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As climate change intensifies, drought has become a major global engineering and environmental challenge. In critical areas such as agricultural production, accurate drought monitoring is vital for the sustainable development of regional agriculture. Currently, despite extensive use of traditional meteorological stations and remote sensing methods, these approaches have proven to be inadequate in capturing the full extent of drought information and adequately reflecting spatial characteristics. Therefore, to improve the accuracy of drought forecasts and achieve predictions across extensive areas, this paper employs deep learning models, specifically introducing an attention-weighted long short-term memory network model (AW-LSTM), constructs a composite drought monitoring index (CDMI) and validates the model. Results show that: (1) The AW-LSTM model significantly outperforms traditional long short-term memory (LSTM), support vector machine (SVM) and artificial neural network (ANN) models in drought monitoring, offering not only better applicability in meteorological and agricultural drought monitoring but also the ability to accurately predict drought events one month in advance compared to machine learning models, providing a new method for precise and comprehensive regional drought assessment. (2) The Huang-Huai-Hai Plain has shown significant regional variations in drought conditions across different years and months, with the drought situation gradually worsening in the northern part of Hebei Province, Beijing, Tianjin, the southern part of Huai North and the central part of Henan Province from 2001 to 2022, while drought conditions in the northern part of Huai North, southern Shandong Province, western Henan Province and southwestern Hebei Province have been alleviated. (3) During the sowing (June) and harvesting (September) periods for summer maize, the likelihood of drought occurrences is higher, necessitating flexible adjustments to agricultural production strategies to adapt to varying drought conditions. [ABSTRACT FROM AUTHOR]

The water supply of water resources allocation under extreme drought is insufficient, and the limited available water resources make it urgent to tap the potential of water supply. In this paper, the Yellow River water supply area in Henan Province is taken as an example to study the multi-water source allocation under extreme drought. According to the Palmer Drought Severity Index (PDSI), the extreme drought years are selected, and the water supply and demand balance in the extreme drought years is analyzed, and the water shortage degree of each water supply area is obtained. In this paper, unconventional water, flood resource utilization and elastic exploitation of groundwater are used as potential water sources. Different water supply scenarios are set up according to different potential tapping measures, and multi-scenario supply increase under extreme drought is explored. A multi-water source allocation model with the goal of minimizing water shortage is constructed, and a multi-scenario supply increase allocation scheme is proposed, which provides a basis for the study of water supply increase allocation to alleviate the drought degree of the the Yellow River Water Supply Area in Henan. Through the Multi-scenario potential tapping of multiple water sources, the existing potential water volume can be maximized, which is conducive to reducing the water supply pressure and water use restrictions of conventional water sources, improving the support capacity and guarantee capacity of water resources, and reducing the economic and social development bottlenecks caused by extreme drought. [ABSTRACT FROM AUTHOR]

Under the background of global warming, it is very important to explore the spatial–temporal evolution and causes of regional drought/flood for the realization of drought relief and social sustainable development. In this paper, the spatial–temporal characteristics of drought/flood in the Henan section of the Yellow River in recent 51 years were analyzed on the basis of the Standardized Precipitation Evapotranspiration Index (SPEI), and the methods such as wavelet analysis and empirical orthogonal decomposition. Combined with sunspot, atmospheric circulation factors and the operation of reservoir, the response of drought/flood to various factors were explored by using the sliding correlation analysis and cross wavelet analysis. The results showed that: (1) In terms of time scale, the SPEI-12 showed an insignificant trend of aridity on the whole and with main cycle of 18 years and sub cycles of 4 years and 8 years. (2) The first spatial distribution mode coefficients were all positive, indicating that the drought/flood changes in the entire region were consistent, while the second mode coefficients showed the east–west reverse differences, reflecting opposite drought/flood characteristics in the east and west. (3) In terms of causes, droughts and floods were affected by natural factors. The SPEI-12 had a period of 8–12 years with the sunspot phase logarithm and it had resonance periods of different time scales with the Arctic Oscillation (AO), El Niño-Southern Oscillation (ENSO) and Pacific Decadal Oscillation (PDO), but it did not have a significant resonance period with the North Atlantic Oscillation (NAO). The flood storage and drought compensation function of the reservoir affected the occurrence of drought/flood events under natural conditions. [ABSTRACT FROM AUTHOR]

