Environmental Modeling and Assessment 9: 77–89, 2004. 2004 Kluwer Academic Publishers. Printed in the Netherlands. Application of SMR to modeling watersheds in the Catskill Mountains Vishal K. Mehta a, M. Todd Walter a,∗, Erin S. Brooks b, Tammo S. Steenhuis a, Michael F. Walter a, Mark Johnson a, Jan Boll b and Dominique Thongs c a Department of Biological and Environmental Engineering, Cornell University, Ithaca, NY 14853-5701, USA E-mail: [email protected] b Department of Biological and Agricultural Engineering, Moscow, ID 83844, Russia c New York City Dept. of Environmental Protection, Kingston, NY 12401, USA Very few hydrological models commonly used in watershed management are appropriate for simulating the saturation excess runoff. The Soil Moisture Routing model (SMR) was developed specifically to predict saturation excess runoff from variable source areas, especially for areas where shallow interflow controls saturation. A recent version of SMR was applied to two rural catchments in the Catskill Mountains to evaluate its ability to simulate the hydrology of these systems. Only readily available meteorological, topographical, and landuse information from published literature and governmental agencies was used. Measured and predicted streamflows showed relatively good agreement; the average Nash–Sutcliffe efficiency for the two watersheds were R2 = 72% and R2 = 63%. Distributed soil moisture contents and the locations of hydrologically sensitive areas were also predicted well. Keywords: Soil Moisture Routing model, distributed watershed modeling, saturation excess runoff, variable source area hydrology, dis- tributed soil moisture, NYC watersheds 1. Introduction able to watershed managers, planners, and other practition- ers (e.g., [7–9]). In humid, temperate climates runoff is commonly gener- The Soil Moisture Routing model (SMR) was developed ated from areas in the landscape where the soil has saturated primarily for practitioners to simulate VSA hydrology in to the surface (e.g., [1]). This process is commonly referred shallow-soil interflow driven systems [10]. Specifically, to as saturation excess runoff. A counterpart runoff process SMR is a management tool used to locate runoff-contributing is infiltration excess runoff, which occurs when the rainfall areas that vary spatially and temporally with the rise and intensity exceeds the land’s infiltration capacity (e.g., [2,3]). fall of shallow, transient, perched groundwater. One im- Because humid, well-vegetated areas, such as the Catskill petus for developing SMR was the need for a hydrologi- Mountains of New York State, commonly have higher soil cal model that could be used to develop land management infiltration capacities than rainfall intensities, infiltration ex- practices in the New York City (NYC) watersheds for water cess is rare and saturation excess is the dominant runoff quality [11]. SMR is especially well suited to the NYC wa- process [4]. Due to drainage and evapotranspiration, the tersheds in the Catskill Mountains, which are characterized location and extent of saturated runoff-generating areas are by hilly topography with shallow, permeable soils overlay- dynamic. The term variable source area (VSA) hydrology ing restrictive bedrock or fragipans [10]. Accurately esti- refers to this type of system [1]. mating the locations of runoff source areas in the landscape There are very few models that are designed to simu- is important to developing effective management practices late VSA hydrology, and fewer yet that have modest enough [12]. For management purposes, SMR is used to iden- data-input requirements to be readily used by watershed tify Hydrologically Sensitive Areas (HSA), which are ar- planners and managers. Most commonly used models rep- eas prone to being runoff source areas, and typically form resent relatively large portions of watersheds as lumped hy- where shallow, perched groundwater accumulates and satu- drological units and, therefore, cannot be used to identify rates the soil profile. Knowing the spatio-temporal distrib- discrete, saturated areas in the landscape, e.g., AGNPS [5], ution of HSA’s allows watershed planners to develop man- SWAT, SWRRB [6]. Many of these models also base the agement strategies that avoid placing potential pollutants on runoff potential of each portion of the landscape on landuse these HSA’s. and soil infiltration capacity and, thus, these models are im- Prior to developing SMR, hydrologists working in the plicitly most applicable to infiltration excess dominated sys- NYC watersheds considered TOPMODEL [13,14] as a pos- tems. Of the few distributed models that simulate VSA hy- sible hydrological model to use in the Catskills because it drology, most require large amounts of input data, much of was a popular semi-distributed hydrological model that sim- ulates VSA hydrology [15]. However, it was discarded be- which is typically unavailable, making these models unten- cause its conceptual basis was not well-suited for shallow ∗ Corresponding author. systems, driven by transient interflow [10,16,17]. 