Storylines: an alternative approach to representing uncertainty in physical aspects of climate change

Investigating the relationships between climate extremes and crop yield can help us understand how unfavourable climatic conditions affect crop production. In this study, two statistical models, multiple linear regression and random forest, were used to identify rainfall extremes indices affecting wheat yield in three different regions of the New South Wales wheat belt. The results show that the random forest model explained 41–67% of the year-to-year yield variation, whereas the multiple linear regression model explained 34–58%. In the two models, 3-month timescale standardized precipitation index of Jun.–Aug. (SPI_JJA), Sep.–Nov. (SPI_SON), and consecutive dry days (CDDs) were identified as the three most important indices which can explain yield variability for most of the wheat belt. Our results indicated that the inter-annual variability of rainfall in winter and spring was largely responsible for wheat yield variation, and pre-growing season rainfall played a secondary role. Frequent shortages of rainfall posed a greater threat to crop growth than excessive rainfall in eastern Australia. We concluded that the comparison between multiple linear regression and machine learning algorithm proposed in the present study would be useful to provide robust prediction of yields and new insights of the effects of various rainfall extremes, when suitable climate and yield datasets are available.     

Sketch Maps and Qualitative GIS: Using Cartographies of Individual Spatial Narratives in Geographic Research

Mental and sketch maps have a long tradition in modern geography. Little theoretical and methodological distinction has been made, however, between different hand mapping approaches. Mental maps emerged from behavioral geography of the spatial science tradition as a technique to understand human behaviors based on peoples’ perceptions of their spatial environment. More recently, sketch maps have been used in participatory and qualitative geographic information systems (QGIS) to develop cartographies of group and individual spatial narratives. They are a tool that helps achieve QGIS's objectives of collecting unique spatial data of individual experiences, visualizing socio-spatial processes, breaking down particular barriers of positionality in research, and developing new uses of GIS. Two case studies illustrate the use of sketch maps in research, including a project examining job accessibility among working poor individuals and a study of the experiences of fear and safety in public spaces among lesbian, gay, bisexual, and transgender community members. Sketch maps in QGIS have many methodological merits. They add an invaluable dimension to the qualitative interview process, offer countermapping perspectives, generate detailed spatial information of individuals, and facilitate data interpretation.     

Women and seed management: A study of two villages in Bangladesh

Seed management has until recently been neglected in agrobiodiversity research. Gender dimensions in seed selection, processing, storage and exchange embody critical task areas in the human management of diverse crop species and varieties. This paper focuses on women's roles in seed management in field and homegarden crops in Bangladesh and reports on the findings of a study in two villages in Tangail District. Seventy-five women were surveyed with a questionnaire and subsets interviewed in-depth; focus groups conducted for women and men provided a means of cross-checking responses. Three distinct areas of agrobiodiversity were addressed: the gendered divisions of labour among agricultural tasks; the pervasiveness of seed saving among respondents; and seed management practices and techniques. The results indicate that while men have greater authority over field activities, homegarden production is the exclusive domain of women. Seed management is seen as an extension of women's domestic duties: women are responsible for all seed processing, storage and exchange for field as well as homegarden crops. The vast majority of seeds sown are saved on-farm by women, revealing important cultural, economic and environmental implications for agrobiodiversity conservation and local differences.     

CFSv2 ensemble prediction of the wintertime Arctic Oscillation

Lagged ensembles from the operational Climate Forecast System version 2 (CFSv2) seasonal hindcast dataset are used to assess skill in forecasting interannual variability of the December–February Arctic Oscillation (AO). We find that a small but statistically significant portion of the interannual variance (>20 %) of the wintertime AO can be predicted at leads up to 2 months using lagged ensemble averages. As far as we are aware, this is the first study to demonstrate that an operational model has discernible skill in predicting AO variability on seasonal timescales. We find that the CFS forecast skill is slightly higher when a weighted ensemble is used that rewards forecast runs with the most accurate representations of October Eurasian snow cover extent (SCE), hinting that a stratospheric pathway linking October Eurasian SCE with the AO may be responsible for the model skill. However, further analysis reveals that the CFS is unable to capture many important aspects of this stratospheric mechanism. Model deficiencies identified include: (1) the CFS significantly underestimates the observed variance in October Eurasian SCE, (2) the CFS fails to translate surface pressure anomalies associated with SCE anomalies into vertically propagating waves, and (3) stratospheric AO patterns in the CFS fail to propagate downward through the tropopause to the surface. Thus, alternate boundary forcings are likely contributing to model skill. Improving model deficiencies identified in this study may lead to even more skillful predictions of wintertime AO variability in future versions of the CFS.     

