Effect of spatial data resolution on uncertainty

Pogson, Mark; Smith, Pete

doi:10.1016/j.envsoft.2014.09.021

Cited by 27 publications

(11 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Spatial data quality directly impacts the reliability of habitat maps, predictive models, and sta - tistical description of species−habitat relationships (Menke et al 2009, Moudrý & Šímová 2012). Data quality is conceptually related to spatial scale (Zhang et al 2014, Lecours & De villers 2015, Pogson & Smith 2015. For instance, the finer the data resolution, the more that uncertainty and poor positional accuracy influence relationships between variables (Hanberry 2013).…”

Section: Observational Scale: Representing Nature With Spatial Datamentioning

confidence: 99%

Spatial scale and geographic context in benthic habitat mapping: review and future directions

Lecours¹,

Devillers²,

Schneider³

et al. 2015

Mar. Ecol. Prog. Ser.

156

123

View full text Add to dashboard Cite

Understanding the effects of scale is essential to the understanding of natural ecosystems, particularly in marine environments where sampling is more limited and sporadic than in terrestrial environments. Despite its recognized importance, scale is rarely considered in benthic habitat mapping studies. Lack of explicit statement of scale in the literature is an impediment to better characterization of seafloor pattern and process. This review paper highlights the importance of incorporating ecological scaling and geographical theories in benthic habitat mapping. It reviews notions of ecological scale and benthic habitat mapping, in addition to the way spatial scale influences patterns and processes in benthic habitats. We address how scale is represented in geographic data, how it influences their analysis, and consequently how it influences our understanding of seafloor ecosystems. We conclude that quantification of ecological processes at multiple scales using spatial statistics is needed to gain a better characterization of species−habitat relationships. We offer recommendations on more effective practices in benthic habitat mapping, including sampling that covers multiple spatial scales and that includes as many environmental variables as possible, adopting continuum-based habitat characterization approaches, using statistical analyses that consider the spatial nature of data, and explicit statement of the scale at which the research was conducted. We recommend a set of improved standards for defining benthic habitat. With these standards benthic habitats can be defined as 'areas of seabed that are (geo)statistically significantly different from their surroundings in terms of physical, chemical and biological characteristics, when observed at particular spatial and temporal scales'.

show abstract

Section: Observational Scale: Representing Nature With Spatial Datamentioning

confidence: 99%

Spatial scale and geographic context in benthic habitat mapping: review and future directions

Lecours¹,

Devillers²,

Schneider³

et al. 2015

Mar. Ecol. Prog. Ser.

156

123

View full text Add to dashboard Cite

show abstract

“…8). In this study, we performed simple spatial averaging, but there are other methodologies for smoothing two-dimensional signals (e.g., Räisä-nen and Ylhäisi, 2011;Pogson and Smith, 2015) that could potentially increase signal detection capabilities.…”

Section: Discussionmentioning

confidence: 99%

“…This strategy has been applied in other fields of the atmospheric sciences as well as for general gridded datasets (e.g., Pogson and Smith, 2015), and spatial averaging has been suggested as a means of reducing temperature variability and smoothing biases at the smallest spatial scales within a single model run (Räisänen and Ylhäsi, 2011). This "scale problem" has also been noted as an important consideration when analyzing aerosol indirect effects (McComiskey and Feingold, 2012) and for the detection and attribution of extreme weather events (Angélil et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Maximizing ozone signals among chemical, meteorological, and climatological variability

