An early warning signal for grassland degradation on the Qinghai-Tibetan Plateau

Domain: earth_science
Published: 2023-10-12
Editorial outcome: major_revision

Abstract
Intense grazing may lead to grassland degradation on the Qinghai-Tibetan Plateau, but it is difficult to predict where this will occur and to quantify it. Based on a process-based ecosystem model, we define a productivity-based stocking rate threshold that induces extreme grassland degradation to assess whether and where the current grazing activity in the region is sustainable. We find that the current stocking rate is below the threshold in ~80% of grassland areas, but in 55% of these grasslands the stocking rate exceeds half the threshold. According to our model projections, positive effects of climate change including elevated CO2 can partly offset negative effects of grazing across nearly 70% of grasslands on the Plateau, but only in areas below the stocking rate threshold. Our analysis suggests that stocking rate that does not exceed 60% (within 50% to 70%) of the threshold may balance human demands with grassland protection in the face of climate change.

Introduction
The Qinghai–Tibetan Plateau (QTP), colloquially referred to as the “rooftop” of the world, lies at a mean elevation higher than 4000 m, and covers around 2.5 million km2, corresponding to a quarter of China’s land area1. The QTP is also the “Asian Water Tower”, providing water to 1.9 billion people, and extensive ecosystem benefits, including climate regulation, soil conservation and cultural services2,3.

About 70% of the QTP is covered by different types of alpine grasslands, including desert steppes, alpine steppes and alpine meadows (Fig. 1), making it the highest and largest alpine grassland in the world4,5. Such alpine grasslands provide forage for 12 million yak and 30 million sheep and goats, and their responses and resilience to climate change and human activities strongly affect the livelihood of about 5 million pastoralists and agropastoralists on the QTP6,7. The QTP grasslands, one of the world’s harshest grazing environments, are very fragile and extremely sensitive to climatic conditions1,8. They face increasing impacts from climatic change, and the climate has been warming faster than in many other regions in the world7,9. The ubiquitous warming and overall slight wetting are dominant characteristics of climate change of the QTP that have had positive and negative effects on the alpine grasslands which subsequently have been under great changes in the past decades. Earlier in history, animal farmers tended to maintain relatively small herds just large enough to support their nomadic lifestyle, with some extra animals to buffer the effects of harsh weather. The grazing of these small herds was sustainable because it did not lead to irreversible grassland degradation, defined as a decline in the quality of plants and soils, as well as changes in ecosystem composition, structure, and function10,11. More recently, however, along with climate change, human activities on the QTP have intensified as the population has increased to 12 million, leading to increasing construction of roads and railways, land reclamation, and overgrazing. In particular, the massive increase in livestock grazing on the QTP appear to be a major driver of grassland degradation that reduces the quality of plants and soils11. Altogether, some studies showed that human activities have mainly contributed to the current degradation of the alpine grasslands on QTP12,13.

Even slight changes in climate and human factors can substantially affect the structure and functioning of alpine grasslands on the QTP14–16, leading to sudden, potentially irreversible changes in ecology and functioning17,18. With growing concern on the complex interactions between human activities and the natural ecosystem processes, how to identify the occurrence time of possible abrupt and irreversible state shifts in the alpine grassland ecosystem is essential for sustainable development and is the key for effective management in grassland ecosystem degradation mitigation15,18.

Given the pertinence of grazing to QTP grasslands8,19, we propose to develop a “stocking rate (expressed as the number of sheep unit per hectare of grassland per year in this study) threshold” which would induce an extreme grassland degradation to provide an early implication for grazing activities management. Since grazing in different regions varies according to local climate factors (e.g. precipitation and temperature)2, an effective management strategy for reasonable grazing patterns and grazing period arrangement based on a predicted stocking rate threshold that could induce grassland degradation may support balancing the grazing activities and carrying capacity of the grassland on QTP20,21. Such a threshold may also help to guide grazing management and sustainable development of grasslands on QTP in the future.

The central Chinese government has launched several initiatives to mitigate degradation of grasslands around the country, especially on the QTP3,20–23. The “Grain to Green Program”, initiated in 1999, began restoring grassland from cropland, while the “Retire Livestock and Restore Pastures”, initiated in 2003, and the “Return Grazing to Grassland Project”, initiated in 2004, began replacing grazing areas with uncultivated pastures. While these programs have achieved some success, it remains unclear how to ensure grazing patterns on QTP grasslands that are ecologically sustainable in the long term and can support the livelihood of local populations8. Instead of local scale, in depth understanding of a stocking rate threshold that could induce grassland degradation at regional scale across the QTP could help to propose an early warning signal for grassland degradation to inform the management decisions related to the above-mentioned ecological projects.

Numerous studies have investigated the impact of grazing on QTP grasslands on a local scale, for example in terms of vegetation cover24, species richness and diversity25, above-ground primary production25, soil organic carbon26, soil respiration27 and soil nutrient dynamics28. However, whether these local measurements can be reliably extrapolated to regional scales on the QTP grasslands is unclear, making it difficult to define an appropriate stocking threshold that induces grassland degradation base on statistical hypothesis framework or field observation work29. As a result, so-called “process-based ecosystem modeling”, which integrates information about biogeophysics, biogeochemistry, plant phenology, vegetation dynamics, as well as cycling of carbon, water and energy on the land surface, has shown promise for simulating interactions among vegetation, climate, and human activities30,31, making it well-suited to detect productivity changes in specific ecosystems32. Conducting long-term simulations at grid scale across the whole QTP using a process-based ecosystem model that consider the interaction of different processes among atmosphere, vegetation and soil could provide more insights into the spatial heterogeneity of ecosystem changes and could be extended into the past or the future to take climate change into account.

In this work, we modified a grazing framework for grassland ecosystems and integrated it into a process-based model to explore a stocking rate threshold inducing grassland degradation on the QTP under current conditions and future scenarios with climate change. We adopted net primary productivity (NPP) as an indicator of grassland degradation, which has proven to be a good estimator of ecosystem functioning33 and land degradation34. We assumed that grassland productivity would decrease with increasing stocking rate, and we defined the stocking rate threshold to be the stocking rate at which NPP fell to 1% of the pre-grazing level that was considered as an extreme degradation status for grassland (see Methods). The objectives of this study were to detect a potential stocking rate threshold that could risk grassland extreme degradation on the QTP, apply the stocking rate threshold to identify grassland areas under threat of degradation, and predict when such degradation might occur under current conditions and future conditions of grazing, climate change and elevated CO2. We show that the current stocking rate is below the threshold in ~80% of grassland areas on the Plateau. We further suggest that although climate change including elevated CO2 can partly offset negative effects of grazing across nearly 70% of grasslands on the Plateau, stocking rate that does not exceed 60% of the threshold may balance human demands with grassland protection in the face of climate change.

Results
Spatial pattern of stocking rate on QTP grasslands
The multiyear average stocking rate on the QTP generally increased from northwest to southeast during the period from 1980 to 2017 (Fig. 2a). Greatest stocking rate (>10 sheep units (SU) ha−1 year−1) was found mainly in the east and in the south. Intermediate stocking rate was found in the southeast (5-9 SU ha−1 year−1). Analysis by decade showed that stocking rate increased continuously in the east (~0.1 SU ha−1 year−1 year−1), especially between 1980 and 1989 (Fig. 2b–e). From 1990 to 1999, stocking rate decreased across most of the Plateau, but it has been increasing in the central and eastern regions since 2000.

Patterns of stocking rate threshold on QTP grasslands
Stocking rate thresholds were mapped across the QTP to examine the spatial pattern of intensity of grazing activities that possibly inducing extreme grassland degradation (Fig. 3a). Stocking rate thresholds were highest in the east (greater than 9 SU ha−1 year−1), while they were lowest in the northwest (mostly less than 1.5 SU ha−1 year−1) (Fig. 3a). In general, thresholds are lower in the northwestern part and higher in the southeastern part of QTP. The stocking rate threshold fell between 1 and 4 SU ha−1 year−1 over most of the Plateau (Fig. 3b).

Next, we assessed where current stocking rate lay below or above the local threshold. We found that approximately 80% of the QTP grassland area had a stocking rate below the threshold, particularly in central regions (Fig. 3c). Nevertheless, stocking rates in most of those areas (~55%) were greater than half of the threshold values (Fig. 3d). Areas where actual stocking rate exceeded the threshold, usually within a factor of two (Fig. 3d) were within the same general area as those showing high stocking rate (Fig. 2a).

Time until degradation of QTP grasslands at different stocking rates
Given that stocking rate on most of the QTP appears to lie under stocking rate threshold, we wanted to know how long it would take until grassland became degraded in different areas if we assumed that stocking rate remained at the threshold level, or even higher than the threshold (10% or 30% higher). If stocking rate remained at the threshold, model results projected that the grasslands in the northwest would become degraded within 20 years, compared to more than 80 years for grasslands in the south and southeast (Fig. 4a). Most grasslands on the QTP would become degraded within 40–80 years (Fig. 4d). In general, model results predicted that longer time for grasslands to become degraded as one moved from the northwest to the southeast under the grazing activities at intensity of threshold (Fig. 4a). If stocking rate remained at 10% or 30% above the threshold, we predicted that most grasslands would become degraded within, respectively, 20–50 years or 10–20 years (Fig. 4b, c, d). Generally, grassland degradation accelerated by approximately 25 years or 40 years for these two conditions respectively while comparing the degradation time under the condition exactly with the threshold (Fig. 4d).

The areas where current stocking rate exceeded the threshold were mainly located in the northwest, southern and northeast parts of QTP (Fig. 4e, f). Modeling of these areas indicated that 96.2% of the overgrazed grassland would be degraded within 40 years (Fig. 4f) or 83.0% within 20 years if stocking rate remained at actual levels (Fig. 4e). Since we applied the grazing activities in the simulation from 1980 onwards, it indicated that the grasslands in these areas would be degraded by now. If we reduced stocking rate in these areas to the threshold level, the time until degradation would be more than 60 years in the southern and northeast parts, but still fewer than 10 years in the northwest part (Fig. 4f), which implies that the southern and northeast grasslands of the QTP can currently be considered undegraded, while the northwest grasslands can still be considered degraded.

Ratios of actual stocking rate to the threshold on QTP grasslands
Simulations suggested that climate change and elevated atmospheric CO2 concentration would partly offset the negative effects of grazing activities on 68.3% of QTP grasslands (Fig. 5a, b and Supplementary Note 1), nearly all of which (98.5%) was subject to normal grazing and which accounted for 83.6% of the total grassland area subject to normal grazing (Fig. 5b). Across these “offset” areas, 69.2% showed a ratio of stocking rate to the threshold below 0.6, while 83.0% showed a ratio below 0.7 (Fig. 5c). The beneficial offsets were observed in only 5.3% of overgrazed areas (Fig. 5b), 76.9% of which featured ratios of stocking rate to the threshold below 3.0 (Fig. 5d). Among the remaining 31.7% of QTP grassland areas where climate change and elevated CO2 were not predicted to offset negative effects of grazing (Fig. 5b), 58.5% were overgrazed (Fig. 5b), with ratios of stocking rate to threshold from 2.0 to 6.0 (Fig. 5e), and 41.5% were subject to normal grazing (Fig. 5b), of which 71.3% showed a ratio of stocking rate to threshold larger than 0.6 and 88.4% showed a ratio of stocking rate to threshold larger than 0.5 (Fig. 5f). Although 41.5% of “non-offset areas” were under normal grazing, the positive effects of climate change and elevated CO2 could not compensate the negative effects of grazing on grassland productivity.

Our model suggested that setting the ratio of stocking rate to threshold between 0.5 and 0.7 would preserve 70–80% of the areas that were currently subject to normal grazing and were predicted to experience positive offsets from climate change and CO2 (Fig. 5c, g). This range from 0.5 to 0.7 showed the greatest potential for maximizing the areas subject to normal grazing or overgrazing (both currently in “non-offset areas”) that could benefit from positive offsets due to climate change or CO2 (Fig. 5b, e, f). When we reduced the ratio of stocking rate to threshold to 0.6 in areas where the ratio was >0.6 in an additional simulation, we found that the positive effects of climate change and elevated CO2 could offset the negative effects of grazing on over 89.1% of QTP grassland areas (Fig. 5h), by “converting” 70% of currently “non-offset areas” into “offset areas” (Fig. 5b, h). The “converted” areas come from 78% of overgrazing area and 58% of normal grazing area (Fig. 5b, h).

Future patterns of stocking rate threshold on QTP grasslands
In a further effort to understand the possible changes of stocking rate threshold of QTP grasslands in light of future climate change, we predicted stocking rate thresholds for current condition (Fig. 6a) or under different climate scenarios as defined by representative concentration pathways (RCPs) 2.6, 4.5 and 8.5 (Fig. 6b–d). In all three scenarios, stocking rate thresholds generally increased from northwest to southeast, and they increased with radiation forcing (RCP 8.5 > 4.5 > 2.6), particularly in the south and southeast. Compared to the current climate status, thresholds under all the RCP scenarios were larger, and thresholds > 4.0 SU ha−1 year−1 covered a larger surface area (Fig. 6e). Grassland surface area with a threshold > 7.0 SU ha−1 year−1 was substantially greater under the scenario RCP 8.5 than under any others scenarios (Fig. 6e).

Methods
Underlying model
The TRIPLEX-GHG model is a dynamic global vegetation model that takes into account land surface processes, vegetation phenology, long-term vegetation dynamics, soil biogeochemical processes, greenhouse gas emissions, cycling of nitrogen and phosphorus, wetland area dynamics, and peatland carbon accumulation58–60. The TRIPLEX-GHG modules of vegetation dynamics, land surface, plant phenology, and soil biogeochemistry are based primarily on the Integrated Biosphere Simulator (IBIS) model61,62 (Supplementary Fig. 3). The vegetation in TRIPLEX-GHG was represented by plant functional types (PFTs) and two types of grasses (C3 and C4) were included. Vegetation dynamics was characterized by PFT changes in terms of biomass and leaf area index, with competition among PFTs for sunlight and water and sunlight. Plant phenology was modeled based on the coldest temperatures, thresholds for accumulated growing degree days, and thresholds for 10-day average temperature. Canopy photosynthesis was modeled based on the Farquhar model to calculate gross primary productivity (GPP) and net primary productivity (NPP) for each PFT61,62. An improvement considering nitrogen constraints on estimation of canopy NPP was adopted by applying a soil N availability modifier and dynamic C:N ratios in order to capture how the carbon cycle responds to dynamic nitrogen availability58,63.

For the present study, we focused on the PFT of grasses and integrated a grazing module into the model to estimate stocking rate threshold of QTP grasslands in the face of climate change and elevated CO2. The parameters of the two grass PFTs in the distribution in IBIS model were adjusted to the QTP by Yuan et al.64 (Supplementary Table 1).

Grassland NPP
In this study, we adopted NPP as an indicator to detect degradation of QTP grasslands and to evaluate the effect of climate change, elevated CO2 and grazing activities on the grasslands. NPP, the net carbon assimilated by plants and sensitive to climate change and human activities, is an important proxy index of vegetation growth status and ecosystem health. It has been proved to be a good estimator of ecosystem functioning33 and land degradation34. NPP was calculated as the balance between the carbon gained by GPP and carbon released by plant maintenance respiration, as described by the equation in the IBIS model61,62:

NPP = (1 − η) A g − R m dt (1)

where A g was the gross photosynthesis rate; R m, the sum of maintenance respiration rates; and η, the fraction of carbon lost due to growth respiration. Gross photosynthesis rate was calculated based on the Farquhar equations65 and expressed as the smaller of the light-limited rate or Rubisco-limited rate of photosynthesis in the case of C3 plants, or as the smallest of the light-limited, Rubisco-limited or CO2-limited rates of photosynthesis in the case of C4 plants66. For the calculation of Rubisco-limited rates of photosynthesis, the maximum carboxylation capacity of Rubisco was modified using the leaf C:N ratio in order to take into account potential nitrogen limitation58,63. For the calculation of productivity, a soil N availability modifier was applied58,63.

Grazing processes on QTP grasslands
We constructed the grassland grazing processes framework (Supplementary Fig. 3) based on several studies67–70 and defined the summer grazing season as May to October, and the winter grazing season as November to April.

