Can a regionalized model parameterisation be improved with a limited number of runoff measurements?

Viviroli, Daniel; Seibert, Jan

doi:10.1016/j.jhydrol.2015.07.009

Cited by 25 publications

(22 citation statements)

References 53 publications

(69 reference statements)

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“…They therefore provided information for the regionalization at a time when dominant runoff processes were active. These results are comparable to those of Viviroli and Seibert (), who reported that discharge observations during the snowmelt and ice melt season were most valuable for informing regionalization in snow‐dominated or glaciated catchments. They furthermore showed that more observations were needed to effectively inform regionalization for catchments with a predominantly pluvial regime because of the randomness of rain events between and within years as opposed to the reoccurring process of snowmelt and ice melt.…”

Section: Discussionsupporting

confidence: 89%

“…The result that a limited number of discharge observations can improve predictions in otherwise ungauged catchments is in agreement with Rojas‐Serna et al () and Viviroli and Seibert (), who concluded that a few randomly selected discharge observations or a few observations during mean‐flow conditions proved to be a valuable source of information beyond classical regionalization. The value of such observations for regionalization was generally higher for the attribute‐similarity‐based approach than for the spatial proximity approach.…”

Section: Discussionsupporting

confidence: 87%

See 1 more Smart Citation

Value of a Limited Number of Discharge Observations for Improving Regionalization: A Large‐Sample Study Across the United States

Pool

Viviroli

Seibert

2019

Water Resources Research

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Even in regions considered as densely monitored, most catchments are actually ungauged. Prediction of discharge in ungauged catchments commonly relies on parameter regionalization. While ungauged catchments lack continuous discharge time series, a limited number of observations could still be collected within short field campaigns. Here we analyze the value of such observations for improving parameter regionalization in otherwise ungauged catchments. More specifically, we propose an ensemble modeling approach, where discharge predictions from regionalization with multiple donor catchments are weighted based on the fit between predicted and observed discharge on the dates of the available observations. It was assumed that a total of 3 to 24 observations from a single hydrological year were available as an additional source of information for regionalization. This informed regionalization approach was tested with discharge observations from 10 different hydrological years in a leave‐one‐out cross validation scheme on 579 catchments in the United States using the HBV runoff model. Discharge observations helped to improve the regionalization in up to 94% of the study catchments in 8 out of 10 discharge sampling years. Sampling years characterized by exceptionally high peak discharge, or high annual or winter precipitation were less informative for regionalization. In the least informative years, model efficiency increased with an increasing number of observations. In contrast, in the most informative sampling year, 3 discharge observations provided as much information for regionalization as 24 discharge observations. Overall, discharge observations were most effective in informing regionalization in arid catchments, snow‐dominated catchments, and winter‐precipitation‐dominated catchments.

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Section: Discussionsupporting

confidence: 89%

Section: Discussionsupporting

confidence: 87%

Value of a Limited Number of Discharge Observations for Improving Regionalization: A Large‐Sample Study Across the United States

Pool

Viviroli

Seibert

2019

Water Resources Research

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“…In operational conditions, it is likely that the choice of gauging conditions is not made at random, and that the efficiency of the method could be improved by appropriate gauging strategies. Several authors attempted to define strategies for acquiring point flow data in a modeling perspective [see e.g., Clausen , ; Juston et al ., ; Konz and Seibert , ; Singh and Bardossy , ; Viviroli and Seibert , ]. This could also be investigated in the context of the approach proposed here.…”

Section: Discussionmentioning

confidence: 99%

“…Since the number of catchments is large, this does not prevent obtaining robust results. More advanced sampling strategies could be adopted to improve modeling efficiency [Viviroli and Seibert, 2015], but this was not within the scope of this article. Capacity of the production store (mm; positive) X G2…”

Section: Assessment Methodologymentioning

confidence: 99%

How should a rainfall‐runoff model be parameterized in an almost ungauged catchment? A methodology tested on 609 catchments

