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As clean, renewable energy, photovoltaic (PV) energy can reduce the ozone-layer loss and climate deterioration caused by the use of traditional types of energy to generate electricity. At present, most PV energy products involve the influence of cloud cover on solar radiation. However, the resolution and precision of most cloud cover data are not fine enough to reflect the actual cloud distribution in local areas. This leads to incorrect distribution results of PV energy in areas with high-spatial-variability clouds. Using high-resolution and high-precision cloud cover data obtained by satellite remote sensing to estimate the distribution of PV energy can solve this problem. In this study, the Global Land High-Resolution Cloud Climatology (GLHCC), a 10-day cloud frequency product with a resolution of 1 km and located in China, was used to construct a cloud-based solar radiation estimation model. Using the inverse relationship between cloud cover and solar radiation, the GLHCC was converted into sunshine percentage data. Using meteorological station data in China, a Least Squares Fit (LSF) and error check were carried out on the A-P, Lqbal, Bahel and Sen Models to determine the optimal solar radiation estimation model (Sen Model). Based on the sunshine percentage data, the Sen Model and terrain shielding factors, the distribution of PV energy in China was estimated. Finally, comparing to the Global Horizontal Irradiance (GHI) of the World Bank and the yearly average global irradiance of the Photovoltaic Geographic Information System (PVGIS), PV energy data in this paper more accurately reflected the distribution of PV energy in China, especially in areas with high-spatial-variability clouds. [ABSTRACT FROM AUTHOR]

Based on data from the Gurbantunggut Desert, the largest fixed/semi-fixed desert in China, and ERA5-Land reanalysis data, the long-term variations and spatial surface heat source (SHS) differences in the Gurbantunggut Desert are discussed herein. The results show the following: (1) The hourly SHS at the Kelameili station during the 2013–2021 period was a weak heat source at night; contrastingly, it was a strong heat source during the day. The duration of the hourly SHS increased gradually from January to July, but it decreased gradually from July to December. The daily SHS showed obvious seasonal variation, reaching the maximum in summer and the minimum in winter. The ERA5-Land reanalysis can reproduce all the variation characteristics of the SHS well. (2) The climatology (i.e., multi-year mean) of the monthly SHS intensity was lower than 50 W/m2 during the January–March and September–December periods in the Gurbantunggut Desert, indicating a weak heat source. On the other hand, the climatology recorded in April–August was higher than 50 W/m2, with a strong heat source. From the perspective of spatial distribution, the eastern and western regions of the Gurbantunggut Desert show strong heat sources, while the central region shows weak heat sources. The spatial distribution of the first and second modes of the empirical orthogonal function (EOF) decomposition reflected the consistent spatial variability and a north–south (or east–west) polarity variation of the monthly SHS in the Gurbantunggut Desert, respectively. (3) The yearly SHS showed negative anomalies during the 1950–1954, 1964–1982 and 2004–2015 periods, and positive anomalies during the 1955–1963, 1983–2003 and 2016–2021 periods in the Gurbantunggut Desert. Additionally, the time series of the SHS anomalies was positively correlated with the Interdecadal Pacific Oscillation (IPO) index. During the negative IPO phase, the yearly SHS showed a negative anomaly in the Gurbantunggut Desert, while the yearly SHS showed a positive anomaly during the positive IPO phase in most regions of the Gurbantunggut Desert. [ABSTRACT FROM AUTHOR]

A better understanding of the effects of climate change and land use/cover change (LUCC) on streamflow promotes the long-term water planning and management in the arid regions of northwestern China. In this paper, the Soil and Water Assessment Tool (SWAT) and a separation approach were used to evaluate and separate the effects of climate change and LUCC on streamflow in the Xiying River basin. The SWAT model was calibrated by the hydro-meteorological data from 1980-1989 to obtain the optimum parameters, which were validated by the subsequent application to the period between 1990-2008. Moreover, streamflow under several scenarios with different climate change and land use conditions in 1990-2008 and 2010-2069 were further investigated. Results indicate that, in the period of 1990-2008, the streamflow was dominated by climate change (i.e., changes in precipitation and temperature), which led to a 102.8% increase in the mean annual streamflow, whereas LUCC produced a decrease of 2.8%. Furthermore, in the future period of 2010-2039, the mean annual streamflow will decrease by 5.4% and 4.5% compared with the data of 1961-1990 under scenarios A2 and B2, respectively, while it will decrease by 21.2% and 16.9% in the period of 2040-2069, respectively. [ABSTRACT FROM AUTHOR]

The North China Famine of 1876–1879, known in Chinese as the Dingwu qihuang (丁戊奇荒), is a famous case of drought-induced famine in Chinese history. The purpose of this paper is to provide empirical and historical evidence for understanding the impacts of extreme climate events and major disasters and the mechanisms of adaptation. From the aspects of famine-related migration and the allocation of relief money and grain, the regional interactions in social responses to extreme climate events were analyzed. This paper collected 186 records from historical documents. Regarding the regions as the nodes and the relationships between regions as the links, the spatial patterns of famine-related migration and the allocation of money and grain from 1877 to 1878 were rebuilt. The results show that, firstly, famine-related migration appeared to be spontaneous and short-distanced, with the flow mainly spreading to the surrounding areas and towns. Secondly, as a state administrative action, the relief money and grain from the non-disaster areas were distributed to the disaster areas. However, the distribution of relief grain affected the equilibrium of the food market in non-disaster areas, which led to fluctuations in food prices. [ABSTRACT FROM AUTHOR]

