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Based on the climate-driven yield index (CDYI) series of summer maize in 17 cities and the monthly series of large-scale atmospheric circulation indices (LACI), the responses of yield fluctuations of summer maize to atmospheric circulation anomalies during 1988–2017 were explored in Henan province, north China. The main findings were as follows: (1) with using principal component analysis, east, north, central, and west Henan were identified as four sub-regions with different CDYI variations; (2) with using ensemble empirical mode decomposition, the CDYI in east and north Henan presented a notable 6-year oscillation, while central and west Henan were dominated by a 3-year oscillation; (3) East Pacific/North Pacific Oscillation in January, Eastern Tropical Pacific SST in May, Tropical Southern Atlantic Index in June, and North Pacific in September were the optimal yield prediction signals in east, north, central, and west Henan, respectively; (4) the regression model with predictors as year and various LACI had the ideal forecasting results on actual regional maize yield, with the average relative error ranged from 4.94 to 9.20%.

Carbon-water cycle of terrestrial ecosystem is the important process of energy and mass exchanges, which is greatly influenced by changes of wet/dry conditions. Recently, China is experiencing frequent extreme weather events. Many concerns have been raised to explore how and to what extent dryness/wetness have influenced the carbon-water cycle in China on the regional scale. In this study, we examined the effects of changes of dryness/wetness on gross primary productivity (GPP), evapotranspiration (ET), and water use efficiency (WUE) in terrestrial ecosystems of China during 2001 - 2013 using standardized wet/dry anomaly index and Mann-Kendall method. Our analysis indicated that droughts hit extensively in 2001, 2006, 2009, and 2011, in contrast, extremely severe floods occurred in 2002, 2003, and 2010. Spatially, droughts increased obviously in south China but were alleviated in north China from 2001 to 2013. On average, GPP and WUE of China exhibited increasing trends but ET exhibited a decreasing trend. Severe dry/wet conditions (|SPEI| > 1) decreased GPP, ET, and WUE. Furthermore, the effect of dry stress on GPP, ET, and WUE was much more serious than the effect of wet stress. It was implied that the trends of GPP, ET, and WUE would not change greatly on the long-term scale, but extreme dry/wet events would enhance their change amplitudes in future.

The responses of wheat yield to large-scale atmospheric circulation indices (LACI) were explored in Henan province, north China. With using the annual series of climate-driven yield index (CDYI) extracted from winter wheat yield collected in 17 cites and the monthly series of 15 types of LACI during 1988–2017, the main findings were as follows: (1) this province could be divided into four sub-regions (central-east, west, north, and south Henan) with different CDYI variations; (2) the CDYI in central-east, west, south Henan was dominated by a 3-year oscillation, while the CDYI in north Henan presented a notable 7.5-year oscillation; (3) among the four sub-regions, central-east Henan had the most significant CDYI-LACI relationship, and the higher Nino 1 + 2 in December were a key yield reduction signal; (4) during 2008–2017, the stronger increase of Nino1 + 2_in December had caused the yield decrease in central Henan by 6.58%. In summary, linking wheat yield to LACI anomalies should be instrumental in alleviating the adverse effects of climate change on wheat production.

The regional differences for responses of winter oilseed rape yield to climate changes was not reported in China, and the statistical climate–yield relationships at the provincial scale in south China should be a valuable attempt. With using provincial data of climate variables and oilseed rape yields during 1978–2016, the main findings were as follows: (1) based on 14 provincial climate-induced yield index (CIYI) series for oilseed rape yield, south China could be divided into five sub-regions with different temporal variations in CIYI by using principal component analysis, and they were Yangtze River delta (Region I), central south China (Region II), southwest China (Region III), southernmost China (Region IV) and southeastern coast (Region V), respectively; (2) the correlation and regression analysis between CIYI and monthly climate variables indicated that the precipitation of April, average potential evapotranspiration of November, average potential evapotranspiration of October, average mean temperature of May, and average potential evapotranspiration of September were the key climate variables (KCV) affecting yield in Region I, II, III, IV and V, respectively; (4) among the five sub-regions, the impacts of KCV changes on yields in southwest China were more notable, and the increasing trend of average potential evapotranspiration of November caused the decrease of yield by 2.08% in 1990s and 4.24% after 2000, respectively. These results could be employed as references for the assessment and mitigation of agro-meteorological disasters in south China.

The responds of oilseed rape yield to climate changes was not reported in China, and the climate–yield statistical relationship for winter oilseed rape constructed in Jiangsu province, southeast China should be a valuable attempt. With using climate and yield data at 52 stations in Jiangsu during 1961–2012, the main findings in this study were as follows: (1) correlation analysis between oilseed rape yield and seasonal climate variables indicated that average temperature difference (TD), total rainfalls (RF), total rainy days (RD), total sunshine hours (SH), and average relative humidity (RH) in spring were the key agro-meteorological indicators significantly affecting yield; (2) according to the principal component analysis on the five selected indicators in all stations, this province was classified as four climactic sub-regions for oilseed rape production: north, central-north, central-south, and south Jiangsu, respectively; (3) the significant responses of oilseed rape yield to spring climate variability were detected in central-south and south Jiangsu, and the strong decreasing trends of RF, RD, and RH during study periods in this region had increased yield by up to 0.68%, 1.44%, and 5.68%, respectively; (4) spring RD in the central-south and south Jiangsu was screened as the leading indicator for oilseed rape, and it showed a significant decrease after 1985.

