Runoff change in the Yellow River Basin of China from 1960 to 2020 and its driving factors

Analysing runoff changes and how these are affected by climate change and human activities is deemed crucial to elucidate the ecological and hydrological response mechanisms of rivers.The Indicators of Hydrologic Alteration and the Range of Variability Approach(IHA-RVA)method,as well as the ecological indicator method,were employed to quantitatively assess the degree of hydrologic change and ecological response processes in the Yellow River Basin from 1960 to 2020.Using Budyko's water heat coupling balance theory,the relative contributions of various driving factors(such as precipitation,potential evapotranspiration,and underlying surface)to runoff changes in the Yellow River Basin were quantitatively evaluated.The results show that the annual average runoff and precipitation in the Yellow River Basin had a downwards trend,whereas the potential evapotranspiration exhibited an upwards trend from 1960 to 2020.In approximately 1985,it was reported that the hydrological regime of the main stream underwent an abrupt change.The degree of hydrological change was observed to gradually increase from upstream to downstream,with a range of 34.00%-54.00%,all of which are moderate changes.However,significant differences have been noted among different ecological indicators,with a fluctuation index of 90.00%at the outlet of downstream hydrological stations,reaching a high level of change.After the mutation,the biodiversity index of flow in the middle and lower reaches of the Yellow River was generally lower than that in the base period.The research results also indicate that the driving factor for runoff changes in the upper reach of the Yellow River Basin is mainly precipitation,with a contribution rate of 39.31%-54.70%.Moreover,the driving factor for runoff changes in the middle and lower reaches is mainly human activities,having a contribution rate of 63.70%-84.37%.These results can serve as a basis to strengthen the protection and restoration efforts in the Yellow River Basin and further promote the rational development and use of water resources in the Yellow River.

Quantitative estimation of bubble volume fraction of submarine seep plumes by modeling seismic oceanography data

Submarine seep plumes are a natural phenomenon in which different types of gases migrate through deep or shallow subsurface sediments and leak into seawater in pressure gradient.When detected using acoustic data,the leaked gases frequently exhibit a flame-like structure.We numerically modelled the relationship between the seismic response characteristic and bubble volume fraction to establish the bubble volume fraction in the submarine seep plume.Results show that our models are able to invert and predict the bubble volume fraction from field seismic oceanography data,by which synthetic seismic sections in different dominant frequencies could be numerically simulated,seismic attribute sections(e.g.,instantaneous amplitude,instantaneous frequency,and instantaneous phase)extracted,and the correlation between the seismic attributes and bubble volume fraction be quantitatively determined with functional equations.The instantaneous amplitude is positively correlated with bubble volume fraction,while the instantaneous frequency and bubble volume fraction are negatively correlated.In addition,information entropy is introduced as a proxy to quantify the relationship between the instantaneous phase and bubble volume fraction.As the bubble volume fraction increases,the information entropy of the instantaneous phase increases rapidly at the beginning,followed by a slight upward trend,and finally stabilizes.Therefore,under optimal noise conditions,the bubble volume fraction of submarine seep plumes can be inverted and predicted based on seismic response characteristics in terms of seismic attributes.

Unraveling the mechanisms underlying lake expansion from 2001 to 2020 and its impact on the ecological environment in a typical alpine basin on the Tibetan Plateau

Yanhu Lake basin(YHB)is a typical alpine lake on the northeastern Tibetan Plateau(TP).Its continuous expansion in recent years poses serious threats to downstream major projects.As a result,studies of the mechanisms underlying lake expansion are urgently needed.The elasticity method within the Budyko framework was used to calculate the water balance in the Yanhu Lake basin(YHB)and the neighboring Tuotuo River basin(TRB).Results show intensification of hydrological cycles and positive trends in the lake area,river runoff,precipitation,and potential evapotranspiration.Lake expansion was significant between 2001 and 2020 and accelerated between 2015 and 2020.Precipitation increase was the key factor underlying the hydrological changes,followed by glacier meltwater and groundwater.The overflow of Yanhu Lake was inevitable because it was connected to three other lakes and the water balance of all four lakes was positive.The high salinity lake water diverted downstream will greatly impact the water quality of the source area of the Yangtze River and the stability of the permafrost base of the traffic corridor.

