Derivation of a Tank Model with a Conceptual Rainfall-Infiltration Process

doi:10.3741/jkwra.2006.39.1.047

Cited by 5 publications

(1 citation statement)

References 7 publications

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“…Tank models have been widely applied to various aspects of storm-runoff modeling, including integrating artificial neural networks for parameter optimization (Chen and Adams, 2006), inserting a conceptual rainfall-infiltration process (Park and Cho, 2006), improving the unit hydrograph concept (Yue and Hashino, 2000), and establishing a method for evaluating model parameters (Yokoo et al, 2001). While these studies applied tank models for different purposes, all applications included hydrologic pathways such as overland flow, subsurface flow, and groundwater flow.…”

Section: Modeling Justification and Conceptsmentioning

confidence: 99%

Modeling runoff dynamics from zero-order basins: implications for hydrological pathways

Kim

Sidle

Tsuboyama

2011

Hydrological Research Letters

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Abstract:Although zero-order basins (geomorphic hollows) are important components of headwater catchments, their hydrologic regime has not been thoroughly investigated. A multi-tank model approach is used to simulate flow from zero-order basins in Hitachi Ohta Experimental Watershed, Japan, and simulations are compared with six months of wet season flows. A three-tank model accurately simulated runoff for the 6-month period from basin (FA) with two zero-order basins and deep soils, whereas a two-tank model performed satisfactorily in a zero-order basin with shallower soils (ZB). Characteristics of flow paths were evaluated and the concept of "threshold response" was assessed in simulations. In FA, preferential flow from the upper outlet of Tank 1 only occurred during the two largest storms; no overland flow was simulated. Less rapid subsurface flow emitted from the side outlet of Tank 2 during large and several moderate-size storms. During small storms, no overland, preferential, or subsurface flows occurred. Water depth in Tank 3, which indicates shallow groundwater storage in FA, is highly correlated with 30-day antecedent rainfall. The concept of "threshold response" is evidenced by intermittent quick and moderate flows from Tanks 1 and 2, respectively.

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Section: Modeling Justification and Conceptsmentioning

confidence: 99%

Modeling runoff dynamics from zero-order basins: implications for hydrological pathways

Kim

Sidle

Tsuboyama

2011

Hydrological Research Letters

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Development and application of a simple hydrogeomorphic model for headwater catchments

Sidle

Kim

Tsuboyama

et al. 2011

Water Resources Research

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[1] We developed a catchment model based on a hydrogeomorphic concept that simulates discharge from channel-riparian complexes, zero-order basins (ZOB, basins ZB and FA), and hillslopes. Multitank models simulate ZOB and hillslope hydrological response, while kinematic wave models predict saturation overland runoff from riparian zones and route inputs from ZOB and riparian corridors through the channel. The model was parameterized and tested in the Hitachi Ohta Experiment Watershed, Japan. Tank models were parameterized for a 6 month period from May to October 1992, and these models were then tested for the same 6 month period in 1993. In ZB, with relatively shallower soils, total outflow for the 6 month period in 1993 was underpredicted by 25%. Better predictions were obtained for outflow from FA (deeper soils; À17%) and the entire catchment (À5%). Total runoff from the channel and riparian area depends on the ratio of this area to the total catchment area because this corridor is assumed to be saturated at all times. Stormflow response from ZOB was limited during relatively dry conditions and increased substantially during wetter conditions, especially in ZB, which has shallower soils (1.4 m of average); such effects were diminished in FA (deeper soils) and hillslopes. Outflow from ZB had the highest proportion of rapid flow, while slower flow dominates outflow from FA and hillslopes; these different responses appear to be mainly associated with soil depth and topography. Groundwater recharge, estimated by leakage from the lowermost tank in the models, was as high as 61 mm week À1 from ZB, with lesser recharge from other geomorphic components (18-21 mm week À1). These spatially explicit simulations provide a simpler approach to the greater data demands of distributed hydrologic models without compromising process function.Citation: Sidle, R. C., K. Kim, Y. Tsuboyama, and I. Hosoda (2011), Development and application of a simple hydrogeomorphic model for headwater catchments, Water Resour. Res., 47, W00H13,

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A Review on the Management of Water Resources Information based on Big Data and Cloud Computing

Kim¹,

Kang²,

Jung³

et al. 2016

Journal of Wetlands Research

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핵심용어 : 수자원 정보 관리, 빅 데이터, 클라우드 컴퓨팅 AbstractIn recent, the direction of water resources policy is changing from the typical plan for water use and flood control to the sustainable water resources management to improve the quality of life. This change makes the information related to water resources such as data collection, management, and supply is becoming an important concern for decision making of water resources policy. We had analyzed the structured data according to the purpose of providing information on water resources. However, the recent trend is big data and cloud computing which can create new values by linking unstructured data with structured data. Therefore, the trend for the management of water resources information is also changing. According to the paradigm change of information management, this study tried to suggest an application of big data and cloud computing in water resources field for efficient management and use of water. We examined the current state and direction of policy related to water resources information in Korea and an other country. Then we connected volume, velocity and variety which are the three basic components of big data with veracity and value which are additionally mentioned recently. And we discussed the rapid and flexible countermeasures about changes of consumer and increasing big data related to water resources via cloud computing. In the future, the management of water resources information should go to the direction which can enhance the value(Value) of water resources information by big data and cloud computing based on the amount of data(Volume), the speed of data processing(Velocity), the number of types of data(Variety). Also it should enhance the value(Value) of water resources information by the fusion of water and other areas and by the production of accurate information(Veracity) required for water management and prevention of disaster and for protection of life and property.

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Derivation of a Tank Model with a Conceptual Rainfall-Infiltration Process

Cited by 5 publications

References 7 publications

Modeling runoff dynamics from zero-order basins: implications for hydrological pathways

Modeling runoff dynamics from zero-order basins: implications for hydrological pathways

Development and application of a simple hydrogeomorphic model for headwater catchments

A Review on the Management of Water Resources Information based on Big Data and Cloud Computing

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