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In the field of hydrological modelling, the global and automatic parameter calibration has been a hot issue for many years. Among automatic parameter optimization algorithms, the shuffled complex evolution developed at the University of Arizona (SCE-UA) is the most successful method for stably and robustly locating the global “best” parameter values. Ever since the invention of the SCE-UA, the profession suddenly has a consistent way to calibrate watershed models. However, the computational efficiency of the SCE-UA significantly deteriorates when coping with big data and complex models. For the purpose of solving the efficiency problem, the recently emerging heterogeneous parallel computing (parallel computing by using the multi-core CPU and many-core GPU) was applied in the parallelization and acceleration of the SCE-UA. The original serial and proposed parallel SCE-UA were compared to test the performance based on the Griewank benchmark function. The comparison results indicated that the parallel SCE-UA converged much faster than the serial version and its optimization accuracy was the same as the serial version. It has a promising application prospect in the field of fast hydrological model parameter optimization. [ABSTRACT FROM PUBLISHER]

[Display omitted] • Developed a multiple evapotranspiration (ET) model integration framework to calculate ET. • High accuracy meteorological driven datasets were developed to reduce the uncertainties. • Eddy covariance (EC) observation and water balance method proved the quality of ET estimates. • Regional and spatial–temporal variations of ET in Haihe River Basin (HRB) were analysed. • The increasing ET in HRB was mainly attributed to the increase of the leaf area index. Evapotranspiration (ET) is a key component of the water and carbon cycles. Because it cannot be observed directly on large regional scales at present, many satellite-based ET datasets have been widely used for different purposes. However, their use has been limited at regional and field scales because of their coarse spatial and temporal resolution. In this study, the Bayesian model averaging (BMA) method was used to simulate daily ET values with 500 m spatial resolution in the Haihe River Basin (HRB) from 2000 to 2019. Validation performed with the in-situ observations showed that the BMA ET values had higher accuracy (R2 = 0.69, RMSE = 1.15 mm/day) than those found with individual models. The BMA ET values also had higher accuracy and more credibility based on a water balance equation validation in the HRB. We used interpolated meteorological datasets, reanalysis net radiation products, and remote-sensing datasets to drive the BMA ET model. The mean annual BMA ET in the HRB from 2000 to 2019 was about 601.8 mm/year. The monthly change characteristics of this BMA ET product reflected water consumption characteristics and irrigation regularity of the winter wheat and summer maize rotation system. The BMA ET in the HRB showed a significant increasing trend from 2000 to 2019 of 3.39 mm/y2. The BMA ET values showed a strong positive trend at 80 % of HRB areas. The increasing trend of the BMA ET values was mainly attributed to an increase in the leaf area index. The high spatiotemporal resolution product showed variations in ET trends more accurately than the coarse resolution products. This high spatiotemporal product has great potential for applications in studies of regional microclimate change, interactions between human activities and climate change, drought disaster monitoring, agricultural policy making, and water security. [ABSTRACT FROM AUTHOR]

This paper presented a valuable framework for evaluating the impacts of droughts (single factor) on grassland ecosystems. This framework was defined as the quantitative magnitude of drought impact that unacceptable short-term and long-term effects on ecosystems may experience relative to the reference standard. Long-term effects on ecosystems may occur relative to the reference standard. Net primary productivity (NPP) was selected as the response indicator of drought to assess the quantitative impact of drought on Inner Mongolia grassland based on the Standardized Precipitation Index (SPI) and BIOME-BGC model. The framework consists of six main steps: 1) clearly defining drought scenarios, such as moderate, severe and extreme drought; 2) selecting an appropriate indicator of drought impact; 3) selecting an appropriate ecosystem model and verifying its capabilities, calibrating the bias and assessing the uncertainty; 4) assigning a level of unacceptable impact of drought on the indicator; 5) determining the response of the indicator to drought and normal weather state under global-change; and 6) investigating the unacceptable impact of drought at different spatial scales. We found NPP losses assessed using the new framework were more sensitive to drought and had higher precision than the long-term average method. Moreover, the total and average losses of NPP are different in different grassland types during the drought years from 1961–2009. NPP loss was significantly increased along a gradient of increasing drought levels. Meanwhile, NPP loss variation under the same drought level was different in different grassland types. The operational framework was particularly suited for integrative assessing the effects of different drought events and long-term droughts at multiple spatial scales, which provided essential insights for sciences and societies that must develop coping strategies for ecosystems for such events. [ABSTRACT FROM AUTHOR]

