Habitat evaluation of wild Amur tiger (Panthera tigris altaica) and conservation priority setting in north-eastern China

The Amur Tiger (Panthera tigris altaica) is one of the world’s most endangered species. Recently, habitat fragmentation, food scarcity and human hunting have drastically reduced the population size and distribution areas of Amur tigers in the wild, leaving them on the verge of extinction. Presently, they are only found in the north-eastern part of China. In this study, we developed a reference framework using methods and technologies of analytic hierarchy process (AHP), remote sensing (RS), geographic information system (GIS), GAP analysis and Natural Break (Jenks) classification to evaluate the habitat and to set the conservation priorities for Amur tigers in eastern areas of Heilongjiang and Jilin Provinces of northeast China. We proposed a Habitat Suitability Index (HSI) incorporating 7 factors covering natural conditions and human disturbance. Based on the HSI values, the suitability was classified into five levels from the most to not suitable. Finally, according to results of GAP analysis, we identified six conservation priorities and designed a conservation landscape incorporating four new nature reserves, enlarging two existing ones, and creating four linkages for Amur tigers in northeast China. The case study showed that the core habitats (the most suitable and highly suitable habitats) identified for Amur tigers covered 35,547 km², accounting for approximately 26.71% of the total study area (1,33,093 km²). However, existing nature reserves protected only (7124 km² or) 20.04% of the identified core habitats. Thus, enlargement of current reserves is necessary and urgent for the tiger’s conservation and restoration. Moreover, the establishment of wildlife corridors linking core habitats will provide an efficient reserve network for tiger conservation to maintain the evolutionary potential of Amur tigers facing environmental changes.     

Identification of Swamp Deer (<Emphasis Type="Italic">Cervus duvauceli duvauceli</Emphasis> Cuvier) Potential Habitat in Jhilmil Jheel Conservation Reserve,Uttarakhand, India Using Multi-Criteria Analysis

The present study aims to identify the potential habitat for swamp deer (Cervus duvauceli duvauceli Cuvier) in Jhilmil Jheel Conservation Reserve in the Uttarakhand province of India using multi-criteria analysis. The study area represents one of the last remnant habitats of the flagship species, the swamp deer in Uttarakhand, which is considered as vulnerable. The study showed that only 6.08% of the study area (225 km²) was highly suitable to suitable for the swamp deer. An area of 135.52 km² (60.23%) turned out to be moderately suitable. Within the officially designated Conservation Reserve (area 37.84 km²), 10.91% (4.13 km²) area was found highly suitable to suitable, while 74.19% (28.07 km²) happens to be moderately suitable. Only 14 km² area, which was found as suitable habitat for swamp deer falls short of the space required by a population of 134 animals. The problem could be mitigated if the agricultural land (2.47 km²) adjacent to the Jhilmil Jheel is brought under the Reserve management. This would provide additional area to meet the fodder requirement. The study brings out a particularly grim situation with limited options for conservation and management of the swamp deer in the Indo-Gangetic plains. It also emphasizes the role of geospatial techniques in quick appraisal of habitat attributes and identification of potential sites for protected areas.     

Cocoa Intensification Scenarios and Their Predicted Impact on CO<Subscript>2</Subscript> Emissions,Biodiversity Conservation,and Rural Livelihoods in the Guinea Rain Forest of West Africa

The Guinean rain forest (GRF) of West Africa, identified over 20 years ago as a global biodiversity hotspot, had reduced to 113,000 km² at the start of the new millennium which was 18% of its original area. The principal driver of this environmental change has been the expansion of extensive smallholder agriculture. From 1988 to 2007, the area harvested in the GRF by smallholders of cocoa, cassava, and oil palm increased by 68,000 km². Field results suggest a high potential for significantly increasing crop yields through increased application of seed-fertilizer technologies. Analyzing land-use change scenarios, it was estimated that had intensified cocoa technology, already developed in the 1960s, been pursued in Cote d’Ivoire, Ghana, Nigeria and Cameroon that over 21,000 km² of deforestation and forest degradation could have been avoided along with the emission of nearly 1.4 billion t of CO₂. Addressing the low productivity of agriculture in the GRF should be one of the principal objectives of REDD climate mitigation programs.     

