Monitoring the Effectiveness of Best Management Practices in the Whitewater Watershed, Minnesota

Transcription

1 Monitoring the Effectiveness of Best Management Practices in the Whitewater Watershed, Minnesota Susan Miller Department of Resource Analysis, St. Mary s University of Minnesota, Winona, MN Keywords: holistic watershed management, best management practices, decision support system, water quality, soil erosion, subwatershed, Geographic Information Systems Abstract Soil erosion, due to intensive agricultural practices, has been and continues to be of significant concern in the Whitewater Watershed, a coldwater tributary of the Mississippi River, in southeastern Minnesota. Conservation efforts, in the form of best management practices (BMPs) have been underway for many years and a number of residents have enrolled in various government programs designed to reduce soil loss. However, the effectiveness of these programs is unknown. As it stands, participation in such programs is voluntary and efforts have been non-targeted, resulting in a random and noncontiguous distribution of conservation practices. In addition, many of the practices implemented are designed to reduce soil loss that occurs during an average annual rainfall event. However, studies conducted over extended periods of time (decades) have shown that as much as 60% of total soil erosion occurs during single, infrequent severe rainfall events (Larson, 1997). Therefore, the majority of traditional BMPs aimed at reducing soil loss, which manage only for average annual rainfall, are leaving soil vulnerable to stochastic infrequent storms. A better, and more quantitative, understanding of the effectiveness of such activities is necessary if we are to protect the watershed, and all that flows downstream of it. The Logan Branch of the Whitewater Watershed was selected to be used in a pilot project, managed by the federal Environmental Protection Agency and the Minnesota Pollution Control Agency, to quantify the effectiveness of BMPs implemented under the current enrollment strategy. It is the goal of this paper to report on the selection criteria used to identify the Logan Branch for this pilot project. A model was created based on six criteria dealing with soil erodibility, landuse/landcover, land mass, intensity of current conservation program participation, density of hydrologic and biological sample sites and the potential impact conservation land practices would have on soil conservation. Data for this spatial analysis were obtained from the Whitewater Watershed Project. Based on the given criteria the Logan Branch was identified as the most appropriate subwatershed for the BMP effectiveness pilot project. Geographic Information Systems (GIS), which was used to perform the siting analysis, proved to be an extremely effective tool. The Whitewater Watershed Project (Watershed Project), a collaborative group made up of participants from various local, state and federal agencies, as well as watershed citizens are currently applying GIS technology extensively throughout the watershed and believe it to be an extremely effective tool for holistic watershed management. The technology has been used to improve management efforts within the watershed. It has been used for such things as identifying habitat fragmentation and implementing conservation practices aimed at 1

2 reducing sedimentation and nutrient loading all with the goal of preserving the rich biodiversity of the watershed and to reduce the rapid loss of the watershed s rich topsoil. Several follow-up projects are proposed including the use of GIS technology as a predictive tool that would allow managers to analyze various management alternatives. Introduction Whitewater Watershed Geology, Ecology & the Human Landscape The Whitewater Watershed is a 200,000- acre tributary drainage of the Upper Mississippi River in southeast Minnesota. Three major arteries transport water throughout this drainage; the North, Middle & South Branches all converge near the town of Elba to form the Main Branch of the Whitewater River. At its confluence, the Whitewater River drains into the Mississippi River through a river lake, or backwater, called the Weaver Bottoms. The watershed drains portions of three counties; Winona, Wabasha, and Olmsted, and contains ten small urban communities that touch the watershed or are located completely within its boundary. A variety of natural communities exist in the watershed but the landscape is dominated by agriculture (figure 1). The Watershed flows through an ecological region known as the Driftless area, which remained untouched during the most recent glacial advance. Today the Driftless area is characterized by cold water trout streams and spectacular Figure 1. Whitewater Watershed orientation map 2