Impact of environmental change on runoff in a transitional basin:Tao River Basin from the Tibetan Plateau to the Loess Plateau,China

Quantification of the impacts of environmental changes on runoff in the transitional area from the Tibetan Plateau to the Loess Plateau is of critical importance for regional water resources management.Trends and abrupt change points of the hydro-climatic variables in the Tao River Basin were investigated during 1956-2015.It also quantitatively separates the impacts of climate change and human activities on runoff change in the Tao River by using RCC-WBM model.Results indicate that temperature presented a significant rising trend(0.2℃per decade)while precipitation exhibited an insignificant decreasing trend(3.8 mm per decade)during 1956-2015.Recorded runoff in the Tao River decreased significantly with a magnitude of-13.7 mm per decade and abrupt changes in 1968 and 1986 were identified.Relative to the baseline period(1956-1968),runoff in the two anthropogenic disturbed periods of 1969-1986 and 1987-2015 decreased by 27.8 mm and 76.5 mm,respectively,which can be attributed to human activities(accounting for 69%)and climate change(accounting for 31%).Human activities are the principal drivers of runoff reduction in the Tao River Basin.However,the absolute influences on runoff reductions by the both drivers tend to increase,from 7.7 mm in 1969-1986 to 24.4 mm in 1987-2015 by climate change and from 20.2 mm to 52.2 mm by human activities.

Establishment and evaluation of operation function model for cascade hydropower station

Toward solving the actual operation problems of cascade hydropower stations under hydrologic uncertainty, this paper presents the process of extraction of statistical characteristics from long-term optimal cascade operation, and proposes a monthly operation function algorithm for the actual operation of cascade hydropower stations through the identification, processing, and screening of available information during long-term optimal operation. Applying the operation function to the cascade hydropower stations on the Jinshajiang-Yangtze River system, the modeled long-term electric generation is shown to have high precision and provide benefits. Through comparison with optimal operation, the simulation results show that the operation function proposed retains the characteristics of optimal operation. Also, the inadequacies and attribution of the algorithm are discussed based on case study, providing decision support and reference information for research on large-scale cascade operation work.

Correction of seismic attribute-based smallstructure prediction errors using GPR data-a case study of the Shuguang Coal Mine,Shanxi

Small structures in coal mine working face is one of the main hidden dangers of safe and effi cient production in coal mine.Currently,seismic exploration is often used as the main method for detecting such structures.However,limited by the accuracy of seismic data processing and interpretation,the interpreted location of small structures is often deviated.Ground-penetrating radar(GPR)can detect small structures accurately,but the exploration depth is shallow.The combination of the two methods can improve the exploration accuracy of small structures in coal mine.Aiming at the 1226#working face of Shuguang coal mine,we propose a method of seismic-attributes based small-structure prediction error correction using GPR data.First,we extract the coherence,curvature,and dip attributes from seismic data,that are sensitive to small structures,then by considering factors such as the eff ective detection range of GPR and detection environment,we select two structures from the prediction results of seismic attributes for GPR detection.Finally,based on the relationship between the positions of small structures predicted by the two methods,we use statistical methods to determine the overall off set distance and azimuth of the small structures in the entire study area and use the results as a standard for correcting each structure position.The results show that the GPR data can be used to correct the horizontal position errors of small structures predicted by seismic attribute analysis.The accuracy of the prediction results is greatly improved,with the error controlled within 5 m and reduced by more than 80%.Therefore,the feasibility of the method proposed in this study is verified.

Temporal Trend in Lung Cancer Burden Attributed to Ambient Fine Particulate Matter in Guangzhou, China

Objective To estimate the lung cancer burden that may be attributable to ambient fine particulate matter （PM2.5） pollution in Guangzhou city in China from 2005 to 2013. Methods The data regarding PM2.5 exposure were obtained from the ＆#39;Ambient air pollution exposure estimation for the Global Burden of Disease 2013＇ dataset at 0.1° ×0.1° spatial resolution. Disability-adjusted life years （DALYs） were estimated based on the information of mortality and incidence of lung cancer. Comparative risk analysis and integrated exposure-response function were used to estimate attributed disease burden. Results The population-weighted average concentration of PM2.5 was increased by 34.6% between 1990 and 2013, from 38.37 μg/m3 to 51.31 μg/m^3. The lung cancer DALYs in both men and women were increased by 36.2% from 2005 to 2013. The PM2.5 attributed lung cancer DALYs increased from 12105.0 （8181.0 for males and 3924.0 for females） in 2005 to 16489.3 （11291.7 for males and 5197.6 for females） in 2013. An average of 23.1% lung cancer burden was attributable to PM2.5 pollution in 2013. Conclusion PM2.5 has caused serious but under-appreciated public health burden in Guangzhou and the trend deteriorates. Effective strategies are needed to tackle this major public health problem.

