Statistical identification of orographic effects in the regional analysis of extreme rainfall

Regional models of extreme rainfall must address the spatial variability induced by orographic obstacles. However, the proper detection of orographic effects often depends on the availability of a well‐designed rain gauge network. The aim of this study is to investigate a new method for identifying and characterizing the effects of orography on the spatial structure of extreme rainfall at the regional scale, including where rainfall data are lacking or fail to describe rainfall features thoroughly. We analyse the annual maxima of daily rainfall data in the Campania region, an orographically complex region in Southern Italy, and introduce a statistical procedure to identify spatial outliers in a low order statistic (namely the mean). The locations of these outliers are then compared with a pattern of orographic objects that has been a priori identified through the application of an automatic geomorphological procedure. The results show a direct and clear link between a particular set of orographic objects and a local increase in the spatial variability of extreme rainfall. This analysis allowed us to objectively identify areas where orography produces enhanced variability in extreme rainfall. It has direct implications for rain gauge network design criteria and has led to promising developments in the regional analysis of extreme rainfall. Copyright © 2015 John Wiley & Sons, Ltd.     

Soil property variation mapping through data mining of soil category maps

Spatial information on soil properties is an important input to hydrological models. In current hydrological modelling practices, soil property information is often derived from soil category maps by the linking method in which a representative soil property value is linked to each soil polygon. Limited by the area‐class nature of soil category maps, the derived soil property variation is discontinuous and less detailed than high resolution digital terrain or remote sensing data. This research proposed dmSoil, a data‐mining‐based approach to derive continuous and spatially detailed soil property information from soil category maps. First, the soil–environment relationships are extracted through data mining of a soil map. The similarity of the soil at each location to different soil types in the soil map is then estimated using the mined relationships. Prediction of soil property values at each location is made by combining the similarities of the soil at that location to different soil types and the representative soil property values of these soil types. The new approach was applied in the Raffelson Watershed and Pleasant Valley in the Driftless Area of Wisconsin, United States to map soil A horizon texture (in both areas) and depth to soil C horizon (in Pleasant Valley). The property maps from the dmSoil approach capture the spatial gradation and details of soil properties better than those from the linking method. The new approach also shows consistent accuracy improvement at validation points. In addition to the improved performances, the inputs for the dmSoil approach are easy to prepare, and the approach itself is simple to deploy. It provides an effective way to derive better soil property information from soil category maps for hydrological modelling. Copyright © 2014 John Wiley & Sons, Ltd.     

Elastic models for nonlinear response of rigid passive piles

Recent study indicates that the response of rigid passive piles is dominated by elastic pile–soil interaction and may be estimated using theory for lateral piles. The difference lies in that passive piles normally are associated with a large scatter of the ratio of maximum bending moment over maximum shear force and induce a limiting pressure that is ~1/3 that on laterally loaded piles. This disparity prompts this study. This paper proposes pressure‐based pile–soil models and develops their associated solutions to capture response of rigid piles subjected to soil movement. The impact of soil movement was encapsulated into a power‐law distributed loading over a sliding depth, and load transfer model was adopted to mimic the pile–soil interaction. The solutions are presented in explicit expressions and can be readily obtained. They are capable of capturing responses of model piles in a sliding soil owing to the impact of sliding depth and relative strength between sliding and stable layer on limiting force prior to ultimate state. In comparison with available solutions for ultimate state, this study reveals the 1/3 limiting pressure (of the active piles) on passive piles was induced by elastic interaction. The current models employing distributed pressure for moving soil are more pertinent to passive piles (rather than plastic soil flow). An example calculation against instrumented model piles is provided, which demonstrates the accuracy of the current solutions for design slope stabilising piles. Copyright © 2014 John Wiley & Sons, Ltd.     

Is there a superior conceptual groundwater model structure for baseflow simulation?

Previous work has shown that streamflow response during baseflow conditions is a function of storage, but also that this functional relationship varies among seasons and catchments. Traditionally, hydrological models incorporate conceptual groundwater models consisting of linear or non‐linear storage–outflow functions. Identification of the right model structure and model parameterization however is challenging. The aim of this paper is to systematically test different model structures in a set of catchments where different aquifer types govern baseflow generation processes. Nine different two‐parameter conceptual groundwater models are applied with multi‐objective calibration to transform two different groundwater recharge series derived from a soil‐atmosphere‐vegetation transfer model into baseflow separated from streamflow data. The relative performance differences of the model structures allow to systematically improve the understanding of baseflow generation processes and to identify most appropriate model structures for different aquifer types. We found more versatile and more aquifer‐specific optimal model structures and elucidate the role of interflow, flow paths, recharge regimes and partially contributing storages. Aquifer‐specific recommendations of storage models were found for fractured and karstic aquifers, whereas large storage capacities blur the identification of superior model structures for complex and porous aquifers. A model performance matrix is presented, which highlights the joint effects of different recharge inputs, calibration criteria, model structures and aquifer types. The matrix is a guidance to improve groundwater model structures towards their representation of the dominant baseflow generation processes of specific aquifer types. 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.     

