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Assessment of Erosion Hotspots in a Watershed: Integrating the WEPP Model and GIS in a Case Study in the Peruvian Andes

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Assessment of Erosion Hotspots in a Watershed: Integrating the WEPP Model and GIS in a Case Study in the Peruvian Andes Environmental Modelling & Software 22 (2007) 1175e1183 www.elsevier.com/locate/envsoft Assessment of erosion hotspots in a watershed: Integrating the WEPP model and GIS in a case study in the Peruvian Andes Guillermo A. Baigorria a,*, Consuelo C. Romero b a Agricultural and Biological Engineering Department, University of Florida, Gainesville, FL 32611, USA b Departamento de Suelos, Universidad Nacional Agraria La Molina, Lima 12, Peru Received 8 April 2005; received in revised form 3 January 2006; accepted 9 June 2006 Available online 22 August 2006 Abstract This paper presents a case study in assessment of erosion hotspots in an Andean watershed. To do this, we made use of an interface called Geospatial Modelling of Soil Erosion (GEMSE): a tool that integrates Geographical Information Systems (GIS) with the Water Erosion Predic- tion Project (WEPP) model. Its advantages are: (i) it is independent of any special GIS software used to create maps and to visualize the results; (ii) the results can be used to produce response surfaces relating outputs (e.g. soil loss, runoff) with simple inputs (e.g. climate, soils, topogra- phy); (iii) the scale, resolution and area covered by the different layers can be different among them, which facilitates the use of different sources of information. The objective of this paper is to show GEMSE’s performance in a specific case study of soil erosion in La Encan˜ada watershed (Peru) where the hillslope version of WEPP has been previously validated. Resulting runoff and soil loss maps show the spatial distribution of these processes. Though these maps do not give the total runoff and soil loss at the watershed level, they can be used to identify hotspots that will aid decision makers to make recommendations and plan actions for soil and water conservation. Ó 2006 Elsevier Ltd. All rights reserved. Keywords: Geospatial modeling; WEPP; GIS; Soil loss; Runoff; Andes Software availability 1. Introduction Name of software: Geospatial Modelling of Soil Erosion Modeling has formed the core of a great deal of research focus- (GEMSE) ing on inherently geographic aspects of our environment, and has Developer and contact address: G.A. Baigorria, Frazier Rogers led to the understanding of distributions and spatial relationships Hall, University of Florida, Gainesville, FL 32611, in everything from astronomy to microbiology and chemistry USA (Parks, 1993). In the case of soil erosion, simulation models Coding language: Delphi 7 have become important tools for the analysis of hillslope and wa- Software requirements: Any GIS software only for visualiza- tershed processes and their interactions, and for the development tion purposes and assessment of watershed management scenarios (Santhi Hardware requirements: PCs with Windows 98, Windows et al., 2006; Miller et al., 2007; Lu et al., 2005; Metternicht and 2000 or Windows XP. Gonzales, 2005; He, 2003). Since erosion can adversely affect Program size: 1.1 Mb ecosystems on-site as well as off-site, the estimation of runoff Available since: 2004 and soil loss in catchments is becoming more important as con- cerns about surface water quality increase (Cochrane and Flana- gan, 1999). For this, the ‘‘hotspots’’ (source areas of sediments) within a watershed need to be identified. However, many of the * Corresponding author: Fax: þ352 392 4092. predictive models do not examine the problem in a geographic E-mail address: [email protected]fl.edu (G.A. Baigorria). context (Pullar and Springer, 2000). 1364-8152/$ - see front matter Ó 2006 Elsevier Ltd. All rights reserved. doi:10.1016/j.envsoft.2006.06.012 1176 G.A. Baigorria, C.C. Romero / Environmental Modelling & Software 22 (2007) 1175e1183 Under these circumstances, a Geographical Information southeastern Brazil. The Geo-Spatial Interface for WEPP System (GIS) becomes a valuable tool. A GIS is a powerful (GeoWEPP) (Renschler, 2003) is another example of a tool set of tools for collecting, storing, retrieving at will, transform- that combines GIS and WEPP. It utilizes readily available dig- ing and displaying spatial data from the real world (Burrough, ital geo-referenced information from accessible Internet sour- 1986). GIS has made a tremendous impact in many fields of ces like topographic maps, digital elevation models, land use application, because it allows the manipulation and analysis and soil maps (Renschler et al., 2002), with the aim of evalu- of individual ‘‘layers’’ of spatial data, and it provides tools ating various land-use scenarios to assist with soil and water for analyzing and modeling the interrelationships between conservation planning. For those users of WEPP with no expe- layers (Bonham-Carter, 1996). Coupled to an environmental rience with commercial GIS packages there is a new web- model, a GIS can interpret simulation outputs in a spatial con- based WEPP-GIS system that only requires a user to have text (Pullar and Springer, 2000). It is presumed