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• The extent of flooding in China is greater than in any other country. • Nearly 66% of China's landmass is submerged by flooding. • Financial toll of flooding accounts for approximately 1.42% of the annual GDP. • Various forms of climatic change contribute to flooding. The extent of flooding in China is more significant than in any other country. Our research reveals that approximately 66 % of China's landmass is submerged by flooding, affecting about 50 % of the population. Furthermore, the financial toll of flooding now accounts for approximately 1.42 % of the annual gross domestic product (GDP), which is almost 40 times higher than the corresponding figure for the United States. We have observed that Zhengzhou city in Henan province, which faced a devastating flood in 2021, received a significant amount of rainfall, specifically a total of 552.5 mm within a 24-hour period. The floods in Henan province in 2021 caused considerable damage, including impacting nearly 15 million people, resulting in almost 400 deaths, damaging over 10,000 square kilometers of agricultural land, causing almost $19 billion in economic losses, and leading to the collapse of over 35,000 households and damage to various properties. In a similar manner, the significant flooding that occurred in southern China in 2020 impacted approximately 7.1 million individuals across eight provinces and resulted in 54 fatalities, the collapse of 6,700 houses, and incurred a direct economic loss of US$3.33 billion. We found that rainstorms have significantly increased to 10 % in the last 60 years in China. In this paper, we delved into exploring the existing published articles, reports, and government and authoritative legal texts to analyze the causes and impacts of flooding in China's flood-prone regions and potential mitigation strategies to reduce the repercussions of distressing flood events. We believe this study will help policymakers by providing new insights while formulating policy to reduce the loss caused by high-level flooding. [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]

• Tree ring is an ideal indicator of annual total grain production parameters. • Tree growth synchronized with grain production, and concurrent response to climate. • Reconstruction of total grain productivity based on dendrochronology. • To explore the relationship between total grain sown area and climate change. Climate change affects the growth of regional vegetation; both trees and grain crops often change concurrently, such that the annual radial growth of trees shows consistent inter-annual variations with the total grain crop productivity. However, it remains unclear whether they exhibit concurrent responses to climate factors, and that there lacks of the study on long-term high-resolution variations of grain crops productivity. This paper employs a Pinus massoniana tree-ring series from the Tongbai Mountains to analyze the correlations between tree-ring chronology, local climate data and grain productivity indicators (i.e., total sown grain areas (TSA), total grain outputs (TGO), and average grain yield per hectare (YPH)) of Henan Province in central China. The results indicate that temperature in March and August was the main limiting factor on tree growth, and the best concurrent relationship with tree growth was TSA, which has a correlation of 0.747 (p < 0.001) during 1959–2020. Therefore, a 124-year TSA series in Henan Province was reconstructed using tree-ring data from the Tongbai Mountains, which reveals there were two distinct low periods of total grain sown area in the 1920 s-1930 s and 1980 s-2000 s. There are significant cycles of about 2.57a (p < 0.01), 2.89a (p < 0.05), and 10.95a (p < 0.1), indicating that vegetation growth might be affected by large-scale climate forcing, such as ENSO (2-7a cycle) and sunspot activity (11a cycle). Overall, this study outlines a new approach to understand long-term changes in grain production, which is conducive to grain management and socioeconomic sustainability. [ABSTRACT FROM AUTHOR]

• SMA was used for the first time for the selection of hyperspectral wavebands. • A novel algorithm of SMA-PCC-RF is proposed to invert heavy metal concentrations. • The algorithm was applied to inversion of heavy metal concentrations in mining area. • Spectral transformations can enhance heavy metals and spectral curves correlation. • SMA-PCC-RF algorithms offers the better inversion accuracy for all heavy metals. Heavy metal contamination has long been a concern of intense research within the field of environmental protection. The exacerbation of heavy metal pollution due to mineral resource over-exploitation and the implementation of agricultural modernization has further emphasized the need for rapid monitoring. Although traditional geochemical survey methods have been deemed reliable, they lack the ability to conduct non-destructive and rapid surveys of large areas due to their high cost, low real-time capability, and cumbersome operations. We collected 120 soil samples in the field using Xin'an County, Henan Province as the research region, and obtained hyperspectral curves and contents of the samples using the spectrometer and chemical analysis. After preprocessing the spectral curves, we used the slime mold algorithm (SMA) to preselect feature wavebands, and then the mathematically transformed method was used to improve their correlation. Next, we calculated the correlation coefficients of these wavebands with six heavy metals, and the final modeled wavebands were obtained through precise feature selection based on the criterion that the correlation coefficient's absolute value exceeded 0.298. The inversion model was also established by using adaptive boosting (AdaBoost), gradient boosted decision tree (GBDT), random forest (RF), and partial least squares (PLS). The results indicated that SMA-PCC can effectively downscale the high-dimensional hyperspectral data and obtain the feature wavebands with a high contribution to the modeling. We also observed that the mathematical transformation method improved the relevance between heavy metal elements and spectra. Among the four models, RF showed better overall inversion accuracy for all heavy metals (i.e., Zn: R AdaBoost 2 = 0.85 , R GBDT 2 = 0.56 , R RF 2 = 0.89 , R PLS 2 = 0.57). This paper showcases that the algorithm presented can provide meaningful technical guidance for the large-scale investigation and assessment of heavy metal levels in soil. [ABSTRACT FROM AUTHOR]

