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1.
Forests in the Southeastern United States are predicted to experience future changes in seasonal patterns of precipitation inputs as well as more variable precipitation events. These climate change‐induced alterations could increase drought and lower soil water availability. Drought could alter rooting patterns and increase the importance of deep roots that access subsurface water resources. To address plant response to drought in both deep rooting and soil water utilization as well as soil drainage, we utilize a throughfall reduction experiment in a loblolly pine plantation of the Southeastern United States to calibrate and validate a hydrological model. The model was accurately calibrated against field measured soil moisture data under ambient rainfall and validated using 30% throughfall reduction data. Using this model, we then tested these scenarios: (a) evenly reduced precipitation; (b) less precipitation in summer, more in winter; (c) same total amount of precipitation with less frequent but heavier storms; and (d) shallower rooting depth under the above 3 scenarios. When less precipitation was received, drainage decreased proportionally much faster than evapotranspiration implying plants will acquire water first to the detriment of drainage. When precipitation was reduced by more than 30%, plants relied on stored soil water to satisfy evapotranspiration suggesting 30% may be a threshold that if sustained over the long term would deplete plant available soil water. Under the third scenario, evapotranspiration and drainage decreased, whereas surface run‐off increased. Changes in root biomass measured before and 4 years after the throughfall reduction experiment were not detected among treatments. Model simulations, however, indicated gains in evapotranspiration with deeper roots under evenly reduced precipitation and seasonal precipitation redistribution scenarios but not when precipitation frequency was adjusted. Deep soil and deep rooting can provide an important buffer capacity when precipitation alone cannot satisfy the evapotranspirational demand of forests. How this buffering capacity will persist in the face of changing precipitation inputs, however, will depend less on seasonal redistribution than on the magnitude of reductions and changes in rainfall frequency. 相似文献
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Wetland hydroperiod classification in the western prairies using multitemporal synthetic aperture radar
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Joshua S. Montgomery Chris Hopkinson Brian Brisco Shane Patterson Stewart B. Rood 《水文研究》2018,32(10):1476-1490
Wetlands represent one of the world's most biodiverse and threatened ecosystem types and were diminished globally by about two‐thirds in the 20th century. There is continuing decline in wetland quantity and function due to infilling and other human activities. In addition, with climate change, warmer temperatures and changes in precipitation and evapotranspiration are reducing wetland surface and groundwater supplies, further altering wetland hydrology and vegetation. There is a need to automate inventory and monitoring of wetlands, and as a study system, we investigated the Shepard Slough wetlands complex, which includes numerous wetlands in urban, suburban, and agricultural zones in the prairie pothole region of southern Alberta, Canada. Here, wetlands are generally confined to depressions in the undulating terrain, challenging wetlands inventory and monitoring. This study applied threshold and frequency analysis routines for high‐resolution, single‐polarization (HH) RADARSAT‐2, synthetic aperture radar mapping. This enabled a growing season surface water extent hyroperiod‐based wetland classification, which can support water and wetland resource monitoring. This 3‐year study demonstrated synthetic aperture radar‐derived multitemporal open‐water masks provided an effective index of wetland permanence class, with overall accuracies of 89% to 95% compared with optical validation data, and RMSE between 0.2 and 0.7 m between model and field validation data. This allowed for characterizing the distribution and dynamics of 4 marsh wetlands hydroperiod classes, temporary, seasonal, semipermanent, and permanent, and mapping of the sequential vegetation bands that included emergent, obligate wetland, facultative wetland, and upland plant communities. Hydroperiod variation and surface water extent were found to be influenced by short‐term rainfall events in both wet and dry years. Seasonal hydroperiods in wetlands were particularly variable if there was a decrease in the temporary or semipermanent hydroperiod classes. In years with extreme rain events, the temporary wetlands especially increased relative to longer lasting wetlands (84% in 2015 with significant rainfall events, compared with 42% otherwise). 相似文献
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Determination of subcatchment and watershed boundaries in a complex and highly urbanized landscape