Application of SPARROW Modeling to Understanding Contaminant Fate and Transport from Uplands to Streams

Understanding spatial variability in contaminant fate and transport is critical to efficient regional water‐quality restoration. An approach to capitalize on previously calibrated spatially referenced regression (SPARROW) models to improve the understanding of contaminant fate and transport was developed and applied to the case of nitrogen in the 166,000 km² Chesapeake Bay watershed. A continuous function of four hydrogeologic, soil, and other landscape properties significant (α = 0.10) to nitrogen transport from uplands to streams was evaluated and compared among each of the more than 80,000 individual catchments (mean area, 2.1 km²) in the watershed. Budgets (including inputs, losses or net change in storage in uplands and stream corridors, and delivery to tidal waters) were also estimated for nitrogen applied to these catchments from selected upland sources. Most (81%) of such inputs are removed, retained, or otherwise processed in uplands rather than transported to surface waters. Combining SPARROW results with previous budget estimates suggests 55% of this processing is attributable to denitrification, 23% to crop or timber harvest, and 6% to volatilization. Remaining upland inputs represent a net annual increase in landscape storage in soils or biomass exceeding 10 kg per hectare in some areas. Such insights are important for planning watershed restoration and for improving future watershed models.     

Assessing the Cost of an Invasive Forest Pathogen: A Case Study with Oak Wilt

Economic assessment of damage caused by invasive alien species provides useful information to consider when determining whether management programs should be established, modified, or discontinued. We estimate the baseline economic damage from an invasive alien pathogen, Ceratocystis fagacearum, a fungus that causes oak wilt, which is a significant disease of oaks (Quercus spp.) in the central United States. We focus on Anoka County, Minnesota, a 1,156 km² mostly urban county in the Minneapolis-Saint Paul metropolitan region. We develop a landscape-level model of oak wilt spread that accounts for underground and overland pathogen transmission. We predict the economic damage of tree mortality from oak wilt spread in the absence of management during the period 2007–2016. Our metric of economic damage is removal cost, which is one component of the total economic loss from tree mortality. We estimate that Anoka County has 5.92 million oak trees and 885 active oak wilt pockets covering 5.47 km² in 2007. The likelihood that landowners remove infected oaks varies by land use and ranges from 86% on developed land to 57% on forest land. Over the next decade, depending on the rates of oak wilt pocket establishment and expansion, 76–266 thousand trees will be infected with discounted removal cost of $18–60 million. Although our predictions of removal costs are substantial, they are lower bounds on the total economic loss from tree mortality because we do not estimate economic losses from reduced services and increased hazards. Our predictions suggest that there are significant economic benefits, in terms of damage reduction, from preventing new pocket establishment or slowing the radial growth of existing pockets.     

Rainwater harvesting planning using geospatial techniques and multicriteria decision analysis

Growing water scarcity and global climate change call for more efficient alternatives of water conservation; rainwater harvesting (RWH) is the most promising alternative among others. However, the assessment of RWH potential and the selection of suitable sites for RWH structures are very challenging for the water managers, especially on larger scales. This study addresses this challenge by presenting a fairly robust methodology for evaluating RWH potential and identifying sites/zones for different RWH structures using geospatial and multicriteria decision analysis (MCDA) techniques. The proposed methodology is demonstrated using a case study. The remote sensing data and conventional field data were used to prepare desired thematic layers using ArcGIS© software. Distributed Curve Number method was used to calculate event-based runoffs, based on which annual runoff potential and runoff coefficient maps were generated in the GIS (geographic information system) environment. Thematic layers such as slope, drainage density, and runoff coefficient and their features were assigned suitable weights and then they were integrated in a GIS to generate a RWH potential map of the study area. Zones suitable for different RWH structures were also identified, together with suitable sites for constructing recharge structures (check dams and percolation tanks along the streams). It was found that the study area can be classified into three RWH potential zones: (a) ‘good’ (241 km²), (b) ‘moderate’ (476 km²), and (c) ‘poor’ (287 km²). About 3% of the study area (30 km²) is suitable for constructing farm ponds, while percolation tanks (on the ground) can be constructed in about 2.7% of the area (27 km²). Of the 83 sites identified for the recharge structures, 32 recharge sites are specially suited to the inhabitants because of their proximity. It is concluded that the integrated geospatial and MCDA techniques offer a useful and powerful tool for the planning of rainwater harvesting at a basin or sub-basin scale.     

