Watershed Impact Assessment

1. Watershed Impact Assessment An Introduction to Watershed Modeling using Nonpoint Source Pollution and Erosion Comparison Tool (N-SPECT)

2. Workshop Overview Introduction Tutorial 1 & 2 Watershed Modeling � Theory and Concepts Tutorial 3 & 4 Watershed Delineation � Background Watershed Delineation � Practical Tutorial 5

3. Workshop Objectives Become familiar with the N-SPECT Become comfortable with its application Develop an understanding regarding the theoretical framework that supports N-SPECT Understand how to compare and contrast watershed development scenarios using N-SPECT Learn how to delineate hydrologically correct watersheds � how to check and fix them

4. RGIS-Pacific Northwest Mission: The mission of RGIS is to eliminate the digital divide facing rural America by promoting the transfer of geospatial technologies to under-served rural areas by: providing geospatial tools, technologies, and training to empower local governments, organizations, and citizens to understand and participate in decisions that affect their environment, economy, and quality of life; educating and training a cadre of people to apply geospatial technologies to rural issues;

5. RGIS-Pacific Northwest ~ CSI

6. Linking Land Cover and Water Quality Land cover and water quality are directly linked through runoff generation. Runoff generation is controlled by: Climate Vegetation Land cover and use Soil properties Topography Rainfall characteristics

7. Relationship of Urbanization and Runoff As urbanization increases, impervious areas increase Runoff volume and rate of conveyance to stream network increases Less groundwater recharge because of increases in imperviousness Impervious surfaces (e.g. transport & roof tops) retain heat that is transferred to runoff, increasing stream temperatures. Transport-related imperviousness often exert a greater hydrological impact than rooftop-related imperviousness � directly connected to storm drains!

8. Runoff Pollutants During storms, accumulated pollutants are quickly washed off and rapidly delivered to aquatic systems Types of pollutants Sediment Cropland, construction sites, roadways, forest activities, and stream-bank erosion Nutrients Cropland, lawns and gardens, livestock operations, septic systems, an wildlife Pathogens Livestock, wildlife, septic systems, landfill sites, and urban runoff Toxic contaminants Roadways, croplands, lawns and gardens, and mining operations.

9. Implications for management Land use planners, environmental planners, resource managers are faced with task of anticipating the impacts of their decisions. Want to make the most effective use of resources and mitigate for potential negative impacts � alternative scenario analysis can support the process. Watershed modeling tools offer a powerful tool to comparatively assess the associated impacts of various alternatives upon regional resources.

10. Watershed ModelsHow they can support management decisions Evaluate resource use policies before implementation; Alternative scenario comparison through quantitative assessment of impacts; Directional change concerning environmental metrics; Spatial analysis enabling managers to isolate problematic locations, pollution sources; Development short-term and long-term impact assessments for specific pollutants; Caution is required when applying mathematical models to describe complex hydrologic systems.

11. Watershed Processes

12. Types of Watershed Models Empirical versus physically based (eg. Curve Number vs Kineros) Deterministic versus stochastic (randomness) Lumped versus distributed Continuous versus event Empirical models, in most cases, are centered upon statistical relationships obtained through regression analysis of observed data. The application of empirical models to a wide array of environments is a concern because they are usually only suitable for conditions under which the relationships have been developed. The curve number method (USDA-Soil Conservation Service [SCS], 1972) is an example of this type of approach which has been used to relate land cover and land use to hydrologic model parameters (Hernandez et al., 2000). Physically based models are structured upon physical principles, such as the conservation of mass and momentum. The data input parameters required for these models are usually obtained from field measurements, which often results in this type of modeling being very data intensive. Deterministic models do not take into account the randomness inherent in the data and always reproduce the same results for a given set of parameters (Kalin & Hantush, 2003). In contrast, stochastic models incorporate the uncertainty inherent within the data and for this reason may produce different results for the same parameter set. Lumped models usually consider the entire system as having a homogeneous make-up of specific parameters. Their weakness lies in that the spatial variability of a parameter is often too generalized. For instance, a watershed may be composed of three prominent soil types, each exhibiting different hydrologic characteristics. The lumped approach will classify the entire region as a single soil type which appears on average to be dominant. For large scale regions, such as watersheds that have high levels of heterogeneity, lumped approaches may overgeneralize the characteristics of the region and therefore produce inaccurate hydrologic response predictions. Many recent hydrologic models that employ a lumped approach address this issue of generalization by allowing the modeler to delineate a set of subbasins contained within the entire watershed. Each subbasin is then assigned a potentially different set of lumped parameters that are more reflective of reality. In this way the lumped approach attempts to capture a greater portion of the watershed�s heterogeneity (Beven, 1989). Distributed models, on the other hand, are better suited to account for spatial heterogeneities because they divide the entire system into smaller units, with each unit assuming uniformity among model parameters and initial conditions (Kalin & Hantush, 2003). Finally there are temporal elements to be considered. Some models have been designed to describe the hydrologic response of a region for a single, well-defined storm event. While other models are focused upon assessing the accumulative effect of a region�s hydrologic response to climate conditions over a continuous time frame. Empirical models, in most cases, are centered upon statistical relationships obtained through regression analysis of observed data. The application of empirical models to a wide array of environments is a concern because they are usually only suitable for conditions under which the relationships have been developed. The curve number method (USDA-Soil Conservation Service [SCS], 1972) is an example of this type of approach which has been used to relate land cover and land use to hydrologic model parameters (Hernandez et al., 2000). Physically based models are structured upon physical principles, such as the conservation of mass and momentum. The data input parameters required for these models are usually obtained from field measurements, which often results in this type of modeling being very data intensive. Deterministic models do not take into account the randomness inherent in the data and always reproduce the same results for a given set of parameters (Kalin & Hantush, 2003). In contrast, stochastic models incorporate the uncertainty inherent within the data and for this reason may produce different results for the same parameter set. Lumped models usually consider the entire system as having a homogeneous make-up of specific parameters. Their weakness lies in that the spatial variability of a parameter is often too generalized. For instance, a watershed may be composed of three prominent soil types, each exhibiting different hydrologic characteristics. The lumped approach will classify the entire region as a single soil type which appears on average to be dominant. For large scale regions, such as watersheds that have high levels of heterogeneity, lumped approaches may overgeneralize the characteristics of the region and therefore produce inaccurate hydrologic response predictions. Many recent hydrologic models that employ a lumped approach address this issue of generalization by allowing the modeler to delineate a set of subbasins contained within the entire watershed. Each subbasin is then assigned a potentially different set of lumped parameters that are more reflective of reality. In this way the lumped approach attempts to capture a greater portion of the watershed�s heterogeneity (Beven, 1989). Distributed models, on the other hand, are better suited to account for spatial heterogeneities because they divide the entire system into smaller units, with each unit assuming uniformity among model parameters and initial conditions (Kalin & Hantush, 2003). Finally there are temporal elements to be considered. Some models have been designed to describe the hydrologic response of a region for a single, well-defined storm event. While other models are focused upon assessing the accumulative effect of a region�s hydrologic response to climate conditions over a continuous time frame.

