the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Hydro-PE: gridded datasets of historical and future Penman-Monteith potential evaporation for the United Kingdom
Matthew J. Brown
Alison L. Kay
Rosanna A. Lane
Rhian Chapman
Victoria A. Bell
Eleanor M. Blyth
Abstract. We present two new potential evaporation datasets for the United Kingdom: a historical dataset, Hydro-PE HadUKGrid, which is derived from the HadUK-Grid gridded observed meteorology (1969–2021); and a future dataset, Hydro-PE UKCP18 RCM, which is derived from UKCP18 regional climate projections (1980–2080). Both datasets are suitable for hydrological modelling, and provide Penman-Monteith potential evapotranspiration parameterised for short grass, with and without a correction for interception on days with rainfall. The potential evapotranspiration calculations have been formulated to closely follow the methodology of the existing Meteorological Office Rainfall and Evaporation Calculation System (MORECS) potential evapotranspiration, which has historically been widely used by hydrological modellers in the United Kingdom. The two datasets have been created using the same methodology, to allow seamless modelling from past to future. Hydro-PE HadUK-Grid shows good agreement with MORECS in much of the United Kingdom, although Hydro-PE HadUK Grid is higher in the mountainous regions of Scotland and Wales. This is due to differences in the underlying meteorology, in particular the wind speed, which are themselves due to the different spatial scales of the data. Hydro-PE HadUK-Grid can be downloaded from https://doi.org/10.5285/9275ab7e-6e93-42bc-8e72-59c98d409deb (Brown et al., 2022) and Hydro-PE UKCP18 RCM can be downloaded from https://doi.org/10.5285/eb5d9dc4-13bb-44c7-9bf8-c5980fcf52a4 (Robinson et al., 2021).
Emma L. Robinson et al.
Status: final response (author comments only)
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RC1: 'Comment on essd-2022-288', Anonymous Referee #1, 02 Nov 2022
review of "Hydro-PE: gridded datasets of historical and future Penman-Monteith potential evaporation for the United Kingdom" by Robinson, Brown, Kay, Lane, Chapman, Bell and Blyth
The authors describe two new datasets for Potential EvapoTranspiration (PET) for the UK. These datasets are based on (processed) observations (provide an estimate of the PET values for the current climate) and model climate scenario from UKCP (and provide an estimate of future conditions of PET). The datasets come as an estimate of PET and include a parameterization of inception on days with rain. The approach leading to both datasets and the assumptions are discussed in the documentation in sufficient detail and the embedding in the literature is well done. The manuscript is a very good read and very clear.
However, there are a few concerns that need to be addressed before acceptance of the paper can be considered. These relate to 1) the uncertainty introduced by the need to use monthly mean values for sunshine duration and wind which are interpolated to the daily level and 2) inconsistencies between the input meteorological data for the historic period and the future period - and how these propagate into the PET estimates.
In addition, it would make the documentation of this dataset even more interesting if the manuscript would add a focus on climatology of more extreme situations and their projected changes. The monthly mean perspective is clearly relevant, but many users will be interested in the shortish warm spells with exceptionally sunny weather as these affect hydrology quite profoundly.
These concerns are detailed below.
My advice to the editor is to accept the paper with major revisions.
Main concerns:
1) It is a pity that daily maps for wind speed and sunshine duration (and air pressure) are not available in the HadUK-Grid dataset. The approach taken by the authors is then probably the best way forward. However, there are potentially large effects on the PET estimates because of the strong underestimation of the day-to-day variation in PET values. As the authors state in the manuscript, PET is non-linearly related to the input which makes the underestimation of day-to-day variations a potential problem. While section 5.3 comes some way in demonstrating that the approach of the manuscript works, but the answers are not quite satifying. In my view, an additional analysis needs to done. The UK Met Office's National Climate Information Centre has long records available (starting in the early 1960s) with daily values of sunshine duration. Long series with wind speed are available as well I guess, but can be obtained through NOAA's Global Summary of the Day (or through the ECA&D). These records allow for a day-to-day comparison of selected UK stations of PET using actual daily values and interpolated values.The benefit of this approach - easy to do as you already have all the software in place - is that an assessment of the neglect of day-to-day variability on PET and PETI values can be made directly, also for the summer season (which is left-out in section 5.3). In this assessment, the monthly mean perspective is interesting (and in line with the rest of the manuscript), but a view on the under- or overestimation on the more extreme days (like total overcast days or warm and long sunny spells like those in the recent heat waves) is of particular value to the user.
2) Figs. 10 and 11 compare outcomes of PET values based on the (processed) observations and the UKCP climate scenario's. The manuscript discusses some issues with the model results, like the presence of a bias in the variables etc. What is missing a bit is the identification (and assessment of the severity) of a possible mis-match between model and observations. Apart from a possible bias, differences between observations and model meteorological data include the use of sunshine duration (obs) vs. radiation (model) (at least: this is what I expect that is used when processing the UKCP data). The difference between daily mean temperature (model) vs. Tmax and Tmin (obs) is another. Probably these differences are smaller than any model bias, but it would be good to establish this.
3) With the recent heat waves Europe has seen, many people will be interested in how the dataset performs for these more extreme situations. The current manuscript lacks any detail in this respect and that is a pity. An assessment of the quality of the dataset during dry spells/heat waves is required. The comparison of climatology of extreme PET values - like the climatology of 90th or 95th percentiles in daily PET - between the historical period and the projections is required for the user to gain trust that the dataset is reliable in this respect as well.
Other aspects that authors may want to look into
-) E5: (ground heat flux)
In this section, it appears that ground heat flux and ground heat storage are mixed-up. It was my understanding that the ground heat flux is often neglected when focusing on daily resolution estimates of PE.
-) line 325: the period 1985-1992 is remarkably short for any comparison. I think observational data are available for a more expansive period.
-) line 330: be clear if the standard deviation and correlation analysis involves the daily values or monthly mean values. The reader will see later in this paragraph (line 338) that daily values are used. Note that for the Pearson correlation, the seasonal cycle neds to filtered-away (otherwise the correlation is likely to partly reflect that both datasets capture the seasonal cycle).Citation: https://doi.org/10.5194/essd-2022-288-RC1 -
RC2: 'Comment on essd-2022-288', Anonymous Referee #2, 21 Mar 2023
This paper about two potential evapotranspiration datasets is well organized and documented. The datasets appear relevant and useful for hydrological modelling.
I agree with the thoughtful comments of the first reviewer (https://doi.org/10.5194/essd-2022-288-RC1). Some additional critical discussion of methodological constraints on the results could further increase the usefulness of the data for different applications. Besides the issues from the first review, I do not have other concerns with the manuscript.
Finally, I encourage the authors to consider making the code for their analyis available, which could allow testing other input data, application at different regions, etc.
Citation: https://doi.org/10.5194/essd-2022-288-RC2 -
AC1: 'Author response to reviewers essd-2022-288', Emma Robinson, 19 Apr 2023
We thank both reviewers for their considered comments about the manuscript. Overall we agree that a further assessment of the methodology and analysis of extremes will help potential users to understand the utility of the data set, and will add these to the manuscript.
In addition to the specific responses below, we will also update the text to add some clarifying details about the use of the UKCP18 ensemble, particularly in the context of other available climate projections. We will also publish the potential evaporation (PE) calculation code and add it as an asset to this paper.
Response to RC1:
- We agree that an assessment of the impact of using interpolated variables rather than actual daily values would be of use to potential users. We will identify a data set (or combination of data sets) containing all of the required variables at daily timestep. This will enable us to add an evaluation of the impact on the variability of PE when using interpolated monthly variables compared to the available daily variables. We will also compare with the use of raw monthly means.
- Methods such as the calculation of shortwave radiation from sunshine hours and the use of daily minimum and maximum temperature to estimate the daily mean temperature are well-established when working with station data. However, it is still useful to demonstrate the impact of them on the PE calculations, particularly as the available input variables differ between our historical and future data sets. We will ensure that the data set(s) that we use to address point 1 can also be used to demonstrate this impact, and we will include this in the additional analysis.
- We agree that an assessment of extremes would be a useful addition to this manuscript. We will add the suggested comparison of high percentiles between the historical and future PE data sets, and will also consider this when addressing point 1. We think that an analysis for particular meteorological regimes (eg heatwaves, dry spells) is more detailed than is necessary for this data paper, but that an analysis of high percentiles on a seasonal or monthly basis will address the need to assess the performance at extremes for potential users.
Other concerns:
E5: It is true that ground heat flux is often neglected in calculations of daily PE. However, it is included in the MORECS calculations, and we have retained it for compatibility with the existing MORECS PE. The use of the average daily heat storage to calculate the daily ground heat flux follows the MORECS methodology.
L325: Section 5.2 is specifically a comparison with site-level MORECS data in order to help disentangle the differences between HadUK-Grid and MORECS that are due to the resolution mismatch from other differences. These site-level MORECS data are only available for this relatively short period, but we do not think that a comparison with other site level PE products would add to this particular comparison as they would not be equivalent to MORECS. We note that Section 5.1 does provide a comparison over a longer time period with gridded MORECS and CHESS-PE, albeit at a broader spatial scale. We have targeted these comparisons with MORECS and CHESS-PE due to their widespread use in UK hydrological and ecological modelling.
L330: We will clarify the text to specify that we used daily values to calculate these statistics. We will also calculate the Pearson correlation after filtering for the seasonal cycle for comparison.
Response to RC2:
We intend that addressing the comments of RC1 will provide the added critical discussion of the methodology that RC2 suggests.
We will make our PE calculation code available through github. This will allow researchers to apply it to other input data sets, either observational or model outputs, which will help to expand the understanding of uncertainty in historical and future projections of PE.
Citation: https://doi.org/10.5194/essd-2022-288-AC1
Emma L. Robinson et al.
Data sets
Potential evapotranspiration derived from the UK Climate Projections 2018 Regional Climate Model ensemble 1980-2080 (Hydro-PE UKCP18 RCM) Robinson, E.L.; Kay, A.L.; Brown, M.; Chapman, R.; Bell, V.A.; Blyth, E.M. https://doi.org/10.5285/eb5d9dc4-13bb-44c7-9bf8-c5980fcf52a4
Potential evapotranspiration derived from HadUK-Grid 1km gridded climate observations 1969-2021 (Hydro-PE HadUK-Grid) Brown, M.J.; Robinson, E.L.; Kay, A.L.; Chapman, R.; Bell, V.A.; Blyth, E.M. https://doi.org/10.5285/9275ab7e-6e93-42bc-8e72-59c98d409deb
Emma L. Robinson et al.
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