scholarly journals Hydrological Modeling Approach Using Radar-Rainfall Ensemble and Multi-Runoff-Model Blending Technique

Water ◽  
2019 ◽  
Vol 11 (4) ◽  
pp. 850 ◽  
Author(s):  
Lee ◽  
Kang ◽  
Joo ◽  
Kim ◽  
Kim ◽  
...  
Keyword(s):  
Hydrological Modeling ◽  
Radar Data ◽  
Rainfall Data ◽  
Rainfall Runoff ◽  
Runoff Simulation ◽  
Radar Rainfall ◽  
Topographical Effects ◽  
Reservoir Regulation ◽  
Regulation Model ◽  
Runoff Model

The purpose of this study is to reduce the uncertainty in the generation of rainfall data and runoff simulations. We propose a blending technique using a rainfall ensemble and runoff simulation. To create rainfall ensembles, the probabilistic perturbation method was added to the deterministic raw radar rainfall data. Then, we used three rainfall-runoff models that use rainfall ensembles as input data to perform a runoff analysis: The tank model, storage function model, and streamflow synthesis and reservoir regulation model. The generated rainfall ensembles have increased uncertainty when the radar is underestimated, due to rainfall intensity and topographical effects. To confirm the uncertainty, 100 ensembles were created. The mean error between radar rainfall and ground rainfall was approximately 1.808–3.354 dBR. We derived a runoff hydrograph with greatly reduced uncertainty by applying the blending technique to the runoff simulation results and found that uncertainty is improved by more than 10%. The applicability of the method was confirmed by solving the problem of uncertainty in the use of rainfall radar data and runoff models.

scholarly journals Urban Runoff Simulation: How Do Land Use/Cover Change Patterning and Geospatial Data Quality Impact Model Outcome?

Water ◽  
2020 ◽  
Vol 12 (10) ◽  
pp. 2715
Author(s):  
Amnah Elaji ◽  
Wei Ji
Keyword(s):  
Land Use ◽  
Data Quality ◽  
Metropolitan Area ◽  
Hydrological Modeling ◽  
Urban Landscape ◽  
Urban Runoff ◽  
Geospatial Data ◽  
Rainfall Data ◽  
Runoff Simulation ◽  
Radar Rainfall

With the increase in global urbanization, satellite imagery and other types of geospatial data have been extensively used in urban landscape change research, which includes environmental modeling in order to assess the change impact on urban watersheds. For urban hydrological modeling, as a focus of this study, several related research questions are raised: (1) How sensitive are runoff simulation to land use and land cover change patterning? (2) How will input data quality impact the simulation outcome? (3) How effective is integrating and synthesizing various forms of geospatial data for runoff modeling? These issues were not fully or adequately addressed in previous related studies. With the aim of answering these questions as research objectives, we conducted a spatial land use and land cover (LULC) change analysis and an urban runoff simulation in the Blue River watershed in the Kansas City metropolitan area between 2003 and 2017. In this study, approaches were developed to incorporate the Hydrologic Engineering Center Hydrologic Modeling System (HEC-HMS) model with remote sensing, geographic information systems (GIS), and radar rainfall data. The impact of data quality on the model simulation outcome was also analyzed. The results indicate that there are no significant differences between simulated runoff responses in the two study years (2003 and 2017) due to spatial and temporal heterogeneity of urbanization processes in the region. While the metropolitan area has been experiencing remarkable urban development in the past few decades, the gain in built-up land in the study watershed during the study period is insignificant. On the other hand, the gain in vegetated land caused by forestation activities is offset by a decrease in farmland and grassland. The results show that increasing spatial data resolution does not necessarily or noticeably improve the HEC-HMS model performance or outcomes. Under these conditions, using Next Generation Weather Radar (NEXRAD) rainfall data in the simulation provides a satisfactory fit in hydrographs’ shapes, peak discharge amounts and time after calibration efforts, while they may overestimate the amount of rainfall as compared with gauge data. This study shows that the developed approach of synthesizing satellite, GIS, and radar rainfall data in hydrological modeling is effective and useful for incorporating urban landscape and precipitation change data in dynamic flood risk assessment at a watershed level.