Extreme climate events frequently exert serious effects on terrestrial vegetation activity. However, these effects are still uncertain in widely distributed areas with different climate zones. Transect analysis is important to understand how terrestrial vegetation responds to climate change, especially extreme climate events, by substituting space for time. In this paper, seven extreme climate indices and the Normalized Difference Vegetation Index (NDVI) are employed to examine changes in the extreme climate events and vegetation activity. To reduce the uncertainty of the NDVI, two satellite-derived NDVI datasets, including the third generation Global Inventory Monitoring and Modeling System (GIMMS-3g) NDVI dataset and the NDVI from the National Oceanic and Atmospheric Administration (NOAA) satellites on Star Web Servers (SWS), were employed to capture changes in vegetation activity. The impacts of climate extremes on vegetation activity were then assessed over the period of 1982–2012 using the North–South Transect of Eastern China (NSTEC) as a case. The results show that vegetation activity was overall strengthened from 1982 to 2012 in the NSTEC. In addition, extreme high temperature events revealed an increased trend of approximately 5.15 days per decade, while a weakened trend (not significant) was found in extreme cold temperature events. The strengthened vegetation activities could be associated with enhanced extreme high temperature events and weakened extreme cold temperature events over the past decades in most of the NSTEC. Despite this, inversed changes were also found locally between vegetation activity and extreme climate events (e.g., in the Northeast Plain). These phenomena could be associated with differences in vegetation type, human activity, as well as the combined effects of the frequency and intensity of extreme climate events. This study highlights the importance of accounting for the vital roles of extreme climate effects on vegetation activity. [ABSTRACT FROM AUTHOR]

Based on severe weather reports, surface precipitation observations, surface routine observations, and the European Center for Medium-Range Weather Forecasts ERA5 reanalysis dataset during the warm seasons (May–September) of 2011–2018 over North China, this paper analyzes the statistical characteristics and environmental conditions of three types of severe convective events. Results are compared between events with different altitudes (i.e., mountains and plains), severities (i.e., ordinary and significant), and months. Hail and thunderstorm high winds (THWs) are more common over the mountains whereas short-duration heavy rainfall (SDHR) is more frequent over the plains. The occurrence frequency of severe convective events exhibits distinct monthly and diurnal variations. Analyses of the environmental parameters provide reference for the potential forecasting of severe convective events over this region. Specifically, the 850–500 hPa temperature lapse rate (LR85), pseudo-equivalent potential temperature at 500 hPa (thetase500), and precipitable water (PW) are skillful in distinguishing hail and THW environments from SDHR environments, and thetase500 is useful in discriminating between hail and THW environments. The convective environments over the plains are characterized by significantly higher (lower) PW (LR85) compared with mountains. The skill of these parameters in forecasting the severity of the convective hazards is limited. Probability distributions in the two parameters space indicate that the occurrence of significant hail requires both higher most unstable convective available potential energy (MUCAPE) and stronger 0–6 km bulk wind shear (SHR6) compared with ordinary hail. Compared with ordinary THWs, the significant THWs over the mountains depend more on the SHR6 whereas those over the plains rely more on the MUCAPE. The significant SDHR events over the plains tend to occur under a variety of instability conditions. The thermodynamic parameters (i.e., MUCAPE, thetase500, and downdraft convective available potential energy), and PW are significantly higher in July–August, whereas the LR85 and vertical wind shear are apparently higher in May, June and September. [ABSTRACT FROM AUTHOR]

Systematically observing components of the climate system as well as their processes and interactions are crucial to understand the weather, climate, climate change, etc. In order to launch long-term, continuous, stereoscopic, and integrated meteorological observations for key regions of the climate system in southwestern China where it is sensitive to interactions among multiple layers and exchanges of mass and energy, the Dali National Climate Observatory (DNCO) was established in May 2006. To date, the DNCO has gradually performed an integrated meteorological observation network in a complex terrain region over the southeastern Tibetan Plateau including the conventional observations of weather and climate, and the special observations of radiation, lightning, soil moisture, wind profile, water vapor, water quality, water level, water temperature profile, turbulent fluxes of momentum, sensible heat, latent heat, carbon dioxide, and methane, etc. Furthermore, the DNCO mainly focuses on the field observation experiments and scientific research activities for mountain meteorology. This paper presents an overview of the DNCO including its location, climatology, scientific objectives, research tasks, and existing observation projects. The progresses in observation and associated research including data quality controls and assessments, recent observation results, and regional numerical model tests are summarized. Future works are also discussed. [ABSTRACT FROM AUTHOR]

Examining the effects of climate change on spring maize, and its suitability under dynamic cultivation patterns, will aid strategic decision-making for future agricultural adaptation. This paper investigates the climate suitability of spring maize, based on daily data from 50 meteorological stations, and statistics on maize yield and area at the county level in Jilin Province, China, between 1986 and 2015. Based on a significant correlation between the cultivation patterns indicator ≥10 °C accumulated temperature (AAT10) and the average yield (R2 = 0.503), the yield data are used to determine suitable thresholds for meteorological factors under the dynamic cultivation pattern, and a fuzzy fitness approach is used to evaluate the climate suitability. The results showed a good agreement between suitability estimates and scaled observed yields (average d = 0.705). Moreover, good consistency between cultivation patterns, climate suitability and yield show that the late-maturing varieties of maize have gradually moved northward and eastward, and the areas of high suitability and high yield have gradually expanded eastward. In addition, drought and chilling hazard factors limit the suitability of climate resources, especially in the eastern and western regions. [ABSTRACT FROM AUTHOR]