Deducing the spatial variability of exchange within a longitudinal channel water balance

Developing an appropriate data collection scheme to infer stream–subsurface interactions is not trivial due to the spatial and temporal variability of exchange flowpaths. Within the context of a case study, this paper presents the results from a number of common data collection techniques ranging from point to reach scales used in combination to better understand the spatial complexity of subsurface exchanges, infer the hydrologic conditions where individual influences of hyporheic and groundwater exchange components on stream water can be characterized, and determine where gaps in information arise. We start with a tracer‐based, longitudinal channel water balance to quantify hydrologic gains and losses at a sub‐reach scale nested within two consecutive reaches. Next, we look at groundwater and stream water surface levels, shallow streambed vertical head gradients, streambed and aquifer hydraulic conductivities, water chemistry, and vertical flux rates estimated from streambed temperatures to provide more spatially explicit information. As a result, a clearer spatial understanding of gains and losses was provided, but some limitations in interpreting results were identified even when combining information collected over various scales. Due to spatial variability of exchanges and areas of mixing, each technique frequently captured a combination of groundwater and hyporheic exchange components. Ultimately, this study provides information regarding technique selection, emphasizes that care must be taken when interpreting results, and identifies the need to apply or develop more advanced methods for understanding subsurface exchanges. Copyright © 2013 John Wiley & Sons, Ltd.     

智能卡和出租车轨迹数据中蕴含城市人群活动模式的差异性分析

当前采用交通流数据量化城市人群活动模式研究已经取得了丰硕的研究成果,但是对于同一区域、同一时段不同类型交通流数据反映城市人群活动模式的共性与差异性仍然知之甚少,直接影响了城市人群活动模式挖掘结果的可解释性与实际应用效果。为此,本文旨在对目前广泛采用的智能卡数据（公交和地铁刷卡）和出租车轨迹数据2种重要的交通流数据,从时空分布模式的差异性、行程距离及距离衰减效应的差异性、空间社团结构的差异性3个方面,探索二者反映城市人群活动模式的差异性：① 采用北京市六环以内区域2016年5月9日至15日的智能卡和出租车轨迹数据进行实验分析,研究发现：① 2种交通流反映出行需求的空间分布呈现出高度相关性,但是在同一空间单元上,2种交通流反映出行需求的时间相关性较低;② 2种交通流的使用率在不同空间位置存在明显差异,仅在城市中心区域使用率较为均衡;③ 2种交通流反映人群行程距离的空间分布、距离衰减效应存在明显差异,公共交通对于促进长距离出行更为重要;④ 从2种交通流发现的空间社团结构都显示了城市的多中心结构特征,但是二者发现社团结构存在的差异性表明两种交通方式对城市空间交互起着不同的作用。本研究有助于深入理解多源交通流反映城市人群活动的内在机理,提升城市人群活动模式在城市规划、交通管理等领域的应用效果。     

Understory and small trees contribute importantly to stemflow of a lower montane cloud forest

Stemflow (Sf) measurements in tropical rain and montane forests dominated by large trees rarely include the understory and small trees. In this study, contributions of lower (1‐ to 2‐m height) and upper (>2‐m height and <5‐cm diameter at breast height [DBH]) woody understory, small trees (5 < DBH < 10 cm), and canopy trees (>10‐cm DBH) to Sf per unit ground area (Sf_a) of a Mexican lower montane cloud forest were quantified for 32 days with rainfall (P) during the 2014 wet season. Rainfall, stemflow yield (Sf_y), vegetation height, density, and basal area were measured. Subsequently, stemflow funneling ratios (SFRs) were calculated, and three common methods to scale up Sf_y from individual trees to the stand level (tree‐Sf_y correlation, P‐Sf_y correlation, and mean‐Sf_y extrapolation) were used to calculate Sf_a. Understory woody plants, small trees, and upper canopy trees represented 96%, 2%, and 2%, respectively, of the total density. Upper canopy trees had the lowest SFRs (1.6 ± 0.5 Standard Error (SE) on average), although the lower understory had the highest (36.1 ± 6.4). Small trees and upper understory presented similar SFRs (22.9 ± 5.4 and 20.2 ± 3.9, respectively). Different Sf scaling methods generally yielded similar results. Overall Sf_a during the study period was 22.7 mm (4.5% of rainfall), to which the understory contributed 70.1% (15.9 mm), small trees 10.6% (2.4 mm), and upper canopy trees 19.3% (4.4 mm). Our results strongly suggest that for humid tropical forests with dense understory of woody plants and small trees, Sf of these groups should be measured to avoid an underestimation of overall Sf at the stand level.     

