How Much Variation in Land Surface Phenology can Climate Oscillation Modes Explain at the Scale of Mountain Pastures in Kyrgyzstan?

Climate oscillation modes can shape weather across the globe due to atmospheric teleconnections. We built on the findings of a recent study to assess whether the impacts of teleconnections are detectable and significant in the early season dynamics of highland pastures across five rayons in Kyrgyzstan. Specifically, since land surface phenology (LSP) has already shown to be influenced by snow cover seasