A pronounced decline in northern vegetation resistance to flash droughts from 2001 to 2022

Domain: earth_science
Published: 2025-03-27
Editorial outcome: minor_revision

Abstract
Climate change has led to the transition of droughts into rapid and intensified phenomena known as flash droughts, presenting considerable challenges for risk management, particularly concerning their impact on ecosystem productivity. Quantifying the ecosystem’s capacity to maintain productivity during flash droughts, referred to as ecosystem resistance, is crucial to assess drought impacts. However, it remains uncertain how the resistance of ecosystem productivity to flash drought changes over time. Here we show that vegetation resistance to flash droughts declines by up to 27% (±5%) over the Northern Hemisphere hotspots during 2001-2022, including eastern Asia, western North America, and northern Europe. The notable decline in vegetation resistance is mainly attributed to increased vapour pressure deficit and temperature, and enhanced vegetation structural sensitivity to water availability. Flash droughts pose higher ecological risks than slowly-developing droughts during the growing seasons, where ecosystem productivity experiences faster decline rates with a shorter response time. Our results underscore the limited ecosystem capacity to resist flash droughts under climate change.

Introduction
Droughts are becoming more frequent and intense, with a rapid onset under climate change1–5. Among them, flash droughts have attracted substantial attention recently due to their sudden and devastating impacts on numerous regions worldwide, occurring without sufficient early warnings5,6. For instance, a flash drought in 2012 swept over the U.S. Great Plains during a two-month period, significantly impacting agricultural productivity and the carbon sink7,8. The outsized flash drought impacts are exacerbated by accumulated precipitation deficits in conjunction with elevated evaporative demand resulting from increased temperatures5,9–11. Despite their relatively short duration at the sub-seasonal scale, flash droughts exert rapid stress on ecosystem productivity due to the compound impact of abnormal hydro-meteorological conditions. They impair vegetation physiological functions, including stomatal conductance and biochemical processes12–14, as well as vegetation structure15. Flash droughts have become more frequent in recent decades under a warming climate, with a higher rate of drought intensification5,16–18. However, the capacity of vegetation to resist flash droughts remain largely unexplored and the global hotspots at risk remain largely unidentified, both of which are essential for effective drought risk assessment and management.

Severedroughtscriticallyimpairecosystemstabilityanddiminishterrestrialgrossprimaryproductivity(GPP)19–23. The responses of vegetationtodroughtsvaryacrossbiomesandclimatesintermsofresponsetimesandtheseverityofthereductionofGPP24–27. The responsetimereferstotheperiodfromtheinitialundisturbedvegetationconditiontothepointwherevegetationgrowthisinhibitedbydroughts,andalongerresponsetimeindicatesagreatercapacityoftheecosystemtowithstandwaterdeficits13,28.ThemagnitudeofGPPdeclinesduringdroughtsalsoreflectstheecosystemdroughtsensitivity21,26,29. Vegetation resistance to droughts is modulated by concurrenthydrometeorologicalfactors30,31,droughtcharacteristics32,vegetationphenology33, and vegetation structuraland physiologicalresponses34.Thedominantfactorofcarbonlossesduringdroughtsisassumedtobesoilmoisturedeficit20,35,whilethelimitationofvaporpressuredeficit(VPD)hasbecomemoreevidentinrecentyears30,36. Plantscanenhancewateruseefficiency(WUE)toadapttowaterstressbyclosingstomatatolimitthewaterlossthroughevaporation,albeitattheexpenseofterrestrialcarbonuptake37,38.Recentresearch25,39,40 hasdocumentedawidespreaddeclineinecosystemstabilitytovariousenvironmentalperturbations,includingdroughts,heatwaves,andwildfires,sincetheearly2000s.Despitethesefindings,thereremainsanotablegapinourunderstandingofhowecosystemresistancespecificallytoflashdroughtsvariesinthecontextofongoingclimatechangeapartfromotherexternaldisturbances’influence.Thisknowledgegaphindersourabilitytoaccuratelypredictterrestrialcarbondynamicsunderwaterstressatsub-seasonalscales.

Hereweshowapronounceddeclineinecosystemproductivityresistancetoflashdroughtsacrossnorthernhotspotsfrom2001to2022. We utilized a satellite-derived proxy for ecosystem GPP to investigate space-time variations in vegetation resistance to flash droughts on a global scale. We identified all sub-seasonal droughts using multiple soil moisture datasets from ERA5, GLDASv2.1, and GLEAMv3.8a during the growing seasons of 2001–2022. Subsequently, we classified these droughts into two categories, namely flash and slow droughts, based on the rates of soil moisture dry-down in percentiles during the onset stage (Methods and Fig. 1). Two independent satellite records including GPP from Moderate Resolution Imaging Spectroradiometer (MODIS) and sun-induced chlorophyll fluorescence (SIF) derived from the Orbiting Carbon Observatory-2 (OCO-2), were employed. Specifically, we adopted the declining rates in GPP or SIF percentiles to measure ecosystem resistance to droughts, integrating both the response time and declining magnitudes of ecosystem productivity. We examined the global spatial distributions and temporal trends of GPP resistance to flash droughts. To elucidate the observed trends, we employed a random forest model to attribute them to the hydrometeorological anomalies, drought characteristics, and vegetation responses (more details refer to Methods).

Results
Flashdroughtsaremorelikelytooccurovereastern,southern,andnorthernAsia,easternAmerica,northernEurope,andtheAmazoncomparedtoslowdroughts(SupplementaryFig.1).Basedontheidentifiedflashandslowdroughteventsateachpixel,weestimatedGPP’sresponsetime,decliningmagnitudes,anddecliningratestobothtypesofdroughtsusingMODIS-derivedGPPandOCO-2derivedSIF(Fig.1)comparingtheundisturbedconditionstotheresponseintensityofGPP(SupplementaryFig.2).Here,theseresponseproxiesrepresenttheecosystem’scapacitytowithstanddroughtdisturbances,andthelargerGPPdeclinerateduringdroughtsindicatesthattheecosystem’sphotosyntheticprocessislessresistanttowaterstress.

Forallflashdroughts,theresistanceofGPPisquantifiedbythechangeratesofGPPpercentilesandtheglobalmeanresistanceis−12±0.4%(mean±s.d.)per8daysduringtheGPP’sresponseperiod(Fig.2a).Thedecliningratesaregenerallylargerduringflashdroughtsthanslowdroughtsover66%oftheglobalvegetatedareas(Fig.2b,c).However,inhighlatituderegions,theAmazon,andsoutheasternAsia,thedeclineinphotosyntheticratesunderflashdroughtsisslowerthanslowdroughts.Astheseregionsareenergy-limited,thehighertemperatureandshortwaveradiationcanoffsetthenegativeimpactsofdroughts27,41. Faster declining rates in GPP are related to the faster responsetime,withtheresponsetimeofGPPtoflashdroughtsbeingonly34±0.1daysfromtheunperturbedstatetothemaximumintensityofGPPresponse,whichisshorterthanthe39±0.7daysforslowdroughtsatthesignificancelevelof1%(SupplementaryFig.3).Forthelow-latitudeandmid-latituderegionsexceptforAmazonandsoutheasternAsia,therapiddroughtintensificationofflashdroughtsimposescompoundstressfrombothsoilmoisturedroughtdeficitandatmosphericdrynessonvegetationgrowth,drivingfasterresponseofGPP.Throughcomparingthehydrometeorologicalconditionsduringflashandslowdroughtperiods,weobservedthatbothsoilmoisturedeficitandatmosphericdrynessaremoresevereduringflashdroughtsthanslowdroughts(SupplementaryFig.4),resultinginmorepronouncedlimitationonvegetationphotosynthesisduringflashdroughts.

Furthermore, the differences between resistance to flash and slow droughts are larger in drier climate regions, with drylands exhibiting higher vegetation sensitivity to flash droughts (SupplementaryFig.5).Intermsofdifferentecosystemtypes,GPPofalllandcovertypesexceptfordeciduousneedleleafforestsshowslargerdecliningratesduringflashdroughtsthanthoseduringslowdroughts,asthegrowthofdeciduousneedleleafforestsinarelativelycoldclimateareinfluencedbytemperatureandradiation(SupplementaryFig.6).

To investigate the regional changes in the vegetation productivity resistance to flash droughts, the gridded declining rates were averaged over 27 IPCC SREX regions weighted by grid areas. We find that GPP declining rates over the flash drought periods increased by 6±4%–27±5% in eastern, southern, and northern Asia, northern Europe, western North America, southern Africa, Sahara, and Canada region during 2001–2022 (Fig. 3a and Supplementary Fig. 7). Notably, the significant declines (p<0.01) in GPP resistance to flash droughts are 16±3%, 12±1% and 12±6% over eastern Asia, West North America, and North Europe during 2001–2022. The vegetation in northern regions is increasingly sensitive to flash droughts, with 34% of vegetated land across the Northern Hemisphere exhibiting accelerated declining rates in GPP during flash droughts (Fig. 3a). Instead, the resistance of vegetation over Central North America, East North America, Southeast South America, North-East Brazil, West Africa, and West Asia is increasing during recent two decades (Fig. 3a and Supplementary Fig. 8). Although the resistance trends vary across different regions based on various soil moisture products, there is a high consistency in the trends’ direction across individual results such as eastern Asia, southern Africa, West Africa, and southern South America (Supplementary Fig. 9). Generally, vegetation photosynthesis demonstrates greater sensitivity to flash droughts over most Northern Hemisphere regions (p=0.30), whereas it exhibits more resistance over the Southern Hemisphere (p<0.01).