Grassland biomass consumption. Grassland biomass consumption was calculated based on stocking rate and herbage mass availability. During the summer grazing season, the total biomass needs for livestock consuming (BioGrz t, kg C m−2) was calculated as68:

BioGrz t = LS w × r IT × C coe / 10000.0 (2)

where LS w was livestock weight (kg ha−1), which depends on stocking rate (sheep unit (SU), ha−1 year−1) and initial livestock unit body weight (40 kg); r IT was livestock intake rate, which was set to 0.02 based on the general intake rate of Tibetan-sheep69 and an approximate daily body weight gain of 40 g per sheep69; C coe was the unit conversion factor of dry mass to carbon (0.475); and 10000.0 served to convert from ha to m2. An intake rate of 0.02 meant that Tibetan-sheep consumed biomass each day equivalent to 2% of body weight.

We assumed that in the summer grazing season, 95% of biomass consumption was live grass and 5% dead material. The above ground grass biomass (AGB), live or dead, was assumed to be sufficient for grazing if it exceeded BioGrz t × 0.95 × 2.0. The biomass consumed from live grass (BioGrz l) was calculated in units of kg C m−2 according to the study of Shiyomi et al. (2011)68. We added an exponential function to calculate biomass consumed from live grass, while AGB was allowed to range from BioGrz t × 0.95 to BioGrz t × 0.95 × 2.0 in order to make biomass consumption vary continuously.

BioGrz l = 

BioGrz t × 0.95 × exp(1.8326) when BioGrz t × 0.95 < AGB < BioGrz t × 0.95 × 2.0

AGB × 0.2 when AGB < BioGrz t × 0.95 (3)

The biomass consumed from standing dead grass (BioGrz d-SGS), which the model considered as part of litterfall, was calculated in units of kg C m−2 as:

BioGrz d-SGS = BioGrz t × 0.05 (4)

During the winter grazing season, we assumed the biomass needed for livestock come from dead plant material in the above-ground litter pool. In the winter grazing season, livestock was assumed to lose 25% of body weight69, and this weight loss was distributed equally across all days of the winter grazing season. The daily biomass consumed by livestock was considered to compensate for the difference between livestock daily respiration consumption and body weight loss. Thus,

BioGrz d-WGS = LS w-ESGS × 0.012 − LS w-ESGS × 0.25 / D WGS (5)

where BioGrz d-WGS was the daily biomass consumed from standing dead grass in the winter grazing season; LS w-ESGS, livestock body weight at the end of the summer grazing season; D WGS, the number of days in the winter grazing season; and LS w, livestock body weight, which was adjusted for daily weight loss at a proportion of 0.012 to body weight.

Livestock weight dynamics. Livestock body weight of (LS w) was calculated in daily steps using the equation as:

LS w = LS w + BioGrz l × 0.65 + BioGrz d × 0.45 × 10000.0 / C coe − LS w × 0.012 (6)

where 0.65 and 0.45 represented the digestibility of live and dead plant material, respectively, while 0.012 represented the energy necessary for daily respiration consumption to maintain livestock activity. Indigestible material from live and dead plants was assumed to be excreted by animals at respective excretion rates of 0.35 and 0.55 and to return to the soil pool, where it participated in soil biogeochemical processes. We assumed the negative effect of trampling on above-ground biomass depend on stocking rate with an effect coefficient of 0.8%67,70.

Mapping stocking rate on QTP grasslands
To map stocking rate across QTP grasslands, we collected livestock (including sheep, goat, pig, cattle, yak, horse, mule, donkey, camel etc.) numbers for each province or county across the QTP from provincial or prefecture statistical yearbooks for the period from 1980 to 2017 (National Digital Library of China, http://www.nlc.cn/). We converted livestock numbers into sheep units (SUs) as described in Ren69 (Supplementary Table 2). All data processing and modelling steps related to grazing activity in this study were based on SUs.

We calculated annual average stocking rate for each county as:

SR i = (SU i / A i × R i × S i) + (SU i / A i × R i × W i) (7)

where SR i was the stocking rate in county i (SU ha−1 year−1); SU i, SUs in county i (capita); A i, the area of grassland in county i (ha); R i, the area fraction of grassland in county i that was edible; S i, the area fraction of summer pasture in county i; and W i, the area fraction of winter pasture in county i. To generate dynamic maps of annual stocking rate at the county level, maps of annual grassland distribution and area and distribution were retrieved from the ESA CCI Land Cover time-series dataset (http://www.esa-landcover-cci.org/). The area fractions of edible grassland, summer pasture, and winter pasture for each county were collected from provincial or prefecture statistical yearbooks. Since these data were unavailable on an annual basis, we calculated average values over the years for which data were available. Finally, dynamic maps of stocking rate at the county level were calculated for the period from 1980 to 2017.

Data for model performance evaluation
The modeled stocking rates were compared with designed heavy level of stocking rates in local grazing experiments collected from 38 field sites (Supplementary Fig. 4), and the stocking rate at each site was classified as heavy, medium, or light. We also compared the observed biomass, which were available for 15 sites (Supplementary Fig. 4, Supplementary Table 3), with the modeled biomass in the corresponding grid under different stocking rates.

We compared simulated and eddy covariance retrieved monthly GPP for three flux sites (CN-Dan, CN-Ha2, CN-HaM) involved in FLUXNET Network (https://fluxnet.org/) that located on the QTP to validate the GPP simulation performance of the model (Supplementary Fig. 4).

We also compared the modeled NPP and NPP productions retrieved from Moderate Resolution Imaging Spectroradiometer (MODIS) (http://files.ntsg.umt.edu/data/NTSG_Products/MOD17/GeoTIFF/MOD17A3/GeoTIFF_30arcsec/) and Advanced Very High Resolution Radiometer (AVHRR) (http://www.glass.umd.edu/NPP/AVHRR/) to validate the NPP simulation performance of the model at grid scale.

Model forcing data
Daily meteorological data on precipitation; mean, maximum, and minimum temperatures; relative humidity; solar radiation, and wind speed were generated for whole China between 1960 and 2017 by smooth thin plate spline interpolation71 at a resolution of 0.08333° based on observed data from 2400 national meteorological stations. Then, daily climate data were extracted from this national dataset for the model simulations on the QTP. Soil texture on the QTP was derived from a high-resolution soil texture map of China72, and initial grassland distribution information was derived from the 1:1000000 China vegetation map73. All data were generated with spatial resolution of 0.08333° (~10 km).

Model simulation
The model was firstly set up as a 400-year spin-up simulation with multiyear average historical meteorological data for the period between 1960 and 1990. This allowed the ecosystem carbon pools, especially soil carbon pools, to reach equilibrium. For the simulations to evaluate the effects of climate change, elevated CO2 concentration and stocking rate on NPP of QTP grasslands (Supplementary Note 1), the model ran with daily meteorological data beginning from 1960 to 2017, while grazing activity was applied from 1980 to 2017. Grazing activities on the QTP were applied in the simulations from 1980 (by using the generated stocking rate maps), since this marked the beginning of livestock privatization and dissolution of the collective system on the Plateau, from when many pastoralists increased their herd sizes under a condition of rapid transition to a market economy with exacerbating the trend toward high livestock numbers8,40. Grassland productivity, biomass removed by grazing, and livestock body weight were updated using a daily time step in the model.

Stocking rate threshold detection
We assumed that grassland productivity would decrease with increasing stocking rate. The consumption of NPP (NPP Grz) at each daily time step was calculated as the minimum of biomass consumed from live grass (BioGrz l) that directly depended on stocking rate and the NPP could be allocated to leaf:

NPP Grz = min(NPP D × α leaf, BioGrz l) (8)

where NPP D was daily net primary productivity of grassland (kg C m−2), α leaf was the allocation fraction of total photosynthate to leaf (Supplementary Table 1).

In order to detect the stocking rate threshold for grassland degradation in each grid cell, the model was run under different stocking rates, from non-grazing (stocking rate = 0.0) to maximum possible grazing (stocking rate = 15 SU ha−1 year−1), increasing in a step of 0.5 SU ha−1 year−1 for a period of 120 years from the grazing activities applied on (Supplementary Fig. 5). During this period, multiyear (1980-2017) averages of daily meteorological and CO2 concentration data were used to drive the model in order to eliminate the effects of varied climate and ramping CO2 concentration on NPP in the process of threshold detection. Altogether 31 simulations were performed over the entire QTP. When the NPP of a cell reached 1% of the baseline NPP before the stocking rate was applied, we defined that stocking rate to be the stocking rate threshold for the grid cell, above which stocking rate would lead to degradation based on the indicator of NPP (Supplementary Fig. 5). This simulation strategy was performed on each grassland grid cell (~10 km) of the QTP. Using this approach, and 3720 annual NPP maps (31 scenarios × 120 years) of grassland on the QTP were used to generate a map of stocking rate threshold and a map showing how many years before the grassland in each grid cell at the indicated stocking rate (“time until degradation”). These simulations under different stocking rates were run with average climate and CO2 conditions between 1980 and 2017.

Using a similar approach, we generated stocking rate threshold maps under future climate change. The modeling was run using stocking rates ranging from non-grazing (stocking rate = 0.0) to maximum possible grazing (stocking rate = 15 SU ha−1 year−1) by increasing the grazing level in a step of 0.5 SU ha−1 year−1 under the scenarios of RCP 2.6, 4.5 and 8.5, corresponding to low, medium, or high emissions in the 21st century (Supplementary Table 4). Multiyear (2020–2100) averaged climate and CO2 concentration data for each scenario was used to drive the model. A fourth scenario was considered in which climate conditions and CO2 concentration were held constant at their 2020 levels. A total of 31 simulations across QTP grasslands were performed for each scenario, resulting in stocking rate threshold maps. Model driving data on precipitation, temperature, relative humidity, and radiation of three RCPs were averaged from 24 General Circulation Models (GCMs) at a spatial resolution of 0.5° (Supplementary Table 5).

Model performance evaluation
We make a comparison between the heavy grazing levels designed in the field experiment and stocking rate thresholds simulated in this study as a reference to evaluate the modeled stocking rate threshold (Supplementary Fig. 4). Modeled stocking rates were compared at the county level with “heavy” stocking rates at 38 field sites (Supplementary Fig. 4, Supplementary Table 3) in local grazing experiments in which sites were subjected to “light”, “medium” or “heavy” stocking rate. Although this comparison may be imperfect because stocking rates defined as “heavy” in the literature have an unclear relation to stocking rate thresholds defined in our study, regional mean simulated stocking rate thresholds were quite consistent with designed heavy stocking rates (slope=0.923, R2=0.77, p<0.01; Supplementary Fig. 6). Generally, the heavy stocking rates in experimental studies were slightly greater than the simulated stocking rate thresholds (Supplementary Fig. 6). The difference in spatial scale between our modeling grid (~10 km) and local grazing experiment site (~several hectares) meant that the same modeling grid sometimes contained multiple sites differing substantially in stocking rates designated as heavy in experimental studies (Supplementary Table 3).

We also compared simulated and measured AGB at the sites with different stocking rates for which data were available (Supplementary Table 3). Comparisons were performed on a county scale (grouped the site with county location) (Supplementary Fig. 4, Supplementary Table 3). The two sets of values were generally comparable and showed the gradient influence of stocking rate on AGB (Supplementary Fig. 7). Agreement between simulated and observed AGB values was acceptable across all five regions (R2=0.74, p<0.01; Supplementary Fig. 7).

The simulated GPP values agreed well with the eddy covariance retrieved GPP for the three sites with coefficient of determination all greater than 0.74 (p<0.001) (Supplementary Fig. 8). The discrepancy of GPP between simulated and observed data may be caused by the inconsistent spatial scale between modelling grid (~10 km) and footprint of flux tower.

Multi-year average NPP (2000-2015) was compared between simulated in this study and the productions retrieved from remote sensed data of AVHRR and MODIS at grid scale across QTP (Supplementary Fig. 9) for a reference of model performance evaluation. The simulated NPP values agreed better with the AVHRR data (R2=0.62, Supplementary Fig. 9b) than with the MODIS data (R2=0.47, Supplementary Fig. 9a). Simulated NPP values showed a coefficient of determination of nearly 0.6 when compared with NPP values averaged between the MODIS and AVHRR datasets (Supplementary Fig. 9c). The AVHRR data usually contained greater NPP values than the MODIS data (Supplementary Fig. 9d).

