The effects of soil piping on contributing areas and erosion patterns

Natural piping doubles the dynamic contributing area on the upper Maesnant stream in mid-Wales, mainly through linking points well beyond the riparian zones of seepage to the stream. Both discharge and sediment transport rates in the major pipes are closely related to the size of shallow surface microtopographic hollows in which they lie, and which themselves are largely created by piping erosion. However, pipe dischrges are frequently generated by contributing areas larger than these surface depressions and some pipes run counter to the surface topography. The redistribution and acceleration of hillslope drainage processes by piping has implications for theories of hillslope development, especially through plan-form modifications, and also for channel discharge and erosion.     

Sediment and particulate phosphorus characteristics in grassed waterways from row crop corn and alfalfa fields collected by manual University of Exeter samplers and automatic sampling

Phosphorus (P) export from agricultural lands above known threshold levels can result in adverse impacts to receiving water quality. Phosphorus loss occurs in dissolved and sediment‐bound, or particulate phosphorous (PP), forms, with the latter often dominating losses from row‐cropped systems. To target practices, land managers need good computer models and model developers need good monitoring data. Sediment monitoring data (e.g. radiometric finger printing and sediment P sorption capacity) can help identify sediment source areas and improve models, but require more sediment mass than is typically obtained by automatic sampling. This study compares a simple suspended sediment sampler developed at the University of Exeter (UE) with automatic sampling in intermittent channels draining corn and alfalfa fields. The corn field had a greater runoff coefficient (27%) than alfalfa (11%). No differences were found in enrichment ratios (sediment constituent/soil constituent) in PP (PP_ER) or percent loss on ignition (LOI_ER) between paired UE samplers on corn. The median LOI_ER for the UE samplers (1·9%) did not differ significantly (p > 0·13) from the automatic sampler (2·0%). The PP_ER from the UE samplers was on average 20% lower than the automatic samplers. A correlation (r² = 0·75) was found between sediment PP and % LOI from automatic samplers and UE samplers for particles < 50 µm, while for > 50 µm PP concentration did not change with changes in % LOI. Sediment ammonium‐oxalate extractable metals were similarly related to LOI, with the strongest correlation for iron (r² = 0·71) and magnesium (r² = 0·70). Copyright © 2011 John Wiley & Sons, Ltd.     

Integrated surface–groundwater flow modeling: A free-surface overland flow boundary condition in a parallel groundwater flow model

Interactions between surface and groundwater are a key component of the hydrologic budget on the watershed scale. Models that honor these interactions are commonly based on the conductance concept that presumes a distinct interface at the land surface, separating the surface from the subsurface domain. These types of models link the subsurface and surface domains via an exchange flux that depends upon the magnitude and direction of the hydraulic gradient across the interface and a proportionality constant (a measure of the hydraulic connectivity). Because experimental evidence of such a distinct interface is often lacking in field systems, there is a need for a more general coupled modeling approach.     

Assessing the potential impact of climate change on the UK’s electricity network

We investigate how weather affects the UK’s electricity network, by examining past data of weather-related faults on the transmission and distribution networks. By formalising the current relationship between weather-related faults and weather, we use climate projections from a regional climate model (RCM) to quantitatively assess how the frequency of these faults may change in the future. This study found that the incidences of both lightning and solar heat faults are projected to increase in the future. There is evidence that the conditions that cause flooding faults may increase in the future, but a reduction cannot be ruled out. Due to the uncertainty associated with future wind projections, there is no clear signal associated with the future frequency of wind and gale faults, however snow, sleet and blizzard faults are projected to decrease due to a reduction in the number of snow days.     