Location‐based social media behavior and perception: Views of university students

Location‐based social media (LBSM), a specific type of volunteered geographic information (VGI), is increasingly being used as a spatial data source for researchers in geography and related disciplines. Many questions, though, have been raised about VGI data in terms of its quality and its contributors. While a number of studies have explored users’ demographics and motivations for contribution to explicitly geographic forms of VGI, such as OpenStreetMap and Wikimapia, few have focused on these aspects with implicitly geographic forms of VGI, such as LBSM (for example, Twitter and Instagram). This study, through use of an online survey, specifically assesses the LBSM behavior and perceptions of 253 university students, noting differences found in gender, race, and academic standing. We find that the greatest differences are those between males and females, rather than through race or academic standing, and LBSM appears less biased than other forms of VGI.     

The molar volume of FeO–MgO–Fe2O3–Cr2O3–Al2O3–TiO2 spinels

We define and calibrate a new model of molar volume as a function of pressure, temperature, ordering state, and composition for spinels in the supersystem (Mg, Fe²⁺)(Al, Cr, Fe³⁺)₂O₄ ? (Mg, Fe²⁺)₂TiO₄. We use 832 X-ray and neutron diffraction measurements performed on spinels at ambient and in situ high-P, T conditions to calibrate end-member equations of state and an excess volume model for this system. The effect on molar volume of cation ordering over the octahedral and tetrahedral sites is captured with linear dependence on Mg²⁺, Al³⁺, and Fe³⁺ site occupancy terms. We allow standard-state volumes and coefficients of thermal expansion of the end members to vary within their uncertainties during extraction of the mixing properties, in order to achieve the best fit. Published equations of state of the various spinel end members are analyzed to obtain optimal values of the bulk modulus and its pressure derivative, for each explicit end member. For any spinel composition in the supersystem, the model molar volume is obtained by adding excess volume and cation order-dependent terms to a linear combination of the five end-member volumes, estimated at pressure and temperature using the high-T Vinet equation of state. The preferred model has a total of 9 excess volume and order-dependent parameters and fits nearly all experiments to within 0.02 J/bar/mol, or better than 0.5 % in volume. The model is compared to the current MELTS spinel model with a demonstration of the impact of the model difference on the estimated spinel-garnet lherzolite transition pressure.     

Jupiter’s South South Temperate Zone vortices: Observations and simulations

The region in Jupiter’s atmosphere with the highest density of anticyclonic spot-like vortices is the region known as the South South Temperate Zone (SSTZ), which is located between the eastward jet at ≈−42.9° latitude and the westward jet at ≈−39.2° latitude. We present a characterization of the spots found in this region based on ground-based and Hubble Space Telescope observations from the years 1993 to 2007. Mergers have been reported between spots in this region, similar to those observed for the White Ovals in the latitudinal domain immediately equatorward (northward). We use a multilayer model to perform numerical simulations that capture the details of a well-observed merger event involving multiple interacting vortices. We find that the vertical stratification has an important effect in the outcome of the interaction between spots. In particular it can play a determining role on whether or not a cyclone embedded between two approaching anticyclones can inhibit their merging. From our simulations we conclude that the background static stability of the atmosphere in the SSTZ is better characterized by an average value of .     

The importance of incorporating diurnally fluctuating stream discharge in stream temperature energy balance models

Although stream temperature energy balance models are useful to predict temperature through time and space, a major unresolved question is whether fluctuations in stream discharge reduce model accuracy when not exactly represented. However, high‐frequency (e.g., subdaily) discharge observations are often unavailable for such simulations, and therefore, diurnal streamflow fluctuations are not typically represented in energy balance models. These fluctuations are common due to evapotranspiration, snow pack or glacial melt, tidal influences within estuaries, and regulated river flows. In this work, we show when to account for diurnally fluctuating streamflow. To investigate how diurnal streamflow fluctuations affect predicted stream temperatures, we used a deterministic stream temperature model to simulate stream temperature along a reach in the Quilcayhuanca Valley, Peru, where discharge varies diurnally due to glacial melt. Diurnally fluctuating streamflow was varied alongside groundwater contributions via a series of computational experiments to assess how uncertainty in reach hydrology may impact simulated stream temperature. Results indicated that stream temperatures were more sensitive to the rate of groundwater inflow to the reach compared with the timing and amplitude of diurnal fluctuations in streamflow. Although incorporating observed diurnal fluctuations in discharge resulted in a small improvement in model RMSE, we also assessed other diurnal discharge signals and found that high amplitude signals were more influential on modelled stream temperatures when the discharge peaked at specific times. Results also showed that regardless of the diurnal discharge signal, the estimated groundwater flux to the reach only varied from 1.7% to 11.7% of the upstream discharge. However, diurnal discharge fluctuations likely have a stronger influence over longer reaches and in streams where the daily range in discharge is larger, indicating that diurnal fluctuations in stream discharge should be considered in certain settings.