Spatial distribution of snow depth based on geographically weighted regression kriging in the Bayanbulak Basin of the Tianshan Mountains,China

Snow depth is a general input variable in many models of agriculture,hydrology,climate and ecology.This study makes use of observational data of snow depth and explanatory variables to compare the accuracy and effect of geographically weighted regression kriging(GWRK)and regression kriging(RK)in a spatial interpolation of regional snow depth.The auxiliary variables are analyzed using correlation coefficients and the variance inflation factor(VIF).Three variables,Height,topographic ruggedness index(TRI),and land surface temperature(LST),are used as explanatory variables to establish a regression model for snow depth.The estimated spatial distribution of snow depth in the Bayanbulak Basin of the Tianshan Mountains in China with a spatial resolution of 1 km is obtained.The results indicate that 1)the result of GWRK's accuracy is slightly higher than that of RK(R~2=0.55 vs.R~2=0.50,RMSE(root mean square error)=0.102 m vs.RMSE=0.077 m);2)for the subareas,GWRK and RK exhibit similar estimation results of snow depth.Areas in the Bayanbulak Basin with a snow depth greater than 0.15m are mainly distributed in an elevation range of 2632.00–3269.00 m and the snow in this area comprises 45.00–46.00% of the total amount of snow in this basin.However,the GWRK resulted in more detailed information on snow depth distribution than the RK.The final conclusion is that GWRK is better suited for estimating regional snow depth distribution.     

Assessment of phytoplankton class abundance using fluorescence excitation-emission matrix by parallel factor analysis and nonnegative least squares

The feasibility of using fluorescence excitation-emission matrix (EEM) along with parallel factor analysis (PARAFAC) and nonnegative least squares (NNLS) method for the differentiation of phytoplankton taxonomic groups was investigated. Forty-one phytoplankton species belonging to 28 genera of five divisions were studied. First, the PARAFAC model was applied to EEMs, and 15 fluorescence components were generated. Second, 15 fluorescence components were found to have a strong discriminating capability based on Bayesian discriminant analysis (BDA). Third, all spectra of the fluorescence component compositions for the 41 phytoplankton species were spectrographically sorted into 61 reference spectra using hierarchical cluster analysis (HCA), and then, the reference spectra were used to establish a database. Finally, the phytoplankton taxonomic groups was differentiated by the reference spectra database using the NNLS method. The five phytoplankton groups were differentiated with the correct discrimination ratios (CDRs) of 100% for single-species samples at the division level. The CDRs for the mixtures were above 91% for the dominant phytoplankton species and above 73% for the subdominant phytoplankton species. Sixteen of the 85 field samples collected from the Changjiang River estuary were analyzed by both HPLC-CHEMTAX and the fluorometric technique developed. The results of both methods reveal that Bacillariophyta was the dominant algal group in these 16 samples and that the subdominant algal groups comprised Dinophyta, Chlorophyta and Cryptophyta. The differentiation results by the fluorometric technique were in good agreement with those from HPLC-CHEMTAX. The results indicate that the fluorometric technique could differentiate algal taxonomic groups accurately at the division level.     

Surface energy balance of Bayi Ice Cap in the middle of Qilian Mountains,China

Energy balance at the glacier surface is important for understanding the impacts of climate change on glaciers. Here, we analyzed the characteristics of the glacier surface energy fluxes along with their contributions to glacier melt on Bayi Ice Cap in Qilian Mountains by using a point-scale energy balance model. The half-hourly meteorological data from an automatic weather station (AWS) located on the glacier was used to drive the energy balance model. The model simulated results could accurately represent the mass-balance observations from the stake near the weather station during summer 2016. Our results showed the net radiation (86%) played an important role in the surface energy balance, and the contribution of the turbulent heat fluxes (14%) to the energy budget was relatively less important. A distinct behavior of energy balance, as compared to other continental glaciers in China (e.g., two adjacent glaciers Laohugou No. 12 Glacier and Qiyi Glacier), is the fact that a sustained period of positive turbulent latent flux exists on Bayi Ice Cap during August, causing faster melt rate in the month of August. Our study also presented the effect of frequent summer snowfall in slowing down surface melt by changing the surface albedo during the beginning of the melting season.     

Comparison of the spatio-temporal dynamics of vegetation between the Changbai Mountains of eastern Eurasia and the Appalachian Mountains of eastern North America

