River evaporation,condensation and heat fluxes within a first‐order tributary of Catamaran Brook (New Brunswick,Canada)

Stream temperature plays an important role in many biotic and abiotic processes, as it influences many physical, chemical and biological properties in rivers. As such, a good understanding of the thermal regime of rivers is essential for effective fisheries management and the protection aquatic habitats. Moreover, a thorough understanding of underlying physical processes and river heat fluxes is essential in the development of better and more adaptive water temperature models. Very few studies have measured river evaporation and condensation and subsequently calculated corresponding heat fluxes in small tributary streams, mainly because microclimate data (data collected within the stream environment) are essential and rarely available. As such, the present study will address these issues by measuring river evaporation and condensation in tributary 1 (Trib 1, a small tributary within Catamaran Brook) using floating minipans. The latent heat flux and other important fluxes were calculated. Results showed that evaporation was low within the small Trib 1 of Catamaran Brook, less than 0.07 mm day^?1. Results showed that condensation played an important role in the latent heat flux. In fact, condensation was present during 34 of 92 days (37%) during the summer, which occurred when air temperature was greater than water temperature by 4–6 °C. Heat fluxes within this small stream showed that solar radiation dominated the heat gains and long‐wave radiation dominated the heat losses. © 2015 Her Majesty the Queen in Right of Canada. Hydrological Processes. © 2015 John Wiley & Sons, Ltd.     

Frequency analysis of nonstationary annual maximum flood series using the time‐varying two‐component mixture distributions

The most popular practice for analysing nonstationarity of flood series is to use a fixed single‐type probability distribution incorporated with the time‐varying moments. However, the type of probability distribution could be both complex because of distinct flood populations and time‐varying under changing environments. To allow the investigation of this complex nature, the time‐varying two‐component mixture distributions (TTMD) method is proposed in this study by considering the time variations of not only the moments of its component distributions but also the weighting coefficients. Having identified the existence of mixed flood populations based on circular statistics, the proposed TTMD was applied to model the annual maximum flood series of two stations in the Weihe River basin, with the model parameters calibrated by the meta‐heuristic maximum likelihood method. The performance of TTMD was evaluated by different diagnostic plots and indexes and compared with stationary single‐type distributions, stationary mixture distributions and time‐varying single‐type distributions. The results highlighted the advantages of TTMD with physically‐based covariates for both stations. Besides, the optimal TTMD models were considered to be capable of settling the issue of nonstationarity and capturing the mixed flood populations satisfactorily. Copyright © 2016 John Wiley & Sons, Ltd.     

Generation of continuous surface soil moisture dataset using combined optical and thermal infrared images

Surface soil moisture (SSM) is a critical variable for understanding water and energy flux between the atmosphere and the Earth's surface. An easy to apply algorithm for deriving SSM time series that primarily uses temporal parameters derived from simulated and in situ datasets has recently been reported. This algorithm must be assessed for different biophysical and atmospheric conditions by using actual geostationary satellite images. In this study, two currently available coarse‐scale SSM datasets (microwave and reanalysis product) and aggregated in situ SSM measurements were implemented to calibrate the time‐invariable coefficients of the SSM retrieval algorithm for conditions in which conventional observations are rare. These coefficients were subsequently used to obtain SSM time series directly from Meteosat Second Generation (MSG) images over the study area of a well‐organized soil moisture network named REMEDHUS in Spain. The results show a high degree of consistency between the estimated and actual SSM time series values when using the three SSM dataset‐calibrated time‐invariable coefficients to retrieve SSM, with coefficients of determination (R²) varying from 0.304 to 0.534 and root mean square errors ranging from 0.020 m³/m³ to 0.029 m³/m³. Further evaluation with different land use types results in acceptable debiased root mean square errors between 0.021 m³/m³ and 0.048 m³/m³ when comparing the estimated MSG pixel‐scale SSM with in situ measurements. These results indicate that the investigated method is practical for deriving time‐invariable coefficients when using publicly accessed coarse‐scale SSM datasets, which is beneficial for generating continuous SSM dataset at the MSG pixel scale.     

