Downscaling potential evapotranspiration to the urban canyon

Koelbing, Merle; Schuetz, Tobias; Weiler, Markus

doi:https://doi.org/10.5194/hess-2021-24

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https://doi.org/10.5194/hess-2021-24

Preprints

04 Feb 2021

| 04 Feb 2021

Status: this preprint was under review for the journal HESS but the revision was not accepted.

Downscaling potential evapotranspiration to the urban canyon

Merle Koelbing, Tobias Schuetz, and Markus Weiler

Abstract. The future increase in urban population will lead to progressing urbanization with urban sprawl and densification. Urbanized areas show distinct changes in their hydrological behaviour, water quality and climate. In the last decades, the ability of urban hydrological models to represent the dynamic hydrological behaviour of the different surface types has been improved continuously. Dissenting from the urban surface which is mostly represented in high spatial resolution, the climatic input to these models, such as precipitation and potential evapo(transpi)ration, is usually observed at one or several reference climate stations that are representing a mesoscale urban foot print area or rural conditions. From urban climate studies it is known, that the meteorological variables that are governing potential evapotranspiration (E_p) can be highly variable even on a small spatial scale. Consequently, we expect E_p at the street level to be affected by this variability as well.

We observed the urban microclimate with a mobile climate station and a rotational principle at 16 different locations in two differently oriented street canyons with vegetated and non-vegetated sections, respectively, during three seasons (spring, summer, autumn) in Freiburg, in southwestern Germany. With these observations, we simulated E_p at the street level using FAO-56 Penman-Monteith reference evapotranspiration and compared it to reference E_p derived at a rooftop station. We found that E_p on street level is negatively influenced by changes in shortwave radiation and that it is barely sensitive to changes in the other input climate variables. Significant linear relationships between the relative differences in hourly and daily short-wave radiation input and E_p at the street level have been established. The application of these relationships allows to simulate E_p at the street level for any location in a city based on simulated (or observed) short wave time series and observations at a reference climate station. Our findings can be transferred easily to existing urban hydrologic models to improve modelling results with a more precise estimate of potential evapotranspiration on street level.

Received: 13 Jan 2021 – Discussion started: 04 Feb 2021

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.

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Merle Koelbing, Tobias Schuetz, and Markus Weiler

Status: closed

RC1:
'Comment on hess-2021-24', Anonymous Referee #1, 01 Mar 2021
This paper presents an FAO56-based analysis of so-claimed "potential evapotranspiration" at an urban street scale using mobile measurements. Although the mobile measurements look interesting and may be valuable to understand the urban micrometeorology, the major flaw, improper use of FAO56 framework, prohibits this paper from publication in its current form:

1. the assumption of FAO56 for a reference crop with "a height of 0.12 m, a fixed surface resistance of 70 s m^-1 and an albedo of 0.23" fails on (almost) all urban surfaces, which are usually characterised with impervious surfaces and rather patchy greenery: it is thus inappropriate to use such a surface to represent urban canyons. Besides, the FAO56 assumed reference crop is inherently positioned in a more homogeneous context, where the measurements take at screen level (e.g., 2 m agl) is within the inertial sublayer (or above roughness sublayer). As such, the study area of this work can by no means be considered suitable for FAO56 applications.

2. Moreover, even if FAO56 can be boldly applied to the urban canyons using the mobile measurements, the surface heat flux (i.e., Q_G in eqn 1) seems missing, leading to questionable estimates of available energy (i.e., Q^*-Q_G) as well as E_P.

Given the concerns above, I cannot recommend publication of this paper in HESS.