3 severe blufflands. The Whitewater Watershed has three distinct landscapes that follow its major branches as they drain from southwest to northeast to the Whitewater s confluence with the Mississippi River, near the town of Weaver, Minnesota. The gently sloping headwaters of the watershed were once covered in tall grass prairie, but are now dominated by homogeneous agricultural land. The middle portion of the watershed is marked by a dramatic landscape transition to steep bluff hillsides and dense forests. The lower watershed landscape contains an expansive floodplain made up of wetlands, forests and prairies. Because the watershed has such a variety of habitat types it contains a high diversity of flora and fauna (Rogers, 2002). Whitewater Watershed Project Conservation efforts in the Whitewater Watershed began in the early 1900 s with the public proclamation of the Mississippi River as a major navigable waterway, and the subsequent installation, by the US Army Corps of Engineers, of the Lock and Dams (L&D) throughout the River. In the 1930s, the Army Corp of Engineers began construction of the Lock & Dam system on the upper reaches of the Mississippi River, forever changing the river s landscape (Davis, 2002). Fearing what the proposed navigation system along with a diked and drained floodplain would mean for fish and wildlife, a visionary Chicago businessman named Will Dilg founded the Izaak Walton League to fight for the establishment of the Upper Mississippi Wildlife and Fish Refuge, which was established in Also at this time the concept of holistic watershed management gained in popularity. The fundamental belief that the health of a river system was directly related to the sum of its parts (tributaries) was a popular paradigm and the holistic watershed approach to conservation efforts was adopted as a part of the long-range management response to saving the Mississippi River ecosystem (Hawkins, 1998). Conservation work in the Whitewater Watershed began a few years later, but was not structured into a formal organization until the 1990 s when the Whitewater Watershed Joint Powers Board (JPB) was established. The JPB, made up of six local citizens with vested interest in the health and preservation of the Watershed, was developed to set goals, identify needs, complete an environmental assessment study, and to organize State and Federal agencies with regard to their biological and anthropogenic activities within the watershed. The activities included the development of a series of funding proposals to the various agencies involved in the project, and to focus conservation activities within the Watershed. An advisory board made up of highly skilled staff from various participating agencies guided policy, implemented by the JPB. The advisory board is made up of members from the three counties Soil and Water Conservation District offices, State agencies such as the Pollution Control Agency (MNPCA) and the Department of Natural Resources (MNDNR), as well as the equivalent Federal agencies like the US Fish & Wildlife Service (USFWS), the USDA s Natural Resource Conservation Service (NRCS) and the Environmental Protection Agency (EPA). 3

4 GIS in the Watershed GIS technology is being used in the Whitewater Watershed as a tool to successfully implement the holistic, adaptive approach to Watershed management. GIS is being used to visualize the linkages between land treatment, water quality and habitat preservation. As a management tool, GIS promotes productive discussions and allows policy makers to make more informed decisions. GIS technology lends itself precisely to conservation and to holistic watershed based management. A GIS can be used to ask questions like what, where and how restoration efforts should be implemented. This gives land managers the tools needed to more efficiently and effectively answer difficult land management questions. Holistic watershed management is an adaptive ecosystem-based approach to resource management, the success of which is greatly enhanced with the use of GIS. Because of this, the JPB, under the influence of key agency advisors, agreed to and secured funding for the development of the Whitewater Watershed Decision Support System (DSS) the base of which consists of hundreds of GIS data layers. The Whitewater DSS contains data for a variety of both natural and anthropogenic features in the watershed such as soils data, landuse/landcover, hydrology, geology, infrastructure, and land ownership information as well as various conservation practices within the watershed. The data is used by staff from nearly 20 government agencies for a variety of applications. Uses of the GIS data have been as diverse as the partner agencies involved in the Watershed Project - some are interested in developing very sophisticated tracking and reporting applications while others are interested in using the technology to identify critical landscape elements in the hopes of reconnecting fragmented features of the watershed to prevent continued environmental degradation, all of which detract from the high quality of life both in and outside this biologically unique watershed. One of the goals of the Whitewater Project is to help conservation efforts, and one of the most powerful tools used to achieve this goal is simple map-making. Showing the relative location of sensitive landscape components on a simple paper map allows relationships to be seen that may have otherwise gone unnoticed. At the most simplistic level maps provoke story telling, which evolves into discussions about landuse changes and comparisons of landscape elements over time, the lessons of which hopefully drive conservation efforts on the land. The Watershed Project uses GIS and maps for conservation, promotional and education purposes. The Watershed project continues to collect, update, and create new GIS data sets - which are used for a variety of analytical purposes by participants of the Watershed project. They actively track conservation efforts in the watershed such as Public Law 566 Watershed Protection and Flood Prevention Act (PL-566), Conservation Reserve Program (CRP), Reinvest in Minnesota (RIM), and nutrient management activities. They track biological and water quality monitoring efforts in the watershed. They develop complex databases to aid in tracking and reporting efforts pertaining to the conservation practices mentioned above. The Watershed DSS is used to rate elements 4

