Small-Scale Spatial Variability of Soil Nutrients and Vegetation Properties in Semi-Arid Northern China

Abstract.The spatial variability of farmland soil nutrients on different scales is important for farming as it forms the basis for the efficient utilization of soil nutrients and precision fertilization. Survey points were distributed throughout the study area on three different scales (county, field, and block). Research on the scale effect of the spatial variability of available nitrogen (AN), available phosphorus (AP), and available potassium (AK) involved a combination of classical statistics, geostatistics, and Geographic Information System (GIS) techniques. Results indicated that the three kinds of nutrients presented moderate variation intensity on the three scales. All of the nutrients tested exhibited strong spatial autocorrelation, indicating that spatial variability was primarily affected by structural factors, including climate, soil type and topography. As the sampling scale decreased, the nutrients showing weak variation at the large scale exhibited great variation at the small scale; the spatial autocorrelation of these three nutrients first became greater and then weakened; the distance of the spatial autocorrelation shortened gradually. Furthermore, the patch density value of the soil nutrient map increased, which indicated that the distribution of nutrients tended to be more fragile. When combined, sampling methods on the multi-scale allowed us to obtain real and systematic soil information. This study explored scale characteristics and the effects of spatial variability with regards to the primary nutrients available on farmland and provided a theoretical basis to effectively understand the nutrient status of regional farmland and improve the efficacy of soil sampling. Keywords: Multi-scale, Geostatistics, Patch density, Fractal dimension, Kriging interpolation.

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Effect of the Assimilation Frequency of Radar Reflectivity on Rain Storm Prediction by Using WRF-3DVAR

Remote Sensing ◽

10.3390/rs13112103 ◽

2021 ◽

Vol 13 (11) ◽

pp. 2103

Author(s):

Yuchen Liu ◽

Jia Liu ◽

Chuanzhe Li ◽

Fuliang Yu ◽

Wei Wang

Keyword(s):

Data Assimilation ◽

Northern China ◽

Horizontal Resolution ◽

Radar Reflectivity ◽

Small Scale ◽

Time Interval ◽

Outer Domain ◽

Storm Prediction ◽

The Impact ◽

Assimilation Time

An attempt was made to evaluate the impact of assimilating Doppler Weather Radar (DWR) reflectivity together with Global Telecommunication System (GTS) data in the three-dimensional variational data assimilation (3DVAR) system of the Weather Research Forecast (WRF) model on rain storm prediction in Daqinghe basin of northern China. The aim of this study was to explore the potential effects of data assimilation frequency and to evaluate the outputs from different domain resolutions in improving the meso-scale NWP rainfall products. In this study, four numerical experiments (no assimilation, 1 and 6 h assimilation time interval with DWR and GTS at 1 km horizontal resolution, 6 h assimilation time interval with radar reflectivity, and GTS data at 3 km horizontal resolution) are carried out to evaluate the impact of data assimilation on prediction of convective rain storms. The results show that the assimilation of radar reflectivity and GTS data collectively enhanced the performance of the WRF-3DVAR system over the Beijing-Tianjin-Hebei region of northern China. It is indicated by the experimental results that the rapid update assimilation has a positive impact on the prediction of the location, tendency, and development of rain storms associated with the study area. In order to explore the influence of data assimilation in the outer domain on the output of the inner domain, the rainfall outputs of 3 and 1 km resolution are compared. The results show that the data assimilation in the outer domain has a positive effect on the output of the inner domain. Since the 3DVAR system is able to analyze certain small-scale and convective-scale features through the incorporation of radar observations, hourly assimilation time interval does not always significantly improve precipitation forecasts because of the inaccurate radar reflectivity observations. Therefore, before data assimilation, the validity of assimilation data should be judged as far as possible in advance, which can not only improve the prediction accuracy, but also improve the assimilation efficiency.

