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Rainfall interception loss remains one of the most uncertain fluxes in the global water balance, hindering water management in forested regions and precluding an accurate formulation in climate models. Here, a synthesis of interception loss data from past field experiments conducted worldwide is performed, resulting in a meta-analysis comprising 166 forest sites and 17 agricultural plots. This meta-analysis is used to constrain a global process-based model driven by satellite-observed vegetation dynamics, potential evaporation and precipitation. The model considers sub-grid heterogeneity and vegetation dynamics and formulates rainfall interception for tall and short vegetation separately. A global, 40-year (1980–2019), 0.1∘ spatial resolution, daily temporal resolution dataset is created, analysed and validated against in situ data. The validation shows a good consistency between the modelled interception and field observations over tall vegetation, both in terms of correlations and bias. While an underestimation is found in short vegetation, the degree to which it responds to in situ representativeness errors and difficulties inherent to the measurement of interception in short vegetated ecosystems is unclear. Global estimates are compared to existing datasets, showing overall comparable patterns. According to our findings, global interception averages to 73.81 mm yr−1 or 10.96 × 103 km3 yr−1, accounting for 10.53 % of continental rainfall and approximately 14.06 % of terrestrial evaporation. The seasonal variability of interception follows the annual cycle of canopy cover, precipitation, and atmospheric demand for water. Tropical rainforests show low intra-annual vegetation variability, and seasonal patterns are dictated by rainfall. Interception shows a strong variance among vegetation types and biomes, supported by both the modelling and the meta-analysis of field data. The global synthesis of field observations and the new global interception dataset will serve as a benchmark for future investigations and facilitate large-scale hydrological and climate research.

Soil moisture (SM) is a vital variable controlling water and energy exchange between the atmosphere and land surface. Spatiotemporally continuous SM information is urgently needed for large-scale meteorological and hydrological applications. Considering the weakness of the penalized least square regression based on the discrete cosine transform (DCT-PLS) method when the missing data are not evenly distributed in the original data set, this study proposes an in situ observation-combined DCT-PLS (ODCT-PLS) to reconstruct missing values of daily surface SM from the Climate Change Initiative program of the European Space Agency (ESA CCI). The result of the reconstruction for ESA CCI SM data in the Xiliaohe River Basin from 2013 to 2020 showed that the SM reconstructed by ODCT-PLS was in better agreement with in situ soil moisture compared with that reconstructed by DCT-PLS, with the average correlation coefficient (CORR) increasing by 0.3636, the average root mean squared error (RMSE) decreasing by 0.0109 m3/m3 and the average BIAS decreasing by 0.0047 m3/m3. Compared with the original ESA CCI SM, DCT-PLS and ODCT-PLS can both restore the spatial variation of SM in the study area. The reconstruction method proposed in our study provides a valuable alternative to reconstruct the three-dimensional geophysical dataset with spatially or temporally continuous data gap.

IntroductionConductance-photosynthesis (Gs-A) models, accompanying with light use efficiency (LUE) models for calculating carbon assimilation, are widely used for estimating canopy stomatal conductance (Gs) and transpiration (Tc) under the two-leaf (TL) scheme. However, the key parameters of photosynthetic rate sensitivity (gsu and gsh) and maximum LUE (ϵmsu and ϵmsh) are typically set to temporally constant values for sunlit and shaded leaves, respectively. This may result in Tc estimation errors, as it contradicts field observations.MethodsIn this study, the measured flux data from three temperate deciduous broadleaved forests (DBF) FLUXNET sites were adopted, and the key parameters of LUE and Ball-Berry models for sunlit and shaded leaves were calibrated within the entire growing season and each season, respectively. Then, the estimations of gross primary production (GPP) and Tc were compared between the two schemes of parameterization: (1) entire growing season-based fixed parameters (EGS) and (2) season-specific dynamic parameters (SEA).ResultsOur results show a cyclical variability of ϵmsu across the sites, with the highest value during the summer and the lowest during the spring. A similar pattern was found for gsu and gsh, which showed a decrease in summer and a slight increase in both spring and autumn. Furthermore, the SEA model (i.e., the dynamic parameterization) better simulated GPP, with a reduction in root mean square error (RMSE) of about 8.0 ± 1.1% and an improvement in correlation coefficient (r) of 3.7 ± 1.5%, relative to the EGS model. Meanwhile, the SEA scheme reduced Tc simulation errors in terms of RMSE by 3.7 ± 4.4%.DiscussionThese findings provide a greater understanding of the seasonality of plant functional traits, and help to improve simulations of seasonal carbon and water fluxes in temperate forests.

The response of blue and green water to climate and land-use change in the Ganjiang River Basin (GRB) is evaluated via the SWAT model that combines three scenarios (the land-use/land-cover (LULC), climate change, and integrated climate and LULC change scenarios) in the 2040s (2031–2050) and 2060s (2051–2070). The results indicate that, for the GRB, cropland, woodland, and grassland show a decreasing trend, while build-up and water areas show an increasing trend in terms of future land-use change. The climatic conditions projected using NORESM1-M model data under the RCP4.5 and RCP8.5 scenarios suggest, respectively, increases in precipitation (31.17 and 27.24 mm), maximum temperature (2.25 and 2.69 °C), and minimum temperature (1.96 and 2.58 °C). Under climate change conditions, blue water is estimated to decrease by up to 16.89 and 21.4 mm under RCP4.5 and RCP8.5, while green water is estimated to increase up to 19.14 and 20.22 mm, respectively. Under the LULC changes, blue water is projected to increase by up to 5.50 and 7.57 mm, while green water shows decreases of 4.05 and 7.80 mm for the LULC2035 and LULC2055 scenarios, respectively. Under the four combined LULC and climate change conditions (RCP4.5_2040s, RCP4.5_2060s, RCP8.5_2040s, and RCP8.5_2060s), blue water tends to decrease by 0.67, 7.47, 7.28, and 9.99 mm, while green water increases by 19.24, 20.8, 13.87, and 22.30 mm. The influence of climate variation on blue and green water resources is comparatively higher than that of the integrated impacts of climate and land-use changes. The results of this study offer a scientific reference for the water resources management and planning department responsible for scheduling water resource management plan in the GRB. HIGHLIGHTS This study investigates the effects of climate changes and land-use changes on blue/green water.; The NORESM1-M model data from the CMIP5 are combined with the SWAT model to analyse the influence of future climate changes on blue/green water.; The CA–Markov model is applied to generate future land-use scenarios.; Multiple climate and land-use scenarios are set to quantitatively analyse the changes of blue/green water.

