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Changes in large-scale circulation, especially El Niño–Southern Oscillation (ENSO), have significant impacts on dust activities over the dust source and downwind regions. However, these impacts present an interdecadal pattern, and it remains less clear which factors lead to the interdecadal variability of the ENSO impact on dust activities over northwestern South Asia, although previous studies have discussed the response of interannual dust activities over northwestern South Asia to the ENSO circle. Based on the linear regression model and MERRA-2 atmospheric aerosol reanalysis data, this study investigated the interdecadal variability of the ENSO impact on dust activities as well as the associated possible atmospheric drivers under two different warming phases over northwestern South Asia. Results indicated that the relationship between ENSO and dust column mass density (DUCMASS) experienced an obvious shift from the accelerated global warming period (1982–1996) to the warming hiatus period (2000–2014). The change in Atlantic and Indian Ocean sea surface temperature anomaly (SSTA) patterns weakened the impact of ENSO on dust activities over northwestern South Asia during 1982–1996, while the change in Pacific Decadal Oscillation (PDO) strengthened ENSO's effect when it was in phase with ENSO. Both the Atlantic and Indian Ocean SSTA patterns were modulated by the duration of ENSO events (i.e., continuing and emerging ENSO). This study provides new insights into numerical simulation involving the influence of atmospheric teleconnections on the variability of dust activities and their influence mechanisms.

The change of large-scale circulation, especially El Niño-Southern Oscillation (ENSO), play an important role in the interdecadal variability of dust activities over the dust source and downwind regions. However, the detailed factors that lead to the interdecadal variability of the ENSO impact on dust activities over the northwestern South Asia remain less clear, although previous studies have discussed the response of the interannual dust activities over the northwestern South Asia to the ENSO circle. Based on the linear regression model and MERRA-2 atmospheric aerosol reanalysis data, this study investigated the interdecadal variability of the ENSO impact on dust activities as well as the associated possible atmospheric drivers under two different warming phases over the northwestern South Asia. Results indicated that the relationship between ENSO and surface dust mass concentration (DUSMASS) experienced an obvious shift from the accelerated global warming period (1982–1996) to the warming hiatus period (2000–2014). The change of Atlantic SSTA pattern weakened the impact of ENSO on dust activities over the northwestern South Asia, while that of Indian Ocean SSTA pattern and PDO tended to strengthen ENSO’s effect. Both the Atlantic and Indian Ocean SSTA patterns were modulated by the duration of ENSO events (i.e., continuing and emerging ENSO). The Eurasian continent and Indian Ocean thermal contrast was less likely to cause the shift of ENSO–DUSMASS relationship. This study provides new sights to numerical simulation involving the influence of atmospheric teleconnections on the variability of dust activities and their influence mechanisms.

In South Asia, annual land cover and land use (LCLU) is a severe issue in the field of earth science because it affects regional climate, global warming, and human activities. Therefore, it is vital essential to obtain correct information on the LCLU in the South Asia regions. LULC annual map covering the entire period is the primary dataset for climatological research. Although the LULC annual global map was produced from the MODIS dataset in 2001, this limited the perspective of the climatological analysis. This study used AVHRR GIMMS NDVI3g data from 2001 to 2015 to randomly forests classify and produced a time series of the annual LCLU map of the South Asia. The MODIS land cover products (MCD12Q1) are used as data from reference for trained classifiers. The results were verified using of the annual map of LCLU time series, and the space-time dynamics of the LCLU map were shown in the last 15 years, from 2001 to 2015. The overall precision of our 15-year land cover map simplifies 16 classes, which is 1.23% and 86.70% significantly maximum as compared to the precision of the MODIS data map. Findings of the past 15 years shows the changing detection that forest land, savanna, farmland, urban and established land, arid land, and cultivated land have increased; by contrast, woody prairie, open shrub-lands, permanent ice and snow, mixed forests, grasslands, evergreen broadleaf forests, permanent wetlands, and water bodies have been significantly reduced over South Asia regions.