Serna

Lebecherel

Perrin

et al. 2016

Water Resources Research

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This paper examines catchments that are almost ungauged, i.e., catchments for which only a small number of point flow measurements are available. In these catchments, hydrologists may still need to simulate continuous streamflow time series using a rainfall‐runoff model, and the methodology presented here allows using few point measurements for model parameterization. The method combines regional information (parameter sets of neighboring gauged stations) and local information (contributed by the point measurements) within a framework where the relative weight of each source of information is made dependent on the number of point measurements available. This approach is tested with two different hydrological models on a set of 609 catchments in France. The results show that on average a few flow measurements can significantly improve the simulation efficiency, and that 10 measurements can reduce the performance gap between the gauged and ungauged situations by more than 50%. The added value of regional information progressively decreases until being almost insignificant when sufficient flow measurements are available. Model parameters tend to come closer to the values obtained by calibration in fully gauged conditions as the number of point flow measurements increases.

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“…To reduce the uncertainty introduced by regionalization, many researchers have tried to improve parameter estimation by introducing limited information from ungauged basins. For example, Viviroli and Seibert (2015) combined short-term streamflow observations with parameter regionalization and showed that parameter identifications could be improved compared with using information only from donor basins. Many recent works have tried to use available in situ or remote sensing observations of hydrological processes other than streamflow for model calibration, e.g., soil moisture (e.g., Silvestro et al, 2015;Vrugt et al, 2002), evapotranspiration (Vervoort et al, 2014;Winsemius et al, 2008), and groundwater level (e.g., Khu et al, 2008), as a new direction to solve the calibration problem.…”

Section: Introductionmentioning

confidence: 99%

Physically based distributed hydrological model calibration based on a short period of streamflow data: case studies in four Chinese basins

Sun

Wang

et al. 2017

Hydrol. Earth Syst. Sci.

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Abstract. Physically based distributed hydrological models are widely used for hydrological simulations in various environments. As with conceptual models, they are limited in data-sparse basins by the lack of streamflow data for calibration. Short periods of observational data (less than 1 year) may be obtained from fragmentary historical records of previously existing gauging stations or from temporary gauging during field surveys, which might be of value for model calibration. However, unlike lumped conceptual models, such an approach has not been explored sufficiently for physically based distributed models. This study explored how the use of limited continuous daily streamflow data might support the application of a physically based distributed model in datasparse basins. The influence of the length of the observation period on the calibration of the widely applied soil and water assessment tool model was evaluated in four Chinese basins with differing climatic and geophysical characteristics. The evaluations were conducted by comparing calibrations based on short periods of data with calibrations based on data from a 3-year period, which were treated as benchmark calibrations of the four basins, respectively. To ensure the differences in the model simulations solely come from differences in the calibration data, the generalized likelihood uncertainty analysis scheme was employed for the automatic calibration and uncertainty analysis. In the four basins, contrary to the common understanding of the need for observations over a period of several years, data records with lengths of less than 1 year were shown to calibrate the model effectively, i.e., performances similar to the benchmark calibrations were achieved. The models of the wet Jinjiang and Donghe basins could be effectively calibrated using a shorter data record (1 month), compared with the dry Heihe and upstream Yalongjiang basins (6 months). Even though the four basins are very different, when using 1-year or 6-month (covering a whole dry season or rainy season) data, the results show that data from wet seasons and wet years are generally more reliable than data from dry seasons and dry years, especially for the two dry basins. The results demonstrated that this idea could be a promising approach to the problem of calibration of physically based distributed hydrological models in datasparse basins, and findings from the discussion in this study are valuable for assessing the effectiveness of short-period data for model calibration in real-world applications.

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Can a regionalized model parameterisation be improved with a limited number of runoff measurements?

Cited by 25 publications

References 53 publications

Value of a Limited Number of Discharge Observations for Improving Regionalization: A Large‐Sample Study Across the United States

Value of a Limited Number of Discharge Observations for Improving Regionalization: A Large‐Sample Study Across the United States

How should a rainfall‐runoff model be parameterized in an almost ungauged catchment? A methodology tested on 609 catchments

Physically based distributed hydrological model calibration based on a short period of streamflow data: case studies in four Chinese basins

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