Hydrogeology Journal

, Volume 18, Issue 6, pp 1413–1423

Modeling watershed rainfall–runoff relations using impervious surface-area data with high spatial resolution

  • Yuyu Zhou
  • Yeqiao Wang
  • Arthur J. Gold
  • Peter V. August
Paper

Abstract

A distributed object-based rainfall–runoff simulation (DORS) model with incorporation of detailed impervious surface-area (ISA) data, derived from digital true-color orthophotography data with high spatial resolution, was developed. This physically based model simulates hydrologic processes of precipitation interception, infiltration, runoff, evapotranspiration, change of soil moisture, change of water-table depth, runoff routing, groundwater routing, and channel-flow routing. The modeling processes take objects based on land-cover types as fundamental spatial units in order to reduce data volume, increase computational efficiency, strengthen representation of watersheds, and utilize the data in variable scales. US Geological Survey stream-gaging data were used to validate the temporal variation of simulated discharge within two watersheds in Rhode Island State. The ratio of absolute error to the mean and the Nash coefficient in the validation period are 7.2% and 0.90 for the first watershed, and 8.0% and 0.77 for the second watershed, respectively. The results indicate that the DORS model is able to capture the relationship between rainfall and runoff in the study area, and that it is applicable in the further study of ISA impacts on the water cycle and associated pollution problems. The results also demonstrate that the performance of the hydrologic simulation is improved with ISA data with high spatial resolution.

Keywords

Remote sensing Rainfall/runoff Hydrological modeling USA 

Modélisation des relations pluie–débit à l’échelle du bassin versant utilisant des données à haute résolution spatiale sur les surfaces imperméables

Résumé

Un modèle basé pluie-débit (DORS) incorporant des données détaillées sur les surfaces imperméables (ISA) dérivées d’orthophotographies numériques couleur haute résolution a été développé. Ce modèle à base physique simule les processus d’interception des précipitations, infiltration, ruissellement de surface, évapotranspiration, variations de l’humidité du sol et du niveau piézométrique. Il simule aussi l’écoulement superficiel, celui des eaux souterraines et le transfert hydrographique. La modélisation individualise des objets en fonction de l’occupation du sol et les définit en tant qu’unités spatiales afin de réduire le volume de données, d’augmenter l’efficacité du traitement numérique, d’améliorer la représentation des bassins versants et d’utiliser les données à des échelles variables. Les données des débits relatifs à deux cours d’eau de l’Etat de Rhode Island fournies par le US Geological Survey (USGS) ont été utilisées pour valider les variations temporelles des débits simulés. Sur la période de validation, le rapport de l’erreur absolue rapportée à la moyenne et le coefficient de Nash sont respectivement de 7.2% et 0.90 pour le premier bassin versant et de 8.0% et 0.77 pour le second. Les résultats indiquent que le modèle DORS est capable d’établir la relation pluie-débit dans la zone d’étude et qu’il est applicable aux autres études d’impact des ISA sur le cycle de l’eau et aux problèmes de pollution associés. Les résultats démontrent aussi que l’utilisation des données ISA à haute résolution spatiale améliore la performance de la simulation hydrologique.

Modelación de las relaciones cuenca–precipitación–escurrimiento usando datos areales de superficies impermeables con alta resolución especial

Resumen

Se desarrolló un modelo de simulación precipitación–escurrimiento basado en objetos distribuidos (DORS) con la incorporación de detallados datos areales de superficies impermeables (ISA), a partir de datos de ortofotográficos digitales de color verdadero con una alta resolución espacial. Este modelo de bases físicas simula los procesos hidrológicos de la intercepción de la precipitación, infiltración, escurrimiento, evapotranspiración, cambios en la humedad del suelo, cambios en la profundidad del nivel freático y la propagación del escurrimiento, de las aguas subterráneas y del flujo en canales. El proceso de modelación toma objetos apoyados en los tipos de cubierta del suelo como unidades espaciales fundamentales con el objeto de reducir el volumen de datos, incrementar la eficiencia computacional, fortalecer la representación de las cuencas, y utilizar los datos en escalas variables. Se utilizan las mediciones de las aguas superficiales del US Geological Survey para validar la variación temporal de la descarga simulada en el interior de dos cuencas en el Estado de Rhode Island. El cociente entre el error absoluto y el medio y el coeficiente de Nash en el período de validación son 7.2% y 0.90 para la primera cuenca, y 8.0% y 0.77 para la segunda cuenca, respectivamente. Los resultados indican que el modelo DORS es capaz de capturar la relación entre la precipitación y el escurrimiento en el área de estudio, y que es aplicable en el estudio ulterior de impactos ISA en el ciclo del agua y en problemas asociados de contaminación. Los resultados también demuestran que la bondad de la simulación hidrológica se mejora con los datos ISA con alta resolución espacial.