Rapid intensification (RI) progress is the main challenge that precludes the improvement of tropical cyclone (TC) intensity estimates. In this paper, a composite study of the observable characteristics of 11-year TCs undergoing RI and non-RI in the Western North Pacific were conducted using CloudSat tropical cyclone (CSTC) dataset. 2B-GEOPROF, 2B-CLDCLASS, and 2B-CWC-RO products in the CSTC dataset were used to construct radar reflectivity, cloud class, and cloud water-content distributions for each category, respectively. The results show that radar echo statistics have an arc-like contour profile for all five categories, with two distinct modes of reflectivity distributions separated by the melting layer. RI has the highest frequency expanding through the whole reflectivity range along with the broadest coverage in the upper branch, and a “continuity” of reflectivity distribution from −10 to 10 dBZ at 6–11 km could be a vital indicator of TC intensification. A sharp slope can be observed in the lower branch due to heavy precipitation attenuation. Vertical distributions of cloud types show that all categories have similar cloud compositions, and deep convective clouds are apparently to play an important role in maintaining the TC intensification. Liquid water content (LWC) curve exhibits a bimodal distribution with two peaks at around 1.5 and 5 km, while ice water content (IWC) curve is much smoother and has one peak at around 9 km. It is suggested that TCs with larger LWC around freezing level or/and larger IWC at upper level tend to intensify within the next 24 hours.

Satellite-derived Normalized Difference Vegetation index (NDVI) data records offer important sources for long term correlation modelling over West Africa. In this study, we assessed long range correlations in half monthly NDVI records over West Africa from 1982 to 2011 using GIMMS NDVI. In our analysis, we assessed (a) the annual and seasonal trends obtained using Ordinary Linear Regression, (b) the detrended lag-1-autocorrelation C(1), (c) the Detrended Fluctuation Analysis (DFA) scaling Hurst exponent h and (d) the Multifractal (MF) characteristics of NDVI. Results show that there exist some patterns or trends in the records that persist over time. The value of C(1) for NDVI was obtained as 0.989 is significant at 95% confidence interval. Consequently, the scaling h values of the Hurst DFA showed that about 37.4, 20.5, 41.7 and 0.5% of the vegetated areas are anti-correlated (h

The accurate and timely spatial distribution information of various crop types is vital for food security. In this study, the 2-band enhanced vegetation index (EVI2) data from Moderate Resolution Imaging Spectroradiometer (MODIS) were combined with sparse representation approach to identify the distribution of various crop types in Shandong Province, China. Three groups of input variables derived from EVI2 including annual time series EVI2 (TS), harmonic features (HF), and combined vector formed by harmonic features and texture features (HFT) were used. The online dictionary learning and orthogonal matching pursuit algorithms were applied to generate the dictionary and solve the sparse coefficients of the identified samples, respectively. Then, the label of the identified samples can be obtained according to the minimum residuals between the dictionary and the sparse coefficients of the identified samples. At the provincial level, the validation based on the statistical data showed that three groups of input variables presented lower than ±25% at relative errors, and input variables of HFT performed better than the other two. At the municipal level, the results achieved by using input variables of HFT also agreed well with the statistics with the coefficient of determination R2 > 0.85 for wheat and maize, as well as R2 > 0.71 for peanut and cotton during 2014-2016. These results demonstrate that the combination of the input variables of HFT derived from time series MODIS EVI2 data and sparse representation approach can be used for crop identification in the study area.

Drought, a climate-related disaster impacting a variety of sectors, poses challenges for millions of people in South Asia. Accurate and complete drought information with a proper monitoring system is very important in revealing the complex nature of drought and its associated factors. In this regard, deep learning is a very promising approach for delineating the non-linear characteristics of drought factors. Therefore, this study aims to monitor drought by employing a deep learning approach with remote sensing data over South Asia from 2001–2016. We considered the precipitation, vegetation, and soil factors for the deep forwarded neural network (DFNN) as model input parameters. The study evaluated agricultural drought using the soil moisture deficit index (SMDI) as a response variable during three crop phenology stages. For a better comparison of deep learning model performance, we adopted two machine learning models, distributed random forest (DRF) and gradient boosting machine (GBM). Results show that the DFNN model outperformed the other two models for SMDI prediction. Furthermore, the results indicated that DFNN captured the drought pattern with high spatial variability across three penology stages. Additionally, the DFNN model showed good stability with its cross-validated data in the training phase, and the estimated SMDI had high correlation coefficient R2 ranges from 0.57~0.90, 0.52~0.94, and 0.49~0.82 during the start of the season (SOS), length of the season (LOS), and end of the season (EOS) respectively. The comparison between inter-annual variability of estimated SMDI and in-situ SPEI (standardized precipitation evapotranspiration index) showed that the estimated SMDI was almost similar to in-situ SPEI. The DFNN model provides comprehensive drought information by producing a consistent spatial distribution of SMDI which establishes the applicability of the DFNN model for drought monitoring.