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "The authors define grazing capacities for areas of the Qinghai-Tibetan Plateau (QTP) using a process-based ecosystem model. They then compare those capacities against recent stocking rates and speak of stocking relative to a threshold they have identified. Climate projections are then used to infer changes into the future. The writing is of high quality, although the work would benefit from additional edits. The analyses too are interesting. I have several overarching comments, and a few more minor points.\nI was quite excited and impressed when first reviewing the manuscript to learn that the authors had identified thresholds in grazing intensity in QTP. The definition of quantitative thresholds in sustainability science is notoriously difficult. But I think this is related to the multiple meanings of “threshold”. The exciting, and I think more typical, interpretation in sustainability science is of a quantitative measure of use over which a non-linear response will be seen, leading to a dramatic decline in resource availability. Many examples, a classic being the rapid collapse of Atlantic cod fisheries in the northwestern Atlantic in 1992. Identifying those thresholds a priori is extraordinarily challenging and exciting. Imagine, as the authors’ title suggests, that early warning signals for approaching grazing intensity thresholds had been identified (as suggested on lines 68 and 70). That would be a great advancement. In reading the work I find that threshold is used in another context, to speak of recommended limits not to be exceeded. That is useful, but typical. I worry that the authors will add confusion by using “threshold” where the long-standing rangeland term “carrying capacity” is instead intended. I would suggest the authors consider alternative language to describe their recommendations, avoiding the term that may cause confusion. Put another way, lines 251-252 say “… our threshold approach may prove a more sophisticated and, in the long run, more effective way to ensure sustainable grazing.” But how is this approach distinct from other explorations of grazing carrying capacities, using simulation or otherwise?\nThe authors fail to compare their results to the works of many others who have mapped global grassland degradation (e.g., Yang 1992; Wu et al. 2009; Ho-Young et al. 2015; Cao et al. 2019; Bardgett et al. 2021; Sun et al. 2022; Zhou et al. 2023, recognizing some are too new to be considered). Some of these works either include figures that show degradation in the QTP or are specific to the QTP. The portion of QTP degraded includes a broad range of estimates, some as high as 50%. To be clear, the authors cite degradation of the QTP, but do not place their grazing threshold in the context of the remainder of the literature. The authors say that 80% of the QTP is below some threshold stocking level. But they also say they seek to define “the stress that grassland could take for supporting productivity before a degradation condition is triggered” lines 74-75). That suggests that the authors view at least 80% of the QTP as undegraded. Comparing the contrasting finding to that reported in the literature would improve the manuscript. Perhaps defining the threshold cited (related to the previous point) and how it relates to degradation, if it does, would be helpful. The authors then calculate (lines 137-143 and many other locations) how long areas with grazing exceeding their threshold would take to become degraded. That suggests that the authors see only some small fraction of the 20% of land exceeding the threshold as currently degraded. Contradictions in this regard in the writing itself and in the writing compared to the literature exist and should be rectified.\nI invite the authors to consider the section starting at line 158 for review. It is not as clear as the earlier material. I think it is because the actual grazing intensity and threshold intensities inform statistics, and so two percentages are often provided, which is a little confusing. So “… nearly 89% and 72% of the GI ratio larger than 0.5 and 0.6 under normal grazing condition, respectively” and “… to offset the negative effects of grazing with area percentage of 72% or 89% …” is a little tough going for readers. The same information may be shared in almost as pithy a way while untangling the combined statements.\nSome observed grazing rates converted to categories were used in assessment, which is helpful. It seems there remains a great deal of remotely sensed information amenable to these broad-scale analyses that is suitable for simulation assessment as well, and readily available. Typical data used for a model that has NPP estimates at its core would be NPP and GPP estimates, plus NDVI or EVI, from MODIS, for example. The authors may wish to describe why those data were not used in assessment or incorporate their use.\nLine 33. There is ambiguity in the 70% figure. Is it 70% of the area in question? Or 70% of the effect offset? Something else?\nLine 48. Ensuring the “ecological security of the QTP” may be briefly expanded upon. I’m curious, anyway.\nLine 102. “Processed model” sounds a little unusual. Perhaps “process-based model”?\nLine 112. Briefly define sheep units for those working in other regions.\nLines 163-164. “To some extent” may be removed given that “partly” was used.\nLine 167. GI is introduced without having been defined.\nLines 251-252 introduce a comparison without the thing being compared against. It speaks of a more effective way, compared to … I’m not speaking of rangeland science here, only the writing. “compared to traditional approaches” would be sufficient, for example.\nMay lines 413-415 (“When the NPP of a cell reached nearly to zero under a certain grazing intensity, then we defined that grazing intensity to be the threshold value for the grid cell, such that intensities above that value would lead to degradation.”) somehow find their way into the body of the manuscript? This is really the heart of the matter, the means in which you defined a threshold value (the confusing name aside). A summary of this approach in the Introduction or Results would be very helpful, and require just a few words (e.g., a threshold was crossed when NPP was < 0 [but using another word for “threshold”]).", "reviewer_2": "The manuscript entitled “Early warning of grazing-induced thresholds of grassland degradation on the Qinghai-Tibetan Plateau”, by Zhu et al. addresses the impact of grazing intensity on net primary productivity (NPP) along the Qinghai-Tibetan Plateau (QTP) grassland ecosystems, using a process-based ecosystem modeling. The particular objectives were (i) to determine the grazing intensities that induce an abrupt reduction in NPP, (ii) to apply these findings to identify grassland areas under threat of degradation, and (iii) to predict when such degradation might occur under current and future grazing and climate conditions. The main results indicate that most grasslands of the QTP are managed with grazing intensities below those that induce the abrupt reduction in NPP, and that maintaining the grazing intensity around 60% of that induces the abrupt change has the potential to sustain the human demand without inducing grassland degradation. These aspects are critical since this extensive area is mostly covered by grasslands that sustain millions of domestic herbivores, which in turn are the livelihoods of millions of people.\nThe manuscript is clear and the study is well performed. The findings are novel and have the potential to generate some international interest in its field of study. However, there are some issues that should be addressed. My main concern is the unique ecological attribute and the relationship to estimating thresholds (grazing intensity and NPP), which can lead to misunderstanding degradation processes since the study did not consider other critical ecological attributes and other aspects of grazing management beyond the stocking rate (here grazing intensity). On the one hand, degradation processes can be evidenced through ecosystem attributes different from NPP, which can be more appropriate to early detect degradation (e.g. Kéfi et al., 2007; Berdugo et al., 2017, 2020; Oñatibia & Aguiar, 2023). For example, degradation may occur without inducing changes in NPP (see Verón et al., 2006). On the other hand, grazing management strategies are much more complex than grazing intensity. For example, grazing and rest periods and the spatial distribution of herbivores in the landscape are key factors to control land degradation and promote productivity (see e.g. Briske, 2017). I suggest the authors expand the section on potential limitations of the study including a discussion of these issues. Besides, both contextualizing the study and comparing the main findings with existing global literature (other than just for the QTP) on early warning signals of degradation and ecosystem thresholds are also recommended.\nRegarding the model validation, the field sites are relatively few and cover a small area compared to the large extent of the whole study area. This weakness is not fatal, but the authors should discuss this aspect, maybe also expanding the section about the potential limitations of the study. \nFinally, a minor issue is that the treatment given to the term “threshold” is vague and sometimes unclear (e.g. lines 67, 74, 78, 88, 102, and see specific comments below). This is important since a central role is given to this term throughout the manuscript. In general, it is not completely clear how it relates to an early warning signal for grassland degradation. Definitions of threshold and degradation (and their relationship) from the introduction will help to understand the main ideas addressed. A more precise alternative could be directly referring to grazing-induced abrupt NPP reduction instead of threshold and degradation.\nSPECIFIC COMMENTS\nLines 1-2. I suggest revisiting the title after considering the comments on what thresholds represent and how they are related to early warning signals of grassland degradation.\nLines 26-27. Suitable threshold? Threshold of what? Shouldn't it be “suitable grazing intensity”?\nLines 50-51. The authors indicate that QTP grasslands are very fragile. Please, justify this statement and add citations. If this information is that found in lines 64-66, please, avoid repetition.\nLines 58-62. Please define degradation. A definition of what grassland degradation represents to the authors would help make this section clearer.\nLine 140. Remove “that”.\nLines 220-227. Areas described as overgrazed seem to be generally the most arid (e.g. northwest). If this is correct, authors can discuss the potential interaction between increasing grazing and aridity (see e.g. Gaitán et al., 2018; Oñatibia et al., 2020).\nLine 366-368. If grazing intensity is the annual average, its units could be SU/ha/year (stocking rate units) instead of density units (SU/ha).\nLine 425. I suggest moving this Figure with an expanded legend to the main manuscript.\nLine 608-609. I suggest that authors indicate what the white color represents on the map; if it means the absence of data or if represents other vegetation types different from steppes and meadows.\nFigure legends. Information is generally lacking for the figures to be self-explanatory, particularly those in the supplementary material.\nReferences\nBerdugo, M., Kéfi, S., Soliveres, S., & Maestre, F. T. (2017). Plant spatial patterns identify alternative ecosystem multifunctionality states in global drylands. Nature ecology & evolution, 1(2), 0003.\nBerdugo, M., Delgado-Baquerizo, M., Soliveres, S., Hernández-Clemente, R., Zhao, Y., Gaitán, J. J., ... & Maestre, F. T. (2020). Global ecosystem thresholds driven by aridity. Science, 367(6479), 787-790.\nBriske, D. D. (2017). Rangeland systems: processes, management and challenges (p. 661). Springer Nature.\nGaitán, J. J., Bran, D. E., Oliva, G. E., Aguiar, M. R., Buono, G. G., Ferrante, D., ... & Maestre, F. T. (2018). Aridity and overgrazing have convergent effects on ecosystem structure and functioning in Patagonian rangelands. Land Degradation & Development, 29(2), 210-218.\nKéfi, S., Rietkerk, M., Alados, C. L., Pueyo, Y., Papanastasis, V. P., ElAich, A., & De Ruiter, P. C. (2007). Spatial vegetation patterns and imminent desertification in Mediterranean arid ecosystems. Nature, 449(7159), 213-217.\nOñatibia, G. R., Amengual, G., Boyero, L., & Aguiar, M. R. (2020). Aridity exacerbates grazing induced rangeland degradation: A population approach for dominant grasses. Journal of Applied ‐ Ecology, 57(10), 1999-2009.\nOñatibia, G. R., & Aguiar, M. R. (2023). On the early warning signal of degradation in drylands: Patches or plants? Journal of Ecology, 111(2), 428-435.\nVerón, S. R., Paruelo, J. M., & Oesterheld, M. (2006). Assessing desertification. Journal of arid environments, 66(4), 751-763.", "reviewer_3": "The manuscript ”Early warning of grazing-induced thresholds of grassland degradation on the Qinghai-Tibetan Plateau“ by Zhu & Chen et al. attempts to identify grazing intensity thresholds that would lead to grassland degradation. The question is of broad relevance, and such results could potentially be used as the basis for future grazing management in the area.\nOverall the manuscript did not provide enough information for me to assess the results and conclusions drawn critically. The model, in particular the vegetation dynamical component, is not sufficiently described in either the main text or supplementary materials to allow appropriate assessment of the presented results and conclusions. The authors refer to two published manuscripts (Zhang et al., 2017; Zhu et al., 2014). I have endeavoured to follow the cited manuscripts to gain a better understanding of the model used but have done so unsuccessfully.\nIn Zhu et al., (2014), the model description leads to a string of further citations: “The TRIPLEX-GHG model (Peng et al., 2013) is based on the legacy of well-established and published models that include IBIS (Foley et al., 1996), DNDC (Li, 2000), TRIPLEX (Peng et al., 2002) and CASACNP (Wang et al., 2010). ”. The cited Peng et al. (2013) paper does not appear to exist. At best, I can find an abstract submitted to the European geoscience Union (EGU), which is associated with TRIPLEX-GHG. This manuscript, however, indicates that IBIS represents the vegetation model component.\nZhang et al. (2017) explicitly cite the above-mentioned EGU abstract (Integrating greenhouse gas emission processes into a dynamic global vegetation model of TRIPLEX-GHG) at the beginning of the description of the model. Model improvements by (Liu et al., 2005) are also mentioned here. Based on the equation for NPP provided by the authors (line 303), I am guessing that the model version they are using is not that developed by Liu et al. (2005) and does not include any nutrient constraints on growth or photosynthesis. This is guesswork, though, and I did not see any code submitted, which could have been checked.\nOne of the major results presented by Zhu & Chen et al. is that the positive effects of climate change can offset, to a large extent, grazing. Based on the evidence presented, it is not clear whether such positive effects are as a result of temperature, precipitation, or increasing CO2. Presumably (this is not stated), the authors have turned off all other plant functional types other than temperate C3 grasses or similar for the present study. The extent to which CO2 may fertilise C3 grasses is uncertain (Reich et al., 2018). It is not clear what IBIS version the authors used and whether this version limits photosynthetic rate via any mechanism, e.g. nutrient limitation. There are no future change simulations such as those detailed in Tab.S1.1 that include grazing, and changing climate, but have CO2 fixed. Such simulations would have allowed assessment of the magnitude to which the CO2 fertilisation effect was responsible for the positive effects of climate change. Further, potential interactions between nutrients, plant growth and photosynthesis, and nutrient removal via grazing do not appear to be included in the model.\nAdditionally, the grazing threshold used (NPP close to zero) is not defined well. I remain uncertain about what “close” means. This threshold would need to be accurately defined and extensively justified as it is a pivotal metric in the manuscript.\nOverall the authors attempt to answer a highly relevant question, but the model (perhaps) used is not yet developed to the extent to sufficiently answer it. In any case, the authors have not provided enough information for me to critically asses the methodology.\nMinor comments:\nThere was a general lack of transparency in the description of model and methods.\nText size often changes within sentences and at many points in the manuscript e.g. lines 71-72.\nThere were many grammatical and language errors which could have been easily identified prior to submission. I won’t list all of these as there are tools available that could be used to flag such errors.\nLine 61 - “leading to degradation” a concise definition of degradation is necessary.\nLine 63 – “In parallel, the productivity of alpine grasslands has fallen by 30% since late 1990s 8, 12 “. The studies cited here are in relation to the QTP and the authors here indicate that productivity has decreased since the 1990s. The results presented by the authors (S1.1 a) indicate that productivity (NPP) has increased over the majority of the QTP for this approximate time period based on their simulations. I may have missed something; how do the authors explain this apparent contradiction?\nLines 91-100 Numerous changes in text size.\nLines 91-100 This paragraph sets the reasoning for the present study. The issue mentioned is that it is not clear whether local measurements can be extrapolated to regional scales. The authors should provide more detail about how their modelling approach can do this better and be more explicit about which processes they expect to be able to represent better with their approach.\nLines 101-106 Numerous changes in text size.\nLines 101-106 Given that one of the main objectives of the study was to define a grazing intensity threshold, it would be good to have this threshold defined and justified here. It remains uncertain to me exactly how this threshold is defined and what its jusfitication is.\nFuture change scenarios: “Spatial pattern of future grazing intensity thresholds on QTP grasslands ” (line 186) & Fig. 6 (line 646) – to what extent are the simulated increases in grazing intensity thresholds driven by increasing CO2 alone?\nFigure 5 is very difficult to understand.\nLines 434 – 445 It is difficult to understand. It is not clear to me what “When this comparison was repeated at the regional level after clustering sites by location” means and what was done. It is apparent from Fig. S2.6 Tab. S2.2 that the simulated grazing intensity threshold is almost always above the grazing level at the site, ca. 25/38. The authors write that potential causes for this may be the inconsistent spatial scale between the modelling grid (10km * 10km) and the grazing experimental site. Does the model simulate a 10x10 km patch of vegetation, or are they simulating something smaller (e.g. 1 Ha) and assuming this is representative of a 10x10km grid? What was the spatial scale of the site-level grazing experiments? The authors also mention that the site-level grazing experiments may have a grazing intensity that is in excess of the intensity threshold. How do they justify this? There are presumably measurements collected at these sites, e.g. grass growth (NPP), that could be used to justify this assumption. Based on the threshold methodology of the authors, wouldn't exceeding this threshold imply grass NPP at these sites is zero? Have the authors considered the alternative possibility that their model is wrong?\nSupplement\nFig. S2.6 - what is a) and what is b)? This is not specified.\nFig. S2.7 – the difference between observed and simulated grass biomass seems rather large, up to 300% at some sites and grazing intensities.\nThe “biomass for grazing” equations line 330/331 did not appear correctly. Perhaps this is just a rendering issue, though.\nGrazing intensity threshold with NPP close to zero – based on the NPP equation (line 303), wouldn’t zero NPP imply GPP was equal to maintenance respiration and there is no growth? Detailed justification for the choice of threshold is necessary as this is crucial to the manuscript. At what time-step is grass growth calculated? At what time-step grass biomass removed by grazing? I can’t figure out whether this happends at daily, monthly, or yearly time steps.