The Influence of Agroforestry and Other Land-Use Types on the Persistence of a Sumatran Tiger (<Emphasis Type="Italic">Panthera tigris sumatrae</Emphasis>) Population: An Individual-Based Model Approach

The importance of preserving both protected areas and their surrounding landscapes as one of the major conservation strategies for tigers has received attention over recent decades. However, the mechanism of how land-use surrounding protected areas affects the dynamics of tiger populations is poorly understood. We developed Panthera Population Persistence (PPP)—an individual-based model—to investigate the potential mechanism of the Sumatran tiger population dynamics in a protected area and under different land-use scenarios surrounding the reserve. We tested three main landscape compositions (single, combined and real land-uses of Tesso-Nilo National Park and its surrounding area) on the probability of and time to extinction of the Sumatran tiger over 20 years in Central Sumatra. The model successfully explains the mechanisms behind the population response of tigers under different habitat landscape compositions. Feeding and mating behaviours of tigers are key factors, which determined population persistence in a heterogeneous landscape. All single land-use scenarios resulted in tiger extinction but had a different probability of extinction within 20 years. If tropical forest was combined with other land-use types, the probability of extinction was smaller. The presence of agroforesty and logging concessions adjacent to protected areas encouraged the survival of tiger populations. However, with the real land-use scenario of Tesso-Nilo National Park, tigers could not survive for more than 10 years. Promoting the practice of agroforestry systems surrounding the park is probably the most reasonable way to steer land-use surrounding the Tesso-Nilo National Park to support tiger conservation.     

Effects of Sea-Level Rise and Anthropogenic Development on Priority Bird Species Habitats in Coastal Georgia, USA

We modeled changes in area of five habitats, tidal-freshwater forest, salt marsh, maritime shrub-scrub (shrub), maritime broadleaf forest (oak) and maritime narrowleaf (pine) forest, in coastal Georgia, USA, to evaluate how simultaneous habitat loss due to predicted changes in sea level rise (SLR) and urban development will affect priority bird species of the south Atlantic coastal plain by 2100. Development rates, based on regional growth plans, were modeled at 1% and 2.5% annual urban growth, while SLR rates, based on the Intergovernmental Panel on Climate Change’s A1B mean and maximum scenarios, were modeled at 52 cm and 82 cm, respectively. SLR most greatly affected the shrub habitat with predicted losses of 35–43%. Salt marsh and tidal forest also were predicted to lose considerable area to SLR (20–45 and 23–35%, respectively), whereas oak and pine forests had lesser impact from SLR, 18–22% and 11–15%, respectively. Urban development resulted in losses of considerable pine (48–49%) and oak (53–55%) habitat with lesser loss of shrub habitat (21–24%). Under maximum SLR and urban growth, shrub habitat may lose up to 59–64% compared to as much as 62–65% pine forest and 74–75% oak forest. Conservation efforts should focus on protection of shrub habitat because of its small area relative to other terrestrial habitats and use by Painted Buntings (Passerina ciris), a Partners In Flight (PIF) extremely high priority species. Tidal forests also deserve protection because they are a likely refuge for forest species, such as Northern Parula and Acadian Flycatcher, with the decline of oak and pine forests due to urban development.     

