Effectiveness of hybrid ensemble machine learning models for landslide susceptibility analysis:Evidence from Shimla district of North-west Indian Himalayan region

The Indian Himalayan region is frequently experiencing climate change-induced landslides.Thus,landslide susceptibility assessment assumes greater significance for lessening the impact of a landslide hazard.This paper makes an attempt to assess landslide susceptibility in Shimla district of the northwest Indian Himalayan region.It examined the effectiveness of random forest(RF),multilayer perceptron(MLP),sequential minimal optimization regression(SMOreg)and bagging ensemble(B-RF,BSMOreg,B-MLP)models.A landslide inventory map comprising 1052 locations of past landslide occurrences was classified into training(70%)and testing(30%)datasets.The site-specific influencing factors were selected by employing a multicollinearity test.The relationship between past landslide occurrences and influencing factors was established using the frequency ratio method.The effectiveness of machine learning models was verified through performance assessors.The landslide susceptibility maps were validated by the area under the receiver operating characteristic curves(ROC-AUC),accuracy,precision,recall and F1-score.The key performance metrics and map validation demonstrated that the BRF model(correlation coefficient:0.988,mean absolute error:0.010,root mean square error:0.058,relative absolute error:2.964,ROC-AUC:0.947,accuracy:0.778,precision:0.819,recall:0.917 and F-1 score:0.865)outperformed the single classifiers and other bagging ensemble models for landslide susceptibility.The results show that the largest area was found under the very high susceptibility zone(33.87%),followed by the low(27.30%),high(20.68%)and moderate(18.16%)susceptibility zones.The factors,namely average annual rainfall,slope,lithology,soil texture and earthquake magnitude have been identified as the influencing factors for very high landslide susceptibility.Soil texture,lineament density and elevation have been attributed to high and moderate susceptibility.Thus,the study calls for devising suitable landslide mitigation measures in the study area.Structural measures,an immediate response system,community participation and coordination among stakeholders may help lessen the detrimental impact of landslides.The findings from this study could aid decision-makers in mitigating future catastrophes and devising suitable strategies in other geographical regions with similar geological characteristics.

Runoff Modeling in Ungauged Catchments Using Machine Learning Algorithm-Based Model Parameters Regionalization Methodology

Model parameters estimation is a pivotal issue for runoff modeling in ungauged catchments.The nonlinear relationship between model parameters and catchment descriptors is a major obstacle for parameter regionalization,which is the most widely used approach.Runoff modeling was studied in 38 catchments located in the Yellow–Huai–Hai River Basin(YHHRB).The values of the Nash–Sutcliffe efficiency coefficient(NSE),coefficient of determination(R2),and percent bias(PBIAS)indicated the acceptable performance of the soil and water assessment tool(SWAT)model in the YHHRB.Nine descriptors belonging to the categories of climate,soil,vegetation,and topography were used to express the catchment characteristics related to the hydrological processes.The quantitative relationships between the parameters of the SWAT model and the catchment descriptors were analyzed by six regression-based models,including linear regression(LR)equations,support vector regression(SVR),random forest(RF),k-nearest neighbor(kNN),decision tree(DT),and radial basis function(RBF).Each of the 38 catchments was assumed to be an ungauged catchment in turn.Then,the parameters in each target catchment were estimated by the constructed regression models based on the remaining 37 donor catchments.Furthermore,the similaritybased regionalization scheme was used for comparison with the regression-based approach.The results indicated that the runoff with the highest accuracy was modeled by the SVR-based scheme in ungauged catchments.Compared with the traditional LR-based approach,the accuracy of the runoff modeling in ungauged catchments was improved by the machine learning algorithms because of the outstanding capability to deal with nonlinear relationships.The performances of different approaches were similar in humid regions,while the advantages of the machine learning techniques were more evident in arid regions.When the study area contained nested catchments,the best result was calculated with the similarity-based parameter regionalization scheme because of the high catchment density and short spatial distance.The new findings could improve flood forecasting and water resources planning in regions that lack observed data.