Soil moisture (SM) plays an important role in regulating terrestrial–atmospheric water circulation and energy balance. Most of the existing studies have explored the dynamic patterns of SM based on experimental methods. However, the analysis of large-scale regions and long-term SM sequences was limited. Alternatively, satellite remote sensing data is a potential source for SM analysis for large-scale basins. Therefore, the SM data from the European Space Agency (ESA) Climate Change Initiative (CCI) from 2000 to 2015 is used in this paper to analyze the SM spatial-temporal changes in the Yellow River Basin (YRB). Further, the Normalized Difference Vegetation Index (NDVI) and meteorological data are used to explore the relationships between SM and NDVI, precipitation, air temperature, and wind speed, respectively. The results showed that the overall trend of SM in the YRB was decreasing from southeast to northwest during the past 16 years. The upper reaches of the YRB had shown a humid trend, with a value of 0.00047 m3·m−3·year−1, mainly due to the increase in precipitation; there was an obvious drought trend in the middle reaches of the YRB, especially in Shanxi Province and Henan Province, with a value of −0.00030 m3·m−3·year−1, which may be owed to vegetation greening increasing the soil evaporation. Overall, it is determined that the main factors influencing SM changes were NDVI and precipitation, followed by air temperature and wind speed. This study can provide a scientific basis for the spatial-temporal distribution characteristics and attributions of SM in the YRB over a long time series. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Natural Resources / Ziran Ziyuan Xuebao is the property of Journal of Natural Resources and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

• The matrix representation of grey possibility function is provided. • The solution method of possibility function's turning points is given. • The "maximum criterion" decision paradox is solved. • A variable stage self-correcting grey clustering model is proposed. • The meteorological drought grade of 17 stations in china henan province is evaluated. The grey clustering model based on the possibility function is frequently used in system evaluation, but the setting of the possibility function is frequently subjective, which results in the weak credibility of the clustering results. In addition, the "maximum criterion" decision paradox of the model has fuzzy boundaries, making the clustering process impossible to program. Based on this, the possibility function is represented as a matrix in the paper, and the possibility function is calculated by an optimization model to guarantee the objectivity of the clustering results. A variable stage self-correcting grey clustering model is built by using the kernel weight transformation as the primary tool, "entropy subtraction" as the signal of the decision paradox, and "entropy non-subtraction" as the model's termination signal. The model's Matlab source code is provided to reduce the complexity of the model. The illustration and comparative analysis demonstrate the model's logic and efficacy. Using the model for the evaluation of the meteorological drought grade in Henan Province can offer a fresh perspective for meteorological departments to make choices and assessments. [ABSTRACT FROM AUTHOR]

:Drought has been a major natural disaster for mankind since ancient times. Even today, with the development of science and technology, the disastrous consequences caused by droughts are still everywhere. In this paper, the evolution characteristics of the drought and its correlation with circulation in Henan Province from 1970 to 2019 are analyzed by using the methods of standardized precipitation evapotrans- piration index (SPEI), Mann Kendall test and cross wavelet analysis. The results show that based on SPEI value, the climate in Henan Province has gradually become arid in recent 50 years. In terms of spatial variation, the drought in spring, summer and winter is significant in the northwest and relatively humid in the southeast. From the circulation correlation, the North Atlantic Oscillation (NAO), Arctic Oscillation (AO) and Southern Oscillation (SOI) have a strong correlation with droughts in spring, summer and autumn in Henan Province, and the Pacific Decadal Oscillation (PDO) and SOI have a strong inter-annual correlation with droughts in Henan Province. [ABSTRACT FROM AUTHOR]

