克里雅绿洲地下水埋深与土壤盐分时空分异及耦合分析

在干旱区绿洲平原地带,地下水埋深与土壤盐分时空分异明显,且二者存在高度的交互耦合关系。为实现分时段、分区域对地下水进行综合利用及对土壤盐渍化进行有效治理,以克里雅绿洲为研究区,根据20个地下水样点及8个土壤样点的监测数据,采用经典统计学方法、地统计分析法及GIS技术对研究区地下水埋深与土壤盐分的时空分异进行了研究,并运用耦合系数模型计算了二者之间的耦合度。结果表明:地下水埋深夏秋季较深,冬春季较浅且研究区西北浅,东南深,由西北向东南递增;土壤盐分含量夏秋季快速下降,冬春季迅速上升且研究区西高东低,由西向东呈条带状递减分布;地下水埋深与土壤盐分含量之间存在交互耦合的关系。     

毛乌素沙地海流兔河流域水文地质特征

海流兔河流域是毛乌素沙地的代表性流域,开展以流域为单元的研究,将为毛乌素沙地的流域水资源配置提供科学依据。通过系统分析海流兔河流域的含水层展布和地下水分布特征,创新性地将含水层厚度和地下水埋深的分区进行小波图像融合后,得到了新的水文地质参数分区。结果表明：海流兔河流域的第四系及白垩系为该区域的巨厚含水层系统,其中白垩系含水层由南向北逐渐增厚,整体上该流域地下水资源量丰富。克里金指数模型适用于海流兔河流域的地下水埋深插值,该模型得到的流域地下水埋深为0—43 m,其中地下水埋深3 m以上的区域占流域的主体部分。以Symlets小波函数作为离散小波变换的图像融合技术对海流兔河流域的水文地质参数分区具有较强的适用性。     

黑河中游张掖绿洲地下水时空变异性分析

在干旱、半干旱区,地下水在维护生态可持续发展方面起着重要作用。地统计作为一种决策和管理工具已被许多学者应用于地下水的时空变异性研究。以黑河中游的张掖绿洲为例,运用地统计与GIS相结合的方法研究了地下水埋深的空间变异特征,绘制了各期地下水空间分布图。结果表明,主要受不同尺度的结构性因素的影响,地下水埋深具有强烈的空间相关性,变异函数为各向异性结构。对Kriging插值结果表明,张掖绿洲地下水埋深整体上从西南到东北逐渐变浅,且呈普遍下降趋势。     

极端干旱沙漠中无沙埋干扰时几种固沙植物栽植试验研究

沙埋干扰是沙漠地区乡土植物生存繁殖的必要条件。在塔克拉玛干沙漠腹地,无沙埋干扰地段大多是高矿化度地下水埋深较浅的地段,这些地段盐渍化较重。7a定点观测和试验表明,塔里木沙漠公路沿线丘间地栽植固沙植物逐渐死亡的主要因素不是沙层水分和沙层盐分,而是高矿化度地下水埋深太浅(0.8~1.0 m),即这类地段植物死亡的原因主要是由于植物长期吸收高矿化度地下水使得体内盐分积聚过多无法调节而造成的。因此,在干旱区高矿化度地下水埋深太浅的地段,无沙埋干扰时建立固沙植被应谨慎。     

塔里木河下游长期输水条件下河流剖面地下水埋深估算

准确估计输水条件下河岸地下水埋深的动态变化,可以量化生态输水量与地下水埋深的响应关系,并由此估计自然河道所需输水量及持续时间,这对于干旱区水资源管理的可持续发展具有重要的科学意义。结合塔里木河下游20 a生态输水监测数据,用发展的包含地下水和土壤水的拟二维地下水模型,对输水条件下塔里木河下游上、中、下段3个断面(英苏、阿拉干和依干不及麻)的地下水埋深变化进行20 a长期模拟。通过率定期和后11 a(2010—2020年)的地下水埋深模拟结果与站点数据比较,发现两者较一致,证明该模型在塔里木河下游河岸断面地下水长期模拟上的合理性和适用性。然后根据3个断面20 a的模拟结果分析输水条件下地下水埋深和土壤水的长期变化及其对生态输水的响应。结果表明:经过20 a的生态输水,英苏、阿拉干和依干不及麻3个断面上的地下水位和土壤湿度都有明显的上升,地下水位埋深从输水前的8 m左右抬升到输水后的4 m左右,土壤湿度从最初的0.20上升到0.35以上,特别是自2009年以来,随年输水量增加,地下水位和土壤湿度增加幅度明显。生态输水与地下水的年际变化有一定的滞后性,由于土壤湿度和地下水位表现为正相关关系,这使得土壤湿度对输水量也有滞后性的特点。相比于河水流量,地下水水平传导率的取值对断面地下水埋深变化起着更重要的作用。另外,输水量与地下水的年际变化表明塔里木河下游河岸要想获得持续的生态效益,需要对河道提供间歇性的生态输水。     

