Impact of model structure on flow simulation and hydrological realism: from a lumped to a semi-distributed approach

Garavaglia, Federico; Lay, Matthieu Le; Gottardi, Frédéric; Garçon, Rémy; Gailhard, Joël; Paquet, Emmanuel; Mathevet, Thibault

doi:10.5194/hess-21-3937-2017

Cited by 43 publications

(24 citation statements)

References 47 publications

(45 reference statements)

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“…However, would the model performance change if more or less virtual stations were used? For this purpose, n random stations were selected for model 575 calibration; the remaining stations were used for cross-validation (KlemeŠ, 1986;Gharari et al, 2013;Garavaglia et al, 2017). This was repeated to cover all combinations of n stations and for n = 1, 2 … 17.…”

Section: Number Of Virtual Stations Used For Model Calibration and Evmentioning

confidence: 99%

Using altimetry observations combined with GRACE to select parameter sets of a hydrological model in data scarce regions

Hulsman¹,

Winsemius²,

Michailovsky³

et al. 2019

Preprint

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Abstract. To ensure reliable model understanding of water movement and distribution in terrestrial systems, sufficient and good quality hydro-meteorological data are required. Limited availability of ground measurements in the vast majority of river basins world-wide increase the value of alternative data sources such as satellite observations in modelling. In the absence of directly observed river discharge data, other variables such as remotely sensed river water level may provide valuable information for the calibration and evaluation of hydrological models. This study investigates the potential of the use of remotely sensed river water level, i.e. altimetry observations, from multiple satellite missions to identify parameter sets for a hydrological model in the semi-arid Luangwa River Basin in Zambia. A distributed process-based rainfall runoff model with sub-grid process heterogeneity was developed and run on a daily timescale for the time period 2002 to 2016. Following a step-wise approach, various parameter identification strategies were tested to evaluate the potential of satellite altimetry data for model calibration. As a benchmark, feasible model parameter sets were identified using traditional model calibration with observed river discharge data. For the parameter identification using remote sensing, data from the Gravity Recovery and Climate Experiment (GRACE) were used in a first step to restrict the feasible parameter sets based on the seasonal fluctuations in total water storage. In a next step, three alternative ways of further restricting feasible model parameter sets based on satellite altimetry time-series from 18 different locations, i.e. virtual stations, along the Luangwa River and its tributaries were compared. In the calibrated benchmark case, daily river flows were reproduced relatively well with an optimum Nash-Sutcliffe efficiency of ENS,Q = 0.78 (5/95th percentiles of all feasible solutions ENS,Q,5/95 = 0.61 – 0.75). When using only GRACE observations to restrict the parameter space, assuming no discharge observations are available, an optimum of ENS,Q = −1.4 (ENS,Q,5/95 = −2.3 – 0.38) with respect to discharge was obtained. Depending on the parameter selection strategy, it could be shown that altimetry data can contain sufficient information to efficiently further constrain the feasible parameter space. The direct use of altimetry based river levels frequently over-estimated the flows and poorly identified feasible parameter sets due to the non-linear relationship between river water level and river discharge (ENS,Q,5/95 = −2.9 – 0.10); therefore, this strategy was of limited use to identify feasible model parameter sets. Similarly, converting modelled discharge into water levels using rating curves in the form of power relationships with two additional free calibration parameters per virtual station resulted in an over-estimation of the discharge and poorly identified feasible parameter sets (ENS,Q,5/95 = −2.6 – 0.25). However, accounting for river geometry proved to be highly effective; this included using river cross-section and gradient information extracted from global high-resolution terrain data available on Google Earth, and applying the Strickler-Manning equation with effective roughness as free calibration parameter to convert modelled discharge into water levels. Many parameter sets identified with this method reproduced the hydrograph and multiple other signatures of discharge reasonably well with an optimum of ENS,Q = 0.60 (ENS,Q,5/95 = −0.31 – 0.50). It was further shown that more accurate river cross-section data improved the water level simulations, modelled rating curve and discharge simulations during intermediate and low flows at the basin outlet at which detailed on-site cross-section information was available. For this case, the Nash-Sutcliffe efficiency with respect to river water levels increased from ENS,SM,GE = −1.8 (ENS,SM,GE,5/95 = −6.8 – −3.1) using river geometry information extracted from Google Earth to ENS,SM,ADCP = 0.79 (ENS,SM,ADCP,5/95 = 0.6 – 0.74) using river geometry information obtained from a detailed survey in the field. It could also be shown that increasing the number of virtual stations used for parameter selection in the calibration period can considerably improve the model performance in spatial split sample validation. The results provide robust evidence that in the absence of directly observed discharge data for larger rivers in data scarce regions, altimetry data from multiple virtual stations combined with GRACE observations have the potential to fill this gap when combined with readily available estimates of river geometry, thereby allowing a step towards more reliable hydrological modelling in poorly or ungauged basins.