Regional agricultural drought vulnerability (RADV) is a complex nonlinear problem caused by the interaction of multiple factors, and an objective and systematic method is proposed by this paper to identify its influencing factors, which plays an important role in preventing and regulating the risks of regional agricultural drought. Firstly, to provide a reference for the evaluation problem in selecting the number of factors, the influencing factors affecting RADV are revealed by using the method of phase space reconstruction (PSR). Secondly, to rank the importance of influencing factors, a grey trend relational analysis (TGRA) method is proposed, considering the dynamic development relationship between the RADV index and the influencing factors and integrating the absolute and relative variation of sequences in each corresponding period. Finally, to reduce the collinearity between the influencing factors, a grey trend relational clustering (TGRC) analysis method is proposed. According to the above steps, the process of identifying factors based on PSR-TGRC method is formed. Taking Henan Province as an example, 14 main influencing factors and their effects on RADV are identified from all 42 factors, and the identification results which are consistent with the actual drought relief work show the rationality and practicality of PSR-TGRC method and provide theoretical support for formulating strategies of regional agricultural disaster prevention and mitigation. [ABSTRACT FROM AUTHOR]

Variable selection is the basis of GM (J,N) model, and also one of the keyfactors that affect grey model performance. Based on the adaptability between actual data and theoretical model, this paper recognizes the parameter estimation of grey model as a general linear model and discusses the effect of variable selection on parameter estimation and prediction performance. It is proved that discarding those relevant variables which have little influence is helpful to improve the modeling accuracy. Then, a variable selection method for GM(J,N) model under RMS, 18 and AIC criteria is proposed, and the calculation order and calculation method are given. Finally, the method is applied to predict the agricultural drought vulnerability in Xinyang City, Henan Province. The results show that the method not only identifies the main influencing factors of drought vulnerability but also further improves the prediction accuracy. [ABSTRACT FROM AUTHOR]

As a major grain-producing province in China's Central Plains, Henan Province is severely impacted by drought, making the study of agricultural drought characteristics in the region crucial. Theil–Sen (Sen) trend analysis, the Mann–Kendall (M-K) test and the Hurst index method were used to systematically analyze the spatial variation characteristics of agricultural drought based on the Temperature Vegetation Dryness Index (TVDI). The results show that: (1) The drought occurs in central, northwestern and southern Henan on an annual scale. The drought situation will continue to increase in northern, eastern northeastern and central Henan. (2) The drought in spring, summer and winter showed an increasing trend, but the opposite trend was observed in autumn. The increasing trend of drought in each season is mainly distributed in northern, central and eastern Henan. (3) The drought in January, February, April, July, September and December showed an increasing trend, while the drought in the other 6 months showed a decreasing trend. The increase in drought during July and August was not pronounced, while the drought situation in September remained largely unchanged. The distribution of drought across the other months exhibited varying patterns across different regions. Overall, the drought trend in Henan Province is on the rise, displaying distinct seasonal and regional patterns in its temporal and spatial distribution. The results can provide a reference for Henan Province to formulate effective measures of drought resistance and disaster reduction to ensure grain production. [ABSTRACT FROM AUTHOR]

Characterized by soil moisture content and plant growth, agricultural drought occurs when the soil moisture content is lower than the water requirement of plants. Microwave remote sensing observation has the advantages of all-weather application and sensitivity to soil moisture change. However, microwave remote sensing can only invert 0~5 cm of soil surface moisture, so it cannot effectively reflect the drought situation of farmland. Therefore, this study took Henan Province as the study area, used soil moisture active and passive (SMAP) satellite soil moisture data, employed NDVI, LST, and ET as the independent variables, and took the drought grade on the sample as the dependent variable. Using the 2017–2019 data as the training set and the 2020 data as the testing set, a random forest drought monitoring model with comprehensive influence of multiple factors was constructed based on the training set data. In the process of model training, the cross-validation method was employed to establish and verify the model. This involved allocating 80% of the sample data for model construction and reserving 20% for model verification. The results demonstrated an 85% accuracy on the training set and an 87% accuracy on the testing set. Additionally, two drought events occurring during the winter wheat growing period in Henan Province were monitored, and the validity of these droughts was confirmed using on-site soil moisture and the vegetation supply water index (VSWI). The findings indicated a high incidence of agricultural drought in the southwestern part of Henan Province, while the central and northern regions experienced a lower incidence during the jointing to heading and filling stages. Subsequently, leveraging the results from the random forest drought monitoring, this study conducted a time series analysis using the Mann–Kendall test and a spatial analysis employing Moran's I index to examine the temporal and spatial distribution of agricultural drought in Henan Province. This analysis aimed to unveil trends in soil moisture changes affecting agricultural drought, as observed via the SMAP satellite (NASA). The results suggested a possible significant spatial auto-correlation in the occurrence of agricultural drought. [ABSTRACT FROM AUTHOR]

