ALBERT

Description: Carbon (C) and nitrogen (N) released from biomass burning have multiple effects on the Earth's biogeochemical cycle, climate change, and ecosystem. These effects depend on the relative abundances of C and N species emitted, which vary with fuel type and combustion conditions. This study systematically investigates the emission characteristics of biomass burning under different fuel moisture contents, through controlled burning experiments with biomass and soil samples collected from a typical alpine forest in North America. Fuel moisture in general lowers combustion efficiency, shortens flaming phase, and introduces prolonged smoldering before ignition. It increases emission factors of incompletely oxidized C and N species, such as carbon monoxide (CO) and ammonia (NH3). Substantial particulate carbon and nitrogen (up to 4 times C in CO and 75% of N in NH3) were also generated from high-moisture fuels, maily associated with the pre-flame smoldering. This smoldering process emits particles that are larger and contain lower elemental carbon fractions than soot agglomerates commonly observed in flaming smoke. Hydrogen (H)/C ratio and optical properties of particulate matter from the high-moisture fuels show their resemblance to plant cellulous and brown carbon, respectively. These findings have implications for modeling biomass burning emissions and impacts.

Description: High-resolution mapping of fuel combustion and CO2 emission provides valuable information for modeling pollutant transport, developing mitigation policy, and for inverse modeling of CO2 fluxes. Previous global emission maps included only few fuel types, and emissions were estimated on a grid by distributing national fuel data on an equal per capita basis, using population density maps. This process distorts the geographical distribution of emissions within countries. In this study, a sub-national disaggregation method (SDM) of fuel data is applied to establish a global 0.1° × 0.1° geo-referenced inventory of fuel combustion (PKU-FUEL) and corresponding CO2 emissions (PKU-CO2) based upon 64 fuel sub-types for the year 2007. Uncertainties of the emission maps are evaluated using a Monte Carlo method. It is estimated that CO2 emission from combustion sources including fossil fuel, biomass, and solid wastes in 2007 was 11.2 Pg C yr−1 (9.1 Pg C yr−1 and 13.3 Pg C yr−1 as 5th and 95th percentiles). Of this, emission from fossil fuel combustion is 7.83 Pg C yr−1, which is very close to the estimate of the International Energy Agency (7.87 Pg C yr−1). By replacing national data disaggregation with sub-national data in this study, the average 95th minus 5th percentile ranges of CO2 emission for all grid points can be reduced from 417 to 68.2 Mg km−2 yr−1. The spread is reduced because the uneven distribution of per capita fuel consumptions within countries is better taken into account by using sub-national fuel consumption data directly. Significant difference in per capita CO2 emissions between urban and rural areas was found in developing countries (2.08 vs. 0.598 Mg C/(cap. × yr)), but not in developed countries (3.55 vs. 3.41 Mg C/(cap. × yr)). This implies that rapid urbanization of developing countries is very likely to drive up their emissions in the future.

Description: The surface runoff process in fluvial/pluvial flood modelling is often simulated employing a two-dimensional (2-D) diffusive wave approximation described by grid based digital elevation models (DEMs). However, this approach may cause potential problems when using the 2-D surface flow model which exchanges flows through adjacent cells, with conventional sink removal algorithms which also allow for flow exchange along diagonal directions, due to the existence of artificial depression in DEMs. In this paper, we propose an effective method for filling artificial depressions in DEM so that the problem can be addressed. We firstly analyse two types of depressions in DEMs and demonstrate the issues caused by the current depression filling algorithms using the surface flow simulations from the MIKE SHE model built for a medium-sized basin in Southeast England. The proposed depression-filling algorithm for 2-D overland flow modelling is applied and evaluated by comparing the simulated flows at the outlet of the catchment represented by DEMs at various resolutions (50 m, 100 m and 200 m). The results suggest that the existence of depressions in DEMs can substantially influence the overland flow estimation and the new depression filling algorithm is shown to be effective in tackling this issue based upon the comparison of simulations for sink-dominated and sink-free DEMs, especially in the areas with relatively flat topography.

