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

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.

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

Hydrology and Earth System Sciences ◽

10.5194/hess-13-99-2009 ◽

2009 ◽

Vol 13 (2) ◽

pp. 99-114 ◽

Cited By ~ 87

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

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Insights from a new methodology to optimize rain gauge weighting for rainfall-runoff models

10.5194/hess-2019-450 ◽

2019 ◽

Author(s):

Ashley J. Wright ◽

David E. Robertson ◽

Jeffrey P. Walker ◽

Valentijn R. N. Pauwels

Keyword(s):

Conceptual Understanding ◽

Flood Forecasting ◽

Rain Gauge ◽

Hydrological Models ◽

Rainfall Runoff ◽

Model Dynamics ◽

Areal Rainfall ◽

The Impact ◽

Streamflow Simulation ◽

Selection Of

Abstract. Floods continue to devastate societies and their economies. Resilient societies commonly incorporate flood forecasting into their strategy to mitigate the impact of floods. Hydrological models which simulate the rainfall-runoff process are at the core of flood forecasts. To date operational flood forecasting models use areal rainfall estimates that are based on geographical features. This paper introduces a new methodology to optimally blend the weighting of gauges for the purpose of obtaining superior flood forecasts. For a selection of 7 Australian catchments this methodology was able to yield improvements of 15.3 % and 7.1 % in optimization and evaluation periods respectively. Catchments with a low gauge density, or an overwhelming majority of gauges with a low proportion of observations available, are not well suited to this new methodology. Models which close the water balance and demonstrate internal model dynamics that are consistent with a conceptual understanding of the rainfall-runoff process yielded consistent improvement in streamflow simulation skill.

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

Water Science & Technology ◽

10.2166/wst.2014.466 ◽

2014 ◽

Vol 71 (1) ◽

pp. 31-37 ◽

Cited By ~ 27

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.

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

10.5194/egusphere-egu2020-1972 ◽

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>&#160;</div><div>&#160;</div>

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Areal rainfall estimation using moving cars as rain gauges – a modelling study

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-6-4737-2009 ◽

2009 ◽

Vol 6 (4) ◽

pp. 4737-4772

Author(s):

U. Haberlandt ◽

M. Sester

Keyword(s):

River Basin ◽

Research Question ◽

Rain Gauge ◽

Indicator Kriging ◽

High Time Resolution ◽

Rainfall Estimation ◽

Time Step ◽

Rain Gauges ◽

Areal Rainfall ◽

Rain Gauge Network

Abstract. Optimal spatial assessment of short-time step precipitation for hydrological modelling is still an important research question considering the poor observation networks for high time resolution data. The main objective of this paper is to present a new approach for rainfall observation. The idea is to consider motorcars as moving rain gauges with windscreen wipers as sensors to detect precipitation. This idea is easily technically feasible if the cars are provided with GPS and a small memory chip for recording the coordinates, car speed and wiper frequency. This study explores theoretically the benefits of such an approach. For that a valid relationship between wiper speed and rainfall rate considering uncertainty was assumed here. A simple traffic model is applied to generate motorcars on roads in a river basin. Radar data are used as reference truth rainfall fields. Rainfall from these fields is sampled with a conventional rain gauge network and with several dynamic networks consisting of moving motorcars. Those observed point rainfall data from the different networks are then used to calculate areal rainfall for different scales. Ordinary kriging and indicator kriging are applied for interpolation of the point data with the latter considering uncertain rainfall observation by cars e.g. according to a discrete number of windscreen wiper operation classes. The results are compared with the true values from the radar observations. The study is carried out for the 3300 km2 Bode river basin located in the Harz Mountains in Northern Germany. The results show, that the idea is theoretically feasible. Only a small portion of the cars needed to be equipped with sensors for sufficient areal rainfall estimation. Regarding the required sensitivity of the potential rain sensors in cars it could be shown, that often a few classes for rainfall observation are enough for satisfactory areal rainfall estimation. The findings of the study suggest also a revisiting of the rain gauge network optimisation problem.

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Assessment of rain-gauge networks using a probabilistic GIS based approach

Hydrology Research ◽

10.2166/nh.2013.042 ◽

2013 ◽

Vol 45 (4-5) ◽

pp. 551-562 ◽

Cited By ~ 19

Author(s):

Mojtaba Shafiei ◽

Bijan Ghahraman ◽

Bahram Saghafian ◽

Saket Pande ◽

Shervan Gharari ◽

...

Keyword(s):

Probabilistic Approach ◽

Rain Gauge ◽

Rainfall Estimation ◽

Acceptance Probability ◽

Rain Gauges ◽

Basin Scale ◽

Gis Environment ◽

Areal Rainfall ◽

Rain Gauge Network ◽

Hydrological Applications

Rain-gauge networks provide estimates of areal rainfall as a crucial input for hydrological applications. Hence, it is important to quantify the performance of a rain-gauge network and evaluate the contribution of each rain-gauge to the overall accuracy of areal rainfall estimation at basin scale. This paper evaluates the performance and augmentation of a rain-gauge network in a large basin in Iran. A probabilistic approach combined with a geographic information system (GIS) framework is applied, in order to assess the accuracy of point rainfall in terms of acceptance probability. A simple equation for calculating the acceptance probability is presented which facilitates the application of the probabilistic approach in a GIS environment. This approach analyzes the number and location of rain-gauges and quantifies each gauge's contribution to the accuracy of rainfall estimation over the study area. Results show that among 33 existing gauges, only 21 have significant effect on areal rainfall estimation while other 12 gauges have marginal contribution to the accuracy of the network. Also, by applying an augmentation algorithm, an optimal rain-gauge network with 28 gauges is formed.

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

10.5194/hess-2016-17 ◽

2016 ◽

Cited By ~ 1

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.

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

Hydrology and Earth System Sciences ◽

10.5194/hess-20-3907-2016 ◽

2016 ◽

Vol 20 (9) ◽

pp. 3907-3922 ◽

Cited By ~ 8

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.

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