Development characteristics and petroleum geological significance of Permian pyroclastic flow volcanic rocks in Western Sichuan Basin,SW China

By examining field outcrops, drilling cores and seismic data, it is concluded that the Middle and Late Permian “Emeishan basalts” in Western Sichuan Basin were developed in two large eruption cycles, and the two sets of igneous rocks are in unconformable contact. The lower cycle is dominated by overflow volcanic rocks;while the upper cycle made up of pyroclastic flow volcanic breccia and pyroclastic lava is typical explosive facies accumulation. With high-quality micro-dissolution pores and ultra-fine dissolution pores, the upper cycle is a set of high-quality porous reservoir. Based on strong heterogeneity and great differences of pyroclastic flow subfacies from surrounding rocks in lithology and physical properties, the volcanic facies and volcanic edifices in Western Sichuan were effectively predicted and characterized by using seismic attribute analysis method and instantaneous amplitude and instantaneous frequency coherence analysis. The pyroclastic flow volcanic rocks are widely distributed in the Jianyang area. Centering around wells YT1, TF2 and TF8, the volcanic rocks in Jianyang area had 3edifice groups and an area of about 500 km^(2), which is the most favorable area for oil and gas exploration in volcanic rocks.

Time-series variation and attribution analysis of downward shortwave radiation over the Yunnan-Kweichow plateau from 1984 to 2018

The downward shortwave radiation(DSR)is a key input parameter for land surface models and climate models.Based on the daily averaged Global Land Surface Satellite downward shortwave radiation(GLASS-DSR)dataset over the Yunnan-Kweichow Plateau(YKP)from 1984 to 2018,this paper analyzes variation trend and breakpoints of DSR.The results show that:annual averaged DSR decreases at a decreasing rate of-1.84 W·m^(-2)·decade^(-1) over the YKP from 1984 to 2018;the overall distribution of interannual averaged DSR shows higher in the mid-west,and gradually decreasing from west to northeast over the YKP;the estimated averaged DSR is larger in spring than in summer due to the influence of the monsoon;monthly averaged DSR reaches its maximum in May and its minimum in December;breakpoints are found in the seasonal and trend components of daily averaged DSR.Eleven driving factors are examined for their effects on DSR variation,including annual average temperature,precipitation,10 m wind speed,aerosol optical thickness(AOT),total cloud cover,elevation,slope,aspect,longitude,latitude,and climate zones.According to thefindings,AOT predominates in the spatio-temporal distribution of DSR over the YKP.This study will contribute to studies related to climate change and highland radiation.

A comparative study of 3D FZI and electrofacies modeling using seismic attribute analysis and neural network technique:A case study of Cheshmeh-Khosh Oil field in Iran

Electrofacies are used to determine reservoir rock properties,especially permeability,to simulate fluid flow in porous media.These are determined based on classification of similar logs among different groups of logging data.Data classification is accomplished by different statistical analysis such as principal component analysis,cluster analysis and differential analysis.The aim of this study is to predict 3D FZI(flow zone index)and Electrofacies(EFACT)volumes from a large volume of 3D seismic data.This study is divided into two parts.In the first part of the study,in order to make the EFACT model,nuclear magnetic resonance(NMR)log parameters were employed for developing an Electrofacies diagram based on pore size distribution and porosity variations.Then,a graph-based clustering method,known as multi resolution graph-based clustering(MRGC),was employed to classify and obtain the optimum number of Electrofacies.Seismic attribute analysis was then applied to model each relaxation group in order to build the initial 3D model which was used to reach the final model by applying Probabilistic Neural Network(PNN).In the second part of the study,the FZI 3D model was created by multi attributes technique.Then,this model was improved by three different artificial intelligence systems including PNN,multilayer feed-forward network(MLFN)and radial basis function network(RBFN).Finally,models of FZI and EFACT were compared.Results obtained from this study revealed that the two models are in good agreement and PNN method is successful in modeling FZI and EFACT from 3D seismic data for which no Stoneley data or NMR log data are available.Moreover,they may be used to detect hydrocarbon-bearing zones and locate the exact place for producing wells for the future development plans.In addition,the result provides a geologically realistic spatial FZI and reservoir facies distribution which helps to understand the subsurface reservoirs heterogeneities in the study area.

