scholarly journals Prediction of Long-Term Health Risk from Radiocesium Deposited on Ground with Consideration of Land-Surface Properties

2021 ◽  
Vol 11 (10) ◽  
pp. 4424
Author(s):  
Hiroshi Yasuda
Keyword(s):  
Effective Dose ◽  
Dose Rate ◽  
Land Surface ◽  
Large Scale ◽  
Model Parameters ◽  
Specific Information ◽  
Radiation Dose Rate ◽  
Terrestrial Environment ◽  
Chronic Radiation

After the Fukushima Daiichi accident, there have been long controversial discussions on “how safe is safe?” between the authorities and the residents in the affected area. This controversy was partly attributable to the way the authorities made a judgement based on the annual effective dose rate; meanwhile, many of the local residents have serious concerns about future consequences for their health caused by chronic radiation exposure, particularly of small children. To settle this controversy, the author presents an approach based on long-term cancer risk projections of female infants, i.e., the most radiosensitive group, following land contamination by radiocesium deposition into ground with different surface conditions; the land was classified into three categories on the basis of decaying patterns of radiation dose rate: “Fast”, “Middle”, and “Slow”. From the results of analyses with an initial dose rate of 20 mGy per year, it was predicted that the integrated lifetime attributable risk (LAR) of cancer mortality of a female person ranged by a factor of 2 from 1.8% (for the Fast area) to 3.6% (for the Slow area) that were clearly higher than the nominal risk values derived from effective dose estimates with median values of environmental model parameters. These findings suggest that accurate site-specific information on the behavioral characteristics of radionuclides in the terrestrial environment are critically important for adequate decision making for protecting people when there is an event accompanied by large-scale radioactive contamination.

scholarly journals Cities Exacerbate Climate Warming

Urban Science ◽  
2021 ◽  
Vol 5 (1) ◽  
pp. 27
Author(s):  
Lahouari Bounoua ◽  
Kurtis Thome ◽  
Joseph Nigro
Keyword(s):  
Surface Temperature ◽  
Land Surface ◽  
Large Scale ◽  
Climate Models ◽  
Warming Trend ◽  
Climate Mitigation ◽  
Temperature Changes ◽  
Surface Warming ◽  
The Us

Urbanization is a complex land transformation not explicitly resolved within large-scale climate models. Long-term timeseries of high-resolution satellite data are essential to characterize urbanization within land surface models and to assess its contribution to surface temperature changes. The potential for additional surface warming from urbanization-induced land use change is investigated and decoupled from that due to change in climate over the continental US using a decadal timescale. We show that, aggregated over the US, the summer mean urban-induced surface temperature increased by 0.15 °C, with a warming of 0.24 °C in cities built in vegetated areas and a cooling of 0.25 °C in cities built in non-vegetated arid areas. This temperature change is comparable in magnitude to the 0.13 °C/decade global warming trend observed over the last 50 years caused by increased CO2. We also show that the effect of urban-induced change on surface temperature is felt above and beyond that of the CO2 effect. Our results suggest that climate mitigation policies must consider urbanization feedback to put a limit on the worldwide mean temperature increase.

scholarly journals Towards a simple representation of chalk hydrology in land surface modelling

2017 ◽  
Vol 21 (1) ◽  
pp. 459-471 ◽  
Author(s):  
Mostaquimur Rahman ◽  
Rafael Rosolem
Keyword(s):  
Land Surface ◽  
Unsaturated Zone ◽  
Large Scale ◽  
Management Practices ◽  
Preferential Flow ◽  
Water Movement ◽  
Model Parameters ◽  
Catchment Scale ◽  
Surface Modelling ◽  
Land Surface Modelling

