scholarly journals Estimating Rainfall Erosivity from Daily Precipitation Using Generalized Additive Models

2020 ◽  
Vol 2 (1) ◽  
pp. 21
Author(s):  
Konstantinos Vantas ◽  
Epaminondas Sidiropoulos ◽  
Chris Evangelides
Keyword(s):  
Soil Loss ◽  
Generalized Additive Models ◽  
Daily Rainfall ◽  
Additive Models ◽  
Rainfall Erosivity ◽  
Time Step ◽  
Seasonal Periodicity ◽  
Nonlinear Relation ◽  
Sampling Intervals ◽  
Water District

One of the most important natural processes responsible for soil loss is rainfall-induced erosion. The calculation of rainfall erosivity, as defined in the Universal Soil Loss Equation, requires the availability of rainfall data, either continuous breakpoint, or pluviograph, with sampling intervals on the order of minutes. Due to the limited temporal coverage and spatial scarcity of such data, worldwide, alternative equations have been developed that utilize coarser rainfall records, in an effort to estimate erosivity equivalently to that calculated using pluviograph data. This paper presents the application of generalized additive models (GAMs) to estimate erosivity utilizing daily rainfall records. As a case study, pluviograph data with a time step of 30 min from the Water District of Thrace in Greece were used. By applying GAMs, it became possible to model the nonlinear relation between daily rainfall, seasonal periodicity, and rainfall erosivity more effectively, in terms of accuracy, than the application of two well-known nonlinear empirical equations, both on a daily and an annual basis.

scholarly journals Impact Du Changement Climatique Sur L’évolution De L'érosivité Des Pluies Dans Le Rif Occidental (Nord Du Maroc)

2016 ◽  
Vol 12 (32) ◽  
pp. 79 ◽  
Author(s):  
Fatiha Choukri ◽  
Mohamed Chikhaoui ◽  
Mustapha Naimi ◽  
Damien Raclot ◽  
Yannick Pepin ◽  
...  
Keyword(s):  
Water Conservation ◽  
Soil Loss ◽  
Agricultural Practices ◽  
Daily Rainfall ◽  
Soil Protection ◽  
High Temporal Resolution ◽  
Rainfall Erosivity ◽  
Protection Measures ◽  
Pronounced Increase ◽  
R Factor

The rainfall erosivity factor (R factor in Universal Soil Loss Equation), denoting rain energy, is a key factor for soil loss modeling. Its present and future estimation is thus significant for any action related to soil and water conservation and planning. The extended series of precipitations at high temporal resolution, essential to its evaluation, are not readily available in Morocco. The objective of this study is to predict the evolution of rainfall erosivity by 2080 in the Western Rif, based on predictions of daily rain provided by the General Climatic Models (GCMs). To reflect the seasonal variability of rainfall, and thus of R factor, a series of instantaneous rain measured over 35 consecutive years was used to monthly calibrate a model to calculate erosivity based of daily rainfall. The application of this model to the predictions of different GCMs and for various scenarios of climate evolution in Western Rif shows a weak evolution of erosivity on an annual timescale but a very strong evolution of the latter according to seasons with a reduction in R factor during winter and spring, and a pronounced increase during summer and autumn. This discernable change of the seasonality of rainfall erosivity is very useful for adjusting the evolution of agricultural practices and for selecting appropriate soil protection measures.

scholarly journals Determination of erosion rainfall criteria based on natural rainfall measurement and its impact on spatial distribution of rainfall erosivity in the Czech Republic

2019 ◽  
Vol 14 (No. 3) ◽  
pp. 153-162 ◽  
Author(s):  
Jiří Brychta ◽  
Miloslav Janeček
Keyword(s):  
Spatial Distribution ◽  
Temporal Resolution ◽  
Soil Loss ◽  
Rainfall Intensity ◽  
Control Measures ◽  
Rainfall Erosivity ◽  
Time Step ◽  
R Factor ◽  
Soil Losses ◽  
Runoff Plots