As China is suffering from severe fine particle pollution from dense industrialization and urbanization, satellite-derived aerosol optical depth (AOD) has been widely used for estimating particulate matter with an aerodynamic diameter less than 2.5 μm (PM2.5). However, the correlation between satellite AOD and ground-level PM2.5 could be influenced by aerosol vertical distribution, as satellite AOD represents the entire column, rather than just ground-level concentration. Here, a new column-to-surface vertical correction scheme is proposed to improve separation of the near-surface and elevated aerosol layers, based on the ratio of the integrated extinction coefficient within 200–500 m above ground level (AGL), using the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP)) aerosol profile products. There are distinct differences in climate, meteorology, terrain, and aerosol transmission throughout China, so comparisons between vertical correction via CALIOP ratio and planetary boundary layer height (PBLH) were conducted in different regions from 2014 to 2015, combined with the original Pearson coefficient between satellite AOD and ground-level PM2.5 for reference. Furthermore, the best vertical correction scheme was suggested for different regions to achieve optimal correlation with PM2.5, based on the analysis and discussion of regional and seasonal characteristics of aerosol vertical distribution. According to our results and discussions, vertical correction via PBLH is recommended in northwestern China, where the PBLH varies dramatically, stretching or compressing the surface aerosol layer; vertical correction via the CALIOP ratio is recommended in northeastern China, southwestern China, Central China (excluding summer), North China Plain (excluding Beijing), and the spring in the southeast coast, areas that are susceptible to exogenous aerosols and exhibit the elevated aerosol layer; and original AOD without vertical correction is recommended in Beijing and the southeast coast (excluding spring), where the elevated aerosol layer rarely occurs and a large proportion of aerosol is aggregated in near-surface. Moreover, validation experiments in 2016 agreed well with our discussions and conclusions drawn from the experiments of the first two years. Furthermore, suggested vertical correction scheme was applied into linear mixed effect (LME) model, and high cross validation (CV) R2 (~85%) and relatively low root mean square errors (RMSE, ~20 μg/m3) were achieved, which demonstrated that the PM2.5 estimation agreed well with the measurements. When compared to the original situation, CV R2 values and RMSE after vertical correction both presented improvement to a certain extent, proving that the suggested vertical correction schemes could further improve the estimation accuracy of PM2.5 based on sophisticated model in China. Estimating PM2.5 with better accuracy could contribute to a more precise research of ecology and epidemiology, and provide a reliable reference for environmental policy making by governments. [ABSTRACT FROM AUTHOR]

The inter-comparison of different soil moisture (SM) products over the Tibetan Plateau (TP) reveals the inconsistency among different SM products, when compared to in situ measurement. It highlights the need to constrain the model simulated SM with the in situ measured data climatology. In this study, the in situ soil moisture networks, combined with the classification of climate zones over the TP, were used to produce the in situ measured SM climatology at the plateau scale. The generated TP scale in situ SM climatology was then used to scale the model-simulated SM data, which was subsequently used to scale the SM satellite observations. The climatology-scaled satellite and model-simulated SM were then blended objectively, by applying the triple collocation and least squares method. The final blended SM can replicate the SM dynamics across different climatic zones, from sub-humid regions to semi-arid and arid regions over the TP. This demonstrates the need to constrain the model-simulated SM estimates with the in situ measurements before their further applications in scaling climatology of SM satellite products. [ABSTRACT FROM AUTHOR]

In this study, we apply a bibliometric analysis to characterize publication data on droughts, mainly focusing on drought indices (DIs), drought risk (DR), and drought forecast (DF). Data on publications on these selected topics were obtained through the Scopus database, covering the period from 1963 to June 2021. The DI-related publications, based on meteorological, soil moisture, hydrological, remote sensing, and composite/modeled Dis, accounted for 57%, 8%, 4%, 29%, and 2% of the scientific sources, respectively. DI-related studies showed a notable increase since the 1990s, due perhaps to a higher number of major droughts during the last three decades. It was found that USA and China were the two leading countries in terms of publication count and academic influence on the DI, DR, and DF studies. A network analysis of the country of residence of co-authors on DR and DF research highlighted the top three countries, which were the USA, China, and the United Kingdom. The most productive journal for the DI studies was found to be the International Journal of Climatology, whereas Natural Hazards was identified as the first-ranked journal for the DR and DF studies. In relation to individual researchers, Singh VP from the USA was found to be the most prolific author, having the greatest academic influence on DF study, whereas Zhang Q from China was identified as the most productive author on DR study. This bibliometric analysis reveals that further research is needed on droughts in the areas of risk management, water management, and drought management. This review maps trends of previous research in drought science, covering several important aspects, such as drought indices, geographic regions, authors and their collaboration paths, and sub-topics of interest. This article is expected to serve as an index of the current state of knowledge on drought warning systems and as guidance for future research needs. [ABSTRACT FROM AUTHOR]