Based on the precipitation record and single-crop rice yield data at 52 meteorological stations in Jiangsu province, southeast China from 1961 to 2012, a total of 14 precipitation concentration indices (PCI) during rice-growing period (May–October) were calculated, and then, the relationship between PCI and rice yield was explored. Results showed that: (1) the higher precipitation concentration was observed in north Jiangsu, and total precipitation was mainly concentrated in the rainstorm events and July rainfalls. (2) Precipitation concentration was increasing in recent 50 years, which was embodied in the significant increasing trends of precipitation contribution of rainstorm events and August rainfalls occurred in south Jiangsu. (3) The correlation analysis between rice yield and 14 PCI indicated that the precipitation contribution of rainstorm events (CDP6) was more suitable for accessing the losses of rice yield caused by excessive rainfall. (4) During the study period, the losses of rice yield in south Jiangsu exceeded 2%, owing to the stronger increasing trend of CDP6.

Based on daily precipitation records at 52 meteorological stations in Jiangsu Province, southeast China, the space and time changes of characteristics in rainfall erosivity (RE) during 1961–2012 were analyzed. The meteorological and topographical diagnosis methods including Mann–Kendall trend test, Ensemble Empirical Mode Decomposition (EEMD), and K-means cluster analysis (KCA) were used. The result showed that the average annual RE in Jiangsu was 8621.8 (MJ mm ha−1 h−1 a−1), and more than 50% of the annual RE was concentrated in summer. In contrast, the intra-annual distribution of RE in north Jiangsu presented higher concentration. There was an obvious seasonal difference in temporal trends of RE at the scale of station, and most of stations were characterized by increasing RE in winter and summer. Especially all the stations were dominated by significantly increasing RE in winter. By using KCA, Jiangsu could be divided into three sub-regions with different temporal variations in annual RE such as northern, central and southern area, and the southern area was dominated by a significant increasing tendency. Furthermore, EEMD for regional series of annual RE indicated that a 2.5–2.6 year periodical feature was noticeable in each sub-region.

The growth of early rice is often threatened by a phenomenon known as Grain Buds Cold, a period of anomalously cold temperatures during the booting and flowering stage. As a high yield loss due to Grain Buds Cold will lead to increasing insurance premiums, quantifying the impact of weather on crop yield is crucial to the design of weather index insurance. In this study, we propose a new approach to the estimation of premium rates of Grain Buds Cold weather index insurance. A 2-year artificial controlled experiment was utilized to develop logarithmic and linear yield loss models. Additionally, incorporating 51 years of meteorological data, an information diffusion model was used to calculate the probability of different durations of Grain Buds Cold, ranging from 3 to 20 days. The results show that the pure premium rates determined by a logarithmic yield loss model exhibit lower risk and greater efficiency than those determined by a linear yield loss model. The premium rates of Grain Buds Cold weather index insurance were found to fluctuate between 7.085 and 10.151% at the county level in Jiangxi Province, while the premium rates based on the linear yield loss model were higher (ranging from 7.787 to 11.672%). Compared with common statistical methods, the artificial controlled experiment presented below provides a more robust, reliable and accurate way of analyzing the relationship between yield and a single meteorological factor. At the same time, the minimal data requirements of this experimental approach indicate that this method could be very important in regions lacking historical yield and climate data. Estimating weather index insurance accurately will help farmers address extreme cold weather risk under changing climatic conditions.

To study the effects of day and night temperature difference (DIF) on tomato growth, a controlled experiment using Solanum lycopersicum L., cv. Jinguan 5 was conducted. The daily mean temperature (Tm) was maintained at 18°C and 25°C, and the DIF was set at 0°C, 6°C, and 12°C. The results indicated that chlorophyll a (Chl a) and chlorophyll b (Chl b) gradually increased as DIF rose. At 18°C Tm, the carotenoid content reached a maximum at 12°C DIF. The Chl a/Chl b, net photosynthetic rate (PN), photosynthetic rate at irradiation saturation (Pmax), stomatal conductance (gs), intercellular CO2 concentration (Ci), stomatal limitation value, the maximum assimilation rate (Amax), apparent quantum efficiency (Aq), carboxylation efficiency (Ce), superoxide dismutase (SOD), peroxidase (POD), catalase (CAT), gibberellin A3 (GA3), and indole-3-acetic acid (IAA) were highest, while malondialdehyde (MDA) was lowest at 6°C DIF. At 25°C Tm, PN, Pmax, Amax, Aq, Ce, gs, Ci, CAT, POD, GA3, IAA, and zeatin reached th...

Management of forest carbon stocks on public lands is critical to maintaining or enhancing carbon dioxide removal from the atmosphere. Acknowledging this, an array of federal regulations and policies have emerged that requires US National Forests to report baseline carbon stocks and changes due to disturbance and management and assess how management activities and forest plans affect carbon stocks. To address these requirements with the best-available science, we compiled empirical and remotely sensed data covering the National Forests (one fifth of the area of US forest land) and analyzed this information using a carbon modeling framework. We demonstrate how integration of various data and models provides a comprehensive evaluation of key drivers of observed carbon trends, for individual National Forests. The models in this framework complement each other with different strengths: the Carbon Calculation Tool uses inventory data to report baseline carbon stocks; the Forest Carbon Management Framework integrates inventory data, disturbance histories, and growth and yield trajectories to report relative effects of disturbances on carbon stocks; and the Integrated Terrestrial Ecosystem Carbon Model incorporates disturbance, climate, and atmospheric data to determine their relative impacts on forest carbon accumulation and loss. We report results for several National Forests across the USA and compare their carbon dynamics. Results show that recent disturbances are causing some forests to transition from carbon sinks to sources, particularly in the West. Meanwhile, elevated atmospheric carbon dioxide and nitrogen deposition are consistently increasing carbon stocks, partially offsetting declines due to disturbances and aging. Climate variability introduces concomitant interannual variability in net carbon uptake or release. Targeting forest disturbance and post-disturbance regrowth is critical to management objectives that involve maintaining or enhancing future carbon sequestration.

