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• Average start of the photosynthetic period in China was on the 123rd day of the year. • The average start of the photosynthetic period advances at a rate of 4.3 d (10 a)−1. • 64% of the region is temperature controlled, others are precipitation controlled. • Importance of temperature and precipitation determined by pre-season precipitation. • Climate change increases NPP by advancing the start of the photosynthetic period. In the context of global warming, the advancement of spring phenology in northern and temperate regions due to increased temperatures has been widely reported. Early and delayed start of the photosynthetic period (SOP) directly affects the vegetation net primary productivity (NPP). However, the interrelationship between climate change, the SOP, and the NPP is unclear. In this paper, we use the dynamics of decadal daily solar-induced chlorophyll fluorescence data to calculate the response of Chinese vegetation photosynthetic phenology to climate change and its impact on the NPP over the last 20 years. The results found that over the last 20 years, the average SOP in China was on the 123rd day of the year, and the SOP has advanced at an average rate of 4.3 d (10 a)−1, with a faster trend of SOP advancement in highland and high-altitude areas. 64% of SOP in China is controlled by temperature; 36% of the SOP in China is controlled by precipitation, and the relative importance of temperature and precipitation was reversed as the precipitation gradient decreased, with SOP dominated by temperature when pre-season precipitation ≧300 mm, and SOP dominated by precipitation when pre-season precipitation ≦300 mm. Finally, we find that climate change indirectly increases vegetation NPP by advancing SOP. Our study emphasizes the importance of precipitation on phenology. It provides a scientific basis for understanding and predicting the response of spring photosynthetic phenology to climate change and the contribution of spring phenology to carbon estimation in terrestrial ecosystems. [ABSTRACT FROM AUTHOR]

Terrestrial vegetation plays pivotal roles on land-atmosphere interactions, and even global climate change. However, limited attempts have been taken to elucidate the responses of vegetation activity to weather-related drivers (e.g., droughts, floods). This is especially in China with vast area and changeable meteorological conditions. In this paper, the performance of two satellite-based indices, namely vegetation condition index (VCI) and vegetation health index (VHI) were analyzed to detect the vegetation responses to weather-related variations. The dynamics of vegetation activity in China were further examined for the period 1982–2013. We found widely-distributed vegetation stresses in the entire country for a long period (about an average of two months per year) from the VCI and the VHI. In addition, both the two indices indicated increasing vegetation activities over most parts of China during 1982–2013. However, there is no consensus between the two indices at spatial pattern and regional totals. This discrepancy can be due to the negative-correlation assumption of the VHI between the VCI and bright temperature. However, we found that the relationship between the VCI and temperature could be changeable in different regions, especially in China with complex topography, diverse climate conditions and different vegetation types. The findings of this paper highlight the necessity to account for dominant controls on vegetation growth when using the VCI and the VHI to analyze vegetation activity. [ABSTRACT FROM AUTHOR]

• Climate-sensitive nonlinear mixed-effects height-diameter model was developed for 14 tree species in mixed forests. • The inclusion of climate and species diversity improved the H-D model. • The impact of environmental gradients on tree height growth was simulated. • The sensitivity of multiple species in mixed forest to environmental gradient changes varies with species. Models based on height-diameter (H-D) are helpful for estimating forest biomass and carbon storage on a large scale. Climate change has an impact on allometric height-diameter growth. H-D models explaining climatic effects are still inadequate due to the complexity of mixed forests. This paper developed climate-sensitive mixed-effects models for 14 tree species based on 316 natural mixed forest plots in Northeastern China and compared the response of tree height growth to environmental gradients for multiple species. The results showed that the species heterogeneity leads to the differences in scaling exponents in varying degrees. The analysis revealed that incorporating mean annual temperature (MAT), mean annual precipitation (MAP), dominant tree height (HT), basal area of the larger trees (BAL), and Shannon diversity index significantly enhanced the predictive ability of the model. By exploring the tree height change patterns of each tree species under different gradients of environmental factors, we found that increases in temperature, precipitation, site quality, and competition intensity positively affected height growth in mixed forests. In contrast, increases in species diversity inhibited height growth. The degree to which environmental factors regulate the H-D allometric growth of trees in mixed forests is related to species specificity. This paper provides theoretical support for tree height estimates of major tree species in mixed forests under global climate change by introducing environmental factors to improve the H-D model of 14 tree species. [ABSTRACT FROM AUTHOR]

