Based on monthly streamflow data from 372 stations covering the period 1960-2000 and the monthly streamflow data from 41 stations covering the period 2001-2014 across China, human- and climate-induced impacts on hydrological processes were quantified for 10 river basins in China based on development of Budyko-based coupled water-energy balance model. Penman-Monteith potential evapotranspiration model was used to analyze evapotranspiration processes. Besides, elasticity coefficient was also quantified for the impacts of meteorological variables on streamflow changes. The results indicated that: (1) Compared to southern China, streamflow changes are more sensitive to climate changes and human activities in northern China. Generally, relative humidity changes have positive impacts on streamflow changes. However, the maximum temperature, minimum temperature, solar radiation, wind speed and LUCC changes tend to go against streamflow changes. The elasticity coefficients of streamflow changes for meteorological variables are: precipitation > LUCC > relative humidity > solar radiation > maximum temperature > wind speed > minimum temperature; (2) Climate changes during 1980-2000 generally help to increase annual streamflow, and the increase of streamflow by precipitation changes is most evident, and the increase of streamflow depth reaches 12.1 mm. However, impacts of meteorological variables on streamflow changes are shifting from one river basin to another, e.g. the maximum temperature and relative humidity help to increase streamflow in northern China but decrease streamflow magnitude in southern China; (3) In general, human activities tend to decrease streamflow. Changes of streamflow in the Yangtze, Songhua, Northwest, and Southeast river basins are 78.7%, 76.9%, 65.7%, and 84.2%, respectively, which can be attributed to climate changes. However, human activities play a dominant role in modifications of streamflow changes, such as Pearl River basin, Huaihe River basin, Haihe River basin, Yellow River basin, Liaohe River basin and southwest river basins, with fractional contribution being 59.4%, 77.3%, 66.2%, 69.7%, 75.3%, and 70.4%, respectively. Generally, the fraction of human activities and climate changes to streamflow changes in the river basins across China can reach 71.0% and 29.0% respectively in river basins, where climate changes play a dominant role in streamflow changes. The results of this study can be helpful to human mitigation to climate changes in terms of water resources management.
Evapotranspiration (ET) is one of the core processes of water cycle in ecosystem and ET modeling is a hotspot and frontier in the field of the global climate changes. It is therefore important to provide spatiotemporal information of ET across diverse ecosystems in order to predict the response of ecosystem carbon and water cycles to changes in global climate and land use. The SWH model incorporates the Ball-Berry stomatal conductance model and a light use efficiency-based gross primary productivity (GPP) model into the Shuttleworth-Wallace model, which can simulate both ET and GPP. The newly developed SWH model presents a satisfactory prediction ability of simulating ET in a forest and a grassland ecosystem, respectively. However, the SWH model still lacks comprehensive evaluation and uncertainty analysis at regional scale. In this study, we (1) tested the model's performances on estimating ET and GPP at seasonal and annual time scales; (2) quantified the uncertainties of the model parameters and driving variables, including Normalized Difference Vegetation Index, NDVI and meteorological data; (3) quantified the sensitivity of model outputs to the parameters and driving variables; (4) quantified and separated the uncertainties of ET simulation from the parameters and driving variables. Results showed that the SWH model performed well for ET simulation at regional scale as indicated by high coefficient of determination (R2 = 0.75) of linear regression of modeled against measured ET. Among the key parameters in the SWH model, two parameters related to estimating canopy stomatal conductance (g0 and a1) make great contribution to the model uncertainty. Among the forcing variables, NDVI is most critical in estimating GPP, which contributes much to uncertainty in ET simulation. In comparison, the climatic forcing variables contributes less to uncertainty in ET simulation owing to the high accuracy of the climate data (such as radiation and air temperature) or model’s low sensitivities to some variables (such as precipitation).
