Migration and role of zinc in biogeochemical cycles in the Antarctic Ice Sheet and the Southern Ocean

Zinc(Zn),a widespread metal in the Earth’s crust,serves as a crucial nutrient in the Southern Ocean’s primary production.Studies on Zn in Antarctic snow and ice offer insights into the origins of this metal and its transport routes,as well as its impact on the biogeochemical processes within the Antarctic atmosphere–land–ocean system.This review examines research on the spatial and temporal distribution of Zn in Antarctic snow and ice,as well as in Southern Ocean waters.It includes an overview of advanced methods for sampling and analyzing Zn,along with explanations for the observed variations.The review also discusses various sources of Zn as a nutrient to the Southern Ocean.Finally,it addresses prospective issues related to the use of Zn isotopes in identifying atmospheric sources and their biogeochemical effects on the development of the Southern Ocean ecosystem.

Bias Analysis in the Simulation of the Western North Pacific Tropical Cyclone Characteristics by Two High-Resolution Global Atmospheric Models

This study compares the atmosphere-only HighResMIP simulations from FGOALS-f3-H(FGOALS)and MRIAGCM3-2-S(MRI)with respect to tropical cyclone(TC)characteristics over the Western North Pacific(WNP)for the July-October months of 1985-2014.The focus is on investigating the role of the tropical easterly jet over the Western Pacific(WP_TEJ)in modulating the simulation biases in terms of their climatological distribution and interannual variability of WNP TC genesis frequency(TCGF)based on the analysis of the genesis potential index(GPI).Results show that the two models reasonably capture the main TC genesis location,the maximum center of frequency,and track density;however,their biases mainly lie in simulating the intense TCs and TCGF distributions.The MRI better simulates the windpressure relationship(WPR)but overestimates the proportion of super typhoons(SSTYs).At the same time,FGOALS underestimates the WPR and the proportion of SSTYs but better simulates the total WNP TC precipitation.In particular,FGOALS overestimates the TCGF in the northeastern WNP,which is strongly tied to an overestimated WP_TEJ and the enhanced vertical circulation to the north of its entrance region.In contrast,the MRI simulates a weaker WP_TEJ and vertical circulation,leading to a negative TCGF bias in most of the WNP.Both models exhibit comparable capability in simulating the interannual variability of WP_TEJ intensity,but the composite difference of large-scale atmospheric factors between strong and weak WP_TEJ years is overestimated,resulting in larger interannual anomalies of WNP TCGF,especially for FGOALS.Therefore,accurate simulations of the WP_TEJ and the associated oceanic and atmospheric factors are crucial to further improving WNP TC simulations for both models.

Climate Change and Its Effects on Runoff of Kaidu River, Xinjiang, China:A Multiple Time-scale Analysis

This paper applied an integrated method combining grey relation analysis, wavelet analysis and statistical analysis to study climate change and its effects on runoff of the Kaidu River at multi-time scales. Major findings are as follows: 1) Climatic factors were ranked in the order of importance to annual runoff as average annual temperature, average temperature in autumn, average temperature in winter, annual precipitation, precipitation in flood season, average temperature in summer, and average temperature in spring. The average annual temperature and annual precipi- tation were selected as the two representative factors that impact the annual runoff. 2) From the 32-year time scale, the annual runoff and the average annual temperature presented a significantly rising trend, whereas the annual precipita- tion showed little increase over the period of 1957-2002. By changing the time scale from 32-year to 4-year, we ob- served nonlinear trends with increasingly obvious oscillations for annual runoff, average annual temperature, and annual precipitation. 3) The changes of the runoff and the regional climate are closely related, indicating that the runoff change is the result of the regional climate changes. With time scales ranging from 32-year, 16-year, 8-year and to 4-year, there are highly significant linear correlations between the annual runoff and the average annual temperature and the annual precipitation.

