the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
stoPET v1.0: A stochastic potential evapotranspiration generator for simulation of climate change impacts
- 1School of Geographical Sciences, University of Bristol, Bristol, UK
- 2School of Earth and Environmental Sciences, Cardiff University, Cardiff, UK
- 3Water Research Institute, Cardiff University, Cardiff, UK
- 4Earth Research Institute, University of California Santa Barbara, Santa Barbara, USA
- 5Department of Civil Engineering, University of Bristol, UK
- 6Cabot Institute for the Environment, University of Bristol, Bristol, UK
- 7School of Civil and Environmental Engineering, The University of New South Wales (UNSW), Sydney, Australia
- 1School of Geographical Sciences, University of Bristol, Bristol, UK
- 2School of Earth and Environmental Sciences, Cardiff University, Cardiff, UK
- 3Water Research Institute, Cardiff University, Cardiff, UK
- 4Earth Research Institute, University of California Santa Barbara, Santa Barbara, USA
- 5Department of Civil Engineering, University of Bristol, UK
- 6Cabot Institute for the Environment, University of Bristol, Bristol, UK
- 7School of Civil and Environmental Engineering, The University of New South Wales (UNSW), Sydney, Australia
Abstract. Potential evapotranspiration (PET) represents the evaporative demand in the atmosphere for the removal of water from the land and is an essential variable for understanding and modelling land-atmosphere interactions. Weather generators are often used to generate stochastic rainfall time series; however, no such model exists for stochastically generating plausible PET time series. Here we develop a stochastic PET generator, stoPET, by leveraging a recently published global dataset of hourly PET at 0.1° resolution (hPET). stoPET is designed to simulate realistic time series of PET that capture the diurnal and seasonal variability of hPET and to support the simulation of various scenarios of climate change. The parsimonious model is based on a sine function fitted to the monthly average diurnal cycle of hPET, producing parameters that are then used to generate synthetic series of hourly PET at any 0.1° land surface point between 55° N and 55° S. stoPET also incorporates three methods to account for potential future changes in atmospheric evaporative demand to rising global temperature. These include 1) user-defined percentage increase of annual PET; 2) a step change in PET based on a unit increase in temperature, and 3) extrapolation of the historical trend in hPET into the future. We evaluated stoPET at a regional scale and at twelve locations spanning arid and humid climatic regions around the globe. stoPET generates PET distributions that are statistically similar to hPET, capturing its diurnal/seasonal dynamics, indicating that stoPET produces physically plausible diurnal and seasonal PET variability. We provide examples of how stoPET can generate large ensembles of PET for future climate scenario analysis in sectors like agriculture and water resources, with minimal computational demand.
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The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
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The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
- Preprint
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Supplement
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Journal article(s) based on this preprint
Dagmawi Teklu Asfaw et al.
Interactive discussion
Status: closed
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RC1: 'Comment on gmd-2022-128', Anonymous Referee #1, 12 May 2022
- P6L8: What is the format of skewed normal distribution? Please express the distribution. Why do you choose distribution? Is that common for PET? If yes, include the references. Otherwise, statistical test must be performed to ensure the distribution. Is it possible that noise ratio can be negative? If not, other distribution must do better job such as gamma.
- There is no full equation that explains the stochastic simulation model of PET including sine +noise+annual variability. Each element is explained in separate sections. Combined model description must be provided.
- The overall comparison between hPET and stoPET is not acceptable since the hPET was employed to build the stoPET model. Naive or other stochasitc model must be used for comparison.
- Double cycle of seasonal variability shown in Africa of A4 (Figure9) does not seem perform good. Please describe the potential reasons.
- Explanation of the program and data must be provided. Provide specific steps to download the data.
- Fig12: stoPET is the stochastic simulation model. One might have wrong implication that the model was not performed good. Separate panels can be used instead of overlapping.
- For example of Method 1 and 2, isn’t it better with different user-defined-changes at each year. This reviewer suggest that the authors reasonably set up the scenario to change the annual variation.
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AC1: 'Reply on RC1', Dagmawi Asfaw, 25 May 2022
Thank you for your comment.
We provide a response to your comments and corrected the manuscript accordingly.
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RC3: 'Reply on AC1', Taesam Lee, 07 Jun 2022
This reviewer considers that the authors substantially improved the manuscript following the provided comment.
The manuscript seems to be good enough to be publised as is in GMD.
One thing, this reviewer just want to mention, is that he knows that the authors try to present the stochastic series in Fig12 compared to the observation. this reviewer worried that the readers might have wrong implication from overlapping. This reviewer thinks the additional explanation is enough.
T.Lee
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RC3: 'Reply on AC1', Taesam Lee, 07 Jun 2022
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RC2: 'Comment on gmd-2022-128', Anonymous Referee #2, 06 Jun 2022
I'm revising "stoPET v1.0: A stochastic potential evapotranspiration generator for simulation of climate change impacts", the manuscript exposes the adopted practices to obtain stochastic based generation of Potential EvapoTranspiration.