Intermittent records of satellite soil moisture data are major obstacles that constrain their hydrometeorological applications. Based on the European Space Agency Climate Change Initiative (ESA CCI) soil moisture combined product, two machine learning models were employed to reconstruct soil moisture in China during 1979–2019 in both temporal and spatial domains, and latent errors for reconstructed series, as well as their performances for tracing climate extremes, were analyzed. The results showed that with the homogeneity of available data over space, the spatial approach performed well in reproducing the spatial heterogeneity of soil moisture (with medians of the correlation coefficient (CC) above 0.8 and root mean square errors (RMSEs) ranging from 0.02 to 0.03 m3∙m−3). The temporal approach (CC values of 0.7 and RMSEs ranging between 0.02 and 0.03 m3∙m−3) was superior in capturing the seasonality features and the timely and accurate mapping of short-term soil moisture dynamics impacted by rainstorms. However, both approaches failed to identify the location and severity of droughts accurately. The findings highlight the benefits of combining the strengths of both temporal and spatial gap-filling approaches for improving the estimation of missing values and hydrometeorological applications.

The availability of the new generation Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) V06 products facilitates the utility of long-term higher spatial and temporal resolution precipitation data (0.1° × 0.1° and half-hourly) for monitoring and modeling extreme hydrological events in data-sparse watersheds. This study aims to evaluate the utility of IMERG Final run (IMERG-F), Late run (IMERG-L) and Early run (IMERG-E) products, in flood simulations and frequency analyses over the Mishui basin in Southern China during 2000–2017, in comparison with their predecessors, the Tropical Rainfall Measuring Mission Multi-satellite Precipitation Analysis (TMPA) products (3B42RT and 3B42V7). First, the accuracy of the five satellite precipitation products (SPPs) for daily precipitation and extreme precipitation events estimation was systematically compared by using high-density gauge station observations. Once completed, the modeling capability of the SPPs in daily streamflow simulations and flood event simulations, using a grid-based Xinanjiang model, was assessed. Finally, the flood frequency analysis utility of the SPPs was evaluated. The assessment of the daily precipitation accuracy shows that IMERG-F has the optimum statistical performance, with the highest CC (0.71) and the lowest RMSE (8.7 mm), respectively. In evaluating extreme precipitation events, among the IMERG series, IMERG-E exhibits the most noticeable variation while IMERG-L and IMERG-F display a relatively low variation. The 3B42RT exhibits a severe inaccuracy and the improvement of 3B42V7 over 3B42RT is comparatively limited. Concerning the daily streamflow simulations, IMERG-F demonstrates a superior performance while 3B42V7 tends to seriously underestimate the streamflow. With regards to the simulations of flood events, IMERG-F has performed optimally, with an average DC of 0.83. Among the near-real-time SPPs, IMERG-L outperforms IMERG-E and 3B42RT over most floods, attaining a mean DC of 0.81. Furthermore, IMERG-L performs the best in the flood frequency analyses, where bias is within 15% for return periods ranging from 2–100 years. This study is expected to contribute practical guidance to the new generation of SPPs for extreme precipitation monitoring and flood simulations as well as promoting the hydro-meteorological applications.

Flash floods are a type of catastrophic disasters which cause significant losses of life and property worldwide. In recent years, machine learning techniques have become powerful tools for evaluating flash flood susceptibility. This research applies stacking and blending ensemble learning approaches to assess the flash flood potential in Jiangxi, China. Four base models – linear regression, K-nearest neighbours, support vector machine, and random forest – are adopted to build the two ensemble models. All models are evaluated by three metrics (accuracy, true positive rate, and the area under the receiver operating characteristic curve) and compared with a Bayesian approach. The results suggest that the blending approach is superior to all the other models, which has then been selected to evaluate the vulnerability of flash floods for all the catchments in Jiangxi. The derived maps of flash flood susceptibility suggest that over half of the province, in terms of either area or the number of catchments, are prone to flash floods, in particular the north, northeast and south. These empirical findings can help to develop plans for disaster prevention and control, as well as improving public knowledge of flash flood hazards.

The evaluation uncertainty caused by a standard reference itself is harmful to both algorithm developers and data users in substantially understanding the error features and the performance of satellite precipitation products (SPPs). In this study, the Climate Precipitation Center Unified (CPCU) data and the Merged Precipitation Analysis (MPA) data are used as the benchmark to investigate the evaluation uncertainties of satellite precipitation estimates generated by the reference itself. Two SPPs, IMERG-Late and GSMaP-MVK, are employed here. The results show that the approach using two different ground-based precipitation products as the references can effectively reveal the potential evaluation uncertainties. Interestingly, it is found that the evaluation results are prone to resulting in larger uncertainties over semihumid areas. Furthermore, evaluation uncertainty of statistical metrics is closely related to rainfall intensity in that it has a gradually decreasing tendency with increasing rainfall intensities. Additionally, we also found that the dependency of the false alarm ratio (FAR) and root-mean-square error (RMSE) scores on the spatial density of rain gauges is relatively low. Both relative bias (RBIAS) and normalized root-mean-square error (NRMSE) scores for light precipitation (1–5 mm day−1) increase with the spatial density of the rain gauges, suggesting that the evaluation of light precipitation can easily cause uncertainties relative to medium-to-high rain rates. Finally, the minimum gauge density required for different scores and different rainfall intensities is discussed. This study is expected to provide criteria to investigate the reliability of evaluation results for the satellite quantitative precipitation estimation community.

Satellite- and reanalysis-based precipitation products are important data source for precipitation, particularly in areas with a sparse gauge network. Here, five open-access precipitation products, including the newly released China Meteorological Assimilation Driving Datasets for the Soil and Water Assessment Tool (SWAT) model (CMADS) reanalysis dataset and four widely used bias-adjusted satellite precipitation products [SPPs; i.e., Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis 3B42 Version 7 (TMPA 3B42V7), Climate Prediction Center (CPC) morphing technique satellite-gauge blended product (CMORPH-BLD), Climate Hazards Group Infrared Precipitation with Station Data (CHIRPS), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR)], were assessed. These products were first compared with the gauge observed data collected for the upper Huaihe River basin, and then were used as forcing data for streamflow simulation by the Xin’anjiang (XAJ) hydrological model under two scenarios with different calibration procedures. The performance of CMADS precipitation product for the Chinese mainland was also assessed. The results show that: (1) for the statistical assessment, CMADS and CMORPH-BLD perform the best, followed by TMPA 3B42V7, CHIRPS, and PERSIANN-CDR, among which the correlation coefficient (CC) and root-mean-square error (RMSE) values of CMADS are optimal, although it exhibits certain significant negative relative bias (BIAS; −22.72%); (2) CMORPH-BLD performs the best in capturing and detecting rainfall events, while CMADS tends to underestimate heavy and torrential precipitation; (3) for streamflow simulation, the performance of using CMADS as input is very good, with the highest Nash-Sutcliffe efficiency (NSE) values (0.85 and 0.75 for calibration period and validation period, respectively); and (4) CMADS exhibits high accuracy in eastern China while with significant negative BIAS, and the performance declines from southeast to northwest. The statistical and hydrological evaluations show that CMADS and CMORPH-BLD have high potential for observing precipitation. As high negative BIAS values showed up in CMADS evaluation, further study on the error sources from original data and calibration algorithms is necessary. This study can serve as a reference for selecting precipitation products in data-scarce regions with similar climates and topography in the Global Precipitation Measurement (GPM) era.