地下水埋深对半干旱区典型植物群落土壤酶活性的影响

土壤酶活性是反映土壤功能的关键指标,尤其在受到水分限制的半干旱区,土壤水分驱动的土壤酶活性生态功能的变化可以改变土壤养分周转并影响土壤碳质量。地下水埋深对半干旱区典型植物群落土壤酶活性的影响机制尚不明晰。以半干旱区科尔沁沙质草地两种典型植物群落（白草Pennisetum centrasiaticum和差巴嘎蒿Artemisia halodendron）为研究对象,开展地下水埋深模拟试验,地下水埋深分别为0.5、1.0、2.0 m。分析不同土层的土壤理化性质和土壤酶活性,探讨不同地下水位埋深深度下不同植被类型土壤酶活性的变化特征。结果表明：4种土壤水解酶（酸性磷酸酶（AP）、葡萄糖苷酶（βG）、乙酰氨基葡萄糖苷酶（NAG）和亮氨酸氨基肽酶（LAP））和2种氧化还原酶（过氧化氢酶（CL）和多酚氧化酶（POX））活性均受地下水位埋深和植被类型的影响。随着地下水埋深的增加,白草和差巴嘎蒿群落内土壤酶活性分别呈现不显著性和显著性的变化规律。同时,在各处理中土壤酶活性均随土层深度的增加而减小。地下水埋深对科尔沁沙质草地白草与差巴嘎蒿群落的土壤水解酶和氧化还原酶活性产生不同的影响。未来在半干旱区进行植被恢复时,建议考虑不同植物群落对地下水位变化的适应对策的差异,以更好地恢复半干旱区植物群落的地上地下生态系统功能。     

新疆塔里木河干流堤防修建对地下水位的影响

通过2001—2003年对塔里木河干流3个断面18眼地下水监测井数据分析表明，沙子河口断而地下水埋深无明显变化；乌斯满河口断面地下水埋深表现为加深-变浅-加深的过程；阿其河口断面地下水埋深略呈减小趋势。地下水埋深的空间变化特征为：沙子河口断面水分条件最差；乌斯满河口断面的地下水埋深均小于3m，水分条件最好。洪、枯水期地下水埋深变化特征为：沙子河口断面地下水埋深变化很小；乌斯满河口断而和阿其河口断面，由于受提闸放水的影响，2003年地下水埋深普遍小于2002年。塔里木河中游输水堤防的修建对遏制河水在洪水期任意漫流，增加下游来水量起到了积极作用。     

科尔沁沙地典型区地下水、降水变化特征分析

地下水资源作为科尔沁沙地典型区的重要供水来源,研究地下水及其影响因素的变化特征对水资源合理开发利用、生态环境保护有重要的现实意义。以研究区周围左中、通辽和后旗3个典型气象站与7个地下水观测井数据为基础,应用灰色系统理论、线性回归、M-K突变检验、累积距平分析和小波周期分析等研究方法,对科尔沁沙地典型区1951-2015年降水量与地下水埋深进行研究,定性描述降水量变化与地下水埋深变化的响应关系。结果表明：（1）夏季气候倾向率为-18.6 mm·（10 a）^-1和年降水量气候倾向率为-11.7 mm·（10 a）^-1,都呈下降趋势,春、秋和冬三季降水量变化呈上升趋势,其气候倾向率分别为1.45 mm·（10 a）^-1、1.79 mm·（10 a）^-1和0.67 mm·（10 a）^-1。（2）近65 a来,研究区年降水存在2~5 a、7~12 a和18~31 a三个明显特征时间尺度的周期,对应小波方差图存在26 a和10 a两个周期峰值;四季降水量同样存在不同时间尺度的周期。（3）四季和年地下水埋深先呈线性再呈波动式变化,上升趋势显著,增幅分别为0.48 m·（10 a）^-1、0.50 m·（10 a）^-1、0.51 m·（10 a）^-1、0.48 m·（10 a）^-1和0.49 m·（10 a）^-1。（4）地下水埋深时间序列基准期和变异期的分界点为1994年。（5）1994年前,地下水埋深与滞后4 a降水量相关系数为-0.514;1994年后,地下水埋深与滞后8 a降水量相关系数为-0.527。     