However, as I said, the mobile measurements look promising and should/can be be more properly used: e.g., to evaluate human thermal comfort at the street level; and I believe the refined analysis would be suitable for journals on urban environment (e.g., Urban Climate, Building and Environment, etc.).
Citation: https://doi.org/10.5194/hess-2021-24-RC1
- AC1:
  'Reply on RC1', Merle Koelbing, 13 Mar 2021
  Dear anonymous revier#1,
  we appreciate the time for reading our paper and the effort that you have dedicated to provide your feedback on the manuscript. We are grateful for your comments that we like to reply to below:
  1) We kept the surface parameterized to a reference crop (even though this does not represent the ground cover directly at all measurement locations – but in close proximity there was also gras cover) to create comparable boundary conditions at all measurement locations. Our estimates of FAO56 potential evapotranspiration do therefore not reflect actual plant water use, but rather a measure of the atmospheric water demand under the assumptions in the PET model. Under this assumption we were then able to examine the sensitivity of the different variables included in the FAO56-penman-monteith equation to observed changes in the climatic input that occurred at street level in an urban canyon. Such a sensitivity analysis is only possible using a comparable approach and we decided to focus on the idea of potential evapotranspiration – as explained below.
  Usually, in urban hydrological models (but also in catchment models or groundwater recharge models that may include urban areas), actual evapotranspiration is simulated (sometimes only for the fraction of unsealed surfaces) dependent on potential evaporation (Ep) and the physical behavior of soils and plants (e.g. Grimmond & Oke, 1991; Mitchell et al., 2001; Berthier et al., 2006; Rodriguez et al. 2000; Rodriguez et al., 2008 (these studies are cited in the manuscript)). Therefore, these models ask for an input time series of potential evaporation (Ep). Input variables for estimating Ep are usually observed at one or several reference climate stations that are representing a mesoscale urban foot print area (e.g. airport climate stations) or rural conditions. To our knowledge, no adaption of input Ep to the urban microclimate was made so far in common urban hydrologic models. By using these mesoscale Ep rates, we neglect (at least) that climate variables can vary considerably within the city on a small spatial scale.
  With our approach, we deliver a method that can easily be combined with all those existing hydrologic models. By choosing a reference grass surface for the estimation of potential evapotranspiration we take into account that most parks and gardens in central European cities are cultivated as well watered lawns. We could have also chosen another surface, but since the surfaces in cities are highly complex, we believe that results of such a sensitivity analysis would be comparable.
  For example Zipper et al. (2017) were using a similar approach to investigate the urban heat island effect on the evapotranspiration demand in Madison, WI (USA). In a first step, they used FAO56-Penman-Montieth reference potential evapotranspiration for all urban (and rural) measurement locations to focus only on differences in atmospheric conditions between urban and rural sites (which in their study was limited to air temperature and vapor pressure deficit). Wind and radiation input were observed at two rural meteorological stations. Our study goes much further by also including urban/microscale wind speed and shortwave radiation leading to a new result: If only focusing on urban heat, we tend to overestimate urban potential evapotranspiration because the shading of areas on street level is neglected.
  2) We considered Q_G for hourly time steps as given in Allen et al. (1998) eq (45): Q_G = 0.1 * Q* as ground heat flux of a reference grass surface. We did not consider the surface heat flux of the urban canyon walls. Potential evapotranspiration thus is likely to be overestimated by not reducing net radiation by the surface heat flux of the walls. Neglecting wall heat fluxes was mainly due to missing observation data. We could have estimated the heat flux of the building walls with values taken from the literature (e.g. in Miao et al., 2012 (Table (4)); Nunez and Oke, 1977) which of course would have improved the results in terms of estimating real plant water use. On the other hand, we were focusing on a method for improving input Ep for mesoscale (urban) hydrologic models which can easily be applied without too many assumptions that have to be made beforehand. We approached our goal by performing a sensitivity analysis on the climatic input (and in addition incoming longwave radiation). If, in terms of mesoscale urban hydrologic modelling, the surface heat flux for the area under investigation was approximated by a coefficient multiplied by net radiation, we can assume that the overall relationship of our linear regression and the strong sensitivity to changes in incoming shortwave radiation would not change.
  Our suggestion for improvement of our manuscript are:
  to change the title of the manuscript more towards the hydrologic aspect of our study
  
  to explain better the hydrologic point of view and the aim of the study
  
  to include the handling of the surface heat flux more detailed in the method and the discussion section.
  
  Literature not cited in the manuscript:
  Miao, S., Dou, J., Chen, F. et al. Analysis of observations on the urban surface energy balance in Beijing. Sci. China Earth Sci. 55, 1881–1890 (2012). https://doi.org/10.1007/s11430-012-4411-6
  Nunez M, Oke TR (1977) The energy balance of an urban canyon. J Appl Meteorol 16:11–19
  Zipper, S. C., J. Schatz, C. J. Kucharik, and S. P. Loheide II (2017), Urban heat island induced increases in evapotranspirative demand, Geophys. Res. Lett., 44, doi:10.1002/2016GL072190.
  