5 in the watershed, for example figure 2 shows the potential impact, by subwatersheds, of restoration efforts (for details on this ranking system please refer to the results and discussion section of this paper: Criteria 1). This type of ranking is helping to make resource management activities more deliberate and focused. The Whitewater DSS is also used to do siting analysis, at various scales, for specific conservation activities like the development of riparian buffers, as well as larger scale projects like the one presented in this paper, that outlines the development of a model that will determine which subwatershed would be an appropriate place to study the effectiveness of BMP s. 2. Must contain active citizens as indicated by their participation in conservation land manangement programs like CRPP or PL Must contain high concentration of biological and hydrological sample sites 4. Must have an area greater then 10,000 acres 5. At least 75% of the landuse within the subwatershed must be agricultural 6. Should contain at least 50% surface with Highly Erodible Land (HEL) soils Figure 2. Potential Restoration rating per subwatershed Analysis Process The goal of this project is to identify a suitable location within the Whitewater Watershed to study the effect of BMPs. The selection criteria will be based on subwatershed boundaries (Figure 3). A suitable subbasin will be selected based on the following criteria: 1. Must contain at least 1 evaluation unit with a ranking of High or Moderately High Figure 3. Whitewater Watershed subwatershed boundaries Assembly of GIS All data was obtained from the Whitewater DSS. The Whitewater Project developed some data sets from the Whitewater DSS and all others were originally obtained from the MNDNR, the NRCS, the EPA, the US Geologic Survey (USGS) and the Minnesota Department of Transportation (MNDOT). All data use the Universal Transverse Mercator (UTM) Zone 15; North American Datum 1983 (NAD83) coordinate system. 5

6 GIS data sets used for this geographic analysis consist of the following: subwatershed boundaries evaluation units PL-566 CRP whitewater monitoring sites land cover/land use soils Methods The six criteria used in this analysis were concieved of by the author based on availible datasets and on scientific expertise gained from technical staff. All subsequent analyses are based on the geographic extent of 16 subwatershed boundaries, a hybrid data layer created using the MNDNR Minor watershed boundaries data layer and the Whitewater evaluation unit data layer, which was derived from the Agricultural Non-Point Source Pollution (AGNPS) model run on the watershed in the early 1990 s. Criteria 1: Must contain at least 1 evaluation unit with a ranking of High, or Moderately High The data sets used for this analysis include: evaluation units and subwatershed boundaries. In order to identify the subwatersheds that contain at least one evaluation unit with a potential restoration rating of high or moderately high an inverse SELECTION was preformed to select the evaluation units with a rating of low or moderate. The results of this selection were UNIONed with the subwatersheds dataset in order to associate subwatershed names to all evaluation units. A table SELECTION for a null value in the potential restoration rating field was executed, making visible all the high and moderately high evaluation units. A visual analysis was performed to identify subwatersheds that contained the selected evaluation units; all others failed this criterion and were eliminated from the selection process for the remaining criteria. Criteria 2: Must contain active citizens as indicated by their participation in conservation land management programs like CRP or PL-566 The data sets used for this analysis include: PL-566 and CRP tracking layers and the subwatershed boundaries. A MERGE was performed to create a dataset that combined the polygon features from the PL-566 and CRP data layers. An IDENTITY was performed on the merged layer with the subwatershed boundary layer to associate conservation activities with the subwatershed they are located within. A table SUMMARY of the resulting data layer summarized the acres of conservation lands per subwatershed. The results of this summary were exported to a Microsoft Excel spreadsheet where the percent conservation land mass per subwatershed was calculated. Subwatersheds with greater than 5% of their landmass in conservation activities were identified, all other subwatersheds failed this criterion and were eliminated from the selection process for the remaining criteria. Criteria 3: Must contain high concentration of biological and hydrological sample sites 6