\nLiu, J. X., Price, D. T., & Chen, J. A. (2005). Nitrogen controls on ecosystem carbon sequestration: A model implementation and application to Saskatchewan, Canada. ECOLOGICAL MODELLING, 186(2), 178–195.\nReich, P. B., Hobbie, S. E., Lee, T. D., & Pastore, M. A. (2018). Unexpected reversal of C3 versus C4 grass response to elevated CO2 during a 20-year field experiment. Science, 360(6386), 317–320. https://doi.org/10.1126/science.aas9313\nZhang, K., Peng, C., Wang, M., Zhou, X., Li, M., Wang, K., Ding, J., & Zhu, Q. (2017). Process-based TRIPLEX-GHG model for simulating N2O emissions from global forests and grasslands: Model development and evaluation. Journal of Advances in Modeling Earth Systems, 9(5), 2079–2102. https://doi.org/10.1002/2017MS000934\nZhu, Q., Liu, J., Peng, C., Chen, H., Fang, X., Jiang, H., Yang, G., Zhu, D., Wang, W., & Zhou, X. (2014). Modelling methane emissions from natural wetlands by development and application of the TRIPLEX-GHG model. Geoscientific Model Development, 7(3), 981–999. https://doi.org/10.5194/gmd-7-981-2014"}, "author_response": "RESPONSES TO REVIEWERS’ COMMENTS\nReviewer #1 (Remarks to the Author):\nThe authors define grazing capacities for areas of the Qinghai-Tibetan Plateau (QTP) using a process-based ecosystem model. They then compare those capacities against recent stocking rates and speak of stocking relative to a threshold they have identified. Climate projections are then used to infer changes into the future. The writing is of high quality, although the work would benefit from additional edits. The analyses too are interesting. I have several overarching comments, and a few more minor points. I was quite excited and impressed when first reviewing the manuscript to learn that the authors had identified thresholds in grazing intensity in QTP. The definition of quantitative thresholds in sustainability science is notoriously difficult. But I think this is related to the multiple meanings of “threshold”. The exciting, and I think more typical, interpretation in sustainability science is of a quantitative measure of use over which a non-linear response will be seen, leading to a dramatic decline in resource availability. Many examples, a classic being the rapid collapse of Atlantic cod fisheries in the northwestern Atlantic in 1992. Identifying those thresholds a priori is extraordinarily challenging and exciting. Imagine, as the authors’ title suggests, that early warning signals for approaching grazing intensity thresholds had been identified (as suggested on lines 68 and 70). That would be a great advancement.\nRE: Thanks for your constructive comments and positive feedbacks. We have tried our best to improve the quality of MS based on your comments and suggestions! The responses are listed below point by point.\nIn reading the work I find that threshold is used in another context, to speak of recommended limits not to be exceeded. That is useful, but typical. I worry that the authors will add confusion by using “threshold” where the long-standing rangeland term “carrying capacity” is instead intended. I would suggest the authors consider alternative language to describe their recommendations, avoiding the term that may cause confusion. Put another way, lines 251-252 say “… our threshold approach may prove a more sophisticated and, in the long run, more effective way to ensure sustainable grazing.” But how is this approach distinct from other explorations of grazing carrying capacities, using simulation or otherwise?\nRE: Yes, we agree. Firstly, we revised the title of the paper as “A productivity-based grazing intensity threshold as the early warning signal for grassland degradation on the Qinghai-Tibetan Plateau” to make it clearer that the “grassland degradation” is based on the criterion of grassland net primary productivity, and the “threshold” means the grazing intensity threshold which could induce heavily grassland degradation, but not the threshold for grassland ecosystem degradation itself.\nThe “carrying capacity” refers to “the maximum number, density, or biomass of a population that a specific area can sustainably support” (Hartvigsen, 2022). In fact, the threshold we used in this study differs from it. We defined the threshold under the condition that the productivity of grassland reached an extreme degradation status caused by grazing, which could lead to ecosystem collapse (NPP fell to 1% of the original NPP before application of grazing).\nCorrespondingly, we added the definition of “threshold” and criterion of “grassland degradation” in the introduction section following the reviewer’s suggestions as below in the revised version (see lines 107-112).\n“We adopted net primary productivity (NPP) as an indicator of grassland degradation, which has proven to be a good estimator of ecosystem functioning 33 and land degradation 34. We assumed that grassland productivity would decrease with increasing GI, and we defined the GI threshold to be the GI at which NPP fell to 1% of the pregrazing level that was considered as an extreme degradation status for grassland (see Methods).”\nWe also added discussion on the issue of “carrying capacity” and estimated “threshold” in the first part of discussion section as below in the revised version (see lines 218-226).\n“The GI threshold defined in this study differs from the concept of “carrying capacity” that can “sustainably support” 36. The GI threshold may exceed the carrying capacity because GI at the threshold level is predicted to lead to an extreme degradation which is “unsustainable”. Although many previous reports of the percentages of the entire QTP grasslands that have been degraded, the mention percentages were simply taken from earlier work that did not undergo peer review (SI02). In fact, grassland degradation could be divided into different catalogs, including lightly degraded, moderate degraded and heavily degraded grasslands. In this study, we focus on heavily degraded scenarios, which would cause ecosystem collapse.”\nWe rephrased the expression of lines 251-252 (in original version) and added more information as below in the revised version (see lines 281-286).\n“For ensuring sustainable grazing across the entire QTP, our threshold approach based on simulations at the grid scale may prove more sophisticated and more effective in the long run, especially after further refinement, than traditional approaches based on large-scale inventories or local grazing data. Our GI threshold grids can be extended backward in time to assess whether areas have historically been overgrazed, and they can be projected into the future to estimate grazing sustainability in the face of changing climate and elevated CO .”\nRE: Thanks for the valuable suggestions. According to the comments, we collected references as the reviewer mentioned, as well as other relative studies.\nAlthough many experimental studies conducted at site or local scale have evaluated the effects of grazing on grassland structure, soil qualities and other parameters related to productivity, few studies have simultaneously investigated the status of grassland degradation and grazing activities across the entire QTP. Although many previous reports of the percentages of the entire QTP grasslands that have been degraded (Cai et al., 2015; Cao et al., 2019; Dong et al., 2013; Feng et al., 2010; Liu et al., 2008; Ma & Lang, 1999; Ren et al., 2013; Sheehy, 2001; Wang et al., 2015; Wu et al., 2009; Wu et al., 2014; Zhou et al., 2023), the mention percentages were simply taken from earlier work that did not undergo peer review.\nSome studies have analyzed grassland degradation on a global scale, thereby including the QTP without focusing on it. Such work may provide insights relevant for the QTP with a general information, but comparing it with our study is a little bit difficult since the authors did not report any data specifically for the Plateau (Bardgett et al., 2021; Gang et al., 2014; Gibbs & Salmon, 2015; Kwon et al., 2016).\nIn our study, the overgrazing status is defined as grazing intensity lager than the grazing intensity threshold. Our results showed that the overgrazing area percentage was about 20%. Considering the time until degradation across this overgrazing area, 83.0% of the area was within 20 years and 96.2% of the area was within 40 years (Figure 4e). Since we applied the grazing activities from the year of 1980, and that means the overgrazing area may have already been degraded.\nIn fact, grassland degradation could be divided into different catalogs, including lightly, moderate and heavily degraded grasslands. However, these catalogs may not have standard definitions. In this study, we made a definition based on NPP and focused on heavily degraded scenarios, which would cause the collapse of Tibetan grasslands. Our results figured the most serious overgrazing status based on this heavily degraded condition.\nBased on the collected literatures, the rates of grassland degradation and overgrazing on the QTP or parts of the QTP were summarized in the table as below (Table R1.1, Table R1.2).\nTable R1.1 Collected grassland degradation rate on the whole or parts of the QTP (NA: Not Available)\nTable R1.2 Collected grassland overgrazing rate on the whole or parts of the QTP (NA: Not Available)\nOvergrazing rate declared\nStudies Period Methods Reference\nPercentage (Based area)\nBased on grassland\npotential productivity\nYang and Yang 2000 28.6% (QTP) 1990s -\ncalculated using Miami\nmodel\nBased on grassland\npotential productivity\nQian et al. 2007 16% (Qinghai) & 78% (Tibet) 2003~2004 -\ncalculated based on\nNDVI data\n27~89% Yang and Yang 2000\nZhang et al. 2014 1996~2008 NA\n(Different regions across QTP) Qian et al. 2007\nSupervision report of national grasslands in 2006 39% (Qinghai) & 38% (Tibet) 2006 NA\nSupervision report of national grasslands in 2007 38% (Qinghai) & 40% (Tibet) 2007 NA\nSupervision report of national grasslands in 2008 37% (Qinghai) & 38% (Tibet) 2008 NA\nSupervision report of national grasslands in 2009 26% (Qinghai) & 39% (Tibet) 2009 NA\nSupervision report of national grasslands in 2010 25% (Qinghai) & 38% (Tibet) 2010 NA Ministry of Agriculture of People’s Republic\nof China\nSupervision report of national grasslands in 2011 25% (Qinghai) & 32% (Tibet) 2011 NA\n(Report could be obtained from\nSupervision report of national grasslands in 2012 16% (Qinghai) & 29% (Tibet) 2012 NA\nhttp://www.moa.gov.cn/\nSupervision report of national grasslands in 2013 14% (Qinghai) & 22% (Tibet) 2013 NA or http://www.forestdata.cn/index.html)\nSupervision report of national grasslands in 2014 13% (Qinghai) & 19% (Tibet) 2014 NA\nSupervision report of national grasslands in 2015 13% (Qinghai) & 19% (Tibet) 2015 NA\nSupervision report of national grasslands in 2016 11.9% (Qinghai) & 16% (Tibet) 2016 NA\nSupervision report of national grasslands in 2017 9.8% (Qinghai) & 13% (Tibet) 2017 NA\nAccording to the reviewer’s suggestion, we made a comparison between our results and that showed in other collected studies in the discussion section as below in the revised version (see lines 227-232).\n“Our simulations suggest that overgrazing and degradation affect around 20% of QTP grasslands, based on ratios of GI to the threshold. This figure is comparable to the multi-year mean grassland overgrazing rates of 16% for Qinghai and 24% for Tibet between 2010 and 2017, though several studies have suggested degradation rates of 21-40% across the entire QTP from the 1980s to 2000s (Supplementary Information 2). Our results figured the most serious overgrazing status based on heavily degraded scenarios.”\nBased on the collected literatures, we also added a short review (including two tables above) on the degradation and overgrazing status of grassland on the QTP as an additional SI section in the revised version (see SUPPLEMENTARY INFORMATION 2 (SI02)).\nThe authors then calculate (lines 137-143 and many other locations) how long areas with grazing exceeding their threshold would take to become degraded. That suggests that the authors see only some small fraction of the 20% of land exceeding the threshold as currently degraded. Contradictions in this regard in the writing itself and in the writing compared to the literature exist and should be rectified.\nRE: Thanks for pointing out this. For those overgrazing area (~20% in our study), model results indicate the grassland would be degraded within 20 years (83.0% of the overgrazing area) or within 40 years (96.2% of the overgrazing area) if the grazing activities applied at an actual level (Figure 4e). In our study, we applied the grazing activities from the year of 1980, and that means those areas of overgrazing may have already been degraded. According to the reviewer’s comments, we added this information in the revised version as below to avoid confusion (see lines 159-162).\n“Modeling of these areas indicated that 96.2% of the overgrazed grassland would be degraded within 40 years or 83.0% within 20 years if GI remained at current levels (Figure 4e). Since we applied the grazing activities in the simulation from 1980 onwards, it indicated that the grasslands in these areas would be degraded by now.”\nI invite the authors to consider the section starting at line 158 for review. It is not as clear as the earlier material. I think it is because the actual grazing intensity and threshold intensities inform statistics, and so two percentages are often provided, which is a little confusing. So “… nearly 89% and 72% of the GI ratio larger than 0.5 and 0.6 under normal grazing condition, respectively” and “… to offset the negative effects of grazing with area percentage of 72% or 89% …” is a little tough going for readers. The same information may be shared in almost as pithy a way while untangling the combined statements.\nRE: Yes, we rephrased this section and revised the corresponding Figure 5. We cited the percentage numbers as showed exactly in the figure into the main text to avoid confusion as below in the revised version. (see lines 171-196).\nWe made an additional simulation that the grazing intensity was changed to make ratio of GI to threshold was reduced to 0.6 in areas where the ratio was > 0.6. The results were also added to Figure 5 to show a suitable ratio of GI to threshold (60%, ranged from 50% to 70%) may maintain the balance between grassland protection and human demands. (also see lines 171-196).\n“Simulations suggested that climate change and elevated atmospheric CO\n2\nconcentration would partly offset the negative effects of grazing activities on 68.3% of QTP grasslands (Figure 5a, b and Supplementary Information 1), nearly all of which (98.5%) was subject to normal grazing and which accounted for 83.6% of the total grassland area subject to normal grazing (Figure 5b). Across these “offset” areas, 69.2% showed a ratio of GI to the threshold below 0.6, while 83.0% showed a ratio below 0.7 (Figure 5c). The beneficial offsets were observed in only 5.3% of overgrazed areas (Figure 5b), 76.9% of which featured ratios of GI to the threshold below 3.0 (Figure 5d). Among the remaining 31.7% of QTP grassland areas where climate change and elevated CO were not predicted to offset negative effects of grazing (Figure 5b), 58.5%\n2\nwere overgrazed (Figure 5b), with ratios of GI to threshold from 2.0 to 6.0 (Figure 5e), and 41.5% were subject to normal grazing (Figure 5b), of which 71.3% showed a ratio of GI to threshold larger than 0.6 and 88.4% showed a ratio of GI to threshold larger than 0.5 (Figure 5f). Although the areas in “non-offset areas” under normal grazing, the positive effects of climate change and elevated CO could not compensate the\n2\nnegative effects of grazing on grassland productivity.\nOur model suggested that setting the ratio of GI to threshold between 0.5 and 0.7 would preserve 70-80% of the areas that were currently subject to normal grazing and were predicted to experience positive offsets from climate change and CO (Figure 5c,\n2\ng). This range from 0.5 to 0.7 showed the greatest potential for maximizing the areas subject to normal grazing or overgrazing (both currently in “non-offset areas”) that could benefit from positive offsets due to climate change or CO (Figure 5b, e, f). When\n2\nwe reduced the ratio of GI to threshold to 0.6 in areas where the ratio was > 0.6 in an additional simulation, we found that the positive effects of climate change and elevated CO could offset the negative effects of grazing on over 89.1% of QTP grassland areas\n2\n(Figure5 h), by “converting” 70% of currently “non-offset areas” into “offset areas” (Figure 5b, h). The “converted” areas come from 78% of overgrazing area and 58% of normal grazing area (Figure 5b, h).”\nThe revised Figure 5 showed as below (Figure R1.1):\nFigure R1.1 (also Figure 5) Ratios of actual GI to the threshold on QTP grasslands. a. Distribution of areas where the negative effects of grazing are predicted to be offset by the positive effects of climate change and elevated CO on grassland NPP (“offset areas”) and areas where such offset is not predicted\n2\n(“non-offset areas”); b. The bar charts indicated the area percentage of “offset areas” and “non-offset areas” of the grassland on QTP, along with the area percentage (white numbers) of overgrazing (OG, red color) and normal grazing (NG, green color) over each region, as well as the area percentage of OG (red color; blue numbers) and NG (green color; yellow numbers) distributed in offset areas and non-offset areas respectively; c-d. The area percentage histogram and area percentage cumulative curve of the ratio of GI to GI threshold for the “offset areas” subject to normal grazing (NG, green color) (c) or overgrazing (OG, red color) (d); e-f. The area percentage histogram and area percentage cumulative curve of the ratio of GI to GI threshold for the “non-offset areas” of OG (red color) (e) or NG (green color) (f), and different from others, the area percentage of GI ratio in panel f accumulated decreasingly from 0.95 to 0.15; g. The merged area percentage cumulative curves of the ratio of GI to GI threshold subject to NG (green color), extracted from panel c (blue line) and f (red line). h. The bar charts indicate the area percentage of “offset areas” and “non-offset areas” of QTP grasslands, along with the area percentage (white numbers) of OG (red color) or NG (green color) over each region based on an additional simulation that was performed in which the ratio of GI to threshold was set to be 0.6 where the ratio was > 0.6.\nSome observed grazing rates converted to categories were used in assessment, which is helpful. It seems there remains a great deal of remotely sensed information amenable to these broad-scale analyses that is suitable for simulation assessment as well, and readily available. Typical data used for a model that has NPP estimates at its core would be NPP and GPP estimates, plus NDVI or EVI, from MODIS, for example. The authors may wish to describe why those data were not used in assessment or incorporate their use.\nRE: According to reviewer’s suggestion, multi-year average NPP (2000-2015) simulated in this study was compared with the NPP products retrieved from remote sensed data of AVHRR and MODIS at grid scale across the QTP. The results and figures were added in the model performance evaluation section as below in the revised version (see lines 529-536) and the supplementary materials (see Figure S3.9).\n“Multi-year average NPP (2000-2015) was compared between simulated in this study and the productions retrieved from remote sensed data of AVHRR and MODIS at grid scale across QTP (Figure S3.9) for a reference of model performance evaluation. The simulated NPP values agreed better with the AVHRR data (R2 = 0.62, Figure S3.9b) than with the MODIS data (R2 = 0.47, Figure S3.9a). Simulated NPP values showed a coefficient of determination of nearly 0.6 when compared with NPP values averaged between the MODIS and AVHRR datasets (Figure S3.9c). The AVHRR data usually contained greater NPP values than the MODIS data (Figure S3.9d).”\nLine 33. There is ambiguity in the 70% figure. Is it 70% of the area in question? Or 70% of the effect offset? Something else?\nRe: It is 70% of the total grassland area of the QTP. We rephrased the abstract in the revised version. (see lines 27-30).