Rainfall erosivity is the main factor of the USLE or RUSLE equations. Its accuracy depends on recording precision and its temporal resolution, number of stations and their spatial distribution, length of recorded period, recorded period, erosion rainfall criteria, time step of rainfall intensity and interpolation method. This research focuses on erosion rainfall criteria. A network of 32 ombrographic stations, 1-min temporal resolution rainfall data, 35.6-year period and experimental runoff plots were used. We analysed 8951 rainfalls from ombrographic stations, 100 rainfalls and caused soil losses and runoffs from experimental runoff plots. Main parameter which influenced the number of erosion rainfalls was the precondition AND/OR which determines if conditions of rainfall total (H) have to be fulfilled simultaneously with rainfall intensity (I<sub>15</sub> or I<sub>30</sub>) or not. We proved that if parameters I<sub>15 </sub>&gt; 6.25 mm/15 min AND H &gt; 12.5 mm were fulfilled, then 84.2% of rainfalls caused soil loss &gt; 0.5 t/ha and 73.7% ≥ 1 t/ha. In the case of precondition OR only 44.6% of rainfalls caused soil loss &gt; 0.5 t/ha and 33.9% ≥ 1 t/ha. If the precondition AND was fulfilled, there were on average 75.5 rainfalls, average R factor for each rainfall was 21 MJ/ha·cm/h (without units below in the text, according international unit: 210 MJ/ha·mm/h) and average annual R factor was 45.4. In the case of precondition OR there were on average 279 rainfalls but average R factor for each rainfall was only 9.1 and average annual R factor was 67.4. Therefore if the precondition OR is used, R factor values are overestimated due to a high number of rainfalls with no or very low erosive potential. The resulting overestimated soil losses calculated using USLE/RUSLE subsequently cause an overestimation of financial expenses for erosion-control measures.  

scholarly journals Daily mapping of Australian Plague Locust abundance

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Stéphane Mangeon ◽  
Allan Spessa ◽  
Edward Deveson ◽  
Ross Darnell ◽  
Darren J. Kriticos
Keyword(s):  
Generalized Additive Models ◽  
Additive Models ◽  
Pest Species ◽  
Time Step ◽  
High Productivity ◽  
Eastern Australia ◽  
Negative Effect ◽  
Dry Conditions ◽  
Daily Time Step ◽  
Australian Plague Locust

AbstractLocust population outbreaks have been a longstanding problem for Australian agriculture. Since its inception in the mid-1970s, The Australian Plague Locust Commission (APLC) is responsible for monitoring, forecasting and controlling populations of several locust pest species across inland eastern Australia (ca. two million km2). Ground surveys are typically targeted according to prevailing environmental conditions. However, due to the sheer size of the region and limited resources, such surveys remain sparse. Here we develop daily time-step statistical models of populations of Chortoicetes terminifera (Australian plague locust) that can used to predict abundances when observations are lacking, plus uncertainties. We firstly identified key environmental covariates of locust abundance, then examined their relationship with C. terminifera populations by interpreting the responses of Generalized Additive Models (GAM). We also illustrate how estimates of C. terminifera abundance plus uncertainties can be visualized across the region. Our results support earlier studies, specifically, populations peak in grasslands with high productivity, and decline rapidly under very hot and dry conditions. We also identified new relationships, specifically, a strong positive effect of vapour pressure and sunlight, and a negative effect of soil sand content on C. terminifera abundance. Our modelling tool may assist future APLC management and surveillance effort.

scholarly journals Soil erosion assessment of a Himalayan river basin using TRMM data

2015 ◽  
Vol 366 ◽  
pp. 200-200 ◽  
Author(s):  
A. Pandey ◽  
S. K. Mishra ◽  
A. K. Gautam ◽  
D. Kumar
Keyword(s):  
Soil Erosion ◽  
River Basin ◽  
Soil Loss ◽  
Daily Rainfall ◽  
Point Of View ◽  
Rainfall Erosivity ◽  
Severe Erosion ◽  
Basin Area ◽  
Very High ◽  
Himalayan River

