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Abstract Understanding soil erosion and sediment transport from the hillslope scale to the regional scale is crucial for studies on water quality, soil‐water conservation, the lateral carbon cycle, environmental zoning and vulnerability. However, most existing erosion and sediment transport models are only applicable at the hillslope scale or for small watersheds with fine spatial resolutions (typically much less than 1 km). This study presents a process‐based soil erosion and sediment transport model for model applications designed for applications with coarse spatial (e.g., ≥10 km) and temporal (e.g., from hourly to daily) resolutions. This new model, referred to as VIC‐SED, effectively accounts for interactions between erosion and hydrological processes. This is achieved by tightly coupling the erosion processes with a hydrologically based Three‐layer Variable Infiltration Capacity (VIC‐3L) land surface model (LSM) and to a multi‐scale routing (MSR) model. VIC‐SED considers the impacts of (a) the spatio‐temporal variability of rainfall intensity on erosion processes and (b) soil moisture on the soil detachment process. VIC‐SED is evaluated in two watersheds. Results demonstrate that VIC‐SED is capable of reproducing water and suspended sediment discharges at coarse spatial resolutions and varying temporal scales varying from 15‐min to daily intervals. Our study indicates that the VIC‐SED model is a promising tool for studying and assessing the impacts of climate and land cover changes on suspended sediment yields over large regions using coarse spatial and temporal resolutions.

Abstract Background Interstitial lung disease (ILD) remains one of the most important causes of morbidity and mortality in patients with Connective Tissue Diseases (CTD). This study evaluated the impact of hospitalization on mortality in an ethnically and racially diverse cohort of CTD-ILD patients. Methods We conducted a medical records review study at Montefiore Medical Center, Bronx, NY. We included 96 patients and collected data on demographic characteristics, reasons for hospitalization, length of stay, immunosuppressant therapy use, and mortality. We stratified our patients into two cohorts: hospitalized and non-hospitalized. The hospitalized cohort was further subdivided into cardiopulmonary and non-cardiopulmonary admissions. Two-sample tests or Wilcoxon’s rank sum tests for continuous variables and Chi-square or Fisher’s exact tests for categorical variables were used for analyses as deemed appropriate. Results We identified 213 patients with CTD-ILD. Out of them, 96 patients met the study’s inclusion criteria. The majority of patients were females (79%), and self-identified as Hispanic (54%) and Black (40%). The most common CTDs were rheumatoid arthritis (RA) (29%), inflammatory myositis (22%), and systemic sclerosis (15%). The majority (76%) of patients required at least one hospitalization. In the non-hospitalized group, no deaths were observed, however we noted significant increase of mortality risk in hospitalized group (p = 0.02). We also observed that prolonged hospital stay (> 7 days) as well as older age and male sex were associated with increased mortality. Conclusions Prolonged (> 7 days) hospital stay and hospitalization for cardiopulmonary causes, as well as older age and male sex were associated with an increased mortality risk in our cohort of CTD-ILD patients.

Land surface all-wave net radiation (Rn) is crucial in determining Earth's climate by contributing to the surface radiation budget. This study evaluated seven satellite and three reanalysis long-term land surface Rn products under different spatial scales, spatial and temporal variations, and different conditions. The results showed that during 2000-2018, Global Land Surface Satellite Product (GLASS)-Moderate Resolution Imaging Spectroradiometer (MODIS) performed the best (RMSE=25.54 Wm-2, bias=−1.26 Wm-2), followed by ERA5 (the fifth-generation of European Centre for Medium-Range Weather Forecast Reanalysis) (RMSE=32.17 Wm-2, bias=−4.88 Wm-2) and GLASS-AVHRR (Advanced Very-High-Resolution Radiometer) (RMSE=33.10 Wm-2, bias=4.03 Wm-2). During 1983-2018, GLASS-AVHRR and ERA5 ranked top and performed similarly, with RMSE values of 31.70 and 33.08 Wm-2 and biases of −4.56 and 3.48 Wm-2, respectively. The averaged multi-annual mean Rn over the global land surface of satellite products was higher than that of reanalysis products by about 10∼30 Wm-2. These products differed remarkably in long-term trends variations, particularly pre-2000, but no significant trends were observed. Discrepancies were more frequent in satellite data, while reanalysis products showed smoother variations. Large discrepancies were found in regions with high latitudes, reflectance, and elevation which could be attributed to input radiative components, meteorological variables (e.g., cloud properties, aerosol optical thickness), and applicability of the algorithms used. While further research is needed for detailed insights.