The Changbai Mountains and the Appalachian Mountains have similar spatial contexts. The elevation, latitude, and moisture gradients of both mountain ranges offer regional insight for investigating the vegetation dynamics in eastern Eurasia and eastern North America. We determined and compared the spatial patterns and temporal trends in the normalized difference vegetation index (NDVI) in the Changbai Mountains and the Appalachian Mountains using time series data from the Global Inventory Modeling and Mapping Studies 3rd generation dataset from 1982 to 2013. The spatial pattern of NDVI in the Changbai Mountains exhibited fragmentation, whereas NDVI in the Appalachian Mountains decreased from south to north. The vegetation dynamics in the Changbai Mountains had an insignificant trend at the regional scale, whereas the dynamics in the Appalachian Mountains had a significant increasing trend. NDVI increased in 55% of the area of the Changbai Mountains and in 95% of the area of the Appalachian Mountains. The peak NDVI occurred one month later in the Changbai Mountains than in the Appalachian Mountains. The results revealed a significant increase in NDVI in autumn in both mountain ranges. The climatic trend in the Changbai Mountains included warming and decreased precipitation, and whereas that in the Appalachian Mountains included significant warming and increased precipitation. Positive and negative correlations existed between NDVI and temperature and precipitation, respectively, in both mountain ranges. Particularly, the spring temperature and NDVI exhibited a significant positive correlation in both mountain ranges. The results of this study suggest that human actives caused the differences in the spatial patterns of NDVI and that various characteristics of climate change and intensity of human actives dominated the differences in the NDVI trends between the Changbai Mountains and the Appalachian Mountains. Additionally, the vegetation dynamics of both mountain ranges were not identical to those in previous broader-scale studies.     

Estimating aboveground biomass using Pléiades satellite image in a karst watershed of Guizhou Province,Southwestern China

Biomass in karst terrain has rarely been measured because the steep mountainous limestone terrain has limited the ability to sample woody plants.Satellite observation, especially at high spatial resolution, is an important surrogate for the quantification of the biomass of karst forests and shrublands. In this study, an artificial neural network(ANN) model was built using Pléiades satellite imagery and field biomass measurements to estimate the aboveground biomass(AGB) in the Houzhai River Watershed, which is a typical plateau karst basin in Central Guizhou Province, Southwestern China. A back-propagation ANN model was also developed.Seven vegetation indices, two spectral bands of Pléiades imagery, one geomorphological parameter,and land use/land cover were selected as model inputs. AGB was chosen as an output. The AGB estimated by the allometric functions in 78 quadrats was utilized as training data(54 quadrats, 70%),validation data(12 quadrats, 15%), and testing data(12 quadrats, 15%). Data-model comparison showed that the ANN model performed well with an absolute root mean square error of 11.85 t/ha, which was 9.88%of the average AGB. Based on the newly developed ANN model, an AGB map of the Houzhai River Watershed was produced. The average predicted AGB of the secondary evergreen and deciduous broadleaved mixed forest, which is the dominant forest type in the watershed, was 120.57 t/ha. The average AGBs of the large distributed shrubland,tussock, and farmland were 38.27, 9.76, and 11.69 t/ha, respectively. The spatial distribution pattern ofthe AGB estimated by the new ANN model in the karst basin was consistent with that of the field investigation. The model can be used to estimate the regional AGB of karst landscapes that are distributed widely over the Yun-Gui Plateau.     

Stabilization versus decomposition in alpine ecosystems of the Northwestern Caucasus: The results of a tea bag burial experiment

Mountainous areas exhibit highly variable decomposition rates as a result of strong local differences in climate and vegetation type. This paper describes the effect of these factors on two major determinants of the local carbon cycle: litter decomposition and carbon stabilization. In order to adequately reflect local heterogeneity, we have sampled 12 typical plant communities of the Russian Caucasus. In order to minimize confounding effects and encourage comparative studies, we have adapted the widely used tea bag index (TBI) that is typically used in areas with low decomposition. By incubating standardized tea litter for a year, we investigated whether (1) initial litter decomposition rate (k) is negatively correlated with litter stabilization (S) and (2) whether k or S exhibit correlations with altitude and other environmental conditions. Our results show that S and k are not correlated. Altitude, pH, and water content significantly influenced the stabilization factor S, while soil-freezing had no influence. In contrast, none of these factors predicted the decomposition rate k. Based on our data, we argue that collection of decomposition rates alone, as is now common practice, is not sufficient to understand carbon input to soils and can potentially lead to misleading results. Our data on community-specific decomposition and stabilization rates further constrain estimates of litter accumulation in subalpine communities and the potential effects of climate change.     

Spatio-temporal Variation of Soil Respiration and Its Driving Factors in Semi-arid Regions of North China

Soil respiration (SR) is the second-largest flux in ecosystem carbon cycling. Due to the large spatio-temporal variability of environmental factors, SR varied among different vegetation types, thereby impeding accurate estimation of CO₂ emissions via SR. However, studies on spatio-temporal variation of SR are still scarce for semi-arid regions of North China. In this study, we conducted 12-month SR measurements in six land-use types, including two secondary forests (Populus tomentosa (PT) and Robinia pseudoacacia (RP)), three artificial plantations (Armeniaca sibirica (AS), Punica granatum (PG) and Ziziphus jujuba (ZJ)) and one natural grassland (GR), to quantify spatio-temporal variation of SR and distinguish its controlling factors. Results indicated that SR exhibited distinct seasonal patterns for the six sites. Soil respiration peaked in August 2012 and bottomed in April 2013. The temporal coefficient of variation (CV) of SR for the six sites ranged from 76.98% to 94.08%, while the spatial CV of SR ranged from 20.28% to 72.97% across the 12-month measurement. Soil temperature and soil moisture were the major controlling factors of temporal variation of SR in the six sites, while spatial variation in SR was mainly caused by the differences in soil total nitrogen (STN), soil organic carbon (SOC), net photosynthesis rate, and fine root biomass. Our results show that the annual average SR and Q₁₀ (temperature sensitivity of soil respiration) values tended to decrease from secondary forests and grassland to plantations, indicating that the conversion of natural ecosystems to man-made ecosystems may reduce CO₂ emissions and SR temperature sensitivity. Due to the high spatio-temporal variation of SR in our study area, care should be taken when converting secondary forests and grassland to plantations from the point view of accurately quantifying CO₂ emissions via SR at regional scales.     