Estimation of As and Cu Contamination in Agricultural Soils Around a Mining Area by Reflectance Spectroscopy:A Case Study

Concentrations of Iron (Fe), As, and Cu in soil samples from the fields near the Baoshan Mine in Hunan Province, China, were analyzed and soil spectral reflectance was measured with an ASD FieldSpec FR spectroradiometer (Analytical Spectral Devices, Inc., USA) under laboratory condition. Partial least square regression (PLSR) models were constructed for predicting soil metal concentrations. The data pre-processing methods, first and second derivatives (FD and SD), baseline correction (BC), standard normal variate (SNV), multiplicative scatter correction (MSC), and continuum removal (CR), were used for the spectral reflectance data pretreatments. Then, the prediction results were evaluated by relative root mean square error (RRMSE) and coefficients of determination (R 2 ). According to the criteria of minimal RRMSE and maximal R 2 , the PLSR models with the FD pretreatment (RRMSE = 0.24, R 2 = 0.61), SNV pretreatment (RRMSE = 0.08, R 2 = 0.78), and BC-pretreatment (RRMSE = 0.20, R 2 = 0.41) were considered as the final models for predicting As, Fe, and Cu, respectively. Wavebands at around 460, 1 400, 1 900, and 2 200 nm were selected as important spectral variables to construct final models. In conclusion, concentrations of heavy metals in contaminated soils could be indirectly assessed by soil spectra according to the correlation between the spectrally featureless components and Fe; therefore, spectral reflectance would be an alternative tool for monitoring soil heavy metals contamination.

Wavelet Analysis and Nonparametric Test for Climate Change in Tarim River Basin of Xinjiang During 1959-2006

Using wavelet analysis,regression analysis and the Mann-Kendall test,this paper analyzed time-series(1959-2006) weather data from 23 meteorological stations in an attempt to characterize the climate change in the Tarim River Basin of Xinjiang Uygur Autonomous Region,China.Major findings are as follows:1) In the 48-year study period,average annual temperature,annual precipitation and average annual relative humidity all presented nonlinear trends.2) At the 16-year time scale,all three climate indices unanimously showed a rather flat before 1964 and a detectable pickup thereafter.At the 8-year time scale,an S-shaped nonlinear and uprising trend was revealed with slight fluctuations in the entire process for all three indices.Incidentally,they all showed similar pattern of a slight increase before 1980 and a noticeable up-swing afterwards.The 4-year time scale provided a highly fluctuating pattern of periodical oscillations and spiral increases.3) Average annual relative humidity presented a negative correlation with average annual temperature and a positive correlation with annual precipitation at each time scale,which revealed a close dynamic relationship among them at the confidence level of 0.001.4) The Mann-Kendall test at the 0.05 confidence level demonstrated that the climate warming trend,as represented by the rising average annual temperature,was remarkable,but the climate wetting trend,as indicated by the rising annual precipitation and average annual relative humidity,was not obvious.

Assessing the Dynamics of Grassland Net Primary Productivity in Response to Climate Change at the Global Scale

Understanding the net primary productivity(NPP) of grassland is crucial to evaluate the terrestrial carbon cycle. In this study, we investigated the spatial distribution and the area of global grassland across the globe. Then, we used the Carnegie-Ames-Stanford Approach(CASA) model to estimate global grassland NPP and explore the spatio-temporal variations of grassland NPP in response to climate change from 1982 to 2008. Results showed that the largest area of grassland distribution during the study period was in Asia(1737.23 × 104 km^2), while the grassland area in Europe was relatively small(202.83 × 10~4 km^2). Temporally, the total NPP increased with fluctuations from 1982 to 2008, with an annual increase rate of 0.03 Pg C/yr. The total NPP experienced a significant increasing trend from 1982 to 1995, while a decreasing trend was observed from 1996 to 2008. Spatially, the grassland NPP in South America and Africa were higher than the other regions, largely as a result of these regions are under warm and wet climatic conditions. The highest mean NPP was recorded for savannas(560.10 g C/(m^2·yr)), whereas the lowest was observed in open shrublands with an average NPP of 162.53 g C/(m^2·yr). The relationship between grassland NPP and annual mean temperature and annual precipitation(AMT, AP, respectively) varies with changes in AP, which indicates that, grassland NPP is more sensitive to precipitation than temperature.