3D Seismic Attributes for Structural Analysis in Compressional Context: A Case Study from Western Sichuan Basin

Compressional region usually forms complex thrust faults system, which is difficult to identify using traditional migration profiles. The successful application of three-dimensional（3D） seismic attributes analysis greatly reduces the difficulty, and improves the accuracy and efficiency of seismic interpretation and structural analysis. In this paper, we took Qiongxi area in the compressional region of western Sichuan as an example, using two 3D seismic attributes, coherence and instantaneous phase, to identify fault assemblages and variations both vertically and laterally. The results show that the study area mainly consists of NS-, NE- and NEE-trending faults. The NS-trending faults are the largest and have a component of sinistral slip controlling the formation of NEE-trending faults, while the NE-trending faults are intermediate in scale, formed earlier and were cut by the NS-trending faults. Our results demonstrate that using seismic attributes for structural analysis have the following advantages：（1） more details of major fault zones,（2） highlighting minor faults which are hardly traced in seismic migration cube, and（3） easier acquisition of accurate fault systems. The application of seismic attributes provides a new idea for deciphering fine and complicated structures, and will significantly contribute to the development of objective and precise geological interpretation in the future.

Detect and attribute the extreme maize yield losses based on spatio-temporal deep learning

Providing accurate crop yield estimations at large spatial scales and understanding yield losses under extreme climate stress is an urgent challenge for sustaining global food security.While the data-driven deep learning approach has shown great capacity in predicting yield patterns,its capacity to detect and attribute the impacts of climatic extremes on yields remains unknown.In this study,we developed a deep neural network based multi-task learning framework to estimate variations of maize yield at the county level over the US Corn Belt from 2006 to 2018,with a special focus on the extreme yield loss in 2012.We found that our deep learning model hindcasted the yield variations with good accuracy for 2006-2018(R^(2)=0.81)and well reproduced the extreme yield anomalies in 2012(R^(2)=0.79).Further attribution analysis indicated that extreme heat stress was the major cause for yield loss,contributing to 72.5%of the yield loss,followed by anomalies of vapor pressure deficit(17.6%)and precipitation(10.8%).Our deep learning model was also able to estimate the accumulated impact of climatic factors on maize yield and identify that the silking phase was the most critical stage shaping the yield response to extreme climate stress in 2012.Our results provide a new framework of spatio-temporal deep learning to assess and attribute the crop yield response to climate variations in the data rich era.

基于地理探测器的产水服务归因分析——以横断山区为例

Ecosystem services,which include water yield services,have been incorporated into decision processes of regional land use planning and sustainable development.Spatial pattern characteristics and identification of factors that influence water yield are the basis for decision making.However,there are limited studies on the driving mechanisms that affect the spatial heterogeneity of ecosystem services.In this study,we used the Hengduan Mountain region in southwest China,with obvious spatial heterogeneity,as the research site.The water yield module in the InVEST software was used to simulate the spatial distribution of water yield.Also,quantitative attribution analysis was conducted for various geomorphological and climatic zones in the Hengduan Mountain region by using the geographical detector method.Influencing factors,such as climate,topography,soil,vegetation type,and land use type and pattern,were taken into consideration for this analysis.Four key findings were obtained.First,water yield spatial heterogeneity is influenced most by climate-related factors,where precipitation and evapotranspiration are the dominant factors.Second,the relative importance of each impact factor to the water yield heterogeneity differs significantly by geomorphological and climatic zones.In flat areas,the influence of evapotranspiration is higher than that of precipitation.As relief increases,the importance of precipitation increases and eventually,it becomes the most influential factor.Evapotranspiration is the most influential factor in a plateau climate zone,while in the mid-subtropical zone,precipitation is the main controlling factor.Third,land use type is also an important driving force in flat areas.Thus,more attention should be paid to urbanization and land use planning,which involves land use changes,to mitigate the impact on water yield spatial pattern.The fourth finding was that a risk detector showed that Primarosol and Anthropogenic soil areas,shrub areas,and areas with slope<5°and 250-350 should be recognized as water yield important zones,while the corresponding elevation values are different among different geomorphological and climatic zones.Therefore,the spatial heterogeneity and influencing factors in different zones should be fully con-sidered while planning the maintenance and protection of water yield services in the Hengduan Mountain region.