Evolution Characteristics of Government-Industry-University-Research Cooperative Innovation Network for China’s Agriculture and Influencing Factors: Illustrated According to Agricultural Patent Case

Under the special background of China, the cooperative innovation between different government-industry-university-research institutes plays an increasingly important role in the agricultural field. However, the existing literature has paid little attention to it. Considering the cooperation patents, published in the agriculture field stemming from the Full-text Database of China Patents as the study object, the spatial and institutional attribute of the authors as the data source, and by combining the social network and spatial econometrics analysis, this paper analyzes the structure evolution characteristics of cooperative innovation networks of agricultural government-industry-university-research institute in the city level of China in 1985–2014, based on the triple helix theory, with the influence factors discussed. This shows that, 1) since 1985, China's agricultural innovation level has been substantially increased, but the development degree of the cooperative innovation network is low, and the patent cooperation mainly relies on authors in the same unit; 2) enterprises play a leading role in the agricultural cooperative innovation. The effect of the government and hybrid organizations driven by the government is not obvious; 3) the cooperative innovation in the province and city dominates, and a multi-pole pattern has been formed. The cooperative innovation network structure evolves from a single helix empty core and double helix multi core to a double helix hierarchical network; 4) the city's science, education funding and personnel investment are key factors determining the agricultural cooperative innovation, while the agricultural development of the city presents slight negative impacts on it. The spatial mismatch of supply and demand is present in the technical cooperative innovation of China's agriculture. Therefore, the science enhancement and education investment to big agricultural provinces should be promptly implemented.     

Iron Regulation of Wetland Vegetation Performance Through Synchronous Effects on Phosphorus Acquisition Efficiency

Iron-rich groundwater flowing into wetlands is a worldwide environmental pollution phenomenon that is closely associated with the stability of wetland ecosystems. Combined with high phosphorus(P) loading from agricultural runoff, the prediction of the evolution of wetland vegetation affected by compound contamination is particularly urgent. We tested the effects of anaerobic iron-rich groundwater discharge in a freshwater marsh by simulating the effect of three levels of eutrophic water on native plants(Glyceria spiculosa(Fr. Schmidt.) Rosh.). The management of wetland vegetation with 1–20 mg/L Fe input is an efficient method to promote the growth of plants, which showed an optimum response under a 0.10 mg/L P surface water environment. Iron-rich groundwater strongly affects the changes in ecological niches of some wetland plant species and the dominant species. In addition, when the P concentration in a natural body of water is too high, the governance effect of eutrophication might not be as expected. Under iron-rich groundwater conditions, the δ~(13)C values of organs were more depleted, which can partially explain the differences in δ~(13)C in the soil profile. Conversely, the carbon isotope composition of soil organic carbon is indicative of past changes in vegetation. The results of our experiments confirm that iron-rich groundwater discharge has the potential to affect vegetation composition through toxicity modification in eutrophic environments.     

Porosity Distribution Characteristics and Geological Analysis of Brine Storage Medium in the Qaidam Basin,Western China

Underground brine is an unusual water resource that contains abundant mineral resources. It is distributed widely in the Qaidam Basin, western China, a hyperarid inland basin located in the northern Tibetan Plateau. Pores in the brine storage medium act as storage space and transmission channels of underground brine. Therefore, the porosity of brine storage medium determines its ability to store brine. In this study, Mahai Salt Lake was used as the research area as a modern saline lake located in the north area of the Qaidam Basin. A total of 100 porosity samples were collected from eight sampling points in two profiles of the research area at sampling depths of 1.30–314.78 m. The porosity distribution characteristics and influencing factors in brine storage medium were analysed according to the measured porosity data. Based on analysis of the pore structure characteristics, the brine storage medium contains intercrystalline pores, unlike conventional freshwater storage mediums. Moreover, the primary salt rock is susceptible to dissolution by lighter brine, facilitating the formation of secondary porosity. Due to the formation of secondary pores, a porosity greater than 20% remains even at buried depths greater than 100 m. Based on the geological statistical analysis, due to the geographic location, salt formation time, and depositional environment, the porosity values of Mahai Salt Lake do not exhibit a wider distribution, but also show more extreme values than a nearby salt lake. Based on the porosity characteristics by depth, due to the presence of secondary pores, flooding, stratigraphic static pressure, and other factors, porosity shows fluctuations with increasing depth.