Time filter

Source Type

He Y.,Jiangxi Provincial Meteorological Observatory
Journal of Natural Disasters | Year: 2013

With the precipitation data observed at 83 meteorological stations in Jiangxi Province during 1981 to 2010, the trends of frequency and spatiotemporal change of droughts and floods was analyzed using Mann-Kendall tests, wavelet analysis and IDW interpolation based on Z-index classification. Results show that, droughts and floods in recent 30 years present an overall seriously increasing trend, and flood and drought change alternately with an obvious inter-decadal variation. Periodic oscillations of drought-flood events are significant, with floods' return periods of 16, 9 and 5 years, and droughts'-return periods of 18 and 5 years. Spatial distribution of the frequencies of droughts and floods shows that, droughts mainly occur at the juncture of Jiangxi Province and Fujian Province, i. e. the west side of Wuyi Mountain and southern Jiangxi Province, while floods mainly occur in the north of Jiangxi along the Jiujiang region and Guiter basin.


Zhang D.,CAS Beijing Institute of Geographic Sciences and Nature Resources Research | Zhang D.,University of Chinese Academy of Sciences | Liu X.,CAS Beijing Institute of Geographic Sciences and Nature Resources Research | Hong H.,Jiangxi Provincial Meteorological Observatory
Stochastic Environmental Research and Risk Assessment | Year: 2013

Reference evapotranspiration (ET 0) is a key parameter in hydrological and meteorological studies. In this study, the FAO Penman-Monteith equation was used to estimate ET 0, and the change in ET 0 was investigated in China from 1960 to 2011. The results show that a change point around the year 1993 was detected for the annual ET 0 series by the Cramer's test. For the national average, annual ET 0 decreased significantly (P < 0.001) by -14.35 mm/decade from 1960 to 1992, while ET 0 increased significantly (P < 0.05) by 22.40 mm/decade from 1993 to 2011. A differential equation method was used to attribute the change in ET 0 to climate variables. The attribution results indicate that ET 0 was most sensitive to change in vapor pressure, followed by solar radiation, air temperature and wind speed. However, the effective impact of change in climate variable on ET 0 was the product of the sensitivity and the change rate of climate variable. During 1960-1992, the decrease in solar radiation was the main reason of the decrease in ET 0 in humid region, while decrease in wind speed was the dominant factor of decreases in ET 0 in arid region and semi-arid/semi-humid region of China. Decrease in solar radiation and/or wind speed offset the effect of increasing air temperature on ET 0, and together led to the decrease in ET 0 from 1960 to 1992. Since 1993, the rapidly increasing air temperature was the dominant factor to the change in ET 0 in all the three regions of China, which led to the increase in ET 0. Furthermore, the future change in ET 0 was calculated under IPCC SRES A1B and B1 scenarios with projections from three GCMs. The results showed that increasing air temperature would dominate the change in ET 0 and ET 0 would increase by 2.13-10.77, 4.42-16.21 and 8.67-21.27 % during 2020s, 2050s and 2080s compared with the average annual ET 0 during 1960-1990, respectively. The increases in ET 0 would lead to the increase in agriculture water consumption in the 21st century and may aggravate the water shortage in China. © 2013 Springer-Verlag Berlin Heidelberg.


Hong H.,Jiangxi Provincial Meteorological Observatory | Pourghasemi H.R.,Shiraz University | Pourtaghi Z.S.,University of Yazd
Geomorphology | Year: 2016

Landslides are an important natural hazard that causes a great amount of damage around the world every year, especially during the rainy season. The Lianhua area is located in the middle of China's southern mountainous area, west of Jiangxi Province, and is known to be an area prone to landslides. The aim of this study was to evaluate and compare landslide susceptibility maps produced using the random forest (RF) data mining technique with those produced by bivariate (evidential belief function and frequency ratio) and multivariate (logistic regression) statistical models for Lianhua County, China. First, a landslide inventory map was prepared using aerial photograph interpretation, satellite images, and extensive field surveys. In total, 163 landslide events were recognized in the study area, with 114 landslides (70%) used for training and 49 landslides (30%) used for validation. Next, the landslide conditioning factors-including the slope angle, altitude, slope aspect, topographic wetness index (TWI), slope-length (LS), plan curvature, profile curvature, distance to rivers, distance to faults, distance to roads, annual precipitation, land use, normalized difference vegetation index (NDVI), and lithology-were derived from the spatial database. Finally, the landslide susceptibility maps of Lianhua County were generated in ArcGIS 10.1 based on the random forest (RF), evidential belief function (EBF), frequency ratio (FR), and logistic regression (LR) approaches and were validated using a receiver operating characteristic (ROC) curve. The ROC plot assessment results showed that for landslide susceptibility maps produced using the EBF, FR, LR, and RF models, the area under the curve (AUC) values were 0.8122, 0.8134, 0.7751, and 0.7172, respectively. Therefore, we can conclude that all four models have an AUC of more than 0.70 and can be used in landslide susceptibility mapping in the study area; meanwhile, the EBF and FR models had the best performance for Lianhua County, China. Thus, the resultant susceptibility maps will be useful for land use planning and hazard mitigation aims. © 2016 Elsevier B.V.