Abstract. In this study, an attempt has been made to assess the soil erosion of a Himalayan river basin, the Karnali basin, Nepal, using rainfall erosivity (R-factor) derived from satellite-based rainfall estimates (TRMM-3B42 V7). Average annual sediment yield was estimated using the well-known Universal Soil Loss Equation (USLE). The eight-year annual average rainfall erosivity factor (R) for the Karnali River basin was found to be 2620.84 MJ mm ha−1 h−1 year−1. Using intensity–erosivity relationships and eight years of the TRMM daily rainfall dataset (1998–2005), average annual soil erosion was also estimated for Karnali River basin. The minimum and maximum values of the rainfall erosivity factor were 1108.7 and 4868.49 MJ mm ha−1 h−1 year−1, respectively, during the assessment period. The average annual soil loss of the Karnali River basin was found to be 38.17 t ha−1 year−1. Finally, the basin area was categorized according to the following scale of erosion severity classes: Slight (0 to 5 t ha−1 year−1), Moderate (5 to 10 t ha−1 year−1), High (10 to 20 t ha−1 year−1), Very High (20 to 40 t ha−1 year−1), Severe (40 to 80 t ha−1 year−1) and Very Severe (>80 t ha−1 year−1). About 30.86% of the river basin area was found to be in the slight erosion class. The areas covered by the moderate, high, very high, severe and very severe erosion potential zones were 13.09%, 6.36%, 11.09%, 22.02% and 16.64% respectively. The study revealed that approximately 69% of the Karnali River basin needs immediate attention from a soil conservation point of view.

scholarly journals Predicting Malaria Transmission Dynamics in Dangassa, Mali: A Novel Approach Using Functional Generalized Additive Models

2020 ◽  
Vol 17 (17) ◽  
pp. 6339
Author(s):  
François Freddy Ateba ◽  
Manuel Febrero-Bande ◽  
Issaka Sagara ◽  
Nafomon Sogoba ◽  
Mahamoudou Touré ◽  
...  
Keyword(s):  
Wind Speed ◽  
Malaria Incidence ◽  
Generalized Additive Models ◽  
Community Health Center ◽  
Additive Model ◽  
Additive Models ◽  
Functional Regression ◽  
Novel Approach ◽  
Malaria Incidence Rate ◽  
Mean Wind Speed

Mali aims to reach the pre-elimination stage of malaria by the next decade. This study used functional regression models to predict the incidence of malaria as a function of past meteorological patterns to better prevent and to act proactively against impending malaria outbreaks. All data were collected over a five-year period (2012–2017) from 1400 persons who sought treatment at Dangassa’s community health center. Rainfall, temperature, humidity, and wind speed variables were collected. Functional Generalized Spectral Additive Model (FGSAM), Functional Generalized Linear Model (FGLM), and Functional Generalized Kernel Additive Model (FGKAM) were used to predict malaria incidence as a function of the pattern of meteorological indicators over a continuum of the 18 weeks preceding the week of interest. Their respective outcomes were compared in terms of predictive abilities. The results showed that (1) the highest malaria incidence rate occurred in the village 10 to 12 weeks after we observed a pattern of air humidity levels >65%, combined with two or more consecutive rain episodes and a mean wind speed <1.8 m/s; (2) among the three models, the FGLM obtained the best results in terms of prediction; and (3) FGSAM was shown to be a good compromise between FGLM and FGKAM in terms of flexibility and simplicity. The models showed that some meteorological conditions may provide a basis for detection of future outbreaks of malaria. The models developed in this paper are useful for implementing preventive strategies using past meteorological and past malaria incidence.

