Sign. dischare

0 0S0,15 .. .

0.1. ....... %D.

0.5...........

0 5b10 15 20 25 30 36 0 5 10 15 20 26 30 35

In(A/tan J3) In(A/tan P)

(c) Perched subsurface stormflow (d) Groundwater flow20 12

Recharge rag. Recharge reg.SMidline reg. Midline reg.

-e- Discharge reg. 10 -0- Dischargerag. ...e15 - Sin. discharge -A-. Sign. dischagr

8 . . . . . . . . .L) 1 0 ......• ..... ...... .... ...... ........... !ot4 ............

000 5 10 15 20 25 30 35 0 5 10 15 20 25 30 35

In(A/tan R lIn(A/tan I1

Fig. 4: Frequency of occurrence of four different runoff generation mechanisms in the BlueRiver basin, OK: (a) infiltration excess, (b) saturation excess, (c) perched subsurfacestormflow, and (d) groundwater flow.

generation mechanisms depends on the local topographic 4. SOIL MOISTURE DATA ASSIMILATION ANDcharacteristics. Fig. 4(a) shows that infiltration excess FUSIONrunoff is generated in regions serving to recharge thegroundwater aquifer. However, the propensity to have 4.1 Remotely sensed soil moisture datainfiltration excess in the recharging regions is likely theresult of the soils being composed of predominantly clays Soil moisture strongly influences the emission of lowand silts in those regions (Ivanov et al., 2004). The frequency microwave radiation. In particular, thefrequency of occurrence of runoff generation mechanisms emissivity of soils varies from approximately 0.6 for wetin Figs. 4(b),(c), and (d), which all require a saturated soils to 0.9 in dry soils in the L-band (- 1.4 GHz) of theground surface, is highest in regions acting as a source of spectrum (Njoku and Entekhabi, 1996). This wide rangesignificant groundwater discharge (Ivanov, et al., 2004). of emissivity values for soils translates to a dynamicHowever, the frequency of occurrence of perched range in the radiobrightness temperature observed bysubsurface stormflow and groundwater flow exhibits a microwave radiometers that is larger than thewider range of variability than the frequency of radiometer's noise sensitivity. Passive microwave remoteoccurrence of the other two runoff generation sensing of soil moisture is a direct benefit of this largemechanisms. These results depict the ability of the tRIBS signal-to-noise ratio (Njoku and Entekhabi, 1996).model to depict the spatial distribution of the relevant Greater vegetation penetration and lessened atmosphericrunoff generation mechanisms and its relationship to attenuation at longer wavelengths makes the low-topography and groundwater dynamics. frequency microwave range of 1-3 GHz (30-10 cm

wavelength) best suited to soil moisture sensing (Njokuand Entekhabi, 1996).

5

model and observational estimates, each weighted by itsSeveral complications, however, make obtaining associated uncertainty (Gelb, 1974).

remotely sensed soil moisture observations difficult.Specifically, the required antenna size to sense soil Specifically applied to this work, the tRIBS modelmoisture at spatial scales appropriate to trafficability propagates the soil moisture state givenassessment is problematic when sensing at lower hydrometeorological forcing such as precipitation,frequencies. NASA developed the Electronically Scanned temperature and cloudiness. An algorithm is used toThinned Array Radiometer (ESTAR) concept to, among translate the soil moisture state evolved by tRIBS toother tasks, sense soil moisture at a single frequency (1.4 microwave radiobrightness temperatures retrieval whenGHz). The ESTAR sensor could potentially provide soil remotely sensed observations of the soil state becomemoisture data at a scanning swath width of approximately available. Existing grey body radiative transfer models1200 kin, ground spatial resolution of approximately 40 (e.g., Ulaby et al., 1986, Galantowicz et al., 1999) arekm and global mapping capability every 3 days (Levine et examples of algorithms that could be implemented in theal., 1989; Swift, 1993). However, real-time ground tRIBS model.military operations are only partially supported by soilmoisture assessment at these spatiotemporal resolutions. An additional step is required to resolve theOther space-born passive microwave sensors, operating at inconsistency between the spatial scales at which tRIBShigh 6.6-30GHz frequencies (Gloersen and Barath, 1977; simulates soil moisture (10 - 500 m) and those at whichHollinger et al., 1990; Kummerow et al., 2000), as well as space-born radiometers typically observe soil moisturecurrently planned L-band sensors (Njoku et al., 1999) (10 - 100 km). This inconsistency of spatial scales ismay provide data at ground spatial resolutions ranging overcome through the use of optimal downscaling (i.e.from 10 to 100 km. A more effective use of these data transfer of information from larger to smaller spatialwould be to fuse them with modeled soil moisture data scales) procedures. Previous work has successfullythrough the tRIBS model to produce value-added developed procedures to accomplish four-dimensionaltrafficability assessment aids. soil moisture data assimilation into a hydrologic model