7 The data sets used for this analysis include: whitewater monitoring sites and subwatershed boundaries. A point data layer containing information on monitoring activities within the Watershed was used to summarize the number of sites per subwatershed. An IDENTITY was performed on the sample site data layer in order to associate a subwatershed name with each sample site. An attribute table SUMMARY was executed in order to create a merged shapefile containing a count of sample sites per subwatershed. A selection was made on the subsequent dataset for sample site density of greater than 10. The selected subwatersheds were considered to have high occurrences of sample sites, all other subwatersheds failed this criterion and were eliminated from the selection process for the remaining criteria. Criteria 4: Must have an area greater then 10,000 acres The data set used for this analysis was: subwatershed boundaries. The acres per subwatershed were calculated using the X-Tools extension. A selection based on acres greater then 10,000 was performed. All subwatersheds not in this selection failed this criterion and were eliminated from the selection process for the remaining criteria. Criteria 5: At least 75% of the landuse within the subwatershed must be agricultural The data sets used for this analysis include: landuse/landcover and subwatershed boundaries. Agricultural lands for the purposes of this analysis are defined as cultivated land or hay/pasture/grassland based on the dataset used. An IDENTITY was performed on the landuse/landcover layer and the subwatershed layer, associating subwatershed names to all landcover polygons. A SUMMARY was done on this data set in order to calculate the acreage of agricultural land per subwatershed. The results were exported to a Microsoft Excel spreadsheet to determine what percentage of each subwatersheds land mass is agricultural. Subwatersheds with 75% or more agricultural landcover were selected and all other subwatersheds failed this criterion and were eliminated from the selection process for the final remaining criteria. Criteria 6: Should contain at least 50% surface with HEL soils The data sets used for this analysis include: county soils and subwatershed boundaries. Soil data sets from each of the three watershed counties were individually DISSOLVED, based on Highly Erodible Land (HEL) status (for detailed description on available rankings refer to the Discussion and Results section on this criteria). These three data sets were then MERGED together to create a watershed soils layer containing data pertaining to the status of the erodibility of the soil as calculated by NRCS (a.k.a HEL status). The merged layer was also DISSOLVED based on HEL status to create the analysis layer. An IDENTITY was performed between the analysis soil layer and the subwatershed layer in order to associate a subwatershed name to 7