In order to investigate the spatial-temporal evolution pattern and potential driving mechanism of lakes on a long time-scale, based on the K?ppen climate classification, we classify Chinese climate as 4 climate zones, 6 climatic types and select 34 lakes which have reliable dating, and its lake records have certain continuity since the Last Glacial Maximum. At the same time, NCEP/NCAR 0.5°×0.5° 1900-2015 grid data are used to verify our traditional monsoon region which is defined based on water vapor transportation field. Meanwhile, this study uses a series of models, i.e., the NCAR CCSM 3, a lake energy-balance and a lake water-balance model, to examine the lake-level evolution process and potential driving mechanism in monsoonal Asia and arid central Asia since the Last Glacial Maximum. Our results indicate that the evolution of lakes in China is mainly affected by millennial-scale atmospheric circulation, and lake-level changes in all climate zones have no obvious regularity. In the monsoon region, there are two kinds of evolvement rules, a relatively high lake-level in the early and mid-Holocene and a relatively high lake-level in the Last Glacial Maximum and early Holocene. Meanwhile, in the arid region of East Asia controlled by westerlies, there are also two kinds of evolvement rules. One is that the lake-level in mid- and late Holocene is relatively high, and the other is that the lake-level is relatively high in mid-Holocene and the Last Glacial Maximum. This study provides a large amount of new evidence, which reflects the past climate change and mechanism of lake evolvement, as well as a new perspective to comprehensively understand lake-level changes since the Last Glacial Maximum.
Based on the cross-section data of pollutant discharge and socio-economic development in 2012, by using the indicators of total discharge of COD and SO2 and various sources, the spatial differentiation and agglomeration effects of pollutant discharge are measured quantitatively in 339 cities at prefecture level or above in China. Meanwhile, the socio-economic drivers of environmental pollution are estimated through the econometric model, and some suggestions are put forward to reduce and control pollutant discharge. The results show that: (1) The regional differences of the structure of pollutant source are of great significance in China. Specifically, the types of water pollutant sources are mainly included in the region dominated by agricultural source, the region dominated by urban domestic source, and in the region jointly dominated by agricultural and urban domestic source, with the proportions of these regions being 35.40%, 33.92% and 25.66%, respectively. Among them, the discharge intensity of water pollutant is the highest in the region dominated by agricultural source, which are mainly located in North and Northeast China. Meanwhile, the types of air pollutant sources in China are mostly involved in the region absolutely dominated by industrial source, which accounts for 74.63% and is distributed in the vast area of China in addition to the Qinghai-Tibet Plateau. (2) Single and bivariate LISA cluster maps of total discharge and various sources indicate that the agricultural source makes contribution to the spatial agglomeration of high intensity discharge of water pollutants, while the industrial source leads to the concentrated discharge of air pollutants. Especially, in the Bohai Rim region and its economic hinterland, the structure of pollutant sources strengthens the agglomeration effects of pollutant discharge intensity. (3) By means of OLS estimation for nationwide regression model, it is demonstrated that population size, urbanization rate and economic growth are the main driving factors of environmental pollution, and population size is the key factor in China. Moreover, in terms of regression models of regional types, the economic development level makes different contributions to pollutant discharge in various regions, which shows a positive effect on the air pollutant discharge in the region dominated by industrial source, while shows a negative effect on the water pollutant discharge in the region dominated by urban domestic source. In addition, the degree of industrialization has positive influence on the air pollutant discharge in the region absolutely dominated by industrial source.
The global karst area is about 2.2×107 km2, wherein China reaches up to 3.44×106 km2, occupying more than one third of the global karst area. The previous studies have showed that carbon (C) sequestration rate by chemical weathering in Chinese karst zone range from 5 Tg C yr-1 to 18 Tg C yr-1, which are considered as one of important "residual land sink" in global change research. In order to accurately estimate the C sequestration rate in karst area, we put forward the concept of Earth Critical Zone (CZ) for karst ecological system. In this study, we comprehensively understand the C cycle in karst CZ, introduce the estimation of the methods on karst CZ, and then re-evaluate the C sequestration rate in the karst CZ. At last, we systematically discuss the uncertainty of the C sinks in karst CZ. The results show that the rock-weathering-related C sink rate in Chinese karst CZ is about 4.74 Tg C yr-1. In addition, we elaborate the significance of C sequestration to ecological system service in karst CZ, strive to to strengthen the C monitor at rock-soil-water-biological-atmosphere continuum level in the future, and then use different methods to estimate and evaluate C cycle at multi-scale in karst CZ.
