Development of a soil moisture‐based distributed hydrologic model for determining hydrologically based critical source areas

A simple grid cell‐based distributed hydrologic model was developed to provide spatial information on hydrologic components for determining hydrologically based critical source areas. The model represents the critical process (soil moisture variation) to run‐off generation accounting for both local and global water balance. In this way, it simulates both infiltration excess run‐off and saturation excess run‐off. The model was tested by multisite and multivariable evaluation on the 50‐km² Little River Experimental Watershed I in Georgia, U.S. and 2 smaller nested subwatersheds. Water balance, hydrograph, and soil moisture were simulated and compared to observed data. For streamflow calibration, the daily Nash‐Sutcliffe coefficient was 0.78 at the watershed outlet and 0.56 and 0.75 at the 2 nested subwatersheds. For the validation period, the Nash‐Sutcliffe coefficients were 0.79 at the watershed outlet and 0.85 and 0.83 at the 2 subwatersheds. The per cent bias was less than 15% for all sites. For soil moisture, the model also predicted the rising and declining trends at 4 of the 5 measurement sites. The spatial distribution of surface run‐off simulated by the model was mainly controlled by local characteristics (precipitation, soil properties, and land cover) on dry days and by global watershed characteristics (relative position within the watershed and hydrologic connectivity) on wet days when saturation excess run‐off was simulated. The spatial details of run‐off generation and travel time along flow paths provided by the model are helpful for watershed managers to further identify critical source areas of non‐point source pollution and develop best management practices.     

Effects of forest roads on the hydrological response of a small‐scale mountain watershed in Greece

The effects of land use changes on the ecology and hydrology of natural watersheds have long been debated. However, less attention has been given to the hydrological effects of forest roads. Although less studied, several researchers have claimed that streamflow changes related to forest roads can cause a persistent and pervasive effect on hillslope hydrology and the functioning of the channel system. The main potential direct effects of forest roads on natural watersheds hydrologic response are runoff production on roads surfaces due to reduced infiltration rates, interruption of subsurface flow by road cutslopes and rapid transfer of the produced runoff to the stream network through roadside ditches. The aforementioned effects may significantly modify the total volume and timing of the hillslope flow to the stream network. This study uses detailed field data, spatial data, hydro‐meteorological records, as well as numerical simulation to investigate the effects of forest roads on the hydrological response of a small‐scale mountain experimental watershed, which is situated in the east side of Penteli Mountain, Attica, Greece. The results of this study highlight the possible effects of forest roads on the watersheds hydrological response that may significantly influence direct runoff depths and peak flow rates. It is demonstrated that these effects can be very important in permeable watersheds and that more emphasis should be given on the impact of roads on the watersheds hydrological response. Copyright © 2014 John Wiley & Sons, Ltd.     

Including spatial distribution in a data‐driven rainfall‐runoff model to improve reservoir inflow forecasting in Taiwan

Multi‐step ahead inflow forecasting has a critical role to play in reservoir operation and management in Taiwan during typhoons as statutory legislation requires a minimum of 3‐h warning to be issued before any reservoir releases are made. However, the complex spatial and temporal heterogeneity of typhoon rainfall, coupled with a remote and mountainous physiographic context, makes the development of real‐time rainfall‐runoff models that can accurately predict reservoir inflow several hours ahead of time challenging. Consequently, there is an urgent, operational requirement for models that can enhance reservoir inflow prediction at forecast horizons of more than 3 h. In this paper, we develop a novel semi‐distributed, data‐driven, rainfall‐runoff model for the Shihmen catchment, north Taiwan. A suite of Adaptive Network‐based Fuzzy Inference System solutions is created using various combinations of autoregressive, spatially lumped radar and point‐based rain gauge predictors. Different levels of spatially aggregated radar‐derived rainfall data are used to generate 4, 8 and 12 sub‐catchment input drivers. In general, the semi‐distributed radar rainfall models outperform their less complex counterparts in predictions of reservoir inflow at lead times greater than 3 h. Performance is found to be optimal when spatial aggregation is restricted to four sub‐catchments, with up to 30% improvements in the performance over lumped and point‐based models being evident at 5‐h lead times. The potential benefits of applying semi‐distributed, data‐driven models in reservoir inflow modelling specifically, and hydrological modelling more generally, are thus demonstrated. Copyright © 2012 John Wiley & Sons, Ltd.     

Effect of rainfall uncertainty on the performance of physically based rainfall–runoff models

This paper analyses the effect of rain data uncertainty on the performance of two hydrological models with different spatial structures: a semidistributed and a fully distributed model. The study is performed on a small catchment of 19.6 km² located in the north‐west of Spain, where the arrival of low pressure fronts from the Atlantic Ocean causes highly variable rainfall events. The rainfall fields in this catchment during a series of storm events are estimated using rainfall point measurements. The uncertainty of the estimated fields is quantified using a conditional simulation technique. Discharge and rain data, including the uncertainty of the estimated rainfall fields, are then used to calibrate and validate both hydrological models following the generalized likelihood uncertainty estimation (GLUE) methodology. In the storm events analysed, the two models show similar performance. In all cases, results show that the calibrated distribution of the input parameters narrows when the rain uncertainty is included in the analysis. Otherwise, when rain uncertainty is not considered, the calibration of the input parameters must account for all uncertainty in the rainfall–runoff transformation process. Also, in both models, the uncertainty of the predicted discharges increase in similar magnitude when the uncertainty of rainfall input increase.     