A GIS analysis of suitability for construction aggregate recycling sites using regional transportation network and population density features

Aggregate is used in road and building construction to provide bulk, strength, support, and wear resistance. Reclaimed asphalt pavement (RAP) and reclaimed Portland cement concrete (RPCC) are abundant and available sources of recycled aggregate. In this paper, current aggregate production operations in Virginia, Maryland, and the District of Columbia are used to develop spatial association models for the recycled aggregate industry with regional transportation network and population density features.The cost of construction aggregate to the end user is strongly influenced by the cost of transporting processed aggregate from the production site to the construction site. More than 60% of operations recycling aggregate in the mid-Atlantic study area are located within 4.8 km (3 miles) of an interstate highway. Transportation corridors provide both sites of likely road construction where aggregate is used and an efficient means to move both materials and on-site processing equipment back and forth from various work sites to the recycling operations.Urban and developing areas provide a high market demand for aggregate and a ready source of construction debris that may be processed into recycled aggregate. Most aggregate recycling operators in the study area are sited in counties with population densities exceeding 77 people/km² (200 people/mile²). No aggregate recycling operations are sited in counties with less than 19 people/km² (50 people/mile²), reflecting the lack of sufficient long-term sources of construction debris to be used as an aggregate source, as well as the lack of a sufficient market demand for aggregate in most rural areas to locate a recycling operation there or justify the required investment in the equipment to process and produce recycled aggregate.Weights of evidence analyses (WofE), measuring correlation on an area-normalized basis, and weighted logistic regression (WLR), are used to model the distribution of RAP and RPCC operations relative to transportation network and population distribution data. The models can be used on a regional scale to quickly map the relative site suitability for a RAP or RPCC aggregate recycling operation in a particular area based on transportation network and population parameters. The results can be used to identify general areas to be further evaluated on a site-specific basis using more detailed marketplace information. As transportation or population features change due to planning or actual development, the models can be easily revised to reflect these changes.     

Efficiency of a protected-area network in a Mediterranean region: a multispecies assessment with raptors

Three different systems of designating protected areas in a Mediterranean region in southeastern Spain were studied, referring to their effectiveness and efficiency for protecting both the breeding territories and the suitable habitat of a set of ten raptor species. Taking into consideration the varying degrees of endangerment of these species, a map of multispecies conservation values was also drawn up and superimposed on the three protected-area systems studied. In order to compare the levels of protection afforded by the three systems, we considered two indices that measured their relative effectiveness and efficiency. The effectiveness estimated the proportion of territories or optimal habitat protected by the networks while efficiency implicitly considered the area of each system (percentage of breeding territories or optimal habitat protected per 1% of land protected). Overall, our results showed that the most efficient system was that formed by the set of regional parks and reserves (17 protected breeding territories per 100 km²), although, given its small total area, it was by far the least effective (only protecting the 21% of the breeding territories of all species and 17% of the area of high conservation value). The systems formed by the Special Protection Areas (designated under the EU “Birds Directive”) and by the Special Conservation Areas (designated under the EU “Habitats Directive”) notably increased the percentages of protected territories of all species (61%) and area of high conservation value (57%), but their efficiency was not as high as expected in most cases. The overall level of protection was high for all species except for the Lesser Kestrel (Falco naumanni), an endangered falcon that inhabits pseudo-steppe and traditional agricultural habitats, which are clearly underrepresented in the protected-area network of the study region.     

Landscape Planning for Agricultural Non–Point Source Pollution Reduction. II. Balancing Watershed Size,Number of Watersheds,and Implementation Effort