Accurate estimation of the carbon sequestration potential of afforestation helps us better understand the carbon cycle in China and provides the guide for national forest policies. Forest data from China Forestry Statistical Yearbook were used to estimate carbon stocks and explore the carbon sequestration potential in China's forests in the next 100 years. In this study, we estimate the forest biomass carbon storage and carbon sequestration potential of new afforestation in China over the next 100 years based on new afforestation area from China Forestry Statistical Yearbook during 2005-2013 and forest type map in 2010 derived from remote sensing information. In the consideration of annual forest survival rate, carbon pools of the new afforestation are estimated with the forest growth equations for different forest types. The potential changes in China's forest biomass carbon storage between 2005 and 2100 were estimated with reconstructed forest areas. The results show that the total new afforestation area of China are 4394×104 hm2 from 2005 to 2013. With the assumption of continuous natural forest growth, the volume of new afforestation during 2005-2013 will increase to 16.8 billion m3. The biomass and carbon pool will increase to 1.6 Pg and 0.76 Pg C by 2020, respectively. The new afforestation biomass carbon storage will increase by 2.11 Pg C during 2005-2100. The carbon storage of new afforestation over the next 100 years are about 25% of current biomass carbon stocks in forests and are about 1.5 times of total forest carbon sink of the past 20 years. Furthermore, the biomass carbon density of China's afforestation will reach 48.1 Mg C/hm2 by 2100. In China, the new afforestation has played an important role in the increase of forest carbon storage and has a great potential for carbon sequestration. Therefore, forest management in China is of importance to mitigate increases in greenhouse gas emissions.
This paper presents spatial and temporal dynamics of fraction of vegetation in Xinjiang Uygur Autonomous Region of China. Fractional vegetation cover (FVC) was estimated by using MODIS-NDVI data from 2005 to 2015. The study area was divided into 11 ecological and climate regions according to the altitude and land cover. Slope, variability and linear regression model were used to analyze the present situation and future tendency for FVC in Xinjiang and its sub-regions. The BP-ANN neural network analysis was used to predict FVC from 2016 to 2020, and the FVC trend over the entire study area during 2005-2020 was discussed. The results showed that: From 2005 to 2015, FVC increased in general over time, and spatially, decreased from northwest to southeast; In mountain areas, FVC increased in general; desert system showed no significant change, and multi-average FVC was about 0.10. The dynamic change of FVC was mainly caused by precipitation. We observed an improvement of vegetation cover over oasis and desert ecotone. FVC showed a significant increase over oasis. The year 2009 was the turning point with a historical low value. The variation near areas covered by ice and snow, river and lakes was remarkable, showing a change rate of 150%-316%. This change was probably responded by glacial depletion and fluctuation changes of lakes due to global climate change. The ecosystem in northern Xinjiang is obviously better than that in southern and eastern Xinjiang. In terms of oasis, the northern part is improved remarkably (P = 0.001). There was an obvious FVC fluctuation in Yili region compared to other regions. The mountain area showed an obvious degeneration tendency. The local minima point of FVC was observed in Yili in 2008, while it was in the other three regions in 2009. The lag of local minima occurring in the northern and southern parts of the study areas may have been caused by precipitation and temperature variation across the study area. Predicted average FVC from 2016-2020 demonstrated trends and patterns identical to 2005-2015 with some local differences. For example, FVC increases (P = 0.002) during 2005-2020 in general. In desert areas, the trend is from non-significant decrease during 2005-2015 to non-significant increase for 2005-2020. In oasis region, predicted FVC showed a slightly rising trend compared to the obviously rising trend in 2005-2015. The multi-average FVC is above 0.62 and it showed improvement during 2005-2020. For sub-regions and ecosystems, the trend differs significantly between 2005-2015 and 2005-2020. In northern part, the trend in 2005-2020 was almost the same with that of 2005-2015, while in 2016-2020 the tendency was opposite to 2005-2015, with oasis and mountain FVC showing a decreasing trend. In Yili, the general trend in 2005-2020 was almost the same with that of 2005-2015, but the amplitude of variation became smaller in 2016-2020 when compared to early stage and the mountain area showing a remarkably decreasing trend. Our results demonstrated that BP-ANN model can predict FVC in Xinjiang with statistical significance, the coefficient of determination (R2) of 0.95, root-mean-square error of 0.05, suggesting that this method gained a statisifactory result.