天山北麓地下水与自然植被的空间变异及其分形特征

对面积约670km^2的具有干旱气候特征的新疆三工河流域地下水埋深和自然植被盖度进行取样检测，应用地质统计学方法对取得数据进行半方差函数分析，计算均值、方差、标准差、变异系数等传统统计特征值，指出用该方法表示地下水埋深和自然植被盖度引起的困惑。在此基础上，将分形理论与地质统计学原理相结合，计算了三工河流域地下水埋深和自然植被盖度的分形维数，分别为1．3154、1．3677；而它们的变异系数分别为0．．537、0．368。该地区地下水埋深和自然植被盖度在空间上存在显著的变异性，而且它们具有相似的变异特征，即随着地下水埋深的递增(或递减)，自然植被盖度有所减少(或增加)，说明干旱区自然植被盖度对地下水埋深有很大的依赖性。这暗示着在干旱地区影响自然植被盖度的众多因素中最具有决定性的因素是地下水埋深。     

近10 a来民勤盆地地下水埋深的空间异质性分析

 在GIS环境下,采用地统计空间分析技术,利用民勤盆地1999-2008年10?a的地下水位观测资料,在插值获取逐年地下水埋深栅格数据的基础上,对民勤盆地地下水埋深的时空格局和空间异质性进行了分析。结果表明：（1）1999-2008年民勤盆地地下水埋深急剧增大,整体平均每年增加幅度为0.52?m,地下水降落漏斗区逐年扩大,并有与附近漏斗区融合的趋势;（2）1999-2008年10 a间的地下水埋深的空间相关性逐年增大,表明民勤盆地地下水埋深的空间结构性逐年增强,地下水储存的空间连通性增加,地下水位的可恢复性和持续可开采性更为脆弱,影响地下水埋深的随机性因素逐渐下降,而人类活动对地下水埋深的影响越来越强烈。     

基于随机森林模型的干旱绿洲区张掖盆地地下水水质评价

为合理准确评价地下水水质,建立了基于随机森林（RF）模型的地下水水质评价模型,并根据张掖盆地81个地下水采样点的pH值、Cl^-、SO₄^2-、NO₃^-、Na⁺、NH₄⁺含量及总硬度的监测数据,对研究区的地下水水质进行了综合评价。结果表明：盆地地下水水质主要为Ⅱ、Ⅲ、Ⅳ类水,其中甘州区地下水埋藏较深,水体不容易受到来自地面的污染,水质较好,大多数地方为Ⅱ类水;临泽县和高台县地下水埋藏较浅,水质较差,大多数地方为Ⅲ类水,尤其高台县的水位最浅,再加上地处河段下游,污染更为严重,部分地区达到Ⅳ类。根据指标的重要性度量发现影响研究区域地下水水质的主要因子是NO₃^-含量;其次是NH₄⁺、SO₄^2-、Na⁺、Cl^-含量及总硬度、pH值。为验证模型的有效性,将地下水水质评价结果与基于支持向量机（SVM）和人工神经网络（ANN）的地下水水质综合评价模型模拟结果进行对比,3个模型均能很好地评价研究区地下水水质,但RF模型的评价结果更为准确。     

黄河三角洲典型区域地下水动态分析

地下水作为三角洲地区水循环的重要环节,对整合土地利用和水资源管理以及河口湿地自然保护区的存在 与发展具有重要作用。采用surfer 软件绘制的地下水埋深等值线图显示地下水埋深的分布规律为沿黄河河道附近 地下水埋深较深, 滨海以及河间洼地为地下水浅埋区。在SPSS11.5 统计分析软件的帮助下, 采用偏相关分析( Partial 过程) 方法从地下水埋深多年和年内变化两个方面研究黄河三角洲典型地块地下水埋深和降水量、黄河径流量 之间的相关关系。研究结果表明, 黄河三角洲地区地下水动态变化受多个驱动因子的控制, 在不同时间段和不同的 地理位置影响程度不同, 且多年地下水埋深的序列规律性较差, 地下水埋深多年动态和年内动态变化复杂且均不 受径流量和降水量影响。东营市引黄灌溉、引黄蓄水( 城市生活与工业用水等) 、大型引排水利工程的兴建等可能是 导致地下水位持续上升的主要原因, 为今后地下水资源管理和三角洲湿地资源的保护提供依据。     