13. N-SPECT N-SPECT was developed as a screening level tool, not as an exact engineering tool. Provides spatial analytical support to resource managers seeking to understand the nonpoint source pollution and sedimentation associated with land use changes and management decisions. Analyses are approached from the watershed or subwatershed scale and estimate fluxes in: surface water, pollutant, and sediment throughout the landscape. Estimates pollution and erosion within an area using land cover, soils, topography, and precipitation data.

14. Audience Coastal managers Land-use planners Scientists Teachers Development team Dr. David L. Eslinger, Jamie Carter, Margaret VanderWilt, Bev Wilson, Ed Dempsey, Andrew Meredith Major contributors Hawaii Coastal Zone Management Program NOAA Coastal Services Center (CSC) National Ocean Service Pacific Services Center Hawaiian management community

15. Capabilities Estimating runoff depth Estimating pollutant loads and concentrations Identifying areas highly susceptible to erosion by water Estimating sediment loads and concentrations Assessing the relative impacts of land use changes with scenario analysis

16. Tool Implementation N-SPECT requires: ArcView� 8.3 or ArcView 9.x ArcView� Spatial Analyst extension Basic geographic information system (GIS) skills Land cover grid Digital elevation model Precipitation grid Set of land cover pollutant coefficients Water quality standards Soil type data Model Output: Accumulated runoff, pollutant, and sediment load grids Pollutant and sediment concentration grids Pollutant assessment grid Ability to simulate land cover changes, such as developments

17. Functions Rainfall-runoff model - USDA-SCS curve number technique; Sediment yield model � Universal Soil Loss Equation (USLE) � uses both the MUSLE & RUSLE; Pollutant model � Event mean concentration coefficients � need Fortran program to develop custom coefficients.

18. Limitations Omitted processes Atmospheric deposition Groundwater processes Stormwater drainage Stream diversions Snow melt Landslides No time dependency on Runoff dynamics Sediment redeposition Pollutant load

19. Model Setup Data inputs: Digital Elevation Model (DEM) � probably most important! Land cover grid Rainfall grid Soils shapefile R-factor grid (annual erosion) Local pollutants coefficients Water quality standards

20. Common Data Sources Land cover: National Oceanic and Atmospheric Administration (NOAA) Coastal Services Center Land Cover Analysis Multi-Resolution Land Characteristics (MRLC) National Land Cover Database 2001 U.S. Geological Survey (USGS) Land Cover Institute Topography: NOAA Coastal Services Center Topographic Change Mapping USGS National Elevation Dataset Soil: U.S. Department of Agriculture (USDA) Natural Resources Conservation Service (NRCS) Soil Survey Geographic Database R-Factor (rainfall-runoff erosivity factor): USDA NRCS Electronic Field Office Technical Guide (eFOTG) USDA NRCS National RUSLE2 Database Precipitation: Oregon Climate Service PRISM Group NOAA National Climatic Data Center

21. NOAA Coastal Services Center -Tutorial 1 & 2 Tutorial 1: Baseline Analysis (Accumulated Effects) � Basic walk through of the N-SPECT model, presents baseline approximations of various nonpoint pollution variables Tutorial 2: Baseline Analysis with Local Effects � to produce outputs that represent the results of processes occurring in each individual pixel.