  Citation: https://doi.org/10.5194/hess-2021-24-AC1
RC2:
'Comment on hess-2021-24', Anonymous Referee #2, 17 Mar 2021

Nice paper- but still some improvements have to be done: I start with the most important one:

I suggest modifying the title: Problems in downscaling potential evapotranspiration to an urban canyon

Otherwise I would expect at the end of this paper a first concept how to modify ET0 for street cayons with (i) different types of street sizes and (ii) buildings heights. I like to encourage you to do this final step with the help of your experieneces and ET0 sensitivity analysis of cited papers.

You already wrote: Daily Ep is calculated with meteorological data from a reference climate station and is then multiplied by kmc. This coefficient ranges between 0.5 and 1.4 and thereby 60 decreases or increases Ep depending on whether the location is e.g. shaded or influenced by strong winds, respectively (Costello et al., 2000).

So justify and combine this frame with your results and experiences!

Some more suggestions:

Please change the abbreviations of the street names (HS and ES) into their atmosheric orientations, which are much more relevant than the street names: thus HS becomes NS and ES becomes EW

Table 3, especially the ET0 results of the summer days are from my point of view much more relevant than the paper reflects at the moment. Question: why is in one street Epr> Epcont. while in the other street the opposite is true. Does the orientation plays a role? So please look to ETO sensitivity analysis and combine it with the street conditions, i.e. shading conditions and street size.

3. I miss the information on standard climate parameter: both (i) long-term means, and (ii) for the years of your experiments of ET0, summer precipitation, T, net radiation, and climate water balance.

4. I encourage you to give a first recommendation or frame, how people should modify (downscale) ET0 for urban street conditions. Better or first frame for discussing than nothing!

Citation: https://doi.org/10.5194/hess-2021-24-RC2
- AC2: 'Reply on RC2', Merle Koelbing, 11 Apr 2021
  
  Dear anonymous referee#2,
  We appreciate the time for reading our paper and the effort that you have dedicated to provide your feedback on the manuscript. We are grateful for your comments that we like to reply to below:
  a) Regarding the title:
  We understand that the title might be misleading to readers with a climatologic scientific background. If we will be asked to submit a revised version of the manuscript and if the editor is supporting this, we will change the title so that the purpose of our presented method becomes clearer. The aim was to develop a method that allows to adapt input ETo for continuous urban hydrologic modelling to urban (built-up) areas. We chose two differently oriented street canyons with and without trees to represent these areas. The new title might be: Adapting potential evapotranspiration from climate stations to the urban canyon for hydrological models.
  b) Regarding concept/frame, how people should modify (downscale) ETo for urban street conditions:
  We show that modifying ETo (called E_p in our manuscript since it was adapted to incoming longwave radiation) by using the proportion of shading leads to very good results. Street size and building height are affecting mainly the shading situation in the canyon. So, when applying our method to a city with different street sizes und canyon widths, we expect that according to our results, differences in wind speed, air temperature, relative humidity and longwave radiation should play a minor role compared to differences in short wave radiation. The purpose of our method presented in our manuscript is to optimize input ETo for built-up areas of cities represented by an urban canyon. Our data shows that shading has the largest effect on urban ETo compared to the other climate variable. When focusing on urban green spaces, other approaches can be included or combined.
  The idea presented in this paper has already been applied to the model RoGeR (Steinbrich et al., 2021):
  “This physically-based model can map the processes of run-off generation, the groundwater balance and run-off concentration at high temporal and spatial resolution. RoGeR also takes account of processes that hydrological models often neglect, for instance infiltration and interflow through preferential flow paths, or infiltration of lateral run-off on its flow path.” Among other things, RoGeR has been adapted to urban surfaces by using a digital elevation model including building height of the city of interest. Based on usual GIS tools (ArcGIS), the amount of available shortwave radiation can be calculated for each location in the city for each day of the year, dependent on the geographic location and the surrounding building structure. With the calculated GIS-based short-wave radiation we can determine δK↓ and in combination with the parameters shown in Fig. 14 of our manuscript we can describe the change in E_p for each point in the city. For continuous modelling of a long-term water balance, the mean state around the equinox and winter/summer solstice can be used to safe computing time. We could provide an example of the resulting map of ETo for a selected neighborhood if this is of interest to the readers.
  c) Regarding results in Table (3):
  Table 3 shows E_p values which are all calculated for the reference station. It shows, whether the days that we chose for our measurements were representative for the whole period that the measurements were taken in per season. The measurement period begins at day 1 of the mobile measurements and ends at the last day of mobile measurement in each season (Table 2). Therefore, E_p estimations as shown in Table 3 are not reflecting conditions in the street canyons. We will clarify this in the revised version.
  d) Regarding missing standard climate parameters:
  Since we are focusing on ETo (E_p) and not the single observed climate variables, we presented Table 3 to provide reference E_p estimations for each seasonal observation period. Long-term means of precipitation and air temperature are provided in the method section (line 100). Fig. 9 gives an overview over the observed input variables during the measurement period, for both, streets and reference station. We will add the other variables in the method section.
  e) Regarding abbreviations of street names & Costello et al. (2000)
  If we are asked to submit a revised version of the manuscript, we will consider to change the abbreviations of the street names. In addition, we will discuss our results in the light of the results of Costello et al. (2000).
  