et al. 2018

View full text Add to dashboard Cite

Abstract. The detection of meteorological, chemical, or other signals in modeled or observed air quality data -such as an estimate of a temporal trend in surface ozone data, or an estimate of the mean ozone of a particular region during a particular season -is a critical component of modern atmospheric chemistry. However, the magnitude of a surface air quality signal is generally small compared to the magnitude of the underlying chemical, meteorological, and climatological variabilities (and their interactions) that exist both in space and in time, and which include variability in emissions and surface processes. This can present difficulties for both policymakers and researchers as they attempt to identify the influence or signal of climate trends (e.g., any pauses in warming trends), the impact of enacted emission reductions policies (e.g., United States NO x State Implementation Plans), or an estimate of the mean state of highly variable data (e.g., summertime ozone over the northeastern United States). Here we examine the scale dependence of the variability of simulated and observed surface ozone data within the United States and the likelihood that a particular choice of temporal or spatial averaging scales produce a misleading estimate of a particular ozone signal. Our main objective is to develop strategies that reduce the likelihood of overconfidence in simulated ozone estimates. We find that while increasing the extent of both temporal and spatial averaging can enhance signal detection capabilities by reducing the noise from variability, a strategic combination of particular temporal and spatial averaging scales can maximize signal detection capabilities over much of the continental US. For signals that are large compared to the meteorological variability (e.g., strong emissions reductions), shorter averaging periods and smaller spatial averaging regions may be sufficient, but for many signals that are smaller than or comparable in magnitude to the underlying meteorological variability, we recommend temporal averaging of 10-15 years combined with some level of spatial averaging (up to several hundred kilometers). If this level of averaging is not practical (e.g., the signal being examined is at a local scale), we recommend some exploration of the spatial and temporal variability to provide context and confidence in the robustness of the rePublished by Copernicus Publications on behalf of the European Geosciences Union.

show abstract

“…Model-based estimates of soil N 2 O depend on the modelling scale and local conditions. The quality of input data can affect the robustness of model predictions, which for large-scale simulations depends on the coarseness of the spatial input data (Pogson and Smith, 2015). Moreover, models are developed and parameterised using experimental data collected in the lab or at the field under conditions which can be very different to those of the area in which the model is applied.…”

Section: Introductionmentioning

confidence: 99%

Estimating the soil N<sub>2</sub>O emission intensity of croplands in northwest Europe

et al. 2019

View full text Add to dashboard Cite

Abstract. The application of nitrogenous fertilisers to agricultural soils is a major source of anthropogenic N2O emissions. Reducing the nitrogen (N) footprint of agriculture is a global challenge that depends, among other things, on our ability to quantify the N2O emission intensity of the world's most widespread and productive agricultural systems. In this context, biogeochemistry (BGC) models are widely used to estimate soil N2O emissions in agroecosystems. The choice of spatial scale is crucial because larger-scale studies are limited by low input data precision, while smaller-scale studies lack wider relevance. The robustness of large-scale model predictions depends on preliminary and data-demanding model calibration/validation, while relevant studies often omit the performance of output uncertainty analysis and underreport model outputs that would allow a critical assessment of results. This study takes a novel approach to these aspects. The study focuses on arable eastern Scotland – a data-rich region typical of northwest Europe in terms of edaphoclimatic conditions, cropping patterns and productivity levels. We used a calibrated and locally validated BGC model to simulate direct soil N2O emissions along with NO3 leaching and crop N uptake in fields of barley, wheat and oilseed rape. We found that 0.59 % (±0.36) of the applied N is emitted as N2O while 37 % (±6) is taken up by crops and 14 % (±7) is leached as NO3. We show that crop type is a key determinant of N2O emission factors (EFs) with cereals having a low (mean EF<0.6 %), and oilseed rape a high (mean EF=2.48 %), N2O emission intensity. Fertiliser addition was the most important N2O emissions driver suggesting that appropriate actions can reduce crop N2O intensity. Finally, we estimated a 74 % relative uncertainty around N2O predictions attributable to soil data variability. However, we argue that higher-resolution soil data alone might not suffice to reduce this uncertainty.

show abstract

Effect of spatial data resolution on uncertainty

Cited by 27 publications

References 26 publications

Spatial scale and geographic context in benthic habitat mapping: review and future directions

Spatial scale and geographic context in benthic habitat mapping: review and future directions

Maximizing ozone signals among chemical, meteorological, and climatological variability

Estimating the soil N<sub>2</sub>O emission intensity of croplands in northwest Europe

Contact Info

Product

Resources

About

Effect of spatial data resolution on uncertainty

Cited by 27 publications

References 26 publications

Spatial scale and geographic context in benthic habitat mapping: review and future directions

Spatial scale and geographic context in benthic habitat mapping: review and future directions

Maximizing ozone signals among chemical, meteorological, and climatological variability

Estimating the soil N&lt;sub&gt;2&lt;/sub&gt;O emission intensity of croplands in northwest Europe

Contact Info

Product

Resources

About

Estimating the soil N<sub>2</sub>O emission intensity of croplands in northwest Europe