Agricultural non–point source (NPS) pollution poses a severe threat to water quality and aquatic ecosystems. In response, tremendous efforts have been directed toward reducing these pollution inputs by implementing agricultural conservation practices. Although conservation practices reduce pollution inputs from individual fields, scaling pollution control benefits up to the watershed level (i.e., improvements in stream water quality) has been a difficult challenge. This difficulty highlights the need for NPS reduction programs that focus efforts within target watersheds and at specific locations within target watersheds, with the ultimate goal of improving stream water quality. Fundamental program design features for NPS control programs—i.e., number of watersheds in the program, total watershed area, and level of effort expended within watersheds—have not been considered in any sort of formal analysis. Here, we present an optimization model that explores the programmatic and environmental trade-offs between these design choices. Across a series of annual program budgets ranging from $2 to $200 million, the optimal number of watersheds ranged from 3 to 27; optimal watershed area ranged from 29 to 214 km²; and optimal expenditure ranged from $21,000 to $35,000/km². The optimal program configuration was highly dependent on total program budget. Based on our general findings, we delineated hydrologically complete and spatially independent watersheds ranging in area from 20 to 100 km². These watersheds are designed to serve as implementation units for a targeted NPS pollution control program currently being developed in Wisconsin.     

人海和谐视角下沿海城市国土空间开发建设适宜性评价研究——以江苏省沿海地区为例

国土空间开发适宜性评价对合理开发国土空间、优化国土空间利用格局具有重要的科学指导价值。遵循陆海统筹原则,采用短板原理、线性加权求和法、空间叠加分析法,从自然环境适宜性、资源环境承载力两个方面构建适宜性评价指标体系,通过对经济社会统计数据、空间数据的处理叠加,对江苏省沿海城市南通、盐城、连云港国土空间开发适宜性进行评价与分析。研究结果表明,国土空间开发适宜性空间分异明显,江苏沿海地区不适宜、基本不适宜、基本适宜、适宜区域面积分别为14 710.37 km²、3376.18 km²、6776.30 km²、5695.64km²,分别占陆域国土总面积的46.76%、10.73%、21.54%、18.10%,各地级市外围区域保护与发展之间的矛盾较为突出。     

Long‐Term High‐Resolution Radar Rainfall Fields for Urban Hydrology

Accurate records of high‐resolution rainfall fields are essential in urban hydrology, and are lacking in many areas. We develop a high‐resolution (15 min, 1 km²) radar rainfall data set for Charlotte, North Carolina during the 2001‐2010 period using the Hydro‐NEXRAD system with radar reflectivity from the National Weather Service Weather Surveillance Radar 1988 Doppler weather radar located in Greer, South Carolina. A dense network of 71 rain gages is used for estimating and correcting radar rainfall biases. Radar rainfall estimates with daily mean field bias (MFB) correction accurately capture the spatial and temporal structure of extreme rainfall, but bias correction at finer timescales can improve cold‐season and tropical cyclone rainfall estimates. Approximately 25 rain gages are sufficient to estimate daily MFB over an area of at least 2,500 km², suggesting that robust bias correction is feasible in many urban areas. Conditional (rain‐rate dependent) bias can be removed, but at the expense of other performance criteria such as mean square error. Hydro‐NEXRAD radar rainfall estimates are also compared with the coarser resolution (hourly, 16 km²) Stage IV operational rainfall product. Stage IV is adequate for flood water balance studies but is insufficient for applications such as urban flood modeling, in which the temporal and spatial scales of relevant hydrologic processes are short. We recommend the increased use of high‐resolution radar rainfall fields in urban hydrology.     

Foraging areas of king penguins from Macquarie Island in relation to a marine protected area

Twenty-three king penguins (Aptenodytes patagonicus) from Macquarie Island were tracked by satellite during the late incubation period in 1998–1999 to determine the overlap of the foraging zone of king penguins with an area to be declared a marine protected area (MPA) near the island. While all penguins left the colony in an easterly direction and traveled clockwise back to the island, three penguins foraged in the northern parts of the general foraging area and stayed north of 56°S. The remaining 20 penguins ventured south and most crossed 59°S before returning to the island. The total foraging area was estimated to be 156,000 km² with 36,500 km² being most important (where penguins spent >150 hr in total). North-foraging penguins reached on average 331 ± 24 km from the colony compared to 530 ± 76 km for the south-foraging penguins. The latter traveled an average total distance of 1313 ± 176 km, while the northern foragers averaged 963 ± 166 km. Not only did the penguins spend the majority of their foraging time within the boundaries of the proposed MPA, they also foraged chiefly within the boundaries of a highly protected zone. Thus, the MPA is likely to encompass the foraging zone of king penguins, at least during incubation.     