Three newly released, gauge-based precipitation products (GPPs), i.e., the Global Precipitation Climatology Center full data monthly version 2018 (GPCC 8.0), the Climate Research Unit Time Series version 4.03 (CRU TS 4.03), and the Center for Climatic Research-University of Delaware Terrestrial Precipitation: 1900−2017 Gridded Monthly Time Series version 5.01 (UDel 5.01), were evaluated and compared with the China Gauge-based Daily Precipitation Analysis (CGDPA) dataset. The evaluations involved drought monitoring over mainland China by using the Standardized Precipitation Index (SPI). The results indicated that the GPPs performed well in capturing precipitation spatial patterns (correlation coefficients > 0.9) and interannual variations (correlation coefficients > 0.7) for most regions, with the exceptions of the Xinjiang and Qinghai–Tibet plateau (TP) regions. It was also noted that the accuracy of SPIs calculated using GPPs (SPIg) showed no apparent variations at various timescales, and a 3-month SPI metric was selected for further analyzing. When examining the spatiotemporal accuracies of SPIg, we found that they exhibited high levels of consistency, small errors, and high degrees of detectivity in Eastern China, and comparatively high spatial heterogeneity in Western China, particularly in the TP region. The regional mean SPIg performances improved at the time series, as their individual spatial heterogeneities were offset by averaging. The GPPs were able to satisfactorily capture the characteristics of typical drought events, such as drought area, centroids, and severity, for the four selected specific regions. Overall, the long-term GPP records showed great potential in quantifying drought over mainland China, especially in Eastern China and Southwest. Among the three GPPs, it was found that GPCC 8.0 performed the best, in both precipitation estimation and drought monitoring, while no distinct distinctions were apparent between the CRU TS 4.03 and UDel 5.01 products. These results showed that the long-term GPPs could be applied to large-scale drought monitoring, and the findings could provide useful references for selecting gauge-based precipitation data for various hydrometeorological applications.

Flash droughts are extreme phenomena that have been identified using two different approaches. The first approach identifies these events based on unusually rapid intensification rates, whereas the second approach implicitly identifies short-term features. This latter approach classifies flash droughts into two types, namely, precipitation deficit and heat wave flash droughts (denoted as PDFD and HWFD). In this study, we evaluate these two approaches over the Yellow River basin (YRB) to determine which approach provides more accurate information about flash droughts and why. Based on the concept of intensification rate, a new quantitative flash drought identification method focused on soil moisture depletion during the onset–development phase is proposed. Its performance was evaluated by comparing the onset time and spatial dynamics of the identified flash droughts with PDFD and HWFD events identified using the second approach. The results show that the rapid-intensification approach is better able to capture the continuous evolution of a flash drought. Since the approach for identifying PDFD and HWFD events does not consider changes in soil moisture with time, it cannot ensure that the events exhibit rapid intensification, nor can it effectively capture flash droughts’ onset. Evaluation of the results showed that the chosen hydrometeorological variables and corresponding thresholds, particularly that of temperature, are the main reasons for the poor performance of the PDFD and HWFD identification approach. This study promotes a deeper understanding of flash droughts that is beneficial for drought monitoring, early warning, and mitigation.

A multimodel ensemble of general circulation models (GCM) is a popular approach to assess hydrological impacts of climate change at local, regional, and global scales. The traditional multimodel ensemble approach has not considered different uncertainties across GCMs, which can be evaluated from the comparisons of simulations against observations. This study developed a comprehensive index to generate an optimal ensemble for two main climate fields (precipitation and temperature) for the studies of hydrological impacts of climate change over China. The index is established on the skill score of each bias-corrected model and different multimodel combinations using the outputs from phase 5 of the Coupled Model Intercomparison Project (CMIP5). Results show that the optimal ensemble of the nine selected models accurately captures the characteristics of spatial–temporal variabilities of precipitation and temperature over China. We discussed the uncertainty of subset ensembles of ranking models and optimal ensemble based on historical performance. We found that the optimal subset ensemble of nine models has relative smaller uncertainties compared with other subsets. Our proposed framework to postprocess the multimodel ensemble data has a wide range of applications for climate change assessment and impact studies.

Conventional water infrastructure designs for flood and low flows are usually based on the assumption of stationarity of extreme events. However, recent evidence suggests that the influences of climate variability and human activities have made the hypothesis of stationarity questionable. In this study, we used the generalized additive models for location, scale, and shape (GAMLSS) to construct a nonstationary model in which the parameters of the selected distributions were modelled as a function of climatic variables (i.e., climate indices and precipitation) and/or the reservoir index (RI). The nonstationary models were then used to analyse annual flood and low flow frequency at four hydrological stations in the upper reaches of the Huaihe River Basin, including Dapoling (DPL), Changtaiguan (CTG), Zhuganpu (ZGP), and Xixian (XX) stations. Annual floods were represented by the maximum daily streamflow in each year, and low flows were represented by the 95th quantile of the daily streamflow (Q95) in each year. The change point and trend analysis revealed that the flood series of the ZGP station and the low flow series of the DPL and XX stations exhibited significant downward and upward trends (p < 0.1)), respectively. The low flow series of the ZGP station showed a significant change point in 1980 (p < 0.1). GAMLSS modelling results showed that, in comparison with stationary models, nonstationary models that included precipitation and the Arctic Oscillation climate index as covariates for the gamma distribution location parameter provided a superior description of the flood series at the four stations. Nonstationary models that incorporated precipitation and/or RI as covariates for the Weibull distribution parameters fit the low flow series better than stationary models at all stations. Furthermore, we found that nonstationary models outperformed stationary models in terms of flood frequency analysis, covering all flood observation points and capturing the generally decreasing trend in flood series, as well as a decrease in the scatter of estimated flood value magnitudes. For the low flow frequency analysis, the comparison results showed that the nonstationary and stationary models performed identically for the DPL, CTG, and XX stations, where no significant change point was detected. However, for the ZGP station, where a significant change point was detected, the nonstationary models performed better than the stationary models and could accurately capture the changes in the magnitude of the estimated low flow values before and after the change point. Overall, the proposed nonstationary model can serve as a tool for nonstationary frequency analysis of flood and low flow series under the influence of climate variability and reservoir regulations, thus providing a reference for regional water infrastructure design.