The use of Geographic Information Systems (GIS) and Remote Sensing (RS) by climatologists, environmentalists and urban planners for three dimensional modeling and visualization of the landscape is well established. However no previous study has implemented these techniques for 3D modeling of atmospheric aerosols because air quality data is traditionally measured at ground points, or from satellite images, with no vertical dimension. This study presents a prototype for modeling and visualizing aerosol vertical profiles over a 3D urban landscape in Hong Kong. The method uses a newly developed technique for the derivation of aerosol vertical profiles from AERONET sunphotometer measurements and surface visibility data, and links these to a 3D urban model. This permits automated modeling and visualization of aerosol concentrations at different atmospheric levels over the urban landscape in near-real time. Since the GIS platform permits presentation of the aerosol vertical distribution in 3D, it can be related to the built environment of the city. Examples are given of the applications of the model, including diagnosis of the relative contribution of vehicle emissions to pollution levels in the city, based on increased near-surface concentrations around weekday rush-hour times. The ability to model changes in air quality and visibility from ground level to the top of tall buildings is also demonstrated, and this has implications for energy use and environmental policies for the tall mega-cities of the future. [ABSTRACT FROM AUTHOR]

In view of the limited predictability of heavy rainfall (HR) events and the limited understanding of the physical mechanisms governing the initiation and organization of the associated mesoscale convective systems (MCSs), a composite analysis of 58 HR events over the warm sector (i.e., far ahead of the surface cold front), referred to as WSHR events, over South China during the months of April to June 2008~2014 is performed in terms of precipitation, large-scale circulations, pre-storm environmental conditions, and MCS types. Results show that the large-scale circulations of the WSHR events can be categorized into pre-frontal, southwesterly warm and moist ascending airflow, and low-level vortex types, with higher frequency occurrences of the former two types. Their pre-storm environments are characterized by a deep moist layer with >50 mm column-integrated precipitable water, high convective available potential energy with the equivalent potential temperature of ≥340 K at 850 hPa, weak vertical wind shear below 400 hPa, and a low-level jet near 925 hPa with weak warm advection, based on atmospheric parameter composite. Three classes of the corresponding MCSs, exhibiting peak convective activity in the afternoon and the early morning hours, can be identified as linear-shaped, a leading convective line adjoined with trailing stratiform rainfall, and comma-shaped, respectively. It is found that many linear-shaped MCSs in coastal regions are triggered by local topography, enhanced by sea breezes, whereas the latter two classes of MCSs experience isentropic lifting in the southwesterly warm and moist flows. They all develop in large-scale environments with favorable quasi-geostrophic forcing, albeit weak. Conceptual models are finally developed to facilitate our understanding and prediction of the WSHR events over South China. [ABSTRACT FROM AUTHOR]

Surface solar radiation (Rs) is essential to climate studies. Thanks to long-term records from the Advanced Very High-Resolution Radiometers (AVHRR), the recent release of International Satellite Cloud Climatology Project (ISCCP) HXG cloud products provide a promising opportunity for building long-term Rs data with high resolutions (3 h and 10 km). In this study, we compare three satellite Rs products based on AVHRR cloud products over China from 1983 to 2017 with direct observations of Rs and sunshine duration (SunDu)-derived Rs. The results show that SunDu-derived Rs have higher accuracy than the direct observed Rs at time scales of a month or longer by comparing with the satellite Rs products. SunDu-derived Rs is available from the 1960s at more than 2000 stations over China, which provides reliable decadal estimations of Rs. However, the three AVHRR-based satellite Rs products have significant biases in quantifying the trend of Rs from 1983 to 2016 (−4.28 W/m2/decade to 2.56 W/m2/decade) due to inhomogeneity in satellite cloud products and the lack of information on atmospheric aerosol optical depth. To adjust the inhomogeneity of the satellite Rs products, we propose a geographically weighted regression fusion method (HGWR) to merge ISCCP-HXG Rs with SunDu-derived Rs. The merged Rs product over China from 1983 to 2017 with a spatial resolution of 10 km produces nearly the same trend as that of the SunDu-derived Rs. This study makes a first attempt to adjust the inhomogeneity of satellite Rs products and provides the merged high-resolution Rs product from 1983 to 2017 over China, which can be downloaded freely. [ABSTRACT FROM AUTHOR]

Under a declining trend of fog days in China, the duration of fog events since the 1990s reached a significant peak in the late autumn of 2018 over Eastern China. The average anomalous fog days were 4.74 d in November 2018 over Jiangsu Province in Eastern China, with a 1.73 standard deviation departure from climatology. Those fogs can thus be identified as a significantly abnormal climatic event with long duration, strong intensity, and extensive coverage. Based on the daily evolutions and correlations of atmospheric parameters, the dense fogs are revealed to be well configured by favorable metrological conditions such as weak dynamic progress, strong inversion in the lower troposphere and saturated air near the surface. If not disturbed, the intensification or duration of these conditions will further promote and maintain the development of fogs. The anomalous atmospheric background associated with those favorable meteorological conditions is revealed by composing the standardized anomalies of circulation fields during the fog days. Over the fog areas, vortex activities or cold air invasion is effectively hampered and the atmosphere inclines to be stable, due to the anomalous circulation pattern composed of the broadened jet stream, weakened jet core over Eastern China, undermined East Asian trough, declined East Asian winter monsoon, and enhanced anomalous southerly flows that transport abnormal warm and wet air to Eastern China. The vapor supplement is intensified by both sustained anomalous northward wind at the lower troposphere and anomalous westward wind in the near-surface. Overall, the numbers of standardized anomalies of 1000–200-hPa height, temperature, wind, and moisture fields during these fog days all significantly depart from climatology for that locale and time of the season, further demonstrating that the persistent dense fogs over Eastern China in the late autumn of 2018 is an unusual weather event with extreme synoptic-scale departures from normal. [ABSTRACT FROM AUTHOR]