(Reichle et al., 2001, 2002).4.2 Data fusion and assimilation

Complete implementation of data assimilationTwo important sources of estimates of soil moisture frameworks can be frustrated by the large dimensions of a

have been described here: (1) the soil moisture state problem such as the one being considered. Producingevolved by tRIBS through time, and (2) remotely sensed estimates that preserve the benefits of merging modelobservations of near surface soil moisture taken at a forecast information with observational data, but whichparticular instant in time. These two distinct forms of soil are not necessarily statistically optimal, is one way thismoisture state estimation carry uncertainty associated problem can be overcome. Such estimates are known aswith their respective sources. suboptimal solutions. This approach is known as a data

fusion technique. The desired outcome of this work is toThe evolved soil moisture estimate is subject to what produce a combined data fusion and assimilation

is commonly referred to as model uncertainty. This mechanism that can interface with modular modelsuncertainty arises from, among other sources, the elements developed in this or other projects, include bothparameterization of the various hydrological processes, dynamic and static sources of information, and be applieduncertainty in parameter values, numerical round-off to models of large dimension and computational demand.error, and discretization of the land surface into acomputational surface. On the other hand, the remotely 5. TRAFFICABILITY AND SOIL MOISTUREsensed observations of soil moisture are subject to so-called observational or measurement uncertainty. This is Translating the estimated soil moisture state into anuncertainty that is attributed to the sensitivity of the actionable variable from the perspective of Armymicrowave radiometer, data retrieval from the satellite, operations requires several additional steps. The firstand retrieval in the estimation of soil moisture from the involves combining soil properties with the soil moistureradiobrightness temperature. state estimate to produce a variable related to soil strength

and bearing capacity.Using optimal estimation theory that is well

established in the hydrologic sciences, we can produce an One common method of measuring in situ soiloptimal estimate of the soil moisture state by fusing the strength is through the use of a soil profile coneremotely sensed observations of soil moisture with the penetrometer (PCP). The PCP is a field instrumentevolved soil moisture estimate from tRIBS. This soil comprised of slender rod approximately one meter inmoisture state estimate is optimal in the sense that it is a length with a cone shaped end that measures theminimum error estimate produced as a combination of the compressive and frictional resistance of the soil as the

Trafficability index versus soil moisture CONCLUSIONS

Soil moisture is a dynamic land surface variable thatdemonstrates significant spatial variation as a function of

x70 variability in soils, vegetation and landuse as well astopographic position. Through its relationship to soil

21 5strength and bearing capacity, soil moisture is a limitingZ5 factor the spatial scope and timing of Army operations.=40 Conversely, knowing the evolution of soil moisture2 30 characteristics of a region at time and space scales of

tactical operations (-100 m) has the potential todramatically improve trafficability assessments. Wepresent a framework by which to fuse remotely sensed

0 soil moisture products with the soil moisture state evolvedslMoisture [%V) by a distributed parameter hydrologic model to produce

Fig. 5: Trafficability index (RCI) versus volumetric value-added products to aid in detailed trafficabilitysoil moisture. For the M1A2 Abrams, green-amber assessment of Army operations. This framework involvesthreshold is based on 50-pass VCI, and amber-red dynamically updating the soil moisture field evolved bythreshold for 1-pass VSI. the tRIBS model when remotely sensed soil moisture

observations become available using well established dataassimilation procedures. The net effect of this fusion of

PCP is forced vertically downward into the soil column, models in data is to combine information about soilThis resistance, typically reported in units of force per moistures from independent sources to reduce theunit area, is referred to as the cone index (CI). uncertainty in the estimate of the soil moisture state.Multiplying CI by a measure of soil remoldability, Through models that relate remold cone index (RCI) toproduces the remold cone index (RCI) which is a function soil classification and moisture, the spatial distribution ofof the soil density, type and moisture content (Mason, soil moisture can be translated into the spatial distribution2004). Empirical data suggests that RCI tends to decay of RCI. Then, when considering the spatial distribution ofwith increasing soil moisture, and is sensitive to RCI against the vehicle cone index (VCI), an actionablecumulative rainfall (Mason, 2004). Although the map of Green/Amber/Red conditions throughout a givenrelationship varies by vehicle, RCI is closely related region can be produced.important tactical variables such as vehicle average speedand momentum (Mason, 2004). ACKNOWLEDGEMENTS

Using data from Sullivan and Anderson (2000) we This research is supported by Army Research Officehave developed an algorithm by which RCI can be grant W91 1NF-04-1-0119.predicted given one of eight soil classes, and a value ofsoil moisture. This algorithm can be implemented into REFERENCEStRIBS to produce an estimate of RCI at eachcomputational node within the basin being considered, Cabral, M.C., Garrote, L., Bras, R.L., and Entekhabi, D.,given the soil moisture and properties at that 1992: A kinematic model of infiltration and runoffcomputational node, at each timestep of the simulation, generation in layered and sloped soils, Adv. Wat.Such an estimate of RCI can then be considered against Res., 15, 311-324.the tolerable values of vehicle cone index (VCI) for a Entekhabi, D., Rodriguez-Iturbe, I., and Castalli, F., 1996:particular vehicle type. For example, Fig. 5 depicts Mutual interaction of soil moisture state andtrafficability index (RCI) versus soil moisture for a clayey atmospheric processes, J. Hydrology, 184, 3-17.soil. Considering the 1-pass and 50-pass VCI for the Famiglietti, J.S., Rudnicki, J.W., Rodell, M., 1998:M1A2 Abrams allows the delineation of Variability in surface moisture content along aGreen/Amber/Red thresholds based on soil moisture. hillslope transect: Rattlesnake Hill, Texas, J.Along with other landscape variables important to vehicle Hydrology, 210,259-281, 1998.trafficability such as local topographic slope, and landuse Galantowicz, J.F., Entekhabi, D., and Njoku, E.G., 1999:or vegetative cover, the spatial distribution of RCI Tests of sequential data assimilation for retrievingdeveloped from the soil properties and moisture can be profile soil moisture and temperature from observedimplemented in an IWEDA-like framework. The result L-band radiobrightness, IEEE Trans. Geosci. Remotewould be a map of Green/Amber/Red conditions at each Sens., 37, 1860-1870.tRIBS computational node throughout a simulated region.

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