Long-term Trend and Fractal of Annual Runoff Process in Mainstream of Tarim River

Based on the time series data from the Aral hydrological station for the period of 1958-2005,the paper re-veals the long-term trend and fractal of the annual runoff process in the mainstream of the Tarim River by using the wavelet analysis method and the fractal theory.The main conclusions are as follows:1)From a large time scale point of view,i.e.the time scale of 16(24)years,the annual runoff basically shows a slightly decreasing trend as a whole from 1958 to 2005.If the time scale is reduced to 8(23)or 4(22)years,the annual runoff still displays the basic trend as the large time scale,but it has fluctuated more obviously during the period.2)The correlation dimension for the annual runoff process is 3.4307,non-integral,which indicates that the process has both fractal and chaotic characteris-tics.The correlation dimension is above 3,which means that at least four independent variables are needed to describe the dynamics of the annual runoff process.3)The Hurst exponent for the first period(1958-1973)is 0.5036,which equals 0.5 approximately and indicates that the annual runoff process is in chaos.The Hurst exponents for the second(1974-1989)and third(1990-2005)periods are both greater than 0.50,which indicate that the annual runoff process showed a long-enduring characteristic in the two periods.The Hurst exponent for the period from 1990 to 2005 indi-cates that the annual runoff will show a slightly increasing trend in the 16 years after 2005.

Enhancement of High Energy Electron Fluxes and Variation of Atmospheric Electric Field in the Antarctic Region

High-energy electron precipitation in the high latitude regions enhances the ionization of the atmosphere,and subsequently increases the atmospheric conductivities and the vertical electric field of the atmosphere near the ground as well.The High-Energy Electron Flux(HEEF) data measured by the Fengyun-3 meteorological satellite are analyzed together with the data of nearsurface atmospheric vertical electric field measured at the Russian Vostok Station.Three HEEF enhancements are identified and it is shown that when the HEEF increases to a certain level,the local atmospheric vertical electric field near the ground can increase substantially than usual.The response time of the electric field to HEEF enhancement is about 3.7 to 4 days.

Spatial and temporal change patterns of net primary productivity and its response to climate change in the Qinghai–Tibet Plateau of China from 2000 to 2015

The vegetation ecosystem of the Qinghai–Tibet Plateau in China,considered to be the′′natural laboratory′′of climate change in the world,has undergone profound changes under the stress of global change.Herein,we analyzed and discussed the spatial-temporal change patterns and the driving mechanisms of net primary productivity(NPP)in the Qinghai–Tibet Plateau from 2000 to 2015 based on the gravity center and correlation coefficient models.Subsequently,we quantitatively distinguished the relative effects of climate change(such as precipitation,temperature and evapotranspiration)and human activities(such as grazing and ecological construction)on the NPP changes using scenario analysis and Miami model based on the MOD17A3 and meteorological data.The average annual NPP in the Qinghai–Tibet Plateau showed a decreasing trend from the southeast to the northwest during 2000–2015.With respect to the inter-annual changes,the average annual NPP exhibited a fluctuating upward trend from 2000 to 2015,with a steep increase observed in 2005 and a high fluctuation observed from 2005 to 2015.In the Qinghai–Tibet Plateau,the regions with the increase in NPP(change rate higher than 10%)were mainly concentrated in the Three-River Source Region,the northern Hengduan Mountains,the middle and lower reaches of the Yarlung Zangbo River,and the eastern parts of the North Tibet Plateau,whereas the regions with the decrease in NPP(change rate lower than–10%)were mainly concentrated in the upper reaches of the Yarlung Zangbo River and the Ali Plateau.The gravity center of NPP in the Qinghai–Tibet Plateau has moved southwestward during 2000–2015,indicating that the increment and growth rate of NPP in the southwestern part is greater than those of NPP in the northeastern part.Further,a significant correlation was observed between NPP and climate factors in the Qinghai–Tibet Plateau.The regions exhibiting a significant correlation between NPP and precipitation were mainly located in the central and eastern Qinghai–Tibet Plateau,and the regions exhibiting a significant correlation between NPP and temperature were mainly located in the southern and eastern Qinghai–Tibet Plateau.Furthermore,the relative effects of climate change and human activities on the NPP changes in the Qinghai–Tibet Plateau exhibited significant spatial differences in three types of zones,i.e.,the climate change-dominant zone,the human activity-dominant zone,and the climate change and human activity interaction zone.These research results can provide theoretical and methodological supports to reveal the driving mechanisms of the regional ecosystems to the global change in the Qinghai–Tibet Plateau.

