限额配水条件下水资源优化调配耦合模型研究——以阿瓦提县为例

该文建立了-种基于水盐平衡模型(子模型)和作物优化配水模型(子模型)的干旱灌区水资源优化调配耦合模型,通过地下水开采量实现了子模型的耦合,利用VBA编制了耦合模型的计算程序.将该模型应用干阿瓦提灌区,得出水资源联合调配方案,并得到相应的盐平衡结果.     

滹沱河冲积平原浅层地下水有机污染研究

地下水是滹沱河冲积平原主要工农业及生活用水水源,地下水污染程度直接影响到区内居民的健康状况。为阐明人类活动影响下浅层地下水有机污染状况,以滹沱河冲积平原为重点研究区,采集了大量地下水样品进行测试分析。结果表明:区内浅层地下水已经受到有机污染威胁,主要检出有机组分为卤代烃类和氯代苯类,其中三氯甲烷检出率高达14.02%;有机物超标样品较少,但是超标浓度较高,主要超标组分为四氯化碳。利用EPI Suite软件计算可知高检出组分GUS值均较高,有较高的一致性。由于有机物的高毒理性,研究区内有机污染已经不容忽视。     

基于昼夜水位波动法估算地下水蒸散发量的研究——以河西走廊典型绿洲为例

使用多种昼夜水位波动法(White法、Hays法、Loheide法),计算了黑河中游荒漠绿洲过渡带地下水浅埋区生长季典型时段地下水蒸散发(ETg),并将估算结果同彭曼方法获得的潜在蒸散发(PET)、E-601测量的水面蒸发(ET0)和Φ20测量的水面蒸发(ET1)进行相关性分析.结果表明:在几种算法中,Hays法精度最...     

Assessment of river basin habitat quality and its relationship with disturbance factors: A case study of the Tarim River Basin in Northwest China

The status of regional biodiversity is determined by habitat quality.The effective assessment of habitat quality can help balance the relationship between economic development and biodiversity conservation.Therefore,this study used the InVEST model to conduct a dynamic evaluation of the spatial and temporal changes in habitat quality of the Tarim River Basin in southern Xinjiang Uygur Autonomous Region of China by calc ulating the degradation degree levels for habitat types that were caused by threat factors from 1990 to 2018(represented by four periods of 1990,2000,2010 and 2018).Specifically,we used spatial autocorrelation analysis and Getis-Ord Gi*analysis to divide the study area into three heterogeneous units in terms of habitat quality:cold spot areas,hot spot areas and random areas.Hemeroby index,population density,gross domestic product(GDP),altitude and distance from water source(DWS)were then chosen as the main disturbance factors.Linear correlation and spatial regression models were subsequently used to analyze the influences of disturbance factors on habitat quality.The results demonstrated that the overall level of habitat quality in the TRB was poor,showing a continuous degradation state.The intensity of the negative correlation between habitat quality and Hemeroby index was proven to be strongest in cold spot areas,hot spot areas and random areas.The spatial lag model(SLM)was better suited to spatial regression analysis due to the spatial dependence of habitat quality and disturbance factors in heterogeneous units.By analyzing the model,Hemeroby index was found to have the greatest impact on habitat quality in the studied four periods(1990,2000,2010 and2018).The research results have potential guiding significance for the formulation of reasonable management policies in the TRB as well as other river basins in arid areas.     

Projections of temperature extremes based on preferred CMIP5 models:a case study in the Kaidu-Kongqi River basin in Northwest China

The extreme temperature has more outstanding impact on ecology and water resources in arid regions than the average temperature. Using the downscaled daily temperature data from 21 Coupled Model Inter-comparison Project(CMIP) models of NASA Earth Exchange Global Daily Downscaled Projections(NEX-GDDP) and the observation data, this paper analyzed the changes in temporal and spatiotemporal variation of temperature extremes, i.e., the maximum temperature(Tmax) and minimum temperature(Tmin), in the Kaidu-Kongqi River basin in Northwest China over the period 2020–2050 based on the evaluation of preferred Multi-Model Ensemble(MME). Results showed that the Partial Least Square ensemble mean participated by Preferred Models(PM-PLS) was better representing the temporal change and spatial distribution of temperature extremes during 1961–2005 and was chosen to project the future change. In 2020–2050, the increasing rate of Tmax(Tmin) under RCP(Representative Concentration Pathway) 8.5 will be 2.0(1.6) times that under RCP4.5, and that of Tmin will be larger than that of Tmax under each corresponding RCP. Tmin will keep contributing more to global warming than Tmax. The spatial distribution characteristics of Tmax and Tmin under the two RCPs will overall the same; but compared to the baseline period(1986–2005), the increments of Tmax and Tmin in plain area will be larger than those in mountainous area. With the emission concentration increased, however, the response of Tmax in mountainous area will be more sensitive than that in plain area, and that of Tmin will be equivalently sensitive in mountainous area and plain area. The impacts induced by Tmin will be universal and farreaching. Results of spatiotemporal variation of temperature extremes indicate that large increases in the magnitude of warming in the basin may occur in the future. The projections can provide the scientific basis for water and land plan management and disaster prevention and mitigation in the inland river basin.     

