Using landscape characteristics to define an adjusted distance metric for improving kriging interpolations

Interpolation of point measurements using geostatistical techniques such as kriging can be used to estimate values at non-sampled locations in space. Traditional geostatistics are based on the spatial autocorrelation concept that nearby things are more related than distant things. In this study, additional information was used to modify the traditional Euclidean concept of distance into an adjusted distance metric that incorporates similarity in terms of quantifiable landscape characteristics such as topography or land use. This new approach was tested by interpolating soil moisture content, pH and carbon-to-nitrogen (C:N) ratio measured in both the mineral and the organic soil layers at a field site in central Sweden. Semivariograms were created using both the traditional distance metrics and the proposed adjusted distance metrics to carry out ordinary kriging (OK) interpolations between sampling points. In addition, kriging with external drift (KED) was used to interpolate soil properties to evaluate the ability of the adjusted distance metric to incorporate secondary data into interpolations. The new adjusted distance metric typically lowered the nugget associated with the semivariogram, thereby better representing small-scale variability in the measured data compared to semivariograms based on the traditional distance metric. The pattern of the resulting kriging interpolations using KED and OK based on the adjusted distance metric were similar because they represented secondary data and, thus, enhanced small-scale variability compared to traditional distance OK. This created interpolations that agreed better with what is expected for the real-world spatial variation of the measured properties. Based on cross-validation error, OK interpolations using the adjusted distance metric better fit observed data than either OK interpolations using traditional distance or KED.     

Drought Monitoring Using Data Mining Techniques: A Case Study for Nebraska, USA

Drought has an impact on many aspects of society. To help decision makers reduce the impacts of drought, it is important to improve our understanding of the characteristics and relationships of atmospheric and oceanic parameters that cause drought. In this study, the use of data mining techniques is introduced to find associations between drought and several oceanic and climatic indices that could help users in making knowledgeable decisions about drought responses before the drought actually occurs. Data mining techniques enable users to search for hidden patterns and find association rules for target data sets such as drought episodes. These techniques have been used for commercial applications, medical research, and telecommunications, but not for drought. In this study, two time-series data mining algorithms are used in Nebraska to illustrate the identification of the relationships between oceanic parameters and drought indices. The algorithms provide flexibility in time-series analyses and identify drought episodes separate from normal and wet conditions, and find relationships between drought and oceanic indices in a manner different from the traditional statistical associations. The drought episodes were determined based on the Standardized Precipitation Index (SPI) and Palmer Drought Severity Index (PDSI). Associations were observed between drought episodes and oceanic and atmospheric indices that include the Southern Oscillation Index (SOI), the Multivariate ENSO Index (MEI), the Pacific/North American (PNA) index, the North Atlantic Oscillation (NAO) Index, and the Pacific Decadal Oscillation (PDO) Index. The experimental results showed that among these indices, the SOI, MEI, and PDO have relatively stronger relationships with drought episodes over selected stations in Nebraska. Moreover, the study suggests that data mining techniques can help us to monitor drought using oceanic indices as a precursor of drought.     

Nationally Determined Contributions under the Paris Agreement and the costs of delayed action

Current country-level commitments under the Paris Agreement fall short of putting the world on a required trajectory to stay below a 2°C temperature increase compared to pre-industrial levels by the end of the century. Therefore, the timing of increased ambition is hugely important and as such this paper analyses the impact of both the short and long-term goals of the Paris Agreement on global emissions and economic growth. Using the hybrid TIAM-UCL-MSA model we consider the achievement of a 2°C target against a baseline of the Nationally Determined Contributions (NDCs) while also considering the timing of increased ambition of the NDCs by 2030 and the impacts of cost reductions of key low-carbon technologies. We find that the rate of emissions reduction ambition required between 2030 and 2050 is almost double when the NDCs are achieved but not ratcheted up until 2030, and leads to lower levels of economic growth throughout the rest of the century. However, if action is taken immediately and is accompanied by increasingly rapid low-carbon technology cost reductions, then there is almost no difference in GDP compared to the path suggested by the current NDC commitments.

Key policy insights

• Delaying the additional action needed to achieve the 2°C target until 2030 is shown to require twice the rate of emissions reductions between 2030 and 2050.

• Total cumulative GDP over the century is lower when additional action is delayed to 2030 and therefore has an overall negative impact on the economy, even without including climate change damages.

• Increased ratcheting of the NDC commitments should therefore be undertaken sooner rather than later, starting in conjunction with the 2023 Global Stocktake.

• Early action combined with cost reductions in key renewable energy technologies can reduce GDP losses to minimal levels (<1%).

• A 2°C future with technological advancements is clearly possible for a similar cost as a 3.3°C world without these advances, but with lower damages and losses from climate change.