The proposed manuscript is well written and clearly exposed.The obtained results can be applied in a wide range of practical applications, those applications are exposed in the "conclusion" chapter.
An overview of some of the potential application of the proposed algorithm could be provided earlier in the manuscript; e.g. mentioned in the introductory part, so to introduce the point in obtaining detailed hourly series.Fig.5 shows great alignment between stoPET and hPET but the former is derived from the latter: the agreement only shows that the mean un-biased stochastic mechanism worked as planned.
Wouldn't be be more interesting a comparison with a different stochastic source? E.g. Hargreaves computed PET with input from a stochastic weather generator (at higher computational cost)?Incorporating future climate change in stoPET provides 3 methods to include expected changes in PET.
About method 3, adoption of linear trends for timeseries of complex variables can hardly be considered robust. In 4.1.3 when do you consider the beginning for the historical PET start and how long is it?-
AC2: 'Reply on RC2', Dagmawi Asfaw, 09 Jun 2022
The comment was uploaded in the form of a supplement: https://gmd.copernicus.org/preprints/gmd-2022-128/gmd-2022-128-AC2-supplement.pdf
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AC2: 'Reply on RC2', Dagmawi Asfaw, 09 Jun 2022
Peer review completion
Interactive discussion
Status: closed
-
RC1: 'Comment on gmd-2022-128', Anonymous Referee #1, 12 May 2022
- P6L8: What is the format of skewed normal distribution? Please express the distribution. Why do you choose distribution? Is that common for PET? If yes, include the references. Otherwise, statistical test must be performed to ensure the distribution. Is it possible that noise ratio can be negative? If not, other distribution must do better job such as gamma.
- There is no full equation that explains the stochastic simulation model of PET including sine +noise+annual variability. Each element is explained in separate sections. Combined model description must be provided.
- The overall comparison between hPET and stoPET is not acceptable since the hPET was employed to build the stoPET model. Naive or other stochasitc model must be used for comparison.
- Double cycle of seasonal variability shown in Africa of A4 (Figure9) does not seem perform good. Please describe the potential reasons.
- Explanation of the program and data must be provided. Provide specific steps to download the data.
- Fig12: stoPET is the stochastic simulation model. One might have wrong implication that the model was not performed good. Separate panels can be used instead of overlapping.
- For example of Method 1 and 2, isn’t it better with different user-defined-changes at each year. This reviewer suggest that the authors reasonably set up the scenario to change the annual variation.
-
AC1: 'Reply on RC1', Dagmawi Asfaw, 25 May 2022
Thank you for your comment.
We provide a response to your comments and corrected the manuscript accordingly.
-
RC3: 'Reply on AC1', Taesam Lee, 07 Jun 2022
This reviewer considers that the authors substantially improved the manuscript following the provided comment.
The manuscript seems to be good enough to be publised as is in GMD.
One thing, this reviewer just want to mention, is that he knows that the authors try to present the stochastic series in Fig12 compared to the observation. this reviewer worried that the readers might have wrong implication from overlapping. This reviewer thinks the additional explanation is enough.
T.Lee
-
RC3: 'Reply on AC1', Taesam Lee, 07 Jun 2022
-
RC2: 'Comment on gmd-2022-128', Anonymous Referee #2, 06 Jun 2022
I'm revising "stoPET v1.0: A stochastic potential evapotranspiration generator for simulation of climate change impacts", the manuscript exposes the adopted practices to obtain stochastic based generation of Potential EvapoTranspiration.
The proposed manuscript is well written and clearly exposed.The obtained results can be applied in a wide range of practical applications, those applications are exposed in the "conclusion" chapter.
An overview of some of the potential application of the proposed algorithm could be provided earlier in the manuscript; e.g. mentioned in the introductory part, so to introduce the point in obtaining detailed hourly series.Fig.5 shows great alignment between stoPET and hPET but the former is derived from the latter: the agreement only shows that the mean un-biased stochastic mechanism worked as planned.
Wouldn't be be more interesting a comparison with a different stochastic source? E.g. Hargreaves computed PET with input from a stochastic weather generator (at higher computational cost)?Incorporating future climate change in stoPET provides 3 methods to include expected changes in PET.
About method 3, adoption of linear trends for timeseries of complex variables can hardly be considered robust. In 4.1.3 when do you consider the beginning for the historical PET start and how long is it?-
AC2: 'Reply on RC2', Dagmawi Asfaw, 09 Jun 2022
The comment was uploaded in the form of a supplement: https://gmd.copernicus.org/preprints/gmd-2022-128/gmd-2022-128-AC2-supplement.pdf
-
AC2: 'Reply on RC2', Dagmawi Asfaw, 09 Jun 2022
Peer review completion
Journal article(s) based on this preprint
Dagmawi Teklu Asfaw et al.
Dagmawi Teklu Asfaw et al.
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The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
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