In South Asia, annual land cover and land use (LCLU) is a severe issue in the field of earth science because it affects regional climate, global warming, and human activities. Therefore, it is vital essential to obtain correct information on the LCLU in the South Asia regions. LULC annual map covering the entire period is the primary dataset for climatological research. Although the LULC annual global map was produced from the MODIS dataset in 2001, this limited the perspective of the climatological analysis. This study used AVHRR GIMMS NDVI3g data from 2001 to 2015 to randomly forests classify and produced a time series of the annual LCLU map of the South Asia. The MODIS land cover products (MCD12Q1) are used as data from reference for trained classifiers. The results were verified using of the annual map of LCLU time series, and the space-time dynamics of the LCLU map were shown in the last 15 years, from 2001 to 2015. The overall precision of our 15-year land cover map simplifies 16 classes, which is 1.23% and 86.70% significantly maximum as compared to the precision of the MODIS data map. Findings of the past 15 years shows the changing detection that forest land, savanna, farmland, urban and established land, arid land, and cultivated land have increased; by contrast, woody prairie, open shrub-lands, permanent ice and snow, mixed forests, grasslands, evergreen broadleaf forests, permanent wetlands, and water bodies have been significantly reduced over South Asia regions.

Proper estimation and spatiotemporal trend analysis of net primary productivity (NPP) are very important for forest ecosystem monitoring and management. In this article, we investigate the spatiotemporal trend and variability of the Boreal Ecosystem Productivity Simulator (BEPS)-derived forest NPP over Nepal during the period 2000–2015. This study is the first attempt to simulate nation-wide NPP at 500 m spatial resolution at the daily step for the recent 16-year period. In our work, we use a process-based model BEPS, to estimate the NPP in the forest of Nepal. All the meteorological, forest cover, soil data, leaf area index (LAI), tree cover, biomass, and biophysical parameters, required for the model, were downloaded from different sources or prepared, pre-processed and used in the BEPS model to come up with the daily NPP. In spite of some fluctuations in NPP in some years, there is an overall positive change in the NPP throughout the study period. The annual average national NPP has been raised from 1.34 g m−2 day−1 to 1.74 g m−2 day−1 from 2000 to –2015. Examining the inter-annual variation, we found almost 87% of the total forested area has a positive inter-annual variation and 13% negative. The overall average daily NPP per m2 over the entire forest in the entire period ranges from 0.1 to 4.7 g m−2 day−1 with an average of 1.65 g m−2 day−1. Similarly, by physiographic regions, the average NPP in the plain was 2.43 g m−2 day−1, hills 1.83 g m−2 day−1, and mountain 1.41 g m−2 day−1. The total daily NPP for the entire forest area ranges from 3 to 257 tons with an average of 96 tons over the study period. NPP was found to be highly influenced by rainfall, solar radiation, and temperature. While in case of the variation of NPP on different forest types, broad-leaved forest was found to have almost 1.7 times more (NPP 1.97 g m−2 day−1) than that of needle-leaved forest (NPP 1.18 g m−2 day−1). The slope per cent of −2 day−1) for higher NPP among different slope per cent. We found that this method could be more convenient for estimation and studies of the NPP in Nepal. The result from this study gives us important information on intra and inter-annual spatiotemporal trends and variability of NPP in the forest of overall Nepal of different physiographic regions. It also gives us information on the variation in trends of NPP with the change in various climatic and vegetation parameters, which can be important for the proper forest ecosystem monitoring, management, and planning operations.

