High-resolution land cover datasets, composite curve numbers, and storm water retention in the Tampa Bay, FL region

Reistetter, Joseph A.; Russell, Marc

doi:10.1016/j.apgeog.2010.12.005

Cited by 17 publications

(10 citation statements)

References 29 publications

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“…In this study, all the soil types, vegetation types and impervious surface are assumed under the condition of AMC-II in which adjustments to the corresponding CN for dry soils (AMC-I) and wet soils (AMC-III) must be conducted [38]. CN ISA is assigned a value of 98 due to its impermeable characteristic [15,17]. CN S depends largely on the soil type and antecedent moisture condition.…”

Section: Improved Composite Cn Computation Methodsmentioning

confidence: 99%

“…Canters et al (2006) calculated CN at the catchment level by combining impervious surface, vegetation, bare soil and water/shade information [14]. Joseph (2011) proposed CN can be calculated by integrating the percentages of impervious surface, tree canopy density and pervious surface [15]. Furthermore, CN may be calculated via the combination of water, dense forest, and sand [16].…”

Section: Introductionmentioning

confidence: 99%

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Estimating Composite Curve Number Using an Improved SCS-CN Method with Remotely Sensed Variables in Guangzhou, China

Fan

Deng

et al. 2013

Remote Sensing

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Abstract:The rainfall and runoff relationship becomes an intriguing issue as urbanization continues to evolve worldwide. In this paper, we developed a simulation model based on the soil conservation service curve number (SCS-CN) method to analyze the rainfall-runoff relationship in Guangzhou, a rapid growing metropolitan area in southern China. The SCS-CN method was initially developed by the Natural Resources Conservation Service (NRCS) of the United States Department of Agriculture (USDA), and is one of the most enduring methods for estimating direct runoff volume in ungauged catchments. In this model, the curve number (CN) is a key variable which is usually obtained by the look-up table of TR-55. Due to the limitations of TR-55 in characterizing complex urban environments and in classifying land use/cover types, the SCS-CN model cannot provide more detailed runoff information. Thus, this paper develops a method to calculate CN by using remote sensing variables, including vegetation, impervious surface, and soil (V-I-S). The specific objectives of this paper are: (1) To extract the V-I-S fraction images using Linear Spectral Mixture Analysis; (2) To obtain composite CN by incorporating vegetation types, soil types, and V-I-S fraction images; and (3) To simulate direct runoff under the scenarios with precipitation of 57mm (occurred once every five years by average) and 81mm (occurred once every ten years). Our experiment shows that the proposed method is

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Section: Improved Composite Cn Computation Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Estimating Composite Curve Number Using an Improved SCS-CN Method with Remotely Sensed Variables in Guangzhou, China

Fan

Deng

et al. 2013

Remote Sensing

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“…Because our research was focused on the hydrological benefits of green space in urban areas, only the three types of land cover representing urban green space (i.e., tree canopy, lawns, and farmland) were included in the calculations for our study area. For each pixel, 100% single-cover coverage was assumed due to the relatively high-spatial resolution, so that a unique CN value was assigned to each type of urban green space (Reistetter and Russell, 2011;Atkinson, 2012;Fan et al, 2013). Finally, the CN values for urban green space in different AMCs were determined using the TR-55 look-up tables (NRCS, 1986) (Table 2).…”

Section: Model Preparationmentioning

confidence: 99%

Potential reduction in urban runoff by green spaces in Beijing: A scenario analysis

Yao

Chen

Wei

et al. 2015

Urban Forestry & Urban Greening

119

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a b s t r a c tUrban green space provides multiple ecological benefits, among which the reduction of rainfall runoff is important for sustainable urban development, particularly for cities experiencing severe flooding and water hazards. However, the effectiveness of urban green space in mitigating runoff has not been fully determined. We evaluated potential reductions in surface runoff associated with urban green space in central Beijing under a greening scenario using the Soil Conservation Service Curve Number method. The results show that urban green space offers significant potential for runoff mitigation. In 2012, a total of 97.9 million m 3 of excess surface runoff was retained by urban green space; adding nearly 11% more tree canopy was projected to increase runoff retention by >30%, contributing to considerable benefits of urban rainwater regulation. At a more detailed scale, there were apparent internal variations. Urban function zones with >70% developed land showed less mitigation of runoff, while green zones (vegetation >60%), which occupied only 15.54% of the total area, contributed 31.07% of runoff reduction. Runoff reduction by urban green space, however, is influenced by many factors, such as rainfall, soil condition, and urban morphology. In regulating urban runoff, therefore, the priorities and integrating adaptive approaches to urban greening should be combined with compensatory and complementary measures.

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“…Researchers used remotely sensed data to map the boundaries of residential area and density to facilitate planning and construction of water infrastructure [127,150]. Landsat data were used to calculate the NDVI along with climate information and seasons to estimate water consumption for urban agriculture [151].…”

Section: Urban Hydrologymentioning

confidence: 99%

Supporting Global Environmental Change Research: A Review of Trends and Knowledge Gaps in Urban Remote Sensing

et al. 2014

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This paper reviews how remotely sensed data have been used to understand the impact of urbanization on global environmental change. We describe how these studies can support the policy and science communities' increasing need for detailed and up-to-date information on the multiple dimensions of cities, including their social, biological, physical, and infrastructural characteristics. Because the interactions between urban and surrounding areas are complex, a synoptic and spatial view offered from remote sensing is integral to measuring, modeling, and understanding these relationships. Here we focus on OPEN ACCESSRemote Sens. 2014, 6 3880 three themes in urban remote sensing science: mapping, indices, and modeling. For mapping we describe the data sources, methods, and limitations of mapping urban boundaries, land use and land cover, population, temperature, and air quality. Second, we described how spectral information is manipulated to create comparative biophysical, social, and spatial indices of the urban environment. Finally, we focus how the mapped information and indices are used as inputs or parameters in models that measure changes in climate, hydrology, land use, and economics.

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High-resolution land cover datasets, composite curve numbers, and storm water retention in the Tampa Bay, FL region

Cited by 17 publications

References 29 publications

Estimating Composite Curve Number Using an Improved SCS-CN Method with Remotely Sensed Variables in Guangzhou, China

Estimating Composite Curve Number Using an Improved SCS-CN Method with Remotely Sensed Variables in Guangzhou, China

Potential reduction in urban runoff by green spaces in Beijing: A scenario analysis

Supporting Global Environmental Change Research: A Review of Trends and Knowledge Gaps in Urban Remote Sensing

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