We analyzed the relationship between the temporal changes in resistance indicated by the rates and magnitudes/response time of GPP decline for the period 2001–2022 over the IPCC SREX regions during flash droughts (Fig. 3b, c). The temporal changes in resistance are negatively correlated with those in declining magnitudes, with a correlation coefficient of −0.66 (p=0.0002), and positively correlated with response time (r=0.34, p=0.080). This suggests that the higher declining rates in GPP during flash droughts are more influenced by the loss of vegetation photosynthesis (magnitude) than by the response time. This observational igns with previous studies21,42 asserting that drought is increasingly influential on ecosystem carbon uptake, and the drought-induced reduction of GPP is on the rise.

To delve into the contribution of plant traits and environmental factors to the changes in vegetation resistance during flash droughts, we applied the random forest regression model43, a machine learning method, to relate GPP’s declining rates during the drought period to drought characteristics, hydrometeorological conditions, and vegetation responses. Here nine predictors were used to regress GPP resistance to flash droughts at an annual timestep through the random forest model, and the trends of modeled resistance were consistent with the observed trends (Supplementary Fig. 10). Subsequently, we conducted a focused analysis on the influence of the temporal changes of different factors on vegetation resistance to flash droughts.

For eastern Asia, the reduction in percentiles of detrended LAI during flash droughts is increasingly severe (p=0.02), and the increasing LAI sensitivity to flash droughts contributes to the most (53±17%) of the declining trend in resistance (Fig. 4a and Supplementary Fig. 11a). In addition, the declining LAI under water stress also contribute most to the declining resistance over northern Europe and Sahara (Supplementary Fig. 11c, h). Whereas vegetation growth benefits from warming, CO2 fertilization effects, and forest conservation policy44,45, the enhanced vegetation growth can further deplete soil moisture and may be beyond the ecosystem capacity and intensify climate extremes’ ecological impacts on vegetation structure46,47. For West North America, North Asia, and South Asia, the increase in percentiles of VPD and temperature contributes most to the decreased resistance to flash droughts (46±6% ~96±9%; Fig. 4b and Supplementary Fig. 11b, e, f). For southern Africa, the drying soil moisture explains 54±7% of the declining resistance (Supplementary Fig. 11g). The flash drought enhances land-atmosphere interactions, further intensifying water stress on vegetation physiological process and decreasing the stomatal conductance. The onset of flash drought over northeastern Canada becomes earlier before the peak of the growing season (Supplementary Fig. 11d), contributing 56±4% to the temporal changes in resistance. The dominant role in driving faster response of GPP varies across different regions. The decrease in resistance observed in 50% of the regions (4 out of 8 regions) is related to increasing VPD and temperature and decreasing soil moisture. However, there are still large variations of vegetation sensitivity to flash droughts that cannot be explained by the random forest model.

For the increased resistance in certain regions, the delayed drought onset after the peak of the growing season, the increased drought duration, and decreased drought speed, or the increased underlying water use efficiency (uWUE) and decreased LAI sensitivity to droughts played an important role (Fig. 4b and Supplementary Fig. 12), optimizing carbon fixation while minimizing water loss and offsetting negative impacts of droughts on the carbon sink. In addition, longer drought duration prolongs the duration of GPP response, thereby mitigating the decline in GPP rates.

Methods
Three global reanalysis soil moisture datasets were used to identify flash and slow drought events including ECMWF Reanalysis v5 (ERA5)56, The Global Land Data Assimilation System version 2.1 (GLDASv2.1)57, and Global Land Evaporation Amsterdam Model version 3.8a (GLEAM3.8a)58. For ERA5 and GLDASv2.1, the soil moisture data were interpolated into from ground surface down to 1-m. For GLEAMv3.8a, the root-zone soil moisture was used. Considering there is a large uncertainty of soil moisture estimates, here the ensemble mean results based on these three datasets were used59,60. The meteorological conditions during the drought period were also examined, including precipitation (P), potential evapotranspiration (PET), air temperature (Temp), VPD, evapotranspiration (ET), and shortwave radiation (SW) mainly from ERA5. In this study, we processed the hydrometeorological variables from hourly or daily raw data into 8-day timescale to be consistent with the temporal intervals of satellite ecosystem productivity data. The calculation of uWUE (GPP*VPD)/ET61 used corresponding evapotranspiration that was consistent with the corresponding soil moisture data used. The climate classification was based on multiyear mean aridity index (AI=P/PET), and climate regions included hyper-arid (AI<0.05), arid (0.05≤AI<0.2), semi-arid (0.2≤AI<0.5), dry sub-humid (0.5≤AI<0.65), and humid regions (0.65≤AI)62.

Here two independent satellite proxies were used to examine the ecosystem productivity’s response to droughts during the period from 2001 to 2022, including MODIS-derived GPP and SIF from the global OCO-2 SIF product (GOSIF). GPP from MODIS was estimated based on the light-use efficiency model which was formulated as the product of the incident photosynthetically active radiation (PAR), the fraction of absorbed PAR, and the light-use efficiency63, and missing observations were filled by using the linear interpolation method. SIF is considered a major advancement in measuring terrestrial photosynthesis, and it shows a strong relationship with GPP64,65. Specifically, SIF shows earlier responses to drought than traditional vegetation indexes66. Here we used the GOSIF dataset, which was produced using a data-driven approach based on OCO2-SIF, remote sensing data from MODIS, and meteorological reanalysis data67. We also analyzed the changes in vegetation structure during droughts using LAI from Global Land Surface Satellite (GLASS) MODIS LAI products. The land cover classification is based on MODIS. All datasets were interpolated into 0.25° spatial resolution.

Soil moisture acts as the indicator of flash droughts, as the dynamics of soil moisture are typically associated with lower precipitation and humidity and increased solar radiation and temperature6,68 and are closely related to vegetation water stress13,34. The definition of flash and slow droughts refer to Yuan et al.5. Firstly, the soil moisture data were aggregated into the 8-day timescale at each pixel (e.g., Jan 1st to Jan 8th) to be consistent with satellite observation frequency, and were then transformed into percentiles by ranking them at each 8-day timestep from 2001 to 2022. The sub-seasonal droughts start from the soil moisture percentiles below 40% and develop into the minimum values below 20%. Once the soil moisture percentiles recover above the threshold of 20%, the droughts end. To remove events with little ecological influence, the total duration should be no shorter than 4 timesteps (i.e., 32 days), and there should be at least three 8-day time steps with soil moisture percentiles below 20%69. Besides, mean anomalies of ET should be negative (ET percentile <50%) during the drought period, indicating water stresses on vegetation, although higher atmospheric demand may drive more ET during the onset stage of droughts. The identified sub-seasonal droughts were further classified into flash and slow droughts based on the declining rates of soil moisture percentiles. For flash droughts, the average declining rate of soil moisture percentiles during the onset stage is no less than 5% per 8 days, otherwise, they are classified as slow droughts. Based on this definition, the ratio of flash drought events with declining GPP is 99% and this method allows a reasonable quantification of the negative impacts of flash droughts through the constraint of mean ET percentiles <50% and the duration at least lasting for 32 days.

The characteristics of flash droughts were used to investigate the relationship between flash drought and vegetation productivity resistance, including duration, speed of soil moisture intensification, and the timing of drought onset. According to Yuan et al.5, the duration of flash drought was determined as the persistence time starting from the soil moisture below 40th percentile to below 20th percentile, and recovers above the 20th percentile. The onset speed of flash drought was determined as the declining rates in soil moisture percentiles from a relatively normal condition of 40% to the lowest soil moisture percentile during flash drought. Considering the droughts’ impact on vegetation, here we only focused on the growing seasons identified by the multiyear-averaged LAI at an 8-day time scale. If the LAI peak of the growing season lies between April to September, the growing cycle starts from January and ends in December, otherwise, the growing cycle starts from the previous May to the current April. The start and end of the growing season are the points of the minimum LAI in the annual cycle plus 25% of the amplitude between the maximum and minimum values70. If the minimum LAI is greater than 2, the growing season at this grid is considered as a whole year, as the vegetation exists throughout the whole year. The length of the growing seasons is shown in Supplementary Fig. 13.

In this study, we excluded those sub-seasonal drought events if the GPP percentile is below 40% before the onset of droughts, as these cases are difficult to disentangle the influence of droughts on vegetation growth from other external disturbances.

Before analyzing the ecosystem response to droughts, GPP was detrended at each timestep in the annual cycle and transformed into percentiles to remove the seasonal effect and long-term trend40. The response of GPP to flash droughts (or slow droughts) is measured using response time, declining magnitudes, and the rate of GPP decline during the drought period (Fig. 1). For each drought event at a 0.25° grid, first, we determined the timing of the occurrence of the minimum value of GPP percentiles during the drought period, thus the response time of GPP spans from 8 days before flash droughts under normal conditions (GPP percentiles >40%; Supplementary Fig. 2a), to the timing of the minimum GPP percentiles (Supplementary Fig. 2b)13,24. The difference between GPP percentiles 8 days before flash droughts and the minimum GPP percentiles to quantify the declining magnitude21. The slope between the response time and declining magnitude during the response period reflects the sensitivity of GPP to flash droughts (or slow droughts)26, and the larger slopes (larger magnitudes of negative values) indicate the less resistance of GPP to flash or slow droughts.