The term “flash drought” describes a type of drought with rapid onset and strong intensity, which is co-affected by both water-limited and energy-limited conditions. It has aroused widespread attention in related research communities due to its devastating impacts on agricultural production and natural systems. Based on a global reanalysis dataset, we identify flash droughts across China during 1979–2016 by focusing on the depletion rate of weekly soil moisture percentile. The relationship between the rate of intensification (RI) and nine related climate variables is constructed using three machine learning (ML) technologies, namely, multiple linear regression (MLR), long short-term memory (LSTM), and random forest (RF) models. On this basis, the capabilities of these algorithms in estimating RI and detecting droughts (flash droughts and traditional slowly evolving droughts) were analyzed. Results showed that the RF model achieved the highest skill in terms of RI estimation and flash drought identification among the three approaches. Spatially, the RF-based RI performed best in southeastern China, with an average CC of 0.90 and average RMSE of the 2.6 percentile per week, while poor performances were found in the Xinjiang region. For drought detection, all three ML technologies presented a better performance in monitoring flash droughts than in conventional slowly evolving droughts. Particularly, the probability of detection (POD), false alarm ratio (FAR), and critical success index (CSI) of flash drought derived from RF were 0.93, 0.15, and 0.80, respectively, indicating that RF technology is preferable in estimating the RI and monitoring flash droughts by considering multiple meteorological variable anomalies in adjacent weeks to drought onset. In terms of the meteorological driving mechanism of flash drought, the negative precipitation (P) anomalies and positive potential evapotranspiration (PET) anomalies exhibited a stronger synergistic effect on flash droughts compared to slowly developing droughts, along with asymmetrical compound influences in different regions of China. For the Xinjiang region, P deficit played a dominant role in triggering the onset of flash droughts, while in southwestern China, the lack of precipitation and enhanced evaporative demand almost contributed equally to the occurrence of flash drought. This study is valuable to enhance the understanding of flash droughts and highlight the potential of ML technologies in flash drought monitoring.

Objective. To explore the effects of cyclophosphamide combined with vinorelbine in advanced small cell lung cancer (SCLC) and anteroposterior changes in MRI. Methods. The clinical data of 90 patients with advanced SCLC admitted to our hospital from April 2020 to April 2021 were retrospectively analyzed. They were divided into the control group and the study group according to the order of admission, with 45 cases in each group. The control group received the routine treatment, while the study group was treated with cyclophosphamide and vinorelbine to compare the indexes of imaging data and clinical indicators between the two groups before and after treatment. Results. There was no significant difference in the indexes of imaging data between the two groups before treatment P > 0.05 , and the indexes of imaging data in the study group were visibly lower than those in the control group after treatment P < 0.001 . The DCR in the study group was significantly higher than that in the control group after treatment P < 0.05 , while the QLQ-C30 scores and serum indices of the study group after treatment were significantly lower than those of the control group P < 0.001 . Conclusion. Patients with advanced SCLC were treated with cyclophosphamide and vinorelbine, which can effectively improve the quality of life and reduce the expression of inflammatory factors. This treatment model has a higher application value, and the treatment value is also reflected compared with the routine treatment. At the same time, the permeability parameters obtained by MRI can predict the therapeutic effects of cyclophosphamide and vinorelbine, and further studies are helpful to establish a better solution for patients.

Under current global change, the driving force of evolution of drought has gradually transitioned from a single natural factor to a combination of natural and anthropogenic factors. Therefore, widely used standardised drought indices based on assumption of stationarity are challenged and may not accurately assess characteristics of drought processes. In this study, a nonstationary standardised streamflow index (NSSI) that incorporates climate and reservoir indices as external covariates was developed to access nonstationary hydrological drought. The first step of the proposed approach is to apply methods of trend and change point analysis to assess the nonstationarity of streamflow series to determine type of streamflow regime, that is, the natural and altered regime. Then, different nonstationary models were constructed to calculate the NSSI by selecting climate indices as covariates for streamflow series with natural regime, and climate and reservoir indices as covariate for streamflow series with altered regime. Four stations in the upper reaches of the Huaihe River basin, China, were selected to examine the performance of the proposed NSSI. The results indicated that Dapoling (DPL), Changtaiguan (CTG), and Xixian (XX) stations had natural streamflow regimes, while the Nanwan (NW) station had an altered regime. The global deviances of the optimal nonstationary models were 17 (2.2%), 18 (2.9%), 26 (4.0%), and 22 (3.5%) less than those of stationary models for DPL, CTG, NW, and XX stations, respectively. Especially, for the NW station influenced by reservoir regulations, the frequency of slight drought and moderate drought of NSSI was 12.8% lower than and 13.1% greater than those of SSI, respectively. Overall, the NSSI that incorporates the influence of climate variability and reservoir regulations provided more reliable assessment of hydrological drought than the traditional SSI.

The term “Flash drought” describes a type of drought with rapid onset and strong intensity, which is co-affected by both water-limited and energy-limited conditions. It has aroused widespread attention in related research communities due to its devastating impacts on agricultural production and natural system. Based on a global reanalysis dataset, we identify flash droughts across China during 1979~2016 by focusing on the depletion rate of weekly soil moisture percentile. The relationship between the rate of intensification (RI) and nine related climate variables is constructed using three artificial intelligence (AI) technologies, namely, multiple linear regression (MLR), long short-term memory (LSTM), and random forest (RF) models. On this basis, the capabilities of these algorithms for estimating RI and droughts (flash droughts and traditional slowly-evolving droughts) detection were analyzed. Results showed that the RF model achieved the highest skill in terms of RI estimation and flash droughts identification among the three approaches. Spatially, the RF-based RI performed best in the southeastern China, with an average CC of 0.90 and average RMSE of 2.6th percentile per week, while the poor performances were found in Xinjiang region. For drought detection, all three AI technologies presented a better performance in monitoring flash droughts than in conventional slowly-evolving droughts. Particularly, the probability of detection (POD), false alarm ratio (FAR), and critical success index (CSI) of flash drought derived from RF were 0.93, 0.15, and 0.80, respectively, indicating that RF technology is preferable to estimate the RI and monitoring flash droughts by considering multiple meteorological variable anomalies in adjacent weeks of drought onset. In terms of the meteorological driving mechanism of flash drought, the negative precipitation (P) anomalies and positive potential evapotranspiration (PET) anomalies exhibited a stronger synergistic effect on flash droughts comparing to slowly-developing droughts, along with asymmetrical compound influences in different regions over China. For the Xinjiang region, P deficit played a dominant role in triggering the onset of flash droughts, while in the southwestern China, the lack of precipitation and enhanced evaporative demand almost contributed equally to the occurrence of flash drought. This study is valuable to enhance the understanding of flash drought and highlight the potential of AI technologies in flash droughts monitoring.