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Section: Number Of Virtual Stations Used For Model Calibration and Evmentioning

confidence: 99%

Using altimetry observations combined with GRACE to select parameter sets of a hydrological model in data scarce regions

Hulsman¹,

Winsemius²,

Michailovsky³

et al. 2019

Preprint

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show abstract

“…In the absence of directly observed river discharge data, various types of remotely sensed variables provide valuable information for the calibration and evaluation of hydrological models. These include, for instance, remotely sensed time series of river width (Sun et al, 2012(Sun et al, , 2015, flood extent (Montanari et al, 2009;Revilla-Romero et al, 2015), or river and lake water levels from altimetry (Getirana et al, 2009;Getirana, 2010;Sun et al, 2012;Garambois et al, 2017;Pereira-Cardenal et al, 2011;Velpuri et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

“…In typical applications, radar altimetry data from one single or only a few virtual stations were used for model calibration, validation, or data assimilation. These data were mostly obtained from a single satellite mission, either TOPEX/Poseidson or Envisat (Sun et al, 2012;Getirana, 2010;Liu et al, 2015;Pedinotti et al, 2012;Fleischmann et al, 2018;Michailovsky et al, 2013;Bauer-Gottwein et al, 2015). In previous studies, hydrological models have been calibrated or validated successfully with respect to (satellite-based) river water levels, for example by (1) applying the Spearman rank correlation coefficient (Seibert and Vis, 2016;Jian et al, 2017) or by converting modelled discharge to stream levels using (2) rating curves whose parameters are free calibration parameters in the modelling process (Sun et al, 2012;Sikorska and Renard, 2017) or (3) the Strickler-Manning equation to directly estimate water levels over the hydraulic properties of the river (Liu et al, 2015;Hulsman et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

“…Hence, there may be considerable value in simultaneously using altimetry data not only from one single satellite mission, but also in combining data from multiple missions, which has not yet been systematically explored. While promising calibration results using data from Envisat were found by Getirana (2010) in tropical and Liu et al (2015) in snow-dominated regions, altimetry data from multiple sources have not yet been used to calibrate hydrological models in semi-arid regions.…”

Section: Introductionmentioning

confidence: 99%

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Using altimetry observations combined with GRACE to select parameter sets of a hydrological model in a data-scarce region

Hulsman

Winsemius

Michailovsky

et al. 2020

Hydrol. Earth Syst. Sci.

