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Author: John Charles Peters
Publisher:
ISBN:
Category : Hydrologic models
Languages : en
Pages : 20
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Book Description
Author: John Charles Peters
Publisher:
ISBN:
Category : Hydrologic models
Languages : en
Pages : 20
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Book Description
Author: John Charles Peters
Publisher:
ISBN:
Category : Hydrologic models
Languages : en
Pages : 8
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Author: Amrit Bhusal
Publisher:
ISBN:
Category : Hydrologic models
Languages : en
Pages : 0
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Book Description
Hydrology Modeling using HEC-HMS (Hydrological Engineering Centre-Hydrologic Modeling System) is accepted globally for event-based or continuous simulation of the rainfall-runoff operation. Similarly, Machine learning is a fast-growing discipline that offers numerous alternatives suitable for hydrology research's high demands and limitations. Conventional and process-based models such as HEC-HMS are typically created at specific spatiotemporal scales and do not easily fit the diversified and complex input parameters. Therefore, in this research, the effectiveness of Random Forest, a machine learning model, was compared with HEC-HMS for the rainfall-runoff process. In addition, Point gauge observations have historically been the primary source of the necessary rainfall data for hydrologic models. However, point gauge observation does not provide accurate information on rainfall's spatial and temporal variability, which is vital for hydrological models. Therefore, this study also evaluates the performance of satellite and radar precipitation products for hydrological analysis. The results revealed that integrated Machine Learning and physical-based model could provide more confidence in rainfall-runoff and flood depth prediction. Similarly, the study revealed that radar data performance was superior to the gauging station's rainfall data for the hydrologic analysis in large watersheds. The discussions in this research will encourage researchers and system managers to improve current rainfall-runoff simulation models by application of Machine learning and radar rainfall data.
Author: Abhiru Aryal
Publisher:
ISBN:
Category : Meteorological satellites
Languages : en
Pages : 0
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Book Description
Climate change and urbanization causes the increasing challenges of flooding in urban watersheds. Even the rivers identified as non-vulnerable are causing catastrophic damage due to heavy flooding. So, several satellite and radar-based precipitation data are considered to study the watersheds with no gauge station or need recent precipitation data. Weather Radar (NEXRAD)arch, the accuracy of satellite-based precipitation data, Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks - Climate Data Record (PERSIANN-CDR), and radar-based precipitation data, Next Generation Weather Radar (NEXRAD), is evaluated in rainfall-runoff simulation considering Hydrological Engineering Centre-Hydrologic Modeling System (HEC-HMS) and Personal Computer Storm Water Management Model (PCSWMM), respectively. The primary research proposes a framework for modeling the rainfall-runoff process using PERSIANN-CDR and a floodplain map in an ungauged urban watershed. The one-dimensional Hydrologic Engineering Centre-River Analysis System (HEC-RAS) model generates a flood inundation map for the pertinent flooding occurrences from the acquired peak hydrograph, providing a quantifiable display of the inundation extent percentage. The second research uses the PCSWMMs to show the extent of flooding. It also employs the compromise programming method (CPM) to rank the most critical sub-catchments based on three parameters: slope, surface area, and impervious area. Three low-impact development (LID) strategies over the watershed determine the best flood management option. Therefore, the overall study presents a comprehensive framework for flood management in urban watersheds that integrates satellite precipitation data, hydrologic modeling, and LID strategies. The framework can provide an accurate flood-prone zone and help prioritize critical sub-catchments for flood management options. The study proposes using HEC-HMS and PCSWMM models to simulate and analyze interactions between rainfall, runoff, and the extent of the flood zone. Furthermore, LID can be applied to reduce flooding in urban watersheds. Overall, the framework can be helpful for policymakers and system managers to build the watershed's resilience during catastrophic flooding events caused by climate change and urbanization.
Author: William J. Charley
Publisher:
ISBN:
Category : Flood forecasting
Languages : en
Pages : 14
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Book Description
Author: Gregg C. Lusby
Publisher:
ISBN:
Category : Runoff
Languages : en
Pages : 72
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Book Description
Author: Yasuto Tachikawa
Publisher:
ISBN: 9781901502374
Category : Hydrologic models
Languages : en
Pages : 340
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Book Description
Author: Wei Lin
Publisher:
ISBN:
Category : Precipitation (Meteorology)
Languages : en
Pages : 216
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Book Description
Precipitation is one of the most important components contributing to hydrological dynamics. Spatially distributed precipitation data can be obtained by satellite, radar, rain gages, etc, to serve various purposes. Currently, the most commonly used precipitation data still rely on gage-based measurement techniques that provide timely precipitation information with high quality and reliability. The National Oceanic and Atmospheric Administration (NOAA) and its cooperative climate stations are the primary resources of this form of precipitation data at the federal level. For hydrological simulation of precipitation-runoff for a watershed, precipitation is a critical model input that has a significant impact on the certainty and accuracy of simulation. To better understand the hydrological process within Whitewater River Basin in Kansas, the Precipitation-Runoff Model System (PRMS) was applied to this area, where the Cooperative Atmosphere-Surface Exchange Study (CASES) has set up an intensively instrumented site managed by Hydrologic Science Team (HST) of Oregon State University for rainfall data collection. Two rainfall data sources, NOAA and HST, were used in this study to simulate the stream response to rainfall within the basin. Different simulation results were acquired compared and analyzed. The study concluded that better simulation results were obtained with MMS-PRMS using integrated spatially distributed precipitation data, which was not available as a standard NOAA product. For a large basin, it is necessary to collect precipitation data within the area of interest in addition to standard NOAA data to produce an accurate hydrological model. It was suggested that to guarantee the quality of precipitation-runoff simulation using MMS-PRMS, the coverage of each rain gage should not be larger than 40 to 50 square kilometers (about 15-20 square miles). It was also learned that the precipitation data from local supplementary measurements are unlikely to be a satisfactory substitute for current NOAA data in hydrological simulation due to the short time period of measurement. The combination of standard NOAA data and additional data from an intensively measured site, such as CASES, or from radar, would allow more for better simulation.
Author: Luis Garotte
Publisher:
ISBN:
Category : Hydrological forecasting
Languages : en
Pages : 406
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Book Description
A distributed model for real-time rainfall-runoff simulation during floods is presented. The model is called Distributed Basin Simulator. DBS uses information from a distributed hydrologic database to interpret rainfall information in real time. The model is largely based on the detailed topographical information provided by digital elevation models (DEM). Basin representation adopts the rectangular grid of the DEM, and other soil properties, input data and state variables are also represented as data layers using the same scheme. The basic objective is to map the topographically-driven evolution of saturated areas as the storm progresses. Two modes of runoff generation are simulated: infiltration excess runoff and return flow. DBS applies a kinematic model of infiltration to evaluate local runoff generation in grid elements, and also accounts for lateral moisture flow between elements in a simplified manner. The model was successfully calibrated for the Sieve basin. Object-oriented methodologies were applied in model design and implementation. The resulting computer package is called Real-time Interactive Basin Simulator. RIBS combines the distributed basin simulator and a hydrologic database within an interactive real-time framework. Model structure is flexible, and several modes of operation are possible: an off-line calibration mode, an on-line simulation mode and an on-line forecasting mode. RIBS has two graphic user interfaces: a synchronous, model-driven interface and an asynchronous, user-driven interface. The synchronous interface displays simulation results and forecasts in real time as the model progresses.
Author:
Publisher:
ISBN:
Category : Flood forecasting
Languages : en
Pages : 48
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Book Description