Droughts and floods have frequently occurred in Southwest China (SWC) during the past several decades. Yet, the understanding of the mechanism of precipitation in SWC is still a challenge, since the East Asian monsoon and Indian monsoon potentially influence the rainfall in this region. Thus, the prediction of precipitation in SWC has become a difficult and critical topic in climatology. We develop a novel multi-variable network-based method to delineate the relations between the global sea surface temperature anomalies (SSTA) and the precipitation anomalies (PA) in SWC. Our results show that the out-degree patterns in the Pacific, Atlantic and Indian Ocean significantly influence the PA in SWC. In particular, we find that such patterns dominated by extreme precipitation change with the seasons. Furthermore, we uncover that the teleconnections between the global SSTA and rainfall can be described by the in-degree patterns, which dominated by several vital nodes within SWC. Based on the characteristics of these nodes, we find that the key SSTA areas affect the pattern of the nodes in SWC with some specific time delays that could be helpful to improve the long-term prediction of precipitation in SWC. [ABSTRACT FROM AUTHOR]

Annual cycle is fundamental in the East Asian monsoon (EAM) systems, profoundly governing the spatiotemporal distribution of the East Asian rainfall. The present study identified the dominant modes of the annual cycle in the East Asian rainfall based on the Fourier harmonic analysis and the Empirical Orthogonal Function (EOF) decomposition. We evaluated the performance of the first two leading modes (i.e., EOF-1 and EOF-2) in historical experiments (1979–2014) of the 21 released climate models of phase six of the Coupled Model Intercomparison Project (CMIP6). Comparing with the observation, although the CMIP6 models yield the essential fidelity, they still show considerable systematic biases in the amplitude and phase of the annual cycle, especially in east and south China. Most models exhibit substantial phase delays in the EOF-2 mode of the annual cycle. Some specific models (BCC-ESM1, CanESM5, and GFDL-CM4) exhibiting better performance could capture the observed annual cycle and the underlying physics in climatology and interannual variability. The limited fidelity of the EOF-2 mode of the EAM annual cycle primarily hinders the monsoon variability simulation and thus the reliable future projection. Therefore, the dominant modes of the EAM annual cycle act as the evaluate benchmark in the EAM modelling framework. Their improvement could be one possible bias correction strategy for decreasing the uncertainty in the CMIP6 simulation of the EAM. [ABSTRACT FROM AUTHOR]

Global climate change is presenting a variety of challenges to hydrology and water resources because it strongly affects the hydrologic cycle, runoff, and water supply and demand. In this study, we assessed the effects of climate change scenarios on hydrological variables (i.e., evapotranspiration and runoff) by linking the outputs from the global climate model (GCM) with the Soil and Water Assessment Tool (SWAT) for a case study in the Lijiang River Basin, China. We selected a variety of bias correction methods and their combinations to correct the lower resolution GCM outputs of both precipitation and temperature. Then, the SWAT model was calibrated and validated using the observed flow data and corrected historical GCM with the optimal correction method selected. Hydrological variables were simulated using the SWAT model under emission scenarios RCP2.6, RCP4.5, and RCP8.5. The results demonstrated that correcting methods have a positive effect on both daily precipitation and temperature, and a hybrid method of bias correction contributes to increased performance in most cases and scenarios. Based on the bias corrected scenarios, precipitation annual average, temperature, and evapotranspiration will increase. In the case of precipitation and runoff, projection scenarios show an increase compared with the historical trends, and the monthly distribution of precipitation, evapotranspiration, and runoff shows an uneven distribution compared with baseline. This study provides an insight on how to choose a proper GCM and bias correction method and a helpful guide for local water resources management. [ABSTRACT FROM AUTHOR]

Against the background of global climate change and anthropogenic stresses, extreme climate events (ECEs) are projected to increase in both frequency and intensity. Precipitation is one of the main climate parameters for ECE analysis. However, accurate precipitation information for extreme climate events research from dense rain gauges is still difficult to obtain in mountainous or economically disadvantaged regions. Satellite precipitation products (SPPs) with high spatial and temporal resolution offer opportunities to monitor ECE intensities and trends on large spatial scales. In this study, the accuracies of seven SPPs on multiple spatiotemporal scales in the Yangtze River Basin (YRB) during the period of 2003–2017 are evaluated, along with their ability to capture ECE characteristics. The seven products are the Tropical Rainfall Measuring Mission, Climate Hazards Group InfraRed Precipitation with Station Data (CHIRPS) (25), CHIRPS (05), Climate Prediction Center Morphing (CMORPH), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN)-Climate Data Record, PERSIANN-Cloud Classification System, and Global Precipitation Measurement (GPM) IMERG. Rain gauge precipitation data provided by the China Meteorological Administration are adopted as reference data. Various statistical evaluation metrics and different ECE indexes are used to evaluate and compare the performances of the selected products. The results show that CMORPH has the best agreement with the reference data on the daily and annual scales, but GPM IMERG performs relatively well on the monthly scale. With regard to ECE monitoring in the YRB, in general, GPM IMERG and CMORPH provide higher precision. As regards the spatial heterogeneity of the SPP performance in the YRB, most of the examined SPPs have poor accuracy in the mountainous areas of the upper reach. Only CMORPH and GPM IMERG exhibit superior performance; this is because they feature an improved inversion precipitation algorithm for mountainous areas. Furthermore, most SPPs have poor ability to capture extreme precipitation in the estuaries of the lower reach and to monitor drought in the mountainous areas of the upper reach. This study can provide a reference for SPP selection for ECE analysis. [ABSTRACT FROM AUTHOR]

