the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 30 Apr 2024
Spatiotemporal variation of growth-stage specific compound climate extremes for rice in South China: Evidence from concurrent and consecutive compound events
Abstract. There is increasing concern regarding the impact of compound agroclimatic extreme events on crop yield, particularly in the context of projected increases in their frequency and intensity due to climate change. While previous studies have generally focused on compound hot and dry events in maize and wheat using growing-season relative thresholds, the time-variant physiological sensitivity of crops to climate extremes has not been sufficiently considered. We determined the spatiotemporal variations of compound climate extremes (CEs) for single- and late-rice in southern China during 1980–2014 and their underlying drivers using growth-stage specific physiological thresholds. Specifically, we carefully distinguished between concurrent compound events (CCEs) and consecutive compound events (CSEs). Our results indicated an increasing trend of compound hot-dry events for single-rice, but a decreasing trend of compound chilling-rainy events for late-rice. Spatially, the hotspots of compound hot-dry events for single-rice shifted from the lower Yangtze River Basin to its upper stream, and were dominated by the spatial differences in phenology rather than the occurrence of extreme events. The hotspots of compound chilling-rainy events for late-rice remained concentrated near the northwest edges of late-rice growing areas, indicating the limitation of thermal conditions. The occurrence and duration of CCEs was closely related to local temperature-moisture coupling (negative correlation). A path analysis suggested that temperature was the dominant factor influencing the changes in compound hot-dry events for single-rice. For the changes in compound chilling-rainy events for late-rice, the effect of temperature was only slightly larger than that of moisture. Our study has improved the understanding of compound climate extremes in China’s rice production system, and the results provide important information for risk management and adaptation strategies under climate change.
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RC1: 'Comment on esd-2024-8', Anonymous Referee #1, 30 May 2024
This paper investigates the combined climate extremes relevant to rice production in China. The authors analyze concurrent and consecutive compound events relevant for single- and late-rice during 1980–2014, using specific known thresholds. Examining both concurrent and consecutive extremes provides a more comprehensive picture of potential stress on rice crops. However, the manuscript would benefit from addressing some fundamental points and key concerns:
1- Sample Size Concerns: First concern is regarding the sample size of stations, highlighting the potential lack of representativeness for the entire region. Given the substantial spatial heterogeneity of soil moisture, the limited number of stations may not fully capture the diverse conditions across China.
2- Missing Yield Impact Assessment: While the paper mentions rice yield as motivation, it lacks a direct evaluation of how these compound events affect production quantities. It is necessary to incorporate an analysis of yield data to directly assess the impact of compound events on rice production. The paper's association with rice is primarily through growing season definitions, yet there is a noticeable absence of yield estimation. The justification for focusing on rice should be more explicit, particularly considering the absence of yield data.
3- Growing Season Definition Clarity: Specifying whether the growing season definition has fixed planting and harvest dates or adapts based on actual planting times is crucial. Sensitivity analysis to choice of dates is necessary to understand how changes in the selection of growing season start and end could influence the results.
4- Intensity Metric Considerations: The current focus on number of extreme days based on thresholds might overlook the intensity of extreme events. Analyzing the magnitude of temperature or drought deviations could provide deeper insights. The metrics employed in the study center on frequency and the number of days above a threshold but fail to consider the intensity of compound events. It is important to consider the intensity, as a single day with a temperature 10°C above the threshold could have more substantial implications for agriculture than ten days with only 0.5°C above the threshold.
5- Practical Implications and Value Added: Explicitly discussing the practical applications of the research and its contribution to existing knowledge would enhance the paper's value for the scientific community.