Study of the geomagnetic field's regional gradients in Chinese continent using three-dimensional surface Spline model

We combined domestic ground-based and satellite magnetic measurements to create a regional three-dimensional surface Spline(3DSS)gradient model of the main geomagnetic field over the Chinese continent.To improve the precision of the model,we considered the data gap between the ground and satellite data.We compared and analyzed the results of the Taylor polynomial,surface Spline,and CHAOS-6(the CHAMP,?rsted and SAC-C model of Earth’s magnetic field)gradient models.Results showed that the gradients in the south-north and east-west directions of the four models were consistent.The 3DSS model was able to express not only gradients at different altitudes,but also average gradients inside the research area.The two Spline models were able to capture more information on gradient anomalies than were the fitted models.Strong local anomalies were observed in northern Xinjiang,Beijing,and the junction area between Jiangsu and Zhejiang,and the total intensity F decreased whereas the altitude increased.The gradient decreased by 21.69%in the south-north direction and increased by 11.78%in the east-west direction.In addition,the altitude gradient turned from negative to positive while the altitude increased.The Spline model and the two fitted models differed mainly in the field sources they expressed and the modeling theory.

A Hybrid Regional Model for Predicting Ground Deformation Induced by Large-Section Tunnel Excavation

Due to the large number of finite element mesh generated,it is difficult to use full-scale model to simulate largesection underground engineering,especially considering the coupling effect.A regional model is attempted to achieve this simulation.A variable boundary condition method for hybrid regional model is proposed to realize the numerical simulation of large-section tunnel construction.Accordingly,the balance of initial ground stress under asymmetric boundary conditions achieves by applying boundary conditions step by step with secondary development ofDynaflowscripts,which is the key issue of variable boundary conditionmethod implementation.In this paper,Gongbei tunnel based on hybrid regional model involvingmulti-field coupling is simulated.Meanwhile,the variable boundary condition method for regional model is verified against model initialization and the ground deformation due to tunnel excavation is predicted via the proposed hybrid regional model.Compared with the monitoring data of actual engineering,the results indicated that the hybrid regional model has a good prediction effect.

Fusion of Region Extraction and Cross-Entropy SVM Models for Wheat Rust Diseases Classification

Wheat rust diseases are one of the major types of fungal diseases that cause substantial yield quality losses of 15%–20%every year.The wheat rust diseases are identified either through experienced evaluators or computerassisted techniques.The experienced evaluators take time to identify the disease which is highly laborious and too costly.If wheat rust diseases are predicted at the development stages,then fungicides are sprayed earlier which helps to increase wheat yield quality.To solve the experienced evaluator issues,a combined region extraction and cross-entropy support vector machine(CE-SVM)model is proposed for wheat rust disease identification.In the proposed system,a total of 2300 secondary source images were augmented through flipping,cropping,and rotation techniques.The augmented images are preprocessed by histogram equalization.As a result,preprocessed images have been applied to region extraction convolutional neural networks(RCNN);Fast-RCNN,Faster-RCNN,and Mask-RCNN models for wheat plant patch extraction.Different layers of region extraction models construct a feature vector that is later passed to the CE-SVM model.As a result,the Gaussian kernel function in CE-SVM achieves high F1-score(88.43%)and accuracy(93.60%)for wheat stripe rust disease classification.

Zoning Evaluation of Hourly Precipitation in High-resolution Regional Numerical Models over Hainan Island

This study assesses the performance of three high-resolution regional numerical models in predicting hourly rainfall over Hainan Island from April to October for the years from 2020 to 2022.The rainfall amount,frequency,intensity,duration,and diurnal cycle are examined through zoning evaluation.The results show that the China Meteor-ological Administration Guangdong Rapid Update Assimilation Numerical Forecast System(CMA-GD)tends to forecast a higher occurrence of light precipitation.It underestimates the late afternoon precipitation and the occurrence of short-duration events.The China Meteorological Administration Shanghai Numerical Forecast Model System(CMA-SH9)reproduces excessive precipitation at a higher frequency and intensity throughout the island.It overestimates rainfall during the late afternoon and midnight periods.The simulated most frequent peak times of rainfall in CMA-SH9 are 0-1 hour deviations from the observed data.The China Meteorological Administration Mesoscale Weather Numerical Forecasting System(CMA-MESO)displays a similar pattern to rainfall observations but fails to replicate reasonable structure and diurnal variation of frequency-intensity.It underestimates the occurrence of long-duration events and overestimates related rainfall amounts from midnight to early morning.Notably,significant discrepancies are observed in the predictions of the three models for areas with complex terrain,such as the central,southeastern,and southwestern regions of Hainan Island.