The Relationship between Mobility and COVID-19 in Germany: Modeling Case Occurrence using Apple's Mobility Trends Data

2021 ◽  
Author(s):  
Mark David Walker ◽  
Mihály Sulyok
Keyword(s):  
Generalized Additive Models ◽  
Information Criterion ◽  
Additive Models ◽  
Online Data ◽  
German Government ◽  
Explanatory Variables ◽  
Mobility Data ◽  
Community Mobility ◽  
The Relationship ◽  
Potential Use

Abstract Background Restrictions on social interaction and movement were implemented by the German government in March 2020 to reduce the transmission of coronavirus disease 2019 (COVID-19). Apple's “Mobility Trends” (AMT) data details levels of community mobility; it is a novel resource of potential use to epidemiologists. Objective The aim of the study is to use AMT data to examine the relationship between mobility and COVID-19 case occurrence for Germany. Is a change in mobility apparent following COVID-19 and the implementation of social restrictions? Is there a relationship between mobility and COVID-19 occurrence in Germany? Methods AMT data illustrates mobility levels throughout the epidemic, allowing the relationship between mobility and disease to be examined. Generalized additive models (GAMs) were established for Germany, with mobility categories, and date, as explanatory variables, and case numbers as response. Results Clear reductions in mobility occurred following the implementation of movement restrictions. There was a negative correlation between mobility and confirmed case numbers. GAM using all three categories of mobility data accounted for case occurrence as well and was favorable (AIC or Akaike Information Criterion: 2504) to models using categories separately (AIC with “driving,” 2511. “transit,” 2513. “walking,” 2508). Conclusion These results suggest an association between mobility and case occurrence. Further examination of the relationship between movement restrictions and COVID-19 transmission may be pertinent. The study shows how new sources of online data can be used to investigate problems in epidemiology.

scholarly journals Comparing Charlson and Elixhauser comorbidity indices with different weightings to predict in-hospital mortality: an analysis of national inpatient data

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Narayan Sharma ◽  
René Schwendimann ◽  
Olga Endrich ◽  
Dietmar Ausserhofer ◽  
Michael Simon
Keyword(s):  
Hospital Mortality ◽  
Patient Population ◽  
Generalized Additive Models ◽  
Routine Data ◽  
Predictive Performance ◽  
Population Based ◽  
Mortality Prediction ◽  
Additive Models ◽  
General Hospitals ◽  
Net Reclassification Improvement

Abstract Background Understanding how comorbidity measures contribute to patient mortality is essential both to describe patient health status and to adjust for risks and potential confounding. The Charlson and Elixhauser comorbidity indices are well-established for risk adjustment and mortality prediction. Still, a different set of comorbidity weights might improve the prediction of in-hospital mortality. The present study, therefore, aimed to derive a set of new Swiss Elixhauser comorbidity weightings, to validate and compare them against those of the Charlson and Elixhauser-based van Walraven weights in an adult in-patient population-based cohort of general hospitals. Methods Retrospective analysis was conducted with routine data of 102 Swiss general hospitals (2012–2017) for 6.09 million inpatient cases. To derive the Swiss weightings for the Elixhauser comorbidity index, we randomly halved the inpatient data and validated the results of part 1 alongside the established weighting systems in part 2, to predict in-hospital mortality. Charlson and van Walraven weights were applied to Charlson and Elixhauser comorbidity indices. Derivation and validation of weightings were conducted with generalized additive models adjusted for age, gender and hospital types. Results Overall, the Elixhauser indices, c-statistic with Swiss weights (0.867, 95% CI, 0.865–0.868) and van Walraven’s weights (0.863, 95% CI, 0.862–0.864) had substantial advantage over Charlson’s weights (0.850, 95% CI, 0.849–0.851) and in the derivation and validation groups. The net reclassification improvement of new Swiss weights improved the predictive performance by 1.6% on the Elixhauser-van Walraven and 4.9% on the Charlson weights. Conclusions All weightings confirmed previous results with the national dataset. The new Swiss weightings model improved slightly the prediction of in-hospital mortality in Swiss hospitals. The newly derive weights support patient population-based analysis of in-hospital mortality and seek country or specific cohort-based weightings.

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