Spatiotemporal variations and driving factors of habitat quality in the loess hilly area of the Yellow River Basin: A case study of Lanzhou City,China

Rapid industrialization and urbanization have led to the most serious habitat degradation in China, especially in the loess hilly area of the Yellow River Basin, where the ecological environment is relatively fragile. The contradiction between economic development and ecological environment protection has aroused widespread concern. In this study, we used the habitat quality of Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST-HQ) model at different scales to evaluate the dynamic evolution characteristics of habitat quality in Lanzhou City, Gansu Province of China. The spatiotemporal variations of habitat quality were analyzed by spatial autocorrelation. A Geographical Detector (Geodetector) model was used to explore the driving factors that influencing the spatial differentiation of habitat quality, including natural factors, socio-economic factors, and ecological protection factors. The results showed that the habitat quality index of Lanzhou City decreased from 0.4638 to 0.4548 during 2000-2018. The areas with reduced the habitat quality index were mainly located in the Yellow River Basin and Qinwangchuan Basin, where are the main urban areas and the new economic development areas, respectively. The spatial distribution of habitat quality presented a trend of high in the surrounding areas and low in the middle, and showed a significant positive spatial autocorrelation. With the increase of study scale, the spatial distribution of habitat quality changed from concentrated to dispersed. The spatial differentiation of habitat quality in the study area was the result of multiple factors. Among them, topographic relief and slope were the key factors. The synergistic enhancement among these driving factors intensified the spatial differentiation of habitat quality. The findings of this study can provide a scientific basis for land resources utilization and ecosystem restoration in the arid and semi-arid land.     

Check dam extraction from remote sensing images using deep learning and geospatial analysis: A case study in the Yanhe River Basin of the Loess Plateau,China

Check dams are widely used on the Loess Plateau in China to control soil and water losses, develop agricultural land, and improve watershed ecology. Detailed information on the number and spatial distribution of check dams is critical for quantitatively evaluating hydrological and ecological effects and planning the construction of new dams. Thus, this study developed a check dam detection framework for broad areas from high-resolution remote sensing images using an ensemble approach of deep learning and geospatial analysis. First, we made a sample dataset of check dams using GaoFen-2 (GF-2) and Google Earth images. Next, we evaluated five popular deep-learning-based object detectors, including Faster R-CNN, You Only Look Once (version 3) (YOLOv3), Cascade R-CNN, YOLOX, and VarifocalNet (VFNet), to identify the best one for check dam detection. Finally, we analyzed the location characteristics of the check dams and used geographical constraints to optimize the detection results. Precision, recall, average precision at intersection over union (IoU) threshold of 0.50 (AP₅₀), IoU threshold of 0.75 (AP₇₅), and average value for 10 IoU thresholds ranging from 0.50-0.95 with a 0.05 step (AP_50-95), and inference time were used to evaluate model performance. All the five deep learning networks could identify check dams quickly and accurately, with AP_50-95, AP₅₀, and AP₇₅ values higher than 60.0%, 90.0%, and 70.0%, respectively, except for YOLOv3. The VFNet had the best performance, followed by YOLOX. The proposed framework was tested in the Yanhe River Basin and yielded promising results, with a recall rate of 87.0% for 521 check dams. Furthermore, the geographic analysis deleted about 50% of the false detection boxes, increasing the identification accuracy of check dams from 78.6% to 87.6%. Simultaneously, this framework recognized 568 recently constructed check dams and small check dams not recorded in the known check dam survey datasets. The extraction results will support efficient watershed management and guide future studies on soil erosion in the Loess Plateau.