Soil moisture (SM) is a crucial environmental variable, and it plays an important role in energy and water cycles. SM data retrieval based on microwave satellite remote sensing has garnered significant attention due to its spatial continuity, wide observational coverage, and relatively low cost. Validating the accuracy of satellite remote sensing SM products is a critical step in enhancing data credibility, which plays a vital role in ensuring the effective application of satellite remote sensing data across various fields. Firstly, this study focused on Henan Province and evaluated the accuracy of the SMAP Enhanced L3 Radiometer Global and Polar Grid Daily 9 km EASE-Grid Soil Moisture (SPL3SMP_E) product along with its application in agriculture. The evaluation was based on in situ SM data from 55 stations in Henan Province. The assessment metrics used in this study include mean difference (MD), root mean square error (RMSE), unbiased root mean square error (ubRMSE), and the Pearson correlation coefficient (R). The time span of this study is from 2017 to 2020. The evaluation results indicated that the SPL3SMP_E soil moisture product performs well, as reflected by an ubRMSE value of 0.045 (m3/m3), which was relatively close to the product's design accuracy of 0.04 (m3/m3). Moreover, the accuracy of the product was unaffected by temporal factors, but the product exhibited strong spatial aggregation, which was closely related to land use types. Then, this study explored the response of the SPL3SMP_E product to irrigation signals. The precipitation and irrigation data from Henan Province were employed to investigate the response of the SPL3SMP_E soil moisture product to irrigation. Our findings revealed that the SPL3SMP_E soil moisture product was capable of capturing over 70% of irrigation events in the study area, indicating its high sensitivity to irrigation signals in this region. In this study, the SPL3SMP_E product was also employed for monitoring agricultural drought in Henan Province. The findings revealed that the collaborative use of the SPL3SMP_E soil moisture product and machine learning algorithms proves highly effective in monitoring significant drought events. Furthermore, the integration of multiple indices demonstrated a notable enhancement in the accuracy of drought monitoring. Such an evaluation holds significant implications for the effective application of satellite remote sensing SM data in agriculture and other domains. [ABSTRACT FROM AUTHOR]

Drought is a frequent global phenomenon. Solar-induced chlorophyll fluorescence (SIF), an electromagnetic signal, has been proven to be an efficient tool for monitoring and assessing gross primary productivity (GPP) and drought. To address the issue of the sparse resolution of satellite-based SIF, researchers have developed different downscaling algorithms. Recently, the most frequently used SIF products had a spatial resolution of 0.05 degrees. However, these spatial resolution SIF data are not conducive to regional agricultural drought monitoring. In this study, we utilized the global 'OCO-2' solar-induced fluorescence (GOSIF) products along with normalized difference vegetation index (NDVI) and land surface temperature (LST) products. With the powerful advantages offered by Google Earth Engine (GEE), we could conveniently acquire the necessary data. Additionally, employing the random forest (RF) method, we successfully acquired downscaled SIF data at an enhanced spatial resolution of 1 km. Using those downscaled SIF results with 1 km resolution, an SIF anomaly index was established and calculated to monitor drought. Results showed that the RF-based downscaled SIF result followed the same trend as the GOSIF value. Subsequently, correlation coefficients between SIF and GPP were calculated. The downscaled SIF demonstrated a higher correlation with GPP from MODIS compared to 0.05-degree GOSIF, with coefficients of 0.74 and 0.68 in May 2018, respectively. Moreover, the SIF anomaly index showed positive correlations with crop yield; the correlation coefficients were 0.93 for wheat and 0.89 for maize. The drought index had a negative correlation with areas affected by drought, with a correlation coefficient of −0.58. Finally, the SIF anomaly index was used to monitor drought from 2001 to 2020 in Henan Province. The 1 km SIF results obtained through the RF-based downscaled method were deemed reliable, thereby establishing the suitability of the SIF anomaly index for drought monitoring at a regional scale. [ABSTRACT FROM AUTHOR]