Drought is one of the most prominent natural threats to grassland productivity, although the magnitude of this threat is uncertain due to the different drought-levels. However, drought-productivity dynamics has not yet received much attention. It is necessary to establish the method to evaluate quantitatively the effect of different drought-levels on grassland productivity. To better understand the impact of different drought-levels on productivity dynamics, an assessment method to assess the quantitative effects of different drought-levels on grassland productivity was proposed based-on long-term observation data, standardized precipitation index (SPI) and Biome-BGC process model. Based-on assessment indicator of net primary productivity (NPP), NPP loss caused by moderate, severe and extreme drought was dramatically different in grasslands with a significant exponential change with gradient of different drought-levels. Furthermore, NPP loss variation in different grassland types under the same drought level was significantly different. Besides, the effect of drought on NPP gradually decreased by an exponential relationship in desert, typical and meadow steppe. However, the percentage of NPP loss in desert, typical and meadow steppe reduced by 20.5%, 13.1% and 17.5% with U-shaped, respectively. Meanwhile, our results can offer scientific basis to improve assessment impact of extreme climate events used by ecosystem model and data, and cope with carbon cycling management and climate change. • A new method of Net Primary Productivity Loss under Different Drought Levels was proposed in Different Grassland Ecosystems. • NPP loss significantly increased along a gradient of the increasing drought-levels with good exponential relationship. • NPP loss variation in different grassland types under the same drought level was noticeable. • Differential effects of drought on NPP in same grassland types were also found. [ABSTRACT FROM AUTHOR]

Coal mining in northwestern China is an important industry. For the traditional monitoring of water resources in coal-rich regions, a single monitoring well or remote-sensing image is often used to obtain the groundwater level or water body area. The process is restricted by the spatial distribution of monitoring wells and the quality of remote sensing images. The regions of Ordos, Northern Shaanxi (including Yan'an and Yulin cities), herein collectively referred to as OYY, and Shanxi (SX) were studied. Here, groundwater storage anomalies (GWSA) were derived using the gravity recovery and climate experiment (GRACE) satellite data and WaterGAP global hydrology model, and the change trend of groundwater storage (GWS) was explored. Using time series analysis and grey slope relational analysis, the potential driving factors of regional GWSA were derived and considered independent variables. In combination with GWSA, the quantitative relationship between the variables was established by partial least squares regression. Results showed that: (1) the decreasing rate of GWS in OYY and SX reached –0.65 and –1.16 cm/year, respectively, from 2003 to 2014; (2) the main driving factors leading to the reduction of GWS included coal-mining water consumption for OYY and water consumption by coal mining and agricultural irrigation for SX, and the weights of water consumption by coal mining and agricultural irrigation for SX were both 50%. Therefore, GRACE satellite data show good application in groundwater monitoring of coal-mining concentrated areas, providing an important basis for the formulation of water resource management measures. [ABSTRACT FROM AUTHOR]

Droughts have destructive impacts on crop yields and water supplies, and researching droughts is vital for societal stability and human life. This work aimed to assess the spatiotemporal evolution of droughts in mainland China over 1961–2013 using four drought indices. These indices were the percentage of precipitation anomaly (Pa), standard precipitation index (SPI), standard precipitation evapotranspiration index (SPEI) and evaporative demand drought index (EDDI) at multiple timescales ranging from 1-week to 24-month. The variations of the SPI, SPEI and EDDI were compared with historical severe or extreme droughts. The general increases of the Pa, SPI and SPEI, and general decrease of the EDDI, consistently implied an overall relief of drought conditions over 1961–2013. The different drought indices revealed historical drought conditions, including the national extreme droughts in 1961, 1965, 1972, 1978, 1986, 1988, 1992, 1994, 1997, 1999 and 2000, but various drought severity levels were classified for each drought event since the classification standards differed. Although the SPI and SPEI performed better than the EDDI and there were higher correlations between the SPI and the SPEI, all the indices were regional- or station-specific and have identified historical severe or extreme drought events. At shorter timescales, the EDDI revealed earlier onsets and ends of flash droughts, unlike those indicated by the SPI and SPEI. The comparison of the different indices based on the historical drought events confirmed the uses of the Pa, SPI and SPEI for determining continuous droughts and that of the EDDI for identifying flash droughts. [ABSTRACT FROM AUTHOR]

