2020
DOI: 10.5194/egusphere-egu2020-9305
|View full text |Cite
Preprint
|
Sign up to set email alerts
|

Impact of satellite and in situ data assimilation on hydrological predictions

Abstract: <p>The assimilation of different satellite and in-situ products generally improves the hydrological model predictive skill. Most studies have focused on assimilating a single product at a time with the ensemble size subjectively chosen by the modeller. In this study, we use the European-scale Hydrological Predictions for the Environment hydrological model in the Umeälven catchment in northern Sweden with the stream discharge and local reservoir inflow as target variables to objective… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1
1

Citation Types

0
3
0

Year Published

2020
2020
2023
2023

Publication Types

Select...
3

Relationship

1
2

Authors

Journals

citations
Cited by 3 publications
(3 citation statements)
references
References 24 publications
(25 reference statements)
0
3
0
Order By: Relevance
“…The quality of seasonal streamflow forecasts relies on a forecasting chain that includes at least seasonal meteorological forcing, initialization of hydrological model states and a hydrologic model setup (Mazrooei et al, 2015; Pechlivanidis et al, 2014). To improve the forecast quality and further the decision‐making, this chain can be advanced by introducing additional components that allow assimilation of data to set the initial model states (e.g., in situ/Earth observations of soil moisture and snow water equivalent; Draper & Reichle, 2015; Griessinger et al, 2016; Liu et al, 2012; Musuuza et al, 2020), postprocessing of seasonal meteorological forecasts (e.g., bias adjustment and model output statistics; Dobrynin et al, 2018; Manzanas et al, 2019; Zhao et al, 2017), and postprocessing of hydrologic forecasts (e.g., conditioning to local data; Lucatero et al, 2018; Madadgar et al, 2014; Wood & Schaake, 2008). Currently, forecast service development is ad hoc with improvements made to single parts of the forecasting chain when and where available, and with only very limited guidance on the relative importance of each component to the forecasting chain performance (Arheimer et al, 2011; Sinha et al, 2014; Thiboult et al, 2016).…”
Section: Discussionmentioning
confidence: 99%
“…The quality of seasonal streamflow forecasts relies on a forecasting chain that includes at least seasonal meteorological forcing, initialization of hydrological model states and a hydrologic model setup (Mazrooei et al, 2015; Pechlivanidis et al, 2014). To improve the forecast quality and further the decision‐making, this chain can be advanced by introducing additional components that allow assimilation of data to set the initial model states (e.g., in situ/Earth observations of soil moisture and snow water equivalent; Draper & Reichle, 2015; Griessinger et al, 2016; Liu et al, 2012; Musuuza et al, 2020), postprocessing of seasonal meteorological forecasts (e.g., bias adjustment and model output statistics; Dobrynin et al, 2018; Manzanas et al, 2019; Zhao et al, 2017), and postprocessing of hydrologic forecasts (e.g., conditioning to local data; Lucatero et al, 2018; Madadgar et al, 2014; Wood & Schaake, 2008). Currently, forecast service development is ad hoc with improvements made to single parts of the forecasting chain when and where available, and with only very limited guidance on the relative importance of each component to the forecasting chain performance (Arheimer et al, 2011; Sinha et al, 2014; Thiboult et al, 2016).…”
Section: Discussionmentioning
confidence: 99%
“…For example, authors of [30] proposed to choose G from optimality conditions as either a scalar value or a matrix. We considered the classical formulation of Newtonian nudging as in (13). However, our own numerical experiments with constant G proved unsatisfactory.…”
Section: Nudging Coefficientmentioning
confidence: 99%
“…While both the model and observations are imperfect, they contain different kinds of information, and so their combination is able to yield more accurate result than each of them does separately [10]. Such assimilated results agree better with the real state of the system than each of the sources alone, which has been proved in a number of works [11][12][13]. Some studies aimed at measuring the positive impact of combining observations with a model, e.g.…”
Section: Introductionmentioning
confidence: 99%