the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
High-resolution drought simulations and comparison to soil moisture observations in Germany
- 1Helmholtz Centre for Environmental Research – UFZ, Department Computational Hydrosystems, Permoserstraße 15, 04318 Leipzig, Germany
- 2Faculty of Environmental Sciences, Czech University of Life Sciences Prague, Praha-Suchdol 16500, Czech Republic
- 3Helmholtz Centre for Environmental Research – UFZ, Department Monitoring and Exploration Technologies, Permoserstraße 15, 04318 Leipzig, Germany
- 4Forschungszentrum Jülich GmbH, Agrosphere Institute (IBG-3), Germany
- 5Karlsruhe Institute of Technology, IMK-IFU, Ecosystem Matter Fluxes, Kreuzeckbahnstr. 19, 82467 Garmisch-Partenkirchen, Germany
- 1Helmholtz Centre for Environmental Research – UFZ, Department Computational Hydrosystems, Permoserstraße 15, 04318 Leipzig, Germany
- 2Faculty of Environmental Sciences, Czech University of Life Sciences Prague, Praha-Suchdol 16500, Czech Republic
- 3Helmholtz Centre for Environmental Research – UFZ, Department Monitoring and Exploration Technologies, Permoserstraße 15, 04318 Leipzig, Germany
- 4Forschungszentrum Jülich GmbH, Agrosphere Institute (IBG-3), Germany
- 5Karlsruhe Institute of Technology, IMK-IFU, Ecosystem Matter Fluxes, Kreuzeckbahnstr. 19, 82467 Garmisch-Partenkirchen, Germany
Abstract. The 2018–2020 consecutive drought events in Germany resulted in impacts related with several sectors such as agriculture, forestry, water management, industry, energy production and transport. A major national operational drought information system is the German Drought Monitor (GDM), launched in 2014. It provides daily soil moisture (SM) simulated with the mesoscale hydrological model (mHM) and its related soil moisture index at a spatial resolution of 4 × 4 km2. Key to preparedness for extreme drought events are high-resolution information systems. The release of the new soil map BUEK200 allowed to increase the model resolution to ~1.2 × 1.2 km2, which is used in the second version of the GDM. In this paper, we explore the ability to provide drought information on the one-kilometer scale in Germany. Therefore, we compare simulated SM dynamics using homogenized and deseasonalized SM observations to evaluate the high-resolution drought simulations of the GDM. These SM observations are obtained from single profile measurements, spatially distributed sensor networks, cosmic-ray neutron stations and lysimeters at 40 sites in Germany. The results show that the agreement of simulated and observed SM dynamics is especially high in the vegetation period (0.84 median correlation R) and lower in winter (0.59 median R). Lower agreement in winter results from methodological uncertainties in simulations as well as in observations. Moderate but significant improvements between the first and second GDM version to observed SM were found in correlations for autumn (+0.07 median R) and winter (+0.12 median R). The annual drought intensity ranking and the spatial structure of drought events over the past 69 years is comparable for the two GDM versions. However, the higher resolution of the second GDM version allows a much more detailed representation of the spatial variability of SM, which is particularly beneficial for local risk assessments. Furthermore, the results underline that nationwide drought information systems depend both on appropriate simulations of the water cycle and a broad, high-quality observational soil moisture database.
Friedrich Boeing et al.
Status: closed
-
RC1: 'Comment on hess-2021-402', Anonymous Referee #1, 29 Nov 2021
The comment was uploaded in the form of a supplement: https://hess.copernicus.org/preprints/hess-2021-402/hess-2021-402-RC1-supplement.pdf
- AC1: 'Reply on RC1', Friedrich Boeing, 17 Feb 2022
-
RC2: 'Comment on hess-2021-402', Anonymous Referee #2, 25 Jan 2022
Review of “High-resolution drought simulations and comparison to soil moisture observations in Germany” This manuscripts analyses the relationship between soil moisture observations and estimations by models in Germany with focus on drought monitoring. The manuscript is well written and organised. Nevertheless, I would like to include some caveats related to the limitations of the validation approach and the usefulness of the new high spatial resolution data base in order to assess drought severity. I include specific details related to these issues (and others) below (numbers refer to the specific lines of the manuscript):
11- What is “vegetation period”? Is maybe “vegetative active period”?
Table 1- I would like to ask for a technical question. Do you think if the quality of the globcover map is sufficient for the modelling. How is considered the uncertainty of land cover information in the model? I find very high detail of information related to the improvement of the soil maps, map I have the impression that the land cover data is not considered so carefully and it can be strongly relevant to model soil moisture given different water consumption by ecosystem types (even at the scale of species), the role of root structure, root depth, etc.
150- I find very few information related to the meteorological data. There is not information on the number of stations used for each variable, the quality of the data, quality control processes, data gap filling, temporal homogeneity, etc., but also information related to the quality of resulting gridded data (e.g., cross-validation statistics would be useful). Meteorological data can be also an important source of uncertainty in the model outputs…
151-154- What about uncertainty of the Hargreaves-Samani equation to estimate Potential Evapotranspiration? It is widely known that temperature based methods show uncertainties related to physically based models like the Penman-Monteith equation. For example, wind speed and relative humidity may have large importance on PET, even more in non-stationary scenarios charecterised by decreased relative humidity over land and wind speed reduction.
172- Figure 1 > Figure 2.