\n“……The positive effects of climate change and elevated CO can partly offset the\n2\nnegative effects of grazing across nearly 70% of grassland on the Plateau, but only in areas where current GI does not exceed the GI threshold. ……”\nLine 48. Ensuring the “ecological security of the QTP” may be briefly expanded upon. I’m curious, anyway.\nRe: Yes, to make it clearer, we modified the expression as “Such alpine grasslands provide forage for 12 million yak and 30 million sheep and goats, and their responses and resilience to climate change and human activities strongly affect the livelihood of about 5 million pastoralists and agropastoralists on the QTP”. (also see lines 42-45).\nThe original “ecological security” means the resilience of grassland ecosystem, referred from Ecological Security: A Definition. (2021). In M. McDonald (Ed.), Ecological Security: Climate Change and the Construction of Security (pp. 94-122). Cambridge: Cambridge University Press (McDonald, 2021).\nLine 102. “Processed model” sounds a little unusual. Perhaps “process-based model”?\nRe: Yes, it’s revised as suggested.\nLine 112. Briefly define sheep units for those working in other regions.\nRe: Yes, it’s revised as suggested.\nLines 163-164. “To some extent” may be removed given that “partly” was used.\nRe: Yes, it’s revised as suggested.\nLine 167. GI is introduced without having been defined.\nRe: Yes, we checked through the MS and defined GI at its first appearance.\nLines 251-252 introduce a comparison without the thing being compared against. It speaks of a more effective way, compared to … I’m not speaking of rangeland science here, only the writing. “compared to traditional approaches” would be sufficient, for example.\nRe: Yes, lines 251-252 were revised as mentioned before (also see lines 281-286).\nMay lines 413-415 (“When the NPP of a cell reached nearly to zero under a certain grazing intensity, then we defined that grazing intensity to be the threshold value for the grid cell, such that intensities above that value would lead to degradation.”) somehow find their way into the body of the manuscript? This is really the heart of the matter, the means in which you defined a threshold value (the confusing name aside). A summary of this approach in the Introduction or Results would be very helpful, and require just a few words (e.g., a threshold was crossed when NPP was < 0 [but using another word for “threshold”]).\nRe: Yes, according to the reviewer’s suggestion, we defined the grassland degradation and grazing intensity threshold criterion in the introduction section as mentioned above (also see lines 107-112).\nThrough the whole text, we made clearly that the “threshold” means “grazing intensity threshold”. It could induce heavily grassland degradation based on productivity, leading to the collapse of grasslands. It is not the threshold for grassland ecosystem degradation itself.\nReviewer #2 (Remarks to the Author):\nThe manuscript entitled “Early warning of grazing-induced thresholds of grassland degradation on the Qinghai-Tibetan Plateau”, by Zhu et al. addresses the impact of grazing intensity on net primary productivity (NPP) along the Qinghai-Tibetan Plateau (QTP) grassland ecosystems, using a process-based ecosystem modeling. The particular objectives were (i) to determine the grazing intensities that induce an abrupt reduction in NPP, (ii) to apply these findings to identify grassland areas under threat of degradation, and (iii) to predict when such degradation might occur under current and future grazing and climate conditions. The main results indicate that most grasslands of the QTP are managed with grazing intensities below those that induce the abrupt reduction in NPP, and that maintaining the grazing intensity around 60% of that induces the abrupt change has the potential to sustain the human demand without inducing grassland degradation. These aspects are critical since this extensive area is mostly covered by grasslands that sustain millions of domestic herbivores, which in turn are the livelihoods of millions of people. The manuscript is clear and the study is well performed. The findings are novel and have the potential to generate some international interest in its field of study. However, there are some issues that should be addressed.\nRE: Thanks for the reviewer’s positive feedbacks and constructive comments! Our responses are listed below point by point.\nMy main concern is the unique ecological attribute and the relationship to estimating thresholds (grazing intensity and NPP), which can lead to misunderstanding degradation processes since the study did not consider other critical ecological attributes and other aspects of grazing management beyond the stocking rate (here grazing intensity). On the one hand, degradation processes can be evidenced through ecosystem attributes different from NPP, which can be more appropriate to early detect degradation (e.g. Kéfi et al., 2007; Berdugo et al., 2017, 2020; Oñatibia & Aguiar, 2023). For example, degradation may occur without inducing changes in NPP (see Verón et al., 2006). On the other hand, grazing management strategies are much more complex than grazing intensity. For example, grazing and rest periods and the spatial distribution of herbivores in the landscape are key factors to control land degradation and promote productivity (see e.g. Briske, 2017). I suggest the authors expand the section on potential limitations of the study including a discussion of these issues.\nRE: We totally agree with the reviewer that the criteria for grassland degradation should be evaluated comprehensively based on different indicators related to plant and soil of grassland ecosystem, including composition, structure, and function of the grassland ecosystem. In this study, we took NPP as the indicator of grassland degradation as this is an integrated and principal indicator, especially for regional evaluations. We also agree that grazing management strategies are much more complex than grazing intensity.\nAccording to the reviewer’s suggestion and the provided references, we expanded the discussion on potential limitation of our study in the discussion section related to above issues as below in the revised version. (see lines 309-311 and lines 277-279).\n“Refinement of our method of calculating the GI threshold for grassland degradation will require going beyond NPP to take into account multiple indicators related to the plants and soil of the grassland ecosystem 10, 11, 41, 46, 47, 48.”\n“Although we focused on GI in this study, grazing management strategies need to consider additional factors affecting land degradation and productivity, such as the time of grazing and resting and the spatial distribution of herbivores40.”\nBesides, both contextualizing the study and comparing the main findings with existing global literature (other than just for the QTP) on early warning signals of degradation and ecosystem thresholds are also recommended.\nRE: Yes, we agree. Although many experimental studies conducted at site or local scale have evaluated the effects of grazing on grassland structure, soil qualities and other parameters related to productivity, few studies have simultaneously investigated the status of grassland degradation and grazing activities across the entire QTP. Although many previous reports of the percentages of the entire QTP grasslands that have been degraded (Cai et al., 2015; Cao et al., 2019; Dong et al., 2013; Feng et al., 2010; Liu et al., 2008; Ma et al., 1999; Ren et al., 2013; Sheehy, 2001; Wang, et al., 2015; Wu et al., 2009; Wu et al., 2014; Zhou et al., 2023), the mention percentages were simply taken from earlier work that did not undergo peer review.\nSome studies have analyzed grassland degradation on a global scale, thereby including the QTP without focusing on it. Such work may provide insights relevant for the QTP with a general information, but comparing it with our study is a little bit difficult since the authors did not report any data specifically for the Plateau (Bardgett et al., 2021; Gang et al., 2014; Gibbs et al., 2015; Kwon et al., 2016).\nIn our study, the overgrazing status is defined as grazing intensity lager than the grazing intensity threshold. Our results showed that the overgrazing area percentage was about 20%. Considering the time until degradation across this overgrazing area, 83.0% of the area was within 20 years and 96.2% of the area was within 40 years (Figure 4e). Since we applied the grazing activities from year of 1980, and that means the overgrazing area may have already been degraded.\nIn fact, grassland degradation could be divided into different catalogs, including lightly degraded, moderate degraded and heavily degraded grasslands. However, these catalogs may not have standard definitions. In this study, we made a definition based on NPP and focused on heavily degraded scenarios, which would cause ecosystem collapse. Our results figured the most serious overgrazing status based on this heavily degraded condition.\nBased on the collected references, the rates of grassland degradation and overgrazing on the QTP or parts of the QTP were summarized in the table as below (Table R2.1, Table R2.2).\nTable R2.1 Collected grassland degradation rate on the whole or parts of the QTP (NA: Not Available)\nTable R2.2 Collected grassland overgrazing rate on the whole or parts of the QTP (NA: Not Available)\nOvergrazing rate declared\nStudies Period Methods Reference\nPercentage (Based area)\nBased on grassland\npotential productivity\nYang and Yang 2000 28.6% (QTP) 1990s -\ncalculated using Miami\nmodel\nBased on grassland\npotential productivity\nQian et al. 2007 16% (Qinghai) & 78% (Tibet) 2003~2004 -\ncalculated based on\nNDVI data\n27~89% Yang and Yang 2000\nZhang et al. 2014 1996~2008 NA\n(Different regions across QTP) Qian et al. 2007\nSupervision report of national grasslands in 2006 39% (Qinghai) & 38% (Tibet) 2006 NA\nSupervision report of national grasslands in 2007 38% (Qinghai) & 40% (Tibet) 2007 NA\nSupervision report of national grasslands in 2008 37% (Qinghai) & 38% (Tibet) 2008 NA\nSupervision report of national grasslands in 2009 26% (Qinghai) & 39% (Tibet) 2009 NA\nSupervision report of national grasslands in 2010 25% (Qinghai) & 38% (Tibet) 2010 NA Ministry of Agriculture of People’s Republic\nof China\nSupervision report of national grasslands in 2011 25% (Qinghai) & 32% (Tibet) 2011 NA\n(Report could be obtained from\nSupervision report of national grasslands in 2012 16% (Qinghai) & 29% (Tibet) 2012 NA\nhttp://www.moa.gov.cn/\nSupervision report of national grasslands in 2013 14% (Qinghai) & 22% (Tibet) 2013 NA or http://www.forestdata.cn/index.html)\nSupervision report of national grasslands in 2014 13% (Qinghai) & 19% (Tibet) 2014 NA\nSupervision report of national grasslands in 2015 13% (Qinghai) & 19% (Tibet) 2015 NA\nSupervision report of national grasslands in 2016 11.9% (Qinghai) & 16% (Tibet) 2016 NA\nSupervision report of national grasslands in 2017 9.8% (Qinghai) & 13% (Tibet) 2017 NA\nAccording to the reviewer’s suggestion, we made a comparison between our results and that showed in other collected studies in the discussion section as below in the revised version (see lines 227-232).\n“Our simulations suggest that overgrazing and degradation affect around 20% of QTP grasslands, based on ratios of GI to the threshold. This figure is comparable to the multi-year mean grassland overgrazing rates of 16% for Qinghai and 24% for Tibet between 2010 and 2017, though several studies have suggested degradation rates of 21-40% across the entire QTP from the 1980s to 2000s (Supplementary Information 2). Our results figured the most serious overgrazing status based on heavily degraded scenarios.”\nBased on the collected literatures, we also added a short review (including two tables above) on the degradation and overgrazing status of grassland on the QTP as an additional SI section in the revised version (see SUPPLEMENTARY INFORMATION 2 (SI02)).\nAlso, in the revised version, we modified the title as “A productivity-based grazing intensity threshold as the early warning signal for grassland degradation on the Qinghai-Tibetan Plateau” to avoid confusion. In this study, “threshold” means the “grazing intensity threshold” that induces grassland heavy degradation, but not the threshold for grassland ecosystem degradation itself. We think the early warning is specified to the grazing intensity threshold, based on e.g. “time until degradation”, and we hope the study can provide an early implication for grazing activities management on the QTP and preventing the grassland from degradation. (as below or see lines 275-277).\n“From an early warning signal of the grazing intensity threshold determined by “time until degradation”, an early implication was supposed to provide for grazing activities management on the QTP and preventing the grassland from degradation.”\nRegarding the model validation, the field sites are relatively few and cover a small area compared to the large extent of the whole study area. This weakness is not fatal, but the authors should discuss this aspect, maybe also expanding the section about the potential limitations of the study.\nRE: Yes, according to the reviewer’s comments, we expanded the discussion about the limited covering data on model performance evaluation as below in the revised version. (see lines 326-327).\n“Finally, the model performance evaluation should be improved by including more field data covering the entire QTP.”\nAlso, we added a comparison of multi-year average NPP between that simulated in this study and retrieved from remote sensed data of AVHRR and MODIS at grid scale across QTP as below in the revised version (see lines 529-536, Figure S3.9).\n“Multi-year average NPP (2000-2015) was compared between simulated in this study and the productions retrieved from remote sensed data of AVHRR and MODIS at grid scale across QTP (Supplementary Fig. S3.9) for a reference of model performance evaluation. The simulated NPP values agreed better with the AVHRR data (R2 = 0.62, Supplementary Fig. S3.9b) than with the MODIS data (R2 = 0.47, Supplementary Fig. S3.9a). Simulated NPP values showed a coefficient of determination of nearly 0.6 when compared with NPP values averaged between the MODIS and AVHRR datasets (Supplementary Fig. S3.9c). The AVHRR data usually contained greater NPP values than the MODIS data (Supplementary Fig. S3.9d).”\nFinally, a minor issue is that the treatment given to the term “threshold” is vague and sometimes unclear (e.g. lines 67, 74, 78, 88, 102, and see specific comments below). This is important since a central role is given to this term throughout the manuscript.\nRE: Yes, in the revised version, we clearly defined grassland degradation and grazing intensity threshold criterion adopted in our study in the introduction section as below (see lines 107-112).\n“We adopted net primary productivity (NPP) as an indicator of grassland degradation, which has proven to be a good estimator of ecosystem functioning 33 and land degradation 34. We assumed that grassland productivity would decrease with increasing GI, and we defined the GI threshold to be the GI at which NPP fell to 1% of the pregrazing level that was considered as an extreme degradation status for grassland (see Methods).”\nThrough the whole text, we made clearly that the “threshold” means “grazing intensity threshold” that could induce grassland degradation based on productivity, but not the threshold for grassland ecosystem degradation itself.\nFor the line 67 (in original version), we revised the sentence as “Even slight changes in climate and human factors can substantially affect the structure and functioning of alpine grasslands on the QTP 14, 15, 16, leading to sudden, potentially irreversible changes in ecology and functioning 17, 18”. (see lines 63-65).\nFor the line 74 (in original version), we revised the sentence as “Given the pertinence of grazing to QTP grasslands 8, 19, we propose to develop a “grazing intensity (GI) threshold” which would induce an extreme grassland degradation to provide an early implication for grazing activities management.” (see lines 70-72).\nFor the line 78 (in original version), we revised the sentence as “…based on a predicted GI threshold that could induce grassland degradation may support…” (see lines 74-75).\nFor the line 88 (in original version), we revised the sentence as “…in depth understanding of a GI threshold that could induce grassland degradation at regional scale across the QTP…” (see lines 86-87).\nFor the line 102 (in original version), we revised the sentence as “…a grazing framework for grassland ecosystems was modified and integrated into a process-based model to explore a GI threshold inducing grassland degradation on the QTP…” (see lines 105-107).\nIn general, it is not completely clear how it relates to an early warning signal for grassland degradation. Definitions of threshold and degradation (and their relationship) from the introduction will help to understand the main ideas addressed. A more precise alternative could be directly referring to grazing-induced abrupt NPP reduction instead of threshold and degradation.\nRE: Yes, according to the reviewer’s comments, we clearly defined the criterion of grassland degradation and grazing intensity threshold adopted in our study in the introduction section as below in the revised version. (see lines 107-112).\n“We adopted net primary productivity (NPP) as an indicator of grassland degradation, which has proven to be a good estimator of ecosystem functioning 33 and land degradation 34. We assumed that grassland productivity would decrease with increasing GI, and we defined the GI threshold to be the GI at which NPP fell to 1% of the pregrazing level that was considered as an extreme degradation status for grassland (see Methods).”\nAlso, we revised the title as “A productivity-based grazing intensity threshold as the early warning signal for grassland degradation on the Qinghai-Tibetan Plateau” to avoid confusion. In this study, “threshold” means the “grazing intensity threshold”, but not the threshold for grassland ecosystem degradation itself. We think the early warning is specified to the grazing intensity threshold, based on e.g. “time until degradation” (Figure R2.1), and we hope the study can provide an early implication for grazing activities management on the QTP and preventing the grassland from degradation.\nFigure R2.1 (also Figure S3.5) Schematic of how changes in net primary productivity (NPP) of grasslands subject to different grazing intensities (GIs) can be used to determine “grazing intensity (GI) threshold” and “time until degradation”. The process assumed that grassland productivity would decrease as GI increased. The model was run under different GIs, from non-grazing (GI = 0.0 SU ha-1 year-1) to maximum possible grazing (GI = 15 SU ha-1 year-1), increasing in a step of 0.5 SU ha-1 year-1 for a period of 120 years. When NPP fell to 1% of the original NPP before application of the GI, that GI was defined as the GI threshold. The interval between onset of GI and when NPP fell to 1% of the original NPP was defined as the time until degradation.\nSPECIFIC COMMENTS\nLines 1-2. I suggest revisiting the title after considering the comments on what thresholds represent and how they are related to early warning signals of grassland degradation.\nRE: Yes, according to the comment, we revised the title as “A productivity-based grazing intensity threshold as the early warning signal for grassland degradation on the Qinghai-Tibetan Plateau” to avoid confusion. We think the early warning is specified to the grazing intensity threshold, based on e.g. “time until degradation”, and we hope the study can give an early implication for grazing activities management on the QTP and preventing the grassland from degradation.\nLines 26-27. Suitable threshold? Threshold of what? Shouldn't it be “suitable grazing intensity”?\nRE: Yes, we rephrased the abstract in the revised version. (see lines 23-26).\n“Based on an improved process-based ecosystem model, the present work defined a productivity-based grazing intensity (GI) threshold that induces grassland extreme degradation to assess whether and where current grazing activity is sustainable.”\nLines 50-51. The authors indicate that QTP grasslands are very fragile. Please, justify this statement and add citations. If this information is that found in lines 64-66, please, avoid repetition.\nRE: Yes, we added references for this statement. And deleted the redundant information in the text.