Characterization of Air Pollution in Urban Areas of Yangtze River Delta,China

The hallmark of development in the Yangtze River Delta(YRD) of East China has been sprawling urbanization. However, air pollution is a significant problem in these urban areas. In this paper, we investigated and analyzed the air pollution index(API) in four cities(Shanghai, Nanjing, Hangzhou and Ningbo) in the YRD from 2001 to 2012. We attempted to empirically examine the relationship between meteorological factors and air quality in the urban areas of the YRD. According to the monitoring data, the API in Shanghai, Nanjing, Hangzhou slightly declined and that in Ningbo increased over the study period. We analyzed the inter-annual, seasonal, and monthly variations of API, from which we found that the air quality had different temporal changes in the four cities. It was indicated that air quality was poor in winter and spring and best in summer. Furthermore, different weather conditions affected air quality level. The wind direction was considered as an important and influential factor to air pollution, which has an impact on the accumulating or cleaning processes of pollutants. The air quality was influenced by the different wind directions that varied with seasons and cities.

Climate change and Chinese farmers: Perceptions and determinants of adaptive strategies

Farmers’ perceptions, beliefs, adaptive strategies, and barriers regarding climate change are critical to promoting sustainable ecosystems and societal stability. This paper is based on an extensive survey of 1 500 farmers and their households in Henan Province in China during 2013–2014. Henan is the largest agricultural province in China with over 51 million farmers. The survey results showed that approximately 57% of the respondents perceived the direct impact of climate change during the past 10 years, with 70.3% believing that climate change posed a risk to their livelihood. Not surprisingly, most farmers reported that they have adopted new measures to mitigate the negative impacts of climate change. The main barriers hindering farmers’ adopting adaptation measures were lack of funds and timely information. A multinomial logit model revealed that land ownership, knowledge of crop variety and the causes of climate change, as well as the belief of climate change, were all positively related to the likelihood of employing adaptive strategies. Moreover, the percentage of households engaging in agriculture activity, and years of engaging in farming were both negatively correlated with famer’s likelihood of adopting adaptation strategies. More importantly, farmers with high incomes were less likely to adopt adaptive strategies and more willing to engage in other business activities. In conclusion, it is important to communicate climate change related information and government policies in rural areas, promote farmer associations and other educational outreach efforts to assist Chinese farmers to deal with climate change.

Siting of Dark Sky Reserves in China Based on Multi-source Spatial Data and Multiple Criteria Evaluation Method

With the rapid development of population and urbanization and the progress of lighting technology, the influence of artificial light sources has increased.In this context, the problem of light pollution has attracted wide attention.Previous studies have revealed that light pollution can affect biological living environments, human physical and mental health, astronomical observations and many other aspects.Therefore, organizations internationally have begun to advocate for measures to prevent light pollution, many of which are recognized by the International Dark-Sky Association(IDA).In addition to improving public awareness, legal protections, technical treatments and other means, the construction of Dark Sky Reserves(DSR) has proven to be an effective preventive measure.So far, as a pioneer practice in this field, the IDA has identified 11 DSRs worldwide.Based on the DA requirements for DSRs, this paper utilizes NPP-VIIRS nighttime light data and other multi-source spatial data to analyze possible DSR sites in China.The land of China was divided into more than ten thousand 30 km × 30 km fishnets, and constraint and suitable conditions were designated, respectively, as light and cloud conditions, and scale, traffic and attractiveness conditions.Using a multiple criteria evaluation, 1443 fishnets were finally selected as most suitable sites for the construction of DSRs.Results found that less than 25% of China is not subject to light pollution, and less than 13% is suitable for DSR construction, primarily in western and northern areas, including Tibet, Xinjiang, Qinghai, Gansu and Inner Mongolia.

Influence of surface ozone on crop yield of maize in China

This study investigated the adverse effect of surface ozone on the maize yield using a unique panel from 880 counties in China.To identify the impact of elevated surface ozone concentrations,we constructed an econometric model by controlling the impact of climate variables and related economic variables.This study also considered the potential spatial correlation in the measurement of the impact of surface ozone on maize yield.Results confirmed that the increase of ozone concentration decreased the maize yield.Moreover,maize was found to be the most sensitive to ozone at the end of the second month of the growing season.The average annual loss of maize caused by ozone pollution is about 4.234 million tons in 2013–2015,accounting for 1.9%of the average output.

