Investigating regionalization techniques for large-scale hydrological modelling

Pagliero, Liliana; Bouraoui, F.; Diels, Jan; Willems, Patrick; McIntyre, Neil

doi:10.1016/j.jhydrol.2018.12.071

Cited by 54 publications

(31 citation statements)

References 58 publications

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“…The worse performance from regression methods might be due to their original limitation in which the relationship between the model parameters and catchment characteristics is linear and spatially transferable (e.g. Blöschl and Zehe, 2005;Pagliero et al, 2019).…”

Section: Paper Iii: Dependence Of Regionalization Methods On the Compmentioning

confidence: 99%

Dependence of regionalization methods on the complexity of hydrological models in multiple climatic regions

Xue

Magnusson

Huang

et al. 2020

Journal of Hydrology

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Runoff prediction in ungauged basins (PUB) is a challenging but essential research topic from scientific and practical perspectives, and the International Association of Hydrological Sciences (IAHS) was motivated to establish a "Decade on Predictions in Ungauged Basins (PUB): 2003-2012". Regionalization methods are currently regarded as the most common solution for PUB, coupled with hydrological models. Regionalization methods are generally categorized into three classes: spatial proximity, physical similarity and regression. However, knowledge of the applicability of regionalization methods is not clear or consistent, despite the number of extensive studies that have been conducted and considerable progress that has been achieved in recent decades. Due to the nonstationary climate, the reliability of results from historical data for future applications cannot be used undoubtedly. Even worse, PUB studies in cold regions (e.g., Norway) are limited.

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Section: Paper Iii: Dependence Of Regionalization Methods On the Compmentioning

confidence: 99%

Dependence of regionalization methods on the complexity of hydrological models in multiple climatic regions

Xue

Magnusson

Huang

et al. 2020

Journal of Hydrology

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“…Mainstream automated methodologies can be divided into supervised learning and unsupervised learning, which are based on learning methods. Supervised learning is a top-down classification method based on prior knowledge, such as the number and type of regions to be designed [44]. For example, China's key prevention and control regions for soil and water loss were developed using a supervised learning method in accordance with the National Plan for Soil and Water Conservation [45].…”

Section: Selection Of the Partitioning Methodsmentioning

confidence: 99%

Self-Organizing Map Network-Based Soil and Water Conservation Partitioning for Small Watersheds: Case Study Conducted in Xiaoyang Watershed, China

Wang

et al. 2020

Sustainability

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Soil and water conservation partitioning (SWCP) considers complex environmental statutes and development demands and serves as a scientific basis for conducting soil erosion management and practice. However, few studies have researched partitioning in small watersheds (< 50 km2), and guidelines for enabling region-specific measures are lacking. In this study, the Xiaoyang watershed located in the red soil region of southern China was selected as a representative small watershed in which to conduct partitioning. The pressure–state–response (PSR) model was used as a framework for establishing an indicator system that included soil erosion sensitivity, the soil erosion condition, and ecosystem services. With three soil and water conservation variables as the input layer, a one-dimensional self-organizing map was applied to identify clusters in the small watershed. The silhouette width was evaluated to determine the optimal number of regions. Based on the associated results, the Xiaoyang watershed was divided into five regions accounting for 82%, 9%, 8%, 2%, and 1% of the total area, respectively. This study provides a framework on which region-specific soil erosion measures can be planned, and it also provides a partitioning method that can be employed in other areas.

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“…1) ranging from 70 up to 313 km 2 . Several basin attributes have been frequently used for this approach in general, such as basin and river geomorphology, vegetation cover, climate and soil properties, and flow characteristics (Merz and Blöschl 2004;Arsenault and Brissette 2014;Pagliero et al 2019). Based on the data availability and the physical meanings of model parameters, we selected five major basin characteristics including basin area, river length, vegetation cover, average elevation, and basin slope as major criteria for choosing similar sub-basins.…”

Section: Rainfall-runoff Modelingmentioning

confidence: 99%

Flood hazard mapping for data-scarce and ungauged coastal river basins using advanced hydrodynamic models, high temporal-spatial resolution remote sensing precipitation data, and satellite imageries

Trinh

Molkenthin

2021

Nat Hazards

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This paper presents an integrated approach to simulate flooding and inundation for small- and medium-sized coastal river basins where measured data are not available or scarce. By coupling the rainfall–runoff model, the one-dimensional and two-dimensional models, and the integration of these with global tide model, satellite precipitation products, and synthetic aperture radar imageries, a comprehensive flood modeling system for Tra Bong river basin selected as a case study was set up and operated. Particularly, in this study, the lumped conceptual model was transformed into the semi-distributed model to increase the parameter sets of donor basins for applying the physical similarity approach. The temporal downscaling technique was applied to disaggregate daily rainfall data using satellite-based precipitation products. To select an appropriate satellite-derived rainfall product, two high temporal-spatial resolution products (0.1 × 0.1 degrees and 1 h) including GSMaP_GNRT6 and CMORPH_CRT were examined at 1-day and 1-h resolutions by comparing with ground-measured rainfall. The CMORPH_CRT product showed better performance in terms of statistical errors such as Correlation Coefficient, Probability of Detection, False Alarm Ratio, and Critical Success Index. Land cover/land use, flood extent, and flood depths derived from Sentinel-1A imageries and a digital elevation model were employed to determine the surface roughness and validate the flood modeling. The results obtained from the modeling system were found to be in good agreement with collected data in terms of NSE (0.3–0.8), RMSE (0.19–0.94), RPE (− 213 to 0.7%), F1 (0.55), and F2 (0.37). Subsequently, various scenarios of flood frequency with 10-, 20-, 50-, and 100-year return periods under the probability analysis of extreme values were developed to create the flood hazard maps for the study area. The flood hazards were then investigated based on the flood intensity classification of depth, duration, and velocity. These hazard maps are significantly important for flood hazard assessments or flood risk assessments. This study demonstrated that applying advanced hydrodynamic models on computing flood inundation and flood hazard analysis in data-scarce and ungauged coastal river basins is completely feasible. This study provides an approach that can be used also for other ungauged river basins to better understand flooding and inundation through flood hazard mapping.

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Investigating regionalization techniques for large-scale hydrological modelling

Cited by 54 publications

References 58 publications

Dependence of regionalization methods on the complexity of hydrological models in multiple climatic regions

Dependence of regionalization methods on the complexity of hydrological models in multiple climatic regions

Self-Organizing Map Network-Based Soil and Water Conservation Partitioning for Small Watersheds: Case Study Conducted in Xiaoyang Watershed, China

Flood hazard mapping for data-scarce and ungauged coastal river basins using advanced hydrodynamic models, high temporal-spatial resolution remote sensing precipitation data, and satellite imageries

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