Due to the problem of complex aggregate stacking and adhesion, current construction site aggregate grade detection relies on traditional screening methods and single digital image processing technology, which causes inefficiency and segmentation identification difficulties. This problem has become a technical bottleneck in achieving automatic construction site mixed aggregate grade detection. This study constructs a noncontact testing platform for aggregate gradation based on a self-developed sampling and testing device for mixed aggregate gradation and improved deep learning algorithms, which enables rapid testing of mixed aggregate gradation. Based on the principle of instance segmentation, each aggregate in the stacked mixed aggregates collected based on hardware observation is used as an instance to detect independent aggregate targets in the mixed aggregate images with different moisture contents, sand contents and cohesive stacking. An improved aggregate segmentation convolutional neural network model (AS Mask RCNN: Aggregate Segmentation Mask RCNN) is used to achieve the gradation detection of mixed aggregates. This study employed three different types of experiments, and the results showed that the AS Mask RCNN network model achieved an accuracy of over 89.13% in the three experimental situations, and compared the results with those of the Faster RCNN and Mask R-CNN models, with an accuracy improvement of 8.85% and a reduction of 1.29 s in the processing time of a single image segmentation, which can meet the field near real-time detection requirements. The self-developed noncontact testing platform for aggregate grading can adapt to practical applications in complex environments, enabling digital, automated and intelligent noncontact rapid testing of mixed aggregate grading, further improving the accuracy of aggregate grading testing and serving the high-quality development of reservoir dam construction in China. • Aggregate stacking and adhesion affect the intelligent detection of particle size. • A noncontact inspection platform for aggregate grading based on deep learning. • Three different types of comparative experiments were used in this study. • An improved aggregate segmentation convolutional neural network model is proposed. • The study can be applied to actual engineering rockfill material gradation testing. [ABSTRACT FROM AUTHOR]

ABSTRACT Drought is a major natural hazard that can have devastating impacts on regional agriculture, water resources and the environment. To assess the variability and pattern of drought characteristics in the Huang-Huai-Hai ( HHH) Plain, the daily Standardized Precipitation Evapotranspiration Index ( SPEI) is developed based on daily meteorological data in this study. The daily SPEI data are used, including Annual Total Drought Severity ( ATDS), Annual Total Drought Duration ( ATDD) and Annual Drought Frequency ( ADF), which were calculated from 1981 to 2010 at 28 meteorological stations. We used the indices ( ATDS, ATDD and ADF), Hovmöller diagrams and the reliable no parameter statistical methods of the Mann-Kendall test to assess the variability and pattern of drought characteristics for the period from 1981 to 2010 in the HHH plain. The results suggested that severe drought occurred in the 1980s, the late 1990s and the early 2000s, severe drought events occurred in 1981, 1986, 1997 and 2002. Decreasing trends for both ATDS and ATDD were found, and the drought situation did not worsen under global warming during the past 30 years, and the drought situation is alleviating in the entire HHH plain. The northeast and southwest regions of the HHH plain have suffered from more severe drought, and the north region is prone to drought. The results of the study can provide a scientific understanding for the adoption of countermeasures of regional defence against drought and also may serve as a reference point for drought hazard vulnerability analysis. [ABSTRACT FROM AUTHOR]