Citation: https://doi.org/10.5194/esd-2024-8-RC1 -
AC3: 'Reply on RC1', Tao Ye, 31 Jul 2024
The comment was uploaded in the form of a supplement: https://esd.copernicus.org/preprints/esd-2024-8/esd-2024-8-AC3-supplement.pdf
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AC4: 'Reply on RC1', Tao Ye, 31 Jul 2024
The comment was uploaded in the form of a supplement: https://esd.copernicus.org/preprints/esd-2024-8/esd-2024-8-AC4-supplement.pdf
-
AC3: 'Reply on RC1', Tao Ye, 31 Jul 2024
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RC2: 'Comment on esd-2024-8', Anonymous Referee #2, 30 Jun 2024
Review of ‘ Spatiotemporal variation of growth-stage specific compound climate extremes for rice in South China: Evidence from concurrent and consecutive compound events ’
Authors: Ran Sun1,2,3,4, Tao Ye1,2,3,4, Yiqing Liu1,2,3,4, Weihang Liu1,2,3,4, Shuo Chen1,2,3,4
Journal: ESD
General Comment
The text is well structured. The flow is generally clear. Considering phonologically relevant growth stages to assess climatic conditions on crops is indeed interesting and adds to the value of paper. However, I have three major comments:
- You have not directly evalauted how/if your climatic indicators actually impact the yields: I would have expected to see some crop simulation with climatic indicators or at least a correlation analysis between crop and climatic conditions. Check following papers to get some inspiration (Luan et al., 2021; Zhu & Troy, 2018; Zscheischler et al., 2017). The way you presented the result in current version, we cannot even be sure even your indicators matter for crops and impact them.
- The text is rather clear when you generally talk about compound heat and drought and the temperature moisturize coupling, in relation to these two indicators. The text, however, becomes vague when you talk about chilling and rain events and how you tried to associate them to some underlying climatic contributor. L174-176 is very unclear and requires further explanation of the method.
- It is unclear to me why you considered two event types for CSE, according to L152-154. Why two drought or two heat within two growth stage is not considered a consecutive event? In the same lines L153-154 is unclear and requires clarification.
Specific Comment
Abstract: Consider removing the part talking about maize and wheat. The paper focuses on rice and that needs to be brought up in the abstract.
L62-66: Again consider removing the part talking about wheat and maize, and their growths temperature dependent thresholds. I think they distract the reader.
L93: grain-filling and everywhere when you mention this word: consider removing the dash line between grain and filling. For your other stages the dash bridges two stage but grain filling is a distinct stage itself.
L95: I don’t understand why use the term 'dew' sometimes after chilling. Maybe be consistent and use the same terminology or be specific why you need to mention dew in specific parts of the text.
L115: be consistent and use either early rice or single rice. Also, here in L115 it feels like you have three type of rices while I assume there are two rices analyzed in this study.
Fig 2 &3 : It is unclear to me how you considered total days of compound event. Is it the total during study period? – According to L160-163 they should correspond to yearly values but then did you consider an average of duration per year, over the study period and plotted them in these figures?
L236: Please clarify where Hunan is located by geographical lat-lon.
L251-257: I couldn’t understand this part. Please consider heavy modification of the text and clarification.
Fig 4: what is the density in the plots? And what do we learn from it?
L263: sensitivity of PER to what for late rice? – the sentence is generally unclear.
L355: consider removing the first line and directly go to the limitations you think the study has.
References:
Luan, X., Bommarco, R., Scaini, A., & Vico, G. (2021). Combined heat and drought suppress rainfed maize and soybean yields and modify irrigation benefits in the USA. Environmental Research Letters, 16(6), 064023. https://doi.org/10.1088/1748-9326/abfc76
Zhu, X., & Troy, T. J. (2018). Agriculturally Relevant Climate Extremes and Their Trends in the World’s Major Growing Regions. Earth’s Future, 6(4), 656–672. https://doi.org/10.1002/2017EF000687
Zscheischler, J., Orth, R., & Seneviratne, S. I. (2017). Bivariate return periods of temperature and precipitation explain a large fraction of European crop yields. https://doi.org/10.3929/ethz-b-000190400
Citation: https://doi.org/10.5194/esd-2024-8-RC2 -
AC2: 'Reply on RC2', Tao Ye, 31 Jul 2024
The comment was uploaded in the form of a supplement: https://esd.copernicus.org/preprints/esd-2024-8/esd-2024-8-AC2-supplement.pdf
-
RC3: 'Comment on esd-2024-8', Benjamin Poschlod, 01 Jul 2024
The study assesses the occurrence of climatic compound events during the rice growing season in China. Thereby, it relies on 34 years of data (1981 – 2014) of 65 stations across whole China. The study distinguishes between concurrent (CCE) and consecutive (CSE) events, two cropping systems (single-rice for a single harvest per season; late-rice for the second harvest of two harvests per season), and three growing stages. On this base, the authors perform a statistical exploration:
1. Plotting the frequency of event types and assessing a linear trend
2. Mapping the locations, where the events occurred
3. Performing a correlation analysis between event duration and the temperature-moisture coupling
4. Performing a “path analysis” in order to assess the contribution of temperature and moisture to the event duration.The structure of the manuscript is clear; however, I have major concerns regarding the data, methodology, and the interpretation of the results. As the concerns are fundamental, I won’t go into details with minor comments, but only raise the major concerns. Further, I have to note that I agree with the comments of the two other reviewers, where my concerns will partly overlap with.