Ding Q.,Zhejiang Communications Services Company Ltd Consulting Branch | Chen W.,University of Science and Technology of China | Hong H.,Jiangxi Provincial Meteorological Observatory
Geocarto International | Year: 2016

The landslide hazard occurred in Taibai County has the characteristics of the typical landslides in mountain hinterland. The slopes mainly consist of residual sediments and locate along the highway. Most of them are in the less stable state and in high risk during rainfall in flood season especially. The main purpose of this paper is to produce landslide susceptibility maps for Taibai County (China). In the first stage, a landslide inventory map and the input layers of the landslide conditioning factors were prepared in the geographic information system supported by field investigations and remote sensing data. The landslides conditioning factors considered for the study area were slope angle, altitude, slope aspect, plan curvature, profile curvature, distance to faults, distance to rivers, distance to roads, normalized difference vegetation index, lithological unit, rainfall and land use. Subsequently, the thematic data layers of conditioning factors were integrated by frequency ratio (FR), weights of evidence (WOE) and evidential belief function (EBF) models. As a result, landslide susceptibility maps were obtained. In order to compare the predictive ability of these three models, a validation procedure was conducted. The curves of cumulative area percentage of ordered index values vs. the cumulative percentage of landslide numbers were plotted and the values of area under the curve (AUC) were calculated. The predictive ability was characterized by the AUC values and it indicates that all these models considered have relatively similar and high accuracies. The success rate of FR, WOE and EBF models was 0.9161, 0.9132 and 0.9129, while the prediction rate of the three models was 0.9061, 0.9052 and 0.9007, respectively. Considering the accuracy and simplicity comprehensively, the FR model is the optimum method. These landslide susceptibility maps can be used for preliminary land use planning and hazard mitigation purpose. © 2016 Informa UK Limited, trading as Taylor & Francis Group


Zhang D.,CAS Nanjing Institute of Geography and Limnology | Hong H.,Jiangxi Provincial Meteorological Observatory | Zhang Q.,CAS Nanjing Institute of Geography and Limnology | Li X.,CAS Nanjing Institute of Geography and Limnology
Theoretical and Applied Climatology | Year: 2014

Water resources in the Yangtze River, the longest river in China, are of great importance for the water security of China. In this study, 146 years (1865-2010) of streamflow data were used to investigate the changes in streamflow of the Yangtze River. The Mann-Kendall test and wavelet coherence analysis were used to test the change points in annual streamflow. The streamflow data, combined with the meteorological dataset of the Climatic Research Unit (CRU) from 1901 to 2010, showed that the Yangtze River streamflow changes occurred in four major periods over the past century: 1901-1930, 1931-1960, 1961-1990, and 1991-2010. The average annual streamflow for the four periods was 497.26, 499.11, 476.25, and 471.93 mm, respectively. The period from 1901 to 1930 was considered the baseline period for estimating the streamflow changes during the other three periods. We found that the streamflow increase during 1931-1960 was mainly influenced by climatic variation, while the streamflow decrease during 1961-1990 was mainly attributed to human activities because of tremendous population growth and rapid economic development. During the period 1991-2010, both climatic variation and human activities led to a decrease in streamflow, and human activities were still the main driving factor for the streamflow decrease. However, the contribution proportion of human activities to the streamflow decrease during 1991-2010 was much smaller than that during 1961-1990. The estimation results indicated that human activities have become the dominant driving factors of the streamflow changes in the Yangtze River Basin since 1961. Human activities, such as booming socio-economic development and fast population growth, have brought new challenges for water resources management in the Yangtze River Basin. © 2014 Springer-Verlag Wien.


