Yunfeng Qiao

Monitoring the spatio-temporal changes of terrestrial water storage using GRACE data in the Tarim River basin between 2002 and 2015

With the threat of water shortages intensifying, the need to identify the terrestrial water storage (TWS) variation in the Tarim River Basin (TRB) becomes very significant for managing its water resource. Due to the lack of large-scale hydrological data, this study employed the Gravity Recovery and Climate Experiment (GRACE) and Global Land Data Assimilation System (GLDAS) to monitor TWS variation in the TRB during the period of 2002-2015, cooperating with two statistical techniques, Principal Component Analysis (PCA) - Empirical Orthogonal Function (EOF) and Multiple Linear Regression (MLR). Results indicated that (1) the Tropical rainfall measuring mission (TRMM) data can be applied well in the TRB; (2) the EOF result showed that both the time series of TRMM precipitation and GRACE-derived TWS in the TRB between 2002 and 2015 were dominated by the annual signals, which were followed by the semiannual signals; (3) the linear trend for the spatially averaged GRACE-derived TWS changes exhibited an decrease of 1.6±1.1mm/a, and the EOF result indicated a significant decrease of 4.1±1.5mm/a in the north of TRB; (4) while the precipitation variations was the major driver for the TWS changes, the GLDAS-derived TWS (i.e., soil moisture) decrease and ground water decrease played the major role in the TWS decrease in the north of TRB for the significant correlation (P<0.05). The changes of TWS might be linked to excessive exploitation of water resources, increased population, and shrinking water supplies, which would impact on the water level of the lakes or reservoir.

Effect of experimental warming on soil respiration under conventional tillage and no-tillage farmland in the North China Plain

Abstract Understanding the response of soil respiration to global warming in agro-ecosystem is crucial for simulating terrestrial carbon (C) cycle. We conducted an infrared warming experiment under conventional tillage (CT) and no-tillage (NT) farmland for winter wheat and summer maize rotation system in North China Plain (NCP). Treatments include CT with and without warming (CTW and CTN), NT with and without warming (NTW and NTN). The results indicated that warming had no significant effect on soil moisture in irrigated farmland of NCP (P>0.05). The elevated average soil temperature of 1.1–1.6°C in crop growing periods could increase annual soil CO2 emission by 10.3% in CT filed (P>0.05), but significantly increase it by 12.7% in NT field (P

Comprehensive assessment of drought risk in the arid region of Northwest China based on the global palmer drought severity index gridded data

Droughts are extremely widespread natural disasters, which cause the most severe losses among natural disasters. The comprehensive drought risk in Northwest China (NWC) was evaluated based on the self-calibrating (SC) Palmer Drought Severity Index (PDSI) and copula method. The major conclusions are the following: (1) based on the rotated empirical orthogonal function (REOF), a significant consistency in the spatial distribution of the monthly averaged SC-PDSI was observed in NWC, especially in the subregions Inner Mongolia Plateau (IM), Hexi Corridor (HX), and Qiangtang Plateau (QT); (2) the largest frequency was obtained for slight drought and slight wet conditions, while extreme drought and extreme wet showed the lowest values; (3) with respect to the PDSI-th, the Clayton, Arch12, Arch12, Arch12, Arch12, and Frank played the major roles in the copula weight in the subregions IM, HX, Qinghai River Basin (QH), QT, North Xinjiang (NXJ), and South Xinjiang (SXJ), respectively. In terms of PDSI-pm, Arch12, Clayton, Gaussian, Arch12, Clayton, and Clayton dominated the weights of multi-copula functions in the regions IM, HX, QH, QT, NXJ, and SXJ, respectively; and (4) the frequency and probability of droughts in each area differed. The least drought events occurred in the QT and the most emerged in the HX for SC-PDSI.

Impact of environmental factors on water quality at multiple spatial scales and its spatial variation in Huai River Basin, China

Quantitative assessment of water quality and its spatial variation identification, as well as the discernment of primary factors affecting water quality are in its urgent in water environment management. In this study, four key water quality indicators, namely, ammonia nitrogen (NH4+-N), permanganate index (CODMn), total phosphorus (TP) and total nitrogen (TN) at 71 sampling sites were selected to evaluate water quality and its spatial variation identification. More concerns were emphasized on the anthropogenic factors (land use pattern) and natural factors (river density, elevation and precipitation) to quantify the overall water quality variations at different spatial scales. Results showed that the Yi-Shu-Si River sub-basin had a better water quality status than the Huai River sub-basin. The moderate polluted area nearly distributed in the upper and middle reaches of the Shaying River and Guo River. The high cluster centers which were surrounded with CODMn, NH4+-N, TN and TP mainly also distributed in the upper and middle reaches of the Shaying River and Guo River. Redundancy analysis showed that the 200 m buffer area acted as the most sensitive area, which was easily subjected to pollution. The precipitation was identified as the most important variables among all the studied hydrological units, followed by farmland, urban land or elevation. The point source pollution was still existed although the non-point source pollution was also identified. The urban surface runoff pollution was severer than farmland fertilizer loss at the sub-basin scale in flood season, while the farmland showed “small-scale” effects for explaining overall water quality variations. This research is helpful for identifying the overall water quality variations from the scale-process interactions and providing a scientific basis for pollution control and decision making for the Huai River Basin.