that better in- a network connection and web browser (Flanagan et al., tegration of GIS and environmental modeling is possible by 2004). The digital elevation data are processed on the server exploiting the opportunity to combine ever-increasing compu- side to delineate watershed, channels and hillslopes that, tational power, more plentiful digital data, and more advanced once located, WEPP simulations are conducted. Results in models. GIS/modeling tools necessarily encourage the best graphical format are sent as images to the client computer. implementation of new and better ‘‘hybrid’’ tools. According These two last examples’ applicability, however, can fail to Parks (1993), there are three primary reasons for integra- where the availability of digital data is restricted, which often tion: ‘‘(1) spatial representation is critical to environmental occurs in developing countries. problem solving, but GIS currently lack the predictive and re- This paper presents a new tool capable of integrating pro- lated analytic capabilities necessary to examine complex prob- cess-based models with Geographic Information Systems lems; (2) modelling tools typically lack sufficiently flexible (GIS) for improving the analysis of point-estimated results GIS-like spatial analytic components and are often inaccessi- on larger scales. This interface, called Geospatial Modelling ble to potential users less expert than their makers; and (3) of Soil Erosion (GEMSE), makes use of the Water Erosion modeling and GIS technology can both be made more robust Prediction Project (WEPP) model, producing different maps by their linkage and co-evolution.’’ Both GIS and simulation in GIS format as a result of this integration. Analysis of these models have been developed with their own conventions, pro- maps gives insights useful for the evaluation of land resources cedures and limitations. However, linking them at a technical and agricultural sustainability and for estimating risks in a spe- level does not guarantee improved understanding or useful cific area. prediction (Burrough, 1986). More quantitative quality indica- tors, together with spatial statistics and error analysis, are 2. Materials and methods needed to improve the value of GIS/modeling interfaces (Hartkamp et al., 1999). 2.1. The study area A comprehensive description of some of the most popular models of watershed hydrology in the world can be found in Field data for running the model were obtained in the northern Andean Highlands of Peru, in La Encan˜ada watershed. The study area is approximately Singh (1995). As an example, we can mention some of 6000 ha and it is located at 74 0 S latitude and 7816 0 W longitude, ranging them. The TOPMODEL (Beven et al., 1984) was developed between 2950 and 4000 m above sea level (a.s.l.) (Fig. 1a). as a distributed hydrologic model that uses digital elevation Two main climate regimes can be identified during the year in this area: the data and spatial information on soil, vegetation and precipita- rainy season and the dry season. Three automatic weather stations were set up tion to estimate the soil moisture distribution at catchment in the study area to record the climate data on a daily basis. A summary of climate conditions is shown in Table 1. A detailed description about rainfall level, thereby taking account of the spatial heterogeneity of characteristics in the study area is given in Romero (2005) and Romero both topography and soils. One of the most promising of the et al. (in press). physically based models currently used to model erosion is According to the Soil Taxonomy classification (USDA and NRCS, 1998) the Water Erosion Prediction Project (WEPP) model (Flanagan the main soil orders in the watershed are Entisols, Inceptisols and Mollisols and Nearing, 1995). But it was not developed with a flexible (INRENA, 1998). The spatial distribution of the main soil groups is shown in Fig. 1b. In the highest part of the watershed there are deep soils with graphical user interface for spatial and temporal scales appli- a high content of organic matter. Shallow soils are also found; their low or- cations (Renschler, 2003). The first application of WEPP ganic matter content is mainly because the topsoil has been removed by ero- with a raster-based GIS was by Savabi et al. (1995). Another sion. Approximately 65% of the area has a slope gradient less than 15%. Very effort to integrate WEPP and GIS was by Cochrane and steep slopes (up to 65%) are also present, increasing the risk of erosion in this Flanagan (1999) for watershed erosion modeling, using an mountainous area. As steep slopes often occur adjacent to the river, water ero- sion will contribute directly to the river sediment load. interface between Arc View and WEPP. In both cases, the The land use in La Encan˜ada watershed is divided into croplands (55%), integration of WEPP with a GIS was done to facilitate and cultivated pasture (13%), natural pasture (20%) and scrub (12%) (INRENA, improve the application of the model. Another computer 1998). Deep soils with the largest amount of organic matter are used as crop- interface called Erosion Database Interface (EDI) processes lands, with cereals, potato, maize and legumes the most important crops.
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