Shortwave radiation is an influential driver of global hydrological cycle, as its variation will alter evapotranspiration (ET). While climate change discussion extensively examined ET response to temperature, wind speed and land use/cover variations, little is known about the contribution of shortwave radiation balance on ET partly because of data availability. In this study, we applied the newly-released Global LAnd Surface Satellite (GLASS) products to detect changes in shortwave radiation and albedo for 2003–2018 over China, and quantified their contributions on potential evapotranspiration (PET) which is the upper limit of ET and generally the basis to estimate it. The results showed that downward shortwave radiation declined, together with a slight decrease in albedo, causing a decrease of net shortwave radiation. While climate change affected PET, the decreased net shortwave radiation reduced PET during the period. We identified a hotspot area in the north China plain that is one of the main agricultural production regions, as the radiation balance induced a contrasting effect on PET for the warm and the cold seasons. Our study indicates that it is important to consider shortwave radiation balance when estimating PET and ET to make reliable evaluation of climate change on hydrological cycle and ecosystem response.

Study region: Yellow River Basin in China, where streamflow dynamics were significantly impacted by human activities. Study focus: We introduced a deep learning-based method, i.e., Data Integration (DI) with Long Short-Term Memory (LSTM), which leverages Global Flood Awareness System (GloFAS) streamflow data. Multiscale (Catchment, River) attributes were incorporated into the DI LSTM to represent human disturbances on land surface. We employed this method to reconstruct daily streamflow series in 60 human-regulated catchments across the Yellow River Basin, and identified the sensitivity of the DI LSTM model to the multiscale attributes. New hydrological Insights for the Region: Our findings revealed that the DI LSTM model achieved favourable performance in streamflow estimation, with the highest Kling-Gupta efficiency (KGE) reaching up to 0.9, outperforming the Regular LSTM model, which was forced by meteorological variables. Multiscale attributes can enhance the DI model performance, particularly in large catchments with significant human activities. A two-step validation demonstrated the high accuracy of the reconstructed streamflow data across the Yellow River Basin, as the KGEs for streamflow estimation in 40 catchments are over 0.6. In summary, the DI LSTM model shows great potential for reconstructing streamflow in human-regulated catchments in arid regions. The reconstructed daily streamflow data contribute valuable insights for monitoring changing hydrological conditions, especially in regions lacking extensive streamflow monitoring networks.

Guidelines for the management of in-hospital cardiac arrest resuscitation are often drawn from evidence generated in out-of-hospital cardiac arrest populations and applied to the in-hospital setting. Approach to airway management during resuscitation is one example of this phenomenon, with the recommendation to place either a supraglottic airway or endotracheal tube when performing advanced airway management during in-hospital cardiac arrest based mainly in clinical trials conducted in the out-of-hospital setting. The Hospital Airway Resuscitation Trial (HART) is a pragmatic cluster-randomized superiority trial comparing a strategy of first choice supraglottic airway to a strategy of first choice endotracheal intubation during resuscitation from in-hospital cardiac arrest. The design includes a number of innovative elements such as a highly pragmatic design drawing from electronic health records and a novel primary outcome measure for cardiac arrest trials—alive-and-ventilator free days. Many of the topics explored in the design of HART have wide relevance to other trials in in-hospital cardiac arrest populations.

BackgroundOutcome selection is a critically important aspect of clinical trial design. Alive-and-ventilator free days is an outcome measure commonly used in critical care clinical trials, but has not been fully explored in resuscitation science.MethodsA simulation study was performed to explore approaches to the definition and analysis of alive-and-ventilator free days in cardiac arrest populations. Data from an in-hospital cardiac arrest observational cohort and from the Pragmatic Airway Resuscitation Trial were used to inform and conduct the simulations and validate approaches to alive-and-ventilator free days measurement and analysis.FindingsAlive-and-ventilator-free days is a flexible outcome measure in cardiac arrest populations. An approach to alive-and-ventilator free days that assigns -1 days when return of spontaneous circulation is not achieved provides a wider distribution of the outcome and improves statistical power. The optimal approach to the analysis of alive-and-ventilator free days varies based on the expected impact of the intervention under study on rates of return of spontaneous circulation, survival, and ventilator-free survival.ConclusionsAlive-and-ventilator free days adds to the armamentarium of clinical trialists in the field of resuscitation science.