3D Model Construction and Ecological Environment Investigation on a Regional Scale Using UAV Remote Sensing

The acquisition of digital regional-scale information and ecological environmental data has high requirements for structural texture,spatial res-olution,and multiple parameter categories,which is challenging to achieve using satellite remote sensing.Considering the convenient,facilitative,and flexible characteristics of UAV(unmanned air vehicle)remote sensing tech-nology,this study selects a campus as a typical research area and uses the Pegasus D2000 equipped with a D-MSPC2000 multi-spectral camera and a CAM3000 aerial camera to acquire oblique images and multi-spectral data.Using professional software,including Context Capture,ENVI,and ArcGIS,a 3D(three-dimensional)campus model,a digital orthophoto map,and multi-spectral remote sensing map drawing are realized,and the geometric accuracy of typical feature selection is evaluated.Based on a quantitative remote sensing model,the campus ecological environment assessment is performed from the perspectives of vegetation and water body.The results presented in this study could be of great significance to the scientific management and sustainable development of regional natural resources.

Projected Regional 1.50℃and 2.00℃Warming Threshold-crossing Time Worldwide Using the CMIP6 Models

The Paris Agreement aims to limit global warming to well below 2.00℃and pursue efforts to limit the temperature increase to 1.50℃.However,the response of climate change to unbalanced global warming is affected by spatial and temporal sensitivities.To better understand the regional warming response to global warming at 1.50℃and 2.00℃,we detected the 1.50℃and 2.00℃warming threshold-crossing time(WTT)above pre-industrial levels globally using the Coupled Model Intercomparison Project phase 6(CMIP6)models.Our findings indicate that the 1.50℃or 2.00℃WTT differs substantially worldwide.The warming rate of land would be approximately 1.35–1.46 times that of the ocean between 60°N–60°S in 2015–2100.Consequently,the land would experience a 1.50℃(2.00℃)warming at least 10–20 yr earlier than the time when the global mean near-surface air temperature reaches 1.50℃(2.00℃)WTT.Meanwhile,the Southern Ocean between 0°and 60°S considerably slows down the global 1.50℃and 2.00℃WTT.In 2040–2060,over 98.70%(77.50%),99.70%(89.30%),99.80%(93.40%),and 100.00%(98.00%)of the land will have warmed by over 1.50℃(2.00℃)under SSP(Shared Socioeconomic Pathway)1–2.6,SSP2-4.5,SSP3-7.0,and SSP5-8.5,respectively.We conclude that regional 1.50℃(2.00℃)WTT should be fully considered,especially in vulnerable high-latitude and high-altitude regions.

Regional Finite-Fault Source Model for Development of Ground Motion Attenuation Relationship in Sichuan, China

The attenuation relationship of ground motion based on seismology has always been a front subject of engineering earthquake.Among them,the regional finite-fault source model is very important.In view of this point,the general characteristics of regional seism-tectonics,including the dip and depth of the fault plane,are emphasized.According to the statistics of regional seism-tectonics and focal mechanisms in Sichuan,China,and the sensitivity of estimated peak ground acceleration(PGA)attenuation is analyzed,and the dip angle is taken as an average of 70°.Based the statistics of the upper crustal structure and the focal depth of regional earthquakes,the bottom boundary of the sedimentary cover can be used as the upper limit for estimating the depth of upper-edge.The analysis shows that this value is sensitive to PGA.Based on the analysis of geometric relations,the corresponding calculation formula is used,and a set of concepts and steps for building the regional finite-fault source model is proposed.The estimation of source parameters takes into account the uncertainty,the geometric relationship among parameters and the total energy conservation.Meanwhile,a set of reasonable models is developed,which lay a foundation for the further study of regional ground motion attenuation based on seismology.