Description: This study attempts to characterise the manner with which inherent error in radar rainfall estimates input influence the character of the stream flow simulation uncertainty in validated hydrological modelling. An artificial statistical error model described by Gaussian distribution was developed to generate realisations of possible combinations of normalised errors and normalised bias to reflect the identified radar error and temporal dependence. These realisations were embedded in the 5 km/15 min UK Nimrod radar rainfall data and used to generate ensembles of stream flow simulations using three different hydrological models with varying degrees of complexity, which consists of a fully distributed physically-based model MIKE SHE, a semi-distributed, lumped model TOPMODEL and the unit hydrograph model PRTF. These models were built for this purpose and applied to the Upper Medway Catchment (220 km2) in South-East England. The results show that the normalised bias of the radar rainfall estimates was enhanced in the simulated stream flow and also the dominate factor that had a significant impact on stream flow simulations. This preliminary radar-error-generation model could be developed more rigorously and comprehensively for the error characteristics of weather radars for quantitative measurement of rainfall.

Description: Radar rainfall estimates have become increasingly available for hydrological modellers over recent years, especially for flood forecasting and warning over poorly gauged catchments. However, the impact of using radar rainfall as compared with conventional raingauge inputs, with respect to various hydrological model structures, remains unclear and yet to be addressed. In the study presented by this paper, we analysed the flow simulations of the upper Medway catchment of southeast England using the UK NIMROD radar rainfall estimates, using three hydrological models based upon three very different structures (e.g. a physically based distributed MIKE SHE model, a lumped conceptual model PDM and an event-based unit hydrograph model PRTF). We focused on the sensitivity of simulations in relation to the storm types and various rainfall intensities. The uncertainty in radar rainfall estimates, scale effects and extreme rainfall were examined in order to quantify the performance of the radar. We found that radar rainfall estimates were lower than raingauge measurements in high rainfall rates; the resolutions of radar rainfall data had insignificant impact at this catchment scale in the case of evenly distributed rainfall events but was obvious otherwise for high-intensity, localised rainfall events with great spatial heterogeneity. As to hydrological model performance, the distributed model had consistent reliable and good performance on peak simulation with all the rainfall types tested in this study.

Description: This study attempts to characterize the manner with which inherent error in radar rainfall estimates input influence the character of the stream flow simulation uncertainty in validated hydrological modelling. An artificial statistical error model described by Gaussian distribution was developed to generate realizations of possible combinations of normalized errors and normalized bias to reflect the identified radar error and temporal dependence. These realizations were embedded in the 5 km/15 min UK Nimrod radar rainfall data and used to generate ensembles of stream flow simulations using three different hydrological models with varying degrees of complexity, which consists of a fully distributed physically-based model MIKE SHE, a semi-distributed model TOPMODEL and a lumped model PRTF. These models were built for this purpose and applied to the Upper Medway Catchment (220 km2) in South-East England. The results show that the normalized bias of the radar rainfall estimates was enhanced in the simulated stream flow and also the dominate factor that had a significant impact on stream flow simulations. This preliminary radar-error-generation model could be developed more rigorously and comprehensively for the error characteristics of weather radars for quantitative measurement of rainfall.

Description: The surface runoff process in fluvial/pluvial flood modelling is often simulated employing a two-dimensional (2-D) diffusive wave approximation to described by grid based digital elevation models (DEMs). However, a serious problem of this approach may arise when using a 2-D surface flow model which exchanges flows through adjacent cells, or conventional rink removal algorithms which also allow flow to be exchanged along diagonal directions, due to the existence of artificial depression in DEMs. This study firstly analyses the two types of depressions in DEMs and reviews the current depression filling algorithms with a medium sized basin in South-East England, the Upper Medway Catchment (220 km2) used to demonstrate the depression issue in 2-D surface runoff simulation by MIKE SHE with different DEM resolutions (50 m, 100 m and 200 m). An alternative depression-filling algorithm for 2-D overland flow modelling is developed and evaluated by comparing the simulated flows at the outlet of the catchment. This result suggests that the depression estimates at different grid resolution of DEM highly influences overland flow estimation and the new depression filling algorithm is shown to be effective in tackling this issue when comparing simulations in sink-dominated and sink-free digital elevation models, especially for depressions in relatively flat areas on digital land surface models.

