Data assimilation in integrated hydrological modeling using ensemble Kalman filtering: evaluating the effect of ensemble size and localization on filter performance
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Data assimilation in integrated hydrological modeling using ensemble Kalman filtering : evaluating the effect of ensemble size and localization on filter performance. / Rasmussen, Jørn; Madsen, H. ; Jensen, Karsten Høgh; Refsgaard, Jens C.
I: Hydrology and Earth System Sciences, Bind 19, Nr. 7, 2015, s. 2999-3013.Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
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TY - JOUR
T1 - Data assimilation in integrated hydrological modeling using ensemble Kalman filtering
T2 - evaluating the effect of ensemble size and localization on filter performance
AU - Rasmussen, Jørn
AU - Madsen, H.
AU - Jensen, Karsten Høgh
AU - Refsgaard, Jens C.
PY - 2015
Y1 - 2015
N2 - Groundwater head and stream discharge is assimilated using the ensemble transform Kalman filter in an integrated hydrological model with the aim of studying the relationship between the filter performance and the ensemble size. In an attempt to reduce the required number of ensemble members, an adaptive localization method is used. The performance of the adaptive localization method is compared to the more common distance-based localization. The relationship between filter performance in terms of hydraulic head and discharge error and the number of ensemble members is investigated for varying numbers and spatial distributions of groundwater head observations and with or without discharge assimilation and parameter estimation. The study shows that (1) more ensemble members are needed when fewer groundwater head observations are assimilated, and (2) assimilating discharge observations and estimating parameters requires a much larger ensemble size than just assimilating groundwater head observations. However, the required ensemble size can be greatly reduced with the use of adaptive localization, which by far outperforms distance-based localization. The study is conducted using synthetic data only.
AB - Groundwater head and stream discharge is assimilated using the ensemble transform Kalman filter in an integrated hydrological model with the aim of studying the relationship between the filter performance and the ensemble size. In an attempt to reduce the required number of ensemble members, an adaptive localization method is used. The performance of the adaptive localization method is compared to the more common distance-based localization. The relationship between filter performance in terms of hydraulic head and discharge error and the number of ensemble members is investigated for varying numbers and spatial distributions of groundwater head observations and with or without discharge assimilation and parameter estimation. The study shows that (1) more ensemble members are needed when fewer groundwater head observations are assimilated, and (2) assimilating discharge observations and estimating parameters requires a much larger ensemble size than just assimilating groundwater head observations. However, the required ensemble size can be greatly reduced with the use of adaptive localization, which by far outperforms distance-based localization. The study is conducted using synthetic data only.
U2 - 10.5194/hess-19-2999-2015
DO - 10.5194/hess-19-2999-2015
M3 - Journal article
VL - 19
SP - 2999
EP - 3013
JO - Hydrology and Earth System Sciences
JF - Hydrology and Earth System Sciences
SN - 1027-5606
IS - 7
ER -
ID: 147936994