Abstract. Modelling and monitoring of hydrological processes in the unsaturated zone of chalk, a porous medium with fractures, is important to optimize water resource assessment and management practices in the United Kingdom (UK). However, incorporating the processes governing water movement through a chalk unsaturated zone in a numerical model is complicated mainly due to the fractured nature of chalk that creates high-velocity preferential flow paths in the subsurface. In general, flow through a chalk unsaturated zone is simulated using the dual-porosity concept, which often involves calibration of a relatively large number of model parameters, potentially undermining applications to large regions. In this study, a simplified parameterization, namely the Bulk Conductivity (BC) model, is proposed for simulating hydrology in a chalk unsaturated zone. This new parameterization introduces only two additional parameters (namely the macroporosity factor and the soil wetness threshold parameter for fracture flow activation) and uses the saturated hydraulic conductivity from the chalk matrix. The BC model is implemented in the Joint UK Land Environment Simulator (JULES) and applied to a study area encompassing the Kennet catchment in the southern UK. This parameterization is further calibrated at the point scale using soil moisture profile observations. The performance of the calibrated BC model in JULES is assessed and compared against the performance of both the default JULES parameterization and the uncalibrated version of the BC model implemented in JULES. Finally, the model performance at the catchment scale is evaluated against independent data sets (e.g. runoff and latent heat flux). The results demonstrate that the inclusion of the BC model in JULES improves simulated land surface mass and energy fluxes over the chalk-dominated Kennet catchment. Therefore, the simple approach described in this study may be used to incorporate the flow processes through a chalk unsaturated zone in large-scale land surface modelling applications.

scholarly journals Long-term temporal changes in the occurrence of a high forest fire danger in Finland

2012 ◽  
Vol 12 (8) ◽  
pp. 2591-2601 ◽  
Author(s):  
H. M. Mäkelä ◽  
M. Laapas ◽  
A. Venäläinen
Keyword(s):  
Forest Fire ◽  
Forest Fires ◽  
Large Scale ◽  
Weather Conditions ◽  
Temporal Variations ◽  
Fire Danger ◽  
Specific Information ◽  
Climate Data ◽  
Forest Fire Danger

Abstract. Climate variation and change influence several ecosystem components including forest fires. To examine long-term temporal variations of forest fire danger, a fire danger day (FDD) model was developed. Using mean temperature and total precipitation of the Finnish wildfire season (June–August), the model describes the climatological preconditions of fire occurrence and gives the number of fire danger days during the same time period. The performance of the model varied between different regions in Finland being best in south and west. In the study period 1908–2011, the year-to-year variation of FDD was large and no significant increasing or decreasing tendencies could be found. Negative slopes of linear regression lines for FDD could be explained by the simultaneous, mostly not significant increases in precipitation. Years with the largest wildfires did not stand out from the FDD time series. This indicates that intra-seasonal variations of FDD enable occurrence of large-scale fires, despite the whole season's fire danger is on an average level. Based on available monthly climate data, it is possible to estimate the general fire conditions of a summer. However, more detailed input data about weather conditions, land use, prevailing forestry conventions and socio-economical factors would be needed to gain more specific information about a season's fire risk.

scholarly journals Modelling river runoff and estimating its weather-related uncertainty for 11 large-scale rivers located in different regions of the globe

2017 ◽  
Vol 49 (4) ◽  
pp. 1072-1087 ◽  
Author(s):  
Yeugeniy M. Gusev ◽  
Olga N. Nasonova ◽  
Evgeny E. Kovalev ◽  
Georgii V. Aizel
Keyword(s):  
River Runoff ◽  
Land Surface ◽  
Input Data ◽  
Large Scale ◽  
Land Surface Model ◽  
Grid Cell ◽  
Surface Model ◽  
Model Parameters ◽  
Data Sets ◽  
Natural Conditions