78 V.K. Mehta et al. / Application of SMR to modeling watersheds in the Catskill Mountains The objective of this project was to test SMR under hydrological modeling concept with a growing set of tools conditions typical to most management scenarios, specifi- for addressing particular sub-processes as needed such as cally applying the model to medium-sized watersheds, 10– snowmelt, surface flow routing, and macropore flow. Several 50 km2, using only open-access data for inputs. SMR has researchers have contributed to the model’s development and been shown to work well under research settings utilizing its current, fully distributed form [15,18,21–24]. Presented small, easily parameterized watersheds for which on-site here is a brief description of a recent version of SMR with soil characteristics and meteorological data were collected emphasis on changes from the original model [10,25]. One specifically for model testing [10,18]. In essence, these notable change from the early versions of SMR is that the tests demonstrated the adequacy of SMR’s conceptual and shallow soil is modeled as a series of layers rather than mathematical bases. Hydrological models are commonly a single layer to allow more realistic moisture distribution tested using experimental, research watersheds with substan- throughout the soil profile [18,23]. tial monitoring and instrumentation to collect field data that SMR is based on a water balance performed on each grid are used to corroborate and calibrate models. In manage- cell of the watershed (figure 1). For each soil layer of each ment situations, this is an exception rather than the norm, cell, the water balance is calculated at each time step ‘dt’as, and the need to apply modeling tools to typical situations dθi Qin − Qout using only readily available data should be recognized. Fur- Di = P − ETi + − Li − Ri, (1) ther, because models with significantly different structures dt A can perform comparably well in simulating some particular where i is the cell address, D is the soil depth of the cell − output, most commonly, streamflow [19], it is insufficient [L], θ is the volumetric moisture content of the cell [L3L 3], −1 to only show that the model can correctly simulate stream- P is rain plus snowmelt [LT ], ET is the actual evapo- −1] flow hydrographs. Therefore, this study tested SMR’s abil- transpiration [LT , Qin is the lateral inflow from sur- 3 −1 ity to correctly simulate processes internal to a watershed rounding upslope cells [L T ], Qout is the lateral out- − by corroborating simulated soil moisture and saturated ar- flow to surrounding downslope cells [L3T 1],Lis the leak- − eas against field measurements. It was also important to test age from one layer to the layer below [LT 1],R is surface − the model’s reliability for correctly locating saturated areas runoff [LT 1],A is the area of a cell [L2]. Note that for all because, as discussed earlier, reliable estimates of HSA’s is but the surface layer, P is actually leakage from the layer critical to watershed management plans for water quality. above. For the bottom layer, L is the deep percolation out of the soil profile into the bedrock reservoir. Figure 1 illus- trates the components of the water balance for a two-layered 2. SMR overview soil. The water balance is most commonly performed on a daily time step and the number of soil layers used depends The inspiration for SMR’s [10] conceptual basis was a on data availability. Component processes are presented be- hydrologic model for shallow soils [20] that incorporated a low in the order that they are calculated in the model at each philosophy that model complexity should be balanced with time step, for every grid cell in the watershed. The step-wise input-data precision and hydrological-complexity. SMR is a calculation approach keeps simulation time short. Figure 1. Conceptual schematic of SMR. V.K. Mehta et al. / Application of SMR to modeling watersheds in the Catskill Mountains 79 2.1. Precipitation and infiltration 2.4. Deep percolation Precipitation inputs include rainfall and snowmelt. Pre- In SMR, if the soil profile is above field capacity, wa- cipitation when the mean daily temperature is below 0◦C ter drains at a rate controlled by the restricting subsurface falls as snow. Snowmelt is estimated using a temperature in- (bedrock or fragipan) into a lumped ‘groundwater reservoir’. dex method [26] and air temperature is distributed through- Below field capacity there is no drainage. Recharge into this out the watershed using the dry adiabatic lapse rate [23]. conceptual reservoir is determined by the effective subsur- Since saturated hydraulic conductivities and, thus, soil in- face conductivities of the bedrock or fragipan (L in (1)). filtration capacities, are generally higher than rainfall inten- Unfortunately, there is very little information available re- sities [4], all precipitation infiltrates into the soil. Soil mois- garding percolation rates through these materials. This study − ture is then redistributed among the layers proportional to the used an effective fragipan conductivity of 0.01 cm d 1 [23] − available water storage capacity of each layer.