With the increasing risk of people and economies being subject to meteorological disasters, it is of great importance to analyse the spatio-temporal variation of these disasters. In this paper, the possible impacts of meteorological disasters on rice yields in Jiangsu Province, southeast China was studied. A total of 17 rice meteorological disaster indices (RMDI) during 1961–2012 was calculated and analyzed. The spatial distribution of RMDI indicated that south Jiangsu should be classified as a high-intensity zone for heat stress, while north Jiangsu should be classified as a high-intensity zone for precipitation extremes and chilling injury. Changing trends of RMDI were detected by the Mann-Kendall test. Precipitation extremes and heat stress indices presented increasing trends, while the chilling injury indices showed decreasing trends. Correlation analysis between rice yield and RMDI with the first difference method and climate-induced yield method showed that precipitation extremes had more dramatic negative effects than the other two types of meteorological disaster. Principal components analysis provided additional information about the regional differences of changes in meteorological disasters. Jiangsu could be divided into three regions (south, middle, and north) with different and changing patterns for RMDI. Also the Western Pacific Subtropical High Intensity Index and the Pacific Decadal Oscillation had significant influences on these changes. These findings could provide a scientific basis for the prevention or reduction of rice meteorological disasters in Jiangsu Province.

Accurate estimation of light use efficiency (LUE) of plant canopies is essential for calculating gross primary productivity (GPP) using LUE models and is also useful for calibrating process-based models for regional and global applications. A promising method for estimating LUE is through remote sensing of the photochemical reflectance index (PRI). However, there are internal (e.g. pigment concentrations) and external factors (e.g. environmental conditions and sun-target-view geometry) that affect PRI signals. Considering the reflectance difference between sunlit and shaded leaves, the ratio of observed canopy reflectance to leaf reflectance is used to represent the observed fraction of sunlit leaves, and the observed fraction of shaded leaves is calculated with a geometrical optical model. Thus, a canopy-level PRI observation is separated into sunlit and shaded PRI values, and a two-leaf canopy PRI (PRI t ) is calculated as sum of these two values weighted by their respective sunlit and shaded leaf area indices. The usefulness of PRI t in assessing the canopy-level LUE is evaluated with automated multi-angle PRI observations acquired on a flux tower from April to September 2013 over a sub-tropical coniferous forest in southern China. In each 15-minute observation cycle, PRI is observed at four view zenith angles fixed at (37°, 47°, 57°) or (42°, 52°, 62°) and the instantaneous solar zenith angle in the azimuth angle range from 45° to 325° (from the geodetic north). In both the half-hourly and daily time steps, PRI t can effectively improve (> 50% and > 35% increases in R 2 , respectively) the ability as a proxy of LUE derived from the tower flux measurements over the big-leaf PRI taken as the arithmetic average of the multi-angle measurements in a given time interval. In the dry season from July to September, correlations of PRI with LUE at daily time steps are much stronger in the two-leaf case than in the big-leaf case. The correlation between PRI t and LUE is the strongest ( R 2 = 0.785, p t is very effective in detecting the light and low-moderate drought stress on LUE at half-hourly time steps, while ineffective in detecting severe atmospheric water and heat stress, which is probably due to alternative radiative energy sink, i.e. photorespiration. Overall, the two-leaf approach well overcomes some external effects (e.g. sun-target-view geometry) that interfere with PRI signals.

With the increasing risk of meteorological disasters, it is of great importance to analyze the spatiotemporal changes of precipitation extremes and its possible impact on rice productivity, especially in Jiangsu province, southeast China. In this study, we explored the relationships between rice yield and extreme precipitation indices using Mann-Kendall trend test, Pettitt's test, and K-means clustering methods. This study used 10 extreme precipitation indices of the rice growing season (May to October) based on the daily precipitation records and rice yield data at 52 meteorological stations during 1961-2012 in Jiangsu province. The main findings were as follows: (1) correlation results indicated that precipitation extremes occurred in the months of July, August, and October, which had noticeable adverse effects on rice yield; (2) the maximum 7-day precipitation of July and the number of rainy days of August and October should be considered as three key indicators for the precipitation-induced rice meteorological disasters; and (3) most of the stations showed an increasing trends for the maximum 7-day precipitation of July and the number of rainy days of August, while the number of rainy days of October in all the stations demonstrated a decreasing trend. Moreover, Jiangsu province could be divided into two major sub-regions such as north and south areas with different temporal variations in the three key indicators.

This study investigated monthly and annual reference evapotranspiration changes over southwestern China (SWC) from 1960 to 2012, using the Food and Agriculture Organization of the United Nations’ report 56 (FAO-56) Penman–Monteith equation and routine meteorological observations at 269 weather sites. During 1960–2012, the monthly and annual decreased at most sites. Moreover, the SWC regional average trend in annual was significantly negative (p < 0.05); this trend was the same in most months. A new separation method using several numerical experiments was proposed to quantify each driving factor’s contribution to changes and exhibited higher accuracy based on several validation criteria, after which an attribution analysis was performed. Across SWC, the declining annual was mainly due to decreased net radiation (RN). Spatially, the annual changes at most sites in eastern SWC (excluding southeastern West Guangxi) were generally due to RN, whereas wind speed (WND) or vapor pressure deficit (VPD) was the determinant at other sites. Nevertheless, the determinants differed among 12 months. For the whole SWC, increased VPD in February and decreased WND in April, May, and October were the determinant of decreased ; however, decreased RN was the determinant in other months. Overall, the determinant of the monthly changes exhibited a complex spatial pattern. A complete analysis of changes and the related physical mechanisms in SWC is necessary to better understand hydroclimatological extremes (e.g., droughts) and to develop appropriate strategies to sustain regional development (e.g., water resources and agriculture). Importantly, this separation method provides new perspective for quantitative attribution analyses and thus may be implemented in various scientific fields (e.g., climatology and hydrology).

