Parameter-induced uncertainty quantification of soil N 2 O, NO and CO 2 emission from Höglwald spruce forest (Germany) using the LandscapeDNDC model

Assessing the uncertainties of simulation results of ecological models is becoming increasingly important, specifically if these models are used to estimate greenhouse gas emissions on site to regional/national levels. Four general sources of uncertainty effect the outcome of process-based models: (i) uncertainty of information used to initialise and drive the model, (ii) uncertainty of model parameters describing specific ecosystem processes, (iii) uncertainty of the model structure, and (iv) accurateness of measurements (e.g., soil-atmosphere greenhouse gas exchange) which are used for model testing and development. The aim of our study was to assess the simulation uncertainty of the process-based biogeochemical model LandscapeDNDC. For this we set up a Bayesian framework using a Markov Chain Monte Carlo (MCMC) method, to estimate the joint model parameter distribution. Data for model testing, parameter estimation and uncertainty assessment were taken from observations of soil fluxes of nitrous oxide (N2O), nitric oxide (NO) and carbon dioxide (CO2) as observed over a 10 yr period at the spruce site of the Höglwald Forest, Germany. By running four independent Markov Chains in parallel with identical properties (except for the parameter start values), an objective criteria for chain convergence developed by Gelman et al. (2003) could be used. Our approach shows that by means of the joint parameter distribution, we were able not only to limit the parameter space and specify the probability of parameter values, but also to assess the complex dependencies among model parameters used for simulating soil C and N trace gas emissions. This helped to improve the understanding of the behaviour of the complex LandscapeDNDC model while simulating soil C and N turnover processes and associated C and N soil-atmosphere exchange. In a final step the parameter distribution of the most sensitive parameters determining soil-atmosphere C and N exchange were used to obtain the parameter-induced uncertainty of simulated N2O, NO and CO2 emissions. These were compared to observational data of an calibration set (6 yr) and an independent validation set of 4 yr. The comparison showed that most of the annual observed trace gas emissions were in the range of simulated values and were predicted with a high certainty (Root-mean-squared error (RMSE) NO: 2.4 to 18.95 g N ha−1 d−1, N2O: 0.14 to 21.12 g N ha−1 d−1, CO2: 5.4 to 11.9 kg C ha−1 d−1). However, LandscapeDNDC simulations were sometimes still limited to accurately predict observed seasonal variations in fluxes

Ground Profile Recovery from Aerial 3D LiDAR-based Maps

The paper presents the study and implementation of the ground detection methodology with filtration and removal of forest points from LiDAR-based 3D point cloud using the Cloth Simulation Filtering (CSF) algorithm. The methodology allows to recover a terrestrial relief and create a landscape map of a forestry region. As the proof-of-concept, we provided the outdoor flight experiment, launching a hexacopter under a mixed forestry region with sharp ground changes nearby Innopolis city (Russia), which demonstrated the encouraging results for both ground detection and methodology robustness.Comment: 8 pages, FRUCT-2019 conferenc

Effect of bioenergy crops and fast growing trees on hydrology and water quality in the Little Vermilion River Watershed

