基于气温的潜在蒸散发量估算方法在我国西北干旱地区的应用比较

根据石羊河流域两个气象站的历史数据,选取7种基于气温的潜在蒸散发量估算方法,以FAO56-PM法计算的ET作为参考值,对其进行比较分析,最后再用实测蒸发皿蒸发量对这些方法在西北干旱地区的适用性进行评价。结果表明:在两个站使用7种方法的初始参数时,多数产生较大误差。校正参数后两个站所有方法在估算逐月ET和月平均ET时都得到明显改进。改进的Romanenko法在两个站所有月份以及全年误差都最小,其次是Hargreaves和Linacre法。用实测小型蒸发皿蒸发量进行验证时,改进的Romanenko法与其相关系数最高,为0.97,其次是Linacre和Hargreaves法。就气温法而言,校正参数后的Romanenko,Hargreav-es和Linacre法可在此研究区域用于估算ET,并建议优先选择Romanenko法,与小型蒸发皿的折算系数适宜选取0.60。     

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...     

黑河流域叶面积指数的遥感反演

以黑河流域为研究区 ,探寻基于TM影像提取的植被指数 (VIs)与野外实测数据间叶面积指数 (LAI)的反演研究。结果表明 ,各植被指数与LAI均具有较高的相关性 ,整个研究区土壤调整植被指数 (SAVI)具有最高的LAI反演精度 ;把研究区分成绿洲区和山区 ,各种植被指数与LAI的相关系数和反演精度均有很大程度提高 ,同时得到绿洲区SAVI最适于反演LAI ,山区SR最适于反演LAI ,通过这种方式估算出黑河流域叶面积指数图。     

基于SUFI-2算法的SWAT模型在陕西黑河流域径流模拟中的应用

参数率定及敏感性分析是用来提高模型精度,确定模型关键参数及改善模型结构的非常有效的方法。本文基于SUFI-2算法,通过SWAT模型对陕西黑河流域进行了模拟,同时基于SUFI-2算法对模型参数进行了敏感性分析,结果表明：（1）影响黑河流域径流模拟结果精度的主影响因子是scs径流曲线参数;（2）用2005年到2011年的实测径流资料对模型进行了率定与验证,模拟确定性系数 R2和模型效率系数ENS均高于0．8;（3）通过SUFI-2算法与SCE-UA算法比较,发现SUFI-2算法所需时间短,精度更高。     

面积高程积分值计算方法的比较

面积高程积分值(Hypsometric index)能够对流域地貌发育阶段进行定量的描述,在地貌学中得到了广泛的应用.关于该值的计算方法较多,然而,在实际应用中,哪种方法才能带来更便捷准确的结果,尚缺乏探讨.文中基于Arcgis9.3及SRTM-DEM数据,以白龙江流域32个子流域为例,分别利用积分曲线法、体积比例法及起伏比法求取了各子流域的HI值.结果表明:三种方法计算的HI值几乎一致,但起伏比法是计算HI值最高效简捷的方法;积分曲线法及体积比例法在流域数目较少时可以使用,但流域数目较多时不推荐使用.该结果可为利用HI值进行地貌研究提供借鉴.     

河西内陆河流域水资源可利用量与利用现状评价

根据最新的水资源可利用量估算方法的国家规范技术要求,通过中下游河道内生态需水量的计算,对河西内陆河流域3大水系的水资源可利用量进行系统分析和估算,并对3大流域水资源利用现状进行评价.结果表明:河西水资源开发利用程度已远远超出本流域的水资源可利用量,特别是石羊河流域、黑河流域开发利用明显过度,由此引发了一系列社会和生态危机.因此,需要依据流域水资源可利用量,指导全流域水资源合理开发利用、统一配置以及科学管理.     

高寒区潜在蒸散量的计算方法探讨

用黄河源区的玛多、达日、久治和玛曲4个气象站1971-2005年的气温、风速、日照时间、水汽压等资料,对4种常用的潜在蒸散量计算方法在高寒区的适用性进行了分析,并对Makkink方法进行了改进.结果表明:Makkink方法在全年都有明显的低估现象;Priestley and Taylor法除冬、春季外有高估现象;Hargreaves and samani温度法在玛多站全年均有低估现象,在其他站春、秋、冬季有低估现象;国内Penman修正式计算的潜在蒸散量值与FA0 Penman-Monteifh法较接近,但国内Penman修正式在春、夏季有轻微的高估现象;改进的Makkink方法所需资料简便易得,计算精度高,在高寒区具有很强的实用性.     

