Ensemble Hydrometeorological Forecasts Using WRF Hourly QPF and TopModel for a Middle Watershed

Calvetti, Leonardo; Filho, Augusto José Pereira

doi:10.1155/2014/484120

Cited by 22 publications

(24 citation statements)

References 37 publications

(38 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Higher skill of the ensemble mean in comparison with deterministic forecasts has also been found in other flood forecasting systems driven by the WRF model (Calvetti et al, 2014) and other NWP models (Vincendon et al, 2011).…”

Section: Evaluating Discharge Forecastsupporting

confidence: 56%

Streamflow forecasts from WRF precipitation for flood early warning in mountain tropical areas

Rogelis

Werner

2018

Hydrol. Earth Syst. Sci.

View full text Add to dashboard Cite

Abstract. Numerical weather prediction (NWP) models are fundamental to extend forecast lead times beyond the concentration time of a watershed. Particularly for flash flood forecasting in tropical mountainous watersheds, forecast precipitation is required to provide timely warnings. This paper aims to assess the potential of NWP for flood early warning purposes, and the possible improvement that bias correction can provide, in a tropical mountainous area. The paper focuses on the comparison of streamflows obtained from the post-processed precipitation forecasts, particularly the comparison of ensemble forecasts and their potential in providing skilful flood forecasts. The Weather Research and Forecasting (WRF) model is used to produce precipitation forecasts that are post-processed and used to drive a hydrologic model. Discharge forecasts obtained from the hydrological model are used to assess the skill of the WRF model. The results show that post-processed WRF precipitation adds value to the flood early warning system when compared to zeroprecipitation forecasts, although the precipitation forecast used in this analysis showed little added value when compared to climatology. However, the reduction of biases obtained from the post-processed ensembles show the potential of this method and model to provide usable precipitation forecasts in tropical mountainous watersheds. The need for more detailed evaluation of the WRF model in the study area is highlighted, particularly the identification of the most suitable parameterisation, due to the inability of the model to adequately represent the convective precipitation found in the study area.

show abstract

Section: Evaluating Discharge Forecastsupporting

confidence: 56%

Streamflow forecasts from WRF precipitation for flood early warning in mountain tropical areas

Rogelis

Werner

2018

Hydrol. Earth Syst. Sci.

View full text Add to dashboard Cite

show abstract

“…The first examples of ESF for Brazilian cases are shown by Tucci et al (2003 and2008), who showed results of hindcasting experiments of seasonal streamflow forecasts for the rivers Uruguay and Grande. More recently, Calvetti and Pereira (2014), Collischonn et al (2013) and Meller (2013) described experiments of short-to medium-range ensemble flood forecasting. Fernández Bou et al (2015) showed the development of a methodology for 1-month flood forecasting in the upper region of the Uruguay River basin, at the Itá Hydroelectric Power Plant (HPP) reservoir.…”

mentioning

confidence: 99%

Performance of Deterministic and Probabilistic Hydrological Forecasts for the Short-Term Optimization of a Tropical Hydropower Reservoir

Fan

Schwanenberg

Alvarado

et al. 2016

Water Resour Manage

View full text Add to dashboard Cite

Hydropower is the most important source of electricity in Brazil. It is subject to the natural variability of water yield. One building block of the proper management of hydropower assets is the short-term forecast of reservoir inflows as input for an online, event-based optimization of its release strategy. While deterministic forecasts and optimization schemes are the established techniques for short-term reservoir management, the use of probabilistic ensemble forecasts and multi-stage stochastic optimization techniques is receiving growing attention. The present work introduces a novel, mass conservative scenario tree reduction in combination with a detailed hindcasting and closed-loop control experiments for a multipurpose hydropower reservoir in a tropical region in Brazil. The case study is the hydropower project Três Marias, which is operated with two main objectives: (i) hydroelectricity generation and (ii) flood control downstream. In the experiments, precipitation forecasts based on observed data, deterministic and probabilistic forecasts are used to generate streamflow forecasts in a hydrological model over a period of 2 years. Results for a perfect forecast show the potential benefit of the online optimization and indicate a desired forecast lead time of 30 days. In comparison, the use of actual forecasts of up to 15 days shows the practical benefit of Water Resour Manage (2016) operational forecasts, where stochastic optimization (15 days lead time) outperforms the deterministic version (10 days lead time) significantly. The range of the energy production rate between the different approaches is relatively small, between 78% and 80%, suggesting that the use of stochastic optimization combined with ensemble forecasts leads to a significantly higher level of flood protection without compromising the energy production.