To verify the reliability of remote sensing-derived vegetation productivity, we used in-situ observations of GPP at 80 eddy covariance towers where flash droughts/slow droughts occurred during the study period. These observations were derived from FLUXNET2015, AmeriFlux FLUXNET, ICOS Warm Winter 2020, OzFlux, and ChinaFLUX (Supplementary Tab. 1). Although there is a large mismatch in spatial scales between the tower footprint (typically with diameters of a few hundred meters) and the 0.25-degree grid cells, satellite-derived resistance and response time show significant correlations with in-situ results, with correlation coefficients (R) of 0.21/0.29 (MODIS-GPP/SIF with p<0.001/0.002) and 0.33/0.45 (MODIS-GPP/SIF with p<0.001), respectively.

Here three sets of flash and slow drought events were identified using three soil moisture products as mentioned above, and then the response of vegetation productivity to the identified drought events was individually detected using two vegetation productivity products including MODIS-derived GPP and SIF. The temporal dynamics of resistance were analyzed by linear trends and then a two-sided t-test was applied for the individual linear trend derived from six members. The ensemble mean of the six members was used for climatology and trend analysis, and the uncertainty was quantified by the standard deviation through 1000 times bootstrap sampling with the replacement of members60,71.

We applied the random forest model43 to investigate a set of potential drivers of the changes in vegetation resistance to flash droughts, including the vegetation physiological and structural responses indicated by uWUE and LAI, and environmental factors including the soil moisture (SM), Temp, VPD, SW, and drought characteristics (e.g., duration, speed, and onset timing). The percentiles of hydrometeorological variables including SM, Temp, VPD, and SW during the drought period were used to represent the intensity of perturbations, respectively. The drought timing is quantified as the difference between the onset of drought and the peak of LAI in the annual cycle. The negative values of drought timing indicate that droughts start before the peak of the growing season, and vice versa. The random forest model was developed using the regional average GPP resistance to flash drought and the regional mean drought characteristics, hydrometeorological conditions, and vegetation responses averaged during flash drought events over each grid within the region at an annual timestep. We found that the model explained 80-96% of the variations of vegetation resistance across sites and the modeled resistance also showed the same direction of trends with the observed results (Supplementary Fig. 10). Compared with the multiple linear regression model (R2=0.36), the random forest model has a better performance in the simulation of resistance (R2=0.88), by considering the nonlinear interactions between influencing factors and resistance, without a priori assumption about the functional form. Furthermore, we applied the random forest model to disentangle the temporal changes in resistance due to the variations of different factors. To quantify the marginal contributions of each predictor, the control experiment accounted for the dynamics of all predictors, whereas the sensitivity experiment considered all predictors’ dynamics except for the target predictor. The changes in resistance due to the dynamics of a certain factor were disentangled as follows:

ΔYa = Δf(Xt) - Δf(Xt,a,Xt,a0) (1)

in which f is the random forest model, and X and Y represent multiple predictors and ecosystem productivity’s resistance to flash droughts, respectively. The second term on the right side of Eq. (1) is the random forest model simulation considering all predictors’ dynamics except for the predictor of Xa that was kept constant to a certain year of t0, and Δ represents the linear trend of the model simulation. Due to the availability of GLASS LAI, the attribution analysis was based on the period of 2001–2021. For the attribution of each predictor to the temporal changes in resistance, the experiments were conducted with one predictor fixed at each year of 2001–2021, respectively. The contribution of each factor to the temporal changes of GPP resistance was calculated by the ratio of the difference between the resistance trend in the factor-fixed and control experiments to the modeled trend in the control experiment. If there are over 90% of the 21 members of the experiments showing the same direction of change of resistance trend influenced by a certain factor, the contribution of the factor was assumed to be significant.