Characterizing spatial and temporal variation in 18O and 2H content of New Zealand river water for better understanding of hydrologic processes

Time series of hydrogen and oxygen stable isotope ratios (δ²H and δ¹⁸O) in rivers can be used to quantify groundwater contributions to streamflow, and timescales of catchment storage. However, these isotope hydrology techniques rely on distinct spatial or temporal patterns of δ²H and δ¹⁸O within the hydrologic cycle. In New Zealand, lack of understanding of spatial and temporal patterns of δ²H and δ¹⁸O of river water hinders development of regional and national-scale hydrological models. We measured δ²H and δ¹⁸O monthly, together with river flow rates at 58 locations across New Zealand over a two-year period. Results show: (a) general patterns of decreasing δ²H and δ¹⁸O with increasing latitude were altered by New Zealand's major mountain ranges; δ²H and δ¹⁸O were distinctly lower in rivers fed from higher elevation catchments, and in eastern rain-shadow areas of both islands; (b) river water δ²H and δ¹⁸O values were partly controlled by local catchment characteristics (catchment slope, PET, catchment elevation, and upstream lake area) that influence evaporation processes; (c) regional differences in evaporation caused the slope of the river water line (i.e., the relationship between δ²H and δ¹⁸O in river water) for the (warmer) North Island to be lower than that of the (cooler, mountain-dominated) South Island; (d) δ²H seasonal offsets (i.e., the difference between seasonal peak and mean values) for individual sites ranged from 0.50‰ to 5.07‰. Peak values of δ¹⁸O and δ²H were in late summer, but values peaked 1 month later at the South Island sites, likely due to greater snow-melt contributions to streamflow. Strong spatial differences in river water δ²H and δ¹⁸O caused by orographic rainfall effects and evaporation may inform studies of water mixing across landscapes. Generally distinct seasonal isotope cycles, despite the large catchment sizes of rivers studied, are encouraging for transit time analysis applications.     

Spatiotemporal variability in hydrochemistry of shallow groundwater in a small pre‐alpine catchment: The importance of landscape elements

Topography and landscape characteristics affect the storage and release of water and, thus, groundwater dynamics and chemistry. Quantification of catchment scale variability in groundwater chemistry and groundwater dynamics may therefore help to delineate different groundwater types and improve our understanding of which parts of the catchment contribute to streamflow. We sampled shallow groundwater from 34 to 47 wells and streamflow at seven locations in a 20‐ha steep mountainous catchment in the Swiss pre‐Alps, during nine baseflow snapshot campaigns. The spatial variability in electrical conductivity, stable water isotopic composition, and major and trace ion concentrations was large and for almost all parameters larger than the temporal variability. Concentrations of copper, zinc, and lead were highest at sites that were relatively dry, whereas concentrations of manganese and iron were highest at sites that had persistent shallow groundwater levels. The major cation and anion concentrations were only weakly correlated to individual topographic or hydrodynamic characteristics. However, we could distinguish four shallow groundwater types based on differences from the catchment average concentrations: riparian zone‐like groundwater, hillslopes and areas with small upslope contributing areas, deeper groundwater, and sites characterized by high magnesium and sulfate concentrations that likely reflect different bedrock material. Baseflow was not an equal mixture of the different groundwater types. For the majority of the campaigns, baseflow chemistry most strongly resembled riparian‐like groundwater for all but one subcatchment. However, the similarity to the hillslope‐type groundwater was larger shortly after snowmelt, reflecting differences in hydrologic connectivity. We expect that similar groundwater types can be found in other catchments with steep hillslopes and wet areas with shallow groundwater levels and recommend sampling of groundwater from all landscape elements to understand groundwater chemistry and groundwater contributions to streamflow.