- Sample size
The whole analysis is based on 34 years and 65 stations. As the first reviewer, I think that this might be not sufficient to represent the heterogeneity of rice production areas across whole China. More importantly, the low sample size affects also the sampling of compound events. Especially for the hot & dry events (either CCE or CSE), only very few events are found. This severely limits the informative value of the following analyses.
The authors could try to interpolate the growing stage dates using climatic covariates (e.g. growing degree days) in order to better cover the whole rice production area and increase the sample size. - Methodology and Clarity
a) Due to the limitations of the sample size, linear trends of aggregated event frequencies (Fig. 1) and correlation analysis (Figs. 4,5) are subject to big uncertainties. Further, the trend over aggregated event types does not make any sense to me (e.g., I see an increase of H1D1 events, whereas H3D3 events do not seem to increase). The whole hot & dry analysis is based on only 1 to 6 locations (see Fig. 2).
b) The event definition nomenclature (Table 1) does not reflect the choice of thresholds intuitively: “chilling-dew wind” is based on a temperature threshold, not wind. “continuous-rain” is defined as at least three consecutive days with more than 0.1mm/d precipitation and less than an hour of sunshine. This definition includes wide ranges of precipitation (from almost dry to very wet). The sunshine threshold is more specific and might dominate this event definition. So, it’s more “cloudiness” than “continuous rain”.
c) I could not well follow the methodological description in L177-201 and the respective results (Fig.5).
Fig. 5: For the event type H2D1, there is only one event at one location. How can there be a meaningful correlation or “path analysis” between event duration and climate drivers? - Relation to impact
I acknowledge the application of plant-specific absolute thresholds, which are guided by literature (Tab. 1), as well as the separation into three growing stages and two cropping systems. However, the added value is not proven, as there is no assessment of the impact variable (yield). The motivation for the authors’ thresholds comes from literature, which considers the climate driver univariately (e.g., T >= 33°C is harmful for rice, independently from the moisture conditions). However, when jointly occurring with dry soil conditions, this temperature threshold could be at lower temperature.
As the first reviewer comments, the event intensity is not considered in this study. It might be useful to apply bivariate event definitions, which consider the intensity of the marginals. This could be implemented, e.g. via copulas. See Zscheischler et al., 2017 for an application and Salvadori et al., 2016 for the theory. As a starting point, the authors could use their univariate thresholds for the marginals, and apply survival Kendall return periods to assess the bivariate occurrence probability. That probability would then ideally show a higher correlation with the yields than the correlation between each marginal and the yield. - Analysis & interpretation of the results
a) I cannot follow some of the interpretations. In section 3.3 (L244ff) the authors claim to show the “dependence of compound events on temperature-moisture coupling”. The event itself is defined via the joint exceedance of temperature and moisture thresholds. As far as I understand, the “temperature-moisture coupling” is the Pearson rank correlation between temperature and moisture during the growing phase (see L165-176). By definition of a bivariate event, the event occurrence will be dependent on the marginal probabilities and the joint dependence structure. So, I do not see the informative value of section 3.3. and Fig. 4.
Further, regarding Fig. 4: I do not consider it appropriate to assess linear relationships between event duration (total number of event days) on the y-axis versus the temperature-moisture correlation on the x-axis. The kernel density estimates suggest nicely distributed data – in reality there is so few data, that a histogram is more appropriate. Furthermore, this whole analysis again suffers from the sampling. Taking the example of the H1D1 event, 6 locations show events at all. 5 of them are clustered in the north east (see Fig. 2a). By that means, the analysis is sensitive to the spatially inhomogeneous sampling density of locations.
b) Section 3.4 claims to assess the “contribution of temperature and moisture to the changes in compound events”. I do not see how the performed analysis incorporates *changes* in compound events.
For the hot & dry part, this analysis shows a large amount of variability (Figs. 5a,c), which I’d attribute to the low number of sampled events. I would be very careful to (over-)interpret these results.
References:
Salvadori, G., Durante, F., De Michele, C., Bernardi, M., and Petrella, L.: A multivariate copula- based framework for dealing with hazard scenarios and failure probabilities, Water Resour. Res., 52, 3701–3721, https://doi.org/10.1002/2015WR017225, 2016.
