Mean areal rainfall improvement using radar rainfall estimation technique by considering geographic character for dam operation

2020 ◽  
Author(s):  
Seongsim Yoon ◽  
Hongjoon Shin ◽  
Gian Choi
Keyword(s):  
Nuclear Power ◽  
Heavy Rainfall ◽  
Mountainous Area ◽  
Accuracy Improvement ◽  
Radar Rainfall ◽  
Rain Gauges ◽  
Dam Operation ◽  
Areal Precipitation ◽  
Areal Rainfall ◽  
Dam Inflow

<p>Efficiently dam operation is necessary to secure water resources and to respond to floods. For the dam operation, the amount of dam inflow should be accurately calculate. Rainfall information is important for the amount of dam inflow estimation and prediction therefore rainfall should be observed accurately. However, it is difficult to observe the rainfall due to poor density of rain gauges because of the dam is located in the mountainous region. Moreover, ground raingauges are limitted to localized heavy rainfall, which is increasing in frequency due to climate changes. The advantage of radar is that it can obtain high-resolution grid rainfall data because radar can observe the spatial distribution of rainfall. The radar rainfall are less accurate than ground gauge data. For the accuracy improvement of radar rainfall, many adjustment methods using ground gauges, have been suggested. For dam basin, because the density of ground gauge is low, there are limitations when apply the bias adjustment methods. Especially, the localized heavy rainfall occurred in the mountainous area depending on the topography. In this study, we will develop a radar rainfall adjustment method considering the orographic effect. The method considers the elevation to obtain kriged rainfall and apply conditional merging skill for the accuracy improvement of the radar rainfall. Based on this method, we are going to estimate the mean areal precipitation for hydropower dam basin. And, we will compare and evaluate the results of various adjustment methods in term of mean areal precipitation and dam inflow.</p><p>This work was supported by KOREA HYDRO & NUCLEAR POWER CO., LTD (No. 2018-Tech-20)</p><div> </div><div> </div>

scholarly journals Comparative Evaluation of the OTT PARSIVEL2 Using a Collocated Two-Dimensional Video Disdrometer

2017 ◽  
Vol 34 (9) ◽  
pp. 2059-2082 ◽  
Author(s):  
S.-G. Park ◽  
Hae-Lim Kim ◽  
Young-Woong Ham ◽  
Sung-Hwa Jung
Keyword(s):  
Heavy Rainfall ◽  
Drop Diameter ◽  
Two Dimensional ◽  
Radar Rainfall ◽  
Rain Gauges ◽  
Empirical Relations ◽  
Raindrop Size ◽  
Rain Events ◽  
Using Data ◽  
Differential Reflectivity

AbstractThe performance of the OTT second-generation Particle Size Velocity (PARSIVEL2) laser weather sensor is evaluated by comparing it with a collocated two-dimensional video disdrometer (2DVD) and rain gauges using data collected over a total of 36 rain events. A comparison of raindrop size distributions (DSDs) between the 2DVD and two PARSIVEL2 reveals good agreement for weak rainfall rates below approximately 10 mm h−1 and for midsize drops with diameters between 0.6 and 4.0 mm irrespective of rainfall rates, whereas the PARSIVEL2 produces overestimations of large drops with diameters above 4 mm during heavy rainfall above approximately 20 mm h−1. The resultant DSD parameters of the PARSIVEL2 present overestimations of the mean diameter Dm in the normalized gamma function and the maximum drop diameter Dmax, and underestimations of the intercept parameter Nw and total number of drops NT. Furthermore, how the characteristics of DSDs from the PARSIVEL2 affect the polarimetric radar variables, such as differential reflectivity ZDR and specific differential phase KDP, is examined, as well as how these characteristics affect empirical relations required in radar hydrometeorological applications such as quantitative rainfall estimations. Based on these examinations, it can be concluded that the OTT PARSIVEL2 still produces overestimations of large drops and underestimations of small drops during heavy rainfall, similar to older models of PARSIVEL, despite significant improvements to the PARSIVEL2 system, and furthermore that the uses of PARSIVEL2 measurements can act as a source of error in radar hydrometeorological applications such as radar rainfall estimations.

scholarly journals Uncertainties on mean areal precipitation: assessment and impact on streamflow simulations