Inferring air–water temperature relationships from river and catchment properties

Water temperature (Tw) is a key determinant of freshwater ecosystem status and cause for concern under a changing climate. Hence, there is growing interest in the feasibility of moderating rising Tw through management of riparian shade. The Loughborough University Temperature Network (LUTEN) is an array of 36 water and air temperature (Ta) monitoring sites in the English Peak District set‐up to explore the predictability of local Tw, given Ta, river reach, and catchment properties. Year 1 of monitoring shows that 84%–94% of variance in daily Tw is explained by Ta. However, site‐specific logistic regression parameters exhibit marked variation and dependency on upstream riparian shade. Perennial spring flows in the lower River Dove also affect regression model parameters and strongly buffer daily and seasonal mean Tw. The asymptote of the models (i.e. maximum expected Tw) is particularly sensitive to groundwater inputs. We conclude that reaches with spring flows potentially offer important thermal refuges for aquatic organisms against expected long‐term warming of rivers and should be afforded special protection. Copyright © 2013 John Wiley & Sons, Ltd.     

Reevaluating suitability estimates based on dynamics of cropland expansion in the Brazilian Amazon

Agricultural suitability maps are a key input for land use zoning and projections of cropland expansion. Suitability assessments typically consider edaphic conditions, climate, crop characteristics, and sometimes incorporate accessibility to transportation and market infrastructure. However, correct weighting among these disparate factors is challenging, given rapid development of new crop varieties, irrigation, and road networks, as well as changing global demand for agricultural commodities. Here, we compared three independent assessments of cropland suitability to spatial and temporal dynamics of agricultural expansion in the Brazilian state of Mato Grosso during 2001–2012. We found that areas of recent cropland expansion identified using satellite data were generally designated as low to moderate suitability for rainfed crop production. Our analysis highlighted the abrupt nature of suitability boundaries, rather than smooth gradients of agricultural potential, with little additional cropland expansion beyond the extent of the flattest areas (0–2% slope). Satellite-based estimates of the interannual variability in the use of existing crop areas also provided an alternate means to assess suitability. On average, cropland areas in the Cerrado biome had higher utilization (84%) than croplands in the Amazon region of northern Mato Grosso (74%). Areas of more recent expansion had lower utilization than croplands established before 2002, providing empirical evidence for lower suitability or alternative management strategies (e.g., pasture–soya rotations) for lands undergoing more recent land use transitions. This unplanted reserve constitutes a large area of potentially available cropland (PAC) without further expansion, within the management limits imposed for pest management and fallow cycles. Using two key constraints on future cropland expansion, slope and restrictions on further deforestation of Amazon or Cerrado vegetation, we found little available flat land for further legal expansion of crop production in Mato Grosso. Dynamics of cropland expansion from more than a decade of satellite observations indicated narrow ranges of suitability criteria, restricting PAC under current policy conditions, and emphasizing the advantages of field-scale information to assess suitability and utilization.     

Modelling the flow of self‐compacting concrete

A Lagrangian particle‐based method, smooth particle hydrodynamics (SPH), is used in this paper to model the flow of self‐compacting concretes (SCC) with or without short steel fibres. An incompressible SPH method is presented to simulate the flow of such non‐Newtonian fluids whose behaviour is described by a Bingham‐type model, in which the kink in the shear stress vs shear strain rate diagram is first appropriately smoothed out. The viscosity of the SCC is predicted from the measured viscosity of the paste using micromechanical models in which the second phase aggregates are treated as rigid spheres and the short steel fibres as slender rigid bodies. The basic equations solved in the SPH are the incompressible mass conservation and Navier–Stokes equations. The solution procedure uses prediction–correction fractional steps with the temporal velocity field integrated forward in time without enforcing incompressibility in the prediction step. The resulting temporal velocity field is then implicitly projected on to a divergence‐free space to satisfy incompressibility through a pressure Poisson equation derived from an approximate pressure projection. The results of the numerical simulation are benchmarked against actual slump tests carried out in the laboratory. The numerical results are in excellent agreement with test results, thus demonstrating the capability of SPH and a proper rheological model to predict SCC flow and mould‐filling behaviour. Copyright © 2010 John Wiley & Sons, Ltd.     

基于NGM(1,1,k,c)模型和改进切线角的危岩变形预测

针对危岩变形预测问题,本文以非齐次指数序列的灰色模型(NGM)作为危岩变形预测的基本模型,通过对望霞危岩变形的分析结果显示,NGM(1,1,k,c)模型拟合效果明显优于GM(1,1)模型,说明危岩变形趋势更接近于非齐次指数序列。利用NGM(1,1,k,c)模型结合改进切线角可对危岩变形趋势进行分析预测,可作为危岩稳定性和发展趋势的评估依据。