scholarly journals Simulating Typhoon Floods with Gauge Data and Mesoscale-Modeled Rainfall in a Mountainous Watershed

2005 ◽  
Vol 6 (3) ◽  
pp. 306-323 ◽  
Author(s):  
Ming-Hsu Li ◽  
Ming-Jen Yang ◽  
Ruitang Soong ◽  
Hsiao-Ling Huang
Keyword(s):  
Goodness Of Fit ◽  
Hydrological Model ◽  
Mesoscale Model ◽  
Time Lag ◽  
Rain Gauge ◽  
Rainfall Data ◽  
Time Lags ◽  
Distributed Hydrological Model ◽  
Mountainous Watershed ◽  
Gauge Data

Abstract A physically based distributed hydrological model was applied to simulate typhoon floods over a mountainous watershed in Taiwan. The meteorological forcings include the observed gauge rainfall data and the predicted rainfall data from a mesoscale meteorological model, the fifth-generation Pennsylvania State University–National Center for Atmospheric Research (PSU–NCAR) Mesoscale Model (MM5). This study investigates the flood responses of three Typhoons: Zeb (1998), Nari (2001), and Herb (1996), which possessed unique meteorological features and that all produced severe floods. The predicted basin-averaged rainfall hydrographs by the MM5 are compared with that interpreted by rain gauge data to reveal the discrepancies in rainfall peak amounts and time lags, and to explore their subsequent effects on flood generation. The simulated flood hydrographs at the Hsia-Yun station, which is upstream of the Shihmen Reservoir, are compared with observed flood discharges in terms of the amount and time lag of flood peaks. It is shown that the small discrepancy in rainfall peaks and phase lags could be significantly amplified in simulated flood responses of a mountainous watershed. The overall predictive skill of the distributed hydrological model with different rainfall inputs is examined with three parameters, which include the runoff ratio (RR), root-mean-square error (rmse), and goodness of fit (GOF). Although the runoff ratio for the MM5-predicted rainfall is superior to that for the observed gauge rainfall, the simulated hydrographs with observed gauge rainfall have smaller rmse and GOF values for three events. This study shows that the error in flood prediction with the mesoscale-modeled rainfall is mainly caused by the rainfall–peak difference, which arises from the inherent uncertainties in the mesoscale-modeled rainfalls over a mountainous terrain during the typhoon landfall periods.

scholarly journals Influence of Rainfall Data with Different Spatial Resolutions on Flood Forecasting Reliability

10.29007/74bp ◽  
2018 ◽  
Author(s):  
Mamoru Miyamoto ◽  
Kazuhiro Matsumoto
Keyword(s):  
River Discharge ◽  
Hydrological Model ◽  
Radar Data ◽  
Flood Forecasting ◽  
Rainfall Data ◽  
Error Assessment ◽  
Distributed Hydrological Model ◽  
Radar Rainfall ◽  
Convergence Results ◽  
Gauge Data

Recent advancements in precipitation observation technology make it possible to precisely describe the intensity and temporal-spatial distribution of heavy rainfall, which can cause severe floods and inundations. Such technologies have also increased the accuracy of flood forecasting. However, error factors in flood forecasting remain to be solved, originating in not only input data but also model structure and calibration. Thus, this study focused on convergence results of errors in parameter optimization of the PWRI Distributed Hydrological Model and the reproducibility of river discharge. The reliability of ground-gauge and C-band-radar rainfall is compared in terms of flood forecasting under the condition of the minimum error due to calibration. Although the convergence results showed that C-band radar rainfall was superior to ground gauge rainfall, both were equally effective in reproducing river discharge with a high NSE of 0.9 at a station with error assessment. On the other hand, the reproducibility of river discharge with C-band radar data was highly superior to that with ground gauge data at a station without error assessment. This indicates that grid-based high resolution rainfall data is necessary for basin-wide flood forecasting.

scholarly journals Multifractal Comparison of Reflectivity and Polarimetric Rainfall Data from C- and X-Band Radars and Respective Hydrological Responses of a Complex Catchment Model

2017 ◽  
Author(s):  
Igor Paz ◽  
Bernard Willinger ◽  
Auguste Gires ◽  
Laurent Monier ◽  
Christophe Zobrist ◽  
...  
Keyword(s):  
Hydrological Model ◽  
Radar Data ◽  
Rain Gauge ◽  
Distributed Hydrological Model ◽  
Space Resolution ◽  
Rain Gauges ◽  
X Band ◽  
Distributed Simulations ◽  
Rain Gauge Network ◽  
Catchment Model