Carrying out risk assessments of agricultural drought disasters is helpful to understanding agricultural drought quantitatively and scientifically guiding drought prevention and drought relief work. In this paper, the risk assessment system and evaluation index of drought disasters are constructed, and they are composed of a drought risk subsystem, drought exposure subsystem, disaster damage sensitivity subsystem and drought resistance subsystem. Based on the grey matter-element analysis method, the agricultural drought risk evaluation model was established. Grey matter-element analysis method was used to evaluate the risk of agricultural drought in 18 regions of Henan Province, China in 2019. The results validation showed that high drought disaster risk area in Henan province is located in the western, north and the central area. This study provides a new method for the risk assessment of agricultural drought disasters. Understanding the risk in the study area can improve agricultural system resilience. This model could be used to provide support for increasing agricultural drought disaster resilience and risk management efficiency. [ABSTRACT FROM AUTHOR]

Drought exerts a significant impact on the sustainable development of social economy. For sustainable water use and rapid drought response, it is very meaningful to explore the correlation between meteorological drought and hydrological drought. This paper investigates the features of drought evolution based on cross wavelet transform (XWT) and mutual entropy. The meteorological drought was measured by the standardized precipitation index (SPI), while the hydrological drought was evaluated by the standardized runoff index (SRI). The research data were collected from hydrological stations and meteorological stations in the Yellow River basin of Henan Province, China. The main conclusions are as follows: (1) Both meteorological drought and hydrological drought had obvious periodicity, especially on the scale of 2–4a. (2) The evolution of meteorological drought varied greatly from place to place; through the study period, Henan province did not suffer any extreme drought, though the drought situation in the western region has an aggravating trend. (3) Hydrological drought has a strong positive correlation with meteorological drought, and generally lagged behind meteorological drought by 1–2 months in the study area. The research results lay a good basis for drought response in the Yellow River basin. [ABSTRACT FROM AUTHOR]

• 1. The lag degree of hydrological drought is studied by changing the time scale. • 2. Multi-scale standardized indicators can explore the strongest correlation. • 3. According to the synchronization, relevance, correlation to calculate the results. • 4.The scale time with the strongest correlation is the propagation time. To study the propagation law of meteorological droughts to hydrological droughts under variable time scales, and to reveal the synchronisation gap and lag time of hydrological droughts compared to meteorological droughts, and the lag degree of hydrological drought is quantitatively studied. In this paper, the Yellow River Water Supply Area in Henan is taken as the research area, and the propagation time of meteorological drought to hydrological drought under variable time scale is mainly studied. The calculation of drought propagation time is set as two states: interval scale and continuous scale. The Standardized Precipitation Index (SPI) sequence and Standardized Runoff Index (SRI) sequence (1)with interval and continuous scales were obtained by multi-scale standardization of precipitation and runoff, which were used to characterize meteorological drought and hydrological drought respectively. The Mann-Kendall (M−K) test method was used to analyze the evolution of SPI sequence and SRI sequence at monthly scale, and to study the lag time of hydrological drought in four seasons compared with meteorological drought. Combining the Grey Correlation Method and Pearson correlation coefficient methods, the time interval for the propagation of meteorological droughts to hydrological droughts was determined based on the strongest correlation of different combinations of sequences. Finally, the propagation time series is divided into sliding windows to analyse the M−K dynamic trend of propagation time from a seasonal scale perspective. It provides a strong basis for the study of drought propagation based on multi-scale. [ABSTRACT FROM AUTHOR]

