Statistical Modelling of Winter Yield at a Regional Scale

2016 ◽

Vol 20 (6) ◽

pp. 2353-2381 ◽

Cited By ~ 11

Author(s):

Issoufou Ouedraogo ◽

Marnik Vanclooster

Keyword(s):

Statistical Model ◽

Nitrate Concentration ◽

Regional Scale ◽

Shallow Groundwater ◽

Statistical Modelling ◽

Nitrate Pollution ◽

Nitrate Contamination ◽

Data Set ◽

Nitrate Concentrations ◽

Groundwater Systems

Abstract. Contamination of groundwater with nitrate poses a major health risk to millions of people around Africa. Assessing the space–time distribution of this contamination, as well as understanding the factors that explain this contamination, is important for managing sustainable drinking water at the regional scale. This study aims to assess the variables that contribute to nitrate pollution in groundwater at the African scale by statistical modelling. We compiled a literature database of nitrate concentration in groundwater (around 250 studies) and combined it with digital maps of physical attributes such as soil, geology, climate, hydrogeology, and anthropogenic data for statistical model development. The maximum, medium, and minimum observed nitrate concentrations were analysed. In total, 13 explanatory variables were screened to explain observed nitrate pollution in groundwater. For the mean nitrate concentration, four variables are retained in the statistical explanatory model: (1) depth to groundwater (shallow groundwater, typically < 50 m); (2) recharge rate; (3) aquifer type; and (4) population density. The first three variables represent intrinsic vulnerability of groundwater systems to pollution, while the latter variable is a proxy for anthropogenic pollution pressure. The model explains 65 % of the variation of mean nitrate contamination in groundwater at the African scale. Using the same proxy information, we could develop a statistical model for the maximum nitrate concentrations that explains 42 % of the nitrate variation. For the maximum concentrations, other environmental attributes such as soil type, slope, rainfall, climate class, and region type improve the prediction of maximum nitrate concentrations at the African scale. As to minimal nitrate concentrations, in the absence of normal distribution assumptions of the data set, we do not develop a statistical model for these data. The data-based statistical model presented here represents an important step towards developing tools that will allow us to accurately predict nitrate distribution at the African scale and thus may support groundwater monitoring and water management that aims to protect groundwater systems. Yet they should be further refined and validated when more detailed and harmonized data become available and/or combined with more conceptual descriptions of the fate of nutrients in the hydrosystem.

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Investigating the regional-scale pattern of agricultural land abandonment in the Swiss mountains: A spatial statistical modelling approach

Landscape and Urban Planning ◽

10.1016/j.landurbplan.2006.03.004 ◽

2007 ◽

Vol 79 (1) ◽

pp. 65-76 ◽

Cited By ~ 171

Author(s):

Mario Gellrich ◽

Niklaus E. Zimmermann

Keyword(s):

Agricultural Land ◽

Regional Scale ◽

Statistical Modelling ◽

Land Abandonment ◽

Modelling Approach

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Sinkhole susceptibility evaluation in Apulia, southern Italy

10.5194/egusphere-egu21-2154 ◽

2021 ◽

Author(s):

Carmela Vennari ◽

Mauro Rossi ◽

Luca Pisano ◽

Veronica Zumpano ◽

Mario Parise

Keyword(s):