To assess the robustness of the spatio-temporal characteristics of resistance with respect to the threshold choices in defining flash droughts, we conducted a series of sensitivity analyses by altering the thresholds used in the flash drought definition. Specifically, we changed the start point of flash droughts from soil moisture percentiles above 40 to 50%, to represent a relatively normal pre-drought conditions, and the drought termination from soil moisture percentiles recover above 20% to 10%, to represent the extreme drought category. We also adjusted the minimum drought duration from 4 timesteps (32 days) to 3 timesteps (24 days) and considered pre-drought ecosystem productivity levels from above 40% to 50% to represent undisturbed vegetation conditions before droughts. Furthermore, we also examined the spatial distributions and temporal trends using different thresholds. While the absolute values of resistance to flash and slow droughts varied with different thresholds, the results show a high overall consistency in the patterns of resistance differences between flash droughts and slow droughts, as shown in Fig. 2c (R: 0.75–0.98, p<0.01). Additionally, 59%-69% of grids exhibited negative differences, indicating that the ecosystem productivity’s resistance to flash droughts is generally lower than slow droughts (Supplementary Fig. 14). Furthermore, the results with different thresholds show similar temporal changes in resistance over hotspot regions (Supplementary Fig. 15). Overall, the sensitivity analysis demonstrates that the different thresholds do not affect the spatio-temporal dynamics of ecosystem productivity’s resistance to flash droughts. The response time of ecosystem productivity, when GPP percentiles were below 20%, was also faster during flash droughts compared to slow droughts (Supplementary Fig. 16), which is consistent with the faster response time of the minimum GPP percentiles during flash droughts.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "The authors investigate the impact of flash droughts on ecosystem productivity, highlighting a significant decline in\nvegetation resistance across the Northern Hemisphere from 2001 to 2022. They attribute this decline primarily to increased\nvapor pressure deficit, temperature, and vegetation sensitivity to water availability. Their findings show that flash droughts\nlead to faster and more severe declines in ecosystem productivity compared to slowly-developing droughts. The study\nunderscores the urgent need for improved monitoring and management strategies to mitigate the negative effects of flash\ndroughts.\nHow the drought affects ecosystem productivity is a very meaningful topic and has been extensively explored, while the\nresistance of ecosystems to flash droughts has not been analyzed as thoroughly. Overall, the manuscript is well-written, but\nthe findings regarding the impact of flash droughts on ecosystems are not particularly novel (e.g., the finding that flash\ndroughts have more negative effects than slow droughts). Moreover, there are significant omissions and unclear aspects in\nthe methodology.\n1. My biggest criticism of the manuscript is the complete lack of support from site-level observations. For instance, the main\nused satellite GPP data can also be derived from global flux sites. It is necessary to use flux data to validate the findings\nderived from remote sensing data regarding response time, drought resistance, and trends etc.\n2. The study analyzes drought response time on a daily scale, but the core data for GPP and SIF have an 8-day resolution.\nHow can 8-day resolution data accurately reflect changes on a daily scale? This involves analyses such as drought\nidentification and productivity response to drought. The authors have not mentioned this issue or discussed the potential\nuncertainties involved.\n3. Some details in Methods are not clear. For example, in ‘Flash drought identification’, why are the thresholds for drought\nset as soil moisture percentile>40% and recovery as soil moisture percentile>20%? Such thresholds are also found for\ndrought duration setting (like 4 time steps), and excluding decreased GPP before onset of drought (40%). Not giving more\nexplanations would be somewhat arbitrary. For journals like NC, some additional testing is required.\n4. Why not also provide the trend map and driver map by pixels? Fig. 4 is beautiful, but when presenting the results only by\nselected regions, you may lose a lot of spatial details and also include ‘integrated’ results. For example, we can not know\nwhat are the driver of some hotspot regions such as most of Amazon forests and Australia drylands.\nLine 103-104: Since the minimum GPP of flash and slow drought is different, is the response time meaningful?\nParagraph starting from Line 113: May better also examine the different responses across aridity gradients. The results could\nbe more straightforward for understanding.\nFig. 2: No any situation with increasing GPP during drought? Or you have already excluded these cases?\nLine 409: Is this done by each pixel?", "reviewer_2": "Comments on\n“A pronounced decline in northern vegetation resistance to flash\ndroughts from 2001 to 2022”\nManuscript # NCOMMS-24-28130\nby Miao Zhang et al.\nJune 27, 2024\nThismanuscript(MS)aimstoquantifyingtheecosystem’scapacitytomaintainproduc-\ntivityduringflashdroughts.Theauthorsclaimthattheresilienceofvegetationtoflash\ndroughtsintheNorthernHemispherehotspotswilldecreasebyupto27%intheperiod\n2001-2022. Inviewoftheintensificationofflashdroughtsinrecenttimesthisstudyis\ncertainlyappropriateinordertoclarifytheeffectsofdrought.Iconsiderthatthetopic\nofthisMSfitswelltotheportfolioofNCOMMS.However,thereareseveralshortcom-\ningsthatshouldbeclarifiedbeforepublication.\nThispaperhasthefollowingshortcomings:\n• The authors’ aim is to investigate “how the resistance of ecosystem productiv-\nity to flash drought changes over time”. The first shortcoming that needs to be\nclarified is what is a flash drought? The authors used weekly data for their def-\ninition but in current literature there is ambiguity in the definitions and in the\ntemporalresolutionofthedatasetsthatcanbeused(seeOtkinetal.BAMS2018,\nAtmosphere 2021). In my opinion a suitable definition of flash droughts is that\nthe accounts intensification and in which a compound event is occurring, i.e., a\ndeficitofPandTinshortperiodoftime(seeforexamplethe2018-2020inEurope\nreported by Rakovec et al. 2022 https://doi.org/10.1029/2021EF002394). I\nrecommend to make a clarification at the beginning with a justification of the\nchoice.\n• The impacts described by the authors (e.g., l.27) need to have an uncertainty\nestimate. It is well recognised in the literature (here some examples from my\nownwork(noneedtobecited):https://doi.org/10.1175/jhm-d-12-075.1,\nhttps://doi.org/10.1007/s10584-016-1778-y,https://doi.org/10.1038/\ns41558-018-0138-5 ) that both inputs and models outputs have large uncer-\ntaintiesthatarepropagatedalongthemodellingchain. Forthisreason,itisfun-\ndamentaltoquantifytherelateduncertaintiesofallmajorresults.Thisstudylacks\nofasounduncertaintyestimation.\n• There are several accepted alternatives to elucidate non-linear trends in envi-\nronmental variables. Classic test like Mann-Kendall do not work well for non-\nmonotonictrends. Anotherissueistheautocorrelationofthetimeseriesofkey\nvariables(e.g.,soilmoisture,temperature,precipitation,etc.). Forthesereasons,\nARIMAmodelsareoftenusedfornon-monotonictrenddetection.Inthepresent\ncasearandomforestmodelisusedforthispurpose.Pleasejustifyfromastatisti-\ncalperspectivetheadvantagesofthismodel.Second,describethenull-hypothesis\nofthetestandindicatethep-value. Thisinformationandjustificationismissing\ninl.84andinmethodssection.\n• PleasequalifythetrendsobtainedbyeachindependentSMproduct.SMofERA5,\nGLDASv2.1,andGLEAMv3.8a.AreallSMestimatesindicatingthesametrends?\n• What is the test or meaning of statements like this (P<0.01) in l. 105? Is this the\nprobability,ap-value(ofatest),oralevelofsignificance?Authorsshouldprovide\nthe confidence of the value of 38 days followed by the standard error. All key\nresultsshouldhaveanuncertaintyestimate(seepreviouscomment). Intheway\nthattheauthorspresentedtheresultsisnotclear.Incaseofconfidence,itshould\nbegivenas38±xdayswith1%levelofsignificance,i.e.,99%confidenceinterval.\nOthercases:l.125,l.134,l.138-139,l.154.\n• Thereareseveralkindofmodelsthatcanbeusedtoestimatecontributionsfac-\ntors. L.145. JustifytheuseoftheRandomForestmodelsagainstasimplemulti-\nregression model. For example: l.186. “Our results emphasise that extreme hy-\ndrometeorologicalconditionsweakentheterrestrialecosystem’sabilitytowith-\nstand flash droughts under climate change.”. Please quantify these effects and\nindicatetheconfidencewehave.Generalstatementslikethisarenothelpful,\n• Thediscussionistooshortandquitegeneral.\nBasedonthecommentsmentionedaboveandbearinginmindtheNCOMMSpublish-\ningstandardsforaresearcharticle,IrecommendtoreturnthispapertotheAuthors\nformajorrevisions.\nL.Samaniego"}, "author_response": "We appreciate the reviewers’ thorough evaluation of our study. We have carefully\nrevised our manuscript according to the reviewers’ comments, which greatly helped to\nimprove the manuscript.\nIn our revisions, we paid specific attention to 1) improving the robustness of the\nconclusions by estimating uncertainty and performing sensitivity analyses, 2) clarifying\nthe methods by providing details and equations, and 3) expanding the breadth and depth\nof the discussion. Detailed revisions and responses to each comment are provided below.\nThe authors investigate the impact of flash droughts on ecosystem productivity,\nhighlighting a significant decline in vegetation resistance across the Northern\nHemisphere from 2001 to 2022. They attribute this decline primarily to increased vapor\npressure deficit, temperature, and vegetation sensitivity to water availability. Their\nfindings show that flash droughts lead to faster and more severe declines in ecosystem\nproductivity compared to slowly-developing droughts. The study underscores the urgent\nneed for improved monitoring and management strategies to mitigate the negative\neffects of flash droughts.\nHow the drought affects ecosystem productivity is a very meaningful topic and has been\nextensively explored, while the resistance of ecosystems to flash droughts has not been\nanalyzed as thoroughly. Overall, the manuscript is well-written, but the findings\nregarding the impact of flash droughts on ecosystems are not particularly novel (e.g.,\nthe finding that flash droughts have more negative effects than slow droughts).\nMoreover, there are significant omissions and unclear aspects in the methodology.\nResponse: We thank the reviewer for the thoughtful review and comments on our study.