Ecosystem services are comprehensive and quantitative indicators for describing ecosystem&ndash, human interactions. China has experienced rapid urbanization in the past 30 years, which has created a significant impact on regional ecosystem services. However, whether the impact is linear is not clear as yet. In this study, the Jiangsu province, a main body of the Chinese Yangtze River Delta city cluster, was chosen as a case study. Multi-source remotely-sensed geospatial data, including meteorological, land use, vegetation, and socio-economic data, were collected to estimate the total amount of ecosystem services (TESV) and urbanization levels. Subsequently, the relationships between TESV and urbanization indices (i.e., gross domestic product (GDP) per unit area, GPUA, population per unit area, PPUA, and built-up land proportion, BULP) were determined using the Pearson correlation analysis and piecewise linear regression. The primary findings of this study were as follows: (1) There was a distinct spatial pattern in TESV, which gradually increased from west to east with high-value areas located in eastern coastal areas of Jiangsu. Among different land use types, cropland and woodland contributed the most to TESV, (2) The three urbanization indices had spatial patterns, indicating higher urbanization in southern Jiangsu than in central or northern Jiangsu, and (3) Once GPUA and PPUA exceeded threshold values of 3719.55 &times, 104 yuan/km2 and 744.37 person/km2, respectively, TESV sharply decreased with an increase in these indices. However, the BULP showed a linear and significantly negative relationship with TESV at all values, which indicated that the impacts of economic and population growth on TESV lagged behind that of built-up land expansion. These findings provide a potentially significant reference for decision-makers to rationally enhance regional ecosystem services during rapid urbanization processes.

This report assesses how carbon stocks at regional scales and in individual national forests are affected by factors such as timber harvesting, natural disturbances, climate variability, increasing atmospheric carbon dioxide concentrations, and nitrogen deposition. Previous baseline assessments of carbon stocks (https://www.fs.fed.us/managing-land/sc/carbon) evaluated observed trends based on forest inventory data but were limited in ability to reveal detailed causes of these trends. The expanded assessments reported here are based on an extensive disturbance and climate history for each national forest, and two forest carbon models, to estimate the relative impacts of disturbance (e.g., fires, harvests, insect outbreaks, disease) and nondisturbance factors (climate, carbon dioxide concentration, nitrogen deposition). Results are summarized for each region of the National Forest System in the main document. A set of regional appendices to this report provides more detailed information about individual national forests within each region. Results are highly variable across the United States. Generally, carbon stocks are increasing in forests of the eastern United States as these forests continue to recover and grow older after higher historical harvesting rates and periods of nonforest land use. In contrast, carbon stocks in forests of the western United States may be either increasing or decreasing, depending on recent effects of natural disturbances and climate change. The information supports national forest units in assessing carbon stocks, quantifying carbon outcomes of broad forest management strategies and planning, and meeting carbon assessment requirements of the 2012 Planning Rule and directives. Results of these expanded assessments will provide context for project-level decisions, separated from the effects of factors that are beyond land managers’ control.

In order to explore the actual impact of urban spatial morphology on real near-surface wind speed on the urban scale, taking Beijing, China as an example，this study adopted an urban-rural ratio method to construct a WsR (wind speed ratio) to quantify the impact of urbanization on the near-surface wind speed. The spatial morphological parameters such as building height, building density (BD), building standard deviation, floor area ratio(FAR), frontal area index (FAI), roughness length, sky view factor (SVF) and fractal dimension at the 500 m spatial resolution were calculated, and further the relationships of these morphological parameters and WsR was analyzed. Results showed that due to urbanization, the minimum and maximum average annual wind speed of urban areas are 56% and 160% of the suburban wind speed, and urbanization can reduce the wind speed in urban areas at different times by 3% to 27%. On the urban scale, there are significant spatial correlations between the eight morphological parameters and WsR. Except for the quadratic curve of BD and WsR, the other parameters are significantly linearly related to WsR. FAR, FAI and SVF, the three most significant impact factors can individually explain 51%, 48% and 48% of the change of WsR.

A series of nanocomposite (NC) hydrogels containing dendritic polythioether segments were successfully prepared by free radical copolymerization. Due to the multiple thioether functional groups and the strong chelating ability of the thioether groups with metal elements, these hydrogels showed excellent absorption towards Ag nanoparticles. The size of the Ag nanoparticles (AgNPs) embedded in the hydrogel matrix can be regulated by tuning the content of dendritic segments in the polymer matrix. Small-sized AgNPs were formed with increasing of the content of polythioether dendrons. Besides, the Ag release kinetics was investigated. The hydrogels containing dendrons showed higher AgNPs loading and displayed enhanced retention of nanoparticles as compared with the pristine poly-N-isopropyl acrylamide (PNIPAM) hydrogel. Furthermore, these NC hydrogels, especially those containing dendritic segments, showed much better antimicrobial performance for Escherichia coli and anti-algae performance. These NC hydrogels have promising applications in marine antifouling coating and interfaces of biomaterials.