Landslide Susceptibility ◽

Regional Scale ◽

Model Development ◽

Temporal Distribution ◽

Statistical Modelling ◽

Mitigation Strategies ◽

Susceptibility Map ◽

Thematic Layers ◽

Landslide Susceptibility Zonation ◽

Validation Set

In some regions in Italy sinkholes are frequent and pose a serious threat to structures and infrastructures. Apulia region is largely affected by sinkholes of both natural and anthropogenic origin, due to the karst nature of large portions of the regional territory and to high diffusion of artificial cavities.For this reasons, susceptibility, hazard and risk posed by sinkholes must be estimated in order to gain more insights into their spatial and temporal distribution, and to apply appropriate risk management and to take proper mitigation strategies.In order to estimate the susceptibility to sinkholes in Apulia, the ensemble statistical modelling proposed by Rossi et al. (2010) and later refined by Rossi & Reichenbach (2016) is used. This allows assessing susceptibility using differentiated statistical approaches, quantifying accurately the modelling performances, and evaluating the associated uncertainty. In order to obtain accurate and reliable results thematic layers related to the sinkholes occurrence were carefully evauated and selected. This contribution shows the preliminary results of the analyses to evaluate the susceptibility to natural sinkholes, which used &#160;as training dependent (i.e. grouping) set, data extracted from the regional inventory of natural caves, edited by the Apulian Speleological Federation (www.catasto.fspuglia.it), and as validation set the natural sinkholes occurred in Apulia, collected in the chronological catalogue of sinkholes in Italy (Parise & Vennari, 2013, 2017). Appropriate thematic layers, were selected heuristically on the base of the knowledge on the triggering mechanisms and the nature of the phenomenon gained previously in the study area.Resulting regional-scale susceptibility map will be appropriately validated. The methodological procedure will be applied to the evaluation of susceptibility for anthropogenic sinkholes as well.ReferencesParise M. & Vennari C. (2017) Distribution and features of natural and anthropogenic sinkholes in Apulia. In: Renard P. & Bertrand C. (Eds.), EuroKarst 2016, Neuchatel. Advances in the hydrogeology of karst and carbonate reservoirs. Springer, ISBN 978-3-319-45464-1, p. 27-34.Parise M. & Vennari C. (2013) A chronological catalogue of sinkholes in Italy: the first step toward a real evaluation of the sinkhole hazard. Proceedings 8th Multidisciplinary Conference on Sinkholes & the Engineering and Environmental Impacts of Karst, Carlsbad, USA.Rossi, M. & Reichenbach P. (2016) LAND-SE: a software for statistically based landslide susceptibility zonation, version 1.0.&#160;Geoscientific Model Development,&#160;9(10).Rossi M., Guzzetti F., Reichenbach P., Mondini A. C., Peruccacci S. (2010) Optimal landslide susceptibility zonation based on multiple forecasts, Geomorphology, 114, 129&#8211;142.

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Integrated statistical modelling of spatial landslide probability

Natural Hazards and Earth System Sciences Discussions ◽

10.5194/nhessd-3-5677-2015 ◽

2015 ◽

Vol 3 (9) ◽

pp. 5677-5715 ◽

Cited By ~ 5

Author(s):

M. Mergili ◽

H.-J. Chu

Keyword(s):

Regional Scale ◽

Model Performance ◽

Statistical Modelling ◽

Cumulative Probability ◽

Entire Study ◽

Release Probability ◽

Southern Taiwan ◽

The Impact ◽

Landslide Distribution ◽

Entire Study Area

Abstract. Statistical methods are commonly employed to estimate spatial probabilities of landslide release at the catchment or regional scale. Travel distances and impact areas are often computed by means of conceptual mass point models. The present work introduces a fully automated procedure extending and combining both concepts to compute an integrated spatial landslide probability: (i) the landslide inventory is subset into release and deposition zones. (ii) We employ a simple statistical approach to estimate the pixel-based landslide release probability. (iii) We use the cumulative probability density function of the angle of reach of the observed landslide pixels to assign an impact probability to each pixel. (iv) We introduce the zonal probability i.e. the spatial probability that at least one landslide pixel occurs within a zone of defined size. We quantify this relationship by a set of empirical curves. (v) The integrated spatial landslide probability is defined as the maximum of the release probability and the product of the impact probability and the zonal release probability relevant for each pixel. We demonstrate the approach with a 637 km2 study area in southern Taiwan, using an inventory of 1399 landslides triggered by the typhoon Morakot in 2009. We observe that (i) the average integrated spatial landslide probability over the entire study area corresponds reasonably well to the fraction of the observed landside area; (ii) the model performs moderately well in predicting the observed spatial landslide distribution; (iii) the size of the release zone (or any other zone of spatial aggregation) influences the integrated spatial landslide probability to a much higher degree than the pixel-based release probability; (iv) removing the largest landslides from the analysis leads to an enhanced model performance.