Lightning‐flash locations related to the precipitation pattern of the storm

Abstract

Triangulation from paired sferics (100 ± 50 kHz) received at stations 57 km apart gave 42 map locations of lightning flashes belonging to a thunderstorm well‐defined on weather radar. This storm was one of many in a long line along the leading edge of a band of light stratiform precipitation (snow above 3 km). The band was 9 km high and 60 km wide, and the storms were embedded in it. The convective region of the storm studied had a north‐south extent of 20 km, the east‐west extent averaging two‐thirds as much. Cumulus heights progressed from 3 km on the west to 7 ± 1 km on the east. Two‐thirds of the lightning flashes were in the convective region. The remaining third were in 160 km² of the light stratiform precipitation immediately west of it.     

波西米亚块体西部长周期电磁研究

在波西米亚块体西南部做了一条长周期电磁剖面 ,它靠近德国超深钻的位置。用一个二维合成模型对数据做了大体的拟合 ,模型走向东西 ,近地表盖层高度地不均匀畸变。畸变层显示强烈的各向异性 ,优势电导方向为NW -SE至NNW -SSE。讨论了深部区域结构的几个不同的模型 ,其中一个模型可以解释由MT和GDS数据显示的主方向的偏差 ,还讨论了沿特别剖面得到的结果与区域地电结构的关系     

Location and height of intertidal bars on macrotidal ridge and runnel beaches

Previous studies devoted to the morphology and hydrodynamics of ridge and runnel beaches highlight characteristics that deviate from those initially postulated by King and Williams (Geographical Journal, 1949, vol. 113, 70–85) and King (Beaches and Coasts, 1972, Edward Arnold). Disagreements on the morphodynamics of these macrotidal beaches include the position of the ridges relative to the mean neap and spring tide levels, the variation in the height of the ridges across the intertidal profile and, most importantly, whether the ridges are formed by swash or surf zone processes. The morphological characteristics of ridge and runnel beaches from three locations with varying wave, tidal and geomorphic settings were investigated to address these disagreements. Beach profiles from each site were analysed together with water‐level data collected from neighbouring ports. It was found that the ridges occur over the entire intertidal zone. On one site (north Lincolnshire, east England), the ridges are uniformly distributed over the intertidal beach, whereas on the two other sites (Blackpool beach, northwest England, and Leffrinckoucke beach, north France) there is some indication that the ridges appear to occur at preferential locations. Most significantly, the locations of the ridge crests were found to be unrelated to the positions on the intertidal profile where the water level is stationary for the longest time. It was further found that the highest ridges generally occur just above mid‐tide level where tidal non‐stationarity is greatest. These findings argue against the hypothesis that the ridges are formed by swash processes acting at stationary tide levels. It is tentatively suggested that the ridges are the result of a combination of swash and surf zone processes acting across the intertidal zone. Elucidation of the morphodynamic roles of these two types of processes, and other processes such as strong current flows in the runnels, requires further comprehensive field measurements complemented by numerical modelling. Copyright © 2001 John Wiley & Sons, Ltd.     

Inference and uncertainty of snow depth spatial distribution at the kilometre scale in the Colorado Rocky Mountains: the effects of sample size,random sampling,predictor quality,and validation procedures