• For the past 40 years, theBeijing-Tianjin sand source region has been a carbon sink. • As drought intensifies, the negative impact of drought on Net Ecosystem Exchange becomes more pronounced. • In this region, grassland Net Ecosystem Exchange is most affected by and sensitive to drought. • Moisture limitation is the main driver of carbon source/sink change in this region. In the context of global change, the carbon budget in arid and semi-arid regions have changed significantly. Understanding these dynamic features and their response to climate change is essential for regional carbon cycle assessments. The Beijing-Tianjin Sand Source Region (BTSSR), a significant ecological project in China, is central to studies on carbon budget dynamics. While global change intensifies, understanding its carbon budget response to drought is crucial. By integrating data from the Net Ecosystem Exchange (NEE) and the Standardized Precipitation Evapotranspiration Index (SPEI), we analyzed the spatiotemporal attributes of NEE in this region and its response to drought. The BTSSR is currently in a carbon sink state, with the mean NEE being −45.13 gCm−2 and a rate of change at −0.005 gCm−2a-1. The maximum carbon sink occurs during the summer, whereas the winter is characterized by a net release of carbon to the atmosphere. In terms of spatial variation, the NEE shifts from positive to negative from southeast to northwest across the region. The impact of drought on summer NEE is notably significant, with the correlation and sensitivity being −0.717 (p < 0.05) and −3.911, respectively. As drought intensity increases, NEE changes from negative to positive, transforming BTSSR into a carbon source. NEE responded most significantly to strong drought and was less affected by light drought. The NEE of BTSSR showed a significant short-term response to drought, with a lag of 1–4 months. Considering different vegetation types, the temperate desert's NEE is most significantly affected by drought and is the most sensitive, with correlation and sensitivity values of −0.841 (p < 0.05) and −12.570, respectively. In contrast, the warm temperate deciduous broadleaf forest shows stronger resistance to drought. This study elucidates how the carbon budget of different vegetation types in a typical ecological engineering area changes under the background of climate change and human activities, and contributes to the understanding of the impacts of drought events on the carbon cycle of different vegetation types, as well as the response of the latter to the former, especially the lagged response. It provides different perspectives for the subsequent ecological engineering construction. [ABSTRACT FROM AUTHOR]

Understanding the spatiotemporal characteristics of heat wave (HW) is vital for both natural systems and human populations. The Loess Plateau is located in a climate transition zone and is highly sensitive to climate change. Therefore, it is crucial to study HW in this region. Based on instrumental measurements, climate model data, atmospheric circulation data and HW metrics, this study comprehensively analyses past and future HW changes in the Loess Plateau and the influencing factors. From 1961 to 2019, the metrics for daytime heat wave(DHW) and nighttime heat wave(NHW) were as follows: duration (9.28/9.72 days), frequency (1.34/1.4 times, mean intensity (6.24/4.69 °C), maximum intensity (7.83/6.05 °C), and cumulative heat (44.45/32.63 °Cd). All metrics displayed an increasing trend (p < 0.05(NHW)). Geographically, the northern Loess Plateau exhibited the highest intensity and cumulative heat, whereas the southern region experienced longer HW. The solar flux index, western Pacific subtropical high area index, and western Pacific subtropical high intensity index were identified as the primary factors influencing HW in the Loess Plateau. By the end of the 21st century, both the duration and cumulative heat of HW are projected to rise significantly. NHW, in particular, will see extended durations and greater cumulative heat compared to DHW. [ABSTRACT FROM AUTHOR]

To identify the world's severely drought-prone areas, given that the corresponding ground area for a 0.5-degree grid in different latitudes is different, we proposed a more precise spherical area-based statistical method. The corresponding ground area per 0.5-degree grid is obtained by integral calculation in latitude and longitude directions. The analysis of the drought based on the global Standardized Precipitation Evapotranspiration Index dataset from 1902 to 2008, where global, Northern Hemisphere, Southern Hemisphere, and major crop-planting regions from six continents are treated as statistical units. The interannual variability characteristics of the severe drought area for each statistical unit are investigated. To study the spatial distribution characteristics of the global frequency of severe drought, the drought frequency was calculated based on drought events identified by continuous drought months on a grid level. Six major crops (wheat, maize, rice, soybean, barley, and sorghum) were chosen to study the impact of drought events on agriculture. The results suggested that severe droughts in global, Northern Hemisphere, and Southern Hemisphere areas have indicated a downward trend since 1990, but an upward trend overall in all continents except Oceania. The identified drought-prone areas show a patchy distribution and frequently drought-prone areas (with 10–20% occurrence probability of drought) were distributed in regions surrounding chronically drought-prone areas (with more than 20% probability). Global chronically drought-prone areas have increased significantly, from 16.19% in 1902–1949 to 41.09% in 1950–2008. Chronically drought-prone areas of agriculture are located in the center of southern Europe, South America, and eastern Asia. [ABSTRACT FROM AUTHOR]