Analysis of Jiangsu Tidal Flats Reclamation from 1974 to 2012 Using Remote Sensing

Jiangsu has the most abundant tidal flat resources among China＇s coastal provinces. To ease the conflict between the growing population and shrinking usable land and to promote regional sustainable development, large-scale coastal reclamation development activities have been performed in Jiangsu Province since 1949. The present study has integrated multi-source remote sensing images during 1974 to 2012 by using a Multi-point Fast Marching Method （MFMM） to extract the Jiangsu coastal reclamation areas for different time periods. The temporal and spatial patterns of the extent and elevation of the reclamation areas were analysed in order to determine the unused potential for future reclamation. It will provide information necessary to support the development and construction of tidal flats in Jiangsu. Results show that： （1） the reclaimed tidal fiats along the Jiangsu coast cover more than 19.86×10^4 hm^2, of which 13.97×10^4 hm^2 is located in Yancheng, 4.84×10^4 hm^2 in Nantong, and 1.05×10^4 hm^2 in Lianyungang; （2） the average elevation of the reclaimed Jiangsu tidal flats has gradually decreased over the last 40 years, while those in Dongtai and Rudong have showed particularly accelerated decrease since the 1990s; and （3） in 2012, very few unused tidal flats two meters above the sea level are left along the Jiangsu coast, and mainly concentrated in Yancheng. As there are still reserving some coastal land resources between 0-2 m, providing us with a potential for future development. All of these findings may be useful for researchers and local authorities for the development and utilization of tidal flat resources in Jiangsu.

QUANTITATIVE MODELING OF SUSPENDED SEDIMENT IN MIDDLE CHANGJIANG RIVER FROM MODIS

Remote sensing techniques have been widely used to observe bodies of water. Among satellite sensors commonly used for water quality studies, the Moderate-resolution Imaging Spectroradiometer (MODIS) has potential in water quality monitoring, because of its moderate spatial resolution and high temporal resolution. In this paper, the utility of MODIS data for suspended sediment monitoring in the middle Changjiang (Yangtze) River is studied. It is concluded that suspended sediment concentration correlates well with reflectance values (R1-R2)/(R1+R2) retrieved from MODIS 250m image data (R2=0.72, n=41). Based on this correlation, we obtain the empirical model of suspended sediment concentration in the middle Changjiang River from MODIS. It is shown that it is useful for MODIS data to monitor this parameter of water quality.

Multi-step ahead short-term predictions of storm surge level using CNN and LSTM network

Storm surges pose significant danger and havoc to the coastal residents’safety,property,and lives,particularly at offshore locations with shallow water levels.Predictions of storm surges with hours of warning time are important for evacuation measures in low-lying regions and coastal management plans.In addition to experienced predictions and numerical models,artificial intelligence(AI)techniques are also being used widely for short-term storm surge prediction owing to their merits in good level of prediction accuracy and rapid computations.Convolutional neural network(CNN)and long short-term memory(LSTM)are two of the most important models among AI techniques.However,they have been scarcely utilised for surge level(SL)forecasting,and combinations of the two models are even rarer.This study applied CNN and LSTM both individually and in combination towards multi-step ahead short-term storm surge level prediction using observed SL and wind information.The architectures of the CNN,LSTM,and two sequential techniques of combining the models(LSTM–CNN and CNN–LSTM)were constructed via a trial-and-error approach and knowledge obtained from previous studies.As a case study,11 a of hourly observed SL and wind data of the Xiuying Station,Hainan Province,China,were organised as inputs for training to verify the feasibility and superiority of the proposed models.The results show that CNN and LSTM had evident advantages over support vector regression(SVR)and multilayer perceptron(MLP),and the combined models outperformed the individual models(CNN and LSTM),mostly by 4%–6%.However,on comparing the model computed predictions during two severe typhoons that resulted in extreme storm surges,the accuracy was found to improve by over 10%at all forecasting steps.

Channel changes of the Makou-Tianjiazhen reach in the middle Yangtze River during the past 40 years

Quantitative analysis was performed on the filling-scouring process for the river reach within Makou and Tianjiazhen, the middle Yangtze River with the help of GIS and DEM techniques. The research results indicate that the river reach between Makou and Tianjiaz- hen was dominated by the scouring process, and the magnitude of scouring is increasing over time. The intensity of scouring process is more in the deep and narrower river reach than shallower and wider ones. The river reach in the Makou and Tianjiazhen river knot is in frequent scouring and filling process, however the river reach upper to the Makou and lower to the Tianjiazhen river knot is in moderate scouring and filling process. The river reach just upstream or downstream to the river knot （e.g. Makou and Tianjiazhen river knot in this research） is dominated by filling process and the river reach in the river knot is dominated by the scouring process. Research results indicate no changes in the boundary of the river but the scouring and the filling magnitude in specific river channel is strong. The filling and the scouring process of the study river reach is greatly impacted by the sediments and water from the upstream of the study river reach. The construction of the Three Gorges Dam just up- stream to Yichang will cause further decrease of the release of the sediment load to the middle and the lower Yangtze River basin, which will further intensify the scouring process of the river channel in the study river reach.