Requirements for radar rainfall data in urban catchment modelling and control

1997 ◽  
Vol 36 (8-9) ◽  
pp. 13-18
Author(s):  
Georg Johann ◽  
Hans-Reinhard Verworn
Keyword(s):  
Radar Data ◽  
Rainfall Data ◽  
Rainfall Runoff ◽  
Radar Rainfall ◽  
Urban Catchment ◽  
Space Resolution ◽  
Time Space ◽  
Urban Catchments ◽  
The University ◽  
And Control

The use of radar rainfall data as input for storm runoff models can procure a real benefit if the hydrologic response of the watershed studied is strongly dependent on the spatial and temporal heterogeneity of precipitation over its area. In dynamic management of urban catchments rainfall runoff simulations are sensitive to the resolution of the input data. In this study the influence of varios time/space resolutions of radar rainfall data on the results of rainfall-runoff simulations is inverstigated. Therefore, X-band radar rainfall data with high resolution in space (0.5 × 0.5 km) and time (360° scan every second minute), are compared with radar data with 14 min time and 1 × 1 km space resolution as input for the HYSTEM/EXTRAN hydrodynamic model of a small urban catchment. The presented invstigations are realized within the research project “realtime control of a combined sewer system by radar estimates of precipitataon” carried out by the University of Hannover and the Emschergenossenschaft/Lippeverband.

Research on dam inflow analysis based on radar rainfall data

2020 ◽  
Author(s):  
Gian Choi ◽  
Hongjoon Shin ◽  
Seongsim Yoon
Keyword(s):  
Nuclear Power ◽  
Hydrological Modeling ◽  
Radar Data ◽  
Rainfall Data ◽  
Temporal And Spatial Distribution ◽  
Radar Rainfall ◽  
Hydroelectric Dam ◽  
Spatially Distributed ◽  
Dam Inflow ◽  
Inflow Discharge

<p>Estimation of dam inflow using rainfall needs for efficient and timely operation of dam. Accuracy rainfall data is important to estimate dam inflow. Currently, rainfall pattern has volatile temporal and spatial distribution. Dam inflow based on rainfall gauged data is inadequate for operating hydroelectric dam. Radar rainfall has been used as an alternative because radar data provides spatially distributed rainfall. In this study, we estimated inflow discharge for hydroelectric dam using both radar and rain gauged data to find a case to improve the accuracy. Hydrological modeling have been adopted to estimate inflow and based on rainfall data collected from 2018 to 2019.</p><p>This work was supported by KOREA HYDRO & NUCLEAR POWER CO., LTD(No. 2018-Tech-20)</p>

scholarly journals Application of NEXRAD Radar-Based Quantitative Precipitation Estimations for Hydrologic Simulation Using ArcPy and HEC Software

Water ◽  
2020 ◽  
Vol 12 (1) ◽  
pp. 273
Author(s):  
Younghyun Cho
Keyword(s):  
Data Processing ◽  
Spatial Data ◽  
Performance Test ◽  
Hydrological Modeling ◽  
Rainfall Data ◽  
Storm Event ◽  
Rainfall Runoff ◽  
Hydrologic Simulation ◽  
Great Opportunity ◽  
Spatially Distributed