\nWe fully agree that previous research has explored the response of ecosystem\nproductivity to flash droughts across various sites and regions1–9 and shown that flash\ndroughts rapidly stress ecosystem productivity. Our study is novel, however, as it is the\nfirst analysis to show changes in vegetation resistance to flash droughts over the globe\nand to explore the potential drivers. The pronounced decline in vegetation resistance\nduring the past two decades suggests an increasing impact of flash droughts over\nnorthern regions. In the revised manuscript, we have clarified the knowledge gap we\naimed to address, improved the description of the methodology, and better highlighted\nthe novelty of our results throughout.\n1. My biggest criticism of the manuscript is the complete lack of support from site-level\nobservations. For instance, the main used satellite GPP data can also be derived from\n2\nglobal flux sites. It is necessary to use flux data to validate the findings derived from\nremote sensing data regarding response time, drought resistance, and trends etc.\nResponse: Thank you for your constructive comments. This study primarily relies on\nsatellite observations, including MODIS-GPP and SIF, which have provided global\necosystem productivity data since the 2000s, alongside spatial extrapolation of the in-\nsitu data. Eddy covariance towers, which provide direct observations of carbon fluxes,\ncan be used to validate the satellite estimates of GPP’s response to flash droughts/slow\ndroughts. According to the reviewer’s suggestion, we have now used in-situ GPP\nobservations from 80 eddy covariance flux towers to assess the response of GPP to the\nidentified drought events in the revised manuscript. The sites were selected from\napproximately 300 sites over the globe, with records of observations extending over 7\nyears since 2001 and including drought events that occurred during the study period.\nAmong these sites, we identified 117 flash drought events and 73 slow drought events.\nEven with 7+ years of observation at a flux tower site, however, there are a limited\nnumber of drought events at each site, ranging from 0 to 4 annually at hotpots like WNA\nand NEU. This, combined with the inherent level of variability in eddy-covariance flux\nobservations, makes estimating the trend of resistance at the site-level challenging. The\nobservational records are useful for validation of the mean resistance, which we have\nnow included in the manuscript as follows:\n“To verify the reliability of remote sensing-derived vegetation productivity, we used in-\nsitu observations of GPP at 80 eddy covariance towers where flash droughts/slow\ndroughts occurred during the study period. These observations were derived from\nFLUXNET 2015, AmeriFlux FLUXNET, ICOS Warm Winter 2020, OzFlux, and\nChinaFLUX (Extended Data Tab. 1). Although there is a large mismatch in spatial\nscales between the tower footprint (typically with diameters of a few hundred meters)\nand the 0.25-degree grid cells, satellite-derived resistance and response time show\nsignificant correlations with in-situ results, with correlation coefficients (R) of\n3\n0.21/0.29 (MODIS-GPP/SIF with p<0.001/0.002) and 0.33/0.45 (MODIS-GPP/SIF\nwith p<0.001), respectively.” (L675-683)\nExtended Data Tab. 1 Site information including locations and data periods of eddy\ncovariance towers with relatively long records extending over 7 years. The numbers of\nflash droughts and slow droughts were listed during the observed period of each site,\nstarting from 2001.\nName Lat Lon Start End Flash Slow\ndrought drought\nCA-Ca1 49.87 -125.33 1996 2010 0 1\nCA-Ca2 49.87 -125.29 1999 2010 1 0\nCA-Cbo 44.32 -79.93 1994 2020 1 2\nCA-Gro 48.22 -82.16 2003 2014 1 0\nCA-LP1 55.11 -122.84 2007 2020 0 1\nUS-ARM 36.61 -97.49 2003 2020 1 2\nUS-Bar 44.06 -71.29 2004 2021 5 0\nUS-GLE 41.37 -106.24 2005 2020 2 2\nUS-ICh 68.61 -149.30 2007 2021 1 1\nUS-ICs 68.61 -149.31 2007 2020 2 1\nUS-Jo1 32.58 -106.64 2010 2020 2 0\nUS-Jo2 32.58 -106.60 2010 2020 2 0\nUS-KFS 39.06 -95.19 2007 2019 2 2\nUS-KLS 38.77 -97.57 2012 2019 1 0\nUS-Kon 39.08 -96.56 2004 2019 2 0\nUS-MMS 39.32 -86.41 1999 2020 2 4\nUS-MOz 38.74 -92.20 2004 2019 1 1\nUS-Ne1 41.17 -96.48 2001 2020 0 1\nUS-NR1 40.03 -105.55 1998 2016 1 0\nUS-Oho 41.55 -83.84 2004 2013 1 0\nUS-Rpf 65.12 -147.43 2008 2021 1 0\nUS-SRG 31.79 -110.83 2008 2021 6 2\nUS-SRS 31.82 -110.85 2011 2018 0 1\nUS-Ton 38.43 -120.97 2001 2021 0 2\nUS-UMB 45.56 -84.71 2007 2021 2 2\nUS-UMd 45.56 -84.70 2007 2021 2 2\nUS-Var 38.41 -120.95 2000 2021 1 1\nUS-Whs 31.74 -110.05 2007 2020 2 1\nUS-Wkg 31.74 -109.94 2004 2021 1 4\nCN-Din 23.17 112.53 2003 2010 1 2\nAU-ASM -22.28 133.25 2010 2022 0 1\nAU-Cpr -34.00 140.59 2011 2022 3 2\nAU-Cum -33.62 150.72 2012 2022 0 0\nAU-Das -14.16 131.39 2008 2022 1 0\nAU-Dry -15.26 132.37 2008 2022 3 2\nAU-GWW -30.19 120.65 2013 2022 1 1\nAU-Lit -13.18 130.79 2015 2022 1 0\nBE-Bra 51.31 4.52 1996 2020 3 0\nBE-Dor 50.31 4.97 2011 2020 1 0\nBE-Vie 50.30 6.00 1996 2020 1 0\nCH-Cha 47.21 8.41 2005 2020 3 0\nCH-Fru 47.12 8.54 2005 2020 1 1\nCH-Lae 47.48 8.36 2004 2020 2 0\nCH-Oe2 47.29 7.73 2004 2020 1 1\nCZ-BK1 49.50 18.54 2004 2020 0 1\nDE-Geb 51.10 10.91 2001 2020 1 1\nDE-Gri 50.95 13.51 2004 2020 1 1\nDE-Hai 51.08 10.45 2000 2020 3 1\nDE-Kli 50.89 13.52 2004 2020 1 1\nDE-Obe 50.79 13.72 2008 2020 1 0\nDE-Tha 50.96 13.57 1996 2020 1 1\nDK-Sor 55.49 11.64 1996 2020 7 2\nES-Agu 36.94 -2.03 2006 2020 0 1\nES-LJu 36.93 -2.75 2004 2020 2 3\nES-LM1 39.94 -5.78 2014 2020 1 1\nES-LM2 39.93 -5.78 2014 2020 2 1\nFI-Hyy 61.85 24.29 1996 2020 3 1\nFR-Aur 43.55 1.11 2005 2020 1 1\nFR-Bil 44.49 -0.96 2014 2020 0 1\nFR-Gri 48.84 1.95 2004 2020 1 2\n2. The study analyzes drought response time on a daily scale, but the core data for GPP\nand SIF have an 8-day resolution. How can 8-day resolution data accurately reflect\nchanges on a daily scale? This involves analyses such as drought identification and\nproductivity response to drought. The authors have not mentioned this issue or\ndiscussed the potential uncertainties involved.\nflash and slow droughts and their corresponding vegetation responses was performed\nat an 8-day timescale using multiple datasets. This approach aligns with the temporal\nfrequency of satellite-derived observations, including GPP from MODIS10 and SIF11.\nGiven the discrepancies among results from various sources of soil moisture and\necosystem productivity, we utilized the ensemble mean of the response time of\necosystem productivity to flash and slow droughts from the six members which may\nnot perfectly align with the 8-day interval. To address the uncertainty associated with\nthe ensemble mean response time, we included the standard deviation and conducted\nthe significance test of global mean response time using the bootstrapping method. We\nhave revised the manuscript to clarify this issue as follows:\n“Faster declining rates in GPP are related to the faster response time, with the response\ntime of GPP to flash droughts being only 34 0.1 days from the unperturbed state to the\nmaximum intensity of GPP response, which is shorter than the 39 0.7 days for slow\n±\ndroughts at the significance level of 1% (Extended Data Fig. 3).” (L123-127)\n±\n“In this study, we processed the hydrometeorological variables from hourly or daily\nraw data into 8-day timescale to be consistent with the temporal intervals of satellite\necosystem productivity data.” (L588-590)\n“The ensemble mean of the six members was used for climatology and trend analysis,\nand the uncertainty was quantified by the standard deviation through 1000 times\nbootstrap sampling with the replacement of members64,75.” (L689-691)\n3. Some details in Methods are not clear. For example, in ‘Flash drought identification’,\nwhy are the thresholds for drought set as soil moisture percentile>40% and recovery\nas soil moisture percentile>20%? Such thresholds are also found for drought duration\nsetting (like 4 time steps), and excluding decreased GPP before onset of drought (40%).\nNot giving more explanations would be somewhat arbitrary. For journals like NC, some\n6\nadditional testing is required.\nResponse: Thanks for your insightful comments. We have included the sensitivity\nanalysis as follows:\n“Sensitivity analysis\nTo assess the robustness of the spatio-temporal characteristics of resistance with respect\nto the threshold choices in defining flash droughts, we conducted a series of sensitivity\nanalyses by altering the thresholds used in the flash drought definition. Specifically, we\nchanged the start point of flash droughts from soil moisture percentiles above 40% to\n50%, to represent a relatively normal pre-drought conditions, and the drought\ntermination from soil moisture percentiles recover above 20% to 10%, to represent the\nextreme drought category. We also adjusted the minimum drought duration from 4 time\nsteps (32 days) to 3 time steps (24 days) and considered pre-drought ecosystem\nproductivity levels from above 40% to 50%, representing undisturbed vegetation\nconditions. Furthermore, we also examined the spatial distributions and temporal trends\nusing different thresholds. While the absolute values of resistance to flash and slow\ndroughts varied with different thresholds, the results show a high overall consistency in\nthe patterns of resistance differences between flash droughts and slow droughts, as\nshown in Fig. 2c (R: 0.75-0.98, p<0.01). Additionally, 59%-69% of grids exhibited\nnegative differences, indicating that the ecosystem productivity’s resistance to flash\ndroughts is generally lower than slow droughts (Extended Data Fig. 14). Furthermore,\nthe results with different thresholds show similar temporal changes in resistance over\nhotspot regions (Extended Data Fig. 15). Overall, the sensitivity analysis demonstrates\nthat the different thresholds do not affect the spatio-temporal dynamics of ecosystem\nproductivity’s resistance to flash droughts.” (L734-757)\nto flash and slow droughts averaged over 2001-2022 using different drought\nthresholds. In the experiment of DrtOnset-50%, the onset of flash droughts/slow\n8\ndroughts starts where the soil moisture percentiles are higher than 50% and afterward\ndecline. In the experiment of DrtEnd-10%, the termination of droughts is when soil\nmoisture percentiles recover above 10%. Dur-24d represents that droughts persist for\nat least 24 days. Pre-GPP>50% signifies that the pre-drought conditions of GPP are\nhigher than 50%.\nExtended Data Fig. 15 | Regional trends in vegetation productivity resistance over\nthe IPCC SREX regions using different drought thresholds.\n9\n4. Why not also provide the trend map and driver map by pixels? Fig. 4 is beautiful, but\nwhen presenting the results only by selected regions, you may lose a lot of spatial details\nand also include ‘integrated’ results. For example, we can not know what are the driver\nof some hotspot regions such as most of Amazon forests and Australia drylands.