Energy security and sustainability require a suite of biomass crops, including woody species. Short rotation woody crops (SRWCs) such as Populus have great potential as biofuel feedstocks. Quantifying biomass yields of bioenergy crop and hydrologic and water quality responses to growth is important should it be widely planted in the Midwestern U.S. Subsurface tile drainage systems enable the Midwest area to become highly productive agricultural lands, but also create environmental problems like nitrate-N contamination of the water it drains. The Soil and Water Assessment Tool (SWAT) has been used to model watersheds with tile drainage, but the new tile drainage routine in SWAT2012 has not been fully tested. The objectives of this study were to develop algorithms and growth parameters of Populus in Agricultural Land Management Alternative with Numerical Assessment Criteria (ALMANAC) and SWAT models, compare performance of tile drainage routines in SWAT2009 and SWAT2012 in simulating tile drainage, and simulate biomass yields of bioenergy crops and the impacts of their impacts on water quantity and quality for a typical tile-drained watershed in the Midwest USA. The functional components and parameters of hybrid poplar Tristis #1 (Populus balsamifera L. × P.tristis Fisch) and eastern cottonwood (Populus deltoides Bartr.) were determined, and related algorithms improved in ALMANAC and SWAT based on improved simulation of leaf area, plant biomass and biomass partitioning. Long-term (1991-2003) field site and river station data from the Little Vermilion River (LVR) watershed in Illinois were used to evaluate performance of tile drainage routines in SWAT2009 revision 528 (the old routine) and SWAT2012 revision 615 and 645 (the new routine). Calibrated monthly tile flow, surface flow, nitrate in tile and surface flow, sediment and annual corn and soybean yield results at field sites, and flow, sediment load and nitrate load at the river station for the old and new tile drainage routines were compared with observed values. Crop residue from corn stover, perennial grasses, switchgrass and Miscanthus, and hybrid poplar trees were considered as potential bioenergy crops for the LVR watershed. SWAT2012 (Revision 615) with the new tile drainage routine (DRAINMOD routine) and improved perennial grass and tree growth simulation was used to model long-term annual biomass yields, flow, tile flow, sediment load, total nitrogen, nitrate load in flow, nitrate in tile flow, soluble nitrogen, organic nitrogen, total phosphorus, mineral phosphorus and organic phosphorus under various bioenergy scenarios in the LVR watershed. Simulated results from different bioenergy crop scenarios were compared with those from the baseline. Tree growth calibration and validation results showed that improved algorithms of leaf area index (LAI) and biomass simulation and suggested values and potential parameter range for hybrid poplar Tristis #1 and Eastern cottonwood ( Populus deltoides Bartr.) were reasonable, and performance of the modified ALMANAC in simulating LAI, aboveground biomass and root biomass of Populus was good. Performance of the modified SWAT simulated hybrid poplar LAI and aboveground woody biomass (PBIAS: -57 ~ 7%, NSE: 0.94 ~ 0.99, and R2: 0.74 ~ 0.99), and cottonwood aboveground biomass, seasonal mean runoff, mean sediment, mean nitrate-N and total nitrate-N were satisfactory (PBIAS: -39 ~ 11%, NSE: 0.86 ~ 0.99, and R2: 0.93 ~ 0.99). Additionally, tile drainage calibration and validation results indicated that the new routine provides acceptable simulated tile flow (NSE = 0.50 ~ 0.68), and nitrate in tile flow (NSE = 0.50 ~ 0.77) for field sites, while the old routine simulated tile flow (NSE = -0.77~ -0.20) and nitrate in tile flow (NSE = -0.99 ~ 0.21) for the field site with constant tile spacing were unacceptable. The new modified curve number calculation method in revision 645 (NSE = 0.56 ~ 0.82) better simulated surface runoff than revision 615 (NSE = -5.95 ~ 0.5). Bioenergy crop simulation results showed that 38% corn stover removal (66,439 Mg/yr) with combination of Miscanthus both on highly erodible areas and marginal land (19,039 Mg/yr) provided the highest biofeedstock production. Flow, tile flow, erosion and nutrient losses were slightly reduced under bioenergy crop scenarios of Miscanthus, switchgrass, and hybrid poplar on highly erodible areas, marginal land and marginal land with forest. The increase in sediment load and nutrient losses resulting from corn stover removal could be offset under scenarios with various combinations of bioenergy crops. Corn stover removal with bioenergy crops both on highly erodible areas and marginal land could provide more biofuel production relative to the baseline, and was beneficial to hydrology and water quality at the watershed scale. The modified ALMANAC and SWAT can be used for biofeedstock production modeling for Populus. The modified SWAT model can be used for Populus biofeedstock production modeling and hydrologic and water quality response to its growth. The improved algorithms of LAI and biomass simulation for tree growth should also be useful for other process based models, such as SWAT, EPIC and APEX. Tile drainage calibration and validation results provided reasonable parameter sets for the old and new tile drainage routines to accurately simulate hydrologic processes in mildly-sloped watersheds. Bioenergy crop simulation results provided guidance for further research on evaluation of bioenergy crop scenarios in a typical extensively tile-drained watershed in the Midwestern US

Reionization and Cosmology with 21 cm Fluctuations

Measurement of the spatial distribution of neutral hydrogen via the redshifted 21 cm line promises to revolutionize our knowledge of the epoch of reionization and the first galaxies, and may provide a powerful new tool for observational cosmology from redshifts 1<z<4 . In this review we discuss recent advances in our theoretical understanding of the epoch of reionization (EoR), the application of 21 cm tomography to cosmology and measurements of the dark energy equation of state after reionization, and the instrumentation and observational techniques shared by 21 cm EoR and post reionization cosmology machines. We place particular emphasis on the expected signal and observational capabilities of first generation 21 cm fluctuation instruments.Comment: Invited review for Annual Review of Astronomy and Astrophysics (2010 volume

Computing server power modeling in a data center: survey,taxonomy and performance evaluation