\nLines 58-62. Please define degradation. A definition of what grassland degradation represents to the authors would help make this section clearer.\nRE: Yes, we removed some reductant information and rephrased this part as below in the revised version. (see lines 51-56).\n“Earlier in history, animal farmers tended to maintain relatively small herds just large enough to support their nomadic lifestyle, with some extra animals to buffer the effects of harsh weather. The grazing of these small herds was sustainable because it did not lead to irreversible grassland degradation, defined as a decline in the quality of plants and soils, as well as changes in ecosystem composition, structure, and function 10, 11.”\nWe also added definition of grassland degradation used in this study in the final part of the introduction section as mentioned above. (also see lines 107-112).\nLine 140. Remove “that”.\nRE: Yes, it’s revised as suggested.\nLines 220-227. Areas described as overgrazed seem to be generally the most arid (e.g. northwest). If this is correct, authors can discuss the potential interaction between increasing grazing and aridity (see e.g. Gaitán et al., 2018; Oñatibia et al., 2020).\nRE: Thanks for the suggestion. We agree that aridity (or drought trend) and grazing pressure may have a negative synergistic effect on dryland ecosystem as mentioned in the study of Gaitán et al. (2018) and Oñatibia et al. (2020). For the grassland ecosystem on QTP in this study, we found a warming and humidification trend (Figure S2.1). We also checked the multi-year mean precipitation of the periods of 2000-2009 and 2010-2017 and the maps showed below (Figure R2.2a, Figure R2.2b). The precipitation in northwest area of the QTP mostly ranged from 300 to 400 mm/year and the area with precipitation in this range is expanding (Figure R2.2a, Figure R2.2b). A Palmer Drought Severity Index (PDSI) map for the QTP was extracted from a global PDSI data set created by van der Schrier et al. (2013), which was downloaded from ftp://ftp.ceda.ac.uk (Figure R2.2c). Based on the classification of the PDSI (Table R2.3), we can see most area of the northwest area of the QTP is not in dry condition. Therefore, the aridity may not be enhanced for the northwestern part during the studying period.\nFigure R2.2 a. Multi-year mean precipitation on QTP for the period of 2000-2009; b. Multi-year mean precipitation on QTP for the period of 2010-2017; c. PDSI map of QTP extracted form data set of PDSI_CRU_TS_3.10.01_based between 2001 and 2009, ftp://ftp.ceda.ac.uk)\nTable R2.3 PDSI classification in the study of van der Schrier et al. (2013)\nLines 366-368. If grazing intensity is the annual average, its units could be SU/ha/year (stocking rate units) instead of density units (SU/ha).\nRE: Yes, we revised the unit as suggested SU/ha/year all through the text, figures and tables.\nLine 425. I suggest moving this Figure with an expanded legend to the main manuscript.\nRE: Yes, the grazing intensity threshold map under future climate change was presented in the main text as Figure 6.\nLine 608-609. I suggest that authors indicate what the white color represents on the map; if it means the absence of data or if represents other vegetation types different from steppes and meadows.\nRE: The white color on the map represents other land cover types different from steppes and meadows. We added this information in all the related Figure legends in the paper as suggested.\nFigure legends. Information is generally lacking for the figures to be self-explanatory, particularly those in the supplementary material.\nRE: Yes, we added more information on figure legends or on the figures themselves in the revised version.\nReviewer #3 (Remarks to the Author):\nThe manuscript “Early warning of grazing-induced thresholds of grassland degradation on the Qinghai-Tibetan Plateau” by Zhu & Chen et al. attempts to identify grazing intensity thresholds that would lead to grassland degradation. The question is of broad relevance, and such results could potentially be used as the basis for future grazing management in the area.\nRE: Thanks for the reviewer’s positive feedbacks and comments and the responses are listed below point by point.\nOverall the manuscript did not provide enough information for me to assess the results and conclusions drawn critically. The model, in particular the vegetation dynamical component, is not sufficiently described in either the main text or supplementary materials to allow appropriate assessment of the presented results and conclusions. The authors refer to two published manuscripts (Zhang et al., 2017; Zhu et al., 2014). I have endeavoured to follow the cited manuscripts to gain a better understanding of the model used but have done so unsuccessfully. In Zhu et al., (2014), the model description leads to a string of further citations: “The TRIPLEX- GHG model (Peng et al., 2013) is based on the legacy of well-established and published models that include IBIS (Foley et al., 1996), DNDC (Li, 2000), TRIPLEX (Peng et al., 2002) and CASACNP (Wang et al., 2010). ”. The cited Peng et al. (2013) paper does not appear to exist. At best, I can find an abstract submitted to the European geoscience Union (EGU), which is associated with TRIPLEX-GHG. This manuscript, however, indicates that IBIS represents the vegetation model component. Zhang et al. (2017) explicitly cite the above-mentioned EGU abstract (Integrating greenhouse gas emission processes into a dynamic global vegetation model of TRIPLEX-GHG) at the beginning of the description of the model. Model improvements by (Liu et al., 2005) are also mentioned here. Based on the equation for NPP provided by the authors (line 303), I am guessing that the model version they are using is not that developed by Liu et al. (2005) and does not include any nutrient constraints on growth or photosynthesis. This is guesswork, though, and I did not see any code submitted, which could have been checked.\nRE: Thanks to the reviewer for the comments. We apologized for the lack of information on the vegetation dynamical component. The vegetation dynamical module, land surface module, plant phenology module, and soil biogeochemical module of TRIPLEX-GHG is primary based on the model of IBIS (Foley et al., 1996; Kucharik et al., 2000). We declared this in our previous paper of Zhu et al. (2014) and Zhang et al. (2017). And currently we have added different modules into the model including greenhouse gases emission (CH /N O) process, nitrogen and phosphorus cycling\n4 2\nprocess, wetland area dynamic process, peatland carbon accumulation process, and grassland grazing process, etc. The two citations of the model (Peng et al., 2013 on North America Carbon Program and Peng et al. 2013 on EGU) mentioned by the reviewer were presented on two meetings with the purpose of model prototype introduction which were not peer reviewed. The paper of Zhu et al. (2014) is the first publication for the model of TRIPLEX-GHG. Model improvements by Liu et al. (2005) (who is the fourth author of this paper) are fully coupled in current version (also referred in Zhu et al. 2014 and Zhang et al. 2017) and applied in this study. So, nitrogen constraints on NPP were considered in this study. In the revised version, we added more information about the model description in the methodology section as below in the revised version (see lines 332-347).\n“The TRIPLEX-GHG model is a dynamic global vegetation model that takes into account land surface processes, vegetation phenology, long-term vegetation dynamics, soil biogeochemical processes, greenhouse gas emissions, cycling of nitrogen and phosphorus, wetland area dynamics, and peatland carbon accumulation 56, 57, 58, 59. The TRIPLEX-GHG modules of vegetation dynamics, land surface, plant phenology, and soil biogeochemistry are based primarily on the Integrated Biosphere Simulator (IBIS) model 60, 61 (Supplementary Fig. S3.3). The vegetation in TRIPLEX-GHG was represented by plant functional types (PFTs) and two types of grasses (C3 and C4) were included. Vegetation dynamics was characterized by PFT changes in terms of biomass and leaf area index, with competition among PFTs for sunlight and water and sunlight. Plant phenology was modeled based on the coldest temperatures, thresholds for accumulated growing degree days, and thresholds for 10-day average temperature. Canopy photosynthesis was modeled based on the Farquhar model to calculate gross primary productivity (GPP) and net primary productivity (NPP) for each PFT 60, 61. An improvement considering nitrogen constraints on estimation of canopy NPP was adopted by applying a soil N availability modifier and dynamic C:N ratios in order to capture how the carbon cycle responds to dynamic nitrogen availability 58.”\nAnd, we provided the source code with example input data in the manuscript submission system for the reviewers’ information.\nOne of the major results presented by Zhu & Chen et al. is that the positive effects of climate change can offset, to a large extent, grazing. Based on the evidence presented, it is not clear whether such positive effects are as a result of temperature, precipitation, or increasing CO . Presumably (this is not stated), the authors have turned off all other\n2\nplant functional types other than temperate C3 grasses or similar for the present study. The extent to which CO may fertilise C3 grasses is uncertain (Reich et al., 2018). It is\n2\nnot clear what IBIS version the authors used and whether this version limits photosynthetic rate via any mechanism, e.g. nutrient limitation.\nRE: Good question. In this study, we did not consider the precipitation, temperature, and increasing CO concentration separately and then to evaluate the contribution of\n2\neach factor. We considered climate change and increasing CO concentration together\n2\nas an integral impact factor. In current study, we focused on the issues of grazing intensity threshold detection for grassland degradation on the QTP under the changes of the external environment (including climate and CO concentration together) as a\n2\nwhole. We agree with the reviewer that the contribution of different factors to grassland productivity and to the offset effects on the grassland of QTP is worth studying in depth in the future.\nWe added a short discussion on this issue in the potential limitation as below in the revised version. (see lines 323-325).\n“Evaluating the individual contribution of different factors (e.g. precipitation, temperature and raising CO concentration) to the thresholds also could make a better\n2\nunderstanding in grassland ecosystem degradation mitigation under climate change conditions.”\nIn this study, we did not turn off all other plant functional types, and only analyzed the results related to the plant function type of grass to focused on the issues of grazing intensity threshold for grassland degradation on the QTP.\nFor the point that the uncertainty of CO fertilization effects on C3 grasses (Reich\n2\net al., 2018) mentioned by the reviewer, we added this in the discussion section as a potential limitation as below in the revised version. (see lines 321-323).\n“Refinement of our approach should also consider uncertainties in how grasses respond to increasing CO concentration 55 and to potential constrains of nutrients (e.g.\n2\nnutrient removal by grazing).”\nAlso, in the revised version, we added more information about the model description on the vegetation dynamical component and nitrogen constrain process (Liu et al. 2005) as mentioned above. (also see lines 332-347).\nThere are no future change simulations such as those detailed in Tab.S1.1 that include grazing, and changing climate, but have CO fixed. Such simulations would have\n2\nallowed assessment of the magnitude to which the CO fertilization effect was\n2\nresponsible for the positive effects of climate change.\nRE: For the future simulations, our aim was to investigate the patterns of grazing intensity threshold under different Representative Concentration Pathways (RCPs) and make a comparison with that under current climate and CO concentration. The grazing\n2\nintensity threshold was determined by multiple simulations with a grazing intensity gradient from 0 SU/ha/year to 15 SU/ha/year (increasing by a rate of 0.5 SU/ha/year). There were 124 simulations (4 scenarios * 31) all together at the scale of whole QTP. Different from the simulations listed in Table S1.1, we don’t have annual grazing map for the period of 2020-2100 unfortunately, so we currently can’t evaluate the offset effects of future climate change and CO concentration.\n2\ntogether as an integral impact factor to see the changing patterns of grazing intensity threshold in the future. We agree that the climate change and CO fertilization effects\n2\nmay have different contribution on the grazing intensity patterns changing, but currently, we only considered the changes in the external environment (including climate and CO\n2\nconcentration) as a whole and focused on what kind of grazing intensity should be applied under that circumstance in grazing management.\nWe added a short discussion on this issue in the potential limitation as below in the revised version. (see lines 323-325).\n“Evaluating the individual contribution of different factors (e.g. precipitation, temperature and raising CO concentration) to the thresholds also could make a better\n2\nunderstanding in grassland ecosystem degradation mitigation under climate change conditions.”\nFurther, potential interactions between nutrients, plant growth and photosynthesis, and nutrient removal via grazing do not appear to be included in the model.\nRE: For the issues mentioned by the reviewer that potential interactions between nutrients and plant growth, we currently only include the nitrogen constrain in the model. However, nutrient removal via grazing was not considered in the current version of the model, and we added this potential limitation in the discussion section as below in the revised version. (see lines 321-323).\n“Refinement of our approach should also consider uncertainties in how grasses respond to increasing CO concentration 55 and to potential constrains of nutrients (e.g.\n2\nnutrient removal by grazing).”\nAdditionally, the grazing threshold used (NPP close to zero) is not defined well. I remain uncertain about what “close” means. This threshold would need to be accurately defined and extensively justified as it is a pivotal metric in the manuscript.\nRE: Yes, in the revised version, we defined grazing intensity threshold for grassland degradation based on the indicator of NPP in the introduction section, with an accurate statement specified in the text as below. (see lines 107-112).\n“We adopted net primary productivity (NPP) as an indicator of grassland degradation, which has proven to be a good estimator of ecosystem functioning 33 and land degradation 34. We assumed that grassland productivity would decrease with increasing GI, and we defined the GI threshold to be the GI at which NPP fell to 1% of the pregrazing level that was considered as an extreme degradation status for grassland (see Methods).”\nOverall the authors attempt to answer a highly relevant question, but the model (perhaps) used is not yet developed to the extent to sufficiently answer it. In any case, the authors have not provided enough information for me to critically asses the methodology.\nRE: We are sorry for missing the information on the model description. Yes, according to the reviewer’s comments, we added more information about the model in the methodology section for a better understanding of the modeling method as mentioned before (also see lines 332-347).\nIn the method section, we also added more information on how the threshold was detected as below in the revised version (see lines 480-502).\n“We assumed that grassland productivity would decrease with increasing GI. In order to detect the GI threshold for grassland degradation in each grid cell, the model was run under different GIs, from non-grazing (GI = 0.0) to maximum possible grazing (GI = 15 SU ha-1 year-1), increasing in a step of 0.5 SU ha-1 year-1 for a period of 120 years from the grazing activities applied on (Supplementary Fig. S3.5). Altogether 31 simulations were performed over the entire QTP. When the NPP of a cell reached 1% of the baseline NPP before the GI was applied, we defined that GI to be the GI threshold for the grid cell, above which GI would lead to degradation based on the indicator of NPP (Supplementary Fig. S3.5). This simulation strategy was performed on each grassland grid cell (~10km*10km) of the QTP. Using this approach, and 3720 annual NPP maps (31 scenarios × 120 years) of grassland on the QTP were used to generate a map of GI threshold and a map showing how many years before the grassland in each grid cell at the indicated GI (“time until degradation”). These simulations under different GIs were run with average climate and CO conditions between 1980 and 2017.\n(Supplementary Table S3.3). A fourth scenario was considered in which climate conditions and CO concentration were held constant at their 2020 levels. A total of\n2\n31 simulations across QTP grasslands were performed for each scenario, resulting in GI threshold maps. Model driving data on precipitation, temperature, relative humidity, and radiation of three RCPs were averaged from 24 General Circulation Models (GCMs) at a spatial resolution of 0.5 (Supplementary Table S3.4).”\nAnd, we provided the source code with example input data in the manuscript submission system for the reviewers’ information.\nMinor comments:\nThere was a general lack of transparency in the description of model and methods.\nRE: Yes, according to the reviewer’s comments, we added more information about the model in the methodology section (see lines 332-347). In the method section, we also added more information on how the threshold was detected (see lines 480-502).\nAnd, we provided the source code with example input data in the manuscript submission system for the reviewers’ reference.\nText size often changes within sentences and at many points in the manuscript e.g. lines 71-72.\nRE: Yes, we reformatted the text all through the paper in the revised version.\nThere were many grammatical and language errors which could have been easily identified prior to submission. I won’t list all of these as there are tools available that could be used to flag such errors.\nRE: Yes, we have double checked the text and a language editing service was also applied for this revised version.\nLine 61 - “leading to degradation” a concise definition of degradation is necessary.\nRE: Yes, for lines from 58 to 62 in original version, we removed some reductant information and rephrased this part as below in the revised version (see lines 51-56).\n“Earlier in history, animal farmers tended to maintain relatively small herds just large enough to support their nomadic lifestyle, with some extra animals to buffer the effects of harsh weather. The grazing of these small herds was sustainable because it did not lead to irreversible grassland degradation, defined as a decline in the quality of plants and soils, as well as changes in ecosystem composition, structure, and function 10, 11.”\nAs mentioned above, we also added definition of grassland degradation used in this study in the final part of the introduction section as mentioned before. (also see lines 107-112).\nLine 63 – “In parallel, the productivity of alpine grasslands has fallen by 30% since late 1990s 8, 12 “. The studies cited here are in relation to the QTP and the authors here indicate that productivity has decreased since the 1990s. The results presented by the authors (S1.1 a) indicate that productivity (NPP) has increased over the majority of the QTP for this approximate time period based on their simulations. I may have missed something; how do the authors explain this apparent contradiction?\nRE: Thanks to the reviewer for pointing out this. We have double checked the references and found that Dai et al. (2019) cited this number (i.e. 30%) from Dong et al. (2012). However, we did not find the specific percentage for productivity decreasing of QTP, while the study of Dong et al. (2012) focused on the growing season changes on QTP and showed that there was a significant increasing trend for the regional average growing season length during last 50 years, which may imply an increasing productivity. After we reviewed more literatures, the productivity of grassland on QTP were actually reported increasing (Gang et al., 2014; Liu et al., 2019; Wang et al., 2022). After reviewing and double checking the collected references, we deleted this statement in the revised version.