Abstract Lateral movement of soil organic carbon (SOC) induced by soil erosion and runoff changes spatial distributions of SOC, and further changes the land‐atmosphere CO2 exchange and terrestrial carbon budget. However, current ecosystem models do not or only poorly integrate the process of SOC lateral movement and cannot simulate the impacts of soil erosion on the carbon cycle. This study integrated SOC erosion and deposition processes into a process‐based ecosystem model (i.e., Integrated BIosphere Simulator (IBIS)), and separately simulated the lateral movements of dissolved organic carbon (DOC) and particulate organic carbon (POC). The model was evaluated in three river basins in Northeast China that are dominated by cropland, forest, and grassland. The results showed that the model reproduced well the production, erosion, and deposition of DOC and POC. The annual SOC lateral movement (1.34–7.22 g C m−2 yr−1) induced by erosion in the three tested basins was 0.27%–1.45% of the annual net primary production. The model developed in this study has great implications for simulating the lateral movements of SOC in terrestrial ecosystems, which can improve model performance in projecting the terrestrial carbon budget.

Study region: The Loess Plateau in China Study focus: Land surface water bodies are important to ensure water security for agricultural, industrial, domestic, and environmental sectors. In the Loess Plateau, changes in land surface water due to climate change and human activities are a concern. Dam and reservoir constructions are effective to combat erosion and water scarcity, but the impact on surface water dynamics is unclear. In this study, we employ a long-term satellite water product to detect the spatial-temporal variability in surface water at the regional scale on the Loess Plateau and identify the potential cause of climate change and human activities.New hydrological insights for the region: Over the past two decades, the area of permanent water has increased by approximately 800 km2. Surface water expansion is primarily associated with small water bodies (< 1 km2), as their number has roughly doubled, while the number and area of large water bodies have remained stable. This expansion has little correlation with precipitation variation but is highly correlated with water withdrawal for agricultural, industrial, and other sectors. Thus, it is primarily contributed by hydraulic project construction as a response to the increasing water demand. The above findings imply the positive role of hydraulic projects, but it is essential to note that the continuous expansion of surface water might not be sustainable because of natural constraints.

Introduction: Historically, Black and Hispanic patient populations in the Bronx Borough of New York City have experienced the highest rates of social risk factors, and associated poor health outcomes, in New York State. During the pandemic, Bronx communities disproportionately experienced high rates of COVID-19 illness and death. To date, little is known regarding the COVID-19 pandemic’s impact on social risk factors in urban, at-risk communities. This study aimed to determine how social risk factors changed during the pandemic in a Bronx-based patient population. Methods: Study participants were adult patients seen at a Federally Qualified Health Center in the South Bronx. Using a paired longitudinal study design, 300 participants were randomly selected for telephonic outreach during the pandemic from a sample of 865 participants who had been offered a social risk factor screener in the year prior to the pandemic. The outreach survey used included the social risk factor screener and questions regarding COVID-19 illness burden and prior engagement in social services. The McNemar test was used to analyze trends in reported social risks. Results: Housing quality needs, food insecurity, and legal care needs significantly increased during the pandemic. Participants who reported COVID-19 illness burden were 1.47 times more likely to report a social risk factor ( P = .02). No significant relationship was found between prior enrollment in clinic-based social services and degree of reported social risk ( P = .06). Conclusion: Housing quality needs, food insecurity, and legal care needs increased during the COVID-19 pandemic in a predominantly Black and Hispanic identifying urban patient population. Urgently addressing this increase is imperative to achieving health equity in ongoing COVID-19 mitigation efforts.

Abstract To evaluate whether pregnancies conceived via assisted reproductive technology and exposed to Hurricane Katrina (HK), one of the most destructive natural disasters in United States history, were more prone to adverse perinatal outcomes, miscarriages, or sex disparities compared with pregnancies established but not directly exposed to the natural disaster. Retrospective cohort study. Women 18 years and older undergoing fresh ART cycles that resulted in singleton pregnancies and entered in the SART CORS database from 2004 to 2008. Incidence of full-term and preterm delivery, miscarriage rate, infant weight at birth, and infant sex ratio. Total follicle stimulating hormone (FSH) stimulation dosage and number of oocytes retrieved. Between January 2004 and December 2008, a total of 451,848 fresh autologous IVF cycles were recorded in SART CORS, leading to 190,624 pregnancies and 129,499 live births. After controlling for potential confounders, our results demonstrate no association between HK exposure and overall preterm deliveries (