Recent availability of various spatial data, especially for gridded rainfall amounts, provide a great opportunity in hydrological modeling of spatially distributed rainfall–runoff analysis. In order to support this advantage using gridded precipitation in hydrological application, (1) two main Python script programs for the following three steps of radar-based rainfall data processing were developed for Next Generation Weather Radar (NEXRAD) Stage III products: conversion of the XMRG format (binary to ASCII) files, geo-referencing (re-projection) with ASCII file in ArcGIS, and DSS file generation using HEC-GridUtil (existing program); (2) eight Hydrologic Engineering Center’s Hydrologic Modeling System (HEC-HMS) models of ModClark and SCS Unit Hydrograph transform methods for rainfall–runoff flow simulations using both spatially distributed radar-based and basin-averaged lumped gauged rainfall were respectively developed; and (3) three storm event simulations including a model performance test, calibration, and validation were conducted. For the results, both models have relatively high statistical evaluation values (Nash–Sutcliffe efficiency—ENS 0.55–0.98 for ModClark and 0.65–0.93 for SCS UH), but it was found that the spatially distributed rainfall data-based model (ModClark) gives a better fit regarding observed streamflow for the two study basins (Cedar Creek and South Fork) in the USA, showing less requirements to calibrate the model with initial parameter values. Thus, the programs and methods developed in this research possibly reduce the difficulties of radar-based rainfall data processing (not only NEXRAD but also other gridded precipitation datasets—i.e., satellite-based data, etc.) and provide efficiency for HEC-HMS hydrologic process application in spatially distributed rainfall–runoff simulations.

Effect of rainfall data errors on the optimized values of model parameters

Soil Research ◽  
1982 ◽  
Vol 20 (1) ◽  
pp. 15
Author(s):  
WC Boughton ◽  
FT Sefe
Keyword(s):  
Storage Capacity ◽  
Rainfall Data ◽  
Model Parameters ◽  
Similar Magnitude ◽  
Rainfall Runoff ◽  
Data Errors ◽  
Cast Doubt ◽  
Rainfall Runoff Model ◽  
Induced Changes ◽  
Runoff Model

The rainfall input to a rainfall-runoff model was arbitrarily increased and decreased in order to determine the magnitude of corresponding changes in optimized values of the model parameters. The optimized capacities of moisture stores representing surface storage capacity of a catchment changed by average amounts of +24% and -20% as rainfall input was changed by +10% and -10%, respectively. Values of other parameters showed changes of similar magnitude, but there was no uniformity in the magnitude of induced changes from catchment to catchment. The results cast doubt on the validity of relating optimized values of model parameters to physical characteristics of catchments.

scholarly journals Sensitivity Analysis to Investigate the Reliability of the Grid-Based Rainfall-Runoff Model

Water ◽  
2018 ◽  
Vol 10 (12) ◽  
pp. 1839 ◽  
Author(s):  
Mun-Ju Shin ◽  
Yun Choi
Keyword(s):  
Water Movement ◽  
Parameter Sensitivity ◽  
Low Flow ◽  
Rainfall Runoff ◽  
Runoff Simulation ◽  
Rainfall Events ◽  
Flow Calculation ◽  
Rainfall Runoff Model ◽  
Runoff Model ◽  
Grid Based

This study aimed to assess the suitability of the parameters of a physically based, distributed, grid-based rainfall-runoff model. We analyzed parameter sensitivity with a dataset of eight rainfall events that occurred in two catchments of South Korea, using the Sobol’ method. Parameters identified as sensitive responded adequately to the scale of the rainfall events and the objective functions employed. Parameter sensitivity varied depending on rainfall scale, even in the same catchment. Interestingly, for a rainfall event causing considerable runoff, parameters related to initial soil saturation and soil water movement played a significant role in low flow calculation and high flow calculation, respectively. The larger and steeper catchment exhibited a greater difference in parameter sensitivity between rainfall events. Finally, we found that setting an incorrect parameter range that is physically impossible can have a large impact on runoff simulation, leading to substantial uncertainty in the simulation results. The proposed analysis method and the results from our study can help researchers using a distributed rainfall-runoff model produce more reliable analysis results.

scholarly journals The Role of the Spatial Distribution of Radar Rainfall on Hydrological Modeling for an Urbanized River Basin in Japan