Identification of thermal front dynamics in the northern Malacca Strait using ROMS 3D-model

The thermal front in the oceanic system is believed to have a significant effect on biological activity.During an era of climate change,changes in heat regulation between the atmosphere and oceanic interior can alter the characteristics of this important feature.Using the simulation results of the 3D Regional Ocean Modelling System(ROMS),we identified the location of thermal fronts and determined their dynamic variability in the area between the southern Andaman Sea and northern Malacca Strait.The Single Image Edge Detection(SIED)algorithm was used to detect the thermal front from model-derived temperature.Results show that a thermal front occurred every year from 2002 to 2012 with the temperature gradient at the location of the front was 0.3°C/km.Compared to the years affected by El Ni?o and negative Indian Ocean Dipole(IOD),the normal years(e.g.,May 2003)show the presence of the thermal front at every selected depth(10,25,50,and 75 m),whereas El Ni?o and negative IOD during 2010 show the presence of the thermal front only at depth of 75 m due to greater warming,leading to the thermocline deepening and enhanced stratification.During May 2003,the thermal front was separated by cooler SST in the southern Andaman Sea and warmer SST in the northern Malacca Strait.The higher SST in the northern Malacca Strait was believed due to the besieged Malacca Strait,which trapped the heat and make it difficult to release while higher chlorophyll a in Malacca Strait is due to the freshwater conduit from nearby rivers(Klang,Langat,Perak,and Selangor).Furthermore,compared to the southern Andaman Sea,the chlorophyll a in the northern Malacca Strait is easier to reach the surface area due to the shallower thermocline,which allows nutrients in the area to reach the surface faster.

Factors affecting land dissection density in geomorphological regions of China

Land dissection density(LDD)provides morphological evidence regarding prior intense soil erosion and quantifies the distribution of land dissections.A comprehensive understanding of the potential factors influencing the spatial pattern and value of the LDD is vital in geological disasters,soil erosion,and other related domains.Land dissection phenomena in China affects large areas with different morphological,pedological,and climatic characteristics.Prior studies have focused on the potential factors influencing the LDD at a watershed scale.However,these results are insufficient to reflect the status quo of dissection development and its primary influencing factors on a national scale.LDD’s spatial patterns and the dominant factors at a regional scale in millions of square kilometers remain to be ascertained.This study used the geomorphon-based method and the geographical detector model to quantify the spatial pattern of LDD over China and identify the dominant factors affecting this pattern in China’s six first-order geomorphological regions(GR1~GR6).The results yield the following findings:(1)LDD in China ranges from 0~4.55 km/km^(2),which is larger in central and eastern regions than in other regions of China;(2)dominant factors and their dominant risk subcategories vary with each geomorphological region’s primary internal and external forces;(3)the influence of natural factors is more significant on the large regional scale in millions of square kilometers compared to anthropogenic factors;relief degree of land surface(RDLS)is dominant in GR1,GR2,and GR5;the slope is dominant in GR6,soil type is dominant in GR3 and GR4,and lithology plays a critical role in the dominant interactions of GR3,GR4,and GR6;(4)the interactions between factors on LDD’s spatial pattern have a more significant effect than individual factors.

Research on the Digital Economy's Impact on China's Economic Growth:Based on the Variations in Urbanization Levels Across China's Eastern,Central,and Western Regions and Sectoral Heterogeneity

This paper uses inter-provincial panel data from 2011 to 2017,a linear regression model,and a threshold model to conduct empirical analyses of the impact of the digital economy on China's overall economic growth and the three main sectors of industry.The paper then investigates the impact and effects the digital economy has had on the economic growth of the three main sectors of industry in China's eastern,central,and western regions.Finally,the paper investigates the most significant differences among the various regions and the threshold effects of urbanization levels on the relationship between the digital economy and economic growth.The findings indicate a significantly positive correlation between the digital economy and regional economic growth.Moreover,geographical factors notably influence this correlation.The digital economy exerts a positive effect on all sectors of industry.It may not substantially impact industrial development in regions with highly developed infrastructure.Regarding the other regions,the digital economy exhibits varying degrees of impact due to the differences in the specific indicators.The conclusion drawn by the threshold model is that the magnitude of the threshold effect correlates with geographic factors.No threshold effect was observed in the eastern region,while the threshold effect occurred in the central region when the urbanization levels for the provinces were below 0.6645.Similarly,the threshold effect was noted in the western region when the urbanization level was below 0.3931.Considering all of this,the study also offers policy recommendations that will help balance the regional development of digital economies,accelerate the digital transformation of traditional industries,enhance digital infrastructure construction,refine the formulation and implementation of data policy,and establish relevant incentive mechanisms.