• Geographical detector model identifies key influencing factors on dust events. • Interactions of natural and anthropogenic factors on dust events were presented. • Effects of driving factors on dust events have large spatial-temporal disparities. Aeolian dust can influence the climate, air quality, human health, and ecosystems. Dust events in Northern China are the main contributors to dust aerosols in the world, but the impacts of meteorological and anthropogenic factors and their interactions on dust events remain unclear. This study analyzed the spatial and temporal variations of dust event frequencies and quantitatively investigated the impacts of meteorological conditions, anthropogenic factors, and their interactions on dust events using the geographical detector model (GeoDetector) in Northern China. Results revealed that the dust event frequency significantly decreased by 0.006 times yr−1 per site during 1980–2007. At the regional scale, there were large seasonal variations in the effects of meteorological conditions and anthropogenic factors on dust events. Strong winds and soil surface conditions are main drivers of dust events in spring. In summer and autumn, anthropogenic factors have significant impacts on the occurrence of dust events, but the frozen period and relative humidity are major impacting factors in winter. Effects of natural and anthropogenic factors on dust events showed great spatial and seasonal disparities over different vegetation regions. Interactions between two factors enhanced their impacts on the occurrence of dust events. There are also large spatial and seasonal variations in the primary interactions on dust events over different vegetation regions. The findings could help us to better understand the relative importance of various factors on dust events, which has important implications for improving the prediction of dust emission models and developing desertification control strategies. [ABSTRACT FROM AUTHOR]

Neural networks, especially the latest deep learning, have exhibited good ability in estimating surface parameters from satellite remote sensing. However, thorough examinations of neural networks in the estimation of land surface temperature (LST) from satellite passive microwave (MW) observations are still lacking. Here, we examined the performances of the traditional neural network (NN), deep belief network (DBN), and convolutional neural network (CNN) in estimating LST from the AMSR-E and AMSR2 data over the Chinese landmass. The examinations were based on the same training set, validation set, and test set extracted from 2003, 2004, and 2009, respectively, for AMSR-E with a spatial resolution of 0.25°. For AMSR2, the three sets were extracted from 2013, 2014, and 2016 with a spatial resolution of 0.1°, respectively. MODIS LST played the role of "ground truth" in the training, validation, and testing. The examination results show that CNN is better than NN and DBN by 0.1–0.4 K. Different combinations of input parameters were examined to get the best combinations for the daytime and nighttime conditions. The best combinations are the brightness temperatures (BTs), NDVI, air temperature, and day of the year (DOY) for the daytime and BTs and air temperature for the nighttime. By adding three and one easily obtained parameters on the basis of BTs, the accuracies of LST estimates can be improved by 0.8 K and 0.3 K for the daytime and nighttime conditions, respectively. Compared with the MODIS LST, the CNN LST estimates yielded root-mean-square differences (RMSDs) of 2.19–3.58 K for the daytime and 1.43–2.14 K for the nighttime for diverse land cover types for AMSR-E. Validation against the in-situ LSTs showed that the CNN LSTs yielded root-mean-square errors of 2.10–4.72 K for forest and cropland sites. Further intercomparison indicated that ~50% of the CNN LSTs were closer to the MODIS LSTs than ESA's GlobTemperature AMSR-E LSTs, and the average RMSDs of the CNN LSTs were less than 3 K over dense vegetation compared to NASA's global land parameter data record air temperatures. This study helps better the understanding of the use of neural networks for estimating LST from satellite MW observations. [ABSTRACT FROM AUTHOR]