8 each soil polygon. The X-tools Summarize on Multiple Fields was used to SUMMARIZE on HEL status, subwatershed name and acreage. The results of the summary were exported to a MICROSOFT Excel spreadsheet where the acres per subwatershed per HEL ranking were calculated. Subwatersheds with 50% or more HEL were selected, all others failed this criterion and were eliminated from the selection process. The results of this analysis identified all the subwatersheds that contain at least one evaluation unit with a potential restoration rating of moderately high or high. This eliminates the headwater reaches of the watershed including the following subwatersheds: Beaver Creek, Dry Creek, Upper North Branch, Upper South Branch as well as Trout Run, a small subwatershed containing only one evaluation unit with a ranking of moderate (Figure 4). Discussion / Results Criteria 1: Must contain at least one evaluation unit with a ranking of High or Moderately High The potential restoration values assigned to each evaluation unit in the Whitewater Watershed were created based in part on the results of the AGNPS model run on the Watershed in the early 1990s. These values; low, moderate, moderately high, and high, represent the potential impact that conservation activities would be expected to have on the land. A potential restoration rating of low, for example, would imply that little to no impact would be seen on the health of the watershed as a result of establishing best management practices within that subwatershed. In purely monetary terms it can also be defined as where will conservation activities get the biggest bang for their buck investing into conservation based BMP activities in an evaluation unit with a potential restoration rating of high, or moderately high will result in a high value turnover in terms of the effect on the land (for more information on AGNPS and the input variables used to calculate the health of the ecosystem please visit: Figure 4. Subwatershed ranking after analysis of criteria 1 - Must contain at least 1 evaluation unit with a ranking of High or Moderately High Criteria 2: Must contain active citizens as indicated by their participation in conservation land management programs like CRP or PL-566 A variety of BMP activities have been implemented in the Whitewater Watershed; two of these are the federal Conservation Reserve Program, or CRP, and the Whole Farm conservation program known as PL-566. Exhaustive mapping of these activities has occurred for the study area. A hybrid polygon feature containing both conservation activities was created for this analysis. The results of this analysis identified the Upper Middle Creek & Beaver Creek subwatersheds as having the highest percentage of current BMPs, each with just above 10% of their land in 8

9 conservation activities. Percent conservation lands ranged from 2% to 10.6% throughout the Watershed. Subwatersheds with greater then 5% of their landmass in conservation activities were identified and the remaining subwatersheds were eliminated. Eight of the sixteen subwatersheds failed this criterion, further eliminating Crow Spring, Lower Main Branch, Lower North Branch, Middle North Branch, Middle South Branch, and the Upper Main Branch (Dry Creek and Upper South Branch also failed but were eliminated by criteria #1) (Table 1, Figure 5). Figure 5. Subwatershed ranking after analysis of criteria 2 - Must contain active citizens as indicated by their participation in conservation land management programs like CRP or PL-566 Table 1. Data analysis matrix results of criteria 2-5. Conservation Acres Criteria 2 Criteria 3 Criteria 4 Criteria 5 % Land in Conservation Program Sample Sites Total Acres Agricultural Acres % Land in Agriculture Subwatershed Upper South Branch 596 5% 7 12,609 11,596 92% Middle South Branch 1,026 5% 16 22,497 18,389 82% Dry Creek 695 5% 6 15,343 14,562 95% Middle North Branch 991 5% 17 18,321 15,276 83% Upper Middle Branch 1,021 11% 12 9,562 8,665 91% Crow Spring 202 3% 3 6,669 6,246 94% Upper North Branch 1,330 9% 8 15,244 13,835 91% Trout Run 260 6% 6 4,712 3,991 85% Lower Main Branch 200 2% 10 9,840 3,043 31% Beaver Creek 1,075 10% 9 10,611 6,369 60% Lower South Branch 2,379 10% 25 24,737 17,449 71% Lower Middle Branch 1,027 8% 17 13,260 10,011 75% Logan Branch 788 7% 11 10,775 8,892 83% Lower North Branch 363 5% 16 6,921 3,869 56% Upper Main Branch 288 2% 10 12,909 5,693 44% Trout Creek 965 9% 8 11,233 5,876 52% 9