Influencing Factors and Prediction of Risk of Returning to Ecological Poverty in Liupan Mountain Region,China

China has resolved its overall regional poverty in 2020 by attaining moderate societal prosperity.The country has entered a new development stage designed to achieve its second centenary goal.However,ecological fragility and risk susceptibility have increased the risk of returning to ecological poverty.In this paper,the Liupan Mountain Region of China was used as a case study,and the counties were used as the scale to reveal the spatiotempora differentiation and influcing factors of the risk of returning to poverty in study area.The indicator data for returning to ecological poverty from 2011-2020 were collected and summarized in three dimensions:ecological,economic and social.The autoregressive integrated moving average model(ARIMA)time series and exponential smoothing method(ES)were used to predict the multidimensional indicators of returning to ecological poverty for 61 counties(districts)in the Liupan Mountain Region for 2021-2030.The back propagation neural network(BPNN)and geographic information system(GIS)were used to generate the spatial distribution and time variation for the index of the risk of returning to ecological poverty(RREP index).The results show that 1)ecological factors were the main factors in the risk of returning to ecological poverty in Liupan Mountain Region.2)The RREP index for the 61 counties(districts)exhibited a downward trend from 2021-2030.The RREP index declined more in medium-and high-risk areas than in low-risk areas.From 2021 to 2025,the RREP index exhibited a slight downward trend.From 2026 to2030,the RREP index was expected to decline faster,especially from 2029-2030.3)Based on the RREP index,it can be roughly divided into three types,namely,the high-risk areas,the medium-risk areas,and the low-risk areas.The natural resource conditions in lowrisk areas of returning to ecological poverty,were better than those in medium-and high-risk areas.

A synthetic differencein-differences model for the influence of CR Express on Chongqing's economy

Purpose-The China-Europe Railway Express(CR Express)in Chongqing has operated regularly and undergone large-scale development.Its impact on Chongqing's economic growth has become increasingly evident,necessitating further research in this field.Design/methodology/approach-This study employs the opening of CR Express as a quasi-natural experiment,designating Chongqing,which inaugurated the CR Express in 2011,as the treatment group.13 provinces and cities that had not yet opened the CR Express until 2017 were selected as the control group.Utilizing panel data from 14 provinces across China spanning from 2006 to 2017,the synthetic control method(SCM)is employed to synthetically construct Chongqing.To quantify the difference in economic development levels between Chongqing with the operation of the CR express and Chongqing without its operation.Key metrics such as gross domestic product(GDP),per capita GDP,total retail sales of consumer goods,import and export value and the proportions of the secondary and tertiary industries are employed to measure urban economic development capabilities.Chongqing is designated as the experimental group,and a double-difference model is constructed to regress the operation of the CR Express against economic development capabilities.Robustness tests are conducted to validate the analytical results.Findings-The results indicate that,compared to provinces without the operation of the CR Express,the initiation of the CR Express in Chongqing significantly enhances the economic development level of the city.The opening of the CR Express exhibits a pronounced positive impact on Chongqing's economic development,and these findings remain robust and effective even after parallel trend tests and placebo tests.Originalitylvalue-The study represents an expansion of the theoretical framework.In contrast to previous studies that relied on a single indicator such as GDP,this study selects six indicators from the dimensions of economy,trade and industry to measure regional economic development capabilities.Furthermore,employing the grey relational analysis method,the study screens these indicators,thereby providing a theoretical basis for the selection of indicators for measuring regional economic development capabilities.