Hong H.,Jiangxi Provincial Meteorological Observatory | Naghibi S.A.,Tarbiat Modares University | Pourghasemi H.R.,Shiraz University | Pradhan B.,University Putra Malaysia
Arabian Journal of Geosciences | Year: 2016

Landslide susceptibility mapping is among the first works for disaster management and land use planning activities in a mountain area like Ganzhou City. The aims of the current study are to assess GIS-based landslide spatial modeling using four models, namely data-driven evidential belief function (EBF), frequency ratio (FR), maximum entropy (Maxent), and logistic regression (LR), and to compare their performances. At first, a landslide inventory map was prepared according to aerial photographs, satellite images, and extensive field surveys. In total, 3971 landslide events were recognized in the study area that used 2979 landslides (75 %) for modeling and 992 landslide events (25 %) for validation. In the next step, the landslide-conditioning factors, namely slope angle, slope aspect, altitude, plan curvature, profile curvature, topographic wetness index (TWI), slope-length (LS), lithology, normalized difference vegetation index (NDVI), distance from rivers, distance from faults, distance from roads, and rainfall, were derived from the spatial database. Finally, landslide susceptibility maps of Ganzhou City were mapped in ArcGIS based on EBF, FR, Maxent, and LR approaches and were validated using the receiver operating characteristic (ROC) curve. The ROC plot assessment results showed that in the landslide susceptibility maps using the EBF, FR, Maxent, and LR models, the area under the curve (AUC) values were 0.7367, 0.7789, 0.7903, and 0.8237, respectively. Therefore, it can be concluded that all four models have AUC values of more than 0.70 and can be used in landslide susceptibility mapping in the study area. Also, the LR model had the best performance in the current study. Meanwhile, the mentioned models (EBF, FR, Maxent, and LR) showed almost similar results. The resultant susceptibility maps produced in the current study can be useful for land use planning and hazard mitigation purposes in the study area. © 2016, Saudi Society for Geosciences.


Zhang D.,CAS Nanjing Institute of Geography and Limnology | Hong H.,Jiangxi Provincial Meteorological Observatory | Zhang Q.,CAS Nanjing Institute of Geography and Limnology | Nie R.,PLA University of Science and Technology
Climate Research | Year: 2014

Pan-evaporation is an important indicator of atmospheric evaporative demand and is used extensively in agriculture and hydrometeorology. This study investigated changes in panevaporation and the influence of large water bodies on pan-evaporation changes based on the latest data from 1959 to 2012 in the Poyang Lake Basin. The Mann-Kendall test showed that the annual pan-evaporation decreased dramatically (?8.21 mm yr?2) from 1959 to 1973, then de - creased slowly (?5.00 mm yr?2) from 1974 to 1995, while it increased significantly (11.48 mm yr?2) from 1996 to 2012. A differentiation method was used to attribute the changes in climatic variables to the changes in pan-evaporation. A decrease in air temperature dominated the pan-evaporation decrease from 1959 to 1973, whereas decreases in wind speed and solar radiation dominated the pan-evaporation decrease from 1974 to 1995. However, significant increases in wind speed and air temperature dominated the pan-evaporation increase from 1996 to 2012. Furthermore, we studied the influence of Poyang Lake (the largest freshwater lake in China) on the changes in pan-evaporation. The amplitude of the changes in pan-evaporation near Poyang Lake was larger than that away from Poyang Lake. Pan-evaporation near Poyang Lake was more sensitive to changes in air temperature, wind speed, and vapor pressure than that in the regions away from the Poyang Lake, thereby resulting in larger changes in pan-evaporation in areas near the lake. Investigating changes in pan-evaporation is useful for agricultural irrigation planning and water management in the Poyang Lake Basin. © Inter-Research 2014.


Yin J.,Jiangxi Provincial Meteorological Observatory | Du H.-L.,Zhejiang Provincial Meteorological Observatory | Wu J.,Jiangxi Provincial Meteorological Observatory
Journal of Natural Disasters | Year: 2011

For three tropical cyclones ("Bilis", "Saomai" and "kaemi") happened in 2006 that invaded into the interior territory and resulted in the relative strong rainstorm, we comparatively analyzed the vapor and dynamical condition, the unstable energy and the moist potential vorticity. The results show that although " Bilis" just reaches the degree of the strong tropical storm, it induces extremely strong vapor, dynamical, and thermal conditions much stronger than "Saomai" and "Kaemi", after Bilis landes the territory and combined with southern strong southwest monsoon. Therefore, "Bilis" results in the strongest and the larger area rainstorms. These rainstorms are well related with the level of the vertical helicity positive region, the intensity of the vertical direction, and the area invaded. The main involved regions of the rainstorms are consistent with 700hPa 20 line covered regions. The moist potential vorticity of the three typhoons all show the down-negative and up - positive distributions. The function of MPV1 of the moist potential vorticity is larger than that of MPV2.