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Effects of grazing and mowing on vertical distribution of soil nutrients and their stoichiometry (C: N: P) in a semi-arid grassland of North China

CATENA ◽

10.1016/j.catena.2021.105507 ◽

2021 ◽

Vol 206 ◽

pp. 105507

Author(s):

Lingling Chen ◽

Kexin Wang ◽

Taogetao Baoyin

Keyword(s):

Vertical Distribution ◽

Soil Nutrients ◽

North China ◽

Semi Arid

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Understanding Spatial Variability of NO2 in Urban Areas Using Spatial Modelling and Data Fusion Approaches

Atmosphere ◽

10.3390/atmos12020179 ◽

2021 ◽

Vol 12 (2) ◽

pp. 179

Author(s):

Said Munir ◽

Martin Mayfield ◽

Daniel Coca

Keyword(s):

Data Fusion ◽

Spatial Variability ◽

Urban Areas ◽

Road Traffic ◽

Dispersion Model ◽

Low Cost ◽

Spatial Modelling ◽

City Centre ◽

Small Scale ◽

The City

Small-scale spatial variability in NO2 concentrations is analysed with the help of pollution maps. Maps of NO2 estimated by the Airviro dispersion model and land use regression (LUR) model are fused with measured NO2 concentrations from low-cost sensors (LCS), reference sensors and diffusion tubes. In this study, geostatistical universal kriging was employed for fusing (integrating) model estimations with measured NO2 concentrations. The results showed that the data fusion approach was capable of estimating realistic NO2 concentration maps that inherited spatial patterns of the pollutant from the model estimations and adjusted the modelled values using the measured concentrations. Maps produced by the fusion of NO2-LCS with NO2-LUR produced better results, with r-value 0.96 and RMSE 9.09. Data fusion adds value to both measured and estimated concentrations: the measured data are improved by predicting spatiotemporal gaps, whereas the modelled data are improved by constraining them with observed data. Hotspots of NO2 were shown in the city centre, eastern parts of the city towards the motorway (M1) and on some major roads. Air quality standards were exceeded at several locations in Sheffield, where annual mean NO2 levels were higher than 40 µg/m3. Road traffic was considered to be the dominant emission source of NO2 in Sheffield.

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Farm Size, Risk Aversion and Overuse of Fertilizer: The Heterogeneity of Large-Scale and Small-Scale Wheat Farmers in Northern China

Land ◽

10.3390/land10020111 ◽

2021 ◽

Vol 10 (2) ◽

pp. 111

Author(s):

Haixia Wu ◽

Hantao Hao ◽

Hongzhen Lei ◽

Yan Ge ◽

Hengtong Shi ◽

...

Keyword(s):

Risk Aversion ◽

Large Scale ◽

Northern China ◽

Fertilizer Application ◽

Chemical Fertilizer ◽

Farm Size ◽

Small Scale ◽

Fertilizer Use ◽

Deviation Degree ◽

Small Scale Farmers

The excessive use of fertilizer has resulted in serious environmental degradation and a high health cost in China. Understanding the reasons for the overuse of fertilizer is critical to the sustainable development of Chinese agriculture, and large-scale operation is considered as one of the measures to deal with the excessive fertilizer use. Under the premise of fully considering the resource endowment and heterogeneity of large-scale farmers and small-scale farmers in production and management, different production decision-making frameworks were constructed. Based on the 300 large-scale farmers and 480 small-scale farmers in eight provinces of northern China wheat region, we analyzed the optimal fertilizer use amount and its deviation as well as the influencing factors of small-scale and large-scale farmers, then further clarified whether the development of scale management could solve the problem of excessive fertilizer use. The empirical results show that: (1) both small-scale farmers and large-scale farmers deviated from the optimal fertilizer application amount, where the deviation degree of optimal fertilizer application of small-scale farmers is significantly higher than that of large-scale farmers, with a deviation degree of 35.43% and 23.69% for small and large scale farmers, respectively; (2) not all wheat growers in North China had the problem of excessive use of chemical fertilizer, as the optimal level of chemical fertilizer application in Heilongjiang and Inner Mongolia are 346.5 kgha−1 and 335.25 kgha−1, while the actual fertilizer use amount was 337.2 kgha−1 and 324.6 kgha−1, respectively; and (3) the higher the risk aversion level, farmers tended to apply more fertilizer to ensure grain output. Therefore, increasing farm size should be integrated into actions such as improving technological innovation and providing better information transfer to achieve the goal of zero-increase in Chinese fertilizer use.

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