10 Criteria 3: Must contain high concentration of biological and hydrological sample sites The Whitewater DSS is used to actively track biological and hydrological studies that take place within the Watershed. The density of these sample sites was calculated in order to determine the concentration of activities per subwatershed. Subwatersheds with high densities of sample sites were desired because quantitative data used for studying the effectiveness of the BMP s would be readily available. The results of this analysis showed a density range from three to twenty five sites within each subwatershed. A selection was made to identify all subwatersheds with ten or fewer sample sites, the results of which further eliminate the Trout Creek subwatershed from the remaining subwatershed candidates (Figure 6, Table 1). Figure 6. Subwatershed ranking after analysis of criteria 3 - Must contain high concentration of biological and hydrological sample sites Criteria 4: Must have an area greater then 10,000 acres A large sample area was desired in order to study the effectiveness of the BMPs. The subwatershed with an area greater than 10,000 acres was desired. Whitewater subwatersheds range in acreage from 4,700 to 24,700. Five subwatersheds are smaller then 10,000 acres and therefore fail this criterion; of those remaining after selection based on previous criteria only the Upper Middle Branch was further eliminated from the selection pool (Figure 7, Table 1). Figure 7. Subwatershed ranking after analysis of criteria 4 - Must have an area greater then 10,000 acres Criteria 5: At least 75% of the landuse within the subwatershed must be agricultural The major intent of a BMP is to reduce the negative environmental impact that present day agricultural practices have on our land and soil. Therefore, this pilot project required a subwatershed that contains high acreage of agricultural land. The results of this analysis showed a range of between 31% and 95% agricultural land per subwatershed. Ten of the sixteen subwatersheds have 75% and greater landmass in agricultural landuse. Of the six subwatersheds that failed this criteria all but one, the Lower South Branch, have already failed a previous criteria (Figure 8, Table 1). 10

11 Figure 8. Subwatershed ranking after analysis of criteria 5 - At least 75% of the landuse within the subwatershed must be agricultural Criteria 6: Should contain at least 50% surface with HEL soils The basis for identifying highly erodible land (HEL) is the erodibility index (EI) of a soil map unit as defined by the NRCS in their Soil Survey s. The erodibility index of a soil is determined by dividing the potential erodibility for each soil by the soil loss tolerance value established for the soil. The soil loss tolerance value represents the maximum annual rate of soil erosion that could take place without causing a decline in long-term agricultural productivity. A soil map unit with an erodibility index of eight or more is a highly erodible soil map unit. The erodibility index for sheet and rill erosion is represented by the following equation: (R x K x LS)/T = EI where: R = rainfall and runoff factor K = susceptibility of the soil to water erosion LS = combined effects of slope length and steepness T = soil loss tolerance All possible rankings, defined by NRCS, for this calculation are as follows: Highly Erodible Land (HEL): Soil meets the requirements for Highly Erodible Lands as defined below Potentially Highly Erodible Land (PHEL): Range of soil characteristics fall within and outside of the requirements for Highly Erodible Land Potentially Highly Erodible Land*(PHEL*): These PHEL map units are considered HEL based on typical slope percentage and length for determinations made in the office - not in the field Potentially Highly Erodible Land**(PHEL**): These PHEL map units are considered NHEL based on typical slope percentage and length for determinations made in the office - not in the field Non Highly Erodible Land (NHEL): Soil does not meet the requirements for Highly Erodible Lands If the LS factor for the shortest length and minimum percent of slope is used and the (R x K x LS)/T value equals or exceeds 8 then the soil map unit is considered to be Highly Erodible Land (HEL). A soil map unit is considered Potentially Highly Erodible Land (PHEL) if the (R x K x LS)/T value using the minimum LS factor is less than 8, and if the (R x K x LS)/T value using the maximum LS factor is equal to or greater than 8. In other words, if the possible range of LS values for a soil map unit can occur on each side of the erosion index of 8 the soil is determined to be PHEL. If the majority of the possible erosion index values are on one side or the other of the EI=8 line then the soil is called HEL (PHEL*) or NHEL (PHEL**) for the purpose of inoffice HEL determinations. Discrepancies with regard to soil units ranked as PHEL* or PHEL* are addressed with in-field measurements (Svien 2002, Johnson 1996). For the purposes of this analysis all HEL and PHEL* areas were considered to be of high potential erodibility (Svien, 2002). BMPs are 11

