J.Q. Wu, S. Dun, W.J. Elliot, H. Rhee J.R. Frankenberger, D.C. Flanagan P.W. Conrad, R.L. McNearny.

Slides:



Advertisements
Similar presentations
A Model for Evaluating the Impacts of Spatial and Temporal Land Use Changes on Water Quality at Watershed Scale Jae-Pil Cho and Saied Mostaghimi 07/29/2003.
Advertisements

An open source version of the Nonpoint-Source Pollution and Erosion Comparison Tool Climate Tools Café Webinar Dave Eslinger, Ph.D. 3 May, 2012.
Developing Modeling Tools in Support of Nutrient Reduction Policies Randy Mentz Adam Freihoefer, Trip Hook, & Theresa Nelson Water Quality Modeling Technical.
REMM: Riparian Ecosystem Management Model USDA-Agricultural Research Service University of Georgia California State University – Chico USDA-Natural Resources.
Final BMP Modeling Workshop September 29, 2011 UB Geography Department Sponsored by the Buffalo District of the US Army Corps of Engineers.
Development of DRAIN-WARMF Model to Simulate Water Flow & Nitrogen Transport From an Agricultural Watershed: “ Subsurface Flow Component” Shadi Dayyani.
M. Stone, J. Stormont, E. Epp, C. Byrne, S. Rahman, R. Powell, W
Conservation Effects Assessment Project (CEAP) Measuring the Environmental Benefits of Conservation Managing the Agricultural Landscape for Environmental.
Hydrological Modeling for Upper Chao Phraya Basin Using HEC-HMS UNDP/ADAPT Asia-Pacific First Regional Training Workshop Assessing Costs and Benefits of.
Useful Tools for Predicting Erosion from Disturbed Rangelands: Disturbed WEPP for Rangelands The Water Erosion Prediction Project in the Forest Service.
October 5, 2005, The 4th IAHR Symposium on River, Coastal and Estuarine Morphodynamics Field Observation and WEPP Application for Sediment Yield in an.
LTHIA – Upgrades and Training Bernard A. Engel Tong Zhai Larry Theller Agricultural and Biological Department Purdue University In conjunction.
Some of my current research: Modeling sediment delivery on a daily basis for meso-scale catchments: a new tool: LAPSUS-D By: Saskia Keesstra and Arnaud.
GeoWEPP ArcGIS 10.1 Development Team LESAM Lab Team
Erosion Control Short Course Monday, April 23, 2012 San Luis Obispo City/County Library Ron Harben, Project Director California Association of Resource.
Workshop for Watershed Management Using Web-Based Decision Support Tools Bernard A. Engel Jin-Yong Choi.
Upper Brushy Creek Flood Study – Flood mapping and management Rainfall depths were derived using USGS SIR , Atlas of Depth Duration Frequency.
Watershed Characterization System (WCS) and its Modeling Extensions
Surface Water Simulation Group. Comprehensive watershed scale model developed and supported by the USDA-ARS capable of simulating surface and groundwater.
What is RUSLE2 ? R evised U niversal S oil L oss E quation, Version 2 Estimates soil loss from rill and interrill erosion caused by rainfall and overland.
Engineering Hydrology (ECIV 4323)
Soil Water Assessment Tool (SWAT) Model Input
Fort Bragg Cantonment Area Cape Fear River Basin LIDAR data have been used to create digital contours and topographic maps. 1.A Digital Elevation Model.
Soil Conservation: Soil Conservation: towards sustainable agriculture.
Nonpoint Source Pollution Reductions – Estimating a Tradable Commodity Allen R. Dedrick Associate Deputy Administrator Natural Resources & Sustainable.
What makes the The Universal Soil Loss Equation Go ?
SWAT – Land Phase of the Hydrologic Cycle Kristina Schneider Kristi Shaw.
Impact of Climate Change on Flow in the Upper Mississippi River Basin
USDA Process-based Tools for Estimating Runoff, Soil Loss, and Sediment Yield – The WEPP Model Dennis C. Flanagan Research Agricultural Engineer USDA-Agricultural.
FNR 402 – Forest Watershed Management
Modeling Variable Source Area Hydrology With WEPP
GeoWEPP ArcGIS 10.1 for soil erosion project Development Team Haoyi Xiong – Application leading developer Jonathan Goergen - Application co-lead developer.
Ag. & Biological Engineering
Mokelumne Avoided Cost Analysis Technical Committee Meeting: GeoWEPP modeling 1/9/2013 Mary Ellen Miller Michigan Tech Research Institute Bill Elliot,
WEPP: A Process-Based Watershed Runoff and Erosion Model for Watershed Assessment William Conroy, Joan Wu, Shuhui Dun Dept. Biological Systems Engineering.
Predicting Sediment and Phosphorus Delivery with a Geographic Information System and a Computer Model M.S. Richardson and A. Roa-Espinosa; Dane County.
