Significant land cover changes have occurred in the watersheds that contribute runoff to the upper San Pedro River in Sonora, Mexico, and southeast Arizona. These changes, observed using a series of remotely sensed images taken in the 1970s, 1980s, and 1990s, have been implicated in the alteration of the basin hydrologic response. The Cannonsville subwatershed, located in the Catskill/Delaware watershed complex that delivers water to New York City, provides a contrast in land cover change. In this region, the Cannonsville watershed condition has improved over a comparable time period. A landscape assessment tool using a geographic information system (GIS) has been developed that automates the parameterization of the Soil and Water Assessment Tool (SWAT) and KINEmatic Runoff and EROSion (KINEROS) hydrologic models. The Automated Geospatial Watershed Assessment (AGWA) tool was used to prepare parameter input files for the Upper San Pedro Basin, a subwatershed within the San Pedro undergoing significant changes, and the Cannonsville watershed using historical land cover data. Runoff and sediment yield were simulated using these models. In the Cannonsville watershed, land cover change had a beneficial impact on modeled watershed response due to the transition from agriculture to forest land cover. Simulation results for the San Pedro indicate that increasing urban and agricultural areas and the simultaneous invasion of woody plants and decline of grasslands resulted in increased annual and event runoff volumes, flashier flood response, and decreased water quality due to sediment loading. These results demonstrate the usefulness of integrating remote sensing and distributed hydrologic models through the use of GIS for assessing watershed condition and the relative impacts of land cover transitions on hydrologic response.

a b s t r a c tIn this article we present EnviroAtlas, a web-based, open access tool that seeks to meet a range of needs by bringing together environmental, economic and demographic data in an ecosystem services framework. Within EnviroAtlas, there are three primary types of geospatial data: research-derived ecosystem services indicator data in their native resolution, indicator data that have been summarized to standard reporting units, and reference data. Reporting units include watershed basins across the contiguous U.S. and Census block groups throughout featured urban areas. EnviroAtlas includes both current and future drivers of change, such as land use and climate, for addressing issues of adaptation, conservation, equity, and resiliency. In addition to geospatial data, EnviroAtlas includes geospatial and statistical tools, and resources that support research, education, and decision-making. With the development of EnviroAtlas, we facilitate the practice of ecosystem services science by providing a framework to track conditions across political boundaries and assess policies and regulations. EnviroAtlas is a robust research and educational resource, with consistent, systems-oriented information to support nationally, regionally, and locally focused decisions.Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

The Catskill/Delaware reservoirs supply 90% of New York City's drinking water. The City has implemented a series of watershed protection measures, including land acquisition, aimed at preserving water quality in the Catskill/Delaware watersheds. The objective of this study was to examine how relationships between landscape and surface water measurements change between years. Thirty-two drainage areas delineated from surface water sample points (total nitrogen, total phosphorus, and fecal coliform bacteria concentrations) were used in step-wise regression analyses to test landscape and surface-water quality relationships. Two measurements of land use, percent agriculture and percent urban development, were positively related to water quality and consistently present in all regression models. Together these two land uses explained 25 to 75% of the regression model variation. However, the contribution of agriculture to water quality condition showed a decreasing trend with time as overall agricultural land cover decreased. Results from this study demonstrate that relationships between land cover and surface water concentrations of total nitrogen, total phosphorus, and fecal coliform bacteria counts over a large area can be evaluated using a relatively simple geographic information system method. Land managers may find this method useful for targeting resources in relation to a particular water quality concern, focusing best management efforts, and maximizing benefits to water quality with minimal costs.

Floodplains perform several important ecosystem services, including storing water during precipitation events and reducing peak flows, thus reducing flooding of downstream communities. Understanding the relationship between flood inundation and floodplains is critical for ecosystem and community health and well-being, as well as targeting floodplain and riparian restoration. Many communities in the United States, particularly those in rural areas, lack inundation maps due to the high cost of flood modeling. Only 60% of the conterminous United States has Flood Insurance Rate Maps (FIRMs) through the U.S. Federal Emergency Management Agency (FEMA). We developed a 30-meter resolution flood inundation map of the conterminous United States (CONUS) using random forest classification to fill the gaps in the FIRM. Input datasets included digital elevation model (DEM)-derived variables, flood-related soil characteristics, and land cover. The existing FIRM 100-year floodplains, called the Special Flood Hazard Area (SHFA), were used to train and test the random forests for fluvial and coastal flooding. Models were developed for each hydrologic unit code level four (HUC-4) watershed and each 30-meter pixel in the CONUS was classified as floodplain or non-floodplain. The most important variables were DEM-derivatives and flood-based soil characteristics. Models captured 79% of the SFHA in the CONUS. The overall F1 score, which balances precision and recall, was 0.78. Performance varied geographically, exceeding the CONUS scores in temperate and coastal watersheds but were less robust in the arid southwest. The models also consistently identified headwater floodplains not present in the SFHA, lowering performance measures but providing critical information missing in many low-order stream systems. The performance of the random forest models demonstrates the method's ability to successfully fill in the remaining unmapped floodplains in the CONUS, while using only publicly available data and open source software.