231-235- The validation procedure is exclusively based on correlations. Nevertheless, if the main purpose of the manuscript is related to drought monitoring, I think more relevant to assess model outputs during periods of water deficits. For example, it would be useful to check the capability of models to identify duration and magnitude of the dry periods. High correlation could mask a poor goodness between observations and models during dry periods. I would suggest to include statistics focusing on the drought periods in addition to the non-parametric correlations.
170-210- The length of the observation series is not indicated in this section. This information is relevant to assess robustness of the relationship between observations and models. Have the series the same length? How is this considered in the assessment of the signification of the relationships? I think this issue is affecting the validation of the results over the entire section 3.1 since the length of the series affect the degrees of freedom of the correlation analysis. I see in table 3 that the length of the series is between 2 and 5 years, which is too low to provide a robust validation of the model outputs.
Figures 6 and 7. Under my opinion, I do not think that this information is providing an useful output to determine the goodness of providing additional spatial resolution to assess drought severity. Large scale statistics are aggregating the information, being normal that both databases at 4km and 1 km of spatial resolution provide similar results. I think the relevant information of the 1 km modelling approach is not the general large spatial pattern but the local differences that could emerge given higher spatial resolution. This is something interesting to be analysed (e.g. using spatial statistics: the variance between grid cells, the differences between areas characterised by diversity of land cover/soil characteristics) to determine if higher spatial resolution is providing relevant information for drought monitoring and management. Observing Figures 6 and 7 I would say that the higher spatial resolution is really not needed as it basically identifies the same patterns that 4 km grids.
- AC2: 'Reply on RC2', Friedrich Boeing, 17 Feb 2022
Status: closed
-
RC1: 'Comment on hess-2021-402', Anonymous Referee #1, 29 Nov 2021
The comment was uploaded in the form of a supplement: https://hess.copernicus.org/preprints/hess-2021-402/hess-2021-402-RC1-supplement.pdf
- AC1: 'Reply on RC1', Friedrich Boeing, 17 Feb 2022
-
RC2: 'Comment on hess-2021-402', Anonymous Referee #2, 25 Jan 2022
Review of “High-resolution drought simulations and comparison to soil moisture observations in Germany” This manuscripts analyses the relationship between soil moisture observations and estimations by models in Germany with focus on drought monitoring. The manuscript is well written and organised. Nevertheless, I would like to include some caveats related to the limitations of the validation approach and the usefulness of the new high spatial resolution data base in order to assess drought severity. I include specific details related to these issues (and others) below (numbers refer to the specific lines of the manuscript):
11- What is “vegetation period”? Is maybe “vegetative active period”?
Table 1- I would like to ask for a technical question. Do you think if the quality of the globcover map is sufficient for the modelling. How is considered the uncertainty of land cover information in the model? I find very high detail of information related to the improvement of the soil maps, map I have the impression that the land cover data is not considered so carefully and it can be strongly relevant to model soil moisture given different water consumption by ecosystem types (even at the scale of species), the role of root structure, root depth, etc.
150- I find very few information related to the meteorological data. There is not information on the number of stations used for each variable, the quality of the data, quality control processes, data gap filling, temporal homogeneity, etc., but also information related to the quality of resulting gridded data (e.g., cross-validation statistics would be useful). Meteorological data can be also an important source of uncertainty in the model outputs…
151-154- What about uncertainty of the Hargreaves-Samani equation to estimate Potential Evapotranspiration? It is widely known that temperature based methods show uncertainties related to physically based models like the Penman-Monteith equation. For example, wind speed and relative humidity may have large importance on PET, even more in non-stationary scenarios charecterised by decreased relative humidity over land and wind speed reduction.
172- Figure 1 > Figure 2.
231-235- The validation procedure is exclusively based on correlations. Nevertheless, if the main purpose of the manuscript is related to drought monitoring, I think more relevant to assess model outputs during periods of water deficits. For example, it would be useful to check the capability of models to identify duration and magnitude of the dry periods. High correlation could mask a poor goodness between observations and models during dry periods. I would suggest to include statistics focusing on the drought periods in addition to the non-parametric correlations.
170-210- The length of the observation series is not indicated in this section. This information is relevant to assess robustness of the relationship between observations and models. Have the series the same length? How is this considered in the assessment of the signification of the relationships? I think this issue is affecting the validation of the results over the entire section 3.1 since the length of the series affect the degrees of freedom of the correlation analysis. I see in table 3 that the length of the series is between 2 and 5 years, which is too low to provide a robust validation of the model outputs.
Figures 6 and 7. Under my opinion, I do not think that this information is providing an useful output to determine the goodness of providing additional spatial resolution to assess drought severity. Large scale statistics are aggregating the information, being normal that both databases at 4km and 1 km of spatial resolution provide similar results. I think the relevant information of the 1 km modelling approach is not the general large spatial pattern but the local differences that could emerge given higher spatial resolution. This is something interesting to be analysed (e.g. using spatial statistics: the variance between grid cells, the differences between areas characterised by diversity of land cover/soil characteristics) to determine if higher spatial resolution is providing relevant information for drought monitoring and management. Observing Figures 6 and 7 I would say that the higher spatial resolution is really not needed as it basically identifies the same patterns that 4 km grids.
- AC2: 'Reply on RC2', Friedrich Boeing, 17 Feb 2022
Friedrich Boeing et al.
Friedrich Boeing et al.
Viewed
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 1,223 | 433 | 25 | 1,681 | 18 | 17 |
- HTML: 1,223
- PDF: 433
- XML: 25
- Total: 1,681
- BibTeX: 18
- EndNote: 17
Viewed (geographical distribution)
| Country | # | Views | % |
|---|
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1