Winter wheat is a staple food crop for most of the world's population, and the area and spatial distribution of winter wheat are key elements in estimating crop production and ensuring food security. However, winter wheat planting areas contain substantial spatial heterogeneity with mixed pixels for coarse- and moderate-resolution satellite data, leading to large errors in crop acreage estimation. This study has developed a phenology-based approach using moderate-resolution (1 km per pixel) satellite data to estimate sub-pixel planting fractions of winter wheat. Based on unmanned aerial vehicle (UAV) observations, the unique characteristics of winter wheat with high vegetation index values at the heading stage (May) and low values at the ripening stage (June) were investigated. The differences in vegetation index between heading and ripening stages increased with the planting fraction of winter wheat, and therefore the planting fractions were estimated by comparing the NDVI differences of a given pixel with those of predetermined pure winter wheat and non-winter wheat pixels. This approach was evaluated using aerial images and agricultural statistical data in an intensive agricultural region, Shandong Province in North China. The method explained 85% and 60% of the spatial variation in municipal- and county-level statistical data, respectively. More importantly, the predetermined pure winter wheat and non-winter wheat pixels can be automatically identified using MODIS data according to their NDVI differences, which strengthens the potential to use this method at regional and global scales without any field observations as references. [ABSTRACT FROM AUTHOR]

In recent years, extreme droughts have been frequent and widespread, and understanding the spatiotemporal variations in drought events in China plays an important role in drought risk management. In this study, using monthly meteorological data at 618 stations, we computed the Standardized Precipitation Evapotranspiration Index with potential evapotranspiration based on the Penman–Monteith equation (SPEI_PM) for drought assessment. Furthermore, we analysed the trend, affected area, frequency and duration of drought in China over the period of 1960–2014 by the piecewise linear regression method (LRM) and Mann–Kendall (MK) method. The results showed that (a) the slight wetting trend reflected during 1960–2014 by the SPEI_PM (0.03/10a (p >.05). This trend was allocated to most parts of northern and southeastern China and was prone to arrive in winter. In contrast, the most significant drying trend was in the Loess Plateau and Yunnan–Kweichow Plateau. (b) The turning point (TP) year of drought trend was 1993. The SPEI_PM was increased in most parts of northern China before 1993 according to the LRM and MK method. However, it decreased after 1993, especially in the south and northwest of China; (c) Over the past 55 years, the areas affected by drought decreased at a rate of −1.23%/10a (p <.05). In detail, these areas decreased at a rate of −3.41%/10a before 1993 (p <.05) and increased at a rate of 2.71%/10a after 1993 (p >.05). (d) The drought frequency in most parts of China was between 15 and 20 during 1960–2014. Furthermore, compared with that in 1960–1977, the drought frequency was decreased in 1978–1995 and then increased in 1996–2014. These findings suggested that the long‐term trend in drought events during 1960–2014 was not significant in China. However, the severity, affected area and frequency of droughts increase after 1993. These results allowed us to understand the changes in the drought across China over the last 55 years, which is important for guiding relevant agricultural activities. [ABSTRACT FROM AUTHOR]

Soil water content (SWC) and soil temperature (ST) are important properties in water-energy balance processes. Our objective was to analyze the SWC and ST variations at different soil depths (0–10, 10–40, 40–100, and 100–200 cm) and different timescales (daily, monthly, and annual) affected by precipitation and air temperature at seven sub-regions and entire mainland China. The sine function was used to fit the variations of daily and monthly ST. The results showed that the monthly and annual SWC and ST values were relatively low in northwest China and the alpine region of Qinghai-Tibetan plateau; however, higher values were shown in east China. SWC and ST fluctuated both randomly and periodically, especially at the 0–10 cm depth. The daily and monthly ST had regular time-lags and typical periodical changes, which could be fitted by a sine function for both the grids and sub-regions in China with a coefficient of determination (R2) of 0.855. Further, the correlations between SWC and precipitation were good in southern and northeastern China, but poor in northwestern China and Qinghai-Tibet Plateau at the depths < 40 cm. The correlations between ST and AT within the depths < 100 cm were generally good (R2 > 0.76). In conclusion, the spatiotemporal distribution of SWC and ST was greatly affected by precipitation and air temperature. The fitted sine functions for daily and monthly ST are very useful for elementary determination of the long-term mean ST values for a location. [ABSTRACT FROM AUTHOR]