Water ◽  
2019 ◽  
Vol 11 (8) ◽  
pp. 1703 ◽  
Author(s):  
Shakti P. C. ◽  
Tsuyoshi Nakatani ◽  
Ryohei Misumi
Keyword(s):  
River Basin ◽  
Spatial Resolution ◽  
Hydrological Modeling ◽  
Distribution Patterns ◽  
Rainfall Data ◽  
Model Parameters ◽  
Radar Rainfall ◽  
Rain Events ◽  
The Impact ◽  
Hydrological Applications

Recently, the use of gridded rainfall data with high spatial resolutions in hydrological applications has greatly increased. Various types of radar rainfall data with varying spatial resolutions are available in different countries worldwide. As a result of the variety in spatial resolutions of available radar rainfall data, the hydrological community faces the challenge of selecting radar rainfall data with an appropriate spatial resolution for hydrological applications. In this study, we consider the impact of the spatial resolution of radar rainfall on simulated river runoff to better understand the impact of radar resolution on hydrological applications. Very high-resolution polarimetric radar rainfall (XRAIN) data are used as input for the Hydrologic Engineering Center–Hydrologic Modeling System (HEC-HMS) to simulate runoff from the Tsurumi River Basin, Japan. A total of 20 independent rainfall events from 2012–2015 were selected and categorized into isolated/convective and widespread/stratiform events based on their distribution patterns. First, the hydrological model was established with basin and model parameters that were optimized for each individual rainfall event; then, the XRAIN data were rescaled at various spatial resolutions to be used as input for the model. Finally, we conducted a statistical analysis of the simulated results to determine the optimum spatial resolution for radar rainfall data used in hydrological modeling. Our results suggest that the hydrological response was more sensitive to isolated or convective rainfall data than it was to widespread rain events, which are best simulated at ≤1 km and ≤5 km, respectively; these results are applicable in all sub-basins of the Tsurumi River Basin, except at the river outlet.

scholarly journals Preliminary Study of Computational Time Steps in a Physically Based Distributed Rainfall–Runoff Model

Water ◽  
2018 ◽  
Vol 10 (9) ◽  
pp. 1269 ◽  
Author(s):  
Yun Choi ◽  
Mun-Ju Shin ◽  
Kyung Kim
Keyword(s):  
Optimal Parameter ◽  
Computational Time ◽  
Rainfall Runoff ◽  
Runoff Simulation ◽  
Stream Network ◽  
Time Step ◽  
Simulation Results ◽  
Rainfall Runoff Model ◽  
Parameter Values ◽  
Runoff Model

The choice of the computational time step (dt) value and the method for setting dt can have a bearing on the accuracy and performance of a simulation, and this effect has not been comprehensively researched across different simulation conditions. In this study, the effects of the fixed time step (FTS) method and the automatic time step (ATS) method on the simulated runoff of a distributed rainfall–runoff model were compared. The results revealed that the ATS method had less peak flow variability than the FTS method for the virtual catchment. In the FTS method, the difference in time step had more impact on the runoff simulation results than the other factors such as differences in the amount of rainfall, the density of the stream network, or the spatial resolution of the input data. Different optimal parameter values according to the computational time step were found when FTS and ATS were used in a real catchment, and the changes in the optimal parameter values were smaller in ATS than in FTS. The results of our analyses can help to yield reliable runoff simulation results.

scholarly journals Modeling River Runoff Temporal Behavior through a Hybrid Causal–Hydrological (HCH) Method

Water ◽  
2020 ◽  
Vol 12 (11) ◽  
pp. 3137
Author(s):  
Santiago Zazo ◽  
José-Luis Molina ◽  
Verónica Ruiz-Ortiz ◽  
Mercedes Vélez-Nicolás ◽  
Santiago García-López
Keyword(s):  
Hydrological Modeling ◽  
Arma Model ◽  
Causal Modeling ◽  
Change Processes ◽  
Rainfall Runoff ◽  
Insufficient Amount ◽  
Runoff Series ◽  
Rainfall Runoff Model ◽  
The One ◽  
Runoff Model