\nLines 91-100 Numerous changes in text size.\nRE: Yes, we reformatted the text in the revised version.\nLines 91-100 This paragraph sets the reasoning for the present study. The issue mentioned is that it is not clear whether local measurements can be extrapolated to regional scales. The authors should provide more detail about how their modelling approach can do this better and be more explicit about which processes they expect to be able to represent better with their approach.\nRE: Yes, we rephrased this part and highlight the advantages of process-based modelling on the aspects of spatial and temporal scale extrapolation over large region as below in the revised version (see lines 96-104).\n“As a result, so-called “process-based ecosystem modeling”, which integrates information about biogeophysics, biogeochemistry, plant phenology, vegetation dynamics, as well as cycling of carbon, water and energy on the land surface, has shown promise for simulating interactions among vegetation, climate, and human activities 30, 31, making it well-suited to detect productivity changes in specific ecosystems 32. Conducting long-term simulations at grid scale across the whole QTP using a process-based ecosystem model that consider the interaction of different processes among atmosphere, vegetation and soil could provide more insights into the spatial heterogeneity of ecosystem changes and could be extended into the past or the future to take climate change into account.”\nWe also emphasized it in the discussion section as below in the revised version (see lines 281-286).\n“For ensuring sustainable grazing across the entire QTP, our threshold approach based on simulations at the grid scale may prove more sophisticated and more effective in the long run, especially after further refinement, than traditional approaches based on large-scale inventories or local grazing data. Our GI threshold grids can be extended backward in time to assess whether areas have historically been overgrazed, and they can be projected into the future to estimate grazing sustainability in the face of changing climate and elevated CO .”\nRE: Yes, we reformatted the text in the revised version.\nLines 101-106 Given that one of the main objectives of the study was to define a grazing intensity threshold, it would be good to have this threshold defined and justified here. It remains uncertain to me exactly how this threshold is defined and what its jusfitication is. Future change scenarios: “Spatial pattern of future grazing intensity thresholds on QTP grasslands” (line 186) & Fig. 6 (line 646) – to what extent are the simulated increases in grazing intensity thresholds driven by increasing CO alone?\n“We adopted net primary productivity (NPP) as an indicator of grassland degradation, which has proven to be a good estimator of ecosystem functioning 33 and land degradation 34. We assumed that grassland productivity would decrease with increasing GI, and we defined the GI threshold to be the GI at which NPP fell to 1% of the pregrazing level that was considered as an extreme degradation status for grassland (see Methods).”\nIn the method section, we added more information on how the threshold was detected as mentioned before (also see lines 480-502).\nFor the future simulations, our aim was to investigate the patterns of grazing intensity threshold under different Representative Concentration Pathways (RCPs) and make a comparison with that under current climate and CO concentration. In this study,\n2\nwe just considered climate change and increasing CO concentration together as an\n2\nintegral impact factor and did not separately evaluate the individual contribution of each factor (precipitation, temperature, and increasing CO ) to the thresholds.\n2\nas a whole and emphasized what kind of grazing intensity should be applied under that circumstance in grazing management.\nWe added a short discussion on this issue in the potential limitation as below in the revised version. (see lines 323-325).\n“Evaluating the individual contribution of different factors (e.g. precipitation, temperature and raising CO concentration) to the thresholds also could make a better\n2\nunderstanding in grassland ecosystem degradation mitigation under climate change conditions.”\nFigure 5 is very difficult to understand.\nRE: Yes, we revised Figure 5 and rephrased the corresponding section. We cited the percentage numbers as showed exactly in the figure into the main text to avoid confusion as below in the revised version. (see lines 171-196).\nWe made an additional simulation that the grazing intensity was changed to make ratio of GI to threshold was reduced to 0.6 in areas where the ratio was > 0.6. The results were also added to Figure 5 to show a suitable ratio of GI to threshold (60%, ranged from 50% to 70%) may maintain the balance between grassland protection and human demands. (also see lines 171-196).\n“Simulations suggested that climate change and elevated atmospheric CO\n2\nconcentration would partly offset the negative effects of grazing activities on 68.3% of QTP grasslands (Figure 5a, b and Supplementary Information 1), nearly all of which (98.5%) was subject to normal grazing and which accounted for 83.6% of the total grassland area subject to normal grazing (Figure 5b). Across these “offset” areas, 69.2% showed a ratio of GI to the threshold below 0.6, while 83.0% showed a ratio below 0.7 (Figure 5c). The beneficial offsets were observed in only 5.3% of overgrazed areas (Figure 5b), 76.9% of which featured ratios of GI to the threshold below 3.0 (Figure 5d). Among the remaining 31.7% of QTP grassland areas where climate change and elevated CO were not predicted to offset negative effects of grazing (Figure 5b), 58.5%\n2\nwere overgrazed (Figure 5b), with ratios of GI to threshold from 2.0 to 6.0 (Figure 5e), and 41.5% were subject to normal grazing (Figure 5b), of which 71.3% showed a ratio of GI to threshold larger than 0.6 and 88.4% showed a ratio of GI to threshold larger than 0.5 (Figure 5f). Although the areas in “non-offset areas” under normal grazing, the positive effects of climate change and elevated CO could not compensate the\n2\nnegative effects of grazing on grassland productivity.\nOur model suggested that setting the ratio of GI to threshold between 0.5 and 0.7 would preserve 70-80% of the areas that were currently subject to normal grazing and were predicted to experience positive offsets from climate change and CO (Figure 5c,\n2\ng). This range from 0.5 to 0.7 showed the greatest potential for maximizing the areas subject to normal grazing or overgrazing (both currently in “non-offset areas”) that could benefit from positive offsets due to climate change or CO (Figure 5b, e, f). When\n2\nwe reduced the ratio of GI to threshold to 0.6 in areas where the ratio was > 0.6 in an additional simulation, we found that the positive effects of climate change and elevated CO could offset the negative effects of grazing on over 89.1% of QTP grassland areas\n2\n(Figure5 h), by “converting” 70% of currently “non-offset areas” into “offset areas” (Figure 5b, h). The “converted” areas come from 78% of overgrazing area and 58% of normal grazing area (Figure 5b, h).”\nThe revised Figure 5 showed as below (Figure R3.1):\nFigure R3.1 (also Figure 5) Ratios of actual GI to the threshold on QTP grasslands. a. Distribution of areas where the negative effects of grazing are predicted to be offset by the positive effects of climate change and elevated CO on grassland NPP (“offset areas”) and areas where such offset is not predicted\n2\n(“non-offset areas”); b. The bar charts indicated the area percentage of “offset areas” and “non-offset areas” of the grassland on QTP, along with the area percentage (white numbers) of overgrazing (OG, red color) and normal grazing (NG, green color) over each region, as well as the area percentage of OG (red color; blue numbers) and NG (green color; yellow numbers) distributed in offset areas and non-offset areas respectively; c-d. The area percentage histogram and area percentage cumulative curve of the ratio of GI to GI threshold for the “offset areas” subject to normal grazing (NG, green color) (c) or overgrazing (OG, red color) (d); e-f. The area percentage histogram and area percentage cumulative curve of the ratio of GI to GI threshold for the “non-offset areas” of OG (red color) (e) or NG (green color) (f), and different from others, the area percentage of GI ratio in panel f accumulated decreasingly from 0.95 to 0.15; g. The merged area percentage cumulative curves of the ratio of GI to GI threshold subject to NG (green color), extracted from panel c (blue line) and f (red line). h. The bar charts indicate the area percentage of “offset areas” and “non-offset areas” of QTP grasslands, along with the area percentage (white numbers) of OG (red color) or NG (green color) over each region based on an additional simulation that was performed in which the ratio of GI to threshold was set to be 0.6 where the ratio was > 0.6.\nLines 434 – 445 It is difficult to understand. It is not clear to me what “When this comparison was repeated at the regional level after clustering sites by location” means and what was done. It is apparent from Fig. S2.6 Tab. S2.2 that the simulated grazing intensity threshold is almost always above the grazing level at the site, ca. 25/38. The authors write that potential causes for this may be the inconsistent spatial scale between the modelling grid (10km * 10km) and the grazing experimental site. Does the model simulate a 10x10 km patch of vegetation, or are they simulating something smaller (e.g. 1 Ha) and assuming this is representative of a 10x10km grid? What was the spatial scale of the site-level grazing experiments? The authors also mention that the site-level grazing experiments may have a grazing intensity that is in excess of the intensity threshold. How do they justify this? There are presumably measurements collected at these sites, e.g. grass growth (NPP), that could be used to justify this assumption. Based on the threshold methodology of the authors, wouldn't exceeding this threshold imply grass NPP at these sites is zero? Have the authors considered the alternative possibility that their model is wrong?\nRE: Thanks for the valuable points. We rephrased this section in the revised version. The comparison was made at a county scale (grouped the site with county location). We revised the Figure S2.6 (Figure S3.4 in the revised version) and marked the group setting (original Table S2.2, revised Table S3.2) on the revised Figure S3.4 (original Figure S2.4).\nWe also realized that the designed heavy level of grazing intensity in the field grazing experiment may not have substantial connection with our theoretical grazing intensity threshold. The grazing intensity designed in the different filed experiment may have different criterion and could have large variability among different sites, even though these sites are close to each other or located at the same position (Table S3.2).\nWe did the simulation at a 10km×10km spatial scale, and we can see different sites with large variation of designed grazing intensity may located in the same modeling grid cell (Table S3.2).\nIn the revised version, we stated that comparison between the heavy grazing levels designed in the field studies and grazing intensity thresholds simulated in this study as a reference to justify the modeled grazing intensity threshold, which is not a true meaning of model validation.\nWe stated in the original version of MS that the site-level grazing experiments may have a grazing intensity that is in excess of the intensity threshold. It is a possibility inference since there is no clear relation between GIs defined as “heavy” in the literature and GI thresholds defined in our study. So, we removed this statement in the revised version to avoid any misunderstanding.\nThe reviewer had a concern whether the grassland NPP would reach zero when the designed heavy grazing intensity exceeded the modeled grazing intensity threshold. In the revised version, we made a clear statement that when the grassland NPP reduces to 1% of the original NPP level before grazing applied with an application of a certain GI, then that GI was defined as the GI threshold. We also rephrased the statement of “close to zero” in the revised version to avoid misunderstanding.\nIn fact, grassland degradation could be divided into different catalogs, including lightly degraded, moderate degraded and heavily degraded grasslands. In this study, we focus on heavily degraded scenarios, the GI threshold predicted to lead to an extreme degradation, which would cause ecosystem collapse. In our study, we also evaluated the time until degradation when we proposed the grazing intensity threshold, which implied that the grassland NPP would not decrease to the specified low level immediately while grazing was applied at the intensity of threshold.\nBased on the comments of the reviewer, as well as the analysis mentioned above, we rephrased this section as below in the revised version (see lines 505-517).\n“We make a comparison between the heavy grazing levels designed in the field experiment and GI thresholds simulated in this study as a reference to evaluate the modeled GI threshold (Supplementary Fig. S3.4). Modeled GIs were compared at the county level with “heavy” GIs at 38 field sites (Supplementary Fig. S3.4, Tables S3.2) in local grazing experiments in which sites were subjected to “light”, “medium” or “heavy” GI. Although this comparison may be imperfect because GIs defined as “heavy” in the literature have an unclear relation to GI thresholds defined in our study, regional mean simulated GI thresholds were quite consistent with designed heavy grazing intensities (R2 = 0.7853, p < 0.01; Supplementary Fig. S3.6). Generally, the heavy GIs in experimental studies were slightly greater than the simulated GI thresholds (Supplementary Fig. S3.6). The difference in spatial scale between our modeling grid (~10  10 km) and local grazing experiment site (~ several hectares) meant that the same modeling grid sometimes contained multiple sites differing substantially in GIs designated as heavy in experimental studies (Supplementary Table S3.2).”\nSupplement\nFig. S2.6 - what is a) and what is b)? This is not specified.\nRE: Yes, we revised the Figure S2.6 (Figure S3.4 in the revised version) and marked the group setting (original Table S2.2, revised Table S3.2) on the revised Figure S3.4 (original Figure S2.4).\nFig. S2.7 – the difference between observed and simulated grass biomass seems rather large, up to 300% at some sites and grazing intensities. The “biomass for grazing” equations line 330/331 did not appear correctly. Perhaps this is just a rendering issue, though.\nRE: The equations of biomass for grazing was adopted from the approach provided in the study of Shiyomi et al. (2011) and we revised the function only when above ground biomass felled in the range from 𝐵𝑖𝑜𝐺𝑟𝑧 ×0.95 to 𝐵𝑖𝑜𝐺𝑟𝑧 ×0.95×2.0 in order\nt t\nto make the magnitude of biomass consumption in a continues way.\nWe also see there is a large difference between observed and simulated grass biomass at site of Maqu. Thank you for pointing out this. We double checked the five references in which the sites were all declared locating in Maqu county.\nThe original information of the five sites extracted from the references is:\nIf we put them on the map (Figure R3.2), we can see point 22 and 23 are in the wrong place, since Maqu county (the yellow region) is far below these two points (Figure R3.2). Point 24 to 26 are in the right place of Maqu county. We are sure that the original coordinate information in the references (Tan, 2012; Hu, 2015) for point 22 and point 23 must be wrong. The position should be 101.883E, 33.967N, which is the same as point 24 (Figure R3.2). As a result, we extracted the wrong biomass data for point 23 in previous version, which induced the difference in the comparison between simulation and observation. In the revised version, we made a correction for the comparison at point 23 (Figure R3.3, also Figure S3.7). We can see the overall agreement between observed and simulated biomass was also improved (Figure R3.3f, also Figure S3.7f).\nFigure R3.2 Sites location checking for Maqu county.\nFigure R3.3 (also Figure S3.7) Comparison between observed aboveground biomass (red) at experimental field sites and the corresponding simulated aboveground biomass (blue) on grasslands subject to light, medium or heavy grazing intensity.\nWe updated Table S3.2 with the correct position information for points 22 and 23, as well as the GI threshold for these two points as below (also see Table S3.2).\nWe also updated the map of sites distribution (Figure S3.4)\nGrazing intensity threshold with NPP close to zero – based on the NPP equation (line 303), wouldn’t zero NPP imply GPP was equal to maintenance respiration and there is no growth? Detailed justification for the choice of threshold is necessary as this is crucial to the manuscript. At what time-step is grass growth calculated? At what time-step grass biomass removed by grazing? I can’t figure out whether this happens at daily, monthly, or yearly time steps.\nRE: From the view of the equation, zero NPP could be happened under the condition that GPP was equal to maintenance respiration. In our study, we detected the grazing threshold for grassland degradation using the indicator of NPP (specified to 1% of the original NPP level before grazing applied). We can see the NPP will gradually decrease while grazing was applied at intensity of threshold, and finally reached a low level (Figure S3.5). We considered this “near zero” (we rephrased the statement of “close to zero” in the revised version to avoid misunderstanding) level of NPP as an indicator for grassland extreme degradation while comparing to its original level before grazing activity was applied.\nAccording to the reviewer’s comments, in the introduction section, we defined grazing intensity threshold for grassland degradation based on the indicator of NPP as below in the revised version (see lines 107-112).\n“We adopted net primary productivity (NPP) as an indicator of grassland degradation, which has proven to be a good estimator of ecosystem functioning 33 and land degradation 34. We assumed that grassland productivity would decrease with increasing GI, and we defined the GI threshold to be the GI at which NPP fell to 1% of the pregrazing level that was considered as an extreme degradation status for grassland (see Methods).”\nAnd, the information on time-step (a daily time step) of grass growth and biomass removed by grazing was also added in the method section as below (see lines 477-479).\n“Grassland productivity, biomass removed by grazing, and livestock body weight were updated using a daily time step in the model.”\nReviewer comments, second round:\nReviewer #2 (Remarks to the Author):\nThe manuscript now entitled: A productivity-based grazing intensity threshold as the early warning signal for grassland degradation on the Qinghai-Tibetan Plateau, has substantially improved from its previous version. The authors have responded to previous reviews satisfactorily and have addressed my main concern. The manuscript is now much clearer and maintains an adequate length.\nA minor comment is that the authors in one of their responses confuse aridity with drought trend, and argue in relation to the trend when the suggestion pointed to how the spatial variation in aridity interacts with grazing intensity. I suggest incorporating into the discussion section how grazing intensity and aridity can interact.\nReviewer #3 (Remarks to the Author):\nDear Authors,\nThank you for your responses, model dynamics clarification, and code provision, and congratulations on a much-improved manuscript.\nMy previous main point of contention was that the CO2 fertilisation of plant growth might strongly influence the model results. I would be more convinced of the solidity of your results had you chosen to run fixed CO2 simulations, but I concede that these may be beyond the current scope of the manuscript. I hope you do run them.