Sediment Retention model
Runoff Pond Design, Lutsen, MN By Mark Greve. Problem Poplar River increases in sediment load near Lutsen ski hills Structure needed to slow flow and.
WEPP—A Process-Based Hydrology and Erosion Model for Watershed Assessment and Restoration Joan Q. Wu, Markus Flury, Shuhui Dun, R. Cory Greer Washington.
MRC Water Utilisation Programme 20 May 2003 Knowledge Base & DSF Software Presenter: Dr Jon Wicks, Software Integration Specialist in association with.
ArcHydro – Two Components Hydrologic  Data Model  Toolset Credit – David R. Maidment University of Texas at Austin.
Winter Erosion Processes Research at Washington State University Joan Wu, Shuhui Dun Prabhakar Singh, Cory Greer Washington State University Don McCool.
PREDICTION OF SOIL LOSSES. EMPIRICAL WATER EROSION FORMULAS A= k s 0,75 L 1,5 I 1,5 (Kornev,1937) A= k s 1,49 L 1,6 (Zingg,1940) A= k s 0,8 p I 1,2 (Neal,1938)
WEPP—A Process-Based Hydrology and Erosion Model for Watershed Assessment and Restoration Joan Q. Wu 1, William J. Elliot 2, Donald K. McCool 3, Markus.
Modeling experience of non- point pollution: CREAMS (R. Tumas) EPIC (A. Povilaitis and R.Tumas SWRRBWQ (A. Dumbrauskas and R. Tumas) AGNPS (Sileika and.
Adaptation Baselines Through V&A Assessments Prof. Helmy Eid Climate Change Experts (SWERI) ARC Egypt Material for : Montreal Workshop 2001.
WUP-FIN training, 3-4 May, 2005, Bangkok Hydrological modelling of the Nam Songkhram watershed.
Results of Long-Term Experiments With Conservation Tillage in Austria Introduction On-site and off-site damages of soil erosion cause serious problems.
Introduction Conservation of water is essential to successful dryland farming in the Palouse region. The Palouse is under the combined stresses of scarcity.
DRAINMOD APPLICATION ABE 527 Computer Models in Environmental and Natural Resources.
BASINS 2.0 and The Trinity River Basin By Jóna Finndís Jónsdóttir.
Description of WMS Watershed Modeling System. What Model Does Integrates GIS and hydrologic models Uses digital terrain data to define watershed and sub.
LTHIA and Online Watershed Delineation - Tale of a DEM consumer Larry Theller,Bernie Engel, and Tong Zhai Purdue University Agricultural and Biological.
Building an OpenNSPECT Database for Your Watershed Shan Burkhalter and Dave Eslinger National Oceanic and Atmospheric Administration (NOAA) Office for.
Preparing input for the TOPKAPI (TOPographic Kinematic Approximation and Integration) model PRASANNA DAHAL.
Interill Erosion. Interill Detachment and Sediment Delivery to Rills.
U.S. Department of the Interior U.S. Geological Survey Automatic Generation of Parameter Inputs and Visualization of Model Outputs for AGNPS using GIS.
DEVELOPMENT OF A CELL BASED MODEL FOR STREAM FLOW PREDICTION IN UNGAUGED BASINS USING GIS DATA P B Hunukumbura & S B Weerakoon Department of Civil Engineering,
Corn Yield Comparison Between EPIC-View Simulated Yield And Observed Yield Monitor Data by Chad M. Boshart Oklahoma State University.
Black Turtle Land Use Change Hydrologic Impact Evaluation Using Desktop and Web-GIS Capability Kyoung Jae Lim, Bernard A. Engel, Jin-Yong Choi, Jon Harbor,
OBJECTIVES To develop hillslope and watershed erosion models for the Manupali subwatersheds based on the WEPP model; To simulate surface runoff, soil.
SWPPP: Stormwater Pollution Prevention Plan Creating/Implementing a Plan for Compliance.
Sanitary Engineering Lecture 4
NASA BAER Project: Improving Post-Fire Remediation Through Hydrological Modeling NASA Applied Science Program Applied Sciences Program - Wildfires.
SOIL EROSION ASSESSMENT Measurement of Water Erosion Universal Soil Loss Equation (USLE) - predict annual soil loss by water – Wischmeier and Mannering,
Introduction to the PRISM Weather and Climate Mapping System
Application of soil erosion models in the Gumara-Maksegnit watershed
Soil Loss Estimation. USLE – Universal Soil Loss Equation SLEMSA – Soil Loss Estimation Model for Southern Africa.
Predicting the hydrologic and water quality implications of climate and land use change in forested catchments Dennis P. Lettenmaier Department of Civil.
GIS FOR HYDROLOGIC DATA DEVELOPMENT FOR DESIGN OF HIGHWAY DRAINAGE FACILITIES by Francisco Olivera and David Maidment Center for Research in Water Resources.
Presentation transcript:

J.Q. Wu, S. Dun, W.J. Elliot, H. Rhee J.R. Frankenberger, D.C. Flanagan P.W. Conrad, R.L. McNearny

Introduction A crucial component of planning surface mining operations as regulated by the National Pollutant Discharge Elimination System (NPDES) is to estimate potential environmental impacts during and after mining operations Reliable watershed hydrology and erosion models are effective and efficient tools for evaluating postmining site-specific sediment control and reclamation plans for the NPDES

Objectives The objectives of this workshop are  To introduce the newly developed WEPP-Mine, an online GIS interface for the USDA’s WEPP model, as a management tool for western alkaline surface mines  To apply WEPP-Mine, in a case application, to evaluate pre- and postmining watershed hydrological and erosion processes and impacts of BMPs at the Big Sky Mine, eastern Montana, USA  To obtain feedback from and exchange with stakeholders (state regulatory personnel, researchers, private consultants) and other workshop attendees to further refine WEPP-Mine

WEPP WEPP (Water Erosion Prediction Project) was initiated in 1985 as a new ‐ generation water erosion prediction technology for use by federal action agencies involved in soil and water conservation and environmental planning and assessment WEPP was developed by the USDA ‐ ARS with user requirements collected from the Bureau of Land Management (BLM), Forest Service (FS), and Soil Conservation Service (SCS) The WEPP model is a result of a large team efforts involving many scientists and experts

WEPP cont’d WEPP was intended to replace empirically- based erosion prediction technologies (e.g., USLE) for assessing the soil erosion impact of diverse land uses ranging from cotton fields to mountain forests It simulates many of the physical processes important in water erosion, including infiltration, runoff, ET, percolation, subsurface lateral flow, raindrop and flow detachment, sediment transport, deposition, plant growth, residue decomposition, and changes in soil properties

WEPP cont’d The WEPP model can be used for common hillslope applications or on watersheds In addition to WEPP core codes, the current version includes a parameter database and various interfaces, including a GIS and web ‐ based interfaces WEPP technologies have been successfully used in the evaluation of important natural resources issues throughout the US and in many other countries