利用高空间分辨率的不透水地表表面积数据模拟流域-降雨-径流的关系

摘要

我们建立了一个基于对象的降雨-径流模拟 (DORS) 的分布式模型, 该模型耦合了来自数字真彩色高空间分辨率正射投影数据的详细的不透水地表表面积 (ISA) 数据。这个基于物理的模型模拟了降雨截留, 入渗, 径流, 蒸散发, 土壤湿度变化, 地下水埋深变化, 径流路径, 地下水路径以及渠道流路径的水文过程。模拟过程采用的对象是基于土地覆盖类型, 以其作为基础空间单位来降低数据量, 增加计算效率和增强流域的代表性以及在不同的尺度上利用数据。利用美国地质调查局的测流压数据来校正罗得岛州两个流域内排泄量时空变化的模型。校正时间内, 对于第一个流域和第二个流域, 模拟结果相对于平均和纳什系数的绝对误差分别是7.2%和0.9以及8.0%和0.77。结果表明DORS模型可以用来确定研究区降雨和径流的关系, 且在ISA对水循环影响以及相关的污染问题的进一步研究中是适用的。此外, 还证明了利用高空间分辨率的ISA数据提高了水文模拟的性能。

Modelação de relações bacia hidrográfica-precipitação-escoamento utilizando dados de áreas de superfície impermeável com elevada resolução espacial

Resumo

Desenvolveu-se um modelo de distribuição baseado na simulação da precipitação-escoamento (Distributed Object-based Rainfall–runoff Simulation–DORS) com incorporação de dados pormenorizados de áreas de superfície impermeável (Impervious Surface-Area–ISA), obtidos através de ortofotografias digitais de cor verdadeira com elevada resolução espacial. Este modelo, fisicamente baseado, simula os processos hidrológicos de intercepção da precipitação, infiltração, escoamento, evapotranspiração, mudança na humidade do solo, alteração da profundidade ao nível piezométrico, propagação do escoamento, propagação das águas subterrâneas e propagação de fluxo em canais. O processo de modelação considera como unidade espacial fundamental objectos baseados no tipo de ocupação do solo, de forma a reduzir o volume de dados, aumentar a eficiência computacional e fortalecer a representação das bacias hidrográficas, utilizando dados a escalas variáveis. Os dados de medição de caudais dos Serviços Geológicos dos EUA foram utilizados para validar as variações temporais das descargas simuladas em duas bacias hidrográficas localizadas no Estado de Rhode Island. A relação de erro absoluto para a média e o coeficiente de Nash no período de validação foi de 7.2% e 0.90 para a primeira bacia e de 8.0% e 0.77 para a segunda. Os resultados indicam que o modelo DORS é capaz de reproduzir a relação entre a precipitação e o escoamento na área de estudo e que é aplicável aos futuros estudos de impactes de ISA no ciclo hidrológico e aos problemas de poluição associados. Os resultados também demonstram que o desempenho da simulação hidrológica é melhorado com dados de ISA de elevada resolução espacial.