As climate change intensifies, surface vegetation productivity and carbon exchange between terrestrial ecosystems and the atmosphere are significantly affected by the variation of climatic factors. Due to the sensitivity of grasslands to these climatic factors, it is crucial to understand the response of vegetation greenness, or carbon exchange within grasslands, to environment factor dynamics. In this study, we used solar-induced chlorophyll fluorescence (SIF), precipitation (P), vapor pressure deficit (VPD), evaporative stress (ES), and root zone soil moisture (RSM) derived from remote sensing, reanalysis, and assimilation datasets to explore the response of vegetation greenness within Eurasian Steppe to climatic factors. Our results indicated deseasonlization based on the Seasonal-Trend decomposition using Loess (STL) method, which was an effective means to remove the seasonality disturbances that affect the qualification of the relationship between SIF and the four climatic factors. The response of SIF had a time lag effect on these climatic factors, and the longer the response period, the greater the impact on the correlation of SIF with P, VPD, ES, and RSM. We also found, among the four factors, that the response of SIF to ES was the timeliest. The findings of this study emphasized the impact of the seasonality and time lag effect on the dynamic response between variables, and provided references to the attribution and monitoring of vegetation greenness and ecosystem productivity.

Aerosols can affect the atmospheric radiation balance through direct and indirect effects. The formation and development of cloud and precipitation influenced by aerosols differ significantly from each other in different meteorological conditions. In this work, we used the MODIS’s daily Aerosol Optical Depth (AOD), Cloud Effective Radius (CER), Cloud Top Temperature (CTT), Cloud Water Path (CWP) and ECMWF’s Relative Humidity (RH), Vertical Velocity (VV) and Horizontal Wind (HW) (from 2005 to 2008) to reveal the influence of meteorological factors on the distribution of aerosols, and also the correlation between aerosols and clouds. The study was designed in such a way that, the RH, VV, Upwind (UW), Downwind (DW) and CWP were divided into several intervals, to quantify the relationship between AOD and CER by controlling one single variable or two comprehensive variables over the mountains and plains. At the same time, the effect of wind speed and direction on polluted conditions was analyzed through the superposed spatial distribution map of wind and AOD. The conclusions are as follows: (1) The wind coming from mountains dispelled aerosols while the sea breeze invigorated aerosols, and the upwind showed a markedly negative relevance with AOD. (2) The strong upwind contributed to the positive relationship between AOD and CER, and the correlation rose by 38% after excluding the condition where CWP

Recent studies have shown that global Penman-Monteith equation based (PM-based) models poorly simulate water stress when estimating evapotranspiration (ET) in areas having a Mediterranean climate (AMC). In this study, we propose a novel approach using precipitation, vertical root distribution (VRD), and satellite-retrieved vegetation information to simulate water stress in a PM-based model (RS-WBPM) to address this issue. A multilayer water balance module is employed to simulate the soil water stress factor (SWSF) of multiple soil layers at different depths. The water stress factor (WSF) for surface evapotranspiration is determined by VRD information and SWSF in each layer. Additionally, four older PM-based models (PMOV) are evaluated at 27 flux sites in AMC. Results show that PMOV fails to estimate the magnitude or capture the variation of ET in summer at most sites, whereas RS-WBPM is successful. The daily ET resulting from RS-WBPM incorporating recommended VI (NDVI for shrub and EVI for other biomes) agrees well with observations, with R2=0.60 ( RMSE = 18.72 W m−2) for all 27 sites and R2=0.62 ( RMSE = 18.21 W m−2) for 25 nonagricultural sites. However, combined results from the optimum older PM-based models at specific sites show R2 values of only 0.50 ( RMSE = 20.74 W m−2) for all 27 sites. RS-WBPM is also found to outperform other ET models that also incorporate a soil water balance module. As all inputs of RS-WBPM are globally available, the results from RS-WBPM are encouraging and imply the potential of its implementation on a regional and global scale.