Agricultural drought is significantly affecting sustainable crop production. One of climate-based drought indices, the Standardized Precipitation Evapotranspiration Index (SPEI) calculated by the difference between precipitation and potential evapotranspiration (PET), is being more and more widely employed to characterize the spatio-temporal pattern of agricultural drought. However, substantial uncertainties exist in the current PET methods due to unrealistic configurations of the heterogeneity of crop surface characteristics or complicated parameterization scheme. In this study, standard Penman–Monteith (PM) model is modified and derived by meteorological factors and leaf area index (LAI) and albedo data based on remote sensing. Then, the modified SPEI was calculated by the monthly difference between precipitation and PM model integrated remote sensing data (referred as PM RS) over the North China Plain (NCP) between 1982 and 2016. Comparing with the FAO56 reference evapotranspiration (referred as PM RC) and a simplified version of standard PM over open-water surface (referred as PM OW), this study examines how well the PM RS -SPEI captures drought evolution and impacts on crop growth and yield over the NCP in order to evaluate its performance in agricultural drought monitoring. The comparison of SPEIs based on three evapotranspiration equations with soil water moisture in root depth shows that a 6-month lag can be used as the optimum time scale over the NCP. Additionally, the correlation of PM RS -SPEI performs better than PM RC -SPEI and PM OW -SPEI. For typical drought events, PM RS -SPEI6 shows remarkable smaller (larger) severe drought values between April and June (August and October) and a shorter (longer) recovery period in the growing season for wheat (maize). Comparing to PM RC -SPEI6 and PM OW -SPEI6, the PM RS -SPEI6 shows better performance when examining the Vegetation Health Index and Composite Index of Crop Yield Reduction. Additionally, climate-induced crop yield correlates with PM RS -SPEI6 better than PM OW -SPEI6 and PM RC -SPEI6, particularly in the emergence and filling stages for winter wheat, and silking stage for summer maize. These results demonstrate that integrating remotely sensed data can enhance the agricultural drought monitoring accuracy of SPEI. • Standard Penman–Monteith model is derived by remotely sensed land surface data. • New SPEI based on precipitation and modified Penman–Monteith is calculated. • New SPEI performs better at optimum time scale (SPEI6) than traditional SPEI. • New SPEI6 shows smaller (larger) severity for wheat (maize) drought events. [ABSTRACT FROM AUTHOR]

Artificial neural network (ANN)-based data-driven model is an effective and robust tool for multi-input single-output (MISO) system simulation task. However, there are several conundrums which deteriorate the performance of the ANN model. These problems include the hard task of topology design, parameter training, and the balance between simulation accuracy and generalization capability. In order to overcome conundrums mentioned above, a novel hybrid data-driven model named KEK was proposed in this paper. The KEK model was developed by coupling the K-means method for input clustering, ensemble back-propagation (BP) ANN for output estimation, and K-nearest neighbor (KNN) method for output error estimation. A novel calibration method was also proposed for the automatic and global calibration of the KEK model. For the purpose of intercomparison of model performance, the ANN model, KNN model, and proposed KEK model were applied for two applications including the Peak benchmark function simulation and the real-world electricity system daily total load forecasting. The testing results indicated that the KEK model outperformed other two models and showed very good simulation accuracy and generalization capability in the MISO system simulation tasks. [ABSTRACT FROM AUTHOR]

Agricultural drought differs from meteorological, hydrological, and socioeconomic drought, being closely related to soil water availability in the root zone, specifically for crop and crop growth stage. In previous studies, several soil moisture indices ( e.g., the soil moisture index, soil water deficit index) based on soil water availability have been developed for agricultural drought monitoring. However, when developing these indices, it was generally assumed that soil water availability to crops was equal throughout the root zone, and the effects of root distribution and crop growth stage on soil water uptake were ignored. This article aims to incorporate root distribution into a soil moisture-based index and to evaluate the performance of the improved soil moisture index for agricultural drought monitoring. The Huang-Huai-Hai Plain of China was used as the study area. Overall, soil moisture indices were significantly correlated with the crop moisture index ( CMI), and the improved root-weighted soil moisture index ( RSMI) was more closely related to the CMI than averaged soil moisture indices. The RSMI correctly identified most of the observed drought events and performed well in the detection of drought levels. Furthermore, the RSMI had a better performance than averaged soil moisture indices when compared to crop yield. In conclusion, soil moisture indices could improve agricultural drought monitoring by incorporating root distribution. [ABSTRACT FROM AUTHOR]