What are the underlying causes and dynamics of land use conflicts in metropolitan junction areas?A case study of the central Chengdu–Chongqing region in China

Land use conflicts(LUCs),as a spatial manifestation of the conflicts in the human-land relationships,have a profound impact on regional sustainable development.For China’s metropolitan junction areas(MJAs),the existence of“administrative district economies”has made the issue of LUCs more prominent.Based on a case study of the central Chengdu–Chongqing region,we conducted an exploratory spatial data analysis of the evolutionary process of regional LUCs.Furthermore,structural equation modeling was utilized to analyze the dynamic mechanism of LUCs in MJAs,with a particular emphasis on exploring the influences of administrative boundary.The results showed that from 2010 to 2020,LUCs in the central Chengdu–Chongqing region continued to worsen,and the spatial process conflict and spatial structure conflict indices increased by more than 30.0%.The intensification of LUCs in the central Chengdu–Chongqing region from 2010 to 2020 was mainly the result of the deterioration of conflicts in evaluation units with low conflict levels.LUCs in China’s metropolitan areas generally presented a circular gradient distribution,weakening from the core to the periphery,but there were some strong isolated conflict zones in the outer regions.LUCs in China’s MJAs were the result of interactions among multiple factors,e.g.,natural environment,socio-economic development,policy and institutional processes,and administrative boundary effects.Administrative boundary affected the flow of socio-economic elements,changing the supply-and-demand competition of stakeholders for land resources,consequently exerting an indirect influence on LUCs.This study advances the theory of the dynamic mechanism of LUCs,and provides theoretical support for the governance of these conflicts in transboundary areas.

Spatio-temporal variation of depth to groundwater level and its driving factors in arid and semi-arid regions of India

Climate change and increasing anthropogenic activities,such as over-exploitation of groundwater,are exerting unavoidable stress on groundwater resources.This study investigated the spatio-temporal variation of depth to groundwater level(DGWL)and the impacts of climatic(precipitation,maximum temperature,and minimum temperature)and anthropogenic(gross district product(GDP),population,and net irrigated area(NIA))variables on DGWL during 1994-2020.The study considered DGWL in 113 observation wells and piezometers located in arid western plains(Barmer and Jodhpur districts)and semi-arid eastern plains(Jaipur,Ajmer,Dausa,and Tonk districts)of Rajasthan State,India.Statistical methods were employed to examine the annual and seasonal patterns of DGWL,and the generalized additive model(GAM)was used to determine the impacts of climatic and anthropogenic variables on DGWL.During 1994-2020,except for Barmer District,where the mean annual DGWL was almost constant(around 26.50 m),all other districts exhibited increase in DGWL,with Ajmer District experiencing the most increase.The results also revealed that 36 observation wells and piezometers showed a statistically significant annual increasing trend in DGWL and 34 observation wells and piezometers exhibited a statistically significant decreasing trend in DGWL.Similarly,32 observation wells and piezometers showed an statistically significant increasing trend and 37 observation wells and piezometers showed a statistically significant decreasing trend in winter;33 observation wells and piezometers indicated a statistically significant increasing trend and 34 had a statistically significant decreasing trend in post-monsoon;35 observation wells and piezometers exhibited a statistically significant increasing trend and 32 observation wells and piezometers showed a statistically significant decreasing trend in pre-monsoon;and 36 observation wells and piezometers reflected a statistically significant increasing trend and 30 observation wells and piezometers reflected a statistically significant decreasing trend in monsoon.Interestingly,most of the observation wells and piezometers with increasing trends of DGWL were located in Dausa and Jaipur districts.Furthermore,the GAM analysis revealed that climatic variables,such as precipitation,significantly affected DGWL in Barmer District,and DGWL in all other districts was influenced by anthropogenic variables,including GDP,NIA,and population.As a result,stringent regulations should be implemented to curb excessive groundwater extraction,manage agricultural water demand,initiate proactive aquifer recharge programs,and strengthen sustainable management in these water-scarce regions.

Study on the Influence of Lanzhou-Urumqi High-Speed Railway on Regional Accessibility and Economic Development

The Lanzhou-Urumqi high-speed railway is an important part of the railway network connecting Gansu,Qinghai,and Xinjiang,and it is of far-reaching significance in facilitating China’s western development.An accessibility model and a double difference model were built to analyze the impact of the Lanzhou-Urumqi high-speed railway on regional accessibility and economic development of the areas along the line before(2012-2014)and after(2017-2019)its opening.The results show that the regional accessibility remains unchanged before and after the operation of this railway line.However,there is a spatial difference in improvement,that of central cities being better.The opening of the high-speed railway is conducive to driving the overall economic development of the region and promoting the comprehensive and coordinated development of regional economies.