\nResponse: Thanks for your suggestion. The accuracy of trend estimation is\nsignificantly influenced by the sample, and we found that the continuous timeseries\ncould not be obtained due to the insufficient number of flash drought events per year at\npixel scale (Fig. R1). Spatial aggregation is a necessary strength of our analysis to\naddress this issue. We have added a discussion on this issue in the revised manuscript\nas follows:\n“The coarse coverage of resistance at a regional scale could overlook variations across\na wide range of climates and terrestrial ecosystems. Therefore, long-term terrestrial\necosystem productivity datasets at finer spatio-temporal scales based on advancements\nin remote sensing techniques, earth system models, and deep learning algorithms are\nurgently needed to facilitate more detailed studies.” (L278-282)\nFig. R1 | The number of years without occurrences of flash drought at pixels. The values\nfor raw 0.25-degree grids are clustered into 2°× 2° moving windows.\n1 0\nLine 103-104: Since the minimum GPP of flash and slow drought is different, is the\nresponse time meaningful?\nResponse: Thanks for your constructive comment. We tested the response time of\necosystem productivity using a threshold of percentiles of 20% and have included the\nrelated findings in the revised manuscript.\n“The response time of ecosystem productivity, when GPP percentiles were below 20%,\nwas also faster during flash droughts compared to slow droughts (Extended Data Fig.\n16), which is consistent with the faster response time of the minimum GPP percentiles\nduring flash droughts.” (L754-757)\n1 1\nExtended Data Fig. 16 | Response time of the first occurrence of GPP percentiles\n<20% during flash and slow droughts. a, the timing of the occurrence of GPP\npercentiles of 20% during flash droughts (FD). b, the same as (a), but only for slow\ndrought events (SD). c, the difference in response time between flash and slow droughts.\nThe black points indicate the differences are significant at the level of 5%.\nParagraph starting from Line 113: May better also examine the different responses t\n1 2\nacross aridity gradients. The results could be more straightforward for understanding.\nResponse: According to the suggestion, we have now incorporated an analysis of\nvegetation resistance to flash droughts for different climate regions in the revised\nmanuscript as follows:\n“Furthermore, the differences between resistance to flash and slow droughts are more\npronounced in drier climate regions, with drylands exhibiting higher vegetation\nsensitivity to flash droughts (Extended Data Fig. 5).” (L135-137)\n“The meteorological conditions during the drought period were also examined,\nincluding precipitation (P), potential evapotranspiration (PET), air temperature (Temp),\nVPD, evapotranspiration (ET), and shortwave radiation (SW) mainly from ERA5.”\n(L585-588)\n“The climate classification was based on multiyear mean aridity index (AI=P/PET),\nand climate regions include hyper-arid (AI<0.05), arid (0.05≤AI<0.2), semi-arid\n(0.2≤AI<0.5), dry sub-humid (0.5≤AI<0.65), and humid regions (0.65≤AI)66.” (L592-\n595)\n1 3\nExtended Data Fig. 5 | Global distribution of climate regions and resistance of\necosystem productivity to flash and slow droughts for different climate regions. a,\nThe climate classification is based on aridity index (AI=P/PET), including hyper-arid\n(AI<0.05), arid (0.05≤AI<0.2), semi-arid (0.2≤AI<0.5), dry sub-humid (0.5≤AI<0.65),\nand humid regions (0.65≤AI). b, the multi-year average resistance of ecosystem\nproductivity to flash droughts and slow droughts for each climate region during 2001-\n2022. The star indicates the differences in resistance to flash drought and slow drought\nare significant at a 1% level using the Student’s t-test.\nFig. 2: No any situation with increasing GPP during drought? Or you have already\nexcluded these cases?\n1 4\nResponse: Thank you for your comment. We analyzed all flash drought events to\nsystematically investigate the response of GPP and SIF to flash droughts, except for\nthose with vegetation degradation (< 40th percentile) prior to the onset of flash droughts.\nWe have clarified this in the revised manuscript as follows:\n“Based on this definition, the ratio of flash drought events with declining GPP is 99%\nand this method allows a reasonable quantification of the negative impacts of flash\ndroughts through the constraint of mean ET percentiles <50% and the duration at least\nlasting for 32 days.” (L636-640)\n“In this study, we excluded those sub-seasonal drought events if the GPP percentile is\nbelow 40% before the onset of droughts, as these cases are difficult to disentangle the\ninfluence of droughts on vegetation growth from other external disturbances.” (L657-\n659)\nFig. R2 | Ratio of flash drought events causing declines in ecosystem productivity.\nLine 409: Is this done by each pixel?\nResponse: Thanks for your comment. The identification of flash drought events was\nconducted at the pixel level. Please see the details below:\n1 5\n“Firstly, the soil moisture data were aggregated into the 8-day time scale at each pixel\n(e.g., Jan 1st to Jan 8th) to be consistent with satellite observation frequency, and were\nthen transformed into percentiles by ranking them at each 8-day time step from 2001 to\n2022.” (L622-625)\n1 6\nReviewer #2:\nThis manuscript (MS) aims to quantifying the ecosystem’s capacity to maintain\nproductivity during flash droughts. The authors claim that the resilience of vegetation\nto flash droughts in the Northern Hemisphere hotspots will decrease by up to 27% in\nthe period 2001-2022. In view of the intensification of flash droughts in recent times\nthis study is certainly appropriate in order to clarify the effects of drought. I consider\nthat the topic of this MS fits well to the portfolio of NCOMMS. However, there are\nseveral shortcomings that should be clarified before publication.\nResponse: We appreciate the reviewer’s positive comments on our study. We have\nrevised the manuscript based on the following comments, and our responses are\nprovided below.\nThis paper has the following shortcomings:\n• The authors’ aim is to investigate “how the resistance of ecosystem productivity to\nflash drought changes over time”. The first shortcoming that needs to be clarified is\nwhat is a flash drought? The authors used weekly data for their definition but in current\nliterature there is ambiguity in the definitions and in the temporal resolution of the\ndatasets that can be used (see Otkin et al. BAMS 2018, Atmosphere 2021). In my\nopinion a suitable definition of flash droughts is that the accounts intensification and\nin which a compound event is occurring, i.e., a deficit of P and T in short period of time\n(see for example the 2018-2020 in Europe reported by Rakovec et al. 2022\nhttps://doi.org/10.1029/2021EF002394). I recommend to make a clarification at the\nbeginning with a justification of the choice.\nResponse: Thank you for your constructive comment. The most salient characteristic\nof flash droughts is their unusually rapid intensification compared to slowly-developed\ndroughts. To capture these changes at sub-seasonal time scales, pentad or weekly\n1 7\nintervals are typically used12–14. In this study, we adopted an 8-day soil moisture interval\nto be consistent with remote sensing observations of ecosystem productivity11,15, which\ncan capture rapid soil moisture intensification on a sub-seasonal scale. The definition\nof flash droughts we used effectively captures the compounding effects of soil moisture\ndepletion and excessive evaporative demand, as indicated by positive VPD anomalies\nduring flash drought (Extended Data Fig. 4). To clarify the choice and justification of\nthe flash drought indicator and methodology, we have revised the manuscript as follows:\n“In this study, we processed the hydrometeorological variables from hourly or daily\nraw data into 8-day timescale to be consistent with the temporal intervals of satellite\necosystem productivity data.” (L587-589)\n“Soil moisture acts as the indicator of flash droughts, as the dynamics of soil moisture\nare typically associated with lower precipitation and humidity and increased solar\nradiation and temperature6,72 and are closely related to vegetation water stress13,34.”\n(L619-621)\n• The impacts described by the authors (e.g., l.27) need to have an uncertainty estimate.\nIt is well recognised in the literature (here some examples from my own work (no need\nto be cited): https://doi.org/10.1175/jhm-d-12-075.1, https://doi.org/10.1007/s10584-\n016-1778-y, https://doi.org/10.1038/1s41558-018-0138-5 ) that both inputs and models\noutputs have large uncertainties that are propagated along the modelling chain. For\nthis reason, it is fundamental to quantify the related uncertainties of all major results.\nThis study lacks of a sound uncertainty estimation.\nResponse: Thank you for your constructive comment. We agree that addressing\nuncertainty is crucial for enhancing the reliability of our conclusions. We have revised\nour analysis and the manuscript to quantify and present uncertainty values throughout\nas follows:\n“Here we show that vegetation resistance to flash droughts declines by up to 27%\n( 5%) over the Northern Hemisphere hotspots during 2001-2022, including eastern\nAsia, western North America, and northern Europe.” (L33-35)\n±\n1 8\n“For all flash droughts, the resistance of GPP is quantified by the change rates of GPP\npercentiles and the global mean resistance is -12±0.4% (mean±s. d.) per 8 days during\nthe GPP’s response period (Fig. 2a).” (L118-120)\n“Faster declining rates in GPP are related to the faster response time, with the response\ntime of GPP to flash droughts being only 34 0.1 days from the unperturbed state to the\nmaximum intensity of GPP response, which is shorter than the 39 0.7 days for slow\n±\ndroughts at the significance level of 1% (Extended Data Fig. 3).” (L124-128)\n±\n“We find that GPP declining rates over the flash drought periods increased by 6±4%-\n27±5% in eastern, southern, and northern Asia, northern Europe, western North\nAmerica, southern Africa, Sahara, and Canada region during 2001-2022 (Fig. 3a and\nExtended Data Fig. 7). Notably, the significant declines (p<0.01) in GPP resistance to\nflash droughts are 16±3%, 12±1% and 12±6% over eastern Asia, West North America,\nand North Europe during 2001-2022.” (L154-159)\n“For eastern Asia, the reduction in percentiles of detrended LAI during flash droughts\nis increasingly severe (p<0.01), and the increasing LAI sensitivity to flash droughts\ncontributes to the most (47 13%) of the declining trend in resistance (Fig. 4a and\nExtended Data Fig. 11a).” (L207-209)\n±\n“For West North America, North Asia, and South Asia, the increase in percentiles of\nVPD and temperature contributes most to the decreased resistance to flash droughts\n(44 7%~102 10%; Fig. 4b and Extended Data Fig. 11b, e, f). For southern Africa, the\ndrying soil moisture explains 45 6% of the declining resistance.” (L214-218)\n± ±\n±\n“The onset of flash drought over northeastern Canada becomes earlier before the peak\nof the growing season, contributing 53 10% to the temporal changes in resistance.”