地下水位变化对干旱区植被盖度的影响及其空间变异特征

Sampling and testing are conducted on groundwater depth and vegetation coverage in the 670 km^2 of the Sangong River Basin and semi-variance function analysis is made afterwards on the data obtained by the application of geo-statistics. Results showed that the variance curve of the groundwater depth and vegetation coverage displays an exponential model. Analysis of sampling data in 2003 indicates that the groundwater depth and vegetation coverage change similarly in space in this area. The Sangong River Basin is composed of upper oasis, middle ecotone and lower sand dune. In oasis and ecotone, influenced by irrigation of the adjoining oasis, groundwater level has been raised and soil water content also increased compared with sand dune nearby, vegetation developed well. But in the lower reaches of the Sangong River Basin, because of descending of groundwater level, soil water content decreased and vegetation degenerated. From oasis to abandoned land and desert grassland, vegetation coverage and groundwater level changed greatly with significant difference respectively in spatial variation. Distinct but similar spatial variability exists among the groundwater depth and vegetation coverage in the study area, namely, the vegetation coverage decreasing (increasing) as the groundwater depth increases (decreases). This illustrates the great dependence of vegetation coverage on groundwater depth in arid regions and further implies that among the great number of factors affecting vegetation coverage in arid regions, groundwater depth turns out to be the most determinant one.     

Influence of groundwater level change on vegetation coverage and their spatial variation in arid regions

Sampling and testing are conducted on groundwater depth and vegetation coverage in the 670 km2 of the Sangong River Basin and semi-variance function analysis is made afterwards on the data obtained by the application of geo-statistics. Results showed that the variance curve of the groundwater depth and vegetation coverage displays an exponential model. Analysis of sampling data in 2003 indicates that the groundwater depth and vegetation coverage change similarly in space in this area. The Sangong River Basin is composed of upper oasis, middle ecotone and lower sand dune. In oasis and ecotone, influenced by irrigation of the adjoining oasis, groundwater level has been raised and soil water content also increased compared with sand dune nearby, vegetation developed well. But in the lower reaches of the Sangong River Basin, because of descending of groundwater level, soil water content decreased and vegetation degenerated. From oasis to abandoned land and desert grassland, vegetation coverage and groundwater level changed greatly with significant difference respectively in spatial variation. Distinct but similar spatial variability exists among the groundwater depth and vegetation coverage in the study area, namely, the vegetation coverage decreasing (increasing) as the groundwater depth increases (decreases). This illustrates the great dependence of vegetation coverage on groundwater depth in arid regions and further implies that among the great number of factors affecting vegetation coverage in arid regions, groundwater depth turns out to be the most determinant one.     

改进型BP神经网络对民勤绿洲地下水位的模拟预测

以具有代表性的民勤绿洲为研究对象,以Matlab7.0为工作平台,对沙漠绿洲地下水埋深预测的三层前馈神经网络（BP神经网络）进行了改进。输入端因子选取民勤绿洲逐月灌溉量、红崖山水库下泄水量、月降水量、月蒸发量（20 cm）、月平均气温、时间序列6项,输出因子为民勤绿洲地下水位。通过在模型的输入层增加时间序列引导因子的方法使BP神经网络对输入端数据具备时间敏感性;通过Levenberg-Marquardt算法使网络误差最小化,并配合Bayesian正则化使网络的误差平方和、网络权重以及阈值平方和实现最优组合,最后使用相关系数、相对误差、效率系数等指标对模型的模拟结果进行检验。结果表明,通过以上一系列改进可以有效提高模型的模拟精度,增强模型的稳定性,并使模型具有良好的“泛化性”。     

Self-Learning Random Forests Model for Mapping Groundwater Yield in Data-Scarce Areas

Globally, groundwater plays a major role in supplying drinking water for urban and rural population and is used for irrigation to grow crops and in many industrial processes. A novel self-learning random forest (SLRF) model is developed and validated for groundwater yield zonation within the Yeondong Province in South Korea. This study was conducted with an inventory data initially divided randomly into 70% for training and 30% for testing and 13 groundwater-conditioning factors. SLRF was optimized using Bayesian optimization method. We also compared our method to other machine learning methods including support vector machine (SVM), artificial neural networks (ANN), decision trees (DT), and voting ensemble models. Model validation was accomplished using several methods, including a confusion matrix, receiver operating characteristics, cross-validation, and McNemar’s test. Our proposed self-learning method improves random forest (RF) generalization performance by about 23%, with SLRF success rates of 0.76 and prediction rates of 0.83. In addition, the optimized SLRF performed better [according to a threefold cross-validated AUC (area under curve) of 0.75] than that using randomly initialized parameters (0.57). SLRF outperformed all of the other models for the testing dataset (RF, SVM, ANN, DT, and Voted ANN-RF) when the overall accuracy, prediction rate, and cross-validated AUC metrics were considered. The SLRF also estimated the contribution of individual groundwater conditioning factors and showed that the three most influential factors were geology (1.00), profile curvature (0.97), and TWI (0.95). Overall, SLRF effectively modeled groundwater potential, even within data-scarce regions.