The difference between day and night temperature (DIF) is a major environmental factor affecting crop growth, but the mechanisms are not fully understood. We investigated crop performance, root activity and concentrations of low molecular weight organic acids (LMWOAs) secreted by tomato (Lycopersicon esculentum Mill.) root under different DIF conditions. A fixed daily temperature of 25°C and five DIF treatments (−12, −6, 0, 6 and 12°C) were used to grow tomato in a climate chamber. Root/shoot ratio; leaf maximum photosynthetic rate (Pmax); root activity; total nitrogen (N), phosphorus (P) and potassium (K) concentrations in roots; and types and concentrations of LMWOAs were measured at different growth stages. Results showed that positive and negative DIFs inhibited the dry matter accumulation of aerial parts, while 0°C DIF was conducive to the accumulation. Compared to 0°C DIF, positive DIFs significantly increased root dry weight, Pmax, root activity and total N, P and K concentrations in roots,...

Due to the close relationship of climate change with reference evapotranspiration (ETo), detecting changes in ETo spatial distribution and its temporal evolution at local and regional levels is favorable to comprehensively understand climate change-induced impacts on hydrology and agriculture. In this study, the objective is to identify whether climate change has caused variation of ETo spatial distribution in different analysis periods [i.e., long- (20-year), medium- (10-year), and short-term (5-year)] and to investigate its temporal evolution (namely, when these changes happened) at annual and monthly scales in Southwest China (SWC). First, we estimated ETo values using the United Nations Food and Agriculture Organization (FAO) Penman-Monteith equation, based on historical climate data measured at 269 weather sites during 1973–2012. The analysis of variance (ANOVA) results indicated that the spatial pattern of annual ETo had significantly changed during the past 40 years, particularly in west SWC for the long-term analysis period, and west and southeast SWC in both medium- and short-term periods, which corresponded to the percent area of significant differences which were 21.9, 58.0, and 48.2 %, respectively. For investigating temporal evolution of spatial patterns of annual ETo, Duncan’s multiple range test was used, and we found that the most significant changes appeared during 1988–2002 with the significant area of higher than 25.0 %. In addition, for long-, medium-, and short-term analysis periods, the spatial distribution has significantly changed during March, September, November, and December, especially in the corresponding periods of 1988–1997, 1983–1992, 1973–1977, and 1988–2002. All in all, climate change has resulted in significant ETo changes in SWC since the 1970s. Knowledge of climate change-induced spatial distribution of ETo and its temporal evolution would aid in formulating strategies for water resources and agricultural managements.

Currently, US forests constitute a large carbon sink, comprising about 9 % of the global terrestrial carbon sink. Wildfire is the most significant disturbance influencing carbon dynamics in US forests. Our objective is to estimate impacts of climate change, CO2 concentration, and nitrogen deposition on the future net biome productivity (NBP) of US forests until the end of twenty-first century under a range of disturbance conditions. We designate three forest disturbance scenarios under one future climate scenario to evaluate factor impacts for the future period (2011–2100): (1) no wildfires occur but forests continue to age (Saging), (2) no wildfires occur and forest ages are fixed in 2010 (Sfixed_nodis), and (3) wildfires occur according to a historical pattern, consequently changing forest age (Sdis_age_change). Results indicate that US forests remain a large carbon sink in the late twenty-first century under the Sfixed_nodis scenario; however, they become a carbon source under the Saging and Sdis_age_change scenarios. During the period of 2011 to 2100, climate is projected to have a small direct effect on NBP, while atmospheric CO2 concentration and nitrogen deposition have large positive effects on NBP regardless of the future climate and disturbance scenarios. Meanwhile, responses to past disturbances under the Sfixed_nodis scenario increase NBP regardless of the future climate scenarios. Although disturbance effects on NBP under the Saging and Sdis_age_change scenarios decrease with time, both scenarios experience an increase in NBP prior to the 2050s and then a decrease in NBP until the end of the twenty-first century. This study indicates that there is potential to increase or at least maintain the carbon sink of conterminous US forests at the current level if future wildfires are reduced and age structures are maintained at a productive mix. The effects of CO2 on the future carbon sink may overwhelm effects of other factors at the end of the twenty-first century. Although our model in conjunction with multiple disturbance scenarios may not reflect the true conditions of future forests, it provides a range of potential conditions as well as a useful guide to both current and future forest carbon management.

During 1961–2012, the regional average annual potential evapotranspiration (PET) of Southwest China (SWC) and the four subregions (named as SR1, SR2, SR3, and SR4) showed different decreases (excluding SR3); while the breakpoint analysis suggested that PET changes (i.e., sign and magnitude) have shifted. Based on a group of sensitivity experiments with Penman-Monteith equation and a new separating method, the contributions of each climate factor alone (i.e., net radiation, Rn; mean temperature, Tave; wind speed, Wnd; and vapor pressure deficit, Vpd) to PET changes were calculated. Results showed that declined Wnd in SR1, reduced Rn in SR2, SR4, and SWC, and increased Vpd in SR3 were responsible for the PET changes during 1961–2012. However, the determinant factor for each subregion and SWC varied in different segmented periods, which were identified using the breakpoint analysis. The impacts of PET shifts on SWC dryness/wetness (reflected by the 3 month Standardized Precipitation-Evapotranspiration index, SPEI-3) during 1961–2012 were then quantified. Briefly, SPEI-3 changes in SR3, SR4, and SWC had the determinant factor of PET in the first one or two period(s), and precipitation in the last period; while they were attributed to PET (precipitation) in SR1 (SR2) for each segmented period. It is found that PET and precipitation had comparable contributions to the variations in SWC dryness/wetness. Our findings have suggested that more attentions should be paid to the impacts of PET changes and shifts in future studies of dryness/wetness or drought.