Highlights • The grassland fire risk was assessed in remote and data lacking areas. • The grassland fire risk knowledge and experience was transferred from one region to another. • This method could conveniently be used for risk mapping and updating. • It will reduce grassland fire risk management costs. Abstract A new direction of risk assessment research in grassland fire management is data-driven prediction, in which data are collected from particular regions. Since some regions have rich datasets that can easily generate knowledge for risk prediction, and some have no data available, this study addresses how we can leverage the knowledge learned from one grassland risk assessment to assist with a current assessment task. In this paper, we first introduce the transfer learning methodology to map and update risk maps in grassland fire management, and we propose a new grassland fire risk analysis method. In this study, two major grassland areas (Xilingol and Hulunbuir) in northern China are selected as the study areas, and five representative indicators (features) are extracted from grassland fuel, fire climate, accessibility, human and social economy. Taking Xilingol as the source domain (where sufficient labelled data are available) and Hulunbuir as the target domain (which contains insufficient data but requires risk assessment/prediction), we then establish the mapping relationship between grassland fire indicators and the degrees of grassland fire risk by using a transfer learning method. Finally, the fire risk in the Hulunbuir grassland is assessed using the transfer learning method. Experiments show that the prediction accuracy reached 87.5% by using the transfer learning method, representing a significant increase over existing methods. [ABSTRACT FROM AUTHOR]

Crop phenology is co-determined by climate change and crop management. Over recent decades, climate change-related alterations in crop phenology have been observed and reported for various global crops. However, attributing changes in crop phenology to climate change is difficult, because there have been concurrent changes in crop management. In this paper, we isolated and quantified the impacts of climate change and crop management on the changes of wheat phenology in China, during the period 1981–2010, using a first-difference multiple regression model. Our results shows: (1) based on observed phenological data, in spring/winter wheat, the mean sowing and emergence date were delayed by 0.91/2.29 and 0.39/0.73 days decade −1 ; mean anthesis and maturity date advanced by 1.05/2.28 and 0.01/1.42 days decade −1 ; mean length of vegetative growth period (VGP) and whole growth period (WGP) were shortened by 1.09/2.86 and 0.89/3.69 days decade −1 ; mean length of reproductive growth period (RGP) was prolonged by 0.55/0.61 days decade −1 . (2) At most stations, changing direction of wheat phenology affected by isolated impacts of climate change or crop management was consistent with that affected by combined impacts of climate change and crop management. (3) For observed trends of most phenological stages and growth periods, relative contribution from climate change was smaller than from crop management, and average temperature contributed the most among the three contributors (average temperature, cumulative precipitation, and cumulative sunshine hours) to isolated impacts of climate change on wheat phenology. (4) Crop management over the three decades was shown to have helped reduce the lengths of VGP and WGP, but increase the length of RGP for both spring and winter wheat, implying that shorter-duration varieties with a higher yield or better yield stability in changing climate might have been introduced by farmers. [ABSTRACT FROM AUTHOR]

• This paper is the first to map explicitly phenological transition zones. • Both "phenological" and "transitional" characteristics of PTZs were highlighted. • The PTZ dynamics were sensitive, rapid and dramatic in response to climate change. • Three different types of gradient, inclusive and mosaic PTZs were identified. • The method can be easily extended to other areas with no or minor adjustments. Spatially explicit mapping of the Phenological Transition Zone (PTZ) is the prerequisite to PTZ dynamics monitoring and characterization, and is significant for more effective management of natural resources, biodiversity, and conservation areas in the context of climate change. However, to our knowledge there have been no studies to delineate spatially explicit PTZs and there are some potential issues associated with the mapping of other biogeographical transition zones. This paper developed a method to explicitly delineate PTZs continuously from 2003 to 2017 in the study area of the Forest-Grassland Mosaic Region (FGMR) in northeastern China, highlighting both their transitional and phenological characteristics. Annual Normalized Difference Vegetation Index (NDVI) time series were fed into the dynamic-time-warping (DTW) based fuzzy c-medoids (FCMdd) clustering to produce 12 fuzzy phenoregions. Potential PTZ pixels were identified for each pair of fuzzy phenoregions via the fuzzy intersection operator. The Delaunay tessellation field estimator was applied to convert the discrete potential PTZ pixels to continuous weighted density surface. The 7 consistently and 8 intermittently existing PTZs were exhaustively derived via thresholding. Three different types of gradient, inclusive and mosaic PTZs were identified. The PTZ dynamics were rapid and dramatic due to its phenological and transitional nature. This PTZ mapping method can be easily extended to other areas with no or minor adjustments, and it can also be adapted to map other biogeographical transition zones. [ABSTRACT FROM AUTHOR]