\nThe clarification on model dynamics in your response letter and in the manuscript has increased my confidence that CO2 fertilisation effects are considered/constrained within your model; these constraints will therefore reduce the extent to which CO2 is driving your modelled responses and reduce the need for fixed CO2 simulations to examine the influence of CO2 effects to some extent.\nThe paper by LIU et al. (2022)( DOI: 10.5814/j.issn.1674-764x.2022.01.001) provided a very helpful overview of the IBIS model family. There appears to be author overlap. Consider providing this as an additional reference to describe your model.\nI did not get your code running. The code appears complete and executable with adequate instructions. The difficulty was on my side as the necessary Fortran-netcdf libraries were not installed on my standard clusters, and MAC has changed usr/lib paths on my personal computer.\nIn my original review, I stated your key results and their significance. Now that I understand your methodology/model better, I can see no major flaws in the study. In the future, improvements could be made to the vegetation dynamic component, grazer component, and the interaction between the two, but this does not detract from your manuscript's current novelty and significance.\nReviewer #4 (Remarks to the Author):\nFirst, I concur with the original reviewer that the use of the term “Grazing Intensity threshold” is inappropriate. What the authors are actually talking about is stocking rate (SU’s/ha/year). Their model appears to have identified stocking rates that would lead to degradation (i.e. 99% decline in NPP), and mapped how that stocking rate threshold varies across the plateau. This is different from Grazing Intensity, because grazing intensity is typically used to refer to the proportion of NPP grazed in a given year. It is unclear how the model is handling year to year stochasticity in rainfall and NPP, but typically these semiarid grasslands have a high degree of inter-annual variability in NPP, which leads to variability in grazing intensity (high in dry years, wet in low years) if stocking rate remains constant. I would recommend that the authors not use the term grazing intensity, and instead refer to a “stocking rate threshold”.\nA second major concern I have is that it remains unclear to what extent the model predictions have been validated with field data. The authors state that the model assumes NPP declines with increasing grazing intensity, but exactly how this is mathematically implemented is not explained. In many grasslands, we see that as grazing intensity increases up to some optimal level, grazing actually increases NPP (i.e. the grazing optimization hypothesis of McNaughton 1984), but past that point, NPP begins to decline again. Does their model predict a grazing optimization level, and where is this relative to what they are calling the ”grazing intensity threshold”? How has the model been calibrated in terms of the above to belowground partitioning of photosynthate, relative to traits of the dominant gramminoids on the plateau? Does the distribution of grass traits and species vary spatially across the region, and does this affect grazing tolerance? It does not appear that the model accounting for this issue. Also, do the model predictions depend on the temporal pattern of rainfall over time, i.e. the timing and frequency of droughts? Or are model predictions just based on applying mean annual rainfall each year of the simulations? Overall, the authors need to better document how the model has been parameterized and validated for this ecosystem, in order to be confident that the ‘stocking rate threshold’ maps they have generated are actually reliable. To the extent they can do this, the study provides an extremely valuable analysis of how the current distribution of stocking rates in the region relates to likelihood of long-term degradation.\nRESPONSES TO REVIEWERS’ COMMENTS\nReviewer #2 (Remarks to the Author):\nThe manuscript now entitled: A productivity-based grazing intensity threshold as the early warning signal for grassland degradation on the Qinghai-Tibetan Plateau, has substantially improved from its previous version. The authors have responded to previous reviews satisfactorily and have addressed my main concern. The manuscript is now much clearer and maintains an adequate length.\nA minor comment is that the authors in one of their responses confuse aridity with drought trend, and argue in relation to the trend when the suggestion pointed to how the spatial variation in aridity interacts with grazing intensity. I suggest incorporating into the discussion section how grazing intensity and aridity can interact.\nRE: Thanks for your positive feedbacks. Yes, we added discussion about the interaction between grazing intensity and aridity in the revised version as below. (See lines 236-240).\n“The most arid areas on northwest QTP were generally overgrazed and with low grazing intensity threshold, which partially reflected the interaction between the spatial variation of aridity and grazing pressure. Some studies pointed out that increasing aridity could exacerbate the negative effects of overgrazing on grassland ecosystem (Gaitán et al., 2018), and accelerate grassland degradation (Oñatibia et al., 2020).”\nAdded References:\nGaitán JJ, et al. Aridity and Overgrazing Have Convergent Effects on Ecosystem Structure and Functioning in Patagonian Rangelands. Land Degradation & Development 29, 210-218 (2018).\nOñatibia GR, Amengual G, Boyero L, Aguiar MR. Aridity exacerbates grazing-induced rangeland degradation: A population approach for dominant grasses. Journal of Applied Ecology 57, 1999-2009 (2020).\nReviewer #3 (Remarks to the Author):\nDear Authors,\nThank you for your responses, model dynamics clarification, and code provision, and congratulations on a much-improved manuscript.\nRE: Thanks for your positive feedbacks.\nMy previous main point of contention was that the CO fertilization of plant growth\n2\nmight strongly influence the model results. I would be more convinced of the solidity\nof your results had you chosen to run fixed CO simulations, but I concede that these\n2\nmay be beyond the current scope of the manuscript. I hope you do run them.\nRE: Thanks for the comments. We are sorry that we did not fully understand your comments in the previous version and did not state clearly in the method section. Actually, there are two types of simulations in our study.\nFirstly, as showed in supplementary information section 1, in order to evaluate the effects of climate change, elevated CO concentration and stocking rate on grassland\n2\nfrom 1980 to 2017. Then we could evaluate the effects of grazing and climate change/elevated CO concentration on grassland NPP of QTP by the differences\n2\nbetween scenarios.\nSecondly, in order to detect the stocking rate threshold on grassland of QTP (as showed in method section and Figure S3.5), we conducted an ensemble of simulations forced by a multiyear (1980-2017) mean daily meteorological and CO concentration\n2\ndata during the modeling period of 120 years (Figure S3.5). That is to say, in the simulations of threshold detection, the climate and CO condition was actually fixed,\n2\nwhich avoided the influences of climate change and elevated CO concentration on\n2\ngrassland growth. It was similar for the simulations in future period by using multiyear (2020-2100) average climate and CO concentration data for each scenario.\n2\nconcentration data in the ensemble simulations for threshold detection. (See line 489, lines 500-503, and lines 516-517).\n“During this period, multiyear (1980-2017) averages of daily meteorological and CO concentration data were used to drive the model in order to eliminate the effects\n2\nof varied climate and ramping CO concentration on NPP in the process of threshold\n2\ndetection.”\n“Multiyear (2020-2100) averaged climate and CO concentration data for each\n2\nscenario was used to drive the model.”\nThe clarification on model dynamics in your response letter and in the manuscript has increased my confidence that CO fertilization effects are considered/constrained\n2\nwithin your model; these constraints will therefore reduce the extent to which CO is\n2\ndriving your modelled responses and reduce the need for fixed CO simulations to\n2\nexamine the influence of CO effects to some extent. The paper by LIU et al.\n2\n(2022)( DOI: 10.5814/j.issn.1674-764x.2022.01.001) provided a very helpful overview of the IBIS model family. There appears to be author overlap. Consider providing this as an additional reference to describe your model.\nRE: Thanks for the comments and suggestions. We actually conducted simulations with fixed climate and CO to examine stocking rate threshold. We now state it clearly in the\n2\nrevised method section as mentioned above. Yes, we added the reference of Liu et al. (2022) in the revised version.\nI did not get your code running. The code appears complete and executable with adequate instructions. The difficulty was on my side as the necessary Fortran-netcdf libraries were not installed on my standard clusters, and MAC has changed usr/lib paths on my personal computer.\nRE: Yes, it is necessary to install Fortran-netcdf libraries (version 4.0 or later) on the machine. And additional environmental setting could be different among different types of clusters. Based on our experiences, it is a relatively easy way to test the model by installing Cygwin under windows operating system. Just select the latest packages of netcdf, hdf, gfortan during the Cygwin installation.\nIn my original review, I stated your key results and their significance. Now that I understand your methodology/model better, I can see no major flaws in the study. In the future, improvements could be made to the vegetation dynamic component, grazer component, and the interaction between the two, but this does not detract from your manuscript's current novelty and significance.\nRE: Thank you very much for your valuable comments and suggestions. In the future, we will try to improve the model by considering grazer component and vegetation dynamic component, as well as the interaction between them and make further applications.\nReviewer #4 (Remarks to the Author):\nFirst, I concur with the original reviewer that the use of the term “Grazing Intensity threshold” is inappropriate. What the authors are actually talking about is stocking rate (SU’s/ha/year). Their model appears to have identified stocking rates that would lead to degradation (i.e. 99% decline in NPP), and mapped how that stocking rate threshold varies across the plateau. This is different from Grazing Intensity, because grazing intensity is typically used to refer to the proportion of NPP grazed in a given year. It is unclear how the model is handling year to year stochasticity in rainfall and NPP, but typically these semiarid grasslands have a high degree of inter-annual variability in NPP, which leads to variability in grazing intensity (high in dry years, wet in low years) if stocking rate remains constant. I would recommend that the authors not use the term grazing intensity, and instead refer to a “stocking rate threshold”.\nRE: Yes, according the reviewer’s comments, we used the term of “stocking rate threshold” instead of “grazing intensity threshold” in the revised version. Changes were made in main text and supplementary information for all through the text, tables and figures. In this study, daily meteorological data including rainfall was used to drive the model and then output the grassland NPP.\nA second major concern I have is that it remains unclear to what extent the model predictions have been validated with field data. The authors state that the model assumes NPP declines with increasing grazing intensity, but exactly how this is mathematically implemented is not explained. In many grasslands, we see that as grazing intensity increases up to some optimal level, grazing actually increases NPP (i.e. the grazing optimization hypothesis of McNaughton 1984), but past that point, NPP begins to decline again. Does their model predict a grazing optimization level, and where is this relative to what they are calling the “grazing intensity threshold”? How has the model been calibrated in terms of the above to belowground partitioning of photosynthate, relative to traits of the dominant gramminoids on the plateau? Does the distribution of grass traits and species vary spatially across the region, and does this affect grazing tolerance? It does not appear that the model accounting for this issue. Also, do the model predictions depend on the temporal pattern of rainfall over time, i.e. the timing and frequency of droughts? Or are model predictions just based on applying mean annual rainfall each year of the simulations? Overall, the authors need to better document how the model has been parameterized and validated for this ecosystem, in order to be confident that the ‘stocking rate threshold’ maps they have generated are actually reliable. To the extent they can do this, the study provides an extremely valuable analysis of how the current distribution of stocking rates in the region relates to likelihood of long-term degradation.\nRE: Thanks for the comments. For the model validation, in this study, we evaluated the model performance by comparing the simulated results with the collected field observations (including the stocking rates (See Fig. S3.6), the grassland biomass (See Fig. S3.7)), the eddy covariance observations (GPP) (See Fig. S3.8), and the remote sensing NPP productions (NPP retrieved from MODIS and AVHRR) (See Fig. S3.9). We also made a comparison between our results and existing literatures on grazing and degradation status of grassland on the plateau (See supplementary information section 2). The validation indicated that the model is reliable and then we used the model to detect the stocking rate threshold with simulations under different scenarios.\nFor the concerns on NPP, in the revised version, according to the reviewer’s comments, we added more details on the calculation of NPP declining due to grazing activities in the method section as below. (See lines 490-496).\n“The consumption of NPP ((cid:1840)(cid:1842)(cid:1842) ) at each daily timestep was calculated as the\n(cid:3008)(cid:3045)(cid:3053)\nminimum of biomass consumed from live grass ((cid:1828)(cid:1861)(cid:1867)(cid:1833)(cid:1870)(cid:1878) ) that directly depended on\n(cid:3039)\nSR and the NPP could be allocated to leaf:\n(cid:1840)(cid:1842)(cid:1842) = (cid:1865)(cid:1861)(cid:1866)( (cid:1840)(cid:1842)(cid:1842) ∗(cid:2009) ,(cid:1828)(cid:1861)(cid:1867)(cid:1833)(cid:1870)(cid:1878) )\n(cid:3008)(cid:3045)(cid:3053) (cid:3005) (cid:3039)(cid:3032)(cid:3028)(cid:3033) (cid:3039)\nwhere (cid:1840)(cid:1842)(cid:1842) was daily net primary productivity of grassland (kg C m-2), (cid:2009) was\n(cid:3005) (cid:3039)(cid:3032)(cid:3028)(cid:3033)\nthe allocation fraction of total photosynthate to leaf (Supplementary Table S3.1).”\nAs the reviewer mentioned, we did not include specific process of the possibility of compensatory grass growth in response to light or moderate grazing. On one hand, it still remains controversial and is difficult to quantify the compensatory effects in the model currently; on the other hand, in this study, we focused on detecting a stocking rate threshold that would induce extreme grassland degradation. Although there could be a grazing optimization level for grassland NPP under light or moderate grazing condition, we thought the compensatory effects of grazing was not a dominant factor for the estimation of stocking rate threshold that indicating an extreme heavy grazing condition. We also totally agree with the reviewer that it would be meaningful for grazing management if we could predict an optimization grazing level by integrating grazing optimization hypothesis in the model in the future. We discussed this topic in the discussion section as below. (See lines 314-320).\n“Meanwhile, the possibility of compensatory grass growth in response to light or moderate grazing was not included in the simulations, primarily because this possibility remains controversial. Some studies have indicated that it is rare, or that it depends on the type and intensity of environmental stress factors or nutrient availability. Future studies could take compensatory growth into account and explore whether and how it may affect the overall grazing capacity of alpine grasslands as they undergo warming and humidification in the future.”\nIn this study, in fact, we did not include specific classification of grass species and the grazing tolerance in the model calibration and simulations. In our current dynamic vegetation model, we considered two plant function types of grass including C3 and C4 grasses. According to the reviewer’s comments, we added this in the limitation discussion in the revised version as below. (See lines 329-331)\n“Since climate change and grazing activities would affect the structure of palatable and unpalatable grasses, as well as the grazing tolerance, future model improvements could include detailed grass traits and species information instead of only two general grass PFTs.”\nWe added a table to list the major parameters including the allocation fraction of total photosynthate of these two grass plant function types in supplementary information (See Supplementary Table S3.1). The parameters including the allocation fraction of total photosynthate to leaf (aboveground) and root (belowground) were original from Kucharik et al. (2000) and adjustment was adopted from the study of Yuan et al (2011), in which parameters were optimized for the vegetation distribution simulation in China. Currently, the parameters of the allocation fraction of total photosynthate to leaf and root were uniform across the plateau for each plant function type. In the revised version, we added the above information in the method section as below. (See lines 356-358).\n“The parameters of the two grass PFTs in the original IBIS model were adjusted to fit the natural vegetation distribution in China by Yuan et al. (2011) (Supplementary Table S3.1).”\nAnd an additional table in supplementary information as below.\n“\nTable S3.1 Major parameters of plant functional types (PFT) of grasses\nPlant function type m b V cmax,15 T w (℃) GDD 0 σ (m2kg-1) α leaf α root τ leaf (years) τ root (years)\nWarm (C4) grasses 4.0 0.04 4.0 >22.0 >100 20.0 0.45 0.55 1.25 1.00\nCool (C3) grasses 9.0 0.01 25.0 >100 20.0 0.45 0.55 1.00 1.00\nNote: m, slope of stomatal conductance relationship (nondimensional); b, intercept of\nthe stomatal conductance relationship (mol H O m-2 s-1); V , maximum Rubisco\n2 cmax,15\ncapacity of the top leaf (μmol CO m-2 s-1) at 15℃; T , temperature of the warmest\n2 w\nmonth; GDD , growing degree days calculated on a 0℃ base; σ, the specific leaf area;\n0\nα and α , the allocation fraction of total photosynthate to leaf and root; τ and\nleaf root leaf\nτ , the residence time of carbon in leaf and root. The parameters of the two grass\nroot\nPFTs in the original IBIS model (Kucharik et al. 2000) were adjusted to fit the natural\nvegetation distribution in China (Yuan et al. 2011).\n”\nOur model was driven with daily meteorological data including rainfall. The daily-scale data potentially contains the information of the timing and frequency of precipitation. In the revised version, we clearly stated the forcing data used for different simulations scenarios. (See lines 479-482, lines 500-503, and lines 516-517).\n“For the simulations to evaluate the effects of climate change, elevated CO\n2\nconcentration and stocking rate on NPP of QTP grasslands (Supplementary\nInformation 1), the model ran with daily meteorological data beginning from 1960 to 2017, while grazing activity was applied from 1980 to 2017.”\n“During this period, multiyear (1980-2017) averages of daily meteorological and\nCO concentration data were used to drive the model in order to eliminate the effects\n2\nof varied climate and ramping CO concentration on NPP in the process of threshold\n2\ndetection.”\n“Multiyear (2020-2100) averaged climate and CO concentration data for each\n2\nscenario was used to drive the model.”\nAdded References:\nKucharik CJ, et al. Testing the performance of a Dynamic Global Ecosystem Model: Water balance, carbon balance, and vegetation structure. Global Biogeochem Cy 14, 795-825 (2000).\nYuan Q, Zhao D, Wu S, Dai E. Validation of the Integrated Biosphere Simulator in simulating the potential natural vegetation map of China. Ecological Research 26, 917-929 (2011)."}]

Decision letter