Long-term satellite climate data records (CDRs) of clouds and aerosols are used to investigate the aerosol indirect effect (AIE) of cirrus clouds over the global oceans from a climatology perspective. Our study focuses on identifying the sensitive regimes and active regions where AIE signatures easily manifest themselves in the sense of the long-term average of cloud and aerosol variables. The aerosol index (AIX) regimes of AIX < 0.18 and 0.18 < AIX < 0.46 are respectively identified as the sensitive regimes for negative and positive aerosol albedos and lifetime effects of cirrus clouds. Relative humidity first decreases (along with upward motions) and then reverses to increase (along with downward motions) in the first regime of negative aerosol albedo and lifetime effects. Relatively wet and strong upward motions are the favorable meteorological conditions for the second regime of positive aerosol albedo and lifetime effects. Two swath regions extending from 15°N to 30°N over the east coastal oceans of China and the USA are the active regions of positive aerosol albedo effects. Positive aerosol lifetime effects are only active or easy to manifest in the regions where a positive aerosol albedo effect is active. The results based on the long-term averaged satellite observations are valuable for the evaluation and improvement of aerosol-cloud interactions for cirrus clouds in global climate models. [ABSTRACT FROM AUTHOR]

Flow forecasting is an essential topic for flood prevention and mitigation. This study utilizes a data-driven approach, the Long Short-Term Memory neural network (LSTM), to simulate rainfall–runoff relationships for catchments with different climate conditions. The LSTM method presented was tested in three catchments with distinct climate zones in China. The recurrent neural network (RNN) was adopted for comparison to verify the superiority of the LSTM model in terms of time series prediction problems. The results of LSTM were also compared with a widely used process-based model, the Xinanjiang model (XAJ), as a benchmark to test the applicability of this novel method. The results suggest that LSTM could provide comparable quality predictions as the XAJ model and can be considered an efficient hydrology modeling approach. A real-time forecasting approach coupled with the k-nearest neighbor (KNN) algorithm as an updating method was proposed in this study to generalize the plausibility of the LSTM method for flood forecasting in a decision support system. We compared the simulation results of the LSTM and the LSTM-KNN model, which demonstrated the effectiveness of the LSTM-KNN model in the study areas and underscored the potential of the proposed model for real-time flood forecasting. [ABSTRACT FROM AUTHOR]

Carabids are an important insect group in grassland ecosystems and are involved in numerous ecosystem services. Steppes are the most widespread ecosystems in China, but they are under increasing degradation. Despite their importance, little is known about the relationships between environmental variables and carabid community structure in Chinese steppes. We studied the effects of fine-scale factors (soil and vegetation) and coarse-scale factors (climate) on carabid community parameters (abundance, richness, diversity, dominance, and evenness) in three types of steppes (desert, typical, and meadow steppes) in northern China. Carabid communities responded to environmental factors in different ways according to the type of steppe. Climate factors were the most important drivers of community structure, whereas the effects of soil and vegetation were less important. Desert steppe showed the lowest carabid abundance, richness, diversity, and evenness, and the highest dominance. This community is relatively simple and strongly dominated by a few species adapted to the severe conditions of this environment. Typical and meadow steppes showed carabid communities with a more complex structure. As expected on the basis of environmental severity, the most severe ecosystem (i.e., the desert) was only influenced by climatic factors, whereas a certain influence of biotic factors emerged in the other ecosystems. [ABSTRACT FROM AUTHOR]

Climate and land-use change are the two main driving forces that affect watershed hydrological processes. Separately assessing their impacts on hydrology is important for land-use planning and water resource management. In this research, the SWAT (Soil and Water Assessment Tool) and statistical methods were applied to evaluate the effects of climate and land-use change on surface hydrology in the hilly-gully region of the Loess Plateau. The results showed that surface runoff and soil water presented a downward tendency, while evapotranspiration (ET) presented an upward tendency in the Yanhe watershed from 1982 to 2012. Climate is one the dominant factors that influence surface runoff, especially in flooding periods. The average contribution rate of surface runoff on stream flow accounted for 55%, of which the flooding period accounted for 40%. The runoff coefficient declined by 0.21 after 2002 with the land-use change of cropland transformed to grassland and forestland. The soil water exhibited great fluctuation along the Yanhe watershed. In the upstream region, the land-use was the driving force to decline soil water, which reduced the soil water by 51%. Along the spatial distribution, it converted from land-use change to climate variability from northwest to southeast. The ET was more sensitive to land-use change than climate variability in all sub-basins, and increased by 209% with vegetation restoration. To prevent the ecosystem degradation and maintain the inherent ecological functions of rivers, quantitative assessment the influence of climate variability and land-use change on hydrology is of great importance. Such evaluations can provide insight into the extent of land use/cover change on regional water balance and develop appropriate watershed management strategies on the Loess Plateau. [ABSTRACT FROM AUTHOR]