2009 ◽  
Vol 13 (2) ◽  
pp. 99-114 ◽  
Author(s):  
L. Moulin ◽  
E. Gaume ◽  
C. Obled
Keyword(s):  
Flood Forecasting ◽  
Error Model ◽  
Rainfall Runoff ◽  
Rainfall Estimation ◽  
Estimation Errors ◽  
Rain Gauges ◽  
Areal Precipitation ◽  
Areal Rainfall ◽  
Streamflow Simulation

Abstract. This paper investigates the influence of mean areal rainfall estimation errors on a specific case study: the use of lumped conceptual rainfall-runoff models to simulate the flood hydrographs of three small to medium-sized catchments of the upper Loire river. This area (3200 km2) is densely covered by an operational network of stream and rain gauges. It is frequently exposed to flash floods and the improvement of flood forecasting models is then a crucial concern. Particular attention has been drawn to the development of an error model for rainfall estimation consistent with data in order to produce realistic streamflow simulation uncertainty ranges. The proposed error model combines geostatistical tools based on kriging and an autoregressive model to account for temporal dependence of errors. It has been calibrated and partly validated for hourly mean areal precipitation rates. Simulated error scenarios were propagated into two calibrated rainfall-runoff models using Monte Carlo simulations. Three catchments with areas ranging from 60 to 3200 km2 were tested to reveal any possible links between the sensitivity of the model outputs to rainfall estimation errors and the size of the catchment. The results show that a large part of the rainfall-runoff (RR) modelling errors can be explained by the uncertainties on rainfall estimates, especially in the case of smaller catchments. These errors are a major factor limiting accuracy and sharpness of rainfall-runoff simulations, and thus their operational use for flood forecasting.

scholarly journals Areal rainfall estimation using moving cars – computer experiments including hydrological modeling

2016 ◽  
Author(s):  
E. Rabiei ◽  
U. Haberlandt ◽  
M. Sester ◽  
D. Fitzner ◽  
M. Wallner
Keyword(s):  
Spatial Resolution ◽  
Measurement Errors ◽  
Laboratory Experiments ◽  
Hydrological Modeling ◽  
Computer Experiments ◽  
Rainfall Estimation ◽  
Radar Rainfall ◽  
Rain Gauges ◽  
Temporal And Spatial ◽  
Areal Rainfall

Abstract. The need for high temporal and spatial resolution precipitation data for hydrological analyses has been discussed in several studies. Although rain gauges provide valuable information, a very dense rain gauge network is costly. As a result, several new ideas have been emerged to help estimating areal rainfall with higher temporal and spatial resolution. Rabiei et al. (2013) observed that moving cars, called RainCars (RCs), can potentially be a new source of data for measuring rainfall amounts. The optical sensors used in that study are designed for operating the windscreen wipers and showed promising results for rainfall measurement purposes. Their measurement accuracy has been quantified in laboratory experiments. Considering explicitly those errors, the main objective of this study is to investigate the benefit of using RCs for estimating areal rainfall. For that, computer experiments are carried out, where radar rainfall is considered as the reference and the other sources of data, i.e. RCs and rain gauges, are extracted from radar data. Comparing the quality of areal rainfall estimation by RCs with rain gauges and reference data helps to investigate the benefit of the RCs. The value of this additional source of data is not only assessed for areal rainfall estimation performance, but also for use in hydrological modeling. The results show that the RCs considering measurement errors derived from laboratory experiments provide useful additional information for areal rainfall estimation as well as for hydrological modeling. Even assuming higher uncertainties for RCs as obtained from the laboratory up to a certain level is observed practical.

scholarly journals Assessment of spatial uncertainty of heavy local rainfall using a dense gauge network

2018 ◽  
Author(s):  
Sungmin O ◽  
Ulrich Foelsche
Keyword(s):  
Heavy Rainfall ◽  
Extreme Rainfall ◽  
Rain Gauge ◽  
Spatial Uncertainty ◽  
Wet Season ◽  
Rainfall Events ◽  
Rain Gauges ◽  
Areal Rainfall ◽  
The Impact ◽  
Heavy Rainfall Events

