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

Abstract 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 km2 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.

[1]  E. O'Loughlin Prediction of Surface Saturation Zones in Natural Catchments by Topographic Analysis , 1986 .

[2]  M Todd Walter,et al.  Identifying hydrologically sensitive areas: bridging the gap between science and application. , 2006, Journal of environmental management.

[3]  I. Moore,et al.  Digital terrain modelling: A review of hydrological, geomorphological, and biological applications , 1991 .

[4]  C. W. Thornthwaite THE WATER BALANCE , 1955 .

[5]  K. Beven,et al.  THE PREDICTION OF HILLSLOPE FLOW PATHS FOR DISTRIBUTED HYDROLOGICAL MODELLING USING DIGITAL TERRAIN MODELS , 1991 .

[6]  M. Wigmosta,et al.  A distributed hydrology-vegetation model for complex terrain , 1994 .

[7]  J. Boll PROGRESS TOWARD DEVELOPMENT OF A GIS BASED WATER QUALITY MANAGEMENT TOOL FOR SMALL RURAL WATERSHEDS: MODIFICATION AND APPLICATION OF A DISTRIBUTED MODEL. , 1998 .

[8]  K. Beven,et al.  A physically based, variable contributing area model of basin hydrology , 1979 .

[9]  Paul E. Gessler,et al.  Soil-Landscape Modelling and Spatial Prediction of Soil Attributes , 1995, Int. J. Geogr. Inf. Sci..

[10]  Paul D. Bates,et al.  The effect of model configuration on modelled hillslope -riparian interactions , 2003 .

[11]  Thomas A. McMahon,et al.  Physically based hydrologic modeling: 2. Is the concept realistic? , 1992 .

[12]  William J. Elliot,et al.  Spatially and temporally distributed modeling of landslide susceptibility , 2006 .

[13]  Sindhu George,et al.  Principles of Geographic Information Systems , 2002 .

[14]  Carol M. Browner,et al.  National Water Quality Inventory: 1994 Report to Congress , 1995 .

[15]  Carol S. Tatay Level I Stability Analysis (LISA) Documentation for Version 2.0 , 1996 .

[16]  David R. Montgomery,et al.  A process-based model for colluvial soil depth and shallow landsliding using digital elevation data , 1995 .

[17]  Thomas A. McMahon,et al.  Physically based hydrologic modeling: 1. A terrain‐based model for investigative purposes , 1992 .

[18]  Tammo S. Steenhuis,et al.  Hydrologically sensitive areas: Variable source area hydrology implications for water quality risk assessment , 2000 .

[19]  Erin S. Brooks,et al.  Distributed and integrated response of a geographic information system‐based hydrologic model in the eastern Palouse region, Idaho , 2007 .

[20]  Paul E. Gessler,et al.  Modeling Soil–Landscape and Ecosystem Properties Using Terrain Attributes , 2000 .

[21]  Tammo S. Steenhuis,et al.  Scaling Effects on Runoff and Soil Moisture Content in a GIS-Based, Variable-Source-Area Hydrology Model , 1998 .

[22]  T. Saaty,et al.  The Analytic Hierarchy Process , 1985 .

[23]  Carolyn T. Hunsaker,et al.  Hierarchical Approaches to the Study of Water Quality in RiversSpatial scale and terrestrial processes are important in developing models to translate research results to management practices , 1995 .

[24]  Melvin J. Dubnick Army Corps of Engineers , 1998 .

[25]  W. Rawls,et al.  Estimating generalized soil-water characteristics from texture , 1986 .

[26]  R. D. Miller,et al.  Rapid Estimate of Unsaturated Hydraulic Conductivity Function , 1978 .

[27]  R. D. Black,et al.  Partial Area Contributions to Storm Runoff in a Small New England Watershed , 1970 .

[28]  Jacek Malczewski,et al.  GIS and Multicriteria Decision Analysis , 1999 .