Summary: Background: We systematically reviewed the diagnostic accuracy of cervical cancer screening and triage strategies in women living with HIV (WLHIV). Methods: Cochrane Library, Embase, Global Health and Medline were searched for randomised controlled trials, prospective or cross-sectional studies published from database inception to 15 July 2022 reporting diagnostic accuracy of tests in cervical cancer screening and triage of screen-positive WLHIV. Studies were included if they reported the diagnostic accuracy of any cervical cancer screening or triage strategies for the detection of histologically-confirmed high-grade cervical intraepithelial neoplasia (CIN2+/CIN3+) among WLHIV. Summary data were extracted from published reports. Authors were contacted for missing data where applicable. Sensitivity and specificity estimates for CIN2/3+ were pooled using models for meta-analysis of diagnostic accuracy data. Study quality was assessed using the QUADAS-2 tool for the quality assessment of diagnostic accuracy studies. PROSPERO registration:CRD42020189031. Findings: In 38 studies among 18,737 WLHIV, the majority (n=19) were conducted in sub-Saharan Africa. The pooled prevalence was 12.0% (95%CI:9.8-14.1) for CIN2+ and 6.7% (95%CI:5.0-8.4) for CIN3+. The proportion of screen-positive ranged from 3-31% (visual inspection using acetic acid[VIA]); 2-46% (high-grade squamous intraepithelial lesions, and greater [HSIL+] cytology); 20-64% (high-risk[HR]-HPV DNA). In 14 studies, sensitivity and specificity of VIA were variable limiting the reliability of pooled estimates. In 5 studies where majority had histology-confirmed CIN2+, pooled sensitivity was 56.0% (95%CI:45.4-66.1; I2=65%) for CIN2+ and 65.0% (95%CI:52.9-75.4; I2=42%) for CIN3+; specificity for

High-resolution hydrological modeling is crucial for detecting extreme hydrological events and understanding fundamental terrestrial processes. However, spatial resolutions in current hydrological modeling studies have been mostly constrained to relatively coarse resolution (~10–100 km), and they therefore have a difficult time addressing flooding or drought issues with fine resolutions. In this study, a continental-scale high-resolution hydrological modeling framework (0.0625°, ~6 km) driven by remote sensing products was used to detect extreme hydrological event occurrences in China and evaluated based on the Variable Infiltration Capacity (VIC) model. The results showed that the developed model provided more detailed information than the coarser resolution models (a 0.25° and 1°), thereby capturing the timing, duration, and spatial extent of extreme hydrologic events regarding the 2012 Beijing flood and 2009/10 drought in Hai River Basin. Here, the total water storage changes were calculated based on the VIC model (−0.017 mm/year) and Gravity Recovery and Climate Experiment (GRACE) satellite (−0.203 mm/year) to reflect the water availability caused by climate change and anthropogenic factors. This study found that the 0.0625° dataset could capture detailed changes, thereby providing reliable information during occurrences of extreme hydrological events. The high-resolution model integrated with remote sensing products could be used for accurate evaluations of continental-scale extreme hydrological events and can be valuable in understanding its long-term occurrence and water resource security.

Spatiotemporal fusion has provided a feasible way to generate fractional vegetation cover (FVC) data with high spatial and temporal resolution. However, when the currently available spatiotemporal fusion methods are applied over agricultural regions, they usually underestimate high FVC values at the peak vegetation growth stage with medium FVC values as base data. This mainly results from inconsistencies in the temporal variations between fine- and coarse-resolution data if substantial temporal changes occur in vegetation. Therefore, a Spatial and Temporal Fusion method combining with Vegetation Growth Models (STF-VGM) was proposed to address this problem in this study, which incorporates vegetation growth models into the fusion process. Unlike other spatiotemporal fusion methods that mainly rely on changes in coarse-resolution data for prediction, STF-VGM fully utilizes available coarse- and fine-resolution time series data, including uncontaminated information in cloud/cloud shadow contaminated images. By establishing vegetation growth models with time series data, a conversion relationship between coarse- and fine-resolution FVC that changes along with the nonlinear vegetation change process can be extracted. STF-VGM makes prediction based on this variable relationship. A typical agricultural region located in the North China Plain was selected as the study area. The validation results indicated that the prediction accuracy for high FVC values was significantly improved using STF-VGM compared to the commonly used Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) and Flexible Spatiotemporal DAta Fusion (FSDAF) methods (STF-VGM: coefficient of determination (R2) = 0.9491, root mean square error (RMSE) = 0.0650, average difference (AD) = -0.0092; ESTARFM: R2 = 0.9341, RMSE = 0.1127, AD = -0.0631; FSDAF: R2 = 0.9224, RMSE = 0.1110, AD = -0.0599). The satisfactory performance of STF-VGM was also achieved in predicting FVC values at other vegetation growth stages (early growth stage: R2 = 0.8277, RMSE = 0.0440, AD = 0.0027; rapid growth stage: R2 = 0.9183, RMSE = 0.0844, AD = 0.0500). In addition, STF-VGM also has the potential to improve the spatiotemporal fusion accuracy of other vegetation parameters and vegetation indices, which will be evaluated in the future.