Optimized Allocation Model for Regional Agricultural Fertilizer Resources in Southern Xinjiang from the Perspective of Sustainable Development

[Objective] This study was to establish an optimized model for the allocation of agricultural fertilizer resources in Southern Xinjiang from the perspective of sustainable development.[Method] An optimized model for the allocation of agricultural fertilizer resources was established based on their allocation structure.Combined with the actual agricultural production in Aksu areas of Southern Xinjiang,by establishing a rational evaluation index system,under the premise of considering the planting area constraints,the total water resources constraints and the security constraints of food production,we established the empirical optimal allocation model of the regional agricultural fertilizer resources in Aksu area of Southern Xinjiang.Moreover,we solved the model by using the search algorithm of computer and lingo programming.[Result] The increased economic benefit was near to 1.8 billion Yuan by adopting the optimal allocation methods,with a relative increment of about 34.4%.[Conclusion] Our results provided theoretical basis for achieving the sustainable development of agricultural economy in Southern Xinjiang.

Application of jet main region specification model in CFD simulation for room air movement analysis

This paper analyzes the applications of four air terminal device（ATD）models（i.e.,the basic model,the box model,the N-point momentum model,the jet main region specification model）in computational fluid dynamics（CFD）simulation and their performance in case study.A full-scale experiment is performed in an environment chamber,and the measured air velocity and temperature fields are compared with the simulation results by using four ATD models.The velocity and temperature fields are measured by an omni-directional thermo-anemometer system.It demonstrates that the basic model and the box model are not applicable to complicated air terminal devices.At the occupant area,the relative errors between simulated and measured air velocities are less than 20% based on the N-point momentum model and the jet main region specification model.Around the ATD zone,the relative error between the numerical and measured air velocity based on the jet main region specification model is less than 15%.The jet main region specification model is proved to be an applicable approach and a more accurate way to study the airflow pattern around the ATD with complicated geometry.

Multi-Year Simulations and Experimental Seasonal Predictions for Rainy Seasons in China by Using a Nested Regional Climate Model (RegCM_NCC) Part Ⅱ:The Experimental Seasonal Prediction

A nested regional climate model has been experimentally used in the seasonal prediction at the China National Climate Center （NCC） since 2001. The NCC/IAP （Institute of Atmospheric Physics） T63 coupled GCM （CGCM） provides the boundary and initial conditions for driving the regional climate model （RegCM_NCC）. The latter has a 60-km horizontal resolution and improved physical parameterization schemes including the mass flux cumulus parameterization scheme, the turbulent kinetic energy closure scheme （TKE） and an improved land process model （LPM）. The large-scale terrain features such as the Tibetan Plateau are included in the larger domain to produce the topographic forcing on the rain-producing systems. A sensitivity study of the East Asian climate with regard to the above physical processes has been presented in the first part of the present paper. This is the second part, as a continuation of Part Ⅰ. In order to verify the performance of the nested regional climate model, a ten-year simulation driven by NCEP reanalysis datasets has been made to explore the performance of the East Asian climate simulation and to identify the model＇s systematic errors. At the same time, comparative simulation experiments for 5 years between the RegCM2 and RegCM_NCC have been done to further understand their differences in simulation performance. Also, a ten-year hindcast （1991-2000） for summer （June-August）, the rainy season in China, has been undertaken. The preliminary results have shown that the RegCM_NCC is capable of predicting the major seasonal rain belts. The best predicted regions with high anomaly correlation coefficient （ACC） are located in the eastern part of West China, in Northeast China and in North China, where the CGCM has maximum prediction skill as well. This fact may reflect the importance of the largescale forcing. One significant improvement of the prediction derived from RegCM_NCC is the increase of ACC in the Yangtze River valley where the CGCM has a very low, even a negative, ACC. The reason behind this improvement is likely to be related to the more realistic representation of the large-scale terrain features of the Tibetan Plateau. Presumably, many rain-producing systems may be generated over or near the Tibetan Plateau and may then move eastward along the Yangtze River basin steered by upper-level westerly airflow, thus leading to enhancement of rainfalls in the mid and lower basins of the Yangtze River. The real-time experimental predictions for summer in 2001, 2002, 2003 and 2004 by using this nested RegCM-NCC were made. The results are basically reasonable compared with the observations.