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

Rogelis, María Carolina; Werner, Micha

doi:10.5194/hess-22-853-2018

Cited by 43 publications

(29 citation statements)

References 51 publications

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“…The hydrological modelling driven by the in situ observed and remotely sensed precipitation often span periods as long as several decades once the data are available [71,72]. In contrast, the time span of the hydrological modelling driven by the NWP-predicted precipitation is much shorter, as an NWP model commonly demands vast computational resources and computing time, particularly when it applies data assimilation and runs at a very fine grid spacing of 1 km [73][74][75][76]. Thus, to compare the effectivenesses of these different precipitation datasets on hydrological modelling, we focused our study on one short-term rainfall-runoff process and used it as a case study over the WJB watershed.…”

Section: Study Periodmentioning

confidence: 99%

Assessing Hydrological Modelling Driven by Different Precipitation Datasets via the SMAP Soil Moisture Product and Gauged Streamflow Data

Long

Zhang

2018

Remote Sensing

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To compare the effectivenesses of different precipitation datasets on hydrological modelling, five precipitation datasets derived from various approaches were used to simulate a two-week runoff process after a heavy rainfall event in the Wangjiaba (WJB) watershed, which covers an area of 30,000 km2 in eastern China. The five precipitation datasets contained one traditional in situ observation, two satellite products, and two predictions obtained from the Numerical Weather Prediction (NWP) models. They were the station observations collected from the China Meteorological Administration (CMA), the Integrated Multi-satellite Retrievals for Global Precipitation Measurement (GPM IMERG), the merged data of the Climate Prediction Center Morphing (merged CMORPH), and the outputs of the Weather Research and Forecasting (WRF) model and the WRF four-dimensional variational (4D-Var) data assimilation system, respectively. Apart from the outlet discharge, the simulated soil moisture was also assessed via the Soil Moisture Active Passive (SMAP) product. These investigations suggested that (1) all the five precipitation datasets could yield reasonable simulations of the studied rainfall-runoff process. The Nash-Sutcliffe coefficients reached the highest value (0.658) with the in situ CMA precipitation and the lowest value (0.464) with the WRF-predicted precipitation. (2) The traditional in situ observation were still the most reliable precipitation data to simulate the study case, whereas the two NWP-predicted precipitation datasets performed the worst. Nevertheless, the NWP-predicted precipitation is irreplaceable in hydrological modelling because of its fine spatiotemporal resolutions and ability to forecast precipitation in the future. (3) Gauge correction and 4D-Var data assimilation had positive impacts on improving the accuracies of the merged CMORPH and the WRF 4D-Var prediction, respectively, but the effectiveness of the latter on the rainfall-runoff simulation was mainly weakened by the poor quality of the GPM IMERG used in the study case. This study provides a reference for the applications of different precipitation datasets, including in situ observations, remote sensing estimations and NWP simulations, in hydrological modelling.

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Section: Study Periodmentioning

confidence: 99%

Assessing Hydrological Modelling Driven by Different Precipitation Datasets via the SMAP Soil Moisture Product and Gauged Streamflow Data

Long

Zhang

2018

Remote Sensing

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“…The majority of the WRF driven hydrological forecasts reported in literature are conducted in sites where snow processes do not occur (e.g., [6,9,48]). However, some studies that lack information of how snowmelt was handled report good hydrological forecasting results in areas where snow processes, including snowmelt, may occur next to or as a mix with rainfall (e.g.…”

Section: Snowmelt In Discharge Forecastingmentioning

confidence: 99%

“…Rogelis and Werner [6] used Weather Research and Forecasting (WRF) model forecasting ensembles for timely prediction of flash floods in mountain areas in tropical regions. Li et al [7] coupled WRF with hydrological Liuxihe model to extend the flood forecast lead time.…”

Section: Introductionmentioning

confidence: 99%

High-Resolution Discharge Forecasting for Snowmelt and Rainfall Mixed Events

Berezowski

Chybicki

2018

Water

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Discharge events induced by mixture of snowmelt and rainfall are strongly nonlinear due to consequences of rain-on-snow phenomena and snowmelt dependence on energy balance. However, they received relatively little attention, especially in high-resolution discharge forecasting. In this study, we use Random Forests models for 24 h discharge forecasting in 1 h resolution in a 105.9 km 2 urbanized catchment in NE Poland: Biala River. The forcing data are delivered by Weather Research and Forecasting (WRF) model in 1 h temporal and 4 × 4 km spatial resolutions. The discharge forecasting models are set in two scenarios with snowmelt and rainfall and rainfall only predictors in order to highlight the effect of snowmelt on the results (both scenarios use also pre-forecast discharge based predictors). We show that inclusion of snowmelt decrease the forecast errors for longer forecasts' lead times. Moreover, importance of discharge based predictors is higher in the rainfall only models then in the snowmelt and rainfall models. We conclude that the role of snowmelt for discharge forecasting in mixed snowmelt and rainfall environments is in accounting for nonlinear physical processes, such as initial wetting and rain on snow, which cannot be properly modelled by rainfall only.

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“…Studies on improving streamflow forecast accuracy are still fewer than comparable studies related to enhancing the accuracy of QPFs [15].There are various methods for the bias correction of QPFs; in previous studies, the assimilation of radar data with a hybrid approach [16] and the advanced regional prediction system (ARPS) for heavy precipitation have been reported [6]. The Bayesian model averaging [17], machine learning techniques [18], quantile regression [19], and QRF models have all been used as ensemble-based machine learning methods [12]. The findings of previous studies have shown that the forecast accuracy improved after applying different QPF post-processing methods [20].…”

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confidence: 99%

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

Jabbari

Bae

2020

Atmosphere

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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...

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

Cited by 43 publications

References 51 publications

Assessing Hydrological Modelling Driven by Different Precipitation Datasets via the SMAP Soil Moisture Product and Gauged Streamflow Data

Assessing Hydrological Modelling Driven by Different Precipitation Datasets via the SMAP Soil Moisture Product and Gauged Streamflow Data

High-Resolution Discharge Forecasting for Snowmelt and Rainfall Mixed Events

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

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