show abstract

“…The complexity involved in forecasting is extensive, and precipitation has the most complex behavior compared to other meteorological forecasts. Errors in NWP model forecasts are caused by incomplete and inaccurate observations of data assimilation and escalated by the model's dynamic and physical approximations [8]. Although several studies have During the summer, South and North Korea experience torrential rainfall and storms, as has been reported in previous studies [22].…”

Section: Introductionmentioning

confidence: 94%

Improving Ensemble Forecasting Using Total Least Squares and Lead-Time Dependent Bias Correction

Jabbari

Bae

2020

Atmosphere

View full text Add to dashboard Cite

Numerical weather prediction (NWP) models produce a quantitative precipitation forecast (QPF), which is vital for a wide range of applications, especially for accurate flash flood forecasting. The under-and over-estimation of forecast uncertainty pose operational risks and often encourage overly conservative decisions to be made. Since NWP models are subject to many uncertainties, the QPFs need to be post-processed. The NWP biases should be corrected prior to their use as a reliable data source in hydrological models. In recent years, several post-processing techniques have been proposed. However, there is a lack of research on post-processing the real-time forecast of NWP models considering bias lead-time dependency for short-to medium-range forecasts. The main objective of this study is to use the total least squares (TLS) method and the lead-time dependent bias correction method-known as dynamic weighting (DW)-to post-process forecast real-time data. The findings show improved bias scores, a decrease in the normalized error and an improvement in the scatter index (SI). A comparison between the real-time precipitation and flood forecast relative bias error shows that applying the TLS and DW methods reduced the biases of real-time forecast precipitation. The results for real-time flood forecasts for the events of 2002, 2007 and 2011 show error reductions and accuracy improvements of 78.58%, 81.26% and 62.33%, respectively.Atmosphere 2020, 11, 300 2 of 20 focused on improving the prediction skills of NWP models, they have not been able to eliminate the uncertainties included in the NWP model QPFs [9]. Since the performance of hydrological models is sensitive to the forcing data and NWP models are often subject to uncertainties, the input data can be an important source of errors and deficiencies in streamflow forecasting. Therefore, QPFs need to be post-processed and their biases corrected prior to use as reliable data in hydrological models [10].As discussed in several studies, the QPFs produced by the NWP models are usually biased, and in some cases they are severely biased [11]. In previous studies, the accuracy was improved by combining different sources of precipitation and applying the quantile regression forest (QRF) method in the hydrological evaluations [12]. Others improved the forecast precipitation by using post-processing techniques to produce ensemble precipitation predictions (EPPs), for example, using radar precipitation estimates [13]. Statistical bias adjustment/correction is a way to improve the QPF accuracy by reducing its systematic errors through post-processing. Improving the accuracy of the NWP-based QPF by post-processing techniques can be done using methods such as model output statistics (MOS). In recent years, several post-processing techniques have been proposed to develop a statistical relationship between observations and NWP forecasts [10]. There have been previous attempts to decrease the errors of QPFs in hydrometeorological studies [2,14]. Studies on improving streamflow forecas...

show abstract

Ensemble Hydrometeorological Forecasts Using WRF Hourly QPF and TopModel for a Middle Watershed

Cited by 22 publications

References 37 publications

Streamflow forecasts from WRF precipitation for flood early warning in mountain tropical areas

Streamflow forecasts from WRF precipitation for flood early warning in mountain tropical areas

Performance of Deterministic and Probabilistic Hydrological Forecasts for the Short-Term Optimization of a Tropical Hydropower Reservoir

Improving Ensemble Forecasting Using Total Least Squares and Lead-Time Dependent Bias Correction

Contact Info

Product

Resources

About