Watershed management, disaster warning, and hydrological modeling require accurate spatiotemporal precipitation data sets. This paper presents a comprehensive assessment of a gauge-satellite-based precipitation product that merges the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR) satellite precipitation product (SPP) and ground precipitation data at 134 rain gauges in the Xijiang River basin, South China. Two regression-based schemes, principal component regression (PCR) and multiple linear regression (MLR), were used to combine the gauge-based precipitation data and PERSIANN-CDR SPP and were compared at daily and annual scales. Furthermore, a hydrological model Variable Infiltration Capacity was used to calculate streamflow and to evaluate the impact of four different precipitation interpolation methods on the results of the hydrological model at the daily scale. The result shows that the PCR method performs better than MLR and can effectively eliminate the interpolation anomalies caused by terrain differences between observation points and surrounding areas. On the whole, the combined scheme consistently exhibits good performance and thus serves as a suitable tool for producing high-resolution gauge-and satellite-based precipitation datasets.

The multisatellite-retrieved soil moisture (SM) products released by the Europe Space Agency's Climate Change Initiative (ESA CCI) program have been widely used in numerous fields, including drought monitoring. In this article, a cumulative distribution function is applied to match the simulated SM from the Variable Infiltration Capacity (VIC) model and fill in the missing records of ESA CCI SM. The weekly standard SM index (SSI) calculated from the ESA CCI SM dataset is utilized to monitor the agricultural drought over the Yellow River Basin (YRB) during 2000–2012. The performance of the ESA CCI SSI is compared with the Standard Precipitation Index (SPI), the Standard Precipitation Evapotranspiration Index (SPEI), the self-calibrating Palmer Drought Severity Index based on VIC model (VIC-scPDSI) and the anomalies of ESA CCI SM and Normalized Difference Vegetation Index (NDVI). The results show that the interpolated ESA CCI SM is significantly (p

The impact of crucial geographic and climatic factors on the input source errors of integrated multi-satellite precipitation estimates is an important but still unclear issue for both algorithm developers and data users. This study primarily focused on the impacts of the twelve input sources used in the latest Global Satellite Mapping of Precipitation for Global Precipitation Measurement (GPM-GSMaP) for different climatic regions, elevations, and seasons over mainland China. Our evaluation results show that the error features of the input sources from several passive microwave and infrared sensors are related to the accuracy of GPM-GSMaP precipitation estimates. The input sources show larger hits, misses and false biases in the semi-arid and arid regions, for which the false bias was particularly significant. As for the seasonality, the input data sources exhibit a better performance in summer and have relatively lower hits and higher biases in winter. We also found that precipitation retrievals of the input sources are affected by topography in different extents. In terms of passive microwave sensors, the conical-scanning imagers generally outperform the cross-track-scanning sounders, but the sounders-based precipitation estimates have relatively better detection capability. As one of the core sensors for GPM, the microwave imager GMI reveals inadequacies in representing areal rainfall patterns and has relatively large biases especially in the winter and spring seasons, suggesting that the current GMI algorithm might need to be further improved.

Study region: Yunnan Province, China. Study focus: Yunnan Province (YP) is affected by frequent droughts that severely affect local agriculture and the ecological environment. Therefore, the identification of droughts and an analysis of their driving factors are of significant importance for mitigating local drought losses and guiding agricultural practices. This study identified the spatiotemporal distribution and dynamic changes in drought events over YP (period 1961–2018) using the severity-area-duration method. The impact of El Niño-Southern Oscillation (ENSO) on seasonal droughts and their lag period were also quantified by employing the sliding correlation coefficient and cross-wavelet analysis method. New hydrological insights for the region: 74 drought events were identified during 1961–2018 in YP, which were mainly short-duration that occurred in the 1980 s and 2000 s, and most drought centers are located over the northern and eastern parts of YP. We found that a significant correlation and different lag periods exist between Oceanic Niño Index (ONI) and seasonal precipitation in YP. Spring droughts mainly occurred in El Niño years during the 1980 s and the 1990 s, whilst winter droughts mainly occurred in La Niña years during the 1990 s and the 2000 s with a lag period of up to 12 months.

Global satellite precipitation products, such as Tropical Rainfall Measuring Mission (TRMM) and its successor, Global Precipitation Measurement (GPM), have provided hydrologists with a critical precipitation data source for hydrological applications in data-sparse or ungauged basins. This study conducts a comparative analysis on the quality of the TRMM Multi-satellite Precipitation Analysis 3B42V7 and the Integrated Multi-satellite Retrievals for GPM (IMERG) Final Run version 05 precipitation products and their hydrological utilities in the Yellow River source region (YRSR), a mountainous Alpine region in northwestern China, from April 2014 to December 2016. Results indicate that when evaluated against the ground precipitation observations, IMERG generally improves the daily precipitation estimates relative to its predecessor 3B42V7. In addition, and the correlation coefficients (CCs) of IMERG (0.328 and 0.527) are significantly higher at grid and basin scales than those of 3B42V7 (0.287 and 0.458). However, the 3-hourly precipitation estimates from both products poorly correlate with the ground observations at grid and basin scales. By using the grid-based Xinanjiang (GXAJ) hydrological model calibrated with the gauge-based precipitation for daily streamflow simulations, the 3B42V7-driven model run shows acceptable hydrological simulation skill with regard to the Nash-Sutcliffe model efficiency coefficient (NSE = 0.729), whereas IMERG demonstrates improved performance (NSE = 0.810), which is comparable with the gauge-based simulation (NSE = 0.807). Input-specific model recalibration effectively enhances the hydrological performance of both satellite products (NSE = 0.856 for IMERG and NSE = 0.840 for 3B42V7). Additionally, the gauge-benchmarked GXAJ model with 3B42V7 has limited hydrological skill in simulating three historical flood events at 3-hourly time intervals (NSE = −0.070 to 0.702), while IMERG has a slightly better performance (NSE = 0.266–0.792). Model recalibration also significantly improves the simulations of two out of three flood events, and the NSE skill cores of IMERG (0.581–0.901) and 3B42V7 (−0.795 to 0.852) are relatively lower than that of the gauge-based simulation (0.753–0.969) but comparable. Overall, the IMERG and 3B43V7 satellite precipitation products can be adopted as reliable precipitation sources for hydrological simulations at daily and sub-daily time scales in the study area, with IMERG better suited than 3B42V7. Considering that the low CC values exist in both IMERG and 3B42V7 products, in particular, at a sub-daily temporal scale, the GPM research community should further improve the calibration algorithms and enhance the quality of IMERG products in YRSR. Performing bias-correction of satellite precipitation products is also necessary for hydrological modelers to effectively improve their hydrological utilities.

