Predicting river water temperatures using stochastic models: case study of the Moisie River (Québec,Canada)

Successful applications of stochastic models for simulating and predicting daily stream temperature have been reported in the literature. These stochastic models have been generally tested on small rivers and have used only air temperature as an exogenous variable. This study investigates the stochastic modelling of daily mean stream water temperatures on the Moisie River, a relatively large unregulated river located in Québec, Canada. The objective of the study is to compare different stochastic approaches previously used on small streams to relate mean daily water temperatures to air temperatures and streamflow indices. Various stochastic approaches are used to model the water temperature residuals, representing short‐term variations, which were obtained by subtracting the seasonal components from water temperature time‐series. The first three models, a multiple regression, a second‐order autoregressive model, and a Box and Jenkins model, used only lagged air temperature residuals as exogenous variables. The root‐mean‐square error (RMSE) for these models varied between 0·53 and 1·70 °C and the second‐order autoregressive model provided the best results. A statistical methodology using best subsets regression is proposed to model the combined effect of discharge and air temperature on stream temperatures. Various streamflow indices were considered as additional independent variables, and models with different number of variables were tested. The results indicated that the best model included relative change in flow as the most important streamflow index. The RMSE for this model was of the order of 0·51 °C, which shows a small improvement over the first three models that did not include streamflow indices. The ridge regression was applied to this model to alleviate the potential statistical inadequacies associated with multicollinearity. The amplitude and sign of the ridge regression coefficients seem to be more in agreement with prior expectations (e.g. positive correlation between water temperature residuals of different lags) and make more physical sense. Copyright © 2006 John Wiley & Sons, Ltd.     

Influences of local hydroclimatology and teleconnections on Florida's precipitation and temperature variability

Understanding the influences of local hydroclimatology and two large-scale oceanic-atmospheric oscillations (i.e., Atlantic Multidecadal Oscillation (AMO) and El Niño-Southern Oscillation (ENSO)) on seasonal precipitation (P) and temperature (T) relationships for a tropical region (i.e., Florida) is the focus of this study. The warm and cool phases of AMO and ENSO are initially identified using sea surface temperatures (SSTs). The associations of SSTs and regional minimum, maximum and average surface air temperatures (SATs) with precipitation are then evaluated. The seasonal variations in P-SATs and P-SSTs associations considering AMO and ENSO phases for sites in (1) two soil temperature regimes (i.e., thermic and hyperthermic); (2) urban and non-urban regions; and (3) regions with and without water bodies, are analysed using two monthly datasets. The analyses are carried out using trend tests, two association measures, nonparametric and parametric statistical hypothesis tests and kernel density estimates. Decreasing (increasing) trend in precipitation (SATs) is noted in the recent multi-decadal period (1985–2019) compared to the previous one (1950–1984) indicating a progression towards warmer and drier climatic conditions across Florida. Spatially and temporally non-uniform variations in the associations of precipitation with SATs and SSTs are noted. Strong positive (weak negative) P–T associations are noted during the wet (dry) season for both AMO phases and El Niño, while significant (positive) P–T associations are observed across southern Florida during La Niña in the dry season. The seasonal influences are predominant in governing the P–T relationship over the regions with and without water bodies; however, considerable variations between El Niño and La Niña are noted during the dry season. The climate variability influences on P–T correlations for hyperthermic and thermic soil zones are found to be insignificant (significant) during the wet (dry) season. Nonparametric clustering is performed to identify the spatial clusters exhibiting homogeneous P–T relationships considering seasonal and climate variability influences.     

Evaluation of dynamically downscaled reanalysis precipitation data for hydrological application

Skilful and reliable precipitation data are essential for seasonal hydrologic forecasting and generation of hydrological data. Although output from dynamic downscaling methods is used for hydrological application, the existence of systematic errors in dynamically downscaled data adversely affects the skill of hydrologic forecasting. This study evaluates the precipitation data derived by dynamically downscaling the global atmospheric reanalysis data by propagating them through three hydrological models. Hydrological models are calibrated for 28 watersheds located across the southeastern United States that is minimally affected by human intervention. Calibrated hydrological models are forced with five different types of datasets: global atmospheric reanalysis (National Centers for Environmental Prediction/Department of Energy Global Reanalysis and European Centre for Medium‐Range Weather Forecasts 40‐year Reanalysis) at their native resolution; dynamically downscaled global atmospheric reanalysis at 10‐km grid resolution; stochastically generated data from weather generator; bias‐corrected dynamically downscaled; and bias‐corrected global reanalysis. The reanalysis products are considered as surrogates for large‐scale observations. Our study indicates that over the 28 watersheds in the southeastern United States, the simulated hydrological response to the bias‐corrected dynamically downscaled data is superior to the other four meteorological datasets. In comparison with synthetically generated meteorological forcing (from weather generator), the dynamically downscaled data from global atmospheric reanalysis result in more realistic hydrological simulations. Therefore, we conclude that dynamical downscaling of global reanalysis, which offers data for sufficient number of years (in this case 22 years), although resource intensive, is relatively more useful than other sources of meteorological data with comparable period in simulating realistic hydrological response at watershed scales. Copyright © 2013 John Wiley & Sons, Ltd.     