Based on "Cropland Taxes" and "the Number of Households" data recorded in historical documents, this paper estimated cropland area of the Northern Song Dynasty by analyzing some society factors in this dynasty, including land use policies and taxation system. Besides, by quantifying the relationship among population proportion, per capita cropland and cropland spatial pattern in the mid-Northern Song Dynasty, we designed a cropland distribution model. And the model was used to reconstruct cropland area at Lu (administrative region of the Northern Song Dynasty) scale for AD 976, 997, 1066 and 1078. The results are shown as follows: (1) The cropland area of the whole study area for AD 976, 997, 1066 and 1078 of the Northern Song Dynasty were about 468.27 million mu (Chinese area unit, 1 mu=666.7 m2), 495.53 million mu, 697.65 million mu and 731.94 million mu, respectively, and 264 million mu was increased for AD 976-1078. The annual growth rate of cropland area was about 4.4‰, and the reclamation rate (i.e. ratio of cropland area to total land area) increased from 10.7% to 16.8%, and per capita cropland area decreased from 15.7 mu to 8.4 mu. (2) In terms of the characteristics of cropland spatial pattern change, the reclamation rate of the Southeast, Northern and Southwest in the Northern Song territory increased by 12.0%, 5.2% and 1.2%, respectively, and that of some regions of the Yangtze River Plain increased to more than 40%, and for the North China Plain the reclamation rate increased to more than 20%. The reclamation rate of the Southwest (except the Chengdu Plain) in the Northern Song territory was less than 6%. (3) The evaluation results show that the absolute relative error of 84.2% Lu was less than 20%, so the cropland distribution model is feasible. Therefore, our reconstruction results can reflect the spatial-temporal characteristics of cropland area in the Northern Song Dynasty.
Model simulation and scenarios change analysis are the core contents of the future land use change study. In this paper, land use status data of the Three Gorge Reservoir Area (TGRA) in 1990 were used as base data. The relationship between driving factors and land use was analyzed by using binary logistic stepwise regression analysis, based on which land use in 2010 was simulated by CLUE-S model. After the inspection and determination of main parameters impacting driving factors of land use in the TGRA, land use of the TGRA in 2030 was simulated based on four scenarios, including natural growth, food security, emigrant construction and ecological protection. The results were shown as follows: (1) The areas under ROC curves of land use types were both greater than 0.8 under the analysis and inspection of binary logistic model. These land use types include paddy field, dry land, forestland, grassland, construction land and water area. Therefore, it has a strong interpretation ability of driving factors on land use, which can be used in the estimation of land use probability distribution. (2) The Kappa coefficients, verified from the result of land use simulation in 2010, were showed of paddy field 0.9, dry land 0.95, forestland 0.97, grassland 0.84, construction land 0.85 and water area 0.77. So the results of simulation could meet the needs of future simulation and prediction. (3) The results of multi-scenarios simulation show a spatial competition relationship between different land use types, and an influence on food security, emigrant construction and ecological protection in the TGRA, including some land use change such as the large-scale conversion from paddy field to dry land, the occupation of cultivated land, rapid expansion of construction land from forestland and grassland, the reclamation of cultivated land to forestland and grassland, the conversion of forestland and grassland to cultivated land. Therefore, it is necessary to balance the needs of various aspects in land use optimization, so as to achieve the coordination between social economy and ecological environment.
Based on the hydrogeological and topographic map of Guizhou Province, ArcGIS was used to extract the information of nearly 5000 caves in the province, including the altitude, latitude and longitude, the strata, lithology, water system, and structure division. The nearest neighbor index, spatial analysis and coupling analysis methods were used to examine the distribution of 4960 caves in Guizhou Province. The relationship between cave spatial pattern and geographical elements was discussed. Through the point pattern analysis of the caves, it is found that the nearest neighbor index is 0.53, and the variation coefficient is 72.469% tested by Thiessen polygons, which is consistent with the model of aggregate distribution. Through the dot density function method, all the caves were divided into 4 clusters, namely, Zunyi-Tongren strong influence area, Bijie strong influence area, Liupanshui-Qianxinan strong influence area, and Guiyang-Anshun-Qiannan strong influence area, and one weak influence area (Qiandongnan weak influence area). Among them, Anshun-Qiannan-Guiyang strong influence area is the most important area, accounting for 36.63% of the total number of the caves, and 24.67% of the total area of the province. The caves in Guizhou are concentrated in the west and sparsely distributed in the east, shwoing clear "flake"-shaped pattern. Results indicated that the natural factors such as strata, lithology, structure, climate, hydrology, and elevation mainly affect the distribution, quantity, spatial pattern and development of the caves.