Dust storms are one of the major disasters in arid and semi-arid regions. Understanding the impact factors is crucial for early warning and disaster mitigation. Many factors have been affecting the spatiotemporal patterns of dust storms. However, the relative importance of those factors to dust emissions in recent 40 years over the whole dust belt has not been well documented. This study explored the relative importance of those factors to the interannual variation in dust emissions over the whole dust belt. The difference in the primary contributors over two global warming phases was compared to investigate the association of dust emission trend with global warming. The results indicated that the wind regimes, such as the nocturnal low-level jet, were key factors to the wintertime dust emissions over the Sahel. The springtime dust storms related to cold air and cyclones primarily occurred in the southern coast of the Mediterranean and northwestern China. The cold high and heat low were typical mechanisms for the summertime dust emissions, which frequently formed in western North Africa, the Middle East, and northwestern Indian subcontinent. Whereas the land cover and drought conditions play significant roles in the relatively wetter regions, i.e., the southern coast of the Mediterranean, the Ustyurt Plateau, the northwest coast of Indian Ocean, the Thar desert and the Taklimakan desert. The wintertime global warming coincided well with the decreasing trend of dust emissions over the dust sources inland with a more significant effect seen in Asia. The positive anomalies of summertime dust emissions were primarily found over the dust sources in the low-lying coastal areas on the foot of high mountains. Understanding the relative importance of those drivers to dust emission trends and their variation under different warming periods can improve the prediction of dust storm evolutions and mitigate their impacts under future climate change.

Drought over Southwest China occurs frequently and has an obvious seasonal characteristic. Proper management of regional droughts requires knowledge of the expected frequency or probability of specific climate information. This study utilized k-means classification and copulas to demonstrate the regional drought occurrence probability and return period based on trivariate drought properties, i.e., drought duration, severity, and peak. A drought event in this study was defined when 3-month Standardized Precipitation Evapotranspiration Index (SPEI) was less than −0.99 according to the regional climate characteristic. Then, the next step was to classify the region into six clusters by k-means method based on annual and seasonal precipitation and temperature and to establish marginal probabilistic distributions for each drought property in each sub-region. Several copula types were selected to test the best fit distribution, and Student t copula was recognized as the best one to integrate drought duration, severity, and peak. The results indicated that a proper classification was important for a regional drought frequency analysis, and copulas were useful tools in exploring the associations of the correlated drought variables and analyzing drought frequency. Student t copula was a robust and proper function for drought joint probability and return period analysis, which is important for analyzing and predicting the regional drought risks.

The temporal variation of dust emissions over Central Asia in recent decades and the linkages with atmospheric factors have not been well documented. This study investigated the annual and interannual variations of dust emissions in the six main dust emission regions (i.e., Sor Mertvyy Kultuk (SMK), Ustyurt Plateau (UP), Southeast of Aralkum (SA), Eastern shore of the southern Caspian (ESC), Central Karakum desert (CK), and Trans-Unguz Karakum desert (TK)), using the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2) data from 1980 to 2018. The climate linkages with the variation trend were explored using reanalysis data and the climate indices from NOAA Climate Prediction Center (CPC). The results indicated that, in early spring, dust emissions are distinct in all these sources (except for ESC) due to the cold air intrusions from north, northwest, and northeast directions, and the effect of precipitation was limited by the high evaporation. Whereas in summer and/or early autumn, dust emissions were primarily in SA, ESC, CK, and TK because of the strong northeasterly winds, low vegetation, and dry soils. The overall interannual change of dust emissions in Central Asia showed that relatively larger emissions occurred during 1980–1986 and 1993–2003, especially in April. The dust emissions in SA, ESC, and TK in summer were positively correlated with the pressure gradient between northwestern Kazakhstan and Hindu Kush (NWKHKI), while those in SMK were negatively correlated with Caspian Sea High. The dust emissions in the six dust sources were highly correlated with the maximum wind speeds but showed different sensitivities to drought. The Multivariate El Nino-Southern Oscillation Index (MEI) was related to dust emissions in April over coastal Caspian-Sea areas (SMK and ESC), while those in February over the southern parts (ESC, CK, and TK) were significantly correlated with North Atlantic Oscillation/Arctic Oscillation (NAO/AO). These conclusions could contribute to the meteorology research related to dust emissions and also to the decisions made for environmental protection.