Using multivariate statistical techniques and geochemical modelling to identify factors controlling the evolution of groundwater chemistry in a typical transitional area between Taihang Mountains and North China Plain

Identifying the key factors controlling groundwater chemical evolution in mountain-plain transitional areas is crucial for the security of groundwater resources in both headwater basins and downstream plains. In this study, multivariate statistical techniques and geochemical modelling were used to analyse the groundwater chemical data from a typical headwater basin of the North China Plain. Groundwater samples were divided into three groups, which evolved from Group A with low mineralized Ca-HCO₃ water, through Group B with moderate mineralized Ca-SO₄-HCO₃ water, to Group C with highly saline Ca-SO₄ and Ca-Cl water. Water-rock interaction and nitrate contamination were mainly responsible for the variation in groundwater chemistry. Groundwater chemical compositions in Group A were mainly influenced by dissolution of carbonates and cation exchange, and suffered less nitrate contamination, closely relating to their locations in woodland and grassland with less pronounced human interference. Chemical evolution of groundwater in Groups B and C was gradually predominated by the dissolution of evaporites, reverse ion exchange, and anthropogenic factors. Additionally, the results of the inverse geochemical model showed that dedolomitization caused by gypsum dissolution, played a key role in the geochemical evolution from Group A to Group B. Heavy nitrate enrichment in most groundwater samples of Groups B and C was closely associated with the land-use patterns of farmland and residential areas. Apart from the high loads of chemical fertilizers in irrigation return flow as the main source for nitrate contamination, the stagnant zones, flood irrigation pattern, mine drainage, and groundwater-exploitation reduction program were also important contributors for such high mineralization and heavy NO₃⁻ contents in Group C. The important findings of this work not only provide the conceptual framework for the headwater basin but also have important implications for sustainable management of groundwater resources in other headwater basins of the North China Plain.     

深圳岩溶空间发育规律统计分析

粤港澳大湾区建设是国家重大发展战略,而深圳市又是大湾区建设的核心引擎之一。深圳市岩溶地区行政区划上主要发育在龙岗区和坪山区,岩溶地质灾害给地下空间开发利用与城市安全构成极大挑战。通过收集深圳市岩溶勘察钻探数据,从地层岩性、基岩埋深与埋藏类型、地下水主要侵蚀指标、地下水埋深与年变化幅度、溶洞埋深、顶板厚度、洞高、充填情况、线溶率、见洞率、地表岩溶发育密度等方面对深圳市岩溶空间发育特征进行了统计分析。结果表明：深圳市岩溶主要为浅覆盖型,但溶洞空间特征变异性极大。从统计上讲,溶洞平均埋深约为20 m,平均洞高为2.5～4.0 m,以半充填为主,充填物主要为粉质黏土;平均线溶率约为15%,见洞率约为40%,地表岩溶发育密度超过300个/km²,综合上看,深圳场地岩溶发育等级超过90%为强发育。上述主要岩溶特征参数服从对数正态分布和Weibull分布。总体上,灰岩地层溶洞顶板厚度随着岩面埋深而呈现递减的趋势,而大理岩地层溶洞顶板厚度则与岩面埋深无关;已发育的溶洞高度与基岩面埋深、溶洞顶板厚度、地下水主要侵蚀指标及地下水埋深无关或弱正相关。研究成果为深圳市岩溶灾害风险评估提供了重要的先验信息。     

冲孔灌注桩桩孔纠偏预防技术研究及应用

冲孔灌注桩虽然在各类地层都具有广泛的适用性,但是在复杂地质条件下,极易发生偏孔。结合某高层建筑冲孔灌注桩施工中频繁偏孔的情况,对冲孔过程中的偏孔问题进行全面分析研究。以桩孔纠偏预防为目标,从应对孤石和斜硬岩层2个方面来针对性进行研究,应对孤石时,合理控制钻头、冲程及冲孔频率;应对斜硬岩层时,研究了钻头增厚硬质合金块改进技术。经现场应用,有效地避免了偏孔的产生。     