Abstract In this study, we use the Community Land Model Version 5 (CLM5) to investigate how irrigation modulates hydrologic and biogeochemical dynamics in the Upper Columbia‐Priest Rapids (UCPR) watershed, a typical semiarid watershed located in the northwestern United States dominated by cropland. To our knowledge, this constitutes the first application of CLM5 with landscape heterogeneity fully resolved over a watershed. The model is calibrated and evaluated against flux measurements from an AmeriFlux site and the Moderate Resolution Imaging Spectroradiometer (MODIS) products. Two numerical experiments (i.e., irrigated and rainfed) are performed at hyperresolution (~1 km) over the period of 2010–2018, accounting for realistic crop types and management practices. Our results show that irrigation fundamentally alters hydrologic and biogeochemical dynamics of the watershed. By adding 79.6 mm year−1 water in addition to the mean annual precipitation of 204.0 mm year−1, irrigation leads to increases in evapotranspiration and runoff, accompanied by shallower groundwater table depths. Increases in crop productivity in response to irrigation result in more carbon storage in the watershed, and drastically large seasonal fluctuations in soil organic carbon in response to changes in soil temperature and moisture. Irrigation also intensifies the rate of denitrification and mineralization during the growing season, enhancing the interactions between soil mineral nitrogen, the atmosphere, and freshwater systems. Our study demonstrates the potential of CLM5 as an effective tool for understanding hydrological and biogeochemical dynamics in highly managed semiarid watersheds.

Surface upward longwave radiation (SULR) is an indicator of thermal conditions over the Earth’s surface. In this study, we validated the simulated SULR from 51 Coupled Model Intercomparison Project (CMIP6) general circulation models (GCMs) through a comparison with ground measurements and satellite-retrieved SULR from the Clouds and the Earth’s Radiant Energy System, Energy Balanced and Filled (CERES EBAF). Moreover, we improved the SULR estimations by a fusion of multiple CMIP6 GCMs using multimodel ensemble (MME) methods. Large variations were found in the monthly mean SULR among the 51 CMIP6 GCMs; the bias and root mean squared error (RMSE) of the individual CMIP6 GCMs at 133 sites ranged from −3 to 24 W m−2 and 22 to 38 W m−2, respectively, which were higher than those found between the CERES EBAF and GCMs. The CMIP6 GCMs did not improve the overestimation of SULR compared to the CMIP5 GCMs. The Bayesian model averaging (BMA) method showed better performance in simulating SULR than the individual GCMs and simple model averaging (SMA) method, with a bias of 0 W m−2 and an RMSE of 19.29 W m−2 for the 133 sites. In terms of the global annual mean SULR, our best estimation for the CMIP6 GCMs using the BMA method was 392 W m−2 during 2000–2014. We found that the SULR varied between 386 and 393 W m−2 from 1850 to 2014, exhibiting an increasing tendency of 0.2 W m−2 per decade (p < 0.05).

It is of great importance for climate change studies to construct a worldwide, long-term surface downward longwave radiation (Ld, 4–100 μm) dataset. Although a number of global Ld datasets are available, their low accuracies and coarse spatial resolutions limit their applications. This study generated a daily Ld dataset with a 5-km spatial resolution over the global land surface from 2000 to 2018 using atmospheric parameters, which include 2-m air temperature (Ta), relative humidity (RH) at 1000 hPa, total column water vapor (TCWV), surface downward shortwave radiation (Sd), and elevation, based on the gradient boosting regression tree (GBRT) method. The generated Ld dataset was evaluated using ground measurements collected from AmeriFlux, AsiaFlux, baseline surface radiation network (BSRN), surface radiation budget network (SURFRAD), and FLUXNET networks. The validation results showed that the root mean square error (RMSE), mean bias error (MBE), and correlation coefficient (R) values of the generated daily Ld dataset were 17.78 W m−2, 0.99 W m−2, and 0.96 (p < 0.01). Comparisons with other global land surface radiation products indicated that the generated Ld dataset performed better than the clouds and earth’s radiant energy system synoptic (CERES-SYN) edition 4.1 dataset and ERA5 reanalysis product at the selected sites. In addition, the analysis of the spatiotemporal characteristics for the generated Ld dataset showed an increasing trend of 1.8 W m−2 per decade (p < 0.01) from 2003 to 2018, which was closely related to Ta and water vapor pressure. In general, the generated Ld dataset has a higher spatial resolution and accuracy, which can contribute to perfect the existing radiation products.

