Soil Moisture Routing Model Research Articles

Identifying groundwater recharge connections in the Moscow (USA) sub-basin using isotopic tracers and a soil moisture routing model

Globally, aquifers are suffering from large abstractions resulting in groundwater level declines. These declines can be caused by excessive abstraction for drinking water, irrigation purposes or industrial use. Basaltic aquifers also face these conflicts. A large flood basalt area (1.1 × 105 km2) can be found in the Northwest of the USA. This Columbia River Basalt Group (CRBG) consists of a thick series of basalt flows of Miocene age. The two major hydrogeological units (Wanapum and Grand Ronde formations) are widely used for water abstraction. The mean decline over recent decades has been 0.6 m year−1. At present day, abstraction wells are drying up, and base flow of rivers is reduced. At the eastern part of CRBG, the Moscow sub-basin on the Idaho/Washington State border can be found. Although a thick poorly permeable clay layer exists on top of the basalt aquifer, groundwater level dynamics suggest that groundwater recharge occurs at certain locations. A set of wells and springs has been monitored bi-weekly for 9 months for δ18O and δ2H. Large isotopic fluctuations and d-excess values close to the meteoric water line in some wells are indicating that recharge occurs at the granite/basalt interface through lateral flow paths in and below the clay. A soil moisture routing (SMR) model showed that most recharge occurs on the granitic mountains. The basaltic aquifer receives recharge from these sedimentary zones around the granite/basalt interface. The identification of these types of areas is of major importance for future managed-aquifer recharge solutions to solve problems of groundwater depletion.

Dynamic riparian buffer widths from potential non-point source pollution areas in forested watersheds

Efforts to manage National Forests in the USA for wood production, while protecting water quality, are currently constrained by models that do not address the temporal dynamics of variable non-point source (NPS) areas. NPS areas are diffuse sources of contaminants contributed mostly by runoff as a result of different land use activities. Riparian vegetative buffers are often used to control contaminants from NPS areas but defining suitable widths require different policy considerations. In this study, the approach for defining suitable buffer widths is to apply a distributed process-based model that predicts potential NPS areas prone to generating runoff in relation to overland flow distances. A case study of the concept was applied to the 72 km 2 Pete King watershed located in the Clearwater National Forest (CNF) in central Idaho, USA. This grid modeling approach is based on a Geographic Information System (GIS) and it integrates the soil moisture routing (SMR) model with probabilistic analysis. The SMR model is a daily water balance model that simulates the hydrology of forested watersheds using real or stochastically generated climate data, a digital elevation model, soil, and land use data. The probabilistic analysis incorporates the variability of soil depth and accounts for uncertainties associated with the prediction of NPS areas using Monte Carlo simulation. A 1-year simulation for the case study location was performed to examine the spatial and temporal changes in NPS areas prone to generating runoff. The results of the simulation indicate that the seasonal variability of saturated areas determines the spatial dynamics of the potential NPS pollution. Use of this model for the design of riparian buffer widths would increase the effectiveness of decision-making in forest management and planning by mapping or delineating NPS areas likely to transport contaminants to perennial surface water bodies.

Distributed and integrated response of a geographic information system‐based hydrologic model in the eastern Palouse region, Idaho

AbstractVerification of distributed hydrologic models is rare owing to the lack of spatially detailed field measurements and a common mismatch between the scale at which soil hydraulic properties are measured and the scale of a single modelling unit. In this study, two of the most commonly calibrated parameters, i.e. soil depth and the vertical distribution of lateral saturated hydraulic conductivity Ks, were eliminated by a spatially detailed soil characterization and results of a hillslope‐scale field experiment. The soil moisture routing (SMR) model, a geographic information system‐based hydrologic model, was modified to represent the dominant hydrologic processes for the Palouse region of northern Idaho. Modifications included Ks as a double exponential function of depth in a single soil layer, a snow accumulation and melt algorithm, and a simple relationship between storage and perched water depth (PWD) using the drainable porosity. The model was applied to a 2 ha catchment without calibration to measured data. Distributed responses were compared with observed PWD over a 3‐year period on a 10 m × 15 m grid. Integrated responses were compared with observed surface runoff at the catchment outlet. The modified SMR model simulated the PWD fluctuations remarkably well, especially considering the shallow soils in this catchment: a 0·20 m error in PWD is equivalent to only a 1·6% error in predicted soil moisture content. Simulations also captured PWD fluctuations during a year with high spatial variability of snow accumulation and snowmelt rates at upslope, mid‐slope, and toe slope positions with errors as low as 0·09 m, 0·12 m, and 0·12 m respectively. Errors in distributed and integrated model simulations were attributed mostly to misrepresentation of rain events and snowmelt timing problems. In one location in the catchment, simulated PWD was consistently greater than observed PWD, indicating a localized recharge zone, which was not identified by the soil morphological survey. Copyright © 2006 John Wiley & Sons, Ltd.