Recently, the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) products with high spatial (0.1° × 0.1°) and temporal (half-hourly) resolution have become operationally available. It is of crucial importance to comprehensively evaluate these new products before they are applied extensively. This study focuses on statistical and hydrological evaluations of the latest IMERG (Version 05) products: the near-real-time “Early” run and “Late” run IMERG products (IMERG-E and IMERG-L, respectively), and the post-real-time “Final” run IMERG product (IMERG-F) over the mid-latitude humid Mishui basin in South China during 2014–2015, in comparison with their predecessors, the Tropical Rainfall Measuring Mission Multi-satellite Precipitation Analysis (TMPA) products (3B42RT and 3B42V7). The post-real-time IMERG-F presents the best performance among the five satellite precipitation products (SPPs), with the highest daily correlation coefficient (CC) of 0.85 and the lowest daily root-mean square error of 5.58 mm at the basin scale. The near-real-time IMERG-E and IMERG-L demonstrate comparable performance with 3B42V7. In addition, the Taylor diagrams visually demonstrate that the IMERG products are better than the 3B42 products. For hydrological simulations under scenario I (model calibration based on rain gauge observations), the post-real-time IMERG-F performs obviously better than the 3B42V7 does, with a relatively high CC of 0.81, a good Nash–Sutcliffe coefficient of 0.63, and an acceptable relative bias of −3.98%. Both the IMERG-E and IMERG-L demonstrate a better performance than the 3B42RT does. For scenario II (model recalibration based on each satellite dataset), the hydrological performances of both the IMERG and 3B42 products are improved. The IMERG-E, especially, demonstrates streamflow simulation performance comparable to that of the 3B42V7, both for the whole simulation period and flood season, indicating the great potential of the latest near-real-time IMERG-E product for flood simulation and prediction. Overall, this systematic evaluation highlights that the latest IMERG products have desirable hydrological utility in the study region. This study will provide useful guidelines for hydrological applications of the new generation SPPs, as well as IMERG algorithm development.

In the context of global climate change, it is important to monitor abnormal changes in extreme precipitation events that lead to frequent floods. This research used precipitation indices to describe variations in extreme precipitation and analyzed the characteristics of extreme precipitation in four climatic (arid, semi-arid, semi-humid and humid) regions across China. The equidistant cumulative distribution function (EDCDF) method was used to downscale and bias-correct daily precipitation in eight Coupled Model Intercomparison Project Phase 5 (CMIP5) general circulation models (GCMs). From 1961 to 2005, the humid region had stronger and longer extreme precipitation compared with the other regions. In the future, the projected extreme precipitation is mainly concentrated in summer, and there will be large areas with substantial changes in maximum consecutive 5-day precipitation (Rx5) and precipitation intensity (SDII). The greatest differences between two scenarios (RCP4.5 and RCP8.5) are in semi-arid and semi-humid areas for summer precipitation anomalies. However, the area of the four regions with an increasing trend of extreme precipitation is larger under the RCP8.5 scenario than that under the RCP4.5 scenario. The increasing trend of extreme precipitation in the future is relatively pronounced, especially in humid areas, implying a potential heightened flood risk in these areas.

Text-to-Graph extraction aims to automatically extract information graphs consisting of mentions and types from natural language texts. Existing approaches, such as table filling and pairwise scoring, have shown impressive performance on various information extraction tasks, but they are difficult to scale to datasets with longer input texts because of their second-order space/time complexities with respect to the input length. In this work, we propose a Hybrid Span Generator (HySPA) that invertibly maps the information graph to an alternating sequence of nodes and edge types, and directly generates such sequences via a hybrid span decoder which can decode both the spans and the types recurrently in linear time and space complexities. Extensive experiments on the ACE05 dataset show that our approach also significantly outperforms state-of-the-art on the joint entity and relation extraction task., Comment: Accepted by ACL 2021 Findings

As the successor of Tropical Rainfall Measuring Mission, Global Precipitation Measurement (GPM) has released a range of satellite-based precipitation products (SPPs). This study conducts a comparative analysis on the quality of the integrated multisatellite retrievals for GPM (IMERG) and global satellite mapping of precipitation (GSMaP) SPPs in the Yellow River source region (YRSR). This research includes the eight latest GPM-era SPPs, namely, IMERG &ldquo, Early,&rdquo, &ldquo, Late,&rdquo, and &ldquo, Final&rdquo, run SPPs (IMERG-E, IMERG-L, and IMERG-F) and GSMaP gauge-adjusted product (GSMaP-Gauge), microwave-infrared reanalyzed product (GSMaP-MVK), near-real-time product (GSMaP-NRT), near-real-time product with gauge-based adjustment (GSMaP-Gauge-NRT), and real-time product (GSMaP-NOW). In addition, the IMERG SPPs were compared with GSMaP SPPs at multiple spatiotemporal scales. Results indicate that among the three IMERG SPPs, IMERG-F exhibited the lowest systematic errors and the best quality, followed by IMERG-E and IMERG-L. IMERG-E and IMERG-L underestimated the occurrences of light-rain events but overestimated the moderate and heavy rain events. For GSMaP SPPs, GSMaP-Gauge presented the best performance in terms of various statistical metrics, followed by GSMaP-Gauge-NRT. GSMaP-MVK and GSMaP-NRT remarkably overestimated total precipitation, and GSMaP-NOW showed an evident underestimation. By comparing the performances of IMERG and GSMaP SPPs, GSMaP-Gauge-NRT provided the best precipitation estimates among all real-time and near-real-time SPPs. For post-real-time SPPs, GSMaP-Gauge presented the highest capability at the daily scale, and IMERG-F slightly outperformed the other SPPs at the monthly scale. This study is one of the earliest studies focusing on the quality of the latest IMERG and GSMaP SPPs. The findings of this study provide SPP developers with valuable information on the quality of the latest GPM-era SPPs in YRSR and help SPP researchers to refine the precipitation retrieving algorithms to improve the applicability of SPPs.