\n(L220-238)\n±\n“Considering there is a large uncertainty of soil moisture estimates, here the ensemble\nmean results based on these three datasets were used63,64.” (L584-585)\n1 9\n“Here three sets of flash and slow drought events were identified using three soil\nmoisture products as mentioned above, and then the response of vegetation productivity\nto the identified drought events was individually detected using two vegetation\nproductivity products including MODIS-derived GPP and SIF. The temporal dynamics\nof resistance were analyzed by linear trends and then a two-sided t-test was applied for\nthe individual linear trend derived from six members. The ensemble mean of the six\nmembers was used for climatology and trend analysis, and the uncertainty was\nquantified by the standard deviation through 1000 times bootstrap sampling with the\nreplacement of members64,75.” (L684-691)\n• There are several accepted alternatives to elucidate non-linear trends in\nenvironmental variables. Classic test like Mann-Kendall do not work well for\nnonmonotonic trends. Another issue is the autocorrelation of the time series of key\nvariables (e.g., soil moisture, temperature, precipitation, etc.). For these reasons,\nARIMA models are often used for non-monotonic trend detection. In the present case a\nrandom forest model is used for this purpose. Please justify from a statistical\nperspective the advantages of this model. Second, describe the null-hypothesis of the\ntest and indicate the p-value. This information and justification is missing in l.84 and\nin methods section.\nResponse: Thank you for your insightful comment. Considering the limited length (22\nyears) of the time series, the linear trend was adopted to explore the temporal changes\nin ecosystem productivity’s resistance to flash droughts and its potential drivers, as this\nmethod is effective16. The random forest model was used to attribute the effects of the\nvegetation’s physiological and structural responses, and environmental factors on\nchanges in resistance, rather than estimating the trend of resistance. The annual time\nseries of ecosystem productivity’s resistance and its influencing factors were based on\nall flash drought events occurring in the same year, and thus the autocorrelation\ncoefficients are not significant, and we concluded that there was no relationship\n2 0\nbetween the original and lagged time series (Fig. R3). To clarify the attribution method,\nwe have revised the manuscript as follows:\n“The temporal dynamics of the resistance were analyzed by linear trends and a two-\nsided t-test was applied for the individual linear trend derived from six members.”\n(L687-689)\n“Attribution of resistance trends\nThe changes in resistance due to the dynamics of a certain factor were disentangled as\nfollows:\n𝑡 𝑡 𝑡0\nin which is the random f∆or𝑌e𝑎st= m∆o𝑓d(e𝑋l, a)n−d X∆ 𝑓a(n𝑋d −Y𝑎 ,r𝑋ep𝑎re)sent multiple predictors a(n1d)\necosystem productivity’s resistance to flash droughts, respectively. The second term on\n𝑓\nthe right side of equation (1) is the random forest model simulation considering all\npredictors’ dynamics except for the predictor of Xa that was kept constant to a certain\nyear of and here represents the linear trend of the model simulation.” (L715-725)\n𝑡0 ∆\n2 1\nFig. R3 | An example of the partial autocorrelation coefficients of the time series\nof influencing factors over EAS. Here nine factors from three categories include\ndrought characteristics (drought timing, drought speed, and drought duration),\nhydrometeorological conditions during flash droughts (percentiles of shortwave\nradiation (SW), temperature (Temp), vapor pressure deficit (VPD), and soil moisture\n(SM) during flash droughts), and vegetation response (leaf area index (LAI) and\nunderlying water use efficiency (uWUE) to flash droughts).\n• Please qualify the trends obtained by each independent SM product. SM of ERA5,\nGLDASv2.1, and GLEAMv3.8a. Are all SM estimates indicating the same trends?\n2 2\nResponse: Thank you for your constructive comment. We have added the individual\nestimation of linear trends of resistance as follows:\n“Although the resistance trends vary across different regions based on various soil\nmoisture products, there is a high consistency in the trends’ direction across individual\nresults such as eastern Asia, southern Africa, West Africa, and southern South America\n(Extended Data Fig. 9).” (L163-166)\n“Here three sets of flash and slow drought events were identified using three soil\nmoisture products as mentioned above, and then the response of vegetation productivity\nto the identified drought events was individually detected using two vegetation\nproductivity products including MODIS-derived GPP and SIF. The temporal dynamics\nof resistance were analyzed by linear trends and then a two-sided t-test was applied for\nthe individual linear trend derived from six members. The ensemble mean of the six\nmembers was used for climatology and trend analysis, and the uncertainty was\nquantified by the standard deviation through 1000 times bootstrap sampling with the\nreplacement of members64,75.” (L684-691)\n2 3\nExtended Data Fig. 9 | Estimation of the linear trends of resistance to flash\ndroughts based on different soil moisture and GPP/SIF datasets. a, blue and orange\npolygons indicate the regions with significantly negative and positive trends,\nrespectively, and gray polygons indicate the regions with insignificant trends of\nresistance. Black points represent the individual trend with the same direction of the\nensemble mean results at the 10% significance level and white points indicate the same\ndirection of insignificant trends. b-g, Distributions of resistance’s trend derived from\n2 4\nthe individual result obtained using multiple datasets. Black hatchings indicate the trend\nis significant at the 10% significance level.\n• What is the test or meaning of statements like this (P<0.01) in l. 105? Is this the\nprobability, a p-value (of a test), or a level of significance? Authors should provide\nthe confidence of the value of 38 days followed by the standard error. All key results should\nhave an uncertainty estimate (see previous comment). In the way that the authors\npresented the results is not clear. In case of confidence, it should be given as 38±x days\nwith 1% level of significance, i.e., 99% confidence interval. Other cases: l.125, l.134,\nl.138-139, l.154.\nResponse: Here, “P<0.01” indicates that the global mean difference in vegetation\nresponse time between flash droughts and slow droughts is significant at the level of\n1%, as determined by a two-sided t-test. We have added uncertainty estimation\naccordingly; please refer to our earlier response regarding uncertainty estimation.\n• There are several kind of models that can be used to estimate contributions factors.\nL. 145. Justify the use of the Random Forest models against a simple multiregression\nmodel. For example: l.186. “Our results emphasise that extreme hydrometeorological\nconditions weaken the terrestrial ecosystem’s ability to withstand flash droughts under\nclimate change.”. Please quantify these effects and indicate the confidence we have.\nGeneral statements like this are not helpful.\nResponse: In response to your suggestion, we evaluated the performance of the random\nforest (RF) model and the multiple linear regression (Fig. R4). The RF model\noutperforms the multiple linear regression in capturing interannual variability of\nresistance, as RF is constructed without assumptions about model structure and\naccounts for the nonlinear relationships between influencing factors and resistance.\nWhile the dominant factor influencing the dynamics of resistance identified by the RF\nmodel can also be partially detected by the multiple linear regression model (Fig. R5),\nthe contributions of some factors differ significantly between the two models, with a\n2 5\nfew even showing contradictory effects. We have incorporated these finding into the\nrevised manuscript.\n“Compared with the multiple linear regression model (R2=0.36), the random forest\nmodel has a better performance in the simulation of resistance (R2=0.88), by\nconsidering the nonlinear interactions between influencing factors and resistance,\nwithout a priori assumption about the functional form.” (L708-711)\n“Our results emphasize that extreme hydrometeorological conditions weaken the\nterrestrial ecosystem’s ability to withstand flash droughts under climate change,\ndominating the declining trends in resistance in over 50% of hotspot regions, with their\ncontributions ranging from 44% 7% to 102% 10%.” (L272-275)\n± ±\nFig. R4 | Modeled resistance derived from the random forest model and the\nmultiple regression model compared to observed resistance. The observed\nresistance represents regional averages for each year across hotspots, including 9\nregions with significant declining trends in resistance and 6 regions with positive trends.\n2 6\nFig. R5 | Contributions (%) of potential drivers of temporal changes in GPP\nresistance to flash droughts.\n• The discussion is too short and quite general.\nResponse: The discussion has been rewritten to focus more specifically on the\nimplications and limitations of this study as follows:\n“Discussion\nOur combined use of satellite observations, reanalysis hydrometeorological data, and a\nmachine learning model suggests that the accelerated GPP decline during flash droughts\nin recent decades presents challenges for unraveling the mechanism of climate change\nimpacts and monitoring vegetation health and assessing drought-related risks,\nespecially for agricultural regions (e.g., eastern Asia, southern Asia, and western North\nAmerica). Our results reveal the rapid decline rates in terrestrial ecosystems’\nproductivity under short-term intensified droughts, especially in the water-limited\nregions, which were overlooked by previous studies21,51. The concurrent extreme\natmospheric dryness and soil moisture deficits during flash droughts, resulting in\nreduced resistance of plants to these abrupt events. This sharper decline in ecosystem\nproductivity during flash droughts, coupled with shorter recovery intervals due to their\nincreasing frequency5,52, underscores the growing threat they pose to ecosystem\nstability. Future research efforts should delve into how vegetation recovers from flash\n2 7\ndroughts, as post-drought recovery is pivotal in assessing the ecosystem resilience to\ndroughts53.