Improved spatial delineation of streambed properties and water fluxes using distributed temperature sensing

A new method was developed for analysing and delineating streambed water fluxes, flow conditions and hydraulic properties using coiled fibre‐optic distributed temperature sensing or closely spaced discrete temperature sensors. This method allows for a thorough treatment of the spatial information embedded in temperature data by creating a matrix visualization of all possible sensor pairs. Application of the method to a 5‐day field dataset reveals the complexity of shallow streambed thermal regimes. To understand how velocity estimates are affected by violations of assumptions of one‐dimensional, saturated, homogeneous flow and to aid in the interpretation of field observations, the method was also applied to temperature data generated by numerical models of common field conditions: horizontal layering, presence of lateral flow and variable streambed saturation. The results show that each condition creates a distinct signature visible in the triangular matrices. The matrices are used to perform a comparison of the behaviour of one‐dimensional analytical heat‐tracing models. The results show that the amplitude ratio‐based method of velocity calculation leads to the most reliable estimates. The minimum sensor spacing required to obtain reliable velocity estimates with discrete sensors is also investigated using field data. The developed method will aid future heat‐tracing studies by providing a technique for visualizing and comparing results from fibre‐optic distributed temperature sensing installations and testing the robustness of analytical heat‐tracing models. Copyright © 2016 John Wiley & Sons, Ltd.     

伊犁盆地南缘中新生代构造样式与铀成矿关系

伊犁盆地是我国最早发现砂岩型铀矿的地区,近十几年来一直保持砂岩型铀矿找矿的不断突破,盆地南缘自西向东查明了一系列砂岩型铀矿床,这些铀矿床的分布与盆地南缘构造关系密切。本文总结了伊犁盆地南缘构造样式类型,分析了不同构造样式下氧化流体补给、运移规律,并结合铀矿体的空间分布,指出构造样式差异是造成砂岩型铀矿体空间分布的关键因素;从氧化流体补给窗的开启、流体的汇聚和还原性气体聚集的控制等方面讨论了构造样式对铀矿体空间定位的机制,指出单斜稳定地层中纵弯背斜是控制铀空间分布的主要构造,要注意不同构造样式约束下成矿区域的稳定型和地下水补给的多样性,在此基础上讨论了伊犁盆地南缘铀矿的找矿方向及主要矿物类型。     

流域坡面汇流研究现状述评

对当前坡面汇流计算方法的研究进展进行了较为系统的总结与分析,并对坡面汇流的非线性效应以及城市低影响开发中的雨水入渗与蓄集对坡面汇流的控制作用进行了简要分析。从模型简单实用的角度出发,认为以流域时间-面积关系与线性水库相串联的ModClark法等为代表的概念性分布式坡面汇流模型具有良好的发展前景;考虑到基于等流时单元的变动等流时线法在反映雨强非线性影响中存在的问题,认为根据水文响应单元在不同雨强条件下汇流时间的变化,调整其汇流参数以反映坡面汇流的非线性效应,对于流域坡面汇流的分布式模拟更具有实际意义;针对目前低影响开发设施长时间序列大空间尺度的室外降雨径流监测资料普遍较为缺乏的现状,给出了后期应积极选择合适的技术以加强低影响开发性能监测工作的建议。     

滨里海盆地Z油田油藏类型与成藏因素研究

Z油田主要含油层系包括白垩系-中侏罗统低幅度背斜构造油藏、中三叠统盐檐断鼻油藏和上三叠统岩性圈闭油藏3种油藏类型。通过对紧密围绕盐有关的构造和有效盐窗这两个影响Z油田油气成藏的关键因素的研究认为成藏模式为"盐下生成、盐窗沟通、盐边盐间断层输导、高点聚集、后期保存"。Z油田油源充足,盐窗大而有效,多种有效的输导体系,圈闭类型多而好,埋深适中,储盖层发育且配置良好,侧向遮挡条件具备且后期保存条件良好,可作为今后勘探首选目标区。     

准噶尔盆地吉木萨尔凹陷梧桐沟组层序地层划分

准噶尔盆地吉木萨尔凹陷二叠系梧桐沟组作为一套重要的储集岩,具有良好的油气成藏前景,但对其层序划分及地层沉积样式的认识一直存在争议。在构造背景分析的基础上,结合岩芯、测井及地震资料,对研究区梧桐沟组层序地层划分及地层沉积样式研究表明,梧桐沟组沉积时期构造强度较弱,地形较为平缓,地层在全区稳定分布;中晚二叠世盆地发生造山运动,吉木萨尔凹陷东南边缘区域经过构造抬升,上部地层遭受不同程度的剥蚀,形成了现今"底平顶削"的地层样式。以凹陷中部少数地层保存较全的井的地层叠加样式分析为基础,通过井-震结合识别不同类型界面,特别是最大湖泛面,建立了区域层序地层格架,即梧桐沟组为一个完整的长期基准面旋回(三级层序),并在其内部识别出5个中期旋回。梧桐沟组地层在不同的层序发育时期表现出不同的旋回叠加样式:下段沉积时期,即最大湖泛面以下,随着可容纳空间的增大,地层表现为明显的退积叠加样式;上段沉积时期,即最大湖泛面以上,随着可容纳空间的减小,地层表现为明显的进积叠加样式,符合沉积物体积分配原理。