This paper measures and conducts a statistical quantitative analysis of 49 potholes on the subterranean river bedrock of Yinhe Cave, a forming cave in Loushanguan Formation of Middle-Upper Cambrian Series, Guizhou province. After a description on morphological characteristics and spatial distribution, the paper discusses the evolutionary process and influencing factors of the formation. It is concluded that the potholes in the Yinhe River are mostly extremely shallow upside-down omega and wok shaped, and are at a relatively early stage of formation; the major axis and minor axis of the potholes are correlated to some extent, but the average diameter and depth are poorly correlated, indicating that the potholes tend to enlarge the diameter during the floor period and deepen the depth of corrosion during the drought period, as one of the riverbed downcutting forms of subterranean rivers; the potholes of Yinhe Cave develop in three stages: saucer shaped stage, upside-down omega shaped stage, and wok shaped stage, under the influence of such factors as hydrodynamic conditions, tectonic conditions, bed load, bed rock properties and corrosion; cavernous potholes are formed by the erosion and corrosion of running water, instead of glacier, which are essentially different from moulins in formation.
2016 is the 100th anniversary of Professor Shen Yuchang, the founder of river geomorphology in China. To commemorate this event, we reviewed the advances in river geomorphology in China in the past 20 years, which cover the following four topics: (1) river system development and river valley evolution, including the historical development of the Yellow and Yangtze rivers and mountain region river geomorphology; (2) erosion and sediment production processes, including the physically based erosion and sediment yield modeling, scale effect of sediment yield, influence of vegetation on erosion and thresholds, and simulation of hillslope rill development; (3) river channel processes and river patterns, including channel fill and scour behaviors, channel changes and the formative cause of river channel patterns; and (4) study of river geomorphology in the framework of fluvial system, including the couplings between sub-systems, hyperconcentated flows and multiple-agent geomorphologic processes, response of fluvial system to climate change and human activity, sediment-related disasters and the laboratory experiments on fluvial systems. Around 50 years ago, Professors Shen Yuchang and Qian Ning together put forward a strategy for Chinese river researches on the basis of combination of geomorphology with hydrodynamics. In this right direction the river geomorphology in China has achieved an initial breakthrough. In addition, some issues that need to be deepened or solved in the future are addressed.
Professor Wang Nailiang is a famous geomorphologist in China. He worked as a geomorphological science professor for 43 years in Peking University after several years of career in Tsinghua University. He investigated and studied the landforms and the Cenozoic sediments in many regions such as Loess Plateau, North China, Northeast China, Northwest China, and the provinces of Sichuan, Hubei and Guangxi. He achieved great success in research on Cenozoic sediments and geomorphological evolution history, neotectonic movements and tectonic geomorphology, the theories and methods of geomorphology and sedimentology. He worked hard for geomorphological teaching, academic research, scientific activities and international exchange. He contributed greatly to the progress of the geomorphological science in China.
Based on bibliometric analysis and data of Web of Science, this paper selects four major Chinese geographic journals indexed in SCI: Journal of Geographical Sciences, Chinese Geographical Science, Journal of Mountain Science and Journal of Arid Land as research objects. We carried out a bibliometric analysis of the top 50 highly cited articles (a total of 221 papers) published in the four major Chinese geographical journals, with regards to cited frequency, subject distribution, research hotspots of highly cited articles. Furthermore, research contents and hotspots of the four journals were compared and explored. The results show that: (1) Physical Geography is hotspot disciplines of the four journals from discipline distribution, and environmental science plays an increasingly important role in recent years. (2) The keywords category statistics distinguished the key fields of the four journals while the keywords statistics revealed key research topics, with high frequency keywords such as climate change, land use, remote sensing, spatial pattern, and urbanization. (3) Rural development, urbanization, and wetland protection are important fields. What's more, multidisciplinary comprehensive research is the development trend of Geography. Results can be useful in directing the discipline design and focused direction of the journals, and providing reference for the development of Geography.