Zscheischler, J., Orth, R., and Seneviratne, S. I.: Bivariate return periods of temperature and precipitation explain a large fraction of European crop yields, Biogeosciences, 14, 3309–3320, https://doi.org/10.5194/bg-14-3309-2017, 2017.
Citation: https://doi.org/10.5194/esd-2024-8-RC3 -
AC1: 'Reply on RC3', Tao Ye, 31 Jul 2024
The comment was uploaded in the form of a supplement: https://esd.copernicus.org/preprints/esd-2024-8/esd-2024-8-AC1-supplement.pdf
- Sample size
Status: closed
-
RC1: 'Comment on esd-2024-8', Anonymous Referee #1, 30 May 2024
This paper investigates the combined climate extremes relevant to rice production in China. The authors analyze concurrent and consecutive compound events relevant for single- and late-rice during 1980–2014, using specific known thresholds. Examining both concurrent and consecutive extremes provides a more comprehensive picture of potential stress on rice crops. However, the manuscript would benefit from addressing some fundamental points and key concerns:
1- Sample Size Concerns: First concern is regarding the sample size of stations, highlighting the potential lack of representativeness for the entire region. Given the substantial spatial heterogeneity of soil moisture, the limited number of stations may not fully capture the diverse conditions across China.
2- Missing Yield Impact Assessment: While the paper mentions rice yield as motivation, it lacks a direct evaluation of how these compound events affect production quantities. It is necessary to incorporate an analysis of yield data to directly assess the impact of compound events on rice production. The paper's association with rice is primarily through growing season definitions, yet there is a noticeable absence of yield estimation. The justification for focusing on rice should be more explicit, particularly considering the absence of yield data.
3- Growing Season Definition Clarity: Specifying whether the growing season definition has fixed planting and harvest dates or adapts based on actual planting times is crucial. Sensitivity analysis to choice of dates is necessary to understand how changes in the selection of growing season start and end could influence the results.
4- Intensity Metric Considerations: The current focus on number of extreme days based on thresholds might overlook the intensity of extreme events. Analyzing the magnitude of temperature or drought deviations could provide deeper insights. The metrics employed in the study center on frequency and the number of days above a threshold but fail to consider the intensity of compound events. It is important to consider the intensity, as a single day with a temperature 10°C above the threshold could have more substantial implications for agriculture than ten days with only 0.5°C above the threshold.
5- Practical Implications and Value Added: Explicitly discussing the practical applications of the research and its contribution to existing knowledge would enhance the paper's value for the scientific community.
Citation: https://doi.org/10.5194/esd-2024-8-RC1 -
AC3: 'Reply on RC1', Tao Ye, 31 Jul 2024
The comment was uploaded in the form of a supplement: https://esd.copernicus.org/preprints/esd-2024-8/esd-2024-8-AC3-supplement.pdf
-
AC4: 'Reply on RC1', Tao Ye, 31 Jul 2024
The comment was uploaded in the form of a supplement: https://esd.copernicus.org/preprints/esd-2024-8/esd-2024-8-AC4-supplement.pdf
-
AC3: 'Reply on RC1', Tao Ye, 31 Jul 2024
-
RC2: 'Comment on esd-2024-8', Anonymous Referee #2, 30 Jun 2024
Review of ‘ Spatiotemporal variation of growth-stage specific compound climate extremes for rice in South China: Evidence from concurrent and consecutive compound events ’
Authors: Ran Sun1,2,3,4, Tao Ye1,2,3,4, Yiqing Liu1,2,3,4, Weihang Liu1,2,3,4, Shuo Chen1,2,3,4
Journal: ESD
General Comment
The text is well structured. The flow is generally clear. Considering phonologically relevant growth stages to assess climatic conditions on crops is indeed interesting and adds to the value of paper. However, I have three major comments:
- You have not directly evalauted how/if your climatic indicators actually impact the yields: I would have expected to see some crop simulation with climatic indicators or at least a correlation analysis between crop and climatic conditions. Check following papers to get some inspiration (Luan et al., 2021; Zhu & Troy, 2018; Zscheischler et al., 2017). The way you presented the result in current version, we cannot even be sure even your indicators matter for crops and impact them.