Drought is one of the most frequent climate-related disasters occurring in Southwest China, where the occurrence of drought is complex because of the varied landforms, climates and vegetation types. To monitor the comprehensive information of drought from meteorological to vegetation aspects, this paper intended to propose the optimized meteorological drought index (OMDI) and the optimized vegetation drought index (OVDI) from multi-source satellite data to monitor drought in three bio-climate regions of Southwest China. The OMDI and OVDI were integrated with parameters such as precipitation, temperature, soil moisture and vegetation information, which were derived from Tropical Rainfall Measuring Mission (TRMM), Moderate Resolution Imaging Spectroradiometer Land Surface Temperature (MODIS LST), AMSR-E Soil Moisture (AMSR-E SM), the soil moisture product of China Land Soil Moisture Assimilation System (CLSMAS), and MODIS Normalized Difference Vegetation Index (MODIS NDVI), respectively. Different sources of satellite data for one parameter were compared with in situ drought indices in order to select the best data source to derive the OMDI and OVDI. The Constrained Optimization method was adopted to determine the optimal weights of each satellite-based index generating combined drought indices. The result showed that the highest positive correlation and lowest root mean square error (RMSE) between the OMDI and 1-month standardized precipitation evapotranspiration index (SPEI-1) was found in three regions of Southwest China, suggesting that the OMDI was a good index in monitoring meteorological drought; in contrast, the OVDI was best correlated to 3-month SPEI (SPEI-3), and had similar trend with soil relative water content (RWC) in temporal scale, suggesting it a potential indicator of agricultural drought. The spatial patterns of OMDI and OVDI along with the comparisons of SPEI-1 and SPEI-3 for different months in one year or one month in different years showed significantly varied drought locations and areas, demonstrating regional and seasonal fluctuations, and suggesting that drought in Southwest China should be monitored in seasonal and regional level, and more fine distinctions of seasons and regions need to be considered in the future studies of this area.

Forest fires have major impact on ecosystems and greatly impact the amount of greenhouse gases and aerosols in the atmosphere. This paper presents an overview in the forest fire detection, emission estimation, and fire risk prediction in China using satellite imagery, climate data, and various simulation models over the past three decades. Since the 1980s, remotely-sensed data acquired by many satellites, such as NOAA/AVHRR, FY-series, MODIS, CBERS, and ENVISAT, have been widely utilized for detecting forest fire hot spots and burned areas in China. Some developed algorithms have been utilized for detecting the forest fire hot spots at a sub-pixel level. With respect to modeling the forest burning emission, a remote sensing data-driven Net Primary productivity (NPP) estimation model was developed for estimating forest biomass and fuel. In order to improve the forest fire risk modeling in China, real-time meteorological data, such as surface temperature, relative humidity, wind speed and direction, have been used as the model input for improving prediction of forest fire occurrence and its behavior. Shortwave infrared (SWIR) and near infrared (NIR) channels of satellite sensors have been employed for detecting live fuel moisture content (FMC), and the Normalized Difference Water Index (NDWI) was used for evaluating the forest vegetation condition and its moisture status.

Model simulation is an important way to study the effects of climate change on agriculture. Such assessment is subject to a range of uncertainties because of either incomplete knowledge or model technical uncertainties, impeding effective decision-making to climate change. On the basis of uncertainties in the impact assessment at different levels, this article systematically summarizes the sources and propagation of uncertainty in the assessment of the effect of climate change on agriculture in terms of the climate projection, the assessment process, and the crop models linking to climate models. Meanwhile, techniques and methods focusing on different levels and sources of uncertainty and uncertainty propagation are introduced, and shortcomings and insufficiencies in uncertainty processing are pointed out. Finally, in terms of how to accurately assess the effect of climate change on agriculture, improvements to further decrease potential uncertainty are suggested.

The grassland ecosystem in the Northern-Tibet Plateau (NTP) of China is very sensitive to weather and climate conditions of the region. In this study, we investigate the spatial and temporal variations of the grassland ecosystem in the NTP using the NOAA/AVHRR ten-day maximum NDVI composite data of 1981-2001. The relationships among Vegetation Peak-Normalized Difference Vegetation Index (VP-NDVI) and climate variables were quantified for six counties within the NTP. The notable and uneven alterations of the grassland in response to variation of climate and human impact in the NTP were revealed. Over the last two decades of the 20th century, the maximum greenness of the grassland has exhibited high increase, slight increase, no-change, slight decrease and high decrease, each occupies 0.27%, 8.71%, 77.27%, 13.06% and 0.69% of the total area of the NTP, respectively. A remarkable increase (decrease) in VP-NDVI occurred in the central-eastern (eastern) NTP whereas little change was observed in the western and northwestern NTP. A strong negative relationship between VP-NDVI and ET0 was found in sub-frigid, semi-arid and frigid- arid regions of the NTP (i.e., Nakchu, Shantsa, Palgon and Amdo counties), suggesting that the ET0 is one limiting factor affecting grassland degradation. In the temperate-humid, sub-frigid and sub-humid regions of the NTP (Chali and Sokshan counties), a significant inverse correlation between VP-NDVI and population indicates that human activities have adversely affected the grassland condition as was previously reported in the literature. Results from this research suggest that the alteration and degradation of the grassland in the lower altitude of the NTP over the last two decades of the 20th century are likely caused by variations of climate and anthropogenic activities.