Abstract. Hydrology and remote-sensing communities have made use of dense rain-gauge networks for studying rainfall uncertainty and variability. However, in most regions, these dense networks are only available at sub-pixel scales and over short periods of time. Just a few studies have applied a similar approach, employing dense gauge networks, to local-scale areas, which limits the verification of their results in other regions. Using 10-year rainfall measurements from a network of 150 rain gauges, we assess spatial uncertainty in observed heavy rainfall events. The network is located in southeastern Austria over an area of 20 km × 15 km with no significant orography. First, the spatial variability of rainfall in the region was characterised using a correlogram at daily and sub-daily scales. Differences in the spatial structure of rainfall events between wet and dry seasons are apparent and we selected heavy rainfall events, the upper 10 % of wettest days during the wet season, for further analyses because of their high potential for causing hazard risk. Secondly, we investigated uncertainty in estimating mean areal rainfall arising from a limited gauge density. The number of gauges required to obtain areal rainfall with > 20 % accuracy tends to increase roughly following a power law as time scale decreases. Lastly, the impact of spatial aggregation on extreme rainfall was examined using gridded rainfall data with horizontal grid spacings from 0.1° to 0.01°. The spatial scale dependence was clearly observed at high intensity thresholds and high temporal resolutions. Quantitative uncertainty information from this study can guide both data users and producers to estimate uncertainty in their own observational datasets, consequently leading to the rational use of the data in relevant applications. Our findings are generalisable to other plain regions in mid-latitudes, however the degree of uncertainty could be affected by regional variations, like rainfall type or topography.

scholarly journals Areal rainfall estimation using moving cars – computer experiments including hydrological modeling

2016 ◽  
Vol 20 (9) ◽  
pp. 3907-3922 ◽  
Author(s):  
Ehsan Rabiei ◽  
Uwe Haberlandt ◽  
Monika Sester ◽  
Daniel Fitzner ◽  
Markus Wallner
Keyword(s):  
Spatial Resolution ◽  
Measurement Errors ◽  
Laboratory Experiments ◽  
Hydrological Modeling ◽  
Computer Experiments ◽  
Rainfall Estimation ◽  
Radar Rainfall ◽  
Rain Gauges ◽  
Temporal And Spatial ◽  
Areal Rainfall

Abstract. The need for high temporal and spatial resolution precipitation data for hydrological analyses has been discussed in several studies. Although rain gauges provide valuable information, a very dense rain gauge network is costly. As a result, several new ideas have emerged to help estimating areal rainfall with higher temporal and spatial resolution. Rabiei et al. (2013) observed that moving cars, called RainCars (RCs), can potentially be a new source of data for measuring rain rate. The optical sensors used in that study are designed for operating the windscreen wipers and showed promising results for rainfall measurement purposes. Their measurement accuracy has been quantified in laboratory experiments. Considering explicitly those errors, the main objective of this study is to investigate the benefit of using RCs for estimating areal rainfall. For that, computer experiments are carried out, where radar rainfall is considered as the reference and the other sources of data, i.e., RCs and rain gauges, are extracted from radar data. Comparing the quality of areal rainfall estimation by RCs with rain gauges and reference data helps to investigate the benefit of the RCs. The value of this additional source of data is not only assessed for areal rainfall estimation performance but also for use in hydrological modeling. Considering measurement errors derived from laboratory experiments, the result shows that the RCs provide useful additional information for areal rainfall estimation as well as for hydrological modeling. Moreover, by testing larger uncertainties for RCs, they observed to be useful up to a certain level for areal rainfall estimation and discharge simulation.

scholarly journals Uncertainties on mean areal precipitation: assessment and impact on streamflow simulations

2008 ◽  
Vol 5 (4) ◽  
pp. 2067-2110 ◽  
Author(s):  
L. Moulin ◽  
E. Gaume ◽  
C. Obled
Keyword(s):  
Flood Forecasting ◽  
Error Model ◽  
Rainfall Runoff ◽  
Rainfall Estimation ◽  
Estimation Errors ◽  
Rain Gauges ◽  
Areal Precipitation ◽  
Areal Rainfall ◽  
Streamflow Simulation