\nNotably, our results highlight that 37% of the vegetated land over the Northern\nHemisphere shows significant decreasing resistance to flash droughts, warning that\nvegetation resistance to flash droughts in northern regions is probably limited under\ngrowing climate change. Various factors during flash droughts are probably\nundermining different capacities for drought resistance simultaneously. Our results\nemphasize that extreme hydrometeorological conditions weaken the terrestrial\necosystem’s ability to withstand flash droughts under climate change, dominating the\ndeclining trends in resistance in over 50% of hotspot regions, with their contributions\nranging from 44 7% to 102 10%. Vegetation structural overshoot, where warming\nresults to temporary increases in the vegetation carrying capacity is expected to further\n± ±\nexacerbate the sensitivity of vegetation to soil moisture declines54. However, limitations\nmust be considered when interpreting these changes in resistance. The coarse coverage\nof resistance at a regional scale could overlook variations across a wide range of\nclimates and terrestrial ecosystems. Therefore, long-term terrestrial ecosystem\nproductivity datasets at finer spatio-temporal scales based on advancements in remote\nsensing techniques, earth system models, and deep learning algorithms are urgently\nneeded to facilitate more detailed studies.” (L249-282)\n2 8\nReferences\n1. O, S. & Park, S. K. Global ecosystem responses to flash droughts are modulated by\nbackground climate and vegetation conditions. Commun Earth Environ 5, (2024).\n2. Sungmin, O. & Park, S. K. Flash drought drives rapid vegetation stress in arid regions in\nEurope. Environmental Research Letters 18, (2023).\n3. Zheng, X. et al. Flash Droughts Identification Based on an Improved Framework and\nTheir Contrasting Impacts on Vegetation Over the Loess Plateau, China. Water Resour\nRes 58, 1–24 (2022).\n4. Parazoo, N., Osman, M., Pascolini Campbell, M. & Byrne, B. Antecedent Conditions\nMitigate Carbon Loss During Flash- Drought Events. Geophys Res Lett 51, (2024).\n5. Corak, N. K., Otkin, J. A., Ford, T. W. & Lowman, L. E. L. Unraveling phenological and\nstomatal responses to flash drought and implications for water and carbon budgets.\nHydrol Earth Syst Sci 28, 1827–1851 (2024).\n6. Hunt, E. et al. Agricultural and food security impacts from the 2010 Russia flash\ndrought. Weather Clim Extrem 34, 100383 (2021).\n7. Zhang, M. et al. Water-use characteristics and physiological response of moso bamboo to\nflash droughts. Int J Environ Res Public Health 16, (2019).\n8. He, M. et al. Impacts of the 2017 flash drought in the US Northern plains informed by\nsatellite-based evapotranspiration and solar-induced fluorescence. Environmental\nResearch Letters 14, (2019).\n9. Zhang, M. & Yuan, X. Rapid reduction in ecosystem productivity caused by flash\ndroughts based on decade-long FLUXNET observations. Hydrol Earth Syst Sci 24,\n5579-5593 (2020).\n10. Zhao, M. & Running, S. W. Drought-Induced Reduction in Global Terrestrial Net\nPrimary Production from 2000 Through 2009. Science (1979) 329, 940–943 (2010).\n11. Li, X. & Xiao, J. A Global, 0.05-Degree Product of Solar-Induced Chlorophyll\nFluorescence Derived from OCO-2, MODIS, and Reanalysis Data. Remote Sens (Basel)\n11, (2019).\n12. Otkin, J. A. et al. Flash droughts: A review and assessment of the challenges imposed by\n2 9\nrapid-onset droughts in the United States. Bull Am Meteorol Soc 99, 911–919 (2018).\n13. Yuan, X. et al. A global transition to flash droughts under climate change. Science (1979)\n380, 187–191 (2023).\n14. Christian, J. I. et al. Flash drought: A state of the science review. Wiley Interdisciplinary\nReviews: Water e1714, (2024).\n15. Running, S. W. et al. A Continuous Satellite-Derived Measure of Global Terrestrial\nPrimary Production. Bioscience 54, 547–560 (2004).\n16. Fuller, W. A. Introduction to Statistical Time Series. (JOHN WILEY & SONS, INC,\nNew York, 1995). doi:10.1002/9780470316917.\n3 0"}, {"round": 2, "reviewer_comments": {"reviewer_2": "Comments on the revised version of:\nA pronounced decline in northern vegetation resistance to flash droughts from 2001 to 2022\nBy Miao Zhang et al.\nI read the revised version of the manuscript, supplementary information and tye responses to the reviewers. I have to congratulate the authors for the excellent work done and the concise answers to my comments. The MS reads much better after the revision. In this version, the authors provide error estimates in their key findings, which I consider important. Overall, I am satisfied with the text as is.\nOne remark:\nThe code and data should be open source under CC BY licence. Thus this sentence (L591 ff):\n\"All data and code used for the analysis are available on request from the corresponding author and are published online.\",\nshould be changed. Git repositories should be provided.\nConsequently, I recommend publications as is.\nL. Samaniego", "reviewer_3": "I would like to thank the authors for their thorough responses to the review questions presented and I recognize the\nadditional work, time and clarification that they took to address these concerns.\nI do believe that the work/results will add to the literature's body of work and brings a good focus on flash drought centered\non ecosystem responses. This is obviously the tip of the iceberg given the limitations of doing this globally and primarily\nusing remotely sensed indicators given various latency and spatial/temporal factors.\nOne comment I'll add is that I wonder if by moving to the ensemble mean of 6 members over 8 day periods...does this lead to\nreduced detection of rapid onset. In other words, did some of the members perform much better than others in picking up on\nthe onset vs. others? For extreme events (both wet and dry) picking up the signal can be downplayed or even missed when\nfitting to an ensemble mean. Of course, you also don't want a bunch of false alarms either. I'd like to know which members\nperformed the best/worst? This could vary from region to season and would be valuable information to have."}, "author_response": "RESPONSE TO REVIEWERS\nWe sincerely appreciate the reviewers’ efforts for evaluating and enhancing our study.\nThe constructive and insightful comments have significantly improved the quality of\nthe manuscript. Below are our responses to the remaining comments.\nReviewer #2:\nComments:\nI read the revised version of the manuscript, supplementary information and tye\nresponses to the reviewers. I have to congratulate the authors for the excellent work\ndone and the concise answers to my comments. The MS reads much better after the\nrevision. In this version, the authors provide error estimates in their key findings, which\nI consider important. Overall, I am satisfied with the text as is.\nOne remark:\nThe code and data should be open source under CC BY licence. Thus this sentence\n(L591 ff): \"All data and code used for the analysis are available on request from the\ncorresponding author and are published online.\", should be changed. Git repositories\nshould be provided.\nConsequently, I recommend publications as is.\nResponse: Thank you for your positive and thoughtful suggestions. The codes in this\nstudy have now been uploaded to https://github.com/miaozhang2025/Vegetation-\nresistance-to-flash-drought. We have revised the code availability section as follows:\n“Code availability\nThe code for ecosystem productivity resistance to flash droughts is available at\nhttps://github.com/miaozhang2025/Vegetation-resistance-to-flash-drought.” (Line\n419-422 in the tracked version of the revised manuscript)” (Line 422-426 in the tracked\nrevision of the revised manuscript)\nComments\nI would like to thank the authors for their thorough responses to the review questions\npresented and I recognize the additional work, time and clarification that they took to\naddress these concerns.\nI do believe that the work/results will add to the literature's body of work and brings a\ngood focus on flash drought centered on ecosystem responses. This is obviously the tip\nof the iceberg given the limitations of doing this globally and primarily using remotely\nsensed indicators given various latency and spatial/temporal factors.\nOne comment I'll add is that I wonder if by moving to the ensemble mean of 6 members\nover 8 day periods...does this lead to reduced detection of rapid onset. In other words,\ndid some of the members perform much better than others in picking up on the onset vs.\nothers? For extreme events (both wet and dry) picking up the signal can be downplayed\nor even missed when fitting to an ensemble mean. Of course, you also don't want a\nbunch of false alarms either. I'd like to know which members performed the best/worst?\nThis could vary from region to season and would be valuable information to have.\nResponse: Thank you for your positive comments and thoughtful suggestions. In fact,\nwe identified flash drought events and conducted the resistance analysis independently\nfor each of the 6 members (datasets), and then the averages of these results are shown\nin the main results. The rationale is to consider the uncertainty of soil moisture datasets\nover the global scale, we used multiple soil moisture datasets, including ERA5,\nGLDASv2.1, and GLEAMv3.8a (Supplementary Fig. 1). Spatially, all three soil\nmoisture datasets show similar flash drought hotspots, like NEU, SAS, and ENA\n(Supplementary Fig. 1). However, differences exist in the frequency of flash droughts.\nTo assess the reliability of these datasets, we validated the reanalysis gridded soil\nmoisture against in-situ soil moisture from 127 FLUXNET sites with at least 7 years of\ndata at the grid scale. Among the datasets, the three soil moisture datasets show similar\nperformances, with 61, 59, and 60 sites showing significant positive correlations with\nin-situ observations from GLEAMv3.8a, ERA5, and GLDASv2.1m, respectively.\nRegionally, we have now evaluated soil moisture deficits during the 2012 flash drought\nover the central U.S. and the 2022 flash drought in southern China, compared with\ndatasets successfully captured the rapid drought intensification from slightly dry\nconditions in May to extreme dry conditions in June (Fig. R1). However, for the 2022\nYangtze River drought, only GLDASv2.1 captured the rapid drought onset (Fig. R2).\nGiven the varying performances of these reanalysis datasets across regions, we believe\nusing multiple soil moisture sources to identify flash drought events provides a robust\napproach to reduce the uncertainty. Therefore, we used the ensemble mean of\necosystem productivity resistance to flash and slow droughts, which were obtained\nthrough individual soil moisture dataset, to incorporate different valuable information\nfrom different data sources.\nFig. R1 | Soil moisture percentiles during the 2012 flash drought across the continental\nU.S. from GLEAMv3.8a, GLDASv2.1, and ERA5.\nPendergrass, A.G., Meehl, G.A., Pulwarty, R., Hobbins, M., Hoell, A., Aghakouchak, A.,\nBonfils, C.J.W., Gallant, A.J.E., Hoerling, M., Hoffmann, D., Kaatz, L., Lehner, F.,\nLlewellyn, D., Mote, P., Neale, R.B., Overpeck, J.T., Sheffield, A., Stahl, K., Svoboda,\nM., Wheeler, M.C., Wood, A.W., Woodhouse, C.A., 2020. Flash droughts present a new\nchallenge for subseasonal-to-seasonal prediction. Nat Clim Chang 10, 191–199.\nhttps://doi.org/10.1038/s41558-020-0709-0\nYuan, X., Wang, Y., Zhou, S., Li, H., Li, C., 2024. Multiscale causes of the 2022 Yangtze\nmega-flash drought under climate change. Sci China Earth Sci 67, 2649–2660.\nhttps://doi.org/10.1007/s11430-024-1356-x\n5"}]

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