Comparisons of the effects of different drying methods on soil nitrogen fractions:Insights into emissions of reactive nitrogen gases(HONO and NO)

Reactive nitrogen(Nr)emission from soils,e.g.,nitrous acid(HONO)and nitric oxide(NO),is a key process of the global nitrogen(N)cycle and has significant implications for atmospheric chemistry.To understand the underlying mechanisms of soil Nr emissions,air-dried or oven-dried soils are commonly used in the laboratory.To date,few studies have compared the effects of different drying methods on soil Nr gas fluxes and N fractions.Here,the authors studied soil water content,pH,(in)organic N content,and Nr gas fluxes of air-dried,freeze-dried,oven-dried,and fresh soils from different land-use types.The results showed that the soil pH of air-dried and oven-dried samples was significantly lower compared with fresh soil from farmland and grassland,but higher compared with forest soil.The difference in soil pH between freeze-dried and fresh soil(mean±standard deviation:0.52±0.31)was the lowest.In general,all drying methods increased the soil NH4+-N,NO3−-N,and dissolved organic N contents compared with fresh soil(P<0.05).The maximum HONO and NO flux and total emissions during a full wetting–drying cycle of fresh soil were also increased by air-drying and oven-drying(P<0.001),but comparable with freeze-dried soil(P>0.2).In conclusion,all drying methods should be considered for use in studies on the land–atmosphere interface and biogeochemical N cycling,whereas the freeze-drying method might be better for studies involving the measurement of soil Nr gas fluxes.

A Modified Self-adaptive Method for Mapping Annual 30-m Land Use/Land Cover Using Google Earth Engine:A Case Study of Yangtze River Delta

Annual Land Use/Land Cover(LULC)change information at medium spatial resolution(i.e.,at 30 m)is used in applications ranging from land management to achieving sustainable development goals related to food security.However,obtaining annual LULC information over large areas and long periods is challenging due to limitations on computational capabilities,training data,and workflow design.Using the Google Earth Engine(GEE),which provides a catalog of multi-source data and a cloud-based environment,we developed a novel methodology to generate a high accuracy 30-m LULC cover map collection of the Yangtze River Delta by integrating free and public LULC products with Landsat imagery.Our major contribution is a hybrid approach that includes three major components:1)a high-quality training dataset derived from multi-source LULC products,filtered by k-means clustering analysis;2)a yearly 39-band stack feature space,utilizing all available Landsat data and DEM data;and 3)a self-adaptive Random Forest(RF)method,introduced for LULC classification.Experimental results show that our proposed workflow achieves an average classification accuracy of 86.33%in the entire Delta.The results demonstrate the great potential of integrating multi-source LULC products for producing LULC maps of increased reliability.In addition,as the proposed workflow is based on open source data and the GEE cloud platform,it can be used anywhere by anyone in the world.

A novel index to evaluate discretization methods: A case study of flood susceptibility assessment based on random forest

The selection of a suitable discretization method(DM) to discretize spatially continuous variables(SCVs)is critical in ML-based natural hazard susceptibility assessment. However, few studies start to consider the influence due to the selected DMs and how to efficiently select a suitable DM for each SCV. These issues were well addressed in this study. The information loss rate(ILR), an index based on the information entropy, seems can be used to select optimal DM for each SCV. However, the ILR fails to show the actual influence of discretization because such index only considers the total amount of information of the discretized variables departing from the original SCV. Facing this issue, we propose an index, information change rate(ICR), that focuses on the changed amount of information due to the discretization based on each cell, enabling the identification of the optimal DM. We develop a case study with Random Forest(training/testing ratio of 7 : 3) to assess flood susceptibility in Wanan County, China.The area under the curve-based and susceptibility maps-based approaches were presented to compare the ILR and ICR. The results show the ICR-based optimal DMs are more rational than the ILR-based ones in both cases. Moreover, we observed the ILR values are unnaturally small(<1%), whereas the ICR values are obviously more in line with general recognition(usually 10%–30%). The above results all demonstrate the superiority of the ICR. We consider this study fills up the existing research gaps, improving the MLbased natural hazard susceptibility assessments.