Description: A new process-based model TRIPLEX-GHG was developed based on the Integrated Biosphere Simulator (IBIS), coupled with a new methane (CH4) biogeochemistry module (incorporating CH4 production, oxidation, and transportation processes) and a water table module to investigate CH4 emission processes and dynamics that occur in natural wetlands. Sensitivity analysis indicates that the most sensitive parameters to evaluate CH4 emission processes from wetlands are r (defined as the CH4 to CO2 release ratio) and Q10 in the CH4 production process. These two parameters were subsequently calibrated to data obtained from 19 sites collected from approximately 35 studies across different wetlands globally. Being heterogeneously spatially distributed, r ranged from 0.1 to 0.7 with a mean value of 0.23, and the Q10 for CH4 production ranged from 1.6 to 4.5 with a mean value of 2.48. The model performed well when simulating magnitude and capturing temporal patterns in CH4 emissions from natural wetlands. Results suggest that the model is able to be applied to different wetlands under varying conditions and is also applicable for global-scale simulations.

Description: Radar rainfall estimates have become increasingly available for hydrological modellers over recent years, especially for flood forecasting and warning over poorly gauged catchments. However, the impact of using radar rainfall as compared with conventional raingauge inputs, with respect to various hydrological model structures, remains unclear and yet to be addressed. In the study presented by this paper, we analysed the flow simulations of the Upper Medway catchment of Southeast England using the UK NIMROD radar rainfall estimates using three hydrological models based upon three very different structures, e.g. a physically based distributed MIKE SHE model, a lumped conceptual model PDM and an event-based unit hydrograph model PRTF. We focused on the sensitivity of simulations in relation to the storm types and various rainfall intensities. The uncertainty in radar-rainfall estimates, scale effects and extreme rainfall were examined in order to quantify the performance of the radar. We found that radar rainfall estimates were lower than raingauge measurements in high rainfall rates; the resolutions of radar rainfall data had insignificant impact at this catchment scale in the case of evenly distributed rainfall events but was obvious otherwise for high-intensity, localised rainfall events with great spatial heterogeneity. As to hydrological model performance, the distributed model had consistent reliable and good performance on peak simulation with all the rainfall types tested in this study.

Description: In recent years, photochemical smog has been a major cause of air pollution in the metropolitan area of Guangzhou, China, with a continuing increase in the concentrations of photochemical pollutants. The concentration of peroxyacetyl nitrate (PAN) has often been found to reach very high levels, posing a potential threat to the public health. To better understand the changes in PAN concentration and its sources, a study was carried from January to December of 2012 at the Guangzhou Panyu Atmospheric Composition Station (GPACS) to measure the atmospheric concentrations of PAN as well as those of ozone (O3), nitrogen oxides (NOx), and non-methane hydrocarbon (NMHC). These data were analyzed to investigate the quantitative relationships between PAN and its precursors. In the study period, the hourly concentrations of PAN varied from below instrument detection limit to 12.0 ppbv. The yearly mean concentration of PAN was 0.84 ppbv, with the daily mean concentration exceeding 5 ppbv in 32 of the total observation days. Calculations indicate that among the measured NMHC species, alkenes accounted for 53 % of the total NMHC contribution to the PAN production, with aromatics and alkanes accounting for about 11 and 7 % of the total, respectively. During the period of our observation only a modest correlation was found between the concentrations of PAN and O3 for daytime hours, and observed PAN concentrations were relatively high even though the observed NMHCs/NOx ratio was low. This suggests regional air mass transport of pollutants had a major impact on the PAN concentrations in Guangzhou area.