An extreme rainfall event in summer 2018 of Hami city in eastern Xinjiang, China

Extreme rainfall events are rare in inland arid regions, but have exhibited an increasing trend in recent years, causing many casualties and substantial socioeconomic losses. A series of heavy rains that began on July 31st, 2018, battered the Hami prefecture of eastern Xinjiang, China for four days. These rains sparked devastating floods, caused 20 deaths, eight missing, and the evacuation of about 5500 people. This study examines the extreme rainfall event in a historical context and explores the anthropogenic causes based on analysis of multiple datasets (i.e., the observed daily data, the global climate models (GCMs) from the Coupled Model Intercomparison Project Phase 5 (CMIP5), the NCEP/NCAR Reanalysis 1, and the satellite cloud data) and several statistical techniques. Results show that this extraordinarily heavy rainfall was due mainly to the abnormal weather system (e.g., the abnormal subtropical high) that transported abundant water vapor from the Indian Ocean and the East China Sea crossed the high mountains and formed extreme rainfall in Hami prefecture, causing the reservoir to break and form a flood event with treat loss, which is a typical example of a comprehensive analysis of the extreme rainfall event in summer in Northwest China. Also, the fraction of attributable risk (FAR) value was 1.00 when the 2018 July–August RX1day (11.52 mm) was marked as the threshold, supporting the claim of a significant anthropogenic influence on the risk of this extreme rainfall. The results offer insights into the variability of precipitation extremes in arid areas contributing to better manage water-related disasters.

Contributions of internal climate variability in driving global and ocean temperature variations using multi-layer perceptron neural network

Roles of internal climate variabilities regulating global and ocean temperature changes is a hot but complex issue of scientific concern,infuencing the comprehensive policy-making in response to global and regional warming.In this study,the time series of monthly global and ocean mean surface temperature(GST and OST,respectively)since 1866 is successflly reconstructed via natural and anthropogenic forcing factors and internal climate variability by using a Multi-Layer Perceptron(MLP)neural network technique.The MLP demonstrates prominent monthly GST and OST reconstruction skills on both interannual and annual time scales.Most of the warming in GST and OST since 1866 is found to be attributable to anthropogenic forcing,while the multidecadal and interannual GST and OST variations are considerably dominated by Atlantic Multidecadal Oscillation(AMO).Internal climate variabilities like Interdecadal Pacific Oscillation(IPO)can amplify the GST and OST changes and explain the global warming slowdown since 1998.Southern Oscillation Index(SOI)performs a similar role as IPO but to a lesser extent.Changes in OST caused by solar forcing are more considerable than those in GST.Moreover,the"biased warmth"during the Second World War is successfully reconstructed in MLP.AMO and IPO can explain most annual and even sub-annual temperature variations during this period,offering an explanation for the existence of this abnormal warm period other than that it was entirely caused by instrumental errors.The generally high accuracy of reconstructions on interannual and annual time scales can enhance the ability to monitor the prompt feedback of specific external radiative forcings and internal variabilities to changes in climate.

Regression model for estimation of attribute reliability

In this paper,a regression model is developed to estimate attribute reliability in the evidential reasoning(ER)context.By analysing the difference between attribute weight and attribute reliability,a general qualitative definition of attribute reliability is provided.The reliability of an attribute is quantitatively measured in consistence with the qualitative definition in the context of the ER approach.A regression model is then constructed to generate attribute reliabilities by minimising the maximum differences between the real value of attribute reliability and its estimation.Within the post-optimal solution space of attribute reliabilities,an optimisation model is constructed to determine the expected utilities of each alternative in order to generate solutions to multiple attribute decision analysis problems.Asale place selection problem in Qingyang County of Chizhou in Anhui province of China is analysed using the proposed regression model to demonstrate its detailed implementation process,validity and applicability.