Based on the data of monthly precipitation and rice productivity at 61 meteorological stations in Jiangsu during 1966–2010, the possible impact of dryness/wetness variability on rice productivity was explored in this paper. Principal component analysis was applied to the climate-induced rice productivity index (CRPI) series and divided Jiangsu into six sub-regions—south, northwest, east, middle, southwest and northernmost. Mann–Kendall trend test for monthly Standardized Precipitation Index (SPI) series in each sub-regions shows that most of Jiangsu was dominated by wetter tendency in August and drier tendency in September. CRPI had stronger negative correlation with monthly SPI series in the majority of sub-regions. Especially, the SPI in October, March, July and August had significantly negative linear relationship with CRPI in south Jiangsu, northwest Jiangsu, middle Jiangsu and southwest Jiangsu, respectively, which indicated the obvious unfavourable impact of extreme wetness episodes on rice productivity.

Light use efficiency (LUE) models are widely used to estimate gross primary productivity (GPP), a dominant component of the terrestrial carbon cycle. Their outputs are very sensitive to LUE. Proper determination of this parameter is a prerequisite for LUE models to simulate GPP at regional and global scales. This study was devoted to investigating the ability of the photochemical reflectance index (PRI) to track LUE variations for a sub-tropical planted coniferous forest in southern China using tower-based PRI and GPP measurements over the period from day 101 to 275 in 2013. Both half-hourly PRI and LUE exhibited detectable diurnal and seasonal variations, and decreased with increases of vapor pressure deficit (VPD), air temperature (Ta), and photosynthetically active radiation (PAR). Generally, PRI is able to capture diurnal and seasonal changes in LUE. However, correlations of PRI with LUE varied dramatically throughout the growing season. The correlation was the strongest (R2 = 0.6427, p &lt, 0.001) in July and the poorest in May. Over the entire growing season, PRI relates better to LUE under clear or partially cloudy skies (clearness index, CI &gt, 0.3) with moderate to high VPD (&gt, 20 hPa) and high temperatures (&gt, 31 C). Overall, we found that PRI is most sensitive to variations in LUE under stressed conditions, and the sensitivity decreases as the growing conditions become favorable when atmosphere water vapor, temperature and soil moisture are near the optimum conditions.

To address the increasing hazards of climate change to winter oilseed rape production in Jiangsu province, southeast China, a total of 12 agro-meteorological disaster indices (MDI) during 1961-2012 was applied to quantify the disaster impacts of spring precipitation extremes, autumn droughts, winter freezing injuries, and spring chilling injuries. The main findings were as follows: (1) due to the obvious north-south gradient of MDI, this province was classified as four climactic sub-regions: north, central-north, central-south, and south Jiangsu, respectively; (2) in central-north, central-south, and south Jiangsu, the maximum length of dry spell during autumn (DI3), the maximum duration of consecutive rainfalls during spring (PI3), and number of rainy days during spring (PI1) were selected as the leading MDI affecting oilseed rape yield, respectively; (3) each 1d of DI3, PI3, and PI1 increase would result in yield losses of 0.7%, 3.6%, and 1.7% in central-north, central-south, and south Jiangsu, respectively; (4) compared to the weak changes in spring rainfall and autumn drought, the significant weakening of winter-spring cold extremes across Jiangsu had more notable yield increasing effects, especially the strong decrease of freezing events during winter had increased yield by up to 1.4-3.0%.

Accurately measuring regional evapotranspiration (ET) is of great significance for studying global climate change, regional hydrological cycles, and surface energy balance. However, estimating regional ET from mixed vegetation types is still challenging. In this study, the Surface Energy Balance Algorithm for Land (SEBAL) and the Surface Energy Balance System (SEBS) models were applied to estimate surface ET in a small agricultural watershed. Landsat8 satellite images were used as input data to the single-source models. The two models were validated at single point and ecosystem scales. The results showed that both models overestimated ET observations in paddy fields and orange groves but underestimated them in dry farmland. The error was mainly caused by the heterogeneity of the mixed pixels. The linear spectral mixture model and a set of equations were introduced to reduce the simulation error. The revised results showed that the relative precision of SEBAL was improved by 9.87% and 10.06%, respectively. This research is expected to provide new ideas for future development of accurate remote-sensing ET estimations on heterogeneous surfaces.

In this study, the regional variations of climate extremes and its possible impact on rice yield in Jiangsu province, southeast China were investigated. A total of 18 climate extremes indices (CEI) of rice-growing period (May–October) based on the daily climate records and rice yield data at 52 stations during 1961–2012 were calculated and analyzed. The main findings were as follows: (1) due to the remarked regional differences of climate extremes, Jiangsu could be divided into six climatic subregions: westernmost, northwest, north, southwest, south, and southeast corner; (2) trends of 18 indices in the six subregions using Mann–Kendall test indicated that Jiangsu was dominated by an obvious wetting and warming tendency, especially in the southern area; (3) correlation analysis between rice yield and CEI using first-difference and climate-induced yield method showed that the negative influences of precipitation extremes were more notable compared to temperature extremes; (4) P95 [precipitation due to very wet days (> 95th percentile)] should be selected as a key meteorological disasters indicator affecting rice yield in the northwest, north, southwest, and south Jiangsu; (5) the increase of P95 since 1990s was detected in most of Jiangsu, which would bring huge risks to rice growing.