• A deep learning algorithm is used to build a comprehensive drought monitoring model. • Multi-source remote sensing data is processed as input data. • The comprehensive drought model has good applicability in the monitoring of meteorological drought and agricultural drought. • The model successfully monitored a drought process in Henan Province, China. • This study provides a new method for the comprehensive assessment of regional drought. Drought is a popular scientific issue in global climate change research. Accurate monitoring of drought has important implications for the sustainable development of regional agriculture in the context of increasingly complex global climate change. Deep learning is a widely used technique in the field of artificial intelligence. However, ongoing on drought monitoring using deep learning is relatively scarce. In this paper, the various hazard factors in drought development were comprehensively considered based on satellite data including Moderate Resolution Imaging Spectroradiometer (MODIS) and tropical rainfall measuring mission (TRMM) as multi-source remote sensing data. By using the deep learning technique, a comprehensive drought monitoring model was constructed and tested in Henan Province of China as an example. The results showed that the comprehensive drought model has good applicability in the monitoring of meteorological drought and agricultural drought. There was a significant positive correlation between the drought indicators of the model output and the comprehensive meteorological drought index (CI) measured at the site scale. The consistency rate of the drought grade of the two models was 85.6% and 79.8% for the training set and the test set, respectively. The correlation coefficient between the drought index of the model and the standard precipitation evapotranspiration index (SPEI) was between 0.772 and 0.910 (P < 0.01), which indicated a strong level of significance. The correlation coefficient between the drought index of the model and the soil relative moisture at a 10 cm depth was greater than 0.550 (P < 0.01), and there was a good correlation between them. This study provides a new method for the comprehensive assessment of regional drought. [ABSTRACT FROM AUTHOR]

The percentage precipitation anomaly was used to index the effect of drought on winter wheat grown in Henan Province for the years 2011–2020. Of interest was the effect of drought on winter wheat yield and the accurate assessment of the damage done to winter wheat by drought events in order to improve the risk management of winter wheat in the context of drought hazards. The spatial and temporal variability of winter wheat drought risk in Henan Province was determined by analysis of climate data, winter wheat yield, cultivated area, and socio-economic data across three dimensions: exposure or susceptibility to drought, economic–environmental sensitivity to drought, and capacity to resist drought. A drought vulnerability assessment model, based on the entropy value method and a comprehensive fuzzy evaluation, was developed to assess the drought vulnerability of winter wheat in Henan Province compared with the percentage precipitation anomaly model. (1) There were significant spatial differences in the frequency of the five drought categories devised. (2) Areas in which there was a high frequency of mild drought events were mainly in northern and western Henan and southwestern Henan, with the frequency ranging from 17% to 29%. (3) Areas in which there was a high frequency of moderate drought events were mainly in northwestern, central, and southeastern Henan. (4) Areas in which there was a high frequency of severe and extreme drought were mainly in Anyang in northern Henan, Zhengzhou in central Henan, and Xinyang and surrounding areas in southern Henan, with the frequency ranging from 7% to 9.70%. (5) Winter wheat drought vulnerability shows an overall annually increasing trend. The susceptibility dimension had the greatest influence of the three dimensions, followed by economic–environmental sensitivity and then drought resistance, which had the least impact. The model created in this study shows the influence of drought on winter wheat production more intuitively than a conventional fuzzy synthesis, and the results can inform decision-making in winter wheat drought risk assessment and management. [ABSTRACT FROM AUTHOR]