References

  1. Arnold CA Jr, Gibbons CJ (1996) Impervious surface coverage: the emergence of a key urban environmental indicator. J Am Plann Assoc 62:243–258CrossRefGoogle Scholar
  2. Aronica G, Cannarozzo M (2000) Studying the hydrological response of urban catchments using a semi-distributed linear non-linear model. J Hydrol 238:35–43CrossRefGoogle Scholar
  3. Bell VA, Moore RJ (1998) A grid-based distributed flood forecasting model for use with weather radar data: part 1, formulation. Hydrol Earth Syst Sci 2:265–281CrossRefGoogle Scholar
  4. Beven K (1993) Prophecy, reality and uncertainty in distributed hydrological modelling. Adv Water Resour 16:41–51CrossRefGoogle Scholar
  5. Beven K (2001) How far can we go in distributed hydrological modelling? Hydrol Earth Syst Sci 5:1–12CrossRefGoogle Scholar
  6. Boegh E, Thorsen M, Butts MB, Hansen S, Christiansen JS, Abrahamsen P, Hasager CB, Jensen NO, van der Keur P, Refsgaard JC, Schelde K, Soegaard H, Thomsen A (2004) Incorporating remote sensing data in physically based distributed agro-hydrological modelling. J Hydrol 287:279–299CrossRefGoogle Scholar
  7. Bouraoui F, Dillaha TA (2000) ANSWERS-2000: non-point-source nutrient planning model. J Environ Eng 126:1045–1055CrossRefGoogle Scholar
  8. Burns D, Vitvar T, McDonnell J, Hassett J, Duncan J, Kendall C (2005) Effects of suburban development on runoff generation in the Croton River basin, New York, USA. J Hydrol 311:266–281CrossRefGoogle Scholar
  9. Chen JM, Pavlic G, Brown L, Cihlar J, Leblanc SG, White HP, Hall RJ, Peddle D, King DJ, Trofymow JA, Swift EJ, Van der Sanden J, Pellikka P (2002) Derivation and Validation of Canada-wide coarse-resolution leaf area index maps using high-resolution satellite imagery and ground measurements. Remote Sens Environ 80:165–184CrossRefGoogle Scholar
  10. Chen JM, Chen X, Ju W, Geng X (2005) Distributed hydrological model for mapping evapotranspiration using remote sensing inputs. J Hydrol 305:15–39CrossRefGoogle Scholar
  11. Dickinson RE, Henderson-Sellers A, Rosenzweig C, Sellers PJ (1991) Evapotranspiration models with canopy resistance for use in climate models: a review. Agric For Meteorol 54:373–388CrossRefGoogle Scholar
  12. Dunn SM, Mackay R (1995) Spatial variation in evapotranspiration and the influence of land use on catchment hydrology. J Hydrol 171:49–73CrossRefGoogle Scholar
  13. Environmental Protection Agency (EPA) (1998) Estimation of infiltration rate in the vadose zone: compilation of simple mathematical models, vol II. EPA 600-R-97-128b, US EPA, Washington, DCGoogle Scholar
  14. Groffman PM, Bain DJ, Band LE, Belt KT, Brush GS, Grove JM, Pouyat RV, Yesilonis IC, Zipperer WC (2003) Down by the riverside: urban riparian ecology. Front Ecol Environ 1:315–321CrossRefGoogle Scholar
  15. Ivanov VY, Enrique RV, Rafael LB, Dara E (2004) Preserving high-resolution surface and rainfall data in operational-scale basin hydrology: a fully-distributed physically-based approach. J Hydrol 298:80–111CrossRefGoogle Scholar
  16. Jain MK, Kothyari UC, Raju KGR (2004) A GIS based distributed rainfall–runoff model. J Hydrol 299:107–135CrossRefGoogle Scholar
  17. Kirchner JW (2006) Getting the right answers for the right reasons: linking measurements, analyses, and models to advance the science of hydrology. Water Resour Res 42(3):W03S04CrossRefGoogle Scholar
  18. Kite GW (1995) Manual for the SLURP hydrological model. NHRI, Saskatoon, SKGoogle Scholar
  19. Kite GW, Kouwen N (1992) Watershed modeling using land classifications. Water Resour Res 28:3193–3200CrossRefGoogle Scholar
  20. Kouwen N, Soulis ED, Pietroniro A, Donald JR, Harrington RA (1993) Grouped response units for distributed hydrologic modeling. J Water Res Plann Manage 119:289–305CrossRefGoogle Scholar
  21. Kuchment LS, Demidiv VN, Naden PS, Cooper DM, Broadhurst P (1996) Rainfall–runoff modelling of the Ouse basin, North Yorkshire: an application of a physically based distributed model. J Hydrol 181:323–342CrossRefGoogle Scholar
  22. Leonard RA, Knisel WG, Still DA (1987) GLEAMS: groundwater loading effects of agricultural management systems. Trans Am Soc Agri Eng 30:1403–1418Google Scholar
  23. Neitsch SL, Arnold JG, Kiniry JR, Williams JR (2001) Soil and water tssessment tool (SWAT) user’s manual version 2000. Grassland Soil and Water Research Laboratory, Temple, TXGoogle Scholar
  24. Novak A, Wang Y (2004) Effects of suburban sprawl on Rhode Island’s forest: a Landsat view from 1972 to 1999. Northeast Nat 11:67–74CrossRefGoogle Scholar
  25. Ott B, Uhlenbrook S (2004) Quantifying the impact of land-use changes at the event and seasonal time scale using a process-orientated catchment model. Hydrol Earth Syst Sci 8:62–78CrossRefGoogle Scholar