12 designed to prevent soil erosion; therefore a good pilot project subwatershed would contain a fairly high concentration of HEL soils so the effectiveness of any implemented BMP could easily be identified. Subwatersheds with less then 50% of their soil being rating as HEL or PHEL* were eliminated. Six of the sixteen subwatersheds failed this criteria, all of which were previously eliminated from the siting analysis by criteria 1-5. Only two subwatersheds remain as candidates for the BMP pilot project, the Logan Branch and the Lower Middle Branch (Figure 9, Table 2). Figure 9. Subwatershed ranking after analysis of criteria 1-6 Remaining potential sites Table 2. Data analysis matrix results of criteria 6. Criteria 6 Subwatershed HEL PHEL PHEL* PHEL** NHEL %HEL+PHEL* land Upper South Branch 1, ,511 5,943 17% Middle South Branch 6, ,470 8,887 35% Dry Creek 3, , ,420 39% Middle North Branch 6, ,955 7,719 41% Upper Middle Branch 2, , ,290 46% Crow Spring 2, , ,840 46% Upper North Branch 4, ,610 1,225 5,704 54% Trout Run 2, ,036 57% Lower Main Branch 6, ,802 63% Beaver Creek 6, ,185 1,500 64% Lower South Branch 16, ,071 4,155 65% Lower Middle Branch 6, , ,233 69% Logan Branch 4, , ,530 70% Lower North Branch 4, ,372 74% Upper Main Branch 9, ,449 76% Trout Creek 9, ,496 84% A matrix of the final results (Table 3) shows that one subwatershed failed on only one criteria, the Lower South Branch, and five failed only two of the criteria; the Upper North Branch, Middle North Branch, Middle South Branch, Upper Middle Branch and Trout Creek. All others failed at least three of the criteria. Only two subwatersheds passed all six criteria and remained as potential sites for the proposed BMP effectiveness study, the Logan Branch and Lower Middle Branch. A final decision was made by the technical staff involved in the project to propose the Logan Branch subwatershed for the proposed pilot project. The Logan Branch was selected because it falls 12

13 Table 3. Final results matrix Subwatershed Criteria 1 Criteria 2 Criteria 3 Criteria 4 Criteria 5 Criteria 6 Dry Creek failed failed failed passed passed failed Upper South Branch failed failed failed passed passed failed Beaver Creek failed passed failed passed failed passed Trout Run failed passed failed failed passed passed Upper North Branch failed passed failed passed passed passed Crow Spring passed failed failed failed passed failed Lower Main Branch passed failed failed failed failed passed Upper Main Branch passed failed failed passed failed passed Middle North Branch passed failed passed passed passed failed Middle South Branch passed failed passed passed passed failed Lower North Branch passed failed passed failed failed passed Trout Creek passed passed failed passed failed passed Upper Middle Branch passed passed passed failed passed failed Lower South Branch passed passed passed passed failed passed Logan Branch passed passed passed passed passed passed Lower Middle Branch passed passed passed passed passed passed entirely within Olmsted County (the Lower Middle branch straddles two counties), which will reduce the effort needed to coordinate the project with various local governmental agencies. Conclusion This paper outlines the selection criteria used for the siting analysis that identified the Logan Branch of the Whitewater Watershed as an appropriate subwatershed in which to perform a pilot project to study the effectiveness of conservation land practices, known as BMP s, aimed at reducing soil erosion. This decision was made based on the results of six geographically based spatial analyses. A few issues of concern in this analysis process should be noted. The first being that in criteria #3 all biological and hydrologic sample sites were treated equally, when in fact some sample sites could prove to be far more beneficial then others. A more decisive look at the nature of the sample sites should be considered for future analysis, giving a higher weighted value to those sites that would play a more significant role in studying the effectiveness of various conservation practices. Two other points of concern have to do with data quality, how it was created, and its geographic accuracy and scale. The Landuse/Landcover data used in the analysis were the best available datasets, however its quality and scale should be improved if further analysis is desired. The same concern applies to the soils data used for this analysis, which was obtained from the watershed counties. All three data sets are considered the most accurate of those available in digital format; however combining the datasets identified considerable variation in the datasets and should be considered a point of concern during subsequent studies done along county borders. Data accuracy and scale must always be addressed in order to determine if certain data sets are suitable for specific tasks. This project was also successful in showing that GIS is a successful analytical tool and should be used to help focus conservation activities. Future 13