Extreme hydrologic events are getting more frequent under a changing climate, and a reliable hydrological modeling framework is important to understand their mechanism. However, existing hydrological modeling frameworks are mostly constrained to a relatively coarse resolution, unrealistic input information, and insufficient evaluations, especially for the large domain, and they are, therefore, unable to address and reconstruct many of the water-related issues (e.g., flooding and drought). In this study, a 0.0625-degree (~6 km) resolution variable infiltration capacity (VIC) model developed for China from 1970 to 2016 was extensively evaluated against remote sensing and ground-based observations. A unique feature in this modeling framework is the incorporation of new remotely sensed vegetation and soil parameter dataset. To our knowledge, this constitutes the first application of VIC with such a long-term and fine resolution over a large domain, and more importantly, with a holistic system-evaluation leveraging the best available earth data. The evaluations using in-situ observations of streamflow, evapotranspiration (ET), and soil moisture (SM) indicate a great improvement. The simulations are also consistent with satellite remote sensing products of ET and SM, because the mean differences between the VIC ET and the remote sensing ET range from −2 to 2 mm/day, and the differences for SM of the top thin layer range from −2 to 3 mm. Therefore, this continental-scale hydrological modeling framework is reliable and accurate, which can be used for various applications including extreme hydrological event detections.

The downward longwave radiation (Ld, 4–100 μm) is a major component of research for the surface radiation energy budget and balance. In this study, we applied five machine learning methods, namely artificial neural network (ANN), support vector regression (SVR), gradient boosting regression tree (GBRT), random forest (RF), and multivariate adaptive regression spline (MARS), to estimate Ld using ground measurements collected from 27 Baseline Surface Radiation Network (BSRN) stations. Ld measurements in situ were used to validate the accuracy of Ld estimation models on daily and monthly time scales. A comparison of the results demonstrated that the estimates on the basis of the GBRT method had the highest accuracy, with an overall root-mean-square error (RMSE) of 17.50 W m−2 and an R value of 0.96 for the test dataset on a daily time scale. These values were 11.19 W m−2 and 0.98, respectively, on a monthly time scale. The effects of land cover and elevation were further studied to comprehensively evaluate the performance of each machine learning method. All machine learning methods achieved better results over the grass land cover type but relatively worse results over the tundra. GBRT, RF, and MARS methods were found to show good performance at both the high- and low-altitude sites.

Surface all-wave net radiation (Rn) is a crucial variable driving many terrestrial latent heat (LE) models that estimate global LE. However, the differences between different Rn products and their impact on global LE estimates still remain unclear. In this study, we evaluated two Rn products, Global LAnd Surface Satellite (GLASS) beta version Rn and Modern-Era Retrospective Analysis for Research and Applications-version 2 (MERRA-2) Rn, from 2007–2017 using ground-measured data from 240 globally distributed in-situ radiation measurements provided by FLUXNET projects. The GLASS Rn product had higher accuracy (R2 increased by 0.04–0.26, and RMSE decreased by 2–13.3 W/m2) than the MERRA-2 Rn product for all land cover types on a daily scale, and the two Rn products differed greatly in spatial distribution and variations. We then determined the resulting discrepancies in simulated annual global LE using a simple averaging model by merging five diagnostic LE models: RS-PM model, SW model, PT-JPL model, MS-PT model, and SIM model. The validation results showed that the estimated LE from the GLASS Rn had higher accuracy (R2 increased by 0.04–0.14, and RMSE decreased by 3–8.4 W/m2) than that from the MERRA-2 Rn for different land cover types at daily scale. Importantly, the mean annual global terrestrial LE from GLASS Rn was 2.1% lower than that from the MERRA-2 Rn. Our study showed that large differences in satellite and reanalysis Rn products could lead to substantial uncertainties in estimating global terrestrial LE.