WEPP Watershed WEPP discretizes a watershed into hillslopes, channel segments, and impoundments An impoundment can be on the channel network or at the foot of a hillslope

WEPP Inputs Climate  Observed daily values of precipitation (amount, duration, relative time to peak, relative peak intensity), temperatures (max, min), solar radiation, and wind (direction, speed)  Generated with CLIGEN, an auxiliary stochastic climate generator Topography Slope orientation, slope length, and slope steepness at points along the slope profile

WEPP Inputs cont’d Soil  Surface soil hydraulic properties, erosion parameters, and texture data for the soil profile  Soil properties of multiple layers to a maximum depth of 1.8 m can be input Land management  Information and parameters for plant growth, tillage, plant and residue management, initial conditions, contouring, subsurface drainage, and crop rotation

WEPP Outputs Event-by-event summary of runoff and soil erosion Graphical output for soil detachment and sedimentation along a slope profile Daily water balance Plant growth and residue decomposition Snow accumulation and snowmelt and soil frost and thaw Dynamic change of soil properties Sediment yield Return-period analysis

WEPP Impoundments WEPP simulates foothill small ponds behind  Filter fence  Straw bales WEPP also simulates sediment ponds with hydraulic structures  Drop spillway  Perforated riser  Culvert  Emergency spillway  Rock-fill check dam

Drop Spillway

Perforated Riser

Culvert

Emergency Spillway

Rock-fill Check Dam

Filter Fence

WEPP Application to Mining Areas To simulate the effect of mining operations on soil erosion and to evaluate sediment control BMPs, typical WEPP applications to mining areas may involve the assessment of  Premining condition as a baseline against which other scenarios can be compared  Postmining with revegetation  Postmining with revegetation and a sediment pond  Postmining with revegetation and a silt fence

WEPP-Mine WEPP-Mine was developed based on the USDA’s online GIS interface for the WEPP model It provides functions specifically for applications to mining areas  Using user-specified DEMs  Using reclamation maps  Simulating watershed-specific sediment ponds It can be accessed using a web browser at

WEPP-Mine Inputs USGS 30-m DEM USGS 2006 National Land Cover NRCS SSURGO soil data Spatial data automatically retrieved from the online servers by default Soil and landuse can also be customized within the WEPP-Mine interface Special permission is required for uploading user- specified DEMs and reclamation maps

WEPP-Mine Inputs cont’d CLIGEN-generated climate based on long-term monthly statistics is currently used (the use of observed climatic data will be implemented) The CLIGEN database includes more than 2,600 weather stations across the US Weather statistics of the station closest to the watershed outlet is used by default PRISM 800-m gridded monthly averages is applied to the monthly statistics to account for location and elevation differences from the CLIGEN station

WEPP-Mine Outputs Channel network Subcatchments Watershed summary Average annual values of the simulation results Return-period and frequency analysis Flowpath soil loss map Representative hillslope runoff map Representative hillslope soil detachment map Representative hillslope soil loss map

WEPP-Mine Output cont’d

General Steps for WEPP-Mine Applications Select area of interest Generate channel network Select watershed outlet and discretize watershed and subwatersheds View watershed summary Customize watershed inputs Run WEPP Analyze WEPP simulation results

Computer Requirement A computer connected to internet A web browser Following instructions on the web page (select and click buttons)

Premining Simulation WEPP simulation for the premining conditions can be accomplished by following the general steps for WEPP-Mine application without customizing watershed inputs

Premining Simulation cont’d

Postmining Simulation User-specified DEM is used for topographical inputs for postmining conditions A reclamation map can be uploaded for postmining soils and land managements Soils at the disturbed mining areas are composed of mine spoils and a 0.6-m top soil layer if top soil is applied during reclamation Postmining top soil is a mixture of the onsite soil described in the SSURGO database Surface soil hydraulic and erosion parameters were adjusted according to reclamation stages