This paper presents a comparison between rain gauges, C-band and X-band radar data over an instrumented and regulated catchment of the Paris region, as well as their respective hydrological impacts with the help of flow observations and a semi-distributed hydrological model. Both radars confirm the high spatial variability of the rainfall down to their space resolution (respectively one kilometer and 250 m) and therefore underscore limitations of semi-distributed simulations. The use of the polarimetric capacity of the Météo-France C-band radar was limited to corrections of the horizontal reflectivity and its rainfall estimates are adjusted with the help of a rain gauge network. On the contrary, neither calibration was performed for the polarimetric X-band radar of the Ecole des Ponts ParisTech (below called ENPC X-band radar), nor any optimization of its scans. In spite of that and the non-negligible fact that the catchment was much closer to the C-band radar than to the X-band radar (20 km vs. 40 km), the latter seems to perform at least as well as the former, but with a higher scale resolution. This characteristic was best highlighted with the help of a multifractal analysis of the respective radar data, which also shows that the X-band radar was able to pick up a few extremes that were smoothed out by the C-band radar.

scholarly journals In search for relationships between lightning and rainfall with a rectangular grid-box methodology

2009 ◽  
Vol 20 ◽  
pp. 51-56 ◽  
Author(s):  
S. C. Michaelides ◽  
K. Savvidou ◽  
K. A. Nicolaides ◽  
M. Charalambous
Keyword(s):  
Rain Gauge ◽  
Rainfall Data ◽  
Lightning Activity ◽  
Time Lags ◽  
Data Filtering ◽  
National Observatory ◽  
Rectangular Grid ◽  
Rain Gauge Network

Abstract. The rainfall and lightning activity associated with three depressions which affected the area of Cyprus were studied in order to identify possible relationships between them. The lightning data were provided by the National Observatory of Athens, Greece, and were spatially and statistically related to the corresponding rainfall measurements from the rain gauge network of the Cyprus Meteorological Service. The study was carried out by using a rectangular grid-box methodology and various combinations of lightning and rainfall data filtering and time-lags were tested.

scholarly journals Extending flood forecasting lead time in large watershed by coupling WRF QPF with distributed hydrological model

2016 ◽  
Author(s):  
Ji Li ◽  
Yangbo Chen ◽  
Huanyu Wang ◽  
Jianming Qin ◽  
Jie Li
Keyword(s):  
Lead Time ◽  
Hydrological Model ◽  
Southern China ◽  
Weather Forecast ◽  
Forecast Model ◽  
Flood Forecasting ◽  
Rain Gauge ◽  
Model Parameters ◽  
Distributed Hydrological Model ◽  
Flood Warning

Abstract. Long lead time flood forecasting is very important for large watershed flood mitigation as it provides more time for flood warning and emergency responses. Latest numerical weather forecast model could provide 1–15 days quantitative precipitation forecasting products at grid format, by coupling this product with distributed hydrological model could produce long lead time watershed flood forecasting products. This paper studied the feasibility of coupling the Liuxihe Model with the WRF QPF for a large watershed flood forecasting in southern China. The QPF of WRF products has three lead time, including 24 hour, 48 hour and 72 hour, the grid resolution is 20 km × 20 km. The Liuxihe Model is set up with freely downloaded terrain property, the model parameters were previously optimized with rain gauge observed precipitation, and re-optimized with WRF QPF. Results show that the WRF QPF has bias with the rain gauge precipitation, and a post-processing method is proposed to post process the WRF QPF products, which improves the flood forecasting capability. With model parameter re-optimization, the model's performance improves also, it suggests that the model parameters be optimized with QPF, not the rain gauge precipitation. With the increasing of lead time, the accuracy of WRF QPF decreases, so does the flood forecasting capability. Flood forecasting products produced by coupling Liuxihe Model with WRF QPF provides good reference for large watershed flood warning due to its long lead time and rational results.

scholarly journals Effect of radar rainfall time resolution on the predictive capability of a distributed hydrologic model

2010 ◽  
Vol 7 (5) ◽  
pp. 7995-8043 ◽  
Author(s):  
A. Atencia ◽  
M. C. Llasat ◽  
L. Garrote ◽  
L. Mediero
Keyword(s):  
Hydrological Model ◽  
Radar Data ◽  
Hydrologic Model ◽  
Rainfall Data ◽  
Distributed Hydrological Model ◽  
Probability Matching ◽  
Radar Rainfall ◽  
Distributed Hydrologic Model ◽  
Surface Data ◽  
Distributed Hydrological Models

Abstract. The performance of distributed hydrological models depends on the resolution, both spatial and temporal, of the rainfall surface data introduced. The estimation of quantitative precipitation from meteorological radar or satellite can improve hydrological model results, thanks to an indirect estimation at higher spatial and temporal resolution. In this work, composed radar data from a network of three C-band radars, with 6-minutal temporal and 2 × 2 km2 spatial resolution, provided by the Catalan Meteorological Service, is used to feed the RIBS distributed hydrological model. A Window Probability Matching Method (gage-adjustment method) is applied to four cases of heavy rainfall to improve the observed rainfall sub-estimation in both convective and stratiform Z/R relations used over Catalonia. Once the rainfall field has been adequately obtained, an advection correction, based on cross-correlation between two consecutive images, was introduced to get several time resolutions from 1 min to 30 min. Each different resolution is treated as an independent event, resulting in a probable range of input rainfall data. This ensemble of rainfall data is used, together with other sources of uncertainty, such as the initial basin state or the accuracy of discharge measurements, to calibrate the RIBS model using probabilistic methodology. A sensitivity analysis of time resolutions was implemented by comparing the various results with real values from stream-flow measurement stations.