[Display omitted] • The equivalent coefficient is revised according to ability and willingness to pay. • The Best-worst method is used to determine the weight of the allocated indicators. • A 10 km buffer calculation strip is established in the compensated payment area. • The overflow of hydrological adjusting is greater than that of erosion prevention. • Ecological compensation in 2010–2020 is higher than that in 2000–2010. Coordinated governance and horizontal ecological compensation are important ways to realize ecological protection and high-quality development in the Yellow River basin. Taking the middle and lower reaches of the Yellow River as the research object, this paper analyses the compensation relationship and amount of cities along the line in Shaanxi, Shanxi, Henan and Shandong provinces. Firstly, the spillover value of regulation of water flows and erosion prevention of 32 counties along the Yellow River in Shaanxi and Shanxi provinces were calculated during 2000–2010 and 2010–2020 from the perspective of land use/cover change (LUCC). Secondly, the compensation accounting index system was established based on the classification of ecological services given the beneficiaries' individual preferences, and each indicator's weights were determined based on the Best-worst method. Finally, setting the water ecological service value of the 10 km buffer zone on both sides of the Yellow River as the accounting scope, this paper calculated the amount of ecological compensation paid by 16 cities in Shandong and Henan provinces. The results show that: (1) The spillover value of Shaanxi and Shanxi provinces in 2010–2020 is greater than that in 2000–2010, and the values of erosion prevention in both stages are lower than that of regulation of water flows; (2) From the perspective of providers, the highest is 3.68e + 08 yuan in Lvliang from 2000 to 2010, while the highest is 3.60e + 09 yuan in Yulin from 2010 to 2020; (3) From the perspective of beneficiaries, Zhengzhou has the highest amount of compensation in Henan province, which is 2.28e + 08 yuan in 2000–2010 and 9.41e + 08 yuan in 2010–2020 respectively. In Shandong province, Heze pays the highest value of 1.27e + 08 yuan from 2000 to 2010, and Jinan pays the highest value of 9.56e + 08 yuan from 2010 to 2020. The ecological compensation scheme obtained in this paper is more sustainable than the vertical compensation and expands the scope of the existing ecological compensation standards, which can effectively stimulate the enthusiasm for ecological protection, and reduce the financial pressure on the central government. [ABSTRACT FROM AUTHOR]

[Display omitted] • A Water-economy-ecology concept framework is established. • An evaluation index system of 3 aspects including 15 indicators is constructed. • EFAST-Cloud model is innovatively proposed and applied in regional WRCC evaluation. • The WRCC of Henan Province is evaluated at the watershed scale and municipal scale. Water is the basic need for human survival and the development of economy and ecology. Water resources carrying capacity (WRCC) modelling is a fundamental task to explore the interaction mechanism between economy-ecology development and water resources carrying system. To reasonably quantify the regional WRCC, an evaluation method based on Water-economy-ecology (WEE) concept framework and EFAST-Cloud model is proposed in this study, and Henan Province of China is selected as a typical study area. It can both identify the inter-feeding relationships between sub-systems and evaluate the proportion of each grade in detail by reflecting the randomness and fuzziness of the evaluation. A total of 15 indicators including water system, economy system, and ecology system are constructed. The study analyzes the sensitivity of indicators by using Simlab software and then calculates the weight of indicators by using EFAST method. Compared with other traditional methods, EFAST algorithm is a special method for model sensitivity analysis, which can fully consider the coupling effect between indicators. According to the statistical data of 2010, 2015, and 2020, the WRCC at the watershed scale and municipal scale in Henan Province are evaluated through the EFAST-Cloud model. At the watershed scale, the ecology system criterion layer performs best over the multiple years, the Huaihe and Yangtze River basin have better WRCC at the target layer. At the municipal scale, Xuchang, Luohe, and Zhumadian have better WRCC, Puyang, Zhoukou, and Jiaozuo are slightly worse. The results show that WRCC of the study area has shown an overall improvement, but there is still much space for its development. Therefore, targeted recommendations are made for the water, economy, and ecology system respectively. The conclusion of this paper can provide a reference for the government in making water resources management policies. [ABSTRACT FROM AUTHOR]