Abstract. This paper investigates the influence of mean areal rainfall estimation errors on a specific case study: the use of lumped conceptual rainfall-runoff models to simulate the flood hydrographs of three small to medium-sized catchments of the upper Loire river. This area (3200 km2) is densely covered by an operational network of stream and rain gauges. It is frequently exposed to flash floods and the improvement of flood forecasting models is then a crucial concern. Particular attention has been drawn to the development of an error model for rainfall estimation consistent with data in order to produce realistic streamflow simulation uncertainty ranges. The proposed error model combines geostatistical tools based on kriging and an autoregressive model to account for temporal dependence of errors. It has been calibrated and partly validated for hourly mean areal precipitation rates. Simulated error scenarios were propagated into two calibrated rainfall-runoff models using Monte Carlo simulations. Three catchments with areas ranging from 60 to 3200 km2 were tested to reveal any possible links between the sensitivity of the model outputs to rainfall estimation errors and the size of the catchment. The results show that a large part of the rainfall-runoff (RR) modelling errors can be explained by the uncertainties on rainfall estimates, especially in the case of smaller catchments. These errors are a major factor limiting accuracy and sharpness of rainfall-runoff simulations, and thus their operational use for flood forecasting.

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>

Commercial microwave links instead of rain gauges: fiction or reality?

2014 ◽  
Vol 71 (1) ◽  
pp. 31-37 ◽  
Author(s):  
Martin Fencl ◽  
Jörg Rieckermann ◽  
Petr Sýkora ◽  
David Stránský ◽  
Vojtěch Bareš
Keyword(s):  
Rainfall Variability ◽  
A Priori ◽  
Rain Gauges ◽  
Precipitation Estimates ◽  
Spatio Temporal ◽  
Areal Rainfall ◽  
Quantitative Precipitation Estimates ◽  
Commercial Microwave Links ◽  
Unique Dataset

Commercial microwave links (MWLs) were suggested about a decade ago as a new source for quantitative precipitation estimates (QPEs). Meanwhile, the theory is well understood and rainfall monitoring with MWLs is on its way to being a mature technology, with several well-documented case studies, which investigate QPEs from multiple MWLs on the mesoscale. However, the potential of MWLs to observe microscale rainfall variability, which is important for urban hydrology, has not been investigated yet. In this paper, we assess the potential of MWLs to capture the spatio-temporal rainfall dynamics over small catchments of a few square kilometres. Specifically, we investigate the influence of different MWL topologies on areal rainfall estimation, which is important for experimental design or to a priori check the feasibility of using MWLs. In a dedicated case study in Prague, Czech Republic, we collected a unique dataset of 14 MWL signals with a temporal resolution of a few seconds and compared the QPEs from the MWLs to reference rainfall from multiple rain gauges. Our results show that, although QPEs from most MWLs are probably positively biased, they capture spatio-temporal rainfall variability on the microscale very well. Thus, they have great potential to improve runoff predictions. This is especially beneficial for heavy rainfall, which is usually decisive for urban drainage design.

scholarly journals Radar Rainfall Estimation in Morocco: Quality Control and Gauge Adjustment

Hydrology ◽  
2019 ◽  
Vol 6 (2) ◽  
pp. 41 ◽  
Author(s):  
Zahra Sahlaoui ◽  
Soumia Mordane
Keyword(s):  
Positive Impact ◽  
Radar Reflectivity ◽  
Radar Rainfall ◽  
Rain Gauges ◽  
Impact Bias ◽  
African Rainfall ◽  
Precipitation Structure ◽  
Radar Rainfall Estimate ◽  
The Impact

This study focused on investigating the impact of gauge adjustment on the rainfall estimate from a Moroccan C-band weather radar located in Khouribga City. The radar reflectivity underwent a quality check before deployment to retrieve the rainfall amount. The process consisted of clutter identification and the correction of signal attenuation. Thereafter, the radar reflectivity was converted into rainfall depth over a period of 24 h. An assessment of the accuracy of the radar rainfall estimate over the study area showed an overall underestimation when compared to the rain gauges (bias = −6.4 mm and root mean square error [RMSE] = 8.9 mm). The adjustment model was applied, and a validation of the adjusted rainfall versus the rain gauges showed a positive impact (bias = −0.96 mm and RMSE = 6.7 mm). The case study conducted on December 16, 2016 revealed substantial improvements in the precipitation structure and intensity with reference to African Rainfall Climatology version 2 (ARC2) precipitations.

Sign in / Sign up

Export Citation Format

Share Document