scholarly journals Coupling a distributed grid based hydrological model and MM5 meteorological model for flooding alert mapping

2005 ◽  
Vol 2 ◽  
pp. 59-63 ◽  
Author(s):  
B. Tomassetti ◽  
E. Coppola ◽  
M. Verdecchia ◽  
G. Visconti
Keyword(s):  
Hydrological Model ◽  
Mesoscale Model ◽  
Extreme Rainfall ◽  
Mediterranean Area ◽  
Climate Change Signal ◽  
Distributed Hydrological Model ◽  
Meteorological Model ◽  
Extreme Rainfall Events ◽  
Applied Meteorology ◽  
Major Floods

Abstract. The increased number of extreme rainfall events seems to be one of the common feature of climate change signal all over the world (Easterlin et al., 2000; Meehl et al., 2000). In the last few years a large number of floods caused by extreme meteorological events has been observed over the river basins of Mediterranean area and they mainly affected small basins (few hundreds until few thousands of square kilometres of drainage area) . A strategic goal of applied meteorology is now to try to predict with high spatial resolution the segments of drainage network where floods may occur. A possible way to reach this aim is the coupling of meteorological mesoscale model with high resolution hydrological model. In this work few case studies of observed floods in the Italian Mediterranean area will be presented. It is shown how a distributed hydrological model, using the precipitation fields predicted by MM5 meteorological model, is able to highlight the area where the major floods may occur.

scholarly journals Relationships between lightning and rainfall intensities during rainy events in Cyprus

2010 ◽  
Vol 23 ◽  
pp. 87-92 ◽  
Author(s):  
S. Michaelides ◽  
K. Savvidou ◽  
K. Nicolaides
Keyword(s):  
Rain Gauge ◽  
Rainfall Data ◽  
Time Lags ◽  
Spatial Relationships ◽  
Time And Space ◽  
Data Set ◽  
Rain Gauges ◽  
Hourly Rainfall ◽  
Temporal And Spatial ◽  
The Relationship

Abstract. The objective of this work is to study the relationship between the number of lightning recorded by a network of lightning detectors and the amount of rainfall recorded by the network of automatic rain gauges, during rainy events in Cyprus. This study aims at revealing possible temporal and spatial "relationships" between rainfall and lightning intensities. The data used are based on the available records of hourly rainfall data and the "associated" lightning data, with respect to both time and space. The search for temporal and spatial relationships between lightning and rainfall is made by considering various time-lags between lightning and rainfall, and by varying the area around the rain gauge which the associated lightning data set refers to. The methodology adopted in this paper is a statistical one and rainy events registered under the European Project "FLASH" are examined herein.

scholarly journals Point-Based Rainfall Intensity Information System in Mt. Merapi Area by X-Band Radar

2019 ◽  
Vol 14 (1) ◽  
pp. 80-89 ◽  
Author(s):  
Santosa Sandy Putra ◽  
Banata Wachid Ridwan ◽  
Kazuki Yamanoi ◽  
Makoto Shimomura ◽  
Sulistiyani ◽  
...  
Keyword(s):  
Satellite Data ◽  
Early Warning Systems ◽  
Radar Data ◽  
Rain Gauge ◽  
Data Grid ◽  
Rainfall Data ◽  
Rainfall Events ◽  
Rain Gauge Data ◽  
X Band ◽  
Gauge Data

An X-band radar was installed in 2014 at Merapi Museum, Yogyakarta, Indonesia, to monitor pyroclastic and rainfall events around Mt. Merapi. This research aims to perform a reliability analysis of the point extracted rainfall data from the aforementioned newly installed radar to improve the performance of the warning system in the future. The radar data was compared with the monitored rain gauge data from Balai Sabo and the IMERG satellite data from NASA and JAXA (The Integrated Multi-satellitE Retrievals for GPM), which had not been done before. All of the rainfall data was compared on an hourly interval. The comparisons were conducted based on 11 locations that correspond to the ground rainfall measurement stations. The locations of the rain gauges are spread around Mt. Merapi area. The point rainfall information was extracted from the radar data grid and the satellite data grid, which were compared with the rain gauge data. The data were then calibrated and adjusted up to the optimum state. Based on January 2017–March 2018 data, it was obtained that the optimum state has a NSF value of 0.41 and R2value of 0.56. As a result, it was determined that the radar can capture around 79% of the hourly rainfall occurrence around Mt. Merapi area during the chosen calibration period, in comparison with the rain gauge data. The radar was also able to capture nearby 40–50% of the heavy rainfall events that pose risks of lahar. In contrast, the radar data performance in detecting drizzling and light rain types were quite precise (55% of cases), although the satellite data could detect slightly better (60% of cases). These results indicate that the radar sensitivity in detecting the extreme rainfall events must receive higher priority in future developments, especially for applications to the existing Mt. Merapi lahar early warning systems.

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