     

基于遥感和机器学习的内陆水体水深反演技术

文章主要根据机器学习算法（随机森林算法和极端梯度提升算法）和遥感水深反演的原理,利用Sentinel_2多光谱卫星数据和无人船实测水深数据,对内陆水体——梅州水库建立了随机森林（RF）、极端梯度提升（XGBoost）和支持向量机（SVM）水深反演模型,并对反演结果进行对比分析。结果表明：1）RF的训练精度为97%,测试精度为0.80;XGBoost模型的训练精度为97%,测试精度为0.79;SVM的训练精度为90%,测试精度为0.78。说明了在水深预测方面RF模型和XGBoost模型比SVM模型表现更好,对各个区段的水深值较为敏感。2）根据运行时间考察各个模型的效率,其中RF模型从读取数据至输出结果耗时3.92 s;XGBoost模型4.26 s;SVM模型6.66 s。因此,在反演精度和效率上RF模型优于XGBoost模型优于SVM模型,且RF模型的预测结果图细节更加丰富,轮廓更加分明;XGBoost模型次之,但总体效果也较好;SVM模型表现最差。由此可知,机器学习水深反演模型获得的水深结果精度明显提高,解决了传统水深反演模型精度不高的问题。     

Ecological water demand of natural vegetation in the lower Tarim River

We have appraised the relationships between soil moisture, groundwater depth, and plant species diversity in the lower reaches of the Tarim River in western China, by analyzing field data from 25 monitoring wells across eight study sites and 25 permanent vegetation survey plots. It is noted that groundwater depth, soil moisture and plant species diversity are closely related. It has been proven that the critical phreatic water depth is five meters in the lower reaches of the Tarim River. We acquired the mean phreatic evaporation of different groundwater levels every month by averaging the two results of phreatic evaporation using the Qunk and Averyanov formulas. Based on different vegetation types and acreage with different groundwater depth, the total ecological water demand (EWD) of natural vegetation in 2005 was 2.4×108 m3 in the lower reaches of the Tarim River. Analyzing the monthly EWD, we found that the EWD in the growth season (from April to September) is 81% of the year’s total EWD. The EWD in May, June and July was 47% of the year’s total EWD, which indicates the best time for dispensing artificial water. This research aims at realizing the sustainable development of water resources and provides a scientific basis for water resource management and sound collocation of the Tarim River Basin.     

Estimation of ocean primary productivity and its spatio-temporal variation mechanism for East China Sea based on VGPM model

According to calculation results of ocean chlorophyll concentration based on SeaWiFS data by SeaBAM model and synchronous ship-measured data, this research set up an improved model for CaseⅠand CaseⅡwater bodies respectively. The monthly chlorophyll distribution in the East China Sea in 1998 was obtained from this improved model on calculation results of SeaBAM. The euphotic depth distribution in 1998 in the East China Sea is calculated by using remote sensing data of K490 from SeaWiFS according to the relation between the euphotic depth and the oceanic diffuse attenuation coefficient. With data of ocean chlorophyll concentration, euphotic depth, ocean surface photosynthetic available radiation (PAR), daily photoperiod and optimal rate of daily carbon fixation within a water column, the monthly and annual primary productivity spatio-temporal distributions in the East China Sea in 1998 were obtained based on VGPM model. Based on analysis of those distributions, the conclusion can be drawn that there is a clear bimodality character of primary productivity in the monthly distribution in the East China Sea. In detail, the monthly distribution of primary productivity stays the lowest level in winter and rises rapidly to the peak in spring. It gets down a little in summer, and gets up a little in autumn. The daily average of primary productivity in the whole East China Sea is 560.03 mg/m2/d, which is far higher than the average of subtropical ocean areas. The annual average of primary productivity is 236.95 g/m2/a. The research on the seasonal variety mechanism of primary productivity shows that several factors that affect the spatio-temporal distribution may include the chlorophyll concentration distribution, temperature condition, the Yangtze River diluted water variety, the euphotic depth, ocean current variety, etc. But the main influencing factors may be different in each local sea area.