Hong H.,China Earthquake Administration | Hong H.,Jiangxi Provincial Meteorological Observatory | Pradhan B.,University Putra Malaysia | Xu C.,China Earthquake Administration | Tien Bui D.,Telemark University College
Catena | Year: 2015

Preparation of landslide susceptibility map is the first step for landslide hazard mitigation and risk assessment. The main aim of this study is to explore potential applications of two new models such as two-class Kernel Logistic Regression (KLR) and Alternating Decision Tree (ADT) for landslide susceptibility mapping at the Yihuang area (China). The ADT has not been used in landslide susceptibility modeling and this paper attempts a novel application of this technique. For the purpose of comparison, a conventional method of Support Vector Machines (SVM) which has been widely used in the literature was included and their results were assessed. At first, a landslide inventory map with 187 landslide locations for the study area was constructed from various sources. Landslide locations were then spatially randomly split in a ratio of 70/30 for building landslide models and for the model validation. Then a spatial database with a total of fourteen landslide conditioning factors was prepared, including slope, aspect, altitude, topographic wetness index (TWI), stream power index (SPI), sediment transport index (STI), plan curvature, landuse, normalized difference vegetation index (NDVI), lithology, distance to faults, distance to rivers, distance to roads, and rainfall. Using the KLR, the SVM, and the ADT, three landslide susceptibility models were constructed using the training dataset. The three resulting models were validated and compared using the receive operating characteristic (ROC), Kappa index, and five statistical evaluation measures. In addition, pairwise comparisons of the area under the ROC curve were carried out to assess if there are significant differences on the overall performance of the three models. The goodness-of-fits are 92.5% (the KLR model), 88.8% (the SVM model), and 95.7% (the ADT model). The prediction capabilities are 81.1%, 84.2%, and 93.3% for the KLR, the SVM, and the ADT models, respectively. The result shows that the ADT model yielded better overall performance and accurate results than the KLR and SVM models. The KLR model considered slightly better than SVM model in terms of the positive prediction values. The ADT and KLR are the two promising data mining techniques which might be considered to use in landslide susceptibility mapping. The results from this study may be useful for landuse planning and decision making in landslide prone areas. © 2015 Elsevier B.V.


Chen W.,University of Science and Technology of China | Chai H.,China University of Mining and Technology | Zhao Z.,University of Science and Technology of China | Wang Q.,China University of Mining and Technology | Hong H.,Jiangxi Provincial Meteorological Observatory
Environmental Earth Sciences | Year: 2016

The main purpose of this study is to produce reliable susceptibility maps using GIS-based support vector machine (SVM) models and compare their performances for the Qianyang County of Baoji City, Shaanxi Province, China. In this paper, with kernel classifiers of linear, polynomial, radial basis function and sigmoid, the four various types were applied in landslide susceptibility mapping. The important input parameters for the landslide susceptibility assessment were acquired from different sources. Firstly, 81 landslide sites were obtained by aerial photographs, earlier reports and field surveys. Then, the landslide inventory was randomly classified into two datasets: 70 % (56 landslides) for training the models and 30 % (25 landslides) for validation purpose. Secondly, 15 landslide conditioning factors were selected (i.e., slope angle, slope aspect, altitude, plan curvature, profile curvature, distance to faults, distance to rivers, distance to roads, NDVI, STI, SPI, TWI, geomorphology, rainfall, and lithology). Subsequently, with four types of kernel function classifiers based on landslide conditioning factors, landslide susceptibility parameters were obtained using SVM models. Finally, the rationality of landslide susceptibility maps was verified using the receiver operating characteristics with both success rate curve and prediction rate curve. The validation results showed that success rates for the four SVM models were 83.15 % (RBF-SVM), 82.72 % (PL-SVM), 81.77 % (LN-SVM), and 79.99 % (SIG-SVM). The prediction rates for the four SVM models were 77.98 % (RBF-SVM), 77.50 % (PL-SVM), 77.07 % (LN-SVM), and 76.08 % (SIG-SVM), respectively. The results showed that the RBF-SVM model had the highest overall performance. © 2016, Springer-Verlag Berlin Heidelberg.

Loading Jiangxi Provincial Meteorological Observatory collaborators
Loading Jiangxi Provincial Meteorological Observatory collaborators