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Modelling of Wine Production Using Land Surface Temperature and FAPAR—The Case of the Douro Wine Region

Remote Sensing ◽

10.3390/rs11060604 ◽

2019 ◽

Vol 11 (6) ◽

pp. 604

Author(s):

Clarisse Magarreiro ◽

Célia Gouveia ◽

Carla Barroso ◽

Isabel Trigo

Keyword(s):

Surface Temperature ◽

Satellite Data ◽

Land Surface ◽

Regional Climate ◽

Regional Scale ◽

Statistical Modelling ◽

Wine Industry ◽

Vegetative Development ◽

Wine Production ◽

Wine Region

The vegetative development of grapevines is orchestrated by very specific meteorological conditions. In the wine industry vineyards demand diligent monitoring, since quality and productivity are the backbone of the economic potential. Regional climate indicators and meteorological information are essential to winemakers to assure proper vineyard management. Satellite data are very useful in this process since they imply low costs and are easily accessible. This work proposes a statistical modelling approach based on parameters obtained exclusively from satellite data to simulate annual wine production. The study has been developed for the Douro Demarcated Region (DDR) due to its relevance in the winemaking industry. It is the oldest demarcated and controlled winemaking region of the world and listed as one of UNESCO’s World Heritage regions. Monthly variables associated with Land Surface Temperatures (LST) and Fraction of Absorbed Photosynthetic Active Radiation (FAPAR), which is representative of vegetation canopy health, were analysed for a 15-year period (2004 to 2018), to assess their relation to wine production. Results showed that high wine production years are associated with higher than normal FAPAR values during approximately the entire growing season and higher than normal values of surface temperature from April to August. A robust linear model was obtained using the most significant predictors, that includes FAPAR in December and maximum and mean LST values in March and July, respectively. The model explains 90% of the total variance of wine production and presents a correlation coefficient of 0.90 (after cross validation). The retained predictors’ anomalies for the investigated vegetative year (October to July) from 2017/2018 satellite data indicate that the ensuing wine production for the DDR is likely to be below normal, i.e., to be lower than what is considered a high-production year. This work highlights that is possible to estimate wine production at regional scale based solely on low-resolution remotely sensed observations that are easily accessible, free and available for numerous grapevines regions worldwide, providing a useful and easy tool to estimate wine production and agricultural monitoring.

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Understanding problem drug use among young people accessing drug services: A multivariate approach using statistical modelling techniques

PsycEXTRA Dataset ◽

10.1037/e634842007-001 ◽

2004 ◽

Cited By ~ 1

Author(s):

Helen Beckett ◽

Jeremy Heap ◽

Paul McArdle ◽

Eilish Gilvarry ◽

Jane Christian ◽

...

Keyword(s):

Young People ◽

Drug Use ◽

Statistical Modelling ◽

Multivariate Approach ◽

Modelling Techniques ◽

Problem Drug Use ◽

Problem Drug

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Koreans across the Sea: Migration of Laborers to the Metropole, 1910–1937

European Journal of Korean Studies ◽

10.33526/ejks.20191901.161 ◽

2019 ◽

pp. 161-200

Author(s):

Mikwi Cho

Keyword(s):

Capital Accumulation ◽

Regional Scale ◽

Living Conditions ◽

Colonial Period ◽

Global City ◽

Japanese Empire ◽

Chain Migration ◽

Free Mobility

This paper is concerned with Korean farmers who were transformed into laborers during the Korean colonial period and migrated to Japan to enhance their living conditions. The author’s research adopts a regional scale to its investigation in which the emergence of Osaka as a global city attracted Koreans seeking economic betterment. The paper shows that, despite an initial claim to permit the free mobility of Koreans, the Japanese empire came to control this mobility depending on political, social, and economic circumstances of Japan and Korea. For Koreans, notwithstanding poverty being a primary trigger for the abandonment of their homes, the paper argues that their migration was facilitated by chain migration and they saw Japan as a resolution to their economic hardships in the process of capital accumulation by the empire.

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