A model for assessing habitat fragmentation caused by new infrastructures in extensive territories – Evaluation of the impact of the Spanish strategic infrastructure and transport plan

The aim of the present work is to design a model for evaluating the impact of planned infrastructures on species survival at the territorial scale by calculating a connectivity index. The method developed involves determining the effective distance of displacement between patches of the same habitat, simplifying earlier models so that there is no dependence on specific variables for each species. A case study is presented in which the model was used to assess the impact of the forthcoming roads and railways included in the Spanish Strategic Infrastructure and Transport Plan (PEIT, in its Spanish initials).This study took into account the habitats of peninsular Spain, which occupies an area of some 500,000 km². In this territory, the areas deemed to provide natural habitats are defined by Directive 92/43/EEC. The impact of new infrastructures on connectivity was assessed by comparing two scenarios, with and without the plan, for the major new road and railway networks. The calculation of the connectivity index (CI) requires the use of a raster methodology based on the Arc/Info geographical information system (GIS). The actual calculation was performed using a program written in Arc/Info Macro Language (AML); this program is available in FragtULs (Mancebo Quintana, 2007), a set of tools for calculating indicators of fragmentation caused by transport infrastructure (http://topografia.montes.upm.es/fragtuls.html).The indicator of connectivity proposed allows the estimation of the connectivity between all the patches of a territory, with no artificial (non-ecologically based) boundaries imposed. The model proposed appears to be a useful tool for the analysis of fragmentation caused by plans for large territories.     

Multivariate approach for surface water quality mapping with special reference to nitrate enrichment in Sugadaira,Nagano Prefecture (Japan)

The hydrochemical study of the surface water along with land-use/land-cover study of its catchment area is useful for determining its suitability for support to aquatic ecosystem and agricultural purposes. The surface water quality around the wetland in Sugadaira region, Japan, is being affected both by complex hydrogeochemical processes and by anthropogenic activity, mainly intensive agricultural practices. Statistical analysis was carried out in this study using surface water chemistry data to enable hydrochemical evaluation of the water quality based on the ionic constituents, water types, and factors controlling water quality. Results show that the general trend of various ions was found to be Ca²⁺ > Mg²⁺ > Na⁺ > K⁺ and HCO₃ ⁻ > NO₃ ⁻ > SO₄ ²⁻ > Cl⁻. Spatial distribution of water chemistry shows that enrichment of NO₃ ⁻ has taken place along one side of the wetland that is exposed directly to human settlement and agricultural practices. This study is vital considering that pollution in a wetland indicates that poor health of water resources in the region not only makes the situation alarming but also calls for immediate attention.     

AutoRAPID: A Model for Prompt Streamflow Estimation and Flood Inundation Mapping over Regional to Continental Extents

This article couples two existing models to quickly generate flow and flood‐inundation estimates at high resolutions over large spatial extents for use in emergency response situations. Input data are gridded runoff values from a climate model, which are used by the Routing Application for Parallel computatIon of Discharge (RAPID) model to simulate flow rates within a vector river network. Peak flows in each river reach are then supplied to the AutoRoute model, which produces raster flood inundation maps. The coupled tool (AutoRAPID) is tested for the June 2008 floods in the Midwest and the April‐June 2011 floods in the Mississippi Delta. RAPID was implemented from 2005 to 2014 for the entire Mississippi River Basin (1.2 million river reaches) in approximately 45 min. Discretizing a 230,000‐km² area in the Midwest and a 109,500‐km² area in the Mississippi Delta into thirty‐nine 1° by 1° tiles, AutoRoute simulated a high‐resolution (~10 m) flood inundation map in 20 min for each tile. The hydrographs simulated by RAPID are found to perform better in reaches without influences from unrepresented dams and without backwater effects. Flood inundation maps using the RAPID peak flows vary in accuracy with F‐statistic values between 38.1 and 90.9%. Better performance is observed in regions with more accurate peak flows from RAPID and moderate to high topographic relief.     