- The text is rather clear when you generally talk about compound heat and drought and the temperature moisturize coupling, in relation to these two indicators. The text, however, becomes vague when you talk about chilling and rain events and how you tried to associate them to some underlying climatic contributor. L174-176 is very unclear and requires further explanation of the method.
- It is unclear to me why you considered two event types for CSE, according to L152-154. Why two drought or two heat within two growth stage is not considered a consecutive event? In the same lines L153-154 is unclear and requires clarification.
Specific Comment
Abstract: Consider removing the part talking about maize and wheat. The paper focuses on rice and that needs to be brought up in the abstract.
L62-66: Again consider removing the part talking about wheat and maize, and their growths temperature dependent thresholds. I think they distract the reader.
L93: grain-filling and everywhere when you mention this word: consider removing the dash line between grain and filling. For your other stages the dash bridges two stage but grain filling is a distinct stage itself.
L95: I don’t understand why use the term 'dew' sometimes after chilling. Maybe be consistent and use the same terminology or be specific why you need to mention dew in specific parts of the text.
L115: be consistent and use either early rice or single rice. Also, here in L115 it feels like you have three type of rices while I assume there are two rices analyzed in this study.
Fig 2 &3 : It is unclear to me how you considered total days of compound event. Is it the total during study period? – According to L160-163 they should correspond to yearly values but then did you consider an average of duration per year, over the study period and plotted them in these figures?
L236: Please clarify where Hunan is located by geographical lat-lon.
L251-257: I couldn’t understand this part. Please consider heavy modification of the text and clarification.
Fig 4: what is the density in the plots? And what do we learn from it?
L263: sensitivity of PER to what for late rice? – the sentence is generally unclear.
L355: consider removing the first line and directly go to the limitations you think the study has.
References:
Luan, X., Bommarco, R., Scaini, A., & Vico, G. (2021). Combined heat and drought suppress rainfed maize and soybean yields and modify irrigation benefits in the USA. Environmental Research Letters, 16(6), 064023. https://doi.org/10.1088/1748-9326/abfc76
Zhu, X., & Troy, T. J. (2018). Agriculturally Relevant Climate Extremes and Their Trends in the World’s Major Growing Regions. Earth’s Future, 6(4), 656–672. https://doi.org/10.1002/2017EF000687
Zscheischler, J., Orth, R., & Seneviratne, S. I. (2017). Bivariate return periods of temperature and precipitation explain a large fraction of European crop yields. https://doi.org/10.3929/ethz-b-000190400
Citation: https://doi.org/10.5194/esd-2024-8-RC2 -
AC2: 'Reply on RC2', Tao Ye, 31 Jul 2024
The comment was uploaded in the form of a supplement: https://esd.copernicus.org/preprints/esd-2024-8/esd-2024-8-AC2-supplement.pdf
-
RC3: 'Comment on esd-2024-8', Benjamin Poschlod, 01 Jul 2024
The study assesses the occurrence of climatic compound events during the rice growing season in China. Thereby, it relies on 34 years of data (1981 – 2014) of 65 stations across whole China. The study distinguishes between concurrent (CCE) and consecutive (CSE) events, two cropping systems (single-rice for a single harvest per season; late-rice for the second harvest of two harvests per season), and three growing stages. On this base, the authors perform a statistical exploration:
1. Plotting the frequency of event types and assessing a linear trend
2. Mapping the locations, where the events occurred
3. Performing a correlation analysis between event duration and the temperature-moisture coupling
4. Performing a “path analysis” in order to assess the contribution of temperature and moisture to the event duration.The structure of the manuscript is clear; however, I have major concerns regarding the data, methodology, and the interpretation of the results. As the concerns are fundamental, I won’t go into details with minor comments, but only raise the major concerns. Further, I have to note that I agree with the comments of the two other reviewers, where my concerns will partly overlap with.
- Sample size
The whole analysis is based on 34 years and 65 stations. As the first reviewer, I think that this might be not sufficient to represent the heterogeneity of rice production areas across whole China. More importantly, the low sample size affects also the sampling of compound events. Especially for the hot & dry events (either CCE or CSE), only very few events are found. This severely limits the informative value of the following analyses.