棉花全生育期叶片SPAD值的遥感估算模型

叶绿素含量是评估棉花生长状况的重要参数,估算叶绿素含量对于棉花生长监测具有重要意义。以渭北旱塬区种植的棉花为试验材料,测量全生育期棉花叶片SPAD值与冠层反射率光谱,将原始高光谱反射率、一阶微分光谱反射率、不同波段组合的遥感光谱参数分别与SPAD值做相关性分析,用传统回归分析方法构建五种重要光谱参数的SPAD值预测模型,同时,采用PLSR方法建立全生育期SPAD值的估算模型。最后对模型进行检验,筛选出精度最高的模型。建模结果表明,基于多种光谱参数的全生育期PLSR预测模型精度最高、预测效果最好,估算模型的决定系数R~2为0.733,验证模型R~2为0.737。PLSR方法建立的多光谱参数的SPAD值估算模型预测效果显著,利用高光谱技术对棉花SPAD值进行监测,可为全生育期棉花长势遥感监测提供依据。     

基于农业生态区域模型的黑河流域土地资源承载力

采用国内外广泛采用的农业生态学（AEZ）模型,估算黑河流域土地资源承载力,探讨影响土地资源承载力的主要因素,并通过人口压力指数对计算结果进行分析评价,最后与黑河流域的实际情况相结合,给出提高黑河流域人口承载力的相关建议。研究表明：黑河流域人口压力指数为1.13,基本处于适宜状态,但可承载人口密度较低,按人均热量消耗和人均蛋白质消耗计算,分别为15.14人•km-2和13.77人•km-2,大于联合国制定的干旱区人口密度7人•km-2的界限,但小于半干旱区20人•km-2的临界值,而且分布极不均匀,水分是土地资源承载力的主要自然限制因子。     

干旱区内陆河流域人口统计数据的空间化—以黑河流域为例

人口数据对于全球、洲际、区域尺度的人与环境交互作用研究的重要性已经被广泛地认知。研究人口数据与环境的关系不仅要关心人口的数量,更要分析其空间分布。遥感和地理信息系统是人口统计数据实现空间化的有效工具。结合干旱区内陆河流域特征构建适合黑河流域农村和城市人口分布的模型:基于土地利用数据建立回归模型,模拟黑河流域农村人口的空间分布;利用城市边缘距离来表征城市多中心情形,将不规则的城市虚拟成同心圆分布的城市,并将Clark模型和加幂指数模型转换成基于城市边缘距离的形式来模拟研究区城市人口密度。基于以上方法最终获得黑河流域25m格网的人口空间分布结果和尺度上推后的1km格网数据。在乡镇级别对人口空间化结果进行精度验证,并与已有数据库(GPW1995、UNEP/GRID 1995、LandScan 2002和cn2000pop)估计的黑河流域人口数据进行比较,结果均表明本研究采用的方法和模型可以获得更高精度的流域人口空间分布数据。     

风沙区参考作物需水量计算模式的研究

根据包头气象站30a逐旬气象资料和2001年作物生长期逐日资料,用Penman-Monteith公式、Blaney-Criddle公式、Hargreaves公式、Priestley-Taylor公式、Markkink公式估算逐旬和逐日的参考作物需水量(ET0),以Penman-Monteith方法计算结果作为标准来评价其它4种计算方法。经比较分析,用其它4种方法和Penman-Monteith方法计算出的逐旬ET0值均具有较好的相关性,其中以Blaney-Criddle法估算结果最好。4种方法估算的逐日ET0误差较大,相比而言,Markkink法优于其它3种方法。同时,分析了ET0与气温的关系,并建立了适合该地区ET0计算的经验公式,用经验公式估算ET0方法简单,且具有较高的精度。     

Performance and uncertainty analysis of a short-term climate reconstruction based on multi-source data in the Tianshan Mountains region,China