Wind speed data derived from reanalysis datasets has been used in the plan and design of wind farms in China, but the quality of these kinds of data over China remains unknown. In this study, the performances of five sets of reanalysis data, including National Centers for Environmental Predictions (NCEP)-U.S. Department of Energy (DOE) Reanalysis 2 (NCEP-2), Modern-ERA Retrospective Analysis for Research and Applications (MERRA), Japanese 55-year Reanalysis Project (JRA-55), Interim ECMWF Re-Analysis product (ERA-Interim), and 20th Century Reanalysis (20CR) in reproducing the climatology, interannual variation, and long-term trend of near-surface (10 m above ground) wind speed, for the period of 1979–2011 over continental China are comprehensively evaluated. Compared to the gridded data compiled from meteorological stations, all five reanalysis datasets reasonably reproduce the spatial distribution of the climatology of near-surface wind speed, but underestimate the intensity of the near-surface wind speed in most regions except for Tibetan Plateau where the wind speed is overestimated. All five reanalysis datasets show large weaknesses in reproducing the annual cycle of near-surface wind speed averaged over the continental China. The near-surface wind speed derived from the observations exhibit significant decreasing trends over most parts of continental China during 1979 to 2011. Although the spatial patterns of the linear trends reproduced by reanalysis datasets are close to the observation, the magnitudes are weaker in annual, spring, summer and autumn season. The qualities of all reanalysis datasets are limited in winter. For the interannual variability, except for winter, all five reanalysis datasets reasonably reproduce the interannual standard deviation but with larger amplitude. Quantitative comparison indicates that among the five reanalysis datasets, the MERRA (JRA-55) shows the relatively highest (lowest) skill in terms of the climatology and linear trend. These results call for emergent needs for developing high quality reanalysis data that can be used in wind resource assessment and planning. [ABSTRACT FROM AUTHOR]

Cistanche deserticola Ma, a perennial parasitic herb of family Orobanchaceae, is mainly parasitic on the roots of the Haloxylon ammodendron Bunge. In view of this special parasitic relationship, we applied random forest (RF) model to forecast potential geographic distribution, and developed a comprehensive habitat suitability model by integrating bioclimatic and soil factors to assess the suitable distribution of C. deserticola and H. ammodendron across China in 2050s and 2070s under RCP2.6, RCP4.5, and RCP8.5, respectively. We modeled the core potential geographic distribution of C. deserticola by overlaying the distribution of these two species, and analyzed the spatial distribution pattern and migration trend of C. deserticola by using the standard deviational ellipse. In addition, we evaluated the accuracy of RF model through three evaluation indexes, and analyzed the dominant climate factors. The results showed that the core potential distribution areas of C. deserticola are distributed in the Xinjiang Uygur Autonomous Region, the junction of Shaanxi–Gansu–Ningxia provinces, and the Inner Mongolia Autonomous Region. The spatial dispersion would intensify with the increasing of emission scenarios, and the geographical habitat is moving towards higher latitude. Among the three evaluation indexes, the area under the ROC curve (AUC) and True Skill Statistic (TSS) have better assessment results. The main bioclimatic factors affecting the distribution are min temperature of coldest month (Bio6), annual precipitation (Bio12), precipitation of wettest month (Bio13), precipitation of wettest quarter (Bio16), and precipitation of warmest quarter (Bio18), among which the importance of precipitation factors is greater than temperature factors. More importantly, the results of this study could provide some guidance for the improvement of desert forest system, the protection of endangered species and the further improvement of the ecological environment. [ABSTRACT FROM AUTHOR]

Precise quantification of terrestrial gross primary production (GPP) has been recognized as one of the most important components in understanding the carbon balance between the biosphere and the atmosphere. In recent years, although many large-scale GPP estimates from satellite data and ecosystem models have been generated, few attempts have been made to compare the different GPP products at national scales, particularly for various climate zones. In this study, two of the most widely-used GPP datasets were systematically compared over the eight climate zones across China's terrestrial ecosystems from 2001 to 2015, which included the moderate resolution imaging spectroradiometer (MODIS) GPP and the breathing Earth system simulator (BESS) GPP products. Additionally, the coarse (0.05o) GPP estimates from the vegetation photosynthesis model (VPM) at the same time scale were used for auxiliary analysis with the two products. Both MODIS and BESS products exhibited a decreasing trend from the southeast region to the northwest inland. The largest GPP was found in the tropical humid region with 5.49 g C m−2 d−1 and 5.07 g C m−2 d−1 for MODIS and BESS, respectively, while the lowest GPP was distributed in the warm temperate arid region, midtemperate semiarid region and plateau zone. Meanwhile, the work confirmed that all these GPP products showed apparent seasonality with the peaks in the summertime. However, large differences were found in the interannual variations across the three GPP products over different climate regions. Generally, the BESS GPP agreed better than the MODIS GPP when compared to the seasonal and interannual variations of VPM GPP. Furthermore, the spatial correlation analysis between terrestrial GPP and the climatic factors, including temperature and precipitation, indicated that natural rainfall dominated the variability in GPP of Northern China, such as the midtemperate semiarid region, while temperature was a key controlling factor in the Southern China and the Tibet Plateau area. [ABSTRACT FROM AUTHOR]