The uncertainty in traditional hydrological modeling is a challenge that has not yet been overcome. This research aimed to provide a new method called the hybrid causal–hydrological (HCH) method, which consists of the combination of traditional rainfall–runoff models with novel hydrological approaches based on artificial intelligence, called Bayesian causal modeling (BCM). This was implemented by building nine causal models for three sub-basins of the Barbate River Basin (SW Spain). The models were populated by gauging (observing) short runoff series and from long and short hydrological runoff series obtained from the Témez rainfall–runoff model (T-RRM). To enrich the data, all series were synthetically replicated using an ARMA model. Regarding the results, on the one hand differences in the dependence intensities between the long and short series were displayed in the dependence mitigation graphs (DMGs), which were attributable to the insufficient amount of data available from the hydrological records and to climate change processes. The similarities in the temporal dependence propagation (basin memory) and in the symmetry of DMGs validate the reliability of the hybrid methodology, as well as the results generated in this study. Consequently, water planning and management can be substantially improved with this approach.

A Hydrometeorological Modeling Study of a Flash-Flood Event over Catalonia, Spain

2007 ◽  
Vol 8 (3) ◽  
pp. 282-303 ◽  
Author(s):  
A. Amengual ◽  
R. Romero ◽  
M. Gómez ◽  
A. Martín ◽  
S. Alonso
Keyword(s):  
Hydrological Modeling ◽  
Mesoscale Model ◽  
Flash Flood ◽  
Flood Event ◽  
Forecast Errors ◽  
Hydrological Response ◽  
Runoff Simulation ◽  
Management Procedures ◽  
Runoff Model ◽  
Modeling Study

Abstract During the early morning of 10 June 2000, the Catalonia region was affected by a hazardous convective rainfall episode that produced a large increase on flow regimes in many internal catchments of the region. The present modeling study is focused upon the Llobregat basin, the biggest internal catchment with a drainage area of 5040 km2. The first objective of the study is the characterization of the watershed hydrological response to this flash-flood event based on rain gauge data and the Hydrologic Engineering Center’s Hydrological Modeling System (HEC-HMS) runoff model. The HEC-HMS model has been calibrated using five episodes of similar torrential characteristics, and the effects of the spatial segmentation of the basin and of the temporal scale of the input rainfall field have been examined. These kinds of episodes present short recurrence intervals in Mediterranean Spain, and the use of mesoscale forecast driven runoff simulation systems for increasing the lead times of the emergency management procedures is a valuable issue to explore. The second objective uses NCEP and ECMWF analyses to initialize the nonhydrostatic fifth-generation Pennsylvania State University–NCAR Mesoscale Model (MM5) in order to simulate the 10 June 2000 flash-flood episode with appropriate space and time scales to force the runoff model. The final objective analyzes the sensitivity of the catchment’s response to the spatial and temporal uncertainty of the rainfall pattern based on an ensemble of perturbed MM5 simulations. MM5 perturbations are introduced through small shifts and changes in intensity of the precursor upper-level synoptic-scale trough. Main results indicate that 1) an optimum configuration of the runoff model can be clearly defined that best adjusts the simulated basin’s hydrological response to observed peak discharges, their timing, and total volume; 2) the MM5-control driven runoff simulation shows a reasonable reproduction of the observed discharge at the basin’s outlet and appears to be a suitable tool for the hydrometeorological forecasting of flash floods in the Llobregat basin as a whole; and 3) the ensemble of perturbed runoff simulations does not exhibit any relevant degradation of the forecast skill, and some of the members even outperform the control experiment at different stream gauge locations. That is, the catchment is relatively insensitive to rainfall forecast errors of a few tenths of kilometers and no more than 1–2 h.

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