A skewed grey cloud ordered clustering model is proposed to overcome the problem that the existing grey clustering assessment results are inconsistent with objective reality due to the greyness, fuzziness, and randomness of information in the process of agricultural drought loss assessment. The skewed grey cloud model is built by split-combination possibility function, which is based on the normal grey cloud model. The grey cloud constraint interval is solved using the outer envelope curve and expectation curve of the skewed grey cloud, and the classification weight of the index is derived using the Gini coefficient. Then, the grey cloud constraint interval and the weight coefficient of comprehensive decision measure are combined to establish the ordered clustering criterion of skewed grey cloud. The degree of agricultural drought loss in Henan Province is examined from 2006 to 2019, and the assessment results are compared to those of the grey clustering model, normal grey cloud clustering model and comprehensive index method. The results reveal that the skewed grey cloud model's evaluation results are more in line with the actual scenario of agricultural drought in Henan Province, showing the model's applicability and usefulness. [ABSTRACT FROM AUTHOR]

Drought-induced risk has attracted the attention of many scholars, but the risk of combined events caused by drought and high-temperature heat waves still needs further study. Based on MODIS products and meteorological data, the spatiotemporal variation characteristics of summer drought and high-temperature heat waves in the North China Plain from 2000 to 2018 were analyzed by the standardized precipitation evapotranspiration index (SPEI), crop water stress index (CWSI) and high-temperature threshold, and their combined-events risk was evaluated. The results showed that (1) from 2000 to 2018, summer drought in the North China Plain became more severe. Especially in Henan, Anhui and Jiangsu Provinces, drought increased significantly. (2) From 2000 to 2018, the frequency and intensity of high-temperature heat wave events in the North China Plain gradually increased at rates of 0.28 times/10 year and 1.6 °C/10 year, respectively. (3) The slightly high risk and high risk caused by summer drought were mainly distributed in Hebei Province and Tianjin Municipality in the north, and the risk change was characterized by a decrease in the north and an increase in the south. (4) The combined-events risk of summer drought and high-temperature heat waves did not increase significantly, with an increase rate of approximately 0.01/10 year. Among them, the increase rate of combined-events risk in Henan Province was the largest (0.14/10 year), followed by the obvious increase in northern Anhui, Jiangsu and southern Shandong, while the risk in Beijing showed a decreasing trend. The research results have scientific guiding significance for formulating disaster prevention and reduction strategies. [ABSTRACT FROM AUTHOR]

Solar-induced chlorophyll fluorescence (SIF) has been shown to be a powerful proxy for photosynthesis and a promising indicator of drought monitoring, but the ability of high-resolution satellite-derived SIF for drought monitoring has not been widely investigated due to a lack of data. The lack of high spatiotemporal resolution satellite SIF hinders the resolution enhancement of SIF derived by downscaling or reconstruction algorithms. The TROPOspheric Monitoring Instrument (TROPOMI) SIF provides an alternative with finer spatiotemporal resolution. We present an operational downscaling method to generate 500 m 16-day SIF (TSIF) using Neural Networks over a local spatiotemporal window. The results showed that our method is very robust against overfitting, and TSIF has a strong spatiotemporal consistency with TROPOMI SIF (TROPOSIF) with R 2 = 0.956 and RMSE = 0.054   mWm − 2 sr − 1 nm − 1 . Comparison with another SIF product (CASIF) showed a spatiotemporal consistency with TSIF. Comparison with tower gross primary productivity (GPP) from AmeriFlux in California showed a strong correlation with R 2 for multiple ecosystems ranging from 0.58 to 0.88. We explored the capacity of TSIF for monitoring a drought event in Henan, China, showing that TSIF is more sensitive to drought and precipitation compared to the Enhanced Vegetation Index. Our TSIF is a very promising indicator for regional drought monitoring. [ABSTRACT FROM AUTHOR]

Rainfall is a complexity dynamics process. In this paper, our objective is to find the evidence of self-organized criticality (SOC) for rain datasets in China by employing the theory and method of SOC. For this reason, we analyzed the long-term rain records of five meteorological stations in Henan, a central province of China. Three concepts, that is, rain duration, drought duration, accumulated rain amount, are proposed to characterize these rain events processes. We investigate their dynamics property by using scale invariant and found that the long-term rain processes in central China indeed exhibit the feature of self-organized criticality. The proposed theory and method may be suitable to analyze other datasets from different climate zones in China. [ABSTRACT FROM AUTHOR]