View full text Add to dashboard Cite

Abstract. Limited availability of ground measurements in the vast majority of river basins world-wide increases the value of alternative data sources such as satellite observations in hydrological modelling. This study investigates the potential of using remotely sensed river water levels, i.e. altimetry observations, from multiple satellite missions to identify parameter sets for a hydrological model in the semi-arid Luangwa River basin in Zambia. A distributed process-based rainfall–runoff model with sub-grid process heterogeneity was developed and run on a daily timescale for the time period 2002 to 2016. As a benchmark, feasible model parameter sets were identified using traditional model calibration with observed river discharge data. For the parameter identification using remote sensing, data from the Gravity Recovery and Climate Experiment (GRACE) were used in a first step to restrict the feasible parameter sets based on the seasonal fluctuations in total water storage. Next, three alternative ways of further restricting feasible model parameter sets using satellite altimetry time series from 18 different locations along the river were compared. In the calibrated benchmark case, daily river flows were reproduced relatively well with an optimum Nash–Sutcliffe efficiency of ENS,Q=0.78 (5/95th percentiles of all feasible solutions ENS,Q,5/95=0.61–0.75). When using only GRACE observations to restrict the parameter space, assuming no discharge observations are available, an optimum of ENS,Q=-1.4 (ENS,Q,5/95=-2.3–0.38) with respect to discharge was obtained. The direct use of altimetry-based river levels frequently led to overestimated flows and poorly identified feasible parameter sets (ENS,Q,5/95=-2.9–0.10). Similarly, converting modelled discharge into water levels using rating curves in the form of power relationships with two additional free calibration parameters per virtual station resulted in an overestimation of the discharge and poorly identified feasible parameter sets (ENS,Q,5/95=-2.6–0.25). However, accounting for river geometry proved to be highly effective. This included using river cross-section and gradient information extracted from global high-resolution terrain data available on Google Earth and applying the Strickler–Manning equation to convert modelled discharge into water levels. Many parameter sets identified with this method reproduced the hydrograph and multiple other signatures of discharge reasonably well, with an optimum of ENS,Q=0.60 (ENS,Q,5/95=-0.31–0.50). It was further shown that more accurate river cross-section data improved the water-level simulations, modelled rating curve, and discharge simulations during intermediate and low flows at the basin outlet where detailed on-site cross-section information was available. Also, increasing the number of virtual stations used for parameter selection in the calibration period considerably improved the model performance in a spatial split-sample validation. The results provide robust evidence that in the absence of directly observed discharge data for larger rivers in data-scarce regions, altimetry data from multiple virtual stations combined with GRACE observations have the potential to fill this gap when combined with readily available estimates of river geometry, thereby allowing a step towards more reliable hydrological modelling in poorly gauged or ungauged basins.

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“…have been introduced in most of the snow accounting routines (SAR) used in operational hydrology: see e.g. HBV (Bergström, 1975), MOHYSE (Fortin and Turcotte, 2007), CEMANEIGE (Valéry et al, 2014), MORDOR (Garavaglia et al, 2017). The aim of using these parameters is to adapt to 110 local snow processes, but they could be used primarily to compensate for errors in the input data without satisfactorily achieving it.…”

mentioning

confidence: 99%

Should altitudinal gradients of temperature and precipitation inputs be inferred from key parameters in snow-hydrological models?

Ruelland¹

2019

Preprint

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Abstract. This paper evaluates whether snow-covered area and streamflow measurements can help assess altitudinal gradients of temperature and precipitation in data-scarce mountainous areas more realistically than using the usual interpolation procedures. An extensive dataset covering 20 Alpine catchments is used to investigate this issue. Elevation dependency in the meteorological fields is accounted for using two approaches: (i) by estimating the local and time-varying altitudinal gradients from the available gauge network based on deterministic and geostatistical interpolation methods with an external drift; and (ii) by calibrating the local gradients using an inverse snow-hydrological modelling framework. For the second approach, a simple 2-parameter model is proposed to target the temperature/precipitation-elevation relationship and to regionalise air temperature and precipitation from the sparse meteorological network. The coherence of the two approaches is evaluated by benchmarking several hydrological variables (snow-covered area, streamflow and water balance) computed with snow-hydrological models fed with the interpolated datasets and checked against available measurements. Results show that accounting for elevation dependency from scattered observations when interpolating air temperature and precipitation cannot provide sufficiently accurate inputs for models. The lack of high-elevation stations seriously limits correct estimation of lapse rates of temperature and precipitation, which, in turn, affects the performance of the snow-hydrological simulations due to imprecise estimates of temperature and precipitation volumes. Instead, retrieving the local altitudinal gradients using an inverse approach enables increased accuracy in the simulation of snow cover and discharge dynamics, while limiting problems of over-calibration and equifinality.

show abstract

Impact of model structure on flow simulation and hydrological realism: from a lumped to a semi-distributed approach

Cited by 43 publications

References 47 publications

Using altimetry observations combined with GRACE to select parameter sets of a hydrological model in data scarce regions

Using altimetry observations combined with GRACE to select parameter sets of a hydrological model in data scarce regions

Using altimetry observations combined with GRACE to select parameter sets of a hydrological model in a data-scarce region

Should altitudinal gradients of temperature and precipitation inputs be inferred from key parameters in snow-hydrological models?

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