Abstract In order to study the possibility of reproducing river runoff with making use of the land surface model Soil Water–Atmosphere–Plants (SWAP) and information based on global data sets 11 river basins suggested within the framework of the Inter-Sectoral Impact Model Intercomparison Project and located in various regions of the globe under a wide variety of natural conditions were used. Schematization of each basin as a set of 0.5° × 0.5° computational grid cells connected by a river network was carried out. Input data including atmospheric forcing data and land surface parameters based, respectively, on the global WATCH and ECOCLIMAP data sets were prepared for each grid cell. Simulations of river runoff performed by SWAP with a priori input data showed poor agreement with observations. Optimization of a number of model parameters substantially improved the results. The obtained results confirm the universal character of SWAP. Natural uncertainty of river runoff caused by weather noise was estimated and analysed. It can be treated as the lowest limit of predictability of river runoff. It was shown that differences in runoff uncertainties obtained for different rivers depend greatly on natural conditions of a river basin, in particular, on the ratio of deterministic and random components of the river runoff.

scholarly journals Large-scale forest landscape model, design, validation, and application in management of oak decline

2012 ◽  
Author(s):  
◽  
Wenjuan Wang
Keyword(s):  
Forest Inventory ◽  
Large Scale ◽  
Basal Area ◽  
Model Parameters ◽  
Forest Landscape ◽  
Oak Decline ◽  
Model Predictions ◽  
Forest Landscape Model ◽  
Landis Pro

Forest landscape models (FLMs) have increasingly become important tools for exploring forest landscape changes by predicting forest vegetation dynamics over large spatial scales. However, two challenges confronting FLMs have persisted: how to simulate fine, site-scale processes while making large-scale (landscape and regional) simulation feasible, and how to fully take advantage of extensive U.S. Forest Service Inventory and Analysis (FIA) data to initialize and constraint model parameters. In this dissertation, first, a new FLM, LANDIS PRO was developed. In LANDIS PRO, forest succession and dynamics are simulated by incorporating species-, stand-, and landscape-scale processes by tracking number of trees by species age cohort. Because stand-scale resource competition is achieved by implementing rather than simulating the emergent properties of stand development, LANDIS PRO is computationally efficient, which makes large-scale simulation feasible. Since model parameters and simulation results are comparatively straightforward to forest inventory data, current intensive forest inventory data can be directly applied for model initialization and to constrain model parameters. Validation of FLMs is essential to ensure users’ confidence in model predictions and achieve reliable management decision making. To date, validation of FLMs has been limited due to lack of suitable data. However, recent advances in FLMs, together with increasingly available spatiotemporal data make FLM validation feasible. In this dissertation, second, I proposed a framework for validating forest landscape projections from LANDIS PRO using Forest Inventory Analysis (FIA) data. The proposed framework incorporated data assimilation techniques to constrain model parameters and the initial state of the landscape by verifying the initialized landscape and iteratively calibrating the model parameters. The model predictions were rigorously validated against independent FIA data at multiple scales, and the long-term natural successional pattern was also verified against empirical studies. Results showed model predictions were able to capture much of the variation overtime in species basal area and tree density at stand-, landtype- , and landscape-scales. Subsequent long-term predictions of natural succession patterns were consistent with expected changes in tree species density of oak-dominated forests in the absence of disturbance. Lastly, I used LANDIS PRO, a forest landscape model that includes stand-scale species density and basal area to evaluate the potential landscape-scale effects of alternative harvest methods (thinning, clearcutting and group selection) on oak decline mitigation. Projections indicated that forest harvesting can be effective in mitigating oak decline. Group selection and clearcutting were the most effective methods in the management of oak decline in the short-term (20 years) and mid-term (50 years), respectively. However, in the long-run (100 years), there was no significant difference predicted among the three methods.

scholarly journals Development of a global dataset of Wetland Area and Dynamics for Methane Modeling (WAD2M)

2020 ◽  
Author(s):  
Zhen Zhang ◽  
Etienne Fluet-Chouinard ◽  
Katherine Jensen ◽  
Kyle McDonald ◽  
Gustaf Hugelius ◽  
...  
Keyword(s):  
Land Surface ◽  
Large Scale ◽  
Southern Oscillation ◽  
Microwave Remote Sensing ◽  
Global Scale ◽  
Annual Maximum ◽  
Time Step ◽  
Wetland Area ◽  
Wetland Extent