Improving Predictive Models of In‐Stream Phosphorus Concentration Based on Nationally‐Available Spatial Data Coverages

Spatial data are playing an increasingly important role in watershed science and management. Large investments have been made by government agencies to provide nationally‐available spatial databases; however, their relevance and suitability for local watershed applications is largely unscrutinized. We investigated how goodness of fit and predictive accuracy of total phosphorus (TP) concentration models developed from nationally‐available spatial data could be improved by including local watershed‐specific data in the East Fork of the Little Miami River, Ohio, a 1,290 km² watershed. We also determined whether a spatial stream network (SSN) modeling approach improved on multiple linear regression (nonspatial) models. Goodness of fit and predictive accuracy were highest for the SSN model that included local covariates, and lowest for the nonspatial model developed from national data. Septic systems and point source TP loads were significant covariates in the local models. These local data not only improved the models but enabled a more explicit interpretation of the processes affecting TP concentrations than more generic national covariates. The results suggest SSN modeling greatly improves prediction and should be applied when using national covariates. Including local covariates further increases the accuracy of TP predictions throughout the studied watershed; such variables should be included in future national databases, particularly the locations of septic systems.     

Application of SWAT with and without Variable Source Area Hydrology to a Large Watershed

In this study, two different versions of the Soil and Water Assessment Tool (SWAT) model were used to simulate the hydrology and biogeochemical response of the Cannonsville Reservoir watershed, in New York. The first version distributes overland flow in ways that are consistent with variable source area (VSA) hydrology driven by saturation excess runoff, whereas the second version is the standard version of SWAT. These two models were each calibrated for streamflow (Flow), particulate phosphorus (PP), total dissolved phosphorus (TDP), and sediment (Sed) against measured data from the 1,200 km² Cannonsville watershed. The standard version of the model yielded an r² between the measured and simulated data of 0.85, 0.73, 0.70, and 0.72 for Flow, Sed, TDP, and PP, respectively. The VSA version yielded an r² of 0.84, 0.69, 0.72, and 0.53 for Flow, Sed, TDP, and PP, respectively. The two models were then used to determine the maximum upper bound on the reduction in phosphorus loading by removing all of the corn in the watershed. The average reductions between the two models were 65 and 37% for PP and TDP, respectively. The VSA version was also used to estimate the effect of moving corn land in the watershed from the wettest, most runoff prone areas to the driest, least runoff prone areas, which cannot be done directly with the standard SWAT model.     

Identifying Urban Features from LiDAR for a High‐Resolution Urban Hydrologic Model

Light Detection and Ranging (LiDAR), is relatively inexpensive, provides high spatial resolution sampling at great accuracy, and can be used to generate surface terrain and land cover datasets for urban areas. These datasets are used to develop high‐resolution hydrologic models necessary to resolve complex drainage networks in urban areas. This work develops a five‐step algorithm to generate indicator fields for tree canopies, buildings, and artificial structures using Geographic Resources Analysis Support System (GRASS‐GIS), and a common computing language, Matrix Laboratory. The 54 km² study area in Parker, Colorado consists of twenty‐four 1,500 × 1,500 m LiDAR subsets at 1 m resolution with varying degrees of urbanization. The algorithm correctly identifies 96% of the artificial structures within the study area; however, application success is dependent upon urban extent. Urban land use fractions below 0.2 experienced an increase in falsely identified building locations. ParFlow, a three‐dimensional, grid‐based hydrological model, uses these building and artificial structure indicator fields and digital elevation model for a hydrologic simulation. The simulation successfully develops the complex drainage network and simulates overland flow on the impervious surfaces (i.e., along the gutters and off rooftops) made possible through this spatial analysis process.