The evaporation of rainfall intercepted by canopies back into the atmosphere – often referred to as rainfall interception loss – is a significant component of terrestrial evaporation in many ecosystems. The physical process of rainfall interception loss can usually be broken down into three phases: (1) wetting up of the canopy, (2) saturated canopy conditions, and (3) drying out after rainfall has ceased. During each of these phases, the process is affected by many factors, including rainfall characteristics, such as gross rainfall, rainfall intensity and rainfall duration, vegetation characteristics such as canopy height, leaf area and the orientation of branches and leaves, and meteorological conditions such as temperature, wind speed and relative humidity. The Global Land Evaporation Amsterdam Model (GLEAM; Miralles et al. 2011) estimates terrestrial evaporation, including forest rainfall interception loss, at the global scale mostly from satellite data. However, the model estimation of interception loss has not been updated since its release almost 10 years ago (Miralles et al. 2010).In this regard, improving the estimation of interception loss in the model remains a priority. In GLEAM, rainfall interception is estimated using the revised Gash analytical model by Valente et al. (1997), in which the canopy storage and mean wet canopy evaporation rate are both considered constants in both space and time. In addition, only tall-canopy interception is considered. Here we explore the potential of the modified Gash's model by Van Dijk and Bruijnzeel (2001), which uses time variant canopy storage and evaporation functions dependent on leaf area index, for its application at global scales. In addition, due to its dependency on leaf area index, the model is applied to the estimation of rainfall interception loss of low vegetation types such as shrubs and grasses. An extensive meta-analysis of previous interception loss field campaigns provides an extensive archive of data to parameterize and/or validate model estimates over multiple ecosystem types. This presentation provides a general overview of the challenges in rainfall interception loss modelling at global scales and the first results of the global benchmarking of the Valente et al. (1997) and the Van Dijk and Bruijnzeel (2001) formulations against in situ data.References Miralles D G, Gash J H, Holmes T R H, et al. Global canopy interception from satellite observations[J]. Journal of Geophysical Research: Atmospheres, 2010, 115(D16).Miralles D G, Holmes T R H, De Jeu R A M, et al. Global land-surface evaporation estimated from satellite-based observations[J]. Earth Syst. Sci., 2011, 15(2): 453–469.Valente F, David J S, Gash J H C. Modelling interception loss for two sparse eucalypt and pine forests in central Portugal using reformulated Rutter and Gash analytical models[J]. Journal of Hydrology, 1997, 190(1-2): 141-162.Van Dijk A, Bruijnzeel L A. Modelling rainfall interception by vegetation of variable density using an adapted analytical model. Part 1. Model description[J]. Journal of Hydrology, 2001, 247(3-4): 230-238.

How drought changes in the context of global warming is a concerning issue that influences the strategies of drought mitigation and drought management. Based on the simulations of the version 2 of Global Land Data Assimilation System (GLDAS-2.0) during 1948-2016, we revisited the drought trend over China and analyzed the individual contributions of precipitation and potential evapotranspiration (PET) on varied drought patterns. Four composite drought indices including the Aggregate Drought Index (ADI), Joint Drought Deficit Index (JDI), self-calibrating Palmer Drought Severity Index (scPDSI) and Standardized Palmer Drought Index (SPDI) were employed for trend detection. Results showed that all four composite drought indices suggested a significant drying belt spreads from northeastern China to southwestern China, and a significant wetting trend in the “Three river sources” areas. Controversial patterns were mainly located in the northwestern China, Xinjiang districts, and the middle and lower reaches of the Yangtze River, where the SPDI and JDI respectively, overestimated and underestimated the moisture conditions at varying degrees. According to the change point tests, it is found that the drying pattern in the northeastern China occurred since 1970s, where precipitation deficits and expanded PET jointly aggravated the drying process, while for the “Three river sources” areas, the increased precipitation since 2000s is the main driver for the wetting pattern.

Although many previous studies have analysed the impacts of human activities on hydrological drought, studies that analysed these impacts from the perspective of drought termination, a critical re-wetting phase of hydrological drought, are limited. A deeper understanding on how human alter hydrological drought termination phase is essential for improving drought recovery prediction and performance of the drought early warning system. In this study, a comprehensive approach for identifying hydrological drought termination characteristics and quantifying the impact of human activities on drought termination was proposed. This approach, which combines the concept of drought termination (DT), an ‘observed–simulated’ comparison approach, and the variable threshold level method (TLMv), consists of the following steps: (1) reconstruction of natural streamflow using a hydrological model, (2) identification of hydrological drought termination characteristics using TLMv method and the concept of drought termination, and (3) quantification of human influence by comparison of the hydrological drought termination characteristics of human-influenced (observed) series and those of natural (simulated) series. The Laohahe basin, consists of four catchments (Xiquan, Xiaochengzi, Dianzi, and Taipingzhuang) in northern China, was evaluated using the proposed procedure. The study demonstrated that the proposed approach is efficient in quantifying human influence on hydrological drought termination phase. The results revealed that human activities have significant impacts on the hydrological drought termination phase in the Xiaochengzi, Dianzi, and Taipingzhuang catchments. All the average drought termination duration (DTdur), deficit (DTdef), and rate (DTrate) in the human-influenced series of the three catchments (Xiaochengzi, Dianzi, and Taipingzhuang) increased in comparison to those in the natural series, with maximum increases of 230%, 865%, and 35%, respectively. The seasonality of the drought termination phase starts (DTstart) and ends (DTend) for the three catchments exhibited obvious shifts due to human influence. The preferred seasons for DTstart and DTend were shifted to summer and autumn, respectively. The proposed approach and findings of this study may help to gain a deeper understanding of how human activities alter hydrological drought termination severity (drought termination duration, deficit, and rate) and time (drought termination starts or ends).

Global climate change not only affects the processes within the water cycle but also leads to the frequent occurrences of local and regional extreme drought events. In China, spatial and temporal characterizations of drought events and their future changing trends are of great importance in water resources planning and management. In this study, we employed self-calibrating Palmer drought severity index (SC-PDSI), cluster algorithm, and severity-area-duration (SAD) methods to identify drought events and analyze the spatial and temporal distributions of various drought characteristics in China using observed data and CMIP5 model outputs. Results showed that during the historical period (1961&ndash, 2000), the drought event of September 1965 was the most severe, affecting 47.07% of the entire land area of China, and shorter duration drought centers (lasting less than 6 months) were distributed all over the country. In the future (2021&ndash, 2060), under both representative concentration pathway (RCP) 4.5 and RCP 8.5 scenarios, drought is projected to occur less frequently, but the duration of the most severe drought event is expected to be longer than that in the historical period. Furthermore, drought centers with shorter duration are expected to occur throughout China, but the long-duration drought centers (lasting more than 24 months) are expected to mostly occur in the west of the arid region and in the northeast of the semi-arid region.