Abstract. Seasonal and interannual variations in global wetland area is a strong driver of fluctuations in global methane (CH4) emissions. Current maps of global wetland extent vary with wetland definition, causing substantial disagreement and large uncertainty in estimates of wetland methane emissions. To reconcile these differences for large-scale wetland CH4 modeling, we developed a global Wetland Area and Dynamics for Methane Modeling (WAD2M) dataset at ~25 km resolution at equator (0.25 arc-degree) at monthly time-step for 2000–2018. WAD2M combines a time series of surface inundation based on active and passive microwave remote sensing at coarse resolution (~25 km) with six static datasets that discriminate inland waters, agriculture, shoreline, and non-inundated wetlands. We exclude all permanent water bodies (e.g. lakes, ponds, rivers, and reservoirs), coastal wetlands (e.g., mangroves and sea grasses), and rice paddies to only represent spatiotemporal patterns of inundated and non-inundated vegetated wetlands. Globally, WAD2M estimates the long-term maximum wetland area at 13.0 million km2 (Mkm2), which can be separated into three categories: mean annual minimum of inundated and non-inundated wetlands at 3.5 Mkm2, seasonally inundated wetlands at 4.0 Mkm2 (mean annual maximum minus mean annual minimum), and intermittently inundated wetlands at 5.5 Mkm2 (long-term maximum minus mean annual maximum). WAD2M has good spatial agreements with independent wetland inventories for major wetland complexes, i.e., the Amazon Lowland Basin and West Siberian Lowlands, with high Cohen's kappa coefficient of 0.54 and 0.70 respectively among multiple wetlands products. By evaluating the temporal variation of WAD2M against modeled prognostic inundation (i.e., TOPMODEL) and satellite observations of inundation and soil moisture, we show that it adequately represents interannual variation as well as the effect of El Niño-Southern Oscillation on global wetland extent. This wetland extent dataset will improve estimates of wetland CH4 fluxes for global-scale land surface modeling. The dataset can be found at http://doi.org/10.5281/zenodo.3998454 (Zhang et al., 2020).

scholarly journals Development of the global dataset of Wetland Area and Dynamics for Methane Modeling (WAD2M)

2021 ◽  
Vol 13 (5) ◽  
pp. 2001-2023
Author(s):  
Zhen Zhang ◽  
Etienne Fluet-Chouinard ◽  
Katherine Jensen ◽  
Kyle McDonald ◽  
Gustaf Hugelius ◽  
...  
Keyword(s):  
Land Surface ◽  
Large Scale ◽  
Amazon Basin ◽  
Southern Oscillation ◽  
Microwave Remote Sensing ◽  
Annual Maximum ◽  
Time Step ◽  
Wetland Area ◽  
Wetland Extent

Abstract. Seasonal and interannual variations in global wetland area are a strong driver of fluctuations in global methane (CH4) emissions. Current maps of global wetland extent vary in their wetland definition, causing substantial disagreement between and large uncertainty in estimates of wetland methane emissions. To reconcile these differences for large-scale wetland CH4 modeling, we developed the global Wetland Area and Dynamics for Methane Modeling (WAD2M) version 1.0 dataset at a ∼ 25 km resolution at the Equator (0.25∘) at a monthly time step for 2000–2018. WAD2M combines a time series of surface inundation based on active and passive microwave remote sensing at a coarse resolution with six static datasets that discriminate inland waters, agriculture, shoreline, and non-inundated wetlands. We excluded all permanent water bodies (e.g., lakes, ponds, rivers, and reservoirs), coastal wetlands (e.g., mangroves and sea grasses), and rice paddies to only represent spatiotemporal patterns of inundated and non-inundated vegetated wetlands. Globally, WAD2M estimates the long-term maximum wetland area at 13.0×106 km2 (13.0 Mkm2), which can be divided into three categories: mean annual minimum of inundated and non-inundated wetlands at 3.5 Mkm2, seasonally inundated wetlands at 4.0 Mkm2 (mean annual maximum minus mean annual minimum), and intermittently inundated wetlands at 5.5 Mkm2 (long-term maximum minus mean annual maximum). WAD2M shows good spatial agreements with independent wetland inventories for major wetland complexes, i.e., the Amazon Basin lowlands and West Siberian lowlands, with Cohen's kappa coefficient of 0.54 and 0.70 respectively among multiple wetland products. By evaluating the temporal variation in WAD2M against modeled prognostic inundation (i.e., TOPMODEL) and satellite observations of inundation and soil moisture, we show that it adequately represents interannual variation as well as the effect of El Niño–Southern Oscillation on global wetland extent. This wetland extent dataset will improve estimates of wetland CH4 fluxes for global-scale land surface modeling. The dataset can be found at https://doi.org/10.5281/zenodo.3998454 (Zhang et al., 2020).