  26. Paul MJ, Meyer JL (2001) Streams in the urban landscape. Annu Rev Ecol Syst 32:333–365CrossRefGoogle Scholar
  27. Philip JR (1983) Infiltration in one, two, and three dimensions. Procs. National Conference on Advances in Infiltration, Am. Soc. of Agric. Eng., St. Joseph, MI, pp 1–13Google Scholar
  28. Rawls WJ, Ahuja LR, Brakensiek DL, Shirmohammadi A (1992) Infiltration and soil water movement, chapt. 5. In: Maidment DR (ed) Handbook of hydrology. McGraw-Hill, New YorkGoogle Scholar
  29. Reed S, Koren V, Smith M, Zhang Z, Moreda F, Seo DJ, Butts MB, Participants DMIP (2004) Overall distributed model intercomparison project results. J Hydrol 298:27–60CrossRefGoogle Scholar
  30. RIDEM (Rhode Island Department of Environmental Management) (2003) The Greenwich Bay fish kill - August 2003: causes, impacts and responses. RIDEM, Providence, RIGoogle Scholar
  31. Schiff K, Bay S, Stransky C (2002) Characterization of stormwater toxicants from an urban watershed to freshwater and marine organisms. Urban Water 4:215–227CrossRefGoogle Scholar
  32. Schumann AH, Funke R, Schultz GA (2000) Application of a geographic information system for conceptual rainfall–runoff modeling. J Hydrol 240:45–61CrossRefGoogle Scholar
  33. Seth R, Norman P (2001) Effects of urbanization on streamflow in the Atlanta area (Georgia, USA): a comparative hydrologic approach. Hydrol Process 15:1441–1457CrossRefGoogle Scholar
  34. Singh VP (1995) Computer models of watershed hydrology. Water Resources, Highlands Ranch, COGoogle Scholar
  35. Singh VP, Frevert DK (2002) Mathematical models of small watershed hydrology and applications. Water Resources, Highlands Ranch, COGoogle Scholar
  36. Siriwardena L, Finlayson BL, McMahon TA (2006) The impact of land use change on catchment hydrology in large catchments: the Comet River, central Queensland, Australia. J Hydrol 326:199–214CrossRefGoogle Scholar
  37. Todini E (1996) The ARNO rainfall–runoff model. J Hydrol 175:339–382CrossRefGoogle Scholar
  38. van der Sande CJ, de Jong SM, de Roo APJ (2003) A segmentation and classification approach of IKONOS-2 imagery for land cover mapping to improve flood risk and flood damage assessment. Int J Appl Earth Observ Geoinform 4:217–229CrossRefGoogle Scholar
  39. Wegehenkel M, Heinrich U, Uhlemann S, Dunger V, Matschullat J (2006) The impact of different spatial land cover data sets on the outputs of a hydrological model: a modelling exercise in the Ucker catchment, north-east Germany. Phys Chem Earth 31:1075–1088Google Scholar
  40. Wigmosta MS, Vail LW, Lettenmaier DP (1994) A distributed hydrology-vegetation model for complex terrain. Water Resour Res 30:1665–1679CrossRefGoogle Scholar
  41. Wigmosta MS, Nijssen B, Storck P, Lettenmaier DP (2002) The distributed hydrology soil vegetation model. In: Singh VP, Frevert DK (eds) Mathematical models of small watershed hydrology and applications. Water Resources, Highland Ranch, COGoogle Scholar
  42. Wild EC, Nimirosk MT (2004) Estimated water use in the Pawcatuck Basin, southern Rhode Island and southeastern Connecticut, 1995–1996. US Geol Surv Sci Invest Rep 2004-5020, Reston, VAGoogle Scholar
  43. Williams JR (1969) Flood routing with variable travel time or variable storage coefficients. Trans Am Soc Agri Eng 12:100–103Google Scholar
  44. Wood EF, Sivapalan M, Beven KJ, Band LE (1988) Effects of spatial variability and scale with implications to hydrologic modeling. J Hydrol 102:29–47CrossRefGoogle Scholar
  45. Yang D, Oki T, Herath S, Musiake K (2002) A geomorphology-based hydrological model and its applications. In: Singh VP, Frevert DK (eds) Mathematical models of small watershed hydrology and applications. Water Resources, Highlands Ranch, COGoogle Scholar
  46. Zhang W, Ogawa K, Besheng Y, Yamaguchi Y (2000) A monthly stream flow model for estimating the potential changes of river runoff on the projected global warming. Hydrol Process 14:1851–1868CrossRefGoogle Scholar
  47. Zhou Y, Wang Y (2007) An assessment of impervious surface areas in Rhode Island. Northeast Naturalists 14:643–650CrossRefGoogle Scholar
  48. Zhou Y, Wang Y (2008) Extraction of impervious surface areas from high spatial resolution imageries by multiple agent segmentation and classification. Photogramm Eng Remote Sens 74:857–868Google Scholar
  49. Zhou Y, Zhu Q, Chen J, Wang Y, Liu J, Sun R, Tang S (2007) Observation and estimation of net primary productivity in Qilian Mountain, western China. J Environ Manage 85:574–584CrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2010

Authors and Affiliations

  • Yuyu Zhou
    • 1
  • Yeqiao Wang
    • 1
  • Arthur J. Gold
    • 1
  • Peter V. August
    • 1
  1. 1.Department of Natural Resources ScienceUniversity of Rhode IslandKingstonUSA

Personalised recommendations