14 GIS projects within the Watershed should include identifying areas within the Logan Branch that are susceptible to soil erosion and that have a high probability of contributing to ground water contamination. Identifying these areas would be beneficial to ongoing management activities within the watershed. Analyzing current landuse practices within the watershed s riparian corridors would also benefit management planning by identifying areas for possible inclusion into current conservation programs. The development of a soil erosion probability model for the Logan Branch, and eventually the entire watershed, should be strongly considered. The model could be used to simulate various management alternatives, such as the redistribution of CRP acreage along riparian corridors of upland streams, simulating the establishment of buffer strips. The current spatial distribution of soil conservation practices within the Watershed is indiscriminate and fragmented. For example, approximately 6,000 acres of land, scattered throughout the Watershed, is currently enrolled in the nation s Conservation Reserve Program. The CRP program temporarily (minimum of ten years) retires highly erodible cropland, which must be planted to native vegetation making it highly effective conservation land. While land deemed highly erodible by the NRCS and the Farm Service Agency (FSA) is given a higher ranking in the CRP enrollment process, the spatial distribution of the acreage is not a criteria for consideration. CRP plots are usually rectangular in shape (retired farm fields) and arbitrarily distributed. If current CRP acreage was more deliberately dispersed, for example along riparian corridors of upland streams, it would potentially be more effective at curbing runoff from nearby farm fields. Riparian buffer strips have been shown to be a very effective mechanism for trapping nutrients and sediment (Groffman et al. 1992). In conclusion, GIS has proven to be an excellent tool to facilitate conservation work within the Whitewater Watershed. Such a spatial analysis was used in this project to identify the Logan Branch subwatershed for a BMP effectiveness study. The Watershed project has successfully shown that GIS is an effective tool for holistic watershed management, for tracking cumulative changes over time, for modeling alternative management scenarios, and it allows for detailed record keeping and reporting. GIS reduces fragmentation and confusion caused by artificial (or political) boundaries. As mentioned earlier, some agencies involved in the Whitewater project are more programmatic in their approach while others are more visionary. The use of GIS as a tool for conservation encourages an integration of these approaches - which will only benefit the watershed. The power of the Whitewater Watershed DSS, and the exhaustive GIS datasets assembled for it, should now be used to facilitate more complicated analysis and modeling that will aid the BMP effectiveness project which will eventually help to improve the quality of life within the watershed. Acknowledgments I would like to thank Laurie Svien, Mike Davis and Tex Hawkins for their exhaustively vast knowledge and technical expertise, and for sharing it with me I would also like to thank the Whitewater Watershed staff, the 14

15 JPB, and the advisory board for the opportunity to work in this unique watershed, and for the knowledge you all bestowed on me. In addition, I would like to especially thank Dr. Dave McConville, Joe & Galit Breman and my parents, Mary Elizabeth and Allan Miller, for their unwavering support and encouragement. Resource Analysis program, Winona MN. Svien, Lawrence Personal Conversation. Area Resource Conservationist. USDA Natural Resource Conservation Service, Rochester MN. References Davis, Mike Personal Conversation. Senior Mississippi River Biologist, Minnesota Department of Natural Resources, Lake City MN. Groffman, P.M.; A.J. Gold,; R.C. Simmons, Nitrate dynamics in riparian forests: microbial studies. American Society of Agronomy. Journal of Environmental Quality October v. 21 (4): p Hawkins, A (Tex) Personal Conversation. Wildlife Biologist, U.S. Fish & Wildlife Service, 1500 Main Street, Winona MN. Johnson, Paul W Soil Loss Equation Federal Register Notice. FR Doc Filed armbill/1996/usle.html. US Department of Agriculture, Natural Resource Conservation Larson, W.E., M.J. Lindstrom, and T.E. Schumacher The role of severe storms in soil erosion: A problem needing consideration. Journal of Soil and Water Conservation. March-April: Rogers, Marc Assessment of grass/shrub habitat fragmentation in the Whitewater Watershed using GIS and Spatial Linear Regression to model Sensitive Species Population Densities. St. Mary s University 15