Quantifying uncertainty on sediment loads using bootstrap confidence intervals

Slaets, Johanna; Piepho, Hans‐Peter; Schmitter, Petra; Hilger, Thomas; Cadisch, Georg

doi:10.5194/hess-21-571-2017

Cited by 12 publications

(12 citation statements)

References 37 publications

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“…To estimate CIs on the correlation coefficients (Table 1), we utilize non-parametric bootstrapping techniques wherein data from individual counties are resampled in 10,000 iterations. These CIs have a slight asymmetry due to the fixed effects 52 .…”

Section: Methodsmentioning

confidence: 99%

Combined influence of soil moisture and atmospheric evaporative demand is important for accurately predicting US maize yields

Rigden¹,

Mueller²,

Holbrook³

et al. 2020

Nat Food

138

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Understanding the response of agriculture to heat and moisture stress is essential to adapt food systems under climate change. Although evidence of crop yield loss with extreme temperature is abundant, disentangling the roles of temperature and moisture in determining yield has proved challenging, largely due to limited soil moisture data and the tight coupling between moisture and temperature at the land surface. Here, using well-resolved observations of soil moisture from the recently launched Soil Moisture Active Passive satellite, we quantify the contribution of imbalances between atmospheric evaporative demand and soil moisture to maize yield damage in the US Midwest. We show that retrospective yield predictions based on the interactions between atmospheric demand and soil moisture significantly outperform those using temperature and precipitation singly or together. The importance of accounting for this water balance is highlighted by the fact that climate simulations uniformly predict increases in atmospheric demand during the growing season but the trend in root-zone soil moisture varies between models, with some models indicating that yield damages associated with increased evaporative demand are moderated by increased water supply. A damage estimate conditioned only on simulated changes in atmospheric demand, as opposed to also accounting for changes in soil moisture, would erroneously indicate approximately twice the damage. This research demonstrates that more accurate predictions of maize yield can be achieved by using soil moisture data and indicates that accurate estimates of how climate change will influence crop yields require explicitly accounting for variations in water availability.

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Section: Methodsmentioning

confidence: 99%

Combined influence of soil moisture and atmospheric evaporative demand is important for accurately predicting US maize yields

Rigden¹,

Mueller²,

Holbrook³

et al. 2020

Nat Food

138

View full text Add to dashboard Cite

show abstract

“…For RF, many types of uncertainty could be produced, which actually reflect the difference in sampling strategies [123][124][125][126]. For example, prediction uncertainty produced for RF in a previous study based on Monte Carlo resampling [127] was, in fact, measuring the variation in predictions among individual trees rather than by RF.…”

Section: Prediction Uncertaintymentioning

confidence: 99%

A Critical Review of Spatial Predictive Modeling Process in Environmental Sciences with Reproducible Examples in R

2019

Applied Sciences

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Spatial predictive methods are increasingly being used to generate predictions across various disciplines in environmental sciences. Accuracy of the predictions is critical as they form the basis for environmental management and conservation. Therefore, improving the accuracy by selecting an appropriate method and then developing the most accurate predictive model(s) is essential. However, it is challenging to select an appropriate method and find the most accurate predictive model for a given dataset due to many aspects and multiple factors involved in the modeling process. Many previous studies considered only a portion of these aspects and factors, often leading to sub-optimal or even misleading predictive models. This study evaluates a spatial predictive modeling process, and identifies nine major components for spatial predictive modeling. Each of these nine components is then reviewed, and guidelines for selecting and applying relevant components and developing accurate predictive models are provided. Finally, reproducible examples using spm, an R package, are provided to demonstrate how to select and develop predictive models using machine learning, geostatistics, and their hybrid methods according to predictive accuracy for spatial predictive modeling; reproducible examples are also provided to generate and visualize spatial predictions in environmental sciences.

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“…In order to calculate 95 % confidence intervals on the sediment load, a bootstrap method was used that accounts for uncertainty in the discharge and sediment concentration predictions (Slaets et al, 2016; Appendix Sect. A).…”

Section: Sediment Load Estimatesmentioning

confidence: 99%

Sediment trap efficiency of paddy fields at the watershed scale in a mountainous catchment in northwest Vietnam

et al. 2016

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Abstract. Composite agricultural systems with permanent maize cultivation in the uplands and irrigated rice in the valleys are very common in mountainous southeast Asia. The soil loss and fertility decline of the upland fields is well documented, but little is known about reallocation of these sediments within the landscape. In this study, a turbidity-based linear mixed model was used to quantify sediment inputs, from surface reservoir irrigation water and from direct overland flow, into a paddy area of 13 ha. Simultaneously, the sediment load exported from the rice fields was determined. Mid-infrared spectroscopy was applied to analyze sediment particle size. Our results showed that per year, 64 Mg ha−1 of sediments were imported into paddy fields, of which around 75 % were delivered by irrigation water and the remainder by direct overland flow during rainfall events. Overland flow contributed one-third of the received sandy fraction, while irrigated sediments were predominantly silty. Overall, rice fields were a net sink for sediments, trapping 28 Mg ha−1 a−1 or almost half of total sediment inputs. As paddy outflow consisted almost exclusively of silt- and clay-sized material, 24 Mg ha−1 a−1 of the trapped amount of sediment was estimated to be sandy. Under continued intensive upland maize cultivation, such a sustained input of coarse material could jeopardize paddy soil fertility, puddling capacity and ultimately food security of the inhabitants of these mountainous areas. Preventing direct overland flow from entering the paddy fields, however, could reduce sand inputs by up to 34 %.

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Quantifying uncertainty on sediment loads using bootstrap confidence intervals

Cited by 12 publications

References 37 publications

Combined influence of soil moisture and atmospheric evaporative demand is important for accurately predicting US maize yields

Combined influence of soil moisture and atmospheric evaporative demand is important for accurately predicting US maize yields

A Critical Review of Spatial Predictive Modeling Process in Environmental Sciences with Reproducible Examples in R

Sediment trap efficiency of paddy fields at the watershed scale in a mountainous catchment in northwest Vietnam

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