Short-term climate reconstruction, i.e., the reproduction of short-term(several decades) historical climatic time series based on the relationship between observed data and available longer-term reference data in a certain area, can extend the length of climatic time series and offset the shortage of observations. This can be used to assess regional climate change over a much longer time scale. Based on monthly grid climate data from a Coupled Model Inter-comparison Project phase 5(CMIP5) dataset for the period of 1850–2000, the Climatic Research Unit(CRU) dataset for the period of 1901–2000 and the observed data from 53 meteorological stations located in the Tianshan Mountains region(TMR) of China during the period of 1961–2011, we calibrated and validated monthly average temperature(MAT) and monthly accumulated precipitation(MAP) in the TMR using the delta, physical scaling(SP) and artificial neural network(ANN) methods. Performance and uncertainty during the calibration(1971–1999) and verification(1961–1970) periods were assessed and compared using traditional performance indices and a revised set pair analysis(RSPA) method. The calibration and verification processes were subjected to various sources of uncertainty due to the influence of different reconstructed variables, different data sources, and/or different methods used. According to traditional performance indices, both the CRU and CMIP5 datasets resulted in satisfactory calibrated and verified MAT time series at 53 meteorological stations and MAP time series at 20 meteorological stations using the delta and SP methods for the period of 1961–1999. However, the results differed from those obtained by the RSPA method. This showed that the CRU dataset produced a low degree of uncertainty(positive connection degree) during the calibration and verification of MAT using the delta and SP methods compared to the CMIP5 dataset. Overall, the calibrated and verified MAP had a high degree of uncertainty(negative connection degree) regardless of the dataset or reconstruction method used. Therefore, the reconstructed time series of MAT for the period of 1850(or 1901)–1960 based on the CRU and CMIP5 datasets using the delta and SP methods could be used for further study. The results of this study will be useful for short-term(several decades) regional climate reconstruction and longer-term(100 a or more) assessments of regional climate change.     

锡林郭勒草原饲草料作物需水量计算方法比较及相关性分析

根据2004~2005年锡林郭勒典型草原区节水灌溉基地的试验资料和气象资料,通过水量平衡法直接计算作物需水量及根据作物系数Kc和Penman-Monteith公式、Penman修正式、Blaney-Criddle公式、Hargreaves公式、Priestley-Taylor公式、Markkink公式计算参考作物蒸发蒸腾量等间接求解作物需水量。以逐旬需水量的结果进行了相关分析,对以上各种方法计算苜蓿、披碱草和青贮玉米需水量的结果进行了适用性评价;以PM方法的计算结果对常用的6种计算饲草料作物需水量方法进行了评价,根据建立的回归方程实现了PM方法与其它6种方法的相互转换,并用2006年的实测资料对回归方程进行了检验,精度较高。     

基于SWAT模型的内蒙古闪电河流域径流模拟研究

以内蒙古闪电河流域为研究对象,采用分布式水文模型SWAT对闪电河流域水文过程进行模拟,模型采用1962～1975年月径流资料进行参数率定,将1986-2001年作为模型的检验期对模型的适用性进行评估。结果表明:实测与模拟的逐月流量总体较为接近,率定期和验证期的Nash-Sutcliffe效率系数均大于0.60,实测与模拟的月流量之间的相关系数在0.65以上,率定期的多年平均相对误差小于5%。由于人类活动的影响,验证期的模拟误差相对较大,超过12%。总体来看,SWAT模型参数经多次率定之后,能够较好地模拟干旱半干旱地区的径流过程,在同类地区具有一定的适用性。模拟过程中,对于降水量突然增大的月份,SWAT模型对月径流量的模拟结果往往超过实测值一倍,直接导致整个模拟过程的误差偏大。然而,模拟值与降水量在年内分配上却表现出很好的相关性,降水偏大的月份径流量敏感性大。同时,SWAT模型模拟径流结果受参数影响很大,调整融雪参数值,模拟精度在原有基础上相对提高。     

Uncertainty assessment of potential evapotranspiration in arid areas,as estimated by the Penman-Monteith method

The Penman-Monteith(PM)method is the most widely used technique to estimate potential worldwide evapotranspiration.However,current research shows that there may be significant errors in the application of this method in arid areas,although questions remain as to the degree of this estimation error and how different surface conditions may affect the estimation error.To address these issues,we evaluated the uncertainty of the PM method under different underlying conditions in an arid area of Northwest China by analyzing data from 84 meteorological stations and various Moderate Resolution Imaging Spectroradiometer(MODIS)products,including land surface temperature and surface albedo.First,we found that when the PM method used air temperature to calculate the slope of the saturation vapor pressure curve,it significantly overestimated the potential evapotranspiration;the mean annual and July–August overestimation was 83.9 and 36.7 mm,respectively.Second,the PM method usually set the surface albedo to a fixed value,which led to the potential evapotranspiration being underestimated;the mean annual underestimation was 27.5 mm,while the overestimation for July to August was 5.3 mm.Third,the PM method significantly overestimated the potential evapotranspiration in the arid area.This difference in estimation was closely related to the underlying surface conditions.For the entire arid zone,the PM method overestimated the potential evapotranspiration by 33.7 mm per year,with an overestimation of 29.0 mm from July to August.The most significant overestimation was evident in the mountainous and plain nonvegetation areas,in which the annual mean overestimation reached 5%and 10%,respectively;during July,there was an estimation of 10%and 20%,respectively.Although the annual evapotranspiration of the plains with better vegetation coverage was slightly underestimated,overestimation still occurred in July and August,with a mean overestimation of approximately 5%.In order to estimate potential evapotranspiration in the arid zone,it is important that we identify a reasonable parameter with which to calibrate the PM formula,such as the slope of the saturation vapor pressure curve,and the surface albedo.We recommend that some parameters must be corrected when using PM in order to estimate potential evapotranspiration in arid regions.     