Stand age and disturbance data have become more available in recent years and can facilitate modeling studies that integrate and quantify effects of disturbance and nondisturbance factors on carbon dynamics. Since high-quality disturbance and forest age data to support forest dynamic modeling are lacking before 1950, we assumed dynamic equilibrium (carbon neutrality) for the starting conditions of forests with unknown historical disturbance and forest age information. The impacts of this assumption on forest carbon cycle estimation for recent decades have not been systematically examined. In this study, we tested an assumption of disequilibrium conditions for forests with unknown disturbance and age data by randomly assigning ages to them in the model initial year (1900) and analyzed uncertainties for 1951–2010 carbon dynamic simulations compared with the equilibrium assumption. Results show that with the dynamic equilibrium assumption, the total net biome productivity (NBP) of conterminous U.S. forests was 188 ± 60 Tg C yr−1 with 185 ± 56 Tg C yr−1 in living biomass and 3 ± 23 Tg C yr−1 in soil. The C release due to disturbance on average was about 68 ± 55 Tg C yr−1. The disequilibrium assumption causes annual NBP from 1951 to 2010 in conterminous U.S. forests to vary by an average of 13% with the largest impact on the soil carbon component. Uncertainties related to nondisturbance factors have relatively small impacts on NBP estimation (within 10%), while uncertainties related to disturbances cause biases in NBP of 4 to 28%. We conclude that the dynamic equilibrium assumption for the model initialization in 1900 is acceptable for simulating 1951–2010 forest carbon dynamics as long as disturbance and age data are reliable for this later period, although caution should be taken regarding the prior-1950 simulation results because of their greater uncertainties.

Remote sensing of vegetation gross primary production (GPP) is an important step to analyze terrestrial carbon (C) cycles in response to changing climate. The availability of global networks of C flux measurements provides a valuable opportunity to develop remote sensing based GPP algorithms and test their performances across diverse regions and plant functional types (PFTs). Using 70 global C flux measurements including 24 non-forest (NF), 17 deciduous forest (DF) and 29 evergreen forest (EF), we present the evaluation of an upscaled remote sensing based greenness and radiation (GR) model for GPP estimation. This model is developed using enhanced vegetation index (EVI) and land surface temperature (LST) from the Moderate Resolution Imaging Spectroradiometer (MODIS) and global course resolution radiation data from the National Center for Environmental Prediction (NCEP). Model calibration was achieved using statistical parameters of both EVI and LST fitted for different PFTs. Our results indicate that compared to the standard MODIS GPP product, the calibrated GR model improved the GPP accuracy by reducing the root mean square errors (RMSE) by 16%, 30% and 11% for the NF, DF and EF sites, respectively. The standard MODIS and GR model intercomparisons at individual sites for GPP estimation also showed that GR model performs better in terms of model accuracy and stability. This evaluation demonstrates the potential use of the GR model in capturing short-term GPP variations in areas lacking ground measurements for most of vegetated ecosystems globally.

Land surface processes are an important part of the Earth&rsquo, s mass and energy cycles. The application of a land surface process model for farmland in the low-hilly red soil region of southern China continues to draw research attention. Conventional model does not perform well in the simulation of irrigated farmland, because the influence of land surface water is not considered. In this study, an off-line version of the Simple Biosphere model 2 (SiB2) was locally parameterized in a typical farmland of the low-hilly red soil region using field observations and remote sensing data. The performance of SiB2 was then evaluated through comparison to Bowen-ratio direct measurements in a second growing period of rice in 2015 (late rice from 23 July to 31 October). The results show that SiB2 underestimated latent heat flux (LE) by 16.0% and overestimated sensible heat flux (H) by 16.7%, but net radiation flux (Rn) and soil heat flux were reasonably simulated. The single factor sensitivity analysis of Rn, H, and LE modeled in SiB2 indicated that downward shortwave radiation (DSR) and downward longwave radiation (DLR) had a significant effect on Rn simulation. In driving data, DSR, DLR and wind speed (u) were the main factors that could cause a distinct change in sensible heat flux. An irrigation module was added to the original SiB2 model to simulate the influence of irrigated paddy fields according to the sensitivity analysis results of the parameters (C1, bulk boundary-layer resistance coefficient, C2, ground to canopy air-space resistance coefficient, and Ws, volumetric water content at soil surface layer). The results indicate that application of the parameterized SiB2 with irrigation module could be better in southern Chinese farmland.

Water use efficiency (WUE), defined as the ratio of gross primary productivity to evapotranspiration, is an important indicator of the trade-off between water loss and carbon gain. We used a biophysical process-based model to examine the relative importance of climate-induced changes in meteorological factors and leaf area index (LAI) on the changes in WUE in East Asia. Validation showed that our simulation could capture the magnitudes and variations of WUE at 18 flux sites in Asia. Regional results indicated that the highest WUE occurred in boreal forests at high latitudes and the lowest WUE in desert areas of China. Changes in meteorological factors negatively affected WUE in the northwestern, northern, and eastern study regions. Changes in LAI had determinant impacts on changes in WUE in most areas except for those with sparse or low-density vegetation (e.g., western interior China, southeast island countries) where meteorological factors dominated. We conclude that, aside from the impact of meteorological factors on WUE, climate-induced changes in LAI may play a prominent role in regulating WUE changes.