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

• Drought loss models of wheat are built using two different statistical models. • The important SPEI variables recognized by two methods are different. • The random forest-derived model outperforms the stepwise regression model. Agricultural drought loss models provide services for the rapid risk assessment of agricultural disasters, and regional disaster prevention and mitigation efforts. This paper takes wheat as an example, and chooses counties dominated by rain-fed farmland in Henan Province as the study area. Counties dominated by rain-fed farmland are determined by setting a rain-fed threshold that is related to the proportion of the effective irrigation area to the cultivated land area. Modeling samples are screened by considering both drought occurrence time and wheat yield reductions. Under different thresholds (30%, 40%, 50% and 60%), we use the yield loss ratio as the dependent variable and 24 standardized precipitation evapotranspiration index parameters as independent variables to build drought loss models using both a multivariate stepwise regression model and a random forest model. Yield loss ratio from 1990 to 2015 is calculated by decomposing historical wheat yield time series. 24 standardized precipitation evapotranspiration index variables are 1–3 months' time scale standardized precipitation evapotranspiration index during the growth period (from October to May of the following year) of winter wheat in Henan Province. The results show that the random forest-derived model outperforms the stepwise regression model in all tests. The accuracy of all the models increases with an increase of the proportion of the rain-fed threshold. When the rain-fed threshold is 60%, the R2 values of the random forest model and the multivariate stepwise regression equation are 0.720 and 0.523, respectively. The validation results show that the mean absolute error and the root mean square error of the multivariate stepwise regression are 1.38 times and 1.31 times larger than the mean absolute error and the root mean square error from the random forests model. Moreover, both models identify that standardized precipitation evapotranspiration indices in October (sowing/planting stage) and February (overwintering stage) are important variables. However, the multivariate stepwise regression model fails to recognize the importance of standardized precipitation evapotranspiration indices during April–May (filling stage). [ABSTRACT FROM AUTHOR]

Agricultural drought threatens food security. Numerous remote-sensing drought indices have been developed, but their different principles, assumptions and physical quantities make it necessary to compare their suitability for drought monitoring over large areas. Here, we analyzed the performance of three typical remote sensing-based drought indices for monitoring agricultural drought in two major agricultural production regions in Shaanxi and Henan provinces, northern China (predominantly rain-fed and irrigated agriculture, respectively): vegetation health index (VHI), temperature vegetation dryness index (TVDI) and drought severity index (DSI). We compared the agreement between these indices and the standardized precipitation index (SPI), soil moisture, winter wheat yield and National Meteorological Drought Monitoring (NMDM) maps. On average, DSI outperformed the other indices, with stronger correlations with SPI and soil moisture. DSI also corresponded better with soil moisture and NMDM maps. The jointing and grain-filling stages of winter wheat are more sensitive to water stress, indicating that winter wheat required more water during these stages. Moreover, the correlations between the drought indices and SPI, soil moisture, and winter wheat yield were generally stronger in Shaanxi province than in Henan province, suggesting that remote-sensing drought indices provide more accurate predictions of the impacts of drought in predominantly rain-fed agricultural areas. [ABSTRACT FROM AUTHOR]

Regional agricultural drought vulnerability (RADV) is a complex problem caused by the interaction of various factors, and the combination of multiple dimensions of each subregion, factor index and time affects the RADV. Therefore, panel data should be used to reflect the actual situation of the region objectively and comprehensively. Current research on identifying key factors of affecting RADV is relatively scarce from the perspective of panel data. In view of this, in order to classify and identify the key factors, a new panel data grey combined method of comprehensive grey relational analysis (CGRA) and Max-CGRA clustering is proposed, which is applied to identify the key factors of RADV in China's Henan Province. According to the identification results of key factors, the reasons for the change of RADV are further discovered, and the corresponding drought policies and countermeasures that need to be strengthened and controlled are presented. In addition, these results can also provide scientific basis for regional agricultural drought risk control. [ABSTRACT FROM AUTHOR]