• A new ET model (SW_OPT) integrates the SW and the Ts-VI model using PSO. • E, T and ET from SW_OPT were validated in a typical irrigated area of China. • SW_OPT calculates ET and its components with high accuracy. • SW_OPT is significantly more accurate compared to SW_Ori. • SW_OPT has low sensitivity to most of input and empirical parameters. Daily actual evapotranspiration (ET) and its components – soil evaporation (E) and vegetation transpiration (T) – play a key role in water resource management of irrigated areas. Nevertheless, due to large uncertainties in the parameterization of the resistances in irrigated areas, traditional ET models do not always provide accurate ET estimates, especially for its components. This uncertainty is mainly due to the difficulty of determining the empirical parameters accurately of soil and canopy resistances which is debated for a long time and the error in input variables. This paper proposed an optimized Shuttleworth-Wallace model (SW) using the particle swarm optimization (PSO) algorithm to integrate the original SW with the surface temperature-vegetation index (Ts-VI) triangle models (named Shuttleworth & Wallace_Temperature Vegetation Index model, SW_TVI).The performance of the SW_TVI model was significantly improved by optimizing the soil and canopy resistances in the original SW model using discontinuous regional ET estimates from the Ts-VI model under clear-sky conditions. Compared with the original SW model, the root mean squared error (RMSE) and mean absolute deviation (MAD) of the SW_TVI model were reduced by more than 30%, against in situ measurements in the Heihe River Basin. The Bias of the T/ET ratio was also significantly reduced from -22.3% for the original SW model to -5.5% for the SW_TVI model. The annual contributions of E and T to ET were about 20% and 80%, respectively, and had a strong seasonal variation in the typical irrigated area of China. In summary, the SW_TVI model shows three outstanding advantages: (1) it estimates daily continuous E, T, and total ET with high accuracy; (2) it is very robust and insensitive or slightly sensitive to most input variables and empirical parameters; (3) it is independent from ground data. This new SW_TVI model will benefit water resources management in irrigated areas, particularly in arid and semi-arid regions. [ABSTRACT FROM AUTHOR]

• How vegetation greenness and carbon flux respond to extreme droughts was quantified. • Drought caused the largest reduction in GPP than NEP and LAI across China. • Forest was less sensitive to extreme droughts. • Loess Plateau was highlighted as the most vulnerable region. Increasing drought stress under climate change exerting large impacts on vegetation has aroused growing concern, yet how vegetation greenness and carbon cycle respond to extreme drought is not well understood. In this paper, we studied this issue with various data sources including satellite observation, flux sites upscaling estimation, atmospheric inversion, and Dynamic Global Vegetation Model (DGVM) simulations over China during the period 2000–2015. We found a highly consistent negative response of greenness and carbon fluxes to extreme droughts, with the largest reduction in Gross Primary Productivity (GPP, a standardized anomaly of -1.26 ~ -0.33), a slightly smaller extent decrease of Net Ecosystem Productivity (NEP, -0.74 ~ -0.14) due to concurrent decrease of respiration, and reduced Leaf Area Index (LAI, -0.39 ~ -0.20). Besides, there was a large spatial heterogeneity across different regions and biomes, where Loess Plateau was one of the most vulnerable regions. The ambient climate conditions were found to modulate the drought response. Drought sensitivity of greenness and GPP peaked where mean annual temperature is around 25 °C, while that of NEP decreased along increasing precipitation gradients. [ABSTRACT FROM AUTHOR]