Historically, observing snow depth over large areas has been difficult. When snow depth observations are sparse, regression models can be used to infer the snow depth over a given area. Data sparsity has also left many important questions about such inference unexamined. Improved inference, or estimation, of snow depth and its spatial distribution from a given set of observations can benefit a wide range of applications from water resource management, to ecological studies, to validation of satellite estimates of snow pack. The development of Light Detection and Ranging (LiDAR) technology has provided non‐sparse snow depth measurements, which we use in this study, to address fundamental questions about snow depth inference using both sparse and non‐sparse observations. For example, when are more data needed and when are data redundant? Results apply to both traditional and manual snow depth measurements and to LiDAR observations. Through sampling experiments on high‐resolution LiDAR snow depth observations at six separate 1.17‐km² sites in the Colorado Rocky Mountains, we provide novel perspectives on a variety of issues affecting the regression estimation of snow depth from sparse observations. We measure the effects of observation count, random selection of observations, quality of predictor variables, and cross‐validation procedures using three skill metrics: percent error in total snow volume, root mean squared error (RMSE), and R². Extremes of predictor quality are used to understand the range of its effect; how do predictors downloaded from internet perform against more accurate predictors measured by LiDAR? Whereas cross validation remains the only option for validating inference from sparse observations, in our experiments, the full set of LiDAR‐measured snow depths can be considered the ‘true’ spatial distribution and used to understand cross‐validation bias at the spatial scale of inference. We model at the 30‐m resolution of readily available predictors, which is a popular spatial resolution in the literature. Three regression models are also compared, and we briefly examine how sampling design affects model skill. Results quantify the primary dependence of each skill metric on observation count that ranges over three orders of magnitude, doubling at each step from 25 up to 3200. Whereas uncertainty (resulting from random selection of observations) in percent error of true total snow volume is typically well constrained by 100–200 observations, there is considerable uncertainty in the inferred spatial distribution (R²) even at medium observation counts (200–800). We show that percent error in total snow volume is not sensitive to predictor quality, although RMSE and R² (measures of spatial distribution) often depend critically on it. Inaccuracies of downloaded predictors (most often the vegetation predictors) can easily require a quadrupling of observation count to match RMSE and R² scores obtained by LiDAR‐measured predictors. Under cross validation, the RMSE and R² skill measures are consistently biased towards poorer results than their true validations. This is primarily a result of greater variance at the spatial scales of point observations used for cross validation than at the 30‐m resolution of the model. The magnitude of this bias depends on individual site characteristics, observation count (for our experimental design), and sampling design. Sampling designs that maximize independent information maximize cross‐validation bias but also maximize true R². The bagging tree model is found to generally outperform the other regression models in the study on several criteria. Finally, we discuss and recommend use of LiDAR in conjunction with regression modelling to advance understanding of snow depth spatial distribution at spatial scales of thousands of square kilometres. Copyright © 2012 John Wiley & Sons, Ltd.     

Climatic Trends and Temporal Patterns of Phytoplankton Composition, Abundance, and Succession in the Indian River Lagoon, Florida, USA

This paper describes the results of 10 years of water quality monitoring in the Indian River Lagoon Florida, with special emphasis on the relationships between trends in climatic conditions and the distribution, composition, and abundance of the phytoplankton community. The Indian River Lagoon, which spans 220 km of Florida’s east coast, is a region of particular concern because of the rapid rate of human development throughout the region and the hydrologically restricted character of the lagoon, which heightens the potential for algal bloom. Water sampling was carried out on a monthly to twice-monthly basis at six sites located in the northern and central lagoon. The 10-year study included both extended periods of below and above average rainfall. A number of ecologically distinct regions exist within the lagoon, which differ considerably in water exchange properties and watershed inputs. The northern lagoon is characterized by longer water residence times, lower phosphorus concentrations, higher nitrogen concentrations, and more stable salinity conditions than the central lagoon. Mean phytoplankton biovolumes were substantially higher at the sites in the northern lagoon than at the sites in the central lagoon, and algal blooms were more common and intense in the former region. Inter-annual patterns of phytoplankton biovolume were also different in the northern and central lagoon. In the northern lagoon, phytoplankton biovolumes were lowest during the drought period, from the autumn of 1998 to the spring of 2001. By contrast, algal bloom events in the central lagoon were not only less frequent but were not tied to periods of high rainfall. The most widespread and common bloom formers were the potentially toxic dinoflagellate Pyrodinium bahamense var. bahamense and two centric diatoms, Dactyliosolen fragilissimus and Cerataulina pelagica. Many of the biovolume peaks observed over the study period were attributable to these three species. The results of time series modeling of phytoplankton dynamics further highlighted the disparities between the two regions of the lagoon in terms of the suite of parameters that best predict the observed trends in the biomass of phytoplankton. Overall, the outcome of this initial modeling effort in the Indian River Lagoon suggests that time series approaches can help define the factors that influence phytoplankton dynamics.