Extreme precipitation has received much attention because of its implications for hazard assessment and risk management. However, accurate precipitation information for extreme precipitation research from dense rain gauges is still difficult to obtain in developing countries or mountainous regions. Satellite-based precipitation products (SPPs) with high spatial and temporal resolution offer a new way of supplementing data from gauge-based observations. This study aims to evaluate the precision of six SPPs in detail at multiple temporal and spatial scales and explore their ability to capture the characteristics of extreme precipitation from 2009 to 2014 over a semi-arid to semi-humid climate transition area (Wei River basin) in China. The six products are TRMM 3B42RT, TRMM 3B42V7, PERSIANN, PERSIANN CDR, CMORPH RAW, and CRORPH CRT. China gauge-based daily precipitation analysis (CGDPA) provided by the China Meteorological Administration is used as the benchmark reference data. Various statistical evaluation techniques and extreme precipitation indices are used to evaluate and compare the performance of the selected products. The results show that the post real-time products (TRMM 3B42V7, PERSIANN CDR, and CMORPH CRT) agreed better with the reference data than PERSIANN and CMORPH RAW. On a daily scale, TRMM 3B42V7, PERSIANN CDR, and CMORPH CRT displayed similarly good performance. However, at the monthly or annual scale, TRMM 3B42V7 was superior to the other products. With regard to the spatial distribution of precipitation, the datasets performed better over plains and were disappointing over mountainous areas. Additionally, TRMM 3B42V7 provided higher precision and less spatial uncertainty when monitoring extreme precipitation. This study provides a basis for selecting alternative precipitation data for climate transition basins. [ABSTRACT FROM AUTHOR]

Based on the constructed SWAT model in the Qinhuai River Basin, the hydrological response of flooding under different scenarios of temperature and rainfall change is analyzed. The Copula function is then used to calculate and analyze the multivariate flood risk. The results show that the flood peaks increase with the increase of precipitation and decrease with the increase of temperature. The hydrological response of light floods to temperature changes is stronger than that of medium and heavy floods. Additionally, the temperature drop and the precipitation increase lead to a higher flood risk. The flood risk of flood peaks is more sensitive to changes in precipitation. [ABSTRACT FROM AUTHOR]

In the traditional point of view, if there is a significant decreasing trend for a runoff time series, while no significant trend for a precipitation series is present, then an unreliable conclusion will be made that the land surface change is the main contributor to the runoff change. To test it, we selected four sub-watersheds in the Luanhe river basin as the study areas where land use has changed severely. We first detected the long-term rainfall and runoff trend by the Mann–Kendall test, Sen’s slope, and the moving average method, and found that the runoff had a decreasing trend at the 0.05 significance level, while the rainfall had no significant trend in all sub-watersheds. Then an orderly cluster analysis and moving T test method were used to detect the change point of the runoff series. We quantified the contributions of the land surface change and climate variability based on Soil and Water Assessment Tool (SWAT), and the contribution of climate variability accounted for more than 50%, which implies that climate change is the main factor of runoff decrease in the study areas. To further test this, a trend analysis of a reconstructed annual runoff time series under undisturbed conditions has been done. The results showed that in some sub-watersheds, although rainfall series had no significant decreasing trend, the runoff series had significant downward trend. This can be explained by the nonlinear relationship between rainfall and runoff. This study came to a different conclusion from the common view, which observes that runoff decrease is mainly caused by land surface change if rainfall series lacks a significantly decreasing trend. [ABSTRACT FROM AUTHOR]

Continuous and accurate spatiotemporal precipitation data plays an important role in regional climate and hydrology research, particularly in the arid inland regions where rain gauges are sparse and unevenly distributed. The main objective of this study is to evaluate and bias-correct the Tropical Rainfall Measuring Mission (TRMM) 3B42V7 rainfall product under complex topographic and climatic conditions over the Hexi region in the northwest arid region of China with the reference of rain gauge observation data during 2009–2015. A series of statistical indicators were adopted to quantitatively evaluate the error of 3B42V7 and its ability in detecting precipitation events. Overall, the 3B42V7 overestimates the precipitation with Bias of 11.16%, and its performance generally becomes better with the increasing of time scale. The agreements between the rain gauge data and 3B42V7 are very low in cold season, and moderate in warm season. The 3B42V7 shows better correlation with rain gauges located in the southern mountainous and central oasis areas than in the northern extreme arid regions, and is more likely to underestimate the precipitation in high-altitude mountainous areas and overestimate the precipitation in low-elevation regions. The distribution of the error on the daily scale is more related to the elevation and rainfall than in monthly and annual scale. The 3B42V7 significantly overestimates the precipitation events, and the overestimation mainly focuses on tiny amounts of rainfall (0–1 mm/d), which is also the range of false alarm concentration. Bias correction for 3B42V7 was carried out based on the deviation of the average monthly precipitation data during 2009–2015. The bias-corrected 3B42V7 was significantly improved compared with the original product. Results suggest that regional assessment and bias correction of 3B42V7 rainfall product are of vital importance and will provide substantive reference for regional hydrological studies. [ABSTRACT FROM AUTHOR]