Spatially and temporally distributed modeling of landslide susceptibility

Mapping of landslide susceptibility in forested watersheds is important for management decisions. In forested watersheds, especially in mountainous areas, the spatial distribution of relevant parameters for landslide prediction is often unavailable. This paper presents a GIS-based modeling approach that includes representation of the uncertainty and variability inherent in parameters. In this approach, grid-based tools are used to integrate the Soil Moisture Routing (SMR) model and infinite slope model with probabilistic analysis. The SMR model is a daily water balance model that simulates the hydrology of forested watersheds by combining climate data, a digital elevation model, soil, and land use data. The infinite slope model is used for slope stability analysis and determining the factor of safety for a slope. Monte Carlo simulation is used to incorporate the variability of input parameters and account for uncertainties associated with the evaluation of landslide susceptibility. This integrated approach of dynamic slope stability analysis was applied to the 72-km 2 Pete King watershed located in the Clearwater National Forest in north-central Idaho, USA, where landslides have occurred. A 30-year simulation was performed beginning with the existing vegetation covers that represented the watershed during the landslide year. Comparison of the GIS-based approach with existing models (FSmet and SHALSTAB) showed better precision of landslides based on the ratio of correctly identified landslides to susceptible areas. Analysis of landslide susceptibility showed that (1) the proportion of susceptible and non-susceptible cells changes spatially and temporally, (2) changed cells were a function of effective precipitation and soil storage amount, and (3) cell stability increased over time especially for clear-cut areas as root strength increased and vegetation transitioned to regenerated forest. Our modeling results showed that landslide susceptibility is strongly influenced by natural processes and human activities in space and time; while results from simulated outputs show the potential for decision-making in effective forest planning by using various management scenarios and controlling factors that influence landslide susceptibility. Such a process-based tool could be used to deal with real-dynamic systems to help decision-makers to answer complex landslide susceptibility questions.

Application of SMR to Modeling Watersheds in the Catskill Mountains

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 R 2 = 72% and R 2 = 63%. Distributed soil moisture contents and the

A GIS-based variable source area hydrology model

Effective control of nonpoint source pollution from contaminants transported by runoff requires information about the source areas of surface runoff. Variable source hydrology is widely recognized by hydrologists, yet few methods exist for identifying the saturated areas that generate most runoff in humid regions. The Soil Moisture Routing model is a daily water balance model that simulates the hydrology for watersheds with shallow sloping soils. The model combines elevation, soil, and land use data within the geographic information system GRASS, and predicts the spatial distribution of soil moisture, evapotranspiration, saturation-excess overland flow (i.e., surface runoff), and interflow throughout a watershed. The model was applied to a 170 hectare watershed in the Catskills region of New York State and observed stream flow hydrographs and soil moisture measurements were compared to model predictions. Stream flow prediction during non-winter periods generally agreed with measured flow resulting in an average r2 of 0·73, a standard error of 0·01 m3/s, and an average Nash-Sutcliffe efficiency R2 of 0·62. Soil moisture predictions showed trends similar to observations with errors on the order of the standard error of measurements. The model results were most accurate for non-winter conditions. The model is currently used for making management decisions for reducing non-point source pollution from manure spread fields in the Catskill watersheds which supply New York City's drinking water. Copyright © 1999 John Wiley & Sons, Ltd.