The authors could try to interpolate the growing stage dates using climatic covariates (e.g. growing degree days) in order to better cover the whole rice production area and increase the sample size. - Methodology and Clarity
a) Due to the limitations of the sample size, linear trends of aggregated event frequencies (Fig. 1) and correlation analysis (Figs. 4,5) are subject to big uncertainties. Further, the trend over aggregated event types does not make any sense to me (e.g., I see an increase of H1D1 events, whereas H3D3 events do not seem to increase). The whole hot & dry analysis is based on only 1 to 6 locations (see Fig. 2).
b) The event definition nomenclature (Table 1) does not reflect the choice of thresholds intuitively: “chilling-dew wind” is based on a temperature threshold, not wind. “continuous-rain” is defined as at least three consecutive days with more than 0.1mm/d precipitation and less than an hour of sunshine. This definition includes wide ranges of precipitation (from almost dry to very wet). The sunshine threshold is more specific and might dominate this event definition. So, it’s more “cloudiness” than “continuous rain”.
c) I could not well follow the methodological description in L177-201 and the respective results (Fig.5).
Fig. 5: For the event type H2D1, there is only one event at one location. How can there be a meaningful correlation or “path analysis” between event duration and climate drivers? - Relation to impact
I acknowledge the application of plant-specific absolute thresholds, which are guided by literature (Tab. 1), as well as the separation into three growing stages and two cropping systems. However, the added value is not proven, as there is no assessment of the impact variable (yield). The motivation for the authors’ thresholds comes from literature, which considers the climate driver univariately (e.g., T >= 33°C is harmful for rice, independently from the moisture conditions). However, when jointly occurring with dry soil conditions, this temperature threshold could be at lower temperature.
As the first reviewer comments, the event intensity is not considered in this study. It might be useful to apply bivariate event definitions, which consider the intensity of the marginals. This could be implemented, e.g. via copulas. See Zscheischler et al., 2017 for an application and Salvadori et al., 2016 for the theory. As a starting point, the authors could use their univariate thresholds for the marginals, and apply survival Kendall return periods to assess the bivariate occurrence probability. That probability would then ideally show a higher correlation with the yields than the correlation between each marginal and the yield. - Analysis & interpretation of the results
a) I cannot follow some of the interpretations. In section 3.3 (L244ff) the authors claim to show the “dependence of compound events on temperature-moisture coupling”. The event itself is defined via the joint exceedance of temperature and moisture thresholds. As far as I understand, the “temperature-moisture coupling” is the Pearson rank correlation between temperature and moisture during the growing phase (see L165-176). By definition of a bivariate event, the event occurrence will be dependent on the marginal probabilities and the joint dependence structure. So, I do not see the informative value of section 3.3. and Fig. 4.
Further, regarding Fig. 4: I do not consider it appropriate to assess linear relationships between event duration (total number of event days) on the y-axis versus the temperature-moisture correlation on the x-axis. The kernel density estimates suggest nicely distributed data – in reality there is so few data, that a histogram is more appropriate. Furthermore, this whole analysis again suffers from the sampling. Taking the example of the H1D1 event, 6 locations show events at all. 5 of them are clustered in the north east (see Fig. 2a). By that means, the analysis is sensitive to the spatially inhomogeneous sampling density of locations.
b) Section 3.4 claims to assess the “contribution of temperature and moisture to the changes in compound events”. I do not see how the performed analysis incorporates *changes* in compound events.
For the hot & dry part, this analysis shows a large amount of variability (Figs. 5a,c), which I’d attribute to the low number of sampled events. I would be very careful to (over-)interpret these results.
References:
Salvadori, G., Durante, F., De Michele, C., Bernardi, M., and Petrella, L.: A multivariate copula- based framework for dealing with hazard scenarios and failure probabilities, Water Resour. Res., 52, 3701–3721, https://doi.org/10.1002/2015WR017225, 2016.
Zscheischler, J., Orth, R., and Seneviratne, S. I.: Bivariate return periods of temperature and precipitation explain a large fraction of European crop yields, Biogeosciences, 14, 3309–3320, https://doi.org/10.5194/bg-14-3309-2017, 2017.
Citation: https://doi.org/10.5194/esd-2024-8-RC3 -
AC1: 'Reply on RC3', Tao Ye, 31 Jul 2024
The comment was uploaded in the form of a supplement: https://esd.copernicus.org/preprints/esd-2024-8/esd-2024-8-AC1-supplement.pdf
- Sample size
Data sets
A daily 0.25° × 0.25° hydrologically based land surface flux dataset for conterminous China, 1961–2017 Yue Miao and Aihui Wang https://doi.org/10.1016/j.jhydrol.2020.125413
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