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Evaluation of Baseflow Modeling with BlueM.Sim for Long-Term Hydrological Studies in the German Low Mountain Range of Hesse, Germany

Kissel, Michael ; Bach, Michael ; Schmalz, Britta (2024)
Evaluation of Baseflow Modeling with BlueM.Sim for Long-Term Hydrological Studies in the German Low Mountain Range of Hesse, Germany.
In: Hydrology, 2023, 10 (12)
doi: 10.26083/tuprints-00027232
Article, Secondary publication, Publisher's Version

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Item Type: Article
Type of entry: Secondary publication
Title: Evaluation of Baseflow Modeling with BlueM.Sim for Long-Term Hydrological Studies in the German Low Mountain Range of Hesse, Germany
Language: English
Date: 7 May 2024
Place of Publication: Darmstadt
Year of primary publication: 24 November 2023
Place of primary publication: Basel
Publisher: MDPI
Journal or Publication Title: Hydrology
Volume of the journal: 10
Issue Number: 12
Collation: 28 Seiten
DOI: 10.26083/tuprints-00027232
Corresponding Links:
Origin: Secondary publication DeepGreen
Abstract:

So far, research with the hydrological model BlueM.Sim has been focused on reservoir management and integrated river basin modeling. BlueM.Sim is part of the official toolset for estimating immissions into rivers in Hesse (Germany) via long-term continuous modeling. Dynamic runoff modeling from rural catchments is permitted within the Hessian guidelines, but in practice, a constant flow or low flow is used. However, due to increasing water stress in the region caused by climate change, the dynamic modeling of runoff from rural catchments will become necessary. Therefore, dynamic baseflow modeling with BlueM.Sim is of the greatest importance. This study evaluated baseflow modeling with BlueM.Sim in a representative hard-rock aquifer in the German Low Mountain range. Two model setups (Factor Approach (FA): CN method + monthly baseflow; Soil Moisture Approach (SMA): physical soil moisture simulation) were calibrated (validated) for a 9-year (5-year) period. The FA achieved an NSE of 0.62 (0.44) and an LnNSE of 0.64 (0.60) for the calibration and validation periods. The selection of a solution for the successful validation of the FA was challenging and required a selection that overestimated baseflow in the calibration period. This is due to the major disadvantage of the FA, namely, that baseflow can only vary according to an estimated yearly pattern of monthly baseflow factors. However, the data requirements are low, and the estimation of monthly baseflow factors is simple and could potentially be regionalized for Hesse, leading to a better representation of baseflow than in current practice. The SMA achieved better results with an NSE of 0.78 (0.75) and an LnNSE of 0.72 (0.78). The data requirements and model setup are extensive and require the estimation of many parameters, which are limitations to its application in practice. Furthermore, a literature review has shown that a single linear reservoir, as in BlueM.Sim, is not optimal for modeling baseflow in hard-rock aquifers. However, for detailed climate change impact studies in the region with BlueM.Sim, the SMA should be preferred over the FA. It is expected that BlueM.Sim would benefit from implementing a more suitable model structure for baseflow in hard-rock aquifers, resulting in improved water balance and water quality outcomes.

Uncontrolled Keywords: baseflow, hydrological modeling, hard-rock aquifer, long-term continuous modeling, German Low Mountain range
Identification Number: Artikel-ID: 222
Status: Publisher's Version
URN: urn:nbn:de:tuda-tuprints-272322
Additional Information:

This article belongs to the Section Surface Waters and Groundwaters

Classification DDC: 500 Science and mathematics > 550 Earth sciences and geology
600 Technology, medicine, applied sciences > 624 Civil engineering and environmental protection engineering
Divisions: 13 Department of Civil and Environmental Engineering Sciences > Institute of Hydraulic and Water Resources Engineering > Engineering Hydrology and Water Management
Date Deposited: 07 May 2024 12:55
Last Modified: 17 May 2024 08:28
SWORD Depositor: Deep Green
URI: https://tuprints.ulb.tu-darmstadt.de/id/eprint/27232
PPN: 518204359
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