scholarly journals Development and spatialization of a soil potential multifunctionality index for agriculture (Agri-SPMI) at the regional scale. Case study in the Occitanie region (France)

Soil Security ◽  
2022 ◽  
pp. 100034
Author(s):  
Eva Rabot ◽  
Maritxu Guiresse ◽  
Yannis Pittatore ◽  
Marcos Angelini ◽  
Catherine Keller ◽  
...  
Keyword(s):  
Regional Scale ◽  
Multifunctionality Index

Farmland heavy metals can migrate to deep soil at a regional scale: A case study on a wastewater-irrigated area in China

2021 ◽  
Vol 281 ◽  
pp. 116977
Author(s):  
Shushen Yang ◽  
Wenzhao Feng ◽  
Shiqin Wang ◽  
Liang Chen ◽  
Xin Zheng ◽  
...  
Keyword(s):  
Heavy Metals ◽  
Regional Scale ◽  
Deep Soil ◽  
Irrigated Area

scholarly journals Where and how to restore wetland by utilizing storm water at the regional scale: A case study of Fangshan, China

2021 ◽  
Vol 122 ◽  
pp. 107246
Author(s):  
Wenwen Li ◽  
Yuxin Jiang ◽  
Yihao Duan ◽  
Junhong Bai ◽  
Demin Zhou ◽  
...  
Keyword(s):  
Regional Scale ◽  
Storm Water

De-Risking Pre-Salt Exploration: A Regional Scale Re-Imaging Case Study from the Greater Cavendish Area

2021 ◽  
Author(s):  
K. Ramani ◽  
C. Tyagi ◽  
M. Uzcategui Salazar ◽  
A. Cooke ◽  
O. Lewis ◽  
...  
Keyword(s):  
Regional Scale

scholarly journals A Multidisciplinary Tool for the Development of a Regional-scale Geotechnical Model: A Case Study in the North-Western Adriatic Coastal Area

2016 ◽  
Vol 158 ◽  
pp. 546-551 ◽  
Author(s):  
Laura Tonni ◽  
Irene Rocchi ◽  
Nadia Pia Cruciano ◽  
María F. García Martínez ◽  
Luca Martelli ◽  
...  
Keyword(s):  
Coastal Area ◽  
Regional Scale ◽  
The North ◽  
North Western ◽  
Geotechnical Model

scholarly journals The potential for regional-scale bias in top-down CO<sub>2</sub> flux estimates due to atmospheric transport errors

2014 ◽  
Vol 14 (16) ◽  
pp. 23681-23709
Author(s):  
S. M. Miller ◽  
I. Fung ◽  
J. Liu ◽  
M. N. Hayek ◽  
A. E. Andrews
Keyword(s):  
Boundary Layer ◽  
Atmospheric Transport ◽  
Regional Scale ◽  
Co2 Flux ◽  
Atmospheric Co2 ◽  
Co2 Fluxes ◽  
Atmospheric Measurements ◽  
Meteorological Model ◽  
Co2 Transport

Abstract. Estimates of CO2 fluxes that are based on atmospheric data rely upon a meteorological model to simulate atmospheric CO2 transport. These models provide a quantitative link between surface fluxes of CO2 and atmospheric measurements taken downwind. Therefore, any errors in the meteorological model can propagate into atmospheric CO2 transport and ultimately bias the estimated CO2 fluxes. These errors, however, have traditionally been difficult to characterize. To examine the effects of CO2 transport errors on estimated CO2 fluxes, we use a global meteorological model-data assimilation system known as "CAM–LETKF" to quantify two aspects of the transport errors: error variances (standard deviations) and temporal error correlations. Furthermore, we develop two case studies. In the first case study, we examine the extent to which CO2 transport uncertainties can bias CO2 flux estimates. In particular, we use a common flux estimate known as CarbonTracker to discover the minimum hypothetical bias that can be detected above the CO2 transport uncertainties. In the second case study, we then investigate which meteorological conditions may contribute to month-long biases in modeled atmospheric transport. We estimate 6 hourly CO2 transport uncertainties in the model surface layer that range from 0.15 to 9.6 ppm (standard deviation), depending on location, and we estimate an average error decorrelation time of ∼2.3 days at existing CO2 observation sites. As a consequence of these uncertainties, we find that CarbonTracker CO2 fluxes would need to be biased by at least 29%, on average, before that bias were detectable at existing non-marine atmospheric CO2 observation sites. Furthermore, we find that persistent, bias-type errors in atmospheric transport are associated with consistent low net radiation, low energy boundary layer conditions. The meteorological model is not necessarily more uncertain in these conditions. Rather, the extent to which meteorological uncertainties manifest as persistent atmospheric transport biases appears to depend, at least in part, on the energy and stability of the boundary layer. Existing CO2 flux studies may be more likely to estimate inaccurate regional fluxes under those conditions.

scholarly journals Global flood hazard map and exposed GDP comparison: a China case study

2020 ◽  
Author(s):  
Jerom P. M. Aerts ◽  
Steffi Uhlemann-Elmer ◽  
Dirk Eilander ◽  
Philip J. Ward
Keyword(s):  
Return Period ◽  
Flood Hazard ◽  
Regional Scale ◽  
Flood Protection ◽  
Economic Losses ◽  
Return Periods ◽  
Hazard Maps ◽  
High Exposure ◽  
Pluvial Flooding

Abstract. Floods are among the most frequent and damaging natural hazard events in the world. In 2016, economic losses from flooding amounted to $56 bn globally, of which $20 bn occurred in China (Munich Re, 2017). National or regional scale mapping of flood hazard is at present providing an inconsistent and incomplete picture of floods. Over the past decade global flood hazard models have been developed and continuously improved. There is now a significant demand for testing of the global hazard maps generated by these models in order to understand their applicability for international risk reduction strategies and for reinsurance portfolio risk assessments using catastrophe models. We expand on existing methods for comparing global hazard maps and analyse 8 global flood models (GFMs) that represent the current state of the global flood modelling community. We apply our comparison to China as a case study and, for the first time, we include industry models, pluvial flooding, and flood protection standards in the analysis. We find substantial variability between the flood hazard maps in modelled inundated area and exposed GDP across multiple return periods (ranging from 5 to 1500 years) and in expected annual exposed GDP. For example, for the 100 year return period undefended (assuming no flood protection) hazard maps the percentage of total affected GDP of China ranges between 4.4 % and 10.5 % for fluvial floods. For the majority of the GFMs we see only a small increase in inundated area or exposed GDP for high return period undefended hazard maps compared to low return periods, highlighting major limitations in the models’ resolution and their output. The inclusion of industry models which currently model flooding at higher spatial resolution, and which additionally include pluvial flooding, strongly improves the comparison and provides important new benchmarks. Pluvial flooding can increase the expected annual exposed GDP by as much as 1.3 % points. Our study strongly highlights the importance of flood defenses for a realistic risk assessment in countries like China that are characterized by high concentrations of exposure. Even an incomplete (1.74 % of area of China) but locally detailed layer of structural defenses in high exposure areas reduces the expected annual exposed GDP to fluvial and pluvial flooding from 4.1 % to 2.8 %.

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