Streamflow prediction in ungauged basins: analysis of regionalization methods in a hydrologically heterogeneous region of Mexico

Arsenault, R.J.; Breton-Dufour, Mélissa; Poulin, Annie; Dallaire, Gabrielle; Romero-López, Rabindranarth

doi:10.1080/02626667.2019.1639716

Cited by 56 publications

(28 citation statements)

References 43 publications

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“…Yang, Magnusson, Rizzi, et al (2018) made a comprehensive assessment of the regionalization methods in the high latitudes of Norway and found that distance‐based similarity approaches (spatial proximity, physical similarity) performed better than regression‐based approaches. Arsenault, Breton‐Dufour, Poulin, Dallaire, and Romero‐Lopez (2019) also evaluated three regionalization methods in Mexico and discovered that the donor‐based spatial‐proximity regionalization method was to be preferred in semi‐arid and humid ungauged catchments. These results agree with previous conclusions.…”

Section: Hydrological Regionalization Methodsmentioning

confidence: 99%

Regionalization of hydrological modeling for predicting streamflow in ungauged catchments: A comprehensive review

et al. 2020

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Runoff prediction in ungauged and scarcely gauged catchments is a key research field in surface water hydrology. There have been numerous studies before and since the launch of the predictions in ungauged basins (PUB) initiative by the International Association of Hydrological Sciences in 2003. This study critically reviews and assesses the decadal progress in the regionalization of hydrological modeling, which is the major tool for PUB, from 2000 to 2019. This paper found that the journal publications have noticeably increased in terms of PUB in the past 7 years, and research countries have been expanded dramatically since 2013. The regionalization methods are grouped into three categories including similarity-based, regression-based, and hydrological signature-based. There are more detailed researches focusing on the interdisciplinary and profound improvement of each regionalization method. Namely, tremendous efforts have been made and lots of improvements have been carried out in the parameterization domain for the post-PUB period. However, there is still plenty of room to improve the prediction capability in data-sparse regions (e.g., further verification and proof of multi-modeling adaptation and uncertainties description). This paper also discusses possible research directions in the future, including PUB in a changing environment and better utilization of multi-source remote-sensing information.

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Section: Hydrological Regionalization Methodsmentioning

confidence: 99%

Regionalization of hydrological modeling for predicting streamflow in ungauged catchments: A comprehensive review

et al. 2020

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“…Finally, land cover www.nature.com/scientificdata www.nature.com/scientificdata/ fractions are also as expected, with large portions of the United States Midwest having cropland as the dominant land use category and major forested areas in Canada, north-east United States and Western United States. Figure 5 also shows that the Mexican catchments are highly heterogeneous in terms of physiographic attributes 40 .…”

Section: Technical Validationmentioning

confidence: 99%

A comprehensive, multisource database for hydrometeorological modeling of 14,425 North American watersheds

et al. 2020

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the Hydrometeorological Sandbox-École de technologie supérieure (HYSEtS) is a rich, comprehensive and large-scale database for hydrological modelling covering 14425 watersheds in North America. The database includes data covering the period 1950-2018 depending on the type and source of data. The data include a wide array of hydrometeorological data required to perform hydrological and climate change impact studies: (1) watershed properties including boundaries, area, elevation slope, land use and other physiographic information; (2) hydrometric gauging station discharge time-series; (3) precipitation, maximum and minimum daily air temperature time-series from weather station records and from (4) the SCDNA infilled gauge meteorological dataset; (5) the NRCan and Livneh gridded interpolated products' meteorological data; (6) ERA5 and ERA5-Land reanalysis data; and (7) the SNODAS and ERA5-Land snow water equivalent estimates. All data have been processed and averaged at the watershed scale, and provides a solid basis for hydrological modelling, climate change impact studies, model calibration assessment, regionalization method evaluation and essentially any study requiring access to large amounts of spatiotemporally varied hydrometeorological data.

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“…The popular regionalization methods are physical similarity, spatial proximity, and regression 5 . Previously, many studies have been conducted and compared the performance of the regionalization methods to predict total streamflow or direct runoff 6 in the ungauged catchment with various hydrological models (WASMOD 7 , VIC 8 , SWAT 9 , GR4J 10 , HMETS 10 , MOHYSE 10 , and HEC-HMS 11 ) for different regions. Some of them showed that distance-based (spatial proximity, physical similarity) outperformed regression methods 7 , 8 .…”

Section: Introductionmentioning

confidence: 99%

“…Swain and Patra 3 pointed that the spatial proximity between the gauged catchment and the ungauged catchment gave better results than the physical similarity for predicting the continuous streamflow. Arsenault et al 10 studied the efficacy of three regionalization methods: multiple linear regression (MLR), spatial proximity, and physical similarity, to predict current flow in ungauged catchments of Mexico. They showed that transferring a set of parameters from a nearby reservoir is the most efficient method for estimating the runoff in ungauged basins.…”

Section: Introductionmentioning

confidence: 99%

Using machine learning methods for supporting GR2M model in runoff estimation in an ungauged basin

Ditthakit

Pinthong

Salaeh

et al. 2021

Sci Rep

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Estimating monthly runoff variation, especially in ungauged basins, is inevitable for water resource planning and management. The present study aimed to evaluate the regionalization methods for determining regional parameters of the rainfall-runoff model (i.e., GR2M model). Two regionalization methods (i.e., regression-based methods and distance-based methods) were investigated in this study. Three regression-based methods were selected including Multiple Linear Regression (MLR), Random Forest (RF), and M5 Model Tree (M5), and two distance-based methods included Spatial Proximity Approach and Physical Similarity Approach (PSA). Hydrological data and the basin's physical attributes were analyzed from 37 runoff stations in Thailand's southern basin. The results showed that using hydrological data for estimating the GR2M model parameters is better than using the basin's physical attributes. RF had the most accuracy in estimating regional GR2M model’s parameters by giving the lowest error, followed by M5, MLR, SPA, and PSA. Such regional parameters were then applied in estimating monthly runoff using the GR2M model. Then, their performance was evaluated using three performance criteria, i.e., Nash–Sutcliffe Efficiency (NSE), Correlation Coefficient (r), and Overall Index (OI). The regionalized monthly runoff with RF performed the best, followed by SPA, M5, MLR, and PSA. The Taylor diagram was also used to graphically evaluate the obtained results, which indicated that RF provided the products closest to GR2M's results, followed by SPA, M5, PSA, and MLR. Our finding revealed the applicability of machine learning for estimating monthly runoff in the ungauged basins. However, the SPA would be recommended in areas where lacking the basin's physical attributes and hydrological information.

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Streamflow prediction in ungauged basins: analysis of regionalization methods in a hydrologically heterogeneous region of Mexico

Abstract: Streamflow prediction in ungauged basins: analysis of regionalization methods in a hydrologically heterogeneous region of Mexico.

Cited by 56 publications

References 43 publications

Regionalization of hydrological modeling for predicting streamflow in ungauged catchments: A comprehensive review

Regionalization of hydrological modeling for predicting streamflow in ungauged catchments: A comprehensive review

A comprehensive, multisource database for hydrometeorological modeling of 14,425 North American watersheds

Using machine learning methods for supporting GR2M model in runoff estimation in an ungauged basin

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