Various studies have been performed on drought hazard assessment at the national or regional scales, but few studies to date at the global scale, especially on global agriculture. In this paper, we utilized an agricultural drought hazard index (DHI), based on both drought severity and drought occurrence rate, derived from 3-month scale Standardized Precipitation Index (SPI) and the phenology data of main crops (rice, maize, wheat, barley, sorghum and soybean) to assess the agricultural drought hazard grades of the world during 1980-2008. The results indicated that area percentages of high and very high agricultural drought hazard zones were approximately 23.57 and 27.19% of the total agricultural area in the world. Moreover, those zones mostly were distributed in central United States, southeastern South America, most of Europe, southwestern Russia, both southern Congo and Nigeria, east-central and southwest China, Southeast Asia and eastern Australia, and most of those areas were also located in semi-humid or humid climate zones. In addition, some regions above were also found to be the very high agricultural drought hazard zones for the main crops: East-central and southwest China for wheat, maize, rice and soybean; Europe for wheat, maize and barley; Southeast Asia for rice; both central United States and southeastern South America for wheat, maize, soybean and sorghum. [ABSTRACT FROM AUTHOR]

Abstract: China''s capital city, Beijing, has been suffering from sandstorms due to grassland degradation and the large distribution of deserts in western and Northern China, named as the Beijing–Tianjin Sand Source Region (BTSSR). To improve the ecological condition in the BTSSR and to reduce its impacts, the Chinese government has adopted the Beijing–Tianjin Sand Source Control Program since 2001. It is necessary to rigorously evaluate the effectiveness of this 10 years’ program, not only as an essential topic of environmental change in an ecologically vulnerable area, but also as an important aspect of policy efficiency assessments. Toward this aim, this study assessed vegetation changes both temporally and spatially in the areas under the program from 2000 to 2010 with the Moderate-resolution Imaging Spectroradiometer (MODIS) monthly Normalized Difference Vegetation Index (NDVI) data and trend analysis method. The results showed an overall improvement and its spatial variation in vegetation activity. The annual NDVI increased by 0.0121year−1 over 64.33% of the total area, with the greatest increasing trend of NDVI occurring in the spring. However, the change in NDVI varied remarkably in space. This study identified a southwest-to-northeast band in the study area where NDVI decreased notably, while most of the BTSSR experienced a positive trend of NDVI. Although the cause of the increased NDVI in the BTSSR remains uncertain, drought may result in a non-significant increasing trend in vegetation activity and the ecological restoration program may be one of the main driving forces behind the increasing trend in vegetation activity. All of these findings will enrich our knowledge of human activities that impact vegetation in arid and semi-arid environments and will provide a scientific basis for the management of ecological restoration programs. [Copyright &y& Elsevier]

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. [ABSTRACT FROM AUTHOR]

Total precipitable water (TPW) is an important key factor in the global water cycle and climate change. The knowledge of TPW characteristics at spatial and temporal scales could help us to better understand our changing environment. Currently, many algorithms are available to retrieve TPW from optical and microwave sensors. There are still no available TPW data over land from FY-3D MWRI, which was launched by China in 2017. However, the TPW product over land is a key element for the retrieval of many ecological environment parameters. In this paper, an improved algorithm was developed to retrieve TPW over land from the brightness temperature of FY-3D MWRI. The major improvement is that surface emissivity, which is a key parameter in the retrieval of TPW in all-weather conditions, was developed and based on an improved algorithm according to the characteristics of FY-3D MWRI. The improvement includes two aspects, one is selection of appropriate ancillary data in estimating surface emissivity parameter Δε18.7/Δε23.8 in clear sky conditions, and the other is an improvement of the Δε18.7/Δε23.8 estimation function in cloudy conditions according to the band configuration of FY-3D MWRI. Finally, TPW retrieved was validated using TPW observation from the SuomiNet GPS and global distributed Radiosonde Observations (RAOB) networks. According to the validation, TPW retrieved using observations from FY-3D MWRI and ancillary data from Aqua MODIS had the best quality. The root mean square error (RMSE) and correlation coefficient between the retrieved TPW and observed TPW from RAOB were 5.47 and 0.94 mm, respectively. [ABSTRACT FROM AUTHOR]