Spatial-temporal differences in in-stream flow requirement based on GIS： A case study of Yan＇an region, northern Shaanxi

Although water has the central function of the bloodstream in the biosphere especially in arid or semi-arid regions such as Yah＇an region in northwestern China, yet the very limited attention is paid to the role of the water-related processes in ecosystem. In this research, based on continuous nearly 50-year data including runoff volume, sediment discharge as well as sediment accretion from hydrographic stations, and 10-year information of water quality from pollution monitoring stations, the method for measuring in-stream flow requirement has been put forward supported by experiential models and GIS spatial analysis. Additionally, the changes of in-stream flow requirement for environment and economic development have been addressed from spatial-temporal dimensions. The results show that： （1） According to the central streams in Yan＇an region, mean annual in-stream flow requirement reaches 1.0619 billion m^3, and the surface water for economic exploitation is 0.2445 billion m3 （2） Mean annual in-stream flow requirement for sediment transfers in flood period occupies over 80% of the integrated volume in a year. （3） From the 1950s to 1970s, in-stream flow requirement for sediment transfers is comparatively higher, while from the 1980s to 1990s, this requirement presents a decreasing tendency.

Research on runoff variations based on wavelet analysis and wavelet neural network model： A case study of the Heihe River drainage basin （1944-2005）

The Heihe River drainage basin is one of the endangered ecological regions of China. The shortage of water resources is the bottleneck, which constrains the sustainable development of the region. Many scholars in China have done researches concerning this problem. Based on previous researches, this paper analyzed characteristics, tendencies, and causes of annual runoff variations in the Yingluo Gorge （1944-2005） and the Zhengyi Gorge （1954-2005）, which are the boundaries of the upper reaches, the middle reaches, and the lower reaches of the Heihe River drainage basin, by wavelet analysis, wavelet neural network model, and GIS spatial analysis. The results show that： （1） annual runoff variations of the Yingluo Gorge have principal periods of 7 years and 25 years, and its increasing rate is 1.04 m^3/s.10y; （2） annual runoff variations of the Zhengyi Gorge have principal periods of 6 years and 27 years, and its decreasing rate is 2.25 m^3/s.10y; （3） prediction results show that： during 2006-2015, annual runoff variations of the Yingluo and Zhengyi gorges have ascending tendencies, and the increasing rates are respectively 2.04 m^3/s.10y and 1.61 m^3/s.10y; （4） the increase of annual runoff in the Yingluo Gorge has causal relationship with increased temperature and precipitation in the upper reaches, and the decrease of annual runoff in the Zhengyi Gorge in the past decades was mainly caused by the increased human consumption of water resources in the middle researches. The study results will provide scientific basis for making rational use and allocation schemes of water resources in the Heihe River drainage basin.

Factors driving surface deformations in plain area of eastern Zhengzhou City,China

With the rapid socio-economic development and urban expansion,land subsidence has emerged as a major environmental issue,impeding the high-quality development of the plain area in eastern Zhengzhou City,Henan Province,China.However,effective prevention and control of land subsidence in this region have been challenging due to the lack of comprehensive surface deformations monitoring and the quantitative analysis of the factors driving these deformations.In order to accurately identify the dominant factor driving surface deformations in the study area,this study utilized the Persistent Scattered Interferometric Synthetic Aperture Radar(PS-InSAR)technique to acquire the spatio-temporal distribution of surface deformations from January 2018 to March 2020.The acquired data was verified using leveling data.Subsequently,GIS spatial analysis was employed to investigate the responses of surface deformations to the driving factors.The findings are as follows:Finally,the geographical detector model was utilized to quantify the contributions of the driving factors and reveal the mechanisms of their interactions.The findings are as follows:(1)Surface deformations in the study area are dominated by land subsidence,concentrated mainly in Zhongmu County,with a deformation rate of−12.5–−37.1 mm/a.In contrast,areas experiencing surface uplift are primarily located downtown,with deformation rates ranging from 0 mm to 8 mm;(2)Groundwater level,lithology,and urban construction exhibit strong spatial correlations with cumulative deformation amplitude;(3)Groundwater level of the second aquifer group is the primary driver of spatially stratified heterogeneity in surface deformations,with a contributive degree of 0.5328.The contributive degrees of driving factors are significantly enhanced through interactions.Groundwater level and the cohesive soil thickness in the second aquifer group show the strongest interactions in the study area.Their total contributive degree increases to 0.5722 after interactions,establishing them as the primary factors influencing surface deformation patterns in the study area.The results of this study can provide a theoretical basis and scientific support for precise prevention and control measures against land subsidence in the study area,as well as contributing to research on the underlying mechanisms.

A Time-dependent Stochastic Grassland Fire Ignition Probability Model for Hulun Buir Grassland of China

Grassland fire is one of the most important disturbance factors in the natural ecosystems.This paper focuses on the spatial distribution of long-term grassland fire patterns in the Hulun Buir Grassland located in the northeast of Inner Mongolia Autonomous Region in China.The density or ratio of ignition can reflect the relationship between grassland fire and different ignition factors.Based on the relationship between the density or ratio of ignition in different range of each ignition factor and grassland fire events,an ignition probability model was developed by using binary logistic regression function and its overall accuracy averaged up to 81.7%.Meanwhile it was found that daily relative humidity,daily temperature,elevation,vegetation type,distance to county-level road,distance to town are more important determinants of spatial distribution of fire ignitions.Using Monte Carlo method,we developed a time-dependent stochastic ignition probability model based on the distribution of inter-annual daily relative humidity and daily temperature.Through this model,it is possible to estimate the spatial patterns of ignition probability for grassland fire,which will be helpful to the quantitative evaluation of grassland fire risk and its management in the future.

PyLUR:Efficient software for land use regression modeling the spatial distribution of air pollutants using GDAL/OGR library in Python

Land use regression(LUR)models have been widely used in air pollution modeling.This regressionbased approach estimates the ambient pollutant concentrations at un-sampled points of interest by considering the relationship between ambient concentrations and several predictor variables selected from the surrounding environment.Although conceptually quite simple,its successful implementation requires detailed knowledge of the area,expertise in GIS,statistics,and programming skills,which makes this modeling approach relatively inaccessible to novice users.In this contribution,we present a LUR modeling and pollution-mapping software named PyLUR.It uses GDAL/OGR libraries based on the Python platform and can build a LUR model and generate pollutant concentration maps efficiently.This self-developed software comprises four modules:a potential predictor variable generation module,a regression modeling module,a model validation module,and a prediction and mapping module.The performance of the newly developed PyLUR is compared to an existing LUR modeling software called RLUR(with similar functions implemented on R language platform)in terms of model accuracy,processing efficiency and software stability.The results show that PyLUR out-performs RLUR for modeling in the Bradford and Auckland case studies examined.Furthermore,PyLUR is much more efficient in data processing and it has a capability to handle detailed GIS input data.