An R package facilitating sensitivity analysis, calibration and forward simulations with the LPJ-GUESS dynamic vegetation model

2019 ◽  
Vol 111 ◽  
pp. 55-60 ◽  
Author(s):  
Maurizio Bagnara ◽  
Ramiro Silveyra Gonzalez ◽  
Stefan Reifenberg ◽  
Jörg Steinkamp ◽  
Thomas Hickler ◽  
...  
2014 ◽  
Vol 27 (15) ◽  
pp. 5708-5723 ◽  
Author(s):  
Marc P. Marcella ◽  
Elfatih A. B. Eltahir

Abstract This article presents a new irrigation scheme and biome to the dynamic vegetation model, Integrated Biosphere Simulator (IBIS), coupled to version 3 of the Regional Climate Model (RegCM3-IBIS). The new land cover allows for only the plant functional type (crop) to exist in an irrigated grid cell. Irrigation water (i.e., negative runoff) is applied until the soil root zone reaches relative field capacity. The new scheme allows for irrigation scheduling (i.e., when to apply water) and for the user to determine the crop to be grown. Initial simulations show a large sensitivity of the scheme to soil texture types, how the water is applied, and the climatic conditions over the region. Application of the new scheme is tested over West Africa, specifically Mali and Niger, to simulate the potential irrigation of the Niger River. A realistic representation of irrigation of the Niger River is performed by constraining the land irrigated by the annual flow of the Niger River and the amount of arable land in the region as reported by the Food and Agriculture Organization of the United Nations (FAO). A 30-yr simulation including irrigated cropland is compared to a 30-yr simulation that is identical but with no irrigation of the Niger. Results indicate a significant greening of the irrigated land as evapotranspiration over the crop fields largely increases—mostly via increases in transpiration from plant growth. The increase in the evapotranspiration, or latent heat flux (by 65–150 W m−2), causes a significant decrease in the sensible heat flux while surface temperatures cool on average by nearly 5°C. This cooling is felt downwind, where average daily temperatures outside the irrigation are reduced by 0.5°–1.0°C. Likewise, large increases in 2-m specific humidity are experienced across the irrigated cropland (on the order of 5 g kg−1) but also extend farther north and east, reflecting the prevailing surface southwesterlies. Changes (decreases) in rainfall are found only over the irrigated lands of west Mali. The decrease in rainfall can be explained by the large surface cooling and collapse of the boundary layer (by approximately 500 m). Both lead to a reduction in the triggering of convection as the convective inhibition, or negative buoyant energy, is never breached. Nevertheless, the new scheme and land cover allows for a novel line of research that can accurately reflect the effects of irrigation on climate and the surrounding environment using a dynamic vegetation model coupled to a regional climate model.


2019 ◽  
Vol 395 ◽  
pp. 11-22 ◽  
Author(s):  
Mirjam Pfeiffer ◽  
Liam Langan ◽  
Anja Linstädter ◽  
Carola Martens ◽  
Camille Gaillard ◽  
...  

Biometrika ◽  
2019 ◽  
Vol 106 (2) ◽  
pp. 353-367 ◽  
Author(s):  
B Karmakar ◽  
B French ◽  
D S Small

Summary A sensitivity analysis for an observational study assesses how much bias, due to nonrandom assignment of treatment, would be necessary to change the conclusions of an analysis that assumes treatment assignment was effectively random. The evidence for a treatment effect can be strengthened if two different analyses, which could be affected by different types of biases, are both somewhat insensitive to bias. The finding from the observational study is then said to be replicated. Evidence factors allow for two independent analyses to be constructed from the same dataset. When combining the evidence factors, the Type I error rate must be controlled to obtain valid inference. A powerful method is developed for controlling the familywise error rate for sensitivity analyses with evidence factors. It is shown that the Bahadur efficiency of sensitivity analysis for the combined evidence is greater than for either evidence factor alone. The proposed methods are illustrated through a study of the effect of radiation exposure on the risk of cancer. An R package, evidenceFactors, is available from CRAN to implement the methods of the paper.


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