Satellite remote sensing is a practical technique to estimate global precipitation with adequate spatiotemporal resolution in ungauged regions. However, the performance of satellite-based precipitation products is variable and uncertain for the Tibetan Plateau (TP) because of its complex terrain and climate conditions. In this study, we evaluated the abilities of nine widely used satellite-based precipitation products over the Eastern Tibetan Plateau (ETP) and quantified precipitation dynamics over the entire TP. The evaluation was carried out from three aspects, i.e., magnitude agreement, occurrence consistency, and elevation dependency, from grid-cell to regional scales. The results show that the nine satellite-based products exhibited different agreement with gauge-based reference data with median correlation coefficients ranging from 0.15 to 0.95. Three products (climate hazards group infrared precipitation with stations (CHIRPS), multi-source weighted-ensemble precipitation (MSWEP), and tropical rainfall measuring mission multi-satellite precipitation analysis (TMPA)) generally presented the best performance with the reference data, even in complex terrain regions, given their root mean square errors (RMSE) of less than 25 mm/mon. The climate prediction center merged analysis of precipitation (CMAP) product has relatively coarse spatial resolution, but it also exhibited good performance with a bias of less than 20% in watershed scale. Two other products (precipitation estimation from remotely sensed information using artificial neural networks-cloud classification system (PER-CCS) and climate prediction center morphing technique-raw (CMORPH-RAW)) overestimated precipitation with median RMSEs of 87 mm/mon and 45 mm/mon, respectively. All the precipitation products generally exhibited better agreement with the reference data for rainy season and lower-elevation regions. All of the products captured precipitation occurrence well, with hit event over 60%, and similar percentages of missed and false event. According to the evaluation, the four products (CHIRPS, MSWEP, TMPA, and CMAP) revealed that the annual precipitation over the TP fluctuated between 333 mm/yr and 488 mm/yr during the period 2003 to 2015. The study indicates the importance of integration of multiple data sources and post-processing (e.g., gauge data fusion and elevation correction) for satellite-based products and have implications for selection of suitable precipitation products for hydrological modeling and water resources assessment for the TP.

Satellite-based vegetation indices (VIs) and Apparent Thermal Inertia (ATI) derived from temperature change provide valuable information for estimating evapotranspiration (LE) and detecting the onset and severity of drought. The modified satellite-based Priestley-Taylor (MS-PT) algorithm that we developed earlier, coupling both VI and ATI, is validated based on observed data from 40 flux towers distributed across the world on all continents. The validation results illustrate that the daily LE can be estimated with the Root Mean Square Error (RMSE) varying from 10.7 W/m2 to 87.6 W/m2, and with the square of correlation coefficient (R2) from 0.41 to 0.89 (p < 0.01). Compared with the Priestley-Taylor-based LE (PT-JPL) algorithm, the MS-PT algorithm improves the LE estimates at most flux tower sites. Importantly, the MS-PT algorithm is also satisfactory in reproducing the inter-annual variability at flux tower sites with at least five years of data. The R2 between measured and predicted annual LE anomalies is 0.42 (p = 0.02). The MS-PT algorithm is then applied to detect the variations of long-term terrestrial LE over Three-North Shelter Forest Region of China and to monitor global land surface drought. The MS-PT algorithm described here demonstrates the ability to map regional terrestrial LE and identify global soil moisture stress, without requiring precipitation information.

As an important indicator to characterize the surface vegetation, fractional vegetation cover (FVC) with high spatio-temporal resolution is essential for earth surface process simulation. However, due to technical limitations and the influence of weather, it is difficult to generate temporally continuous FVC with high spatio-temporal resolution based on a single remote-sensing data source. Therefore, the objective of this study is to explore the feasibility of generating high spatio-temporal resolution FVC based on the fusion of GaoFen-1 Wide Field View (GF-1 WFV) data and Moderate-resolution Imaging Spectroradiometer (MODIS) data. Two fusion strategies were employed to identify a suitable fusion method: (i) fusing reflectance data from GF-1 WFV and MODIS firstly and then estimating FVC from the reflectance fusion result (strategy FC, Fusion_then_FVC). (ii) fusing the FVC estimated from GF-1 WFV and MODIS reflectance data directly (strategy CF, FVC_then_Fusion). The FVC generated using strategies FC and CF were evaluated based on FVC estimated from the real GF-1 WFV data and the field survey FVC, respectively. The results indicated that strategy CF achieved higher accuracies with less computational cost than those of strategy FC both in the comparisons with FVC estimated from the real GF-1 WFV (CF:R2 = 0.9580, RMSE = 0.0576; FC: R2 = 0.9345, RMSE = 0.0719) and the field survey FVC data (CF: R2 = 0.8138, RMSE = 0.0985; FC: R2 = 0.7173, RMSE = 0.1214). Strategy CF preserved spatial details more accurately than strategy FC and had a lower probability of generating abnormal values. It could be concluded that fusing GF-1 WFV and MODIS data for generating high spatio-temporal resolution FVC with good quality was feasible, and strategy CF was more suitable for generating FVC given its advantages in estimation accuracy and computational efficiency.

