scholarly journals Runoff Simulation Using Radar Rainfall Data.

1996 ◽  
Author(s):  
John C. Peters ◽  
Daniel J. Easton
Keyword(s):  
Rainfall Data ◽  
Runoff Simulation ◽  
Radar Rainfall

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.

scholarly journals Runoff Simulation of An Urban Drainage System Using Radar Rainfall Data

2013 ◽  
Vol 15 (3) ◽  
pp. 413-422 ◽  
Author(s):  
Na Rae Kang ◽  
Hui Seung Noh ◽  
Jong So Lee ◽  
Sang Hun Lim ◽  
Hung Soo Kim
Keyword(s):  
Drainage System ◽  
Rainfall Data ◽  
Urban Drainage ◽  
Runoff Simulation ◽  
Radar Rainfall ◽  
Urban Drainage System

scholarly journals Uncertainty of Flood Forecasting Based on Radar Rainfall Data Assimilation

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Xinchi Chen ◽  
Liping Zhang ◽  
Christopher James Gippel ◽  
Lijie Shan ◽  
Shaodan Chen ◽  
...  
Keyword(s):  
Quantitative Estimation ◽  
Poor Performance ◽  
Statistical Weight ◽  
Rainfall Data ◽  
Precipitation Forecast ◽  
Data Input ◽  
Runoff Simulation ◽  
Radar Rainfall ◽  
Precipitation Measurement ◽  
Using Data

Precipitation is the core data input to hydrological forecasting. The uncertainty in precipitation forecast data can lead to poor performance of predictive hydrological models. Radar-based precipitation measurement offers advantages over ground-based measurement in the quantitative estimation of temporal and spatial aspects of precipitation, but errors inherent in this method will still act to reduce the performance. Using data from White Lotus River of Hubei Province, China, five methods were used to assimilate radar rainfall data transformed from the classifiedZ-Rrelationship, and the postassimilation data were compared with precipitation measured by rain gauges. The five sets of assimilated rainfall data were then used as input to the Xinanjiang model. The effect of precipitation data input error on runoff simulation was analyzed quantitatively by disturbing the input data using the Breeding of Growing Modes method. The results of practical application demonstrated that the statistical weight integration and variational assimilation methods were superior. The corresponding performance in flood hydrograph prediction was also better using the statistical weight integration and variational methods compared to the others. It was found that the errors of radar rainfall data disturbed by the Breeding of Growing Modes had a tendency to accumulate through the hydrological model.

scholarly journals Long-Term Simulation of Daily Streamflow Using Radar Rainfall and the SWAT Model: A Case Study of the Gamcheon Basin of the Nakdong River, Korea

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Huiseong Noh ◽  
Jongso Lee ◽  
Narae Kang ◽  
Dongryul Lee ◽  
Hung Soo Kim ◽  
...  
Keyword(s):  
Swat Model ◽  
Rainfall Data ◽  
Nakdong River ◽  
Runoff Simulation ◽  
Spatiotemporal Resolution ◽  
Radar Rainfall ◽  
Daily Streamflow ◽  
The Nakdong River ◽  
Rain Radar ◽  
Better Than

In recent years, with the increasing need for improving the accuracy of hydrometeorological data, interests in rain-radar are also increasing. Accordingly, with high spatiotemporal resolution of rain-radar rainfall data and increasing accumulated data, the application scope of rain-radar rainfall data into hydrological fields is expanding. To evaluate the hydrological applicability of rain-radar rainfall data depending on the characteristics of hydrological model, this study appliedRgaugeandRradarto a SWAT model in the Gamcheon stream basin of the Nakdong River and analyzed the effect of rainfall data on daily streamflow simulation. The daily rainfall data forRgauge,RZ, andRKDPwere utilized as input data for the SWAT model. As a result of the daily runoff simulation for analysis periods usingRZ(P)andRKDP(P), the simulation which utilizedRgaugereflected the rainfall-runoff characteristics better than the simulations which appliedRZ(P)orRKDP(P). However, in the rainy or wet season, the simulations which utilizedRZ(P)orRKDP(P)were similar to or better than the simulation that appliedRgauge. This study reveals that analysis results and degree of accuracy depend significantly on rainfall characteristics (rainy season and dry season) and QPE algorithms when conducting a runoff simulation with radar.

Stormwater inflow prediction using radar rainfall data compressed by principal component analysis

2006 ◽  
Vol 1 (1) ◽  
Author(s):  
K. Katayama ◽  
K. Kimijima ◽  
O. Yamanaka ◽  
A. Nagaiwa ◽  
Y. Ono
Keyword(s):  
Principal Component Analysis ◽  
Prediction Model ◽  
Principal Components ◽  
Prediction Method ◽  
Principal Component ◽  
Component Analysis ◽  
Rainfall Data ◽  
Radar Rainfall ◽  
Input Variables ◽  
Inflow Prediction

This paper proposes a method of stormwater inflow prediction using radar rainfall data as the input of the prediction model constructed by system identification. The aim of the proposal is to construct a compact system by reducing the dimension of the input data. In this paper, Principal Component Analysis (PCA), which is widely used as a statistical method for data analysis and compression, is applied to pre-processing radar rainfall data. Then we evaluate the proposed method using the radar rainfall data and the inflow data acquired in a certain combined sewer system. This study reveals that a few principal components of radar rainfall data can be appropriate as the input variables to storm water inflow prediction model. Consequently, we have established a procedure for the stormwater prediction method using a few principal components of radar rainfall data.

scholarly journals Optimal Rainfall Estimation by Considering Elevation in the Han River Basin, South Korea

2013 ◽  
Vol 52 (4) ◽  
pp. 802-818 ◽  
Author(s):  
Seong-Sim Yoon ◽  
Deg-Hyo Bae
Keyword(s):  
South Korea ◽  
River Basin ◽  
Flood Control ◽  
Rainfall Data ◽  
Spatiotemporal Variability ◽  
Radar Rainfall ◽  
Mountainous Regions ◽  
Han River ◽  
Orographic Rainfall ◽  
Han River Basin

AbstractMore than 70% of South Korea has mountainous terrain, which leads to significant spatiotemporal variability of rainfall. The country is exposed to the risk of flash floods owing to orographic rainfall. Rainfall observations are important in mountainous regions because flood control measures depend strongly on rainfall data. In particular, radar rainfall data are useful in these regions because of the limitations of rain gauges. However, radar rainfall data include errors despite the development of improved estimation techniques for their calculation. Further, the radar does not provide accurate data during heavy rainfall in mountainous areas. This study presents a radar rainfall adjustment method that considers the elevation in mountainous regions. Gauge rainfall and radar rainfall field data are modified by using standardized ordinary cokriging considering the elevation, and the conditional merging technique is used for combining the two types of data. For evaluating the proposed technique, the Han River basin was selected; a high correlation between rainfall and elevation can be seen in this basin. Further, the proposed technique was compared with the mean field bias and original conditional merging techniques. Comparison with kriged rainfall showed that the proposed method has a lesser tendency to oversmooth the rainfall distribution when compared with the other methods, and the optimal mean areal rainfall is very similar to the value obtained using gauges. It reveals that the proposed method can be applied to an area with significantly varying elevation, such as the Han River basin, to obtain radar rainfall data of high accuracy.

Sign in / Sign up

Export Citation Format

Share Document