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Urban development significantly alters the landscape by introducing widespread impervious surfaces, which quickly convey surface run‐off to streams via stormwater sewer networks, resulting in “flashy” hydrological responses. Here, we present the inadequacies of using raster‐based digital elevation models and flow‐direction algorithms to delineate large and highly urbanized watersheds and propose an alternative approach that accounts for the influence of anthropogenically modified land cover. We use a semi‐automated approach that incorporates conventional drainage networks into overland flow paths and define the maximal run‐off contributing area. In this approach, stormwater pipes are clustered according to their slope attributes, which define flow direction. Land areas drained by each cluster and contributing (or exporting) flow to a topographically delineated catchment were determined. These land masses were subsequently added or removed from the catchment, modifying both the shape and the size. Our results in a highly urbanized Toronto, Canada, area watershed indicate a moderate net increase in the directly connected watershed area by 3% relative to a topographically forced method; however, differences across three smaller scale subcatchments are greater. Compared to topographic delineation, the directly connected watershed areas of both the upper and middle subcatchments decrease by 5% and 8%, respectively, whereas the lower subcatchment area increases by 15%. This is directly related to subsurface storm sewer pipes that cross topographic boundaries. When directly connected subcatchment area is plotted against total streamflow and flashiness indices using this method, the coefficients of variation are greater (0.93 to 0.97) compared to the use of digital elevation model‐derived subcatchment areas (0.78 to 0.85). The accurate identification of watershed and subcatchment boundaries should incorporate ancillary data such as stormwater sewer networks and retention basin drainage areas to reduce water budget errors in urban systems. 相似文献
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A constitutive model that captures the material behavior under a wide range of loading conditions is essential for simulating complex boundary value problems. In recent years, some attempts have been made to develop constitutive models for finite element analysis using self‐learning simulation (SelfSim). Self‐learning simulation is an inverse analysis technique that extracts material behavior from some boundary measurements (eg, load and displacement). In the heart of the self‐learning framework is a neural network which is used to train and develop a constitutive model that represents the material behavior. It is generally known that neural networks suffer from a number of drawbacks. This paper utilizes evolutionary polynomial regression (EPR) in the framework of SelfSim within an automation process which is coded in Matlab environment. EPR is a hybrid data mining technique that uses a combination of a genetic algorithm and the least square method to search for mathematical equations to represent the behavior of a system. Two strategies of material modeling have been considered in the SelfSim‐based finite element analysis. These include a total stress‐strain strategy applied to analysis of a truss structure using synthetic measurement data and an incremental stress‐strain strategy applied to simulation of triaxial tests using experimental data. The results show that effective and accurate constitutive models can be developed from the proposed EPR‐based self‐learning finite element method. The EPR‐based self‐learning FEM can provide accurate predictions to engineering problems. The main advantages of using EPR over neural network are highlighted. 相似文献
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Peng Yue Fan Gao Boyi Shangguan Zheren Yan 《International journal of geographical information science》2020,34(11):2243-2274
ABSTRACT High performance computing is required for fast geoprocessing of geospatial big data. Using spatial domains to represent computational intensity (CIT) and domain decomposition for parallelism are prominent strategies when designing parallel geoprocessing applications. Traditional domain decomposition is limited in evaluating the computational intensity, which often results in load imbalance and poor parallel performance. From the data science perspective, machine learning from Artificial Intelligence (AI) shows promise for better CIT evaluation. This paper proposes a machine learning approach for predicting computational intensity, followed by an optimized domain decomposition, which divides the spatial domain into balanced subdivisions based on the predicted CIT to achieve better parallel performance. The approach provides a reference framework on how various machine learning methods including feature selection and model training can be used in predicting computational intensity and optimizing parallel geoprocessing against different cases. Some comparative experiments between the approach and traditional methods were performed using the two cases, DEM generation from point clouds and spatial intersection on vector data. The results not only demonstrate the advantage of the approach, but also provide hints on how traditional GIS computation can be improved by the AI machine learning. 相似文献
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《Astroparticle Physics》2002,16(4):183-386
Frequency distributions of local muon densities in high-energy extensive air showers (EAS) are presented as signature of the primary cosmic ray energy spectrum in the knee region. Together with the gross shower variables like shower core position, angle of incidence, and the shower sizes, the KASCADE experiment is able to measure local muon densities for two different muon energy thresholds. The spectra have been reconstructed for various core distances, as well as for particular subsamples, classified on the basis of the shower size ratio Nμ/Ne. The measured density spectra of the total sample exhibit clear kinks reflecting the knee of the primary energy spectrum. While relatively sharp changes of the slopes are observed in the spectrum of EAS with small values of the shower size ratio, no such feature is detected at EAS of large Nμ/Ne ratio in the energy range of 1–10 PeV. Comparing the spectra for various thresholds and core distances with detailed Monte Carlo simulations the validity of EAS simulations is discussed. 相似文献
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