scholarly journals Online dynamical downscaling of temperature and precipitation within the <i>i</i>LOVECLIM model (version 1.1)

2018 ◽  
Vol 11 (1) ◽  
pp. 453-466
Author(s):  
Aurélien Quiquet ◽  
Didier M. Roche ◽  
Christophe Dumas ◽  
Didier Paillard
Keyword(s):  
High Resolution ◽  
Land Surface ◽  
Large Scale ◽  
Climate Model ◽  
Dynamical Downscaling ◽  
Atmospheric Model ◽  
Scale Model ◽  
Model Parameters ◽  
Temperature And Precipitation ◽  
Large Scale Model

Abstract. This paper presents the inclusion of an online dynamical downscaling of temperature and precipitation within the model of intermediate complexity iLOVECLIM v1.1. We describe the following methodology to generate temperature and precipitation fields on a 40 km  ×  40 km Cartesian grid of the Northern Hemisphere from the T21 native atmospheric model grid. Our scheme is not grid specific and conserves energy and moisture in the same way as the original climate model. We show that we are able to generate a high-resolution field which presents a spatial variability in better agreement with the observations compared to the standard model. Although the large-scale model biases are not corrected, for selected model parameters, the downscaling can induce a better overall performance compared to the standard version on both the high-resolution grid and on the native grid. Foreseen applications of this new model feature include the improvement of ice sheet model coupling and high-resolution land surface models.

Terminology of long-term geomorphology: a Scandinavian perspective

2009 ◽  
Vol 33 (2) ◽  
pp. 163-182 ◽  
Author(s):  
Karin Ebert
Keyword(s):  
Sea Level ◽  
Land Surface ◽  
Large Scale ◽  
Ice Sheets ◽  
Stepped Surfaces ◽  
Base Level ◽  
History Of ◽  
Land Surfaces ◽  
Over Time

In this paper the terminology used in long-term geomorphology is evaluated. Long-term geomorphology is the study of landforms that are of mostly pre-Quaternary, Cenozoic, Mesozoic or even Palaeozoic age. Many terms have been introduced to name the long-term large-scale landforms that persist to the present. The definitions of many of these terms are ambiguous, have changed over time, and their use and meaning is consequently often unclear. An attempt is made to clarify definitions, when possible, and to facilitate more concise usage of these terms. Long-term geomorphology deals in great parts with the lowering of a land surface to the base level (mostly sea level), leaving a new land surface. The largest group of terms concerns descriptions and genetic models for these kinds of new land surfaces collectively called `base level surfaces' here. Other terms discussed here relate to relict and preglacial landforms and regional terms for stepped surfaces. Terminology is discussed with particular reference to examples from and its use in Scandinavia. There is a long history of long-term geomorphology study in this region. Scandinavia is unique in the respect that pre-Quaternary landforms were repeatedly covered by Quaternary ice sheets but often survived with different degrees of glacial modification.

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