Finer topographic data improves distribution modeling of Picea crassifolia in the northern Qilian Mountains

The Qilian Mountains, a national key ecological function zone in Western China, play a pivotal role in ecosystem services. However, the distribution of its dominant tree species, Picea crassifolia (Qinghai spruce), has decreased dramatically in the past decades due to climate change and human activity, which may have influenced its ecological functions. To restore its ecological functions, reasonable reforestation is the key measure. Many previous efforts have predicted the potential distribution of Picea crassifolia, which provides guidance on regional reforestation policy. However, all of them were performed at low spatial resolution, thus ignoring the natural characteristics of the patchy distribution of Picea crassifolia. Here, we modeled the distribution of Picea crassifolia with species distribution models at high spatial resolutions. For many models, the area under the receiver operating characteristic curve (AUC) is larger than 0.9, suggesting their excellent precision. The AUC of models at 30 m is higher than that of models at 90 m, and the current potential distribution of Picea crassifolia is more closely aligned with its actual distribution at 30 m, demonstrating that finer data resolution improves model performance. Besides, for models at 90 m resolution, annual precipitation (Bio12) played the paramount influence on the distribution of Picea crassifolia, while the aspect became the most important one at 30 m, indicating the crucial role of finer topographic data in modeling species with patchy distribution. The current distribution of Picea crassifolia was concentrated in the northern and central parts of the study area, and this pattern will be maintained under future scenarios, although some habitat loss in the central parts and gain in the eastern regions is expected owing to increasing temperatures and precipitation. Our findings can guide protective and restoration strategies for the Qilian Mountains, which would benefit regional ecological balance.

A Method of Improving Seismic Data Resolution：Comprehensive Inversion of Well logging and Seismic Data

Comprehensive inversion of logging and seismic data presented in this paper is a method to improve seismic data resolution. It involves using ample high-frequency information and complete low-frequency information of known logging to make up for the lack of limited bandwidth of practical seismic recording, obtaining an approximate reflection coefficient sequence (or wave impedance) of high resolution by iterative inversion and providing more reliable seismic evidence for further lithologic interpretation and lateral tracking, correlation and prediction of thin reservoir. The comprehensive inversion can be realized in the following steps: (1) to establish an initial model of higher resolution; (2) to obtain wavelets, and (3) to constrain iterative inversion. The key to this inversion lies in building an initial model. It is assumed from our experience that when the initial model is properly given, iterative inversion can be quickly converged to the ideal result.

Effects of spatial and temporal weather data resolutions on streamflow modeling of a semi-arid basin,Northeast Brazil

One major difficulty in the application of distributed hydrological models is the availability of data with sufficient quantity and quality to perform an adequate evaluation of a watershed and to capture its dynamics.The Soil&Water Assessment Tool(SWAT)was used in this study to analyze the hydrologic responses to different sources,spatial scales,and temporal resolutions of weather inputs for the semi-arid Jaguaribe watershed(73000 km2)in northeastern Brazil.Four different simulations were conducted,based on four groups of weather and precipitation inputs:Group 1-SWAT Weather Generator based on monthly data from four airport weather stations and daily data based on 124 local rain gauges;Group 2-daily local data from 14 weather stations and 124 precipitation gauges;Group 3-Daily values from a global coupled forecast model(NOAA’s Climate Forecast System Reanalysis-CFSR);and Group 4-CFSR data with 124 local precipitation gauges.The four simulations were evaluated using multiple statistical efficiency metrics for four streamflow gauges,using:Nash-Sutcliffe coefficient(NSE),determination coefficient(R2),the ratio of the root mean square to the standard deviation of the observed data(RSR),and the percent bias(PBIAS).The Group 4 simulation performed best overall(provided the best statistical values)with results ranked as“good”or“very good”on all four efficiency metrics suggesting that using CFSR data for weather parameters other than precipitation,coupled with precipitation data from local rain gauges,can provide reasonable hydrologic responses.The second best results were obtained with Group 1,which provided“good”results in three of four efficiency metrics.Group 2 performed worse overall than Groups 1 and 4,probably due to uncertainty related to daily measures and a large percentage of missing data.Groups 2 and 3 were“unsatisfactory”according to three or four of the efficiency metrics,indicating that the choice of weather data is very important.

High-Resolution Geochemical Significance of Lowest Triassic at Majiashan Section, Chaohu, Anhui Province, China

The Permian Triassic boundary (PTB) and the lowest Triassic in the Yangtze region are considered to be the sediments of dysaeroxic and even anoxic environments, due to the dark thin bedded fine deposits, the highly developed parallel beddings with pyrites, the suppression of bio disturbance, and the monotonous fossils. However, the trace fossils there show a rather weak effect of the anoxic event. Meanwhile, the high resolution geochemical data are analyzed with 2 cm interval in the PTB and the lowest Triassic at the Majiashan Section, Chaohu, Anhui Province. The results show that the water depth of Chaohu region in the earliest Triassic was shallow, which might be a feature of the neritic environment. The high resolution geochemical proxies for anoxia have some contrary results. The geochemical data often indicate the dysaeroxic and even anoxic environments during that time, whereas other proxies (such as w (V)/ w (Cr), w (Ni)/ w (Co)) denote that they are normal marine sediments.

Compilation of 1:50,000 vegetation type map with remote sensing images based on mountain altitudinal belts of Taibai Mountain in the North-South transitional zone of China

The compilation of 1:250,000 vegetation type map in the North-South transitional zone and 1:50,000 vegetation type maps in typical mountainous areas is one of the main tasks of Integrated Scientific Investigation of the North-South Transitional Zone of China.In the past,vegetation type maps were compiled by a large number of ground field surveys.Although the field survey method is accurate,it is not only time-consuming,but also only covers a small area due to the limitations of physical environment conditions.Remote sensing data can make up for the limitation of field survey because of its full coverage.However,there are still some difficulties and bottlenecks in the extraction of remote sensing information of vegetation types,especially in the automatic extraction.As an example of the compilation of 1:50,000 vegetation type map,this paper explores and studies the remote sensing extraction and mapping methods of vegetation type with medium and large scales based on mountain altitudinal belts of Taibai Mountain,using multi-temporal high resolution remote sensing data,ground survey data,previous vegetation type map and forest survey data.The results show that:1)mountain altitudinal belts can effectively support remote sensing classification and mapping of 1:50,000 vegetation type map in mountain areas.Terrain constraint factors with mountain altitudinal belt information can be generated by mountain altitudinal belts and 1:10,000 Digital Surface Model(DSM)data of Taibai Mountain.Combining the terrain constraint factors with multi-temporal and high-resolution remote sensing data,ground survey data and previous small-scale vegetation type map data,the vegetation types at all levels can be extracted effectively.2)The basic remote sensing interpretation and mapping process for typical mountains is interpretation of vegetation type-groups→interpretation of vegetation formation groups,formations and subformations→interpretation and classification of vegetation types&subtypes,which is a combination method of top-down method and bottom-up method,not the top-down or the bottom-up classification according to the level of mapping units.The results of this study provide a demonstration and scientific basis for the compilation of large and medium scale vegetation type maps.