IGU commission on the world political map

The International Geographical Union Reports

IGU commission on the world political map     

Modelling ecohydrological feedbacks in forest and grassland plots under a prolonged drought anomaly in Central Europe 2018–2020

Recent studies have highlighted the importance of understanding ecohydrological drought feedbacks to secure water resources under a changing climate and increasing anthropogenic impacts. In this study, we monitored and modelled feedbacks in the soil–plant-atmosphere continuum to the European drought summer 2018 and the following 2 years. The physically based, isotope-aided model EcH₂O-iso was applied to generic vegetation plots (forest and grassland) in the lowland, groundwater-dominated research catchment Demnitzer Millcreek (NE Germany; 66 km²). We included, inter alia, soil water isotope data in the model calibration and quantified changing “blue” (groundwater recharge) and “green” (evapotranspiration) water fluxes and ages under each land use as the drought progressed. Novel plant xylem isotope data were excluded from calibration but were compared with simulated root uptake signatures in model validation. Results indicated inter-site differences in the dynamics of soil water storage and fluxes with contrasting water age both during the drought and the subsequent 2 years. Forest vegetation consistently showed a greater moisture stress, more rapid recovery and higher variability in root water uptake depths from a generally younger soil water storage. In contrast, the grassland site, which had more water-retentive soils, showed higher and older soil water storage and groundwater recharge fluxes. The damped storage and flux dynamics under grassland led to a slower return to younger water ages at depth. Such evidence-based and quantitative differences in ecohydrological feedbacks to drought stress in contrasting soil-vegetation units provide important insights into Critical Zone water cycling. This can help inform future progress in the monitoring, modelling and development of climate mitigation strategies in drought-sensitive lowlands.     

High-temperature emplacement of the Cerro Galán and Toconquis Group ignimbrites (Puna plateau,NW Argentina) determined by TRM analyses

Estimates of pyroclastic flow emplacement temperatures in the Cerro Galán ignimbrite and Toconquis Group ignimbrites were determined using thermal remanent magnetization of lithic clasts embedded within the deposits. These ignimbrites belong to the Cerro Galán volcanic system, one of the largest calderas in the world, in the Puna plateau, NW Argentina. Temperature estimates for the 2.08-Ma Cerro Galán ignimbrite are retrieved from 40 sites in 14 localities (176 measured clasts), distributed at different distances from the caldera and different stratigraphic heights. Additionally, temperature estimates were obtained from 27 sample sites (125 measured clasts) from seven ignimbrite units forming the older Toconquis Group (5.60–4.51 Ma), mainly outcropping along a type section at Rio Las Pitas, Vega Real Grande. The paleomagnetic data obtained by progressive thermal demagnetization show that the clasts of the Cerro Galán ignimbrite have one single magnetic component, oriented close to the expected geomagnetic field at the time of emplacement. Results show therefore that most of the clasts acquired a new magnetization oriented parallel to the magnetic field at the moment of the ignimbrite deposition, suggesting that the clasts were heated up to or above the highest blocking temperature (T _b) of the magnetic minerals (T _b = 580°C for magnetite; T _b = 600–630°C for hematite). We obtained similar emplacement temperature estimations for six out of the seven volcanic units belonging to the Toconquis Group, with the exception of one unit (Lower Merihuaca), where we found two distinct magnetic components. The estimation of emplacement temperatures in this latter case is constrained at 580–610°C, which are lower than the other ignimbrites. These estimations are also in agreement with the lowest pre-eruptive magma temperatures calculated for the same unit (i.e., 790°C; hornblende–plagioclase thermometer; Folkes et al. 2011b). We conclude that the Cerro Galán ignimbrite and Toconquis Group ignimbrites were emplaced at temperatures equal to or higher than 620°C, except for Lower Merihuaca unit emplaced at lower temperatures. The homogeneity of high temperatures from proximal to distal facies in the Cerro Galán ignimbrite provides constraints for the emplacement model, marked by a relatively low eruption column, low levels of turbulence, air entrainment, surface–water interaction, and a high level of topographic confinement, all ensuring minimal heat loss.     

Non-uniform interhemispheric temperature trends over the past 550 years

The warming trend over the last century in the northern hemisphere (NH) was interrupted by cooling from ad 1940 to 1975, a period during which the southern hemisphere experienced pronounced warming. The cause of these departures from steady warming at multidecadal timescales are unclear; the prevailing explanation is that they are driven by non-uniformity in external forcings but recent models suggest internal climate drivers may play a key role. Paleoclimate datasets can help provide a long-term perspective. Here we use tree-rings to reconstruct New Zealand mean annual temperature over the last 550 years and demonstrate that this has frequently cycled out-of-phase with NH mean annual temperature at a periodicity of around 30–60 years. Hence, observed multidecadal fluctuations around the recent warming trend have precedents in the past, strongly implicating natural climate variation as their cause. We consider the implications of these changes in understanding and modelling future climate change.     