• GHG emissions and crop water demand under various scenarios were evaluated. • Changes in planting structure in the study area increased water demand and GHG emissions. • The expansion of the planting area is the main reason for increasing water demand and GHG emissions. • Rice has the greatest impact on GWP during the study period in Heilongjiang. The agriculture sector is one of the largest users of water and a significant source of greenhouse gas (GHG) emissions. The development of low-GHG-emission and water-conserving agriculture will inevitably be the trend in the future. Because of the physiological differences among crops and their response efficiency to external changes, changes in planting structure, climate and input of production factors will have an impact on regional agricultural water use and GHG emissions. This paper systematically analyzed the spatial-temporal evolution characteristics of crop planting structure, climate, and production factor inputs in Heilongjiang Province, the main grain-producing region of China, from 2000 to 2015, and quantified the regional agricultural water use and GHG emissions characteristics under different scenarios by using the Penman-Monteith formula and the Denitrification-Decomposition (DNDC) model. The results showed that the global warming potential (GWP) increased by 15% due to the change in planting structure. A large increase in the proportion of rice and corn sown was the main reason. During the study period, regional climate change had a positive impact on the water-saving and emission reduction of the agricultural industry. The annual water demand per unit area decreased by 19%, and the GWP decreased by 12% compared with that in 2000. The input of fertilizer and other means of production will have a significant impact on GHG emissions from farmlands. The increase in N fertilizer input significantly increased N 2 O emissions, with a 5% increase in GWP. Agricultural water consumption and carbon emissions are affected by changes in climate, input of means of production, and planting structure. Therefore, multiple regulatory measures should be taken in combination with regional characteristics to realize a new layout of planting structure with low emissions, water conservation, and sustainability. [ABSTRACT FROM AUTHOR]

• Conclusions in Zeng et al (2020) AFM Vol 290, 15 August 2020 are debatable • Afforestation does not cause increase in surface water areas in northern China • Rise in precipitation and building water storages increases surface water areas The paper "Large-scale afforestation significantly increases permanent surface water in China's vegetation restoration regions, Agricultural and Forest Meteorology, Volume 290, 15 August 2020, 108001" by Zeng et al. (2020) finds that northern China is greening up and that "vegetation cover is an important factor in controlling permanent water changes". They suggest that afforestation partially caused the increase in surface water areas due to the "significant positive correlations between forest covers and surface water area". The authors suggest that, except precipitation, "climatic factors were not the main factor influencing permanent water". They attribute the increase in area of surface waterbody to the increase in dams, precipitation rise, and afforestation. This commentary aims at clarifying concepts of afforestation-water yield-river flow relations and offers an alternative explanation of the observed expansion of surface water areas in northern China. Using a simple water balance-based approach, we conduct a back-of-envelope calculation and show that afforestation and 'greening up' are not likely to cause an increase in water yield and surface water storage. We argue that the detected rise of permanent surface water changes in the study regions is a result of hydraulic infrastructure construction, urbanization, and increase in precipitation, perhaps not vegetation recovery from afforestation. We believe that large-scale afforestation is not likely to increase surface water resources in northern China as implied in Zeng et al (2020). Future process-based studies are needed to understand the sources of the local precipitation and the effects of revegetation on precipitation, soil improvement, and water yield. [ABSTRACT FROM AUTHOR]

• A novel, simply formulated crop model successfully predicts wheat yields in China. • The model captures the temporal and spatial variations in GPP, biomass and yield. • A predicted positive response of yield to CO 2 counteracts negative thermal response. Climate exerts a major influence on crop development and yield. Despite extensive modelling efforts, there is still considerable uncertainty about the consequences of a changing climate for the yields of major crops. Existing crop models are complex and rely on many assumptions and parameters, motivating a quest for more parsimonious models with stronger theoretical and empirical foundations. This paper presents a prototype of such a model for wheat, informed by measurements of gross primary production (GPP), biomass and yield at research sites across the wheat-growing regions of China. First, GPP was predicted using a recently developed first-principles model driven only by climate, carbon dioxide (CO 2) concentration, and light absorbed by leaves. Modelled GPP was shown to agree well with eddy-covariance measurements. Second, the data were used to show that above-ground biomass (AB) is proportional to time-integrated GPP, and that grain yield shows a saturating relationship with AB. Simple empirical equations based on these findings were combined with modelled GPP to predict yield, including propagated errors due to parameter uncertainty in both the GPP model and the empirical equations. The resulting 'hybrid' model, applied in a variety of climates, successfully predicted measured interannual variations in AB and yield. Third, the model was extended to include a phenology scheme, a mass-balance equation relating mean leaf area index to accumulated GPP over the growth phase, and an independently observed response of leaf mass-per-area to CO 2. Sensitivity analyses and scenario runs with this extended model showed a positive but saturating (at ∼600 ppm) response of yield to rising CO 2 , consistent with experimental evidence. This positive effect was partially counteracted by a net negative response of yield to increasing temperature, caused by increasing photorespiration and an accelerated growth cycle. [ABSTRACT FROM AUTHOR]