Postmining Soil and Landuse Map unitDescriptionLand ManagementsSurface Soils 0Undisturbed or No DataShrubSM Shrub 1Disturbed—FacilitiesPoor grassPaved or Bare Rock 2Not ReclaimedBareMine Spoil 3Pre-ReclamationBareRegraded Mine Spoil 4Natural RevegetationPoor grassSM Top Soil 5Seed Phase IGood grassSM Sod Grass 6Seed Phase IIGood grassSM Bunch Grass 7Trail-completeLow traffic roadSM Skid

User-Specified Maps The required format includes  Raster map in ASCII  30-m resolution  UTM projection  0 for “no data” The corresponding projection file for the map needs to be loaded The IP address of a user is verified for uploading files to the WEPP-Mine server

User-Specified DEM

Reclamation Map

Sediment Pond After a watershed is discretized, one can specify sediment ponds Impoundment inputs include dimensions of the pond and related hydraulic structure parameters Default pond dimensions (stage-area-length relationship) are determined based on horizontal areas encircled by two half ellipses separated by the widest line of the area Inputs for chosen hydraulic structures of a pond are shown after clicking the “Set Structure Parameters” button User inputs override the default values

Sediment Pond cont’d

Case Application

Study Site WEPP-Mine was applied to Watershed III in Area A, Big Sky Mine, a major surface coal mine in southeast Montana

Big Sky Mine Area A Mining completed in 1989 Major reclamation activities (regrading, topsoil replacement, and revegetation) completed in 1992 Since 1984, many watersheds in the Big Sky Mine have been monitored for channel flow and water quality

Field Observations

WEPP Simulations Four WEPP runs were made to examine model performance in simulating the effect of three sediment control BMPs  Premining (natural) condition  Postmining with revegetation  Postmining with revegetation and a sediment pond  Postmining with revegetation and a silt fence

Inputs for Premining Oldest DEM available for the study area NRCS SSURGO soil data USGS National Land Cover dataset for landuse and management Soil and management data acquired using the online WEPP GIS interface

Postmining with Revegetation Topographic map taken from the “Big Sky Mine 2008 Annual Report” Soil and management data for the disturbed areas from the reclamation and bond status report Soil and management data prepared based on field observations

Watershed Delineation: Premining and Postmining Topographic, soil, landuse, and management conditions vary from the mining to postmining period and differ from the natural, premining conditions

Sediment Pond A sediment pond set near the outlet of the watershed  Volume 60,000 m 3  One culvert 2.4 m above bottom  Culvert i.d. 18 cm

Silt Fence A silt fence set on the toe of a hillslope near the watershed outlet  Fence height 1m Curtsey: USDA Forest Service Rocky Mountain Research Station Forestry Sciences Laboratory, Moscow, ID

Return-period Analysis 25-yr WEPP simulations were carried out using observed precipitation and temperature for 1984–2009 from Colstrip climate station (5 mi northwest of the site) and other required climate data generated using CLIGEN Return-period analyses were performed on field observations and WEPP simulations Runoff and sediment yields of WEPP-simulated events with a return period of 2, 5, 10, or 20 yr were compared with field observations

Return-period Analysis Return periods were estimated using Chow’s frequency factor method and Gumbel’s distribution with an annual maxima series following Patra (2000) T: the specified return period X T : the estimated value for a return period T X m and s x : the mean and standard deviation of the annual maxima of the events

Results

Results cont’d Runoff, mmSediment Yield, kg/ha Return Period (yr) Observed Simulated Premining Postmining & Revegetation Postmining & Sediment pond Postmining & Silt fence

Results cont’d WEPP overestimated observed runoff and sediment yield However, WEPP simulation results showed the effectiveness of the sediment control practices A silt fence near the watershed outlet would help to reduce sediment yield slightly from the postmining revegetation condition WEPP simulations indicated a sediment pond to be more effective, with a reduction of sediment yield of 50%

