Hydrologic investigations of radar-rainfall error propagation to rainfall-runoff model hydrographs

Ghimire, Ganesh R.; Krajewski, Witold F.; Ayalew, Tibebu B.; Goska, Radosław

doi:10.1016/j.advwatres.2022.104145

Cited by 29 publications

(14 citation statements)

References 58 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Assuming accurate rainfall data from StageIV is available in advance for post-event analysis, the median KGE increases from 0.33 to 0.62 with a 48-hour lead time. This indicates that forecast errors in rainfall can account for a significant number of streamflow forecast errors, which is consistent with other studies (Ghimire et al, 2022). As a result, streamflow projections are significantly constrained by the accuracy of rainfall forecasts.…”

Section: Discussionsupporting

confidence: 90%

“…Historical data such as streamflow and rainfall observations can be obtained from the federal agencies. Since these are observed data, different radar rainfall products (Seo et al, 2019) would not make a big difference as studied by Ghimire et al (2022). The main data component in this phase is the future data for rainfall since different forecast products may provide different forecast performances and they may further affect our final streamflow output.…”

Section: Real-time Forecast Frameworkmentioning

confidence: 99%

See 1 more Smart Citation

Real-Time Streamflow Forecasting Framework, Implementation and Post-Analysis Using Deep Learning

Xiang¹,

Demir²

2022

Preprint

View full text Add to dashboard Cite

Rainfall-runoff modeling and streamflow prediction using deep learning algorithms have been studied significantly in the last few years. The majority of these studies focus on the simulation and testing of historical datasets. Deployment and operation of a real-time streamflow forecast model using deep learning will face additional data and computational challenges such as inaccurate rainfall forecast data and real-time data assimilation with limited studies guiding on these difficulties. We proposed a real-time streamflow forecast framework that includes pre-event model training using deep learning, real-time data acquisition, and post-event analysis. We implemented the framework for 124 USGS gauged watersheds across Iowa to forecast 120-hour streamflow rates since April 2021. This is the first time deep learning models have been used to predict streamflow in real-time operational settings at a large scale, and we anticipate seeing more real-time implementations of deep learning models in the future.

show abstract

Section: Discussionsupporting

confidence: 90%

Section: Real-time Forecast Frameworkmentioning

confidence: 99%

Real-Time Streamflow Forecasting Framework, Implementation and Post-Analysis Using Deep Learning

Xiang¹,

Demir²

2022

Preprint

View full text Add to dashboard Cite

show abstract

“…The CAbc software (Fundação Centro Tecnológico de Hidráulica, 2002) was used to determine the rain average point accumulated value in the basin, in which the accumulated precipitation plans extracted from the radar images obtained in the ASCI-Grid format were inserted (Figure 3). After the process of reading these files, the software computed the parameters of the distributed rain, with the daily accumulated being used for the hydrological simulation (Shakti et al, 2019;Ghimire et al, 2022).…”

Section: Rainfall Datamentioning

confidence: 99%

“…The hourly data provided from the meteorological radar was applied as the boundary condition for precipitation in the hydrological model. According to the literature (Ghimire et al, 2022), watersheds smaller than 1000 km 2 are more sensitive to the rain spatial variation effects, in the case of the Pinheiros River watershed, it is crucial to correctly determine the amount of volume that will drain to the upper and lower channel, which in turn will affect the flood control operation and for that, the pumped volume.…”

Section: Distributed Rainfall Datamentioning

confidence: 99%

“…Shakti et al (2019) emphasize that the hydrological response is more sensitive to spatial variation during convective rainfall events, these being best represented by resolutions ≤ 1km. Ghimire et al (2022) state that the impact of temporal resolution is more significant in smaller basins (≤ 1000 km 2 ) and that in the flow forecast, an accurate representation of the volume of precipitation in the watershed is essential.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Hydrological modeling using distributed rainfall data to represent the flow in urban watersheds

Amorim

Magalhães

Martins

et al. 2022

RBRH

View full text Add to dashboard Cite

Hydrological models are one of the most effective ways of assessing water behavior and flood risk, although the quality of their results is determined by the input data representativity, especially rainfall. Normally, only rain gauge data is used, unable to represent rain spatial variability. Aiming to reduce the model’s uncertainties, hydrological model performance was evaluated in determining the runoff based on distributed rainfall data applied in an urban watershed with macro drainage structures. A distributed rainfall data, derived from a conditional merging of radar and field measurements, was used as the hydrological model’s input data, and led to very accurate runoff results. The analysis of the results demonstrated that to model urban watersheds with accuracy, distributed rainfall data is required, as well as knowledge about the sewage and drainage systems, reinforcing the need to use tools that are compatible with the site complexity.

show abstract

Enhancing runoff simulation precision in the critical zone through spatiotemporal interpolation of areal rainfall with matrix decomposition

Sheng,

Chen,

Lin

et al. 2023

Hydrological Processes

View full text Add to dashboard Cite

Modelling hydrological process in the critical zone not only contributes to a better understanding of interactions across different Earth surface spheres but also holds significant practical implications for water resource management and disaster prevention. Rainfall‐runoff simulation in critical zones is particularly challenging due to the amalgamation of temporal and spatial complexity, rainfall variability, and data limitations. As a pivotal input variable of hydrological models, accurate estimation of areal rainfall is critical to successful runoff simulation. However, most estimation methods ignore temporal information, thereby increasing uncertainty in rainfall estimation and constraining the precision of rainfall‐runoff simulation. In this study, the matrix decomposition‐based estimation method (F‐SVD), which considers the spatial and temporal correlation of the rainfall process is employed to estimate areal rainfall. The superiority of the method in producing two‐dimensional rainfall information is evaluated through its application in runoff simulation with the Xin'anjiang model. The simulation results of selected flood events in the Jianxi basin in southeast China, spanning from 2009 to 2019, are compared with those of two widely used rainfall estimation methods, namely Arithmetical Mean (AM) and Thiessen Polygons (TP). The results show that (1) F‐SVD not only produces the highest Pearson correlation coefficient between rainfall and runoff series but also reduces the number of flood events with abnormal rainfall‐runoff relationships; (2) the Xin'anjiang model based on F‐SVD achieves the highest Nash‐Sutcliffe efficiency and lowest Relative Error, and performs best in simulating peak flow and its occurrence time as compared to AM and TP. This study contributes to a finer characterization of watershed rainfall distribution, enhancing the accuracy and sharpness of runoff simulation. It provides reliable data support for critical zone research and offers a scientific foundation for rationally allocating and managing water resources.

show abstract

Hydrologic investigations of radar-rainfall error propagation to rainfall-runoff model hydrographs

Cited by 29 publications

References 58 publications

Real-Time Streamflow Forecasting Framework, Implementation and Post-Analysis Using Deep Learning

Real-Time Streamflow Forecasting Framework, Implementation and Post-Analysis Using Deep Learning

Hydrological modeling using distributed rainfall data to represent the flow in urban watersheds

Enhancing runoff simulation precision in the critical zone through spatiotemporal interpolation of areal rainfall with matrix decomposition

Contact Info

Product

Resources

About