  Literature not cited in the manuscript:
  Steinbrich A, Leistert H, Weiler M (2021): RoGeR – ein bodenhydrologisches Modell für die Beantwortung einer Vielzahl hydrologischer Fragen. In Korrespondenz Wasserwirtschaft, 14. Jahrgang, Heft Nr. 2, Februar 2021. DOI: 10.3243/kwe2021.02.004
  
  Citation: https://doi.org/10.5194/hess-2021-24-AC2
EC1: 'Comment on hess-2021-24', Dimitri Solomatine, 15 Apr 2021

Reviewers provided useful comments, and some judgements are tough. There is also a suggestion to change the title. I am of impression however, that the auhtors know how to revise the paper, and they already provided a plan for that. So I encourage the authors to work on the revision. Success! (but try also to enjoy the spring...) Take care!

Citation: https://doi.org/10.5194/hess-2021-24-EC1

Status: closed

RC1:
'Comment on hess-2021-24', Anonymous Referee #1, 01 Mar 2021
This paper presents an FAO56-based analysis of so-claimed "potential evapotranspiration" at an urban street scale using mobile measurements. Although the mobile measurements look interesting and may be valuable to understand the urban micrometeorology, the major flaw, improper use of FAO56 framework, prohibits this paper from publication in its current form:

1. the assumption of FAO56 for a reference crop with "a height of 0.12 m, a fixed surface resistance of 70 s m^-1 and an albedo of 0.23" fails on (almost) all urban surfaces, which are usually characterised with impervious surfaces and rather patchy greenery: it is thus inappropriate to use such a surface to represent urban canyons. Besides, the FAO56 assumed reference crop is inherently positioned in a more homogeneous context, where the measurements take at screen level (e.g., 2 m agl) is within the inertial sublayer (or above roughness sublayer). As such, the study area of this work can by no means be considered suitable for FAO56 applications.

2. Moreover, even if FAO56 can be boldly applied to the urban canyons using the mobile measurements, the surface heat flux (i.e., Q_G in eqn 1) seems missing, leading to questionable estimates of available energy (i.e., Q^*-Q_G) as well as E_P.

Given the concerns above, I cannot recommend publication of this paper in HESS.