考虑基质吸力的非饱和土邓肯-张统计损伤修正模型

为反映非饱和地基土变形全过程,以某基础工程非饱和土为研究对象,开展固结排水三轴压缩试验。研究发现,非饱和土的偏应力-应变曲线形态近似双曲线,基质吸力对土体力学行为影响十分明显,基质吸力越大,土体偏应力越高。根据非饱和土变形特性及工程特点,选取邓肯-张双曲线模型作为基础模型,引入统计损伤理论,假设非饱和土微元强度服从Weibull概率密度分布,建立邓肯-张统计损伤模型。通过搭建初始切线模量与基质吸力的联系,建立一种新的考虑基质吸力的非饱和土邓肯-张统计损伤模型。给出参数解析方法,得到Weibull分布参数经验表达式,从而修正模型。分析不同基质吸力条件下非饱和土损伤累积规律,采用所建模型和传统邓肯-张模型对比验证非饱和土偏应力-应变试验曲线,证明所建模型的可行性和合理性。研究成果为非饱和土的力学特性研究及辨识模拟提供一定参考。     

基于煤层气井生产数据的储层含气量校正新方法

勘探实践发现沁水盆地潘庄、潘河区块及鄂尔多斯盆地保德区块煤层气井累计产量远远大于原始计算的地质探明储量。该现象对体积法计算的煤层气资源储量提出了挑战,同时为全面“上储增效”提出了新的方向。在采用体积法计算煤层气储量时,含气面积、含气量的准确性以及煤岩密度与煤层厚度的非均质特征都会对储量参数的准确性产生影响。其中,由于取心测试过程的局限性,煤层含气量的数值常存在一定的误差。本次研究基于鄂尔多斯盆地和沁水盆地的煤层气井生产数据并结合等温吸附实验结果提出了计算储层临界最低含气量的方法(临界最低法)。将校正后的临界最低含气量与实测含气量(基于美国矿业局直接法(USBM)和史密斯-威廉姆斯法)进行对比,并剖析含气量测试损失量的地质控制机理。结果表明:在中低至中高煤阶(R_o=0.7%~2.1%)范围,临界最低法计算的含气量总体高于其它两种方法计算的含气量,临界最低法在中低煤阶至中高煤阶具有较强的适应性。在高煤阶(R_o=2.1%~2.8%)范围,临界最低法计算结果可以与取心测试结果相互验证。总体上,煤层含气量测试(USBM法)损失量受不同煤阶煤岩孔裂隙发育特征、煤体结构、含气饱和度及逸散时间的影响。含气量测试损失量与孔渗发育特征、构造煤发育程度、含气饱和度及逸散时间呈正相关。此外,针对未取心的煤层气井,可以采用钻井岩屑测试等温吸附参数进而利用临界最低法求取储层含气量,为煤层气进一步的勘探开发提供数据基础。     

岩石热导率校正对大地热流计算值的影响——以渤海湾盆地冀中坳陷为例

岩石热导率是计算大地热流的基础热物性参数,热导率的选取和校正是正确评估热流的基础。当前在冀中坳陷乃至渤海湾盆地的大地热流计算中,多以受季节性气温变化和地下水活动等因素影响较小的古近系和新近系砂泥质岩层作为大地热流的计算层位,岩石热导率也多参考区域内已有定值,而忽略了热导率对水饱和度、温度和压力的依赖性。笔者等从岩石热导率的温压校正和饱水校正角度出发,计算并比较了不同校正方法对热流值的影响。主要认识如下：(1)岩石热导率作为1个受多因素综合影响的热物性参数,其大小与岩性和矿物组分密切相关,同时通常与孔隙率呈负相关,而与密度呈正相关;(2)热导率高温实验结果显示,冀中坳陷砂岩样品热导率总体上随温度的升高而降低,在30～130℃的温度区间热导率降低了6.8%～11.3%,热导率下降率具有随热导率的降低而减小的趋势;由于砂岩和泥岩具有相对较浅的埋深和较低的原位温压条件,温压对热导率的正负效应可以在一定程度上相互抵消,对于热导率小于2 W/(m·K)的样品,温压对其的影响较小。(3)饱水校正后的冀中坳陷JZ03井和JZ04井古近系—新近系砂泥岩热导率的平均偏差分别高达43.0%、47.5%;(4...     

气象产品的广义联合偏差修正方法及其应用

卫星气象产品、气候模式预测数据通常与地面观测数据存在偏差,为保证数据的可靠性和合理性,需要对其进行偏差校正,但偏差校正过程往往受具体区域气象特征、方法本身假定等因素的影响,导致修正效果不佳。为此,本文提出一种广义联合偏差修正方法,相较于现有研究最常用的单变量QM修正方法以及固定多变量修正顺序的JBC修正方法,该方法充分考虑到流域尺度降水和气温双变量的时空相关性,并结合其对径流的主导作用对变量修正顺序进行动态调整,实现了QM法和JBC法的优势互补。在澜沧江-湄公河流域的应用表明：考虑降水、气温相关性可显著改善降水和温度极值的修正效果,尤其是5、6月份,修正后气象与实测数据的纳什系数提升了0.5以上;考虑气象要素的修正次序显著降低了修正后的降水和温度频率分布及均值偏差;利用修正后的气象数据驱动分布式水文模型时,部分月份的径流模拟精度提升了54.3%。