Data centers are large scale, energy-hungry infrastructure serving the increasing computational demands as the world is becoming more connected in smart cities. The emergence of advanced technologies such as cloud-based services, internet of things (IoT) and big data analytics has augmented the growth of global data centers, leading to high energy consumption. This upsurge in energy consumption of the data centers not only incurs the issue of surging high cost (operational and maintenance) but also has an adverse effect on the environment. Dynamic power management in a data center environment requires the cognizance of the correlation between the system and hardware level performance counters and the power consumption. Power consumption modeling exhibits this correlation and is crucial in designing energy-efficient optimization strategies based on resource utilization. Several works in power modeling are proposed and used in the literature. However, these power models have been evaluated using different benchmarking applications, power measurement techniques and error calculation formula on different machines. In this work, we present a taxonomy and evaluation of 24 software-based power models using a unified environment, benchmarking applications, power measurement technique and error formula, with the aim of achieving an objective comparison. We use different servers architectures to assess the impact of heterogeneity on the models' comparison. The performance analysis of these models is elaborated in the paper

Assessing the impacts of land use and land cover change on hydrology of watershed: a case study on Gigel-Abbay Watershed, Lake Tana Basin, Ethiopia

Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies.The population growth for the last 16 years caused changes in land cover of the Gilgel Abbay watershed, Lake Tana basin, Ethiopia. The effects of the land cover changes have impacted on the stream flow of the watershed by changing the magnitude of surface runoff and ground water flow. This study is mainly focusing on the assessment of the impacts of the land cover changes on the stream flow by changing SURQ and GWQ for the wet months (June, July, August) and dry months (January, February, March) through satellite Remote Sensing (RS) and Geographic Information System (GIS) integrated with the SWAT model. ArcGIS used to generate land use and cover maps from Landsat TM and ETM+ acquired, respectively, in 1986 and 2001. The land cover maps were generated using the Maximum Likelihood Algorithm of Supervised Classification. The accuracy of the classified maps was assessed using Confusion Metrics. The result of this analysis showed that the cultivated land has expanded during the study period of 1986-2001. Using the two generated land cover maps, two SWAT models set up were run to evaluate the impacts the land use and cover changes on the stream flow of the study watershed. The performance of the SWAT model was evaluated through sensitivity analysis, calibration, and validation. Ten flow parameters were identified to be sensitive for the stream flow of the study area and used for model calibration. The model calibration was carried out using observed stream flow data from 01 January 1987 to 31 December 1994 and a validation period from 01 January 1995 to 31 December 2001. Both the calibration and validation results showed good match between measured and simulated stream flow data with the coefficient of determination (R2) of 0.93 and Nash-Sutcliffe efficiency (ENS) of 0.95 for the calibration, and R2 of 0.91 and ENS of 0.90 of the validation period. The result of this analysis indicated that the mean monthly stream flow increased by 16.26m3/s for the wet months while for the dry months decreased by 5.41 m3/s. Generally, the analysis indicated that flow during the wet months has increased, while the flow during the dry months decreased. The SURQ increased, while GWQ decreased from 1986 to 2001 due to the increment of cultivated lands. The model results showed that the stream flow characteristics changed due to the land cover changes during the study period

Radiative Transfer Effects on the Lya Forest

Strong observational evidence for a fluctuating ultraviolet background (UVB) has been accumulating through a number of studies of the HI and HeII Lya forest as well as accurate IGM metallicity measurements. UVB fluctuations could arise both from the inhomogeneous distribution of the ionizing sources and/or from radiative transfer (RT) through the filamentary IGM. In this study we investigate, via numerical simulations, the role of RT effects such as shadowing, self-shielding and filtering of the ionizing radiation, in giving raise to a fluctuating UVB. We focus on possible detectable signatures of these effects on quantities derived from Lya forest spectra, as photoionization rate fluctuations, eta parameter (the HeII to HI column density ratio) distributions and the IGM temperature at redshift about 3. We find that RT induces fluctuations up to 60% in the UVB, which are tightly correlated to the density field. The UVB mean intensity is progressively suppressed toward higher densities and photon energies above 4 Ryd, due to the high HeII opacity. Shielding of overdense regions (Delta > 5) from cosmic HeII ionizing radiation, produces a decreaseing trend of eta with overdensity. Furthermore we find that the mean eta value inferred from HI-HeII Lya forest observations can be explained only by properly accounting for the actual IGM opacity. We outline and discuss several implications of our findings.Comment: 13 pages, 10 figures, Accepted for publication in MNRA