Summary WEPP-Mine was developed as a management tool for evaluating potential environmental impacts during and after mining operations WEPP-Mine was applied to a watershed in Area A, Big Sky Mine, southeastern Montana, to assess watershed hydrology and erosion as impacted by surface coal mining activities and postmining reclamation and sediment control practices Three commonly used BMPs: revegetation, sediment basin, and silt fence were evaluated as postmining reclamation management plans Additionally, a baseline scenario, the premining condition, was simulated

Summary cont’d The WEPP simulations demonstrated the effectiveness of the sediment control practices Future efforts are needed to  Further evaluate the WEPP-Mine performance through systematic and statistical comparison of model results and long-term field observations for different mines under different geographic conditions in the western US  Continually refine and develop functions (filter fence, buffer zone) specific for mining applications  Develop a comprehensive database of soil and management for alkaline mines in the western US for using WEPP-Mine

Acknowledgment Funding support from OSM; in-kind support from WSU, US Forest Service, and USDA NSERL Technical exchanges with and support from P. Clark and D. Matt Funding and technical support and data and information from MT DEQ, T. Golnar, J. Calabrese, Dr. E. Hinz. Funding and technical support and assistance in field work from Rosebud Mine engineers and staff

Resources and References (This USDA NSERL site contains extensive documentation and references on the WEPP model, including the free model downloads) Key references on the overview of the WEPP model  Flanagan, D.C., Livingston, S.J. (Eds.), USDA-Water Erosion Prediction Project User Summary. NSERL Rep. No. 11, Natl. Soil Erosion Res. Lab., USDA ARS, West Lafayette, IN, 139 pp.  Flanagan, D.C., Nearing, M.A. (Eds.), USDA-Water Erosion Prediction Project: Hillslope Profile and Watershed Model Documentation. NSERL Rep. No. 10, Natl., oil Erosion Res. Lab., USDA ARS, West Lafayette, IN, 298 pp.  Flanagan, D.C., Ascough II., J.C., Nicks, A.D., Nearing, M.A., Laflen, J.M., Overview of the WEPP erosion prediction model. In: Flanagan, D.C., Nearing, M.A. (Eds.), USDA-Water Erosion Prediction Project Hillslope Profile and Watershed Model Documentation. NSERL Rep. 10, Natl. Soil Erosion Res. Lab., USDA ARS, West Lafayette, IN (Chapter1).  Laflen, J.M., Lane, L.J., Foster, G.R., WEPP—a next generation of erosion prediction technology. J. Soil Water Conserv. 46, 34–38.  Laflen, J.M., Elliot, W.J., Flanagan, D.C., Mayer, C.R., Nearing, M.A., WEPP-predicting water erosion using a process-based model. J. Soil Water Conserv. 52, 96–102.  Laflen, J.M., Flanagan, D.C., Engel, B.A., Soil erosion and sediment yield prediction accuracy using WEPP. Am. Water Res. Assoc. 40, 289–297. Selected papers on modifying and applying the WEPP model by Dr. J. Wu’s group Pieri, L., M. Bittelli, J.Q. Wu, S. Dun, D.C. Flanagan, P. Rossi Pisa, F. Ventura, and F. Salvatorelli, Using the Water Erosion Prediction Project (WEPP) model to simulate field-observed runoff and erosion in the Apennines Mountain Range, Italy, J. Hydrol. 336, 84–97. Zhang, J.X., K-T Chang, and J.Q. Wu, Effects of DEM resolution and source on soil erosion modelling: a case study using the WEPP model, Int. J. Geogr. Info. Sci. 22, 925–942. Dun, S., J.Q. Wu, W.J. Elliot, P.R. Robichaud, D.C. Flanagan, J.R. Frankenberger, R.E. Brown, and A.C. Xu, Adapting the Water Erosion Prediction Project (WEPP) model for forest applications, J. Hydrol. 466, 46–54. Dun, S., J.Q. Wu, D.K. McCool, J.R. Frankenberger, and D.C. Flanagan, Improving frost simulation subroutines of the Water Erosion Prediction Project (WEPP) Model, Trans. ASABE. 53, 1399–1411.