综合遥感与气象信息的小麦白粉病监测方法

<正>小麦白粉病是小麦生产过程中的主要病害之一,常在小麦生育后期爆发,造成严重的减产和品质降低。对该病的准确监测是植保工作的一个重点,对病害的有效防治具有重要意义~([1,2])。近年来,遥感数据的空间连续观察能力及其包含的丰富信息引起作物病害监测领域的广泛关     

基于MOD16数据的焉耆盆地蒸散量变化研究

蒸散量是水资源转化中非常关键的变量,特别是对当前变化环境下干旱区作物耗水量的时空变化与预测具有重要的作用。基于2001—2019年MOD16数据产品,通过遥感反演蒸散量数据,对焉耆盆地实际蒸散量(AET)和潜在蒸散量(PET)的时空变化进行分析,结果表明:(1)MOD16蒸散产品和小型蒸发皿实测数据较为一致(R2=0.94),其精度可以用于分析和探究焉耆盆地蒸散量的时空分布特征。(2)多年平均AET与PET分别为128.7 mm和1381.5 mm,年际变化尺度上AET呈上升趋势,PET呈下降趋势。(3)多年平均AET与PET在空间分布上呈现出明显的差异特征且表现出相反的趋势,年际AET与PET线性倾斜率处于基本不变趋势。(4)AET与PET的变化趋势与焉耆盆地膜下滴灌技术的普及与气象要素(蒸发量、相对湿度、平均气温)的改变具有内在的联系。     

Adaptability of machine learning methods and hydrological models to discharge simulations in datasparse glaciated watersheds

The accurate simulation and prediction of runoff in alpine glaciated watersheds is of increasing importance for the comprehensive management and utilization of water resources. In this study, long shortterm memory(LSTM), a state-of-the-art artificial neural network algorithm, is applied to simulate the daily discharge of two data-sparse glaciated watersheds in the Tianshan Mountains in Central Asia. Two other classic machine learning methods, namely extreme gradient boosting(XGBoost) and support vector regression(SVR), along with a distributed hydrological model(Soil and Water Assessment Tool(SWAT) and an extended SWAT model(SWAT_Glacier) are also employed for comparison. This paper aims to provide an efficient and reliable method for simulating discharge in glaciated alpine regions that have insufficient observed meteorological data. The two typical basins in this study are the main tributaries(the Kumaric and Toxkan rivers) of the Aksu River in the south Tianshan Mountains, which are dominated by snow and glacier meltwater and precipitation. Our comparative analysis indicates that simulations from the LSTM shows the best agreement with the observations. The performance metrics Nash-Sutcliffe efficiency coefficient(NS) and correlation coefficient(R~2) of LSTM are higher than 0.90 in both the training and testing periods in the Kumaric River Basin, and NS and R~2 are also higher than 0.70 in the Toxkan River Basin. Compared to classic machine learning algorithms, LSTM shows significant advantages over most evaluating indices. XGBoost also has high NS value in the training period, but is prone to overfitting the discharge. Compared with the widely used hydrological models, LSTM has advantages in predicting accuracy, despite having fewer data inputs. Moreover, LSTM only requires meteorological data rather than physical characteristics of underlying data. As an extension of SWAT, the SWAT_Glacier model shows good adaptability in discharge simulation, outperforming the original SWAT model, but at the cost of increasing the complexity of the model. Compared with the oftentimes complex semi-distributed physical hydrological models, the LSTM method not only eliminates the tedious calibration process of hydrological parameters, but also significantly reduces the calculation time and costs. Overall, LSTM shows immense promise in dealing with scarce meteorological data in glaciated catchments.