However, as I said, the mobile measurements look promising and should/can be be more properly used: e.g., to evaluate human thermal comfort at the street level; and I believe the refined analysis would be suitable for journals on urban environment (e.g., Urban Climate, Building and Environment, etc.).
Citation: https://doi.org/10.5194/hess-2021-24-RC1
- AC1:
  'Reply on RC1', Merle Koelbing, 13 Mar 2021
  Dear anonymous revier#1,
  we appreciate the time for reading our paper and the effort that you have dedicated to provide your feedback on the manuscript. We are grateful for your comments that we like to reply to below:
  1) We kept the surface parameterized to a reference crop (even though this does not represent the ground cover directly at all measurement locations – but in close proximity there was also gras cover) to create comparable boundary conditions at all measurement locations. Our estimates of FAO56 potential evapotranspiration do therefore not reflect actual plant water use, but rather a measure of the atmospheric water demand under the assumptions in the PET model. Under this assumption we were then able to examine the sensitivity of the different variables included in the FAO56-penman-monteith equation to observed changes in the climatic input that occurred at street level in an urban canyon. Such a sensitivity analysis is only possible using a comparable approach and we decided to focus on the idea of potential evapotranspiration – as explained below.
  Usually, in urban hydrological models (but also in catchment models or groundwater recharge models that may include urban areas), actual evapotranspiration is simulated (sometimes only for the fraction of unsealed surfaces) dependent on potential evaporation (Ep) and the physical behavior of soils and plants (e.g. Grimmond & Oke, 1991; Mitchell et al., 2001; Berthier et al., 2006; Rodriguez et al. 2000; Rodriguez et al., 2008 (these studies are cited in the manuscript)). Therefore, these models ask for an input time series of potential evaporation (Ep). Input variables for estimating Ep are usually observed at one or several reference climate stations that are representing a mesoscale urban foot print area (e.g. airport climate stations) or rural conditions. To our knowledge, no adaption of input Ep to the urban microclimate was made so far in common urban hydrologic models. By using these mesoscale Ep rates, we neglect (at least) that climate variables can vary considerably within the city on a small spatial scale.
  With our approach, we deliver a method that can easily be combined with all those existing hydrologic models. By choosing a reference grass surface for the estimation of potential evapotranspiration we take into account that most parks and gardens in central European cities are cultivated as well watered lawns. We could have also chosen another surface, but since the surfaces in cities are highly complex, we believe that results of such a sensitivity analysis would be comparable.
  For example Zipper et al. (2017) were using a similar approach to investigate the urban heat island effect on the evapotranspiration demand in Madison, WI (USA). In a first step, they used FAO56-Penman-Montieth reference potential evapotranspiration for all urban (and rural) measurement locations to focus only on differences in atmospheric conditions between urban and rural sites (which in their study was limited to air temperature and vapor pressure deficit). Wind and radiation input were observed at two rural meteorological stations. Our study goes much further by also including urban/microscale wind speed and shortwave radiation leading to a new result: If only focusing on urban heat, we tend to overestimate urban potential evapotranspiration because the shading of areas on street level is neglected.
  2) We considered Q_G for hourly time steps as given in Allen et al. (1998) eq (45): Q_G = 0.1 * Q* as ground heat flux of a reference grass surface. We did not consider the surface heat flux of the urban canyon walls. Potential evapotranspiration thus is likely to be overestimated by not reducing net radiation by the surface heat flux of the walls. Neglecting wall heat fluxes was mainly due to missing observation data. We could have estimated the heat flux of the building walls with values taken from the literature (e.g. in Miao et al., 2012 (Table (4)); Nunez and Oke, 1977) which of course would have improved the results in terms of estimating real plant water use. On the other hand, we were focusing on a method for improving input Ep for mesoscale (urban) hydrologic models which can easily be applied without too many assumptions that have to be made beforehand. We approached our goal by performing a sensitivity analysis on the climatic input (and in addition incoming longwave radiation). If, in terms of mesoscale urban hydrologic modelling, the surface heat flux for the area under investigation was approximated by a coefficient multiplied by net radiation, we can assume that the overall relationship of our linear regression and the strong sensitivity to changes in incoming shortwave radiation would not change.
  Our suggestion for improvement of our manuscript are:
  to change the title of the manuscript more towards the hydrologic aspect of our study
  
  to explain better the hydrologic point of view and the aim of the study
  
  to include the handling of the surface heat flux more detailed in the method and the discussion section.
  
  Literature not cited in the manuscript:
  Miao, S., Dou, J., Chen, F. et al. Analysis of observations on the urban surface energy balance in Beijing. Sci. China Earth Sci. 55, 1881–1890 (2012). https://doi.org/10.1007/s11430-012-4411-6
  Nunez M, Oke TR (1977) The energy balance of an urban canyon. J Appl Meteorol 16:11–19
  Zipper, S. C., J. Schatz, C. J. Kucharik, and S. P. Loheide II (2017), Urban heat island induced increases in evapotranspirative demand, Geophys. Res. Lett., 44, doi:10.1002/2016GL072190.
  
  Citation: https://doi.org/10.5194/hess-2021-24-AC1
RC2:
'Comment on hess-2021-24', Anonymous Referee #2, 17 Mar 2021

Nice paper- but still some improvements have to be done: I start with the most important one:

I suggest modifying the title: Problems in downscaling potential evapotranspiration to an urban canyon

Otherwise I would expect at the end of this paper a first concept how to modify ET0 for street cayons with (i) different types of street sizes and (ii) buildings heights. I like to encourage you to do this final step with the help of your experieneces and ET0 sensitivity analysis of cited papers.