Many existing satellite evapotranspiration (ET)-based drought indices have characterized regional drought condition successfully, but the relatively short time span of ET products limits their use in long-term climatological drought assessment. In this study, we assess Evaporative Drought Index (EDI) as a drought monitoring indicator over Northeast China through a retrospective comparison with drought-related indicators. After verifying its utility for detecting documented regional drought events and impacts of drought on crop production, we apply it to improve our understanding of the variation in dryness over Northeast China from 1982 to 2015. Our results illustrate that EDI is generally effective for characterizing terrestrial moisture condition and its standardized formula, namely, Standardized Evaporative Drought Index (sEDI) corresponds well with historical drought events and inter-annual grain crop yields over Northeast China. Although the calculation of sEDI does not directly incorporate precipitation and soil moisture, statistical analyses indicate sEDI can detect drought in accordance with the Standardized Precipitation Index (SPI) and Palmer Drought Severity Index (PDSI), with the highest correlations found in the west part of Northeast China (R < −0.7). Further analysis illustrates sEDI is more related to commonly-used drought metrics over areas with short canopy vegetation (R < −0.5) than woodland (R < −0.2), which suggests precipitation may not be a good representative of drought condition over areas with deep-rooted vegetation. Then, we find 56.5% of Northeast China shows an upward dry trend from 1982 to 2015, which mainly concentrates in the west part of the study area. Conversely, 14.4% of Northeast China shows a significant wetted trend and most of them locate at cropland areas, due to the improved water management. This study suggests that EDI is a feasible method to monitor spatially distributed drought condition and can provide unique drought information not reflected by rainfall deficits, which also can be used to evaluate traditional precipitation-based indicators.

Fractional vegetation cover (FVC) is an essential parameter for characterizing the land surface vegetation conditions and plays an important role in earth surface process simulations and global change studies. The Sentinel-2 missions carrying multi-spectral instrument (MSI) sensors with 13 multispectral bands are potentially useful for estimating FVC. However, the performance of these bands for FVC estimation is unclear. Therefore, the objective of this study was to assess the performance of Sentinel-2 MSI spectral band reflectances on FVC estimation. The samples, including the Sentinel-2 MSI canopy reflectances and corresponding FVC values, were simulated using the PROSPECT + SAIL radiative transfer model under different conditions, and random forest regression (RFR) method was then used to develop FVC estimation models and assess the performance of various band reflectances for FVC estimation. These models were finally evaluated using field survey data. The results indicate that the three most important bands of Sentinel-2 MSI data for FVC estimation are band 4 (Red), band 12 (SWIR2) and band 8a (NIR2). FVC estimation using these bands has a comparable accuracy (root mean square error (RMSE) = 0.085) with that using all bands (RMSE = 0.090). The results also demonstrate that band 12 had a better performance for FVC estimation than the green band (RMSE = 0.097). However, the newly added red-edge bands, with low scores in the RFR model, have little significance for improving FVC estimation accuracy compared with the Red, NIR2 and SWIR2 bands.

Global urban expansion has altered surface aerodynamics and hydrothermal dynamics, aggravating environmental challenges such as urban heat/dry islands. To identify such environmental responses, various physical models, including urban canyon models (UCMs) and land surface models (LSMs), have been developed to represent surface hydrothermal processes. However, UCMs often treat a city as a unified entity and overlook subcity heterogeneity. LSMs are generally designed for natural land covers and lack the capability to capture urban characteristics. To address these limitations, the aim of this study is to couple an urban module with a sophisticated LSM, i.e., the Variable Infiltration Capacity (VIC) model. This coupled model, i.e., the VIC-urban model, is characterized by its ability to coordinate certain critical urban features, including the urban geometry, radiative interactions, and human impacts. Adopting Beijing as an evaluation site, the VIC-urban model shows higher performance than the original version, with excellent accuracy in simulating sensible heat, latent heat, runoff, and land surface temperature (LST). The absolute error is smaller than 25% for the sensible heat and latent heat, and smaller than 12% and 30% for the LST and runoff, respectively, which indicates that VIC-urban can effectively simulate hydrological and thermal fluxes in urban systems. Sensitivity analysis reveals that the roof emissivity and interception capacity exert the greatest impact on the roof temperature and evaporation, and the height-width ratio has the greatest influence on the canyon. Our work introduces a reliable option for large-scale land surface simulations that accounts for urban environments, and is among the first attempts to establish a systematic urban modelling framework of the VIC model. The VIC-urban model enables the analysis of urbanization-induced environmental changes and quantification of environmental variations among different urban configurations. The proposed model can thus offer invaluable insights for urban planners and landscape designers. [ABSTRACT FROM AUTHOR]