Historical development of rainfall‐runoff modeling

Peel, Murray C.; McMahon, TA

doi:10.1002/wat2.1471

Cited by 49 publications

(25 citation statements)

References 130 publications

(177 reference statements)

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“…DL methods have been used in hydrology and meteorology for decades (Daniell, 1991;Halff et al, 1993;Dawson and Wilby, 1998;Wilby et al, 2003;Peel and McMahon, 2020). However, one architecture explicitly designed for time series simulation, the long short-term memory (LSTM) network (Hochreiter et al, 2001;Hochreiter, 1991), has recently demonstrated credible performance for modelling hydrological signatures across the continental United States (CONUS) (Kratzert et al, 2018(Kratzert et al, , 2019Duan et al, 2020;Feng et al, 2020;Gauch et al, 2021b;Fang et al, 2018Fang et al, , 2020.…”

Section: Introductionmentioning

confidence: 99%

Benchmarking data-driven rainfall–runoff models in Great Britain: a comparison of long short-term memory (LSTM)-based models with four lumped conceptual models

Lees

Buechel

Anderson

et al. 2021

Hydrol. Earth Syst. Sci.

119

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Abstract. Long short-term memory (LSTM) models are recurrent neural networks from the field of deep learning (DL) which have shown promise for time series modelling, especially in conditions when data are abundant. Previous studies have demonstrated the applicability of LSTM-based models for rainfall–runoff modelling; however, LSTMs have not been tested on catchments in Great Britain (GB). Moreover, opportunities exist to use spatial and seasonal patterns in model performances to improve our understanding of hydrological processes and to examine the advantages and disadvantages of LSTM-based models for hydrological simulation. By training two LSTM architectures across a large sample of 669 catchments in GB, we demonstrate that the LSTM and the Entity Aware LSTM (EA LSTM) models simulate discharge with median Nash–Sutcliffe efficiency (NSE) scores of 0.88 and 0.86 respectively. We find that the LSTM-based models outperform a suite of benchmark conceptual models, suggesting an opportunity to use additional data to refine conceptual models. In summary, the LSTM-based models show the largest performance improvements in the north-east of Scotland and in south-east of England. The south-east of England remained difficult to model, however, in part due to the inability of the LSTMs configured in this study to learn groundwater processes, human abstractions and complex percolation properties from the hydro-meteorological variables typically employed for hydrological modelling.

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Section: Introductionmentioning

confidence: 99%

Benchmarking data-driven rainfall–runoff models in Great Britain: a comparison of long short-term memory (LSTM)-based models with four lumped conceptual models

Lees

Buechel

Anderson

et al. 2021

Hydrol. Earth Syst. Sci.

119

View full text Add to dashboard Cite

show abstract

“…An overview of some of the bestknown hydrological models at the end of the 20th century can be found in the work by Singh (1995). Peel and McMahon (2020) provide a recent review on the development of runoff models.…”

Section: Catchment Model Developmentsmentioning

confidence: 99%

A retrospective on hydrological catchment modelling based on half a century with the HBV model

Seibert

Bergström

Bergström³

2022

Hydrol. Earth Syst. Sci.

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Abstract. Hydrological catchment models are important tools that are commonly used as the basis for water resource management planning. In the 1960s and 1970s, the development of several relatively simple models to simulate catchment runoff started, and a number of so-called conceptual (or bucket-type) models were suggested. In these models, the complex and heterogeneous hydrological processes in a catchment are represented by a limited number of storage elements and the fluxes between them. While computer limitations were a major motivation for such relatively simple models in the early days, some of these models are still used frequently despite the vast increase in computational opportunities. The HBV (Hydrologiska Byråns Vattenbalansavdelning) model, which was first applied about 50 years ago in Sweden, is a typical example of a conceptual catchment model and has gained large popularity since its inception. During several model intercomparisons, the HBV model performed well despite (or because of) its relatively simple model structure. Here, the history of model development, from thoughtful considerations of different model structures to modelling studies using hundreds of catchments and cloud computing facilities, is described. Furthermore, the wide range of model applications is discussed. The aim is to provide an understanding of the background of model development and a basis for addressing the balance between model complexity and data availability that will also face hydrologists in the coming decades.

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“…An overview of some of the best-known hydrological models at the end of the 20 th century can be found in the work by (Singh, 1995). Peel and McMahon (2020) provide a recent review on the development of runoff models.…”

Section: Early Types Of Hydrological Modelsmentioning

confidence: 99%

A retrospective on hydrological modelling based on half a century with the HBV model

Seibert

Bergström²

2021

Preprint

View full text Add to dashboard Cite

Abstract. Hydrological models are important tools that are commonly used as the basis for water resource management planning. In the 1970s the development of several relatively simple models started and a number of so-called conceptual (or bucket-type) models were suggested. In these models, the complex and heterogeneous hydrological processes in a catchment are represented by a limited number of storage elements and fluxes between these. While a major motivation for such relatively simple models in the early days were computational limitations, today some of these models are still used frequently despite vastly increased computational opportunities. The HBV model, which was first applied about 50 years ago in Sweden, is a typical example of a conceptual catchment model and has gained large popularity over the past 50 years. During several model intercomparisons, the HBV model performed well despite (or because of) its relatively simple model structure. Here, the history of model development from thoughtful considerations of different model structures to modelling studies using hundreds of catchments and cloud computing facilities, is described. Furthermore, the wide range of model applications is discussed. The aim is to provide an understanding of the background of model development and a basis for addressing the balance between model complexity and data availability, which will face hydrologists also in the coming decades.

show abstract

Historical development of rainfall‐runoff modeling

Cited by 49 publications

References 130 publications

Benchmarking data-driven rainfall–runoff models in Great Britain: a comparison of long short-term memory (LSTM)-based models with four lumped conceptual models

Benchmarking data-driven rainfall–runoff models in Great Britain: a comparison of long short-term memory (LSTM)-based models with four lumped conceptual models

A retrospective on hydrological catchment modelling based on half a century with the HBV model

A retrospective on hydrological modelling based on half a century with the HBV model

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