You already wrote: Daily Ep is calculated with meteorological data from a reference climate station and is then multiplied by kmc. This coefficient ranges between 0.5 and 1.4 and thereby 60 decreases or increases Ep depending on whether the location is e.g. shaded or influenced by strong winds, respectively (Costello et al., 2000).

So justify and combine this frame with your results and experiences!

Some more suggestions:

Please change the abbreviations of the street names (HS and ES) into their atmosheric orientations, which are much more relevant than the street names: thus HS becomes NS and ES becomes EW

Table 3, especially the ET0 results of the summer days are from my point of view much more relevant than the paper reflects at the moment. Question: why is in one street Epr> Epcont. while in the other street the opposite is true. Does the orientation plays a role? So please look to ETO sensitivity analysis and combine it with the street conditions, i.e. shading conditions and street size.

3. I miss the information on standard climate parameter: both (i) long-term means, and (ii) for the years of your experiments of ET0, summer precipitation, T, net radiation, and climate water balance.

4. I encourage you to give a first recommendation or frame, how people should modify (downscale) ET0 for urban street conditions. Better or first frame for discussing than nothing!

Citation: https://doi.org/10.5194/hess-2021-24-RC2
- AC2: 'Reply on RC2', Merle Koelbing, 11 Apr 2021
  
  Dear anonymous referee#2,
  We appreciate the time for reading our paper and the effort that you have dedicated to provide your feedback on the manuscript. We are grateful for your comments that we like to reply to below:
  a) Regarding the title:
  We understand that the title might be misleading to readers with a climatologic scientific background. If we will be asked to submit a revised version of the manuscript and if the editor is supporting this, we will change the title so that the purpose of our presented method becomes clearer. The aim was to develop a method that allows to adapt input ETo for continuous urban hydrologic modelling to urban (built-up) areas. We chose two differently oriented street canyons with and without trees to represent these areas. The new title might be: Adapting potential evapotranspiration from climate stations to the urban canyon for hydrological models.
  b) Regarding concept/frame, how people should modify (downscale) ETo for urban street conditions:
  We show that modifying ETo (called E_p in our manuscript since it was adapted to incoming longwave radiation) by using the proportion of shading leads to very good results. Street size and building height are affecting mainly the shading situation in the canyon. So, when applying our method to a city with different street sizes und canyon widths, we expect that according to our results, differences in wind speed, air temperature, relative humidity and longwave radiation should play a minor role compared to differences in short wave radiation. The purpose of our method presented in our manuscript is to optimize input ETo for built-up areas of cities represented by an urban canyon. Our data shows that shading has the largest effect on urban ETo compared to the other climate variable. When focusing on urban green spaces, other approaches can be included or combined.
  The idea presented in this paper has already been applied to the model RoGeR (Steinbrich et al., 2021):
  “This physically-based model can map the processes of run-off generation, the groundwater balance and run-off concentration at high temporal and spatial resolution. RoGeR also takes account of processes that hydrological models often neglect, for instance infiltration and interflow through preferential flow paths, or infiltration of lateral run-off on its flow path.” Among other things, RoGeR has been adapted to urban surfaces by using a digital elevation model including building height of the city of interest. Based on usual GIS tools (ArcGIS), the amount of available shortwave radiation can be calculated for each location in the city for each day of the year, dependent on the geographic location and the surrounding building structure. With the calculated GIS-based short-wave radiation we can determine δK↓ and in combination with the parameters shown in Fig. 14 of our manuscript we can describe the change in E_p for each point in the city. For continuous modelling of a long-term water balance, the mean state around the equinox and winter/summer solstice can be used to safe computing time. We could provide an example of the resulting map of ETo for a selected neighborhood if this is of interest to the readers.
  c) Regarding results in Table (3):
  Table 3 shows E_p values which are all calculated for the reference station. It shows, whether the days that we chose for our measurements were representative for the whole period that the measurements were taken in per season. The measurement period begins at day 1 of the mobile measurements and ends at the last day of mobile measurement in each season (Table 2). Therefore, E_p estimations as shown in Table 3 are not reflecting conditions in the street canyons. We will clarify this in the revised version.
  d) Regarding missing standard climate parameters:
  Since we are focusing on ETo (E_p) and not the single observed climate variables, we presented Table 3 to provide reference E_p estimations for each seasonal observation period. Long-term means of precipitation and air temperature are provided in the method section (line 100). Fig. 9 gives an overview over the observed input variables during the measurement period, for both, streets and reference station. We will add the other variables in the method section.
  e) Regarding abbreviations of street names & Costello et al. (2000)
  If we are asked to submit a revised version of the manuscript, we will consider to change the abbreviations of the street names. In addition, we will discuss our results in the light of the results of Costello et al. (2000).
  