The Palmer drought severity index (PDSI) is physically based with multivariate concepts, but requires complicated calibration and cannot easily be used for multiscale comparison. Standardized drought indices (SDIs), such as the standardized precipitation index (SPI) and standardized precipitation evapotranspiration index (SPEI), are multiscalar and convenient for spatiotemporal comparison, but they are still challenged by their lack of physical basis. In this study, a hybrid multiscalar indicator, the standardized Palmer drought index (SPDI), was used to examine drought properties of two meteorological stations (the Beijing and Guangzhou stations) in China, which have completely different drought climatologies. The results of our case study show that the SPDI is correlated with the well-established drought indices (SPI, SPEI, and PDSI) and presents generally consistent drought/wetness conditions against multiple indicators and literature records. Relative to the PDSI, the SPDI demonstrates invariable statistical characteristics and better comparable drought/wetness frequencies over time and space. Moreover, characteristics of major drought events (drought class, and onset and end times) indicated by the SPDI are generally comparable to those detected by the PDSI. As a physically-based standardized multiscalar drought indicator, the SPDI can be regarded as an effective development of the Palmer drought indices, providing additional choices and tools for practical drought monitoring and assessment. Keywords: Drought, PDSI, Multiscalar index, SPDI, Multifaceted comparison

This study focuses on tracing the error sources of the latest global satellite mapping of precipitation for global precipitation measurement (GPM-GSMaP) over the Tibetan Plateau by the corresponding satellite information flag provided by the retrieval system. We investigated the characteristics of GPM-GSMaP estimates from 11 passive microwave sensors (including 7 imagers and 4 sounders) and 1 geo-infrared data source integrating the morphing technique (i.e., morph). Assessment results show that imagers are generally superior to sounders and morph. Among the seven types of imagers, the Tropical Rainfall Measuring Mission Microwave Imager, which rarely underestimates heavy rain events, exhibits the best performance with total bias of −8.8%. In contrast, the worst performance is found in the special sensor microwave imager/sounder on defense meteorological satellite program (DMSP)-F19 with the largest bias of approximately 39.8%. The GPM Microwave Imager shows acceptable accuracy in detecting heavy rain, but it tends to overestimate light and moderate rain. Compared to the imagers, all the sounders produced larger biases (>120%), although they show lower probability of missing rainfall events. Particularly, the AMSU-A/MHS (i.e., Advanced Microwave Sounding Unit-A; Microwave Humidity Sounder) on national oceanic and atmospheric administration (NOAA) satellites (i.e., NOAA-18 and −19) display higher biases than those on meteorological operational satellite (MetOp) satellites (i.e., MetOp-A and −B), with dramatic overestimation up to 168.4%. Additionally, the error components of morph show a similar pattern to those of sounders, except for substantial underestimation of heavy rain events. The approach of tracing error reported here can enable both GPM-GSMaP users and developers to better understand the error sources of precipitation retrievals.

In this study, the equidistant cumulative distribution function (EDCDF) quantile-based mapping method was used to develop bias-corrected and downscaled monthly precipitation and temperature for China at 0.5° × 0.5° spatial resolution for the period 1961–2099 for eight CMIP5 GCM simulations. The downscaled dataset was constructed by combining observations from 756 meteorological stations across China with the monthly GCM outputs for the historical (1961–2005) and future (2006–99) periods for the lower (RCP2.6), medium (RCP4.5), and high (RCP8.5) representative concentration pathway emission scenarios. The jackknife method was used to cross validate the performance of the EDCDF method and was compared with the traditional quantile-based matching method (CDF method). This indicated that the performance of the two methods was generally comparable over the historic period, but the EDCDF was more efficient at reducing biases than the CDF method across China. The two methods had similar mean absolute error (MAE) for temperature in January and July. The EDCDF method had a slight advantage over the CDF method for precipitation, reducing the MAE by about 0.83% and 1.2% at a significance level of 95% in January and July, respectively. For future projections, both methods exhibited similar spatial patterns for longer periods (2061–90) under the RCP8.5 scenario. However, the EDCDF was more sensitive to a reduction in variability.

Monitoring vegetation dynamics at global scale is equally important in the context of terrestrial ecosystem carbon exchange and climate-biosphere interactions. The Breaks For Additive Seasonal and Trend (BFAST) method and Moderate Resolution Imaging Spectroradiometer (MODIS) 16-day Normalized Difference Vegetation Index (NDVI) at a spatial resolution of 250 m were used to detect vegetation dynamics in Quebec during 2000–2011. The overall agreement between BFAST detected breaks and observed disturbances was about 64% with the highest agreement up to 80% for “Fire” disturbance. The results presented in this study indicated that 25.7% of the total study area experienced NDVI trend changes with one or more breaks during 2000–2011, most of which were detected in the Boreal Shield eco-zone along the coastline of the Gulf of St. Lawrence. Abrupt vegetation changes barely varied under different eco-zones while considerably varied with different land cover types. The abrupt changes areas in 2002 and 2009 were the two greatest, with area percentages of 17.4% and 29.1% of the whole area, respectively. The area percentages of years with abrupt trend changes indicated that abrupt vegetation greening occurred in 2008 and 2009, especially in 2009, with 58.3% of the overall abrupt greening. Abrupt vegetation browning occurred in 2002, 2003, 2005 and 2007, especially in 2002, with 28.2% of the overall abrupt browning. Moreover, our results indicated that the detected vegetation trends varied temporally and spatially. Disturbances from existing field observations or remotely sensed images could only interpret

In this study, a comprehensive evaluation of the reliability of seventeen satellite-, reanalysis-, and gauge-based precipitation products (i.e., IMERG-F, GSMaP-G, TMPA 3B42, CMORPH-CRT, PERSIANN-CDR, CHIRPS, IMERG-E, IMERG-L, GSMaP-RT, TMPA-RT, PERSIANN-RT, ERA5, ERA-Interim, MERRA2, GPCC, CPC, and CRU) was conducted for drought monitoring from June 2000 to December 2019 over mainland China. The girded China monthly Precipitation Analysis Product (CPAP) with a dense station network was used as the benchmark observed precipitation data. The Standardized Precipitation Index (SPI) was used as a drought monitoring tool. The comprehensive evaluation procedures were implemented by a monthly precipitation evaluation, various timescales SPI verification, and typical drought event analysis. The results indicated that the performance of per precipitation product is in accordance with the generally higher monthly accuracy in eastern China than that in western China. The post-process satellite precipitation products, particularly bias-adjusted GPM-based products, i.e., the IMERG-F and GSMaP-G, are significantly better at detecting the monthly precipitation than the reanalysis (ERA5 and ERA-Interim), and near real-time satellite precipitation datasets. The best performances are observed for MERRA2 from reanalysis-based precipitation datasets, and GPCC from gauge-based precipitation products, for both precipitation estimation and drought monitoring. Regarding drought monitoring, the performance ranking of most precipitation products is basically consistent with monthly precipitation estimation. The SPI behaviors based on the bias-adjusted GPM-based products have inferior performance than those in MERRA2 and GPCC at the continental scale. Nevertheless, some discrepancies exist in the reliability level of drought detection in sub-regions and multi-timescales. Integrally, the IMERG-F is evaluated to be the most ideal precipitation data among the seventeen precipitation products for drought monitoring in eastern and southwest China. However, in comparison to the GPCC performance, the accuracy of satellite precipitation products should be further improved in the Xinjiang and Tibet Plateau regions. The outcomes of this study will provide valuable insights into the drought monitoring by using multi-sources open access precipitation products across mainland China.