To improve the understanding of water-vegetation relationships, direct comparative studies assessing the utility of satellite remotely sensed soil moisture, gridded precipitation products, and land surface model output are needed. A case study was investigated for a water-limited, lateral inflow receiving area in northeastern Australia during December 2008 to May 2009. In January 2009, monthly precipitation showed strong positive anomalies, which led to strong positive soil moisture anomalies. The precipitation anomalies disappeared within a month. In contrast, the soil moisture anomalies persisted for months. Positive anomalies of Normalized Difference Vegetation Index (NDVI) appeared in February, in response to water supply, and then persisted for several months. In addition to these temporal characteristics, the spatial patterns of NDVI anomalies were more similar to soil moisture patterns than to those of precipitation and land surface model output. The long memory of soil moisture mainly relates to the presence of clay-rich soils. Modeled soil moisture from four of five global land surface models failed to capture the memory length of soil moisture and all five models failed to present the influence of lateral inflow. This case study indicates that satellite-based soil moisture is a better predictor of vegetation water availability than precipitation in environments having a memory of several months and thus is able to persistently affect vegetation dynamics. These results illustrate the usefulness of satellite remotely sensed soil moisture in ecohydrology studies. This case study has the potential to be used as a benchmark for global land surface model evaluations. The advantages of using satellite remotely sensed soil moisture over gridded precipitation products are mainly expected in lateral-inflow and/or clay-rich regions worldwide.

Around the intensity, frequency, duration, accumulated temperature, and even extremes of high-temperature events, nine selected temperature-related indices were used to explore the space and time changes of rice heat stress in Jiangxi province, southeast China. Several statistical methods including Mann–Kendall trend test (M-K test) and principal component analysis (PCA) were used in this study, and main results were listed as follows: (1) The changes in the intensity indices for high-temperature events were more significant, it was mainly embodied in that more than 80 % of stations had positive trends. (2) R-mode PCA was applied to the multiannual average values of nine selected indices of whole stations, and the results showed that the higher hazard for rice heat stress could be mainly detected in the middle and northeast area of Jiangxi. (3) S-mode PCA was applied to the integrated heat stress index series, and the results demonstrated that Jiangxi could be divided into four sub-regions with different variability in rice heat stress. However, all the sub-regions are dominated by increasing tendencies in rice heat stress since 1990. (4) Further analysis indicated that the western north Pacific sub-tropical high (WPSH) had the significant dominant influence on the rice heat stress in Jiangxi province.

Remote sensing of sun-induced chlorophyll fluorescence (SIF) is a novel optical tool for the assessment of terrestrial photosynthesis or gross primary production (GPP). Several recent studies have demonstrated the strong link between GPP and space-borne retrievals of SIF at broad scales. However, critical gaps remain between short-term small-scale mechanistic understanding and seasonal global observations. Here, we present a model-based analysis of the relationship between SIF and GPP across scales for diverse vegetation types and a range of meteorological conditions, with the ultimate focus on reproducing the environmental conditions during remote sensing measurements. The coupled fluorescence-photosynthesis model SCOPE is used to simulate GPP and SIF at the both leaf and canopy levels for 13 flux sites. Analyses were conducted to investigate the effects of temporal scaling, canopy structure, overpass time, and spectral domain on the relationship between SIF and GPP. The simulated SIF is highly non-linear with GPP at the leaf level and instantaneous time scale and tends to linearize when scaling to the canopy level and daily to seasonal. These relationships are consistent across a wide range of vegetation types. The relationship between SIF and GPP is primarily driven by absorbed photosynthetically active radiation (APAR), especially at the seasonal scale, although the photosynthetic efficiency also contributes to strengthen the link between them. The linearization of their relationship from leaf to canopy and averaging over time is because the overall conditions of the canopy fall within the range of the linear responses of GPP and SIF to light and the photosynthetic capacity. Our results further show that the top-of-canopy relationships between simulated SIF and GPP have similar linearity regardless of whether we used the morning or midday satellite overpass times. Field measurements confirmed these findings. In addition, the simulated red SIF at 685 nm has a similar relationship with GPP as that of far-red SIF at 740 nm at the canopy level. These findings provide model-based evidence to interpret remotely sensed SIF data and their relationship with GPP.

Gross primary productivity (GPP) quantifies the photosynthetic uptake of carbon by ecosystems and is an important component of the terrestrial carbon cycle. Empirical light use efficiency (LUE) models and process-based Farquhar, von Caemmerer, and Berry (FvCB) photosynthetic models are widely used for GPP estimation. In this paper, the MODIS GPP algorithm using the LUE approach and the Boreal Ecosystem Productivity Simulator (BEPS) based on the FvCB model in which a sunlit and shaded leaf separation scheme is evaluated against GPP values derived from eddy-covariance (EC) measurements in a variety of ecosystems. Although the total GPP values simulated using these two models agree within 89% when they are averaged for the conterminous U.S., there are systematic differences between them in terms of their spatial and temporal distribution patterns. The spatial distribution of MODIS GPP therefore differs substantially from that produced by BEPS. These differences may be due to an inherent problem of the LUE modeling approach. When a constant maximum LUE value is used for a biome type, this simplification cannot properly handle the contribution of shaded leaves to the total canopy-level GPP. When GPP is modeled by BEPS as the sum of sunlit and shaded leaf GPP, the problem is minimized, i.e., at the low end, the relative contribution of shaded leaves to GPP is small and at the high end, the relative contribution of shaded leaves is large. Compared with monthly and annual GPP derived from eddy covariance data at 40 tower sites in North America, BEPS performed better than the MODIS GPP algorithm. The difference between MODIS and BEPS GPP widens as with the fraction of shaded leaves increases. The simpler LUE modeling approach should therefore be further improved to reduce this bias issue for effective estimation of regional and temporal GPP distributions.