  Literature not cited in the manuscript:
  Steinbrich A, Leistert H, Weiler M (2021): RoGeR – ein bodenhydrologisches Modell für die Beantwortung einer Vielzahl hydrologischer Fragen. In Korrespondenz Wasserwirtschaft, 14. Jahrgang, Heft Nr. 2, Februar 2021. DOI: 10.3243/kwe2021.02.004
  
  Citation: https://doi.org/10.5194/hess-2021-24-AC2
EC1: 'Comment on hess-2021-24', Dimitri Solomatine, 15 Apr 2021

Reviewers provided useful comments, and some judgements are tough. There is also a suggestion to change the title. I am of impression however, that the auhtors know how to revise the paper, and they already provided a plan for that. So I encourage the authors to work on the revision. Success! (but try also to enjoy the spring...) Take care!

Citation: https://doi.org/10.5194/hess-2021-24-EC1

Merle Koelbing, Tobias Schuetz, and Markus Weiler

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Mar 2024	30	17	4	51
Apr 2024	46	11	5	62
May 2024	30	7	2	39
Jun 2024	34	5	3	42
Jul 2024	33	1	2	36
Aug 2024	37	5	0	42
Sep 2024	28	3	0	31
Oct 2024	28	15	1	44
Nov 2024	16	3	0	19

Cumulative views and downloads (calculated since 04 Feb 2021)

Month	HTML	PDF	XML	Total
Feb 2021	179	52	2	233
Mar 2021	92	30	8	130
Apr 2021	52	24	5	81
May 2021	27	10	0	37
Jun 2021	17	11	0	28
Jul 2021	15	11	1	27
Aug 2021	18	9	0	27
Sep 2021	16	24	0	40
Oct 2021	17	91	1	109
Nov 2021	19	42	2	63
Dec 2021	24	13	0	37
Jan 2022	25	12	0	37
Feb 2022	20	2	0	22
Mar 2022	18	9	1	28
Apr 2022	17	5	1	23
May 2022	13	3	1	17
Jun 2022	7	5	3	15
Jul 2022	24	4	0	28
Aug 2022	8	8	0	16
Sep 2022	5	7	0	12
Oct 2022	8	5	1	14
Nov 2022	9	1	0	10
Dec 2022	5	8	0	13
Jan 2023	18	15	0	33
Feb 2023	10	3	1	14
Mar 2023	5	7	0	12
Apr 2023	6	5	0	11
May 2023	10	5	2	17
Jun 2023	6	6	0	12
Jul 2023	36	13	1	50
Aug 2023	26	9	0	35
Sep 2023	13	5	4	22
Oct 2023	27	7	1	35
Nov 2023	23	5	0	28
Dec 2023	15	3	0	18
Jan 2024	18	7	1	26
Feb 2024	16	7	0	23
Mar 2024	30	17	4	51
Apr 2024	46	11	5	62
May 2024	30	7	2	39
Jun 2024	34	5	3	42
Jul 2024	33	1	2	36
Aug 2024	37	5	0	42
Sep 2024	28	3	0	31
Oct 2024	28	15	1	44
Nov 2024	16	3	0	19

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Cited

Latest update: 20 Nov 2024

Short summary

Based on a unique and comprehensive data set of urban micro-meteorological variables, which were observed with a mobile climate station, we developed a new method to transfer mesoscale reference potential evapotranspiration to the urban microscale in street canyons. Our findings can be transferred easily to existing urban hydrologic models to improve modelling results with a more precise estimate of potential evapotranspiration on street level.


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Downscaling potential evapotranspiration to the urban canyon

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1 citations as recorded by crossref.