Studying on the response of vegetation phenology to climate change at different temporal and spatial scales is important for understanding and predicting future terrestrial ecosystem dynamics and the adaptation of ecosystems to global change. In this study, the Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) dataset and climate data were used to analyze the dynamics of grassland phenology as well as their correlation with climatic factors in different eco-geographic regions and elevation units across the Tibetan Plateau. The results showed that during 2003–2012, the start of the grassland greening season (SOS) appeared later while the end of the growing season (EOS) appeared earlier following the plateau's precipitation and heat gradients from southeast to northwest. The multi-year mean value of SOS showed differences between various eco-geographic regions and was significantly impacted by average elevation and regional average precipitation during spring. Regional mean differences for EOS were mainly regulated by mean temperature during autumn. Changes in trends of SOS in the central and eastern eco-geographic regions were coupled to the mean temperature during spring, advancing by about 7d/°C. However, in the two southwestern eco-geographic regions, SOS was delayed significantly due to the impact of spring precipitation. The results also showed that the SOS occurred later with increasing elevation, as expected, with a delay rate of 0.66 d/100m. For 2003–2012, SOS showed an advancing trend in low-elevation areas, but a delayed trend in high-elevation areas, while EOS was delayed in low-elevation areas, but advanced in high-elevation areas. 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Based on the leaf area index (LAI) data derived from remote sensing information and eco-climate data, the responses of regional ecosystem variations in seasonal and interannual scales to the East Asian monsoon are studied. It is found that the vegetation ecosystems of eastern China are remarkably correlated with the East Asian monsoon in seasonal and interannual scales. In the seasonal timescale, the obvious variations of the vegetation ecosystems occur with the development of the East Asian monsoon from the south in the spring to the north in the autumn. In the interannual scale, high LAI appears in the strong East Asian monsoon year, whereas low LAI is related to the weak East Asian monsoon year. These further lead to the characteristic of “monsoon-driven ecosystem” in the eastern China monsoon region, which can be revealed by LAI.

Maize is one of the major cultivated crops of China, having a central role in ensuring the food security of the country. There has been a significant increase in studies of maize under interactive effects of elevated CO2 concentration ([CO2]) and other factors, yet the interactive effects of elevated [CO2] and increasing precipitation on maize has remained unclear. In this study, a manipulative experiment in Jinzhou, Liaoning province, Northeast China was performed so as to obtain reliable results concerning the later effects. The Open Top Chambers (OTCs) experiment was designed to control contrasting [CO2] i.e., 390, 450 and 550 µmol·mol−1, and the experiment with 15% increasing precipitation levels was also set based on the average monthly precipitation of 5–9 month from 1981 to 2010 and controlled by irrigation. Thus, six treatments, i.e. C550W+15%, C550W0, C450W+15%, C450W0, C390W+15% and C390W0 were included in this study. The results showed that the irrigation under elevated [CO2] levels increased the leaf net photosynthetic rate (P n) and intercellular CO2 concentration (C i) of maize. Similarly, the stomatal conductance (G s) and transpiration rate (T r) decreased with elevated [CO2], but irrigation have a positive effect on increased of them at each [CO2] level, resulting in the water use efficiency (WUE) higher in natural precipitation treatment than irrigation treatment at elevated [CO2] levels. Irradiance-response parameters, e.g., maximum net photosynthetic rate (P nmax) and light saturation points (LSP) were increased under elevated [CO2] and irrigation, and dark respiration (R d) was increased as well. The growth characteristics, e.g., plant height, leaf area and aboveground biomass were enhanced, resulting in an improved of yield and ear characteristics except axle diameter. The study concluded by reporting that, future elevated [CO2] may favor to maize when coupled with increasing amount of precipitation in Northeast China.