Snow cover variability across central Canada (1978–2002) derived from satellite passive microwave data

Twenty-four winter seasons (1978–2002) of mean February snow water equivalent (SWE) values were analyzed in an exploration of the spatial pattern of temporal variability in snow cover across the non-mountainous interior of Canada. The SWE data were derived from space-borne passive microwave brightness temperatures processed with a land cover-sensitive suite of algorithms. Spatial patterns in the frequency and amount of variability were investigated on an annual basis through comparisons with average trends over all 24 years. Changes in temporal variability through time were also investigated by comparing three eight year time periods to general trends. Analyses were synthesized at the ecozone scale in order to link results both to potential land cover influences on algorithm performance and climatological variability in SWE. Prairie and northern ecozones were typically found to be the most variable in terms of SWE magnitude. Analyses indicate that non-treed land cover classes are generally more variable than treed classes. The results also indicate that extreme weather events appear to be occurring with increasing consistency in the Prairie and Arctic regions. Discerning climatologically significant variability in the time series, compared to algorithm-related issues can be a challenge, but in an era of eroding surface observing networks the passive microwave time series represents an important resource for monitoring and detecting trends and variability in terrestrial snow cover.     

Development and Pull-through of Climate Science to Services in China

正1.Introduction The impacts arising from climate change and climate variability pose major challenges to global and regional security and economic prosperity(UNFCCC,2015).Some regions are more at risk than others,through heightened exposure to climatic hazards,and high vulnerability and exposure to such hazards.China,with its rapid economic development,large     

Exploring uncertainty in remotely sensed data with parallel coordinate plots

The existence of uncertainty in classified remotely sensed data necessitates the application of enhanced techniques for identifying and visualizing the various degrees of uncertainty. This paper, therefore, applies the multidimensional graphical data analysis technique of parallel coordinate plots (PCP) to visualize the uncertainty in Landsat Thematic Mapper (TM) data classified by the Maximum Likelihood Classifier (MLC) and Fuzzy C-Means (FCM). The Landsat TM data are from the Yellow River Delta, Shandong Province, China. Image classification with MLC and FCM provides the probability vector and fuzzy membership vector of each pixel. Based on these vectors, the Shannon's entropy (S.E.) of each pixel is calculated. PCPs are then produced for each classification output. The PCP axes denote the posterior probability vector and fuzzy membership vector and two additional axes represent S.E. and the associated degree of uncertainty. The PCPs highlight the distribution of probability values of different land cover types for each pixel, and also reflect the status of pixels with different degrees of uncertainty. Brushing functionality is then added to PCP visualization in order to highlight selected pixels of interest. This not only reduces the visualization uncertainty, but also provides invaluable information on the positional and spectral characteristics of targeted pixels.     

A New Combined Assessment of Mixed Uncertainty in Spatial Models: Conceptualization and Implementation

Uncertainty quantification is not often performed in spatial modeling applications, especially when there is a mixture of probabilistic and non‐probabilistic uncertainties. Furthermore, the effect of positional uncertainty is often not assessed, despite its relevance to geographical applications. Although there has been much work in investigating the aforementioned types of uncertainty in isolation, combined approaches have not been much researched. This has resulted in a lack of tools for conducting mixed uncertainty analyses that include positional uncertainty. This research addresses the issue by first presenting a new, flexible, simulation‐oriented conceptualization of positional uncertainty in geographic objects called F‐Objects. F‐Objects accommodates various representations of uncertainty, while remaining conceptually simple. Second, a new Python‐based framework is introduced, termed Wiggly and capable of conducting mixed uncertainty propagation using fuzzy Monte Carlo simulation (FMCS). FMCS combines both traditional Monte Carlo with fuzzy analysis in a so‐called hybrid approach. F‐Objects is implemented within the Wiggly framework, resulting in a tool capable of considering any combination of: (1) probabilistic variables; (2) fuzzy variables; and (3) positional uncertainty of objects (probabilistic/fuzzy). Finally, a realistic GIS‐based groundwater contamination problem demonstrates how F‐Objects and Wiggly can be used to assess the effect of positional uncertainty.     

Modelling tropical cyclone risks for present and future climate change scenarios using geospatial techniques

Tropical cyclones and their devastating impacts are of great concern to coastal communities globally. An appropriate approach integrating climate change scenarios at local scales is essential for producing detailed risk models to support cyclone mitigation measures. This study developed a simple cyclone risk-modelling approach under present and future climate change scenarios using geospatial techniques at local scales, and tested using a case study in Sarankhola Upazila from coastal Bangladesh. Linear storm-surge models were developed up to 100-year return periods. A local sea level rise scenario of 0.34?m for the year 2050 was integrated with surge models to assess the climate change impact. The resultant storm-surge models were used in the risk-modelling procedures. The developed risk models successfully identified the spatial extent and levels of risk that match with actual extent and levels within an acceptable limit of deviation. The result showed that cyclone risk areas increased with the increase of return period. The study also revealed that climate change scenario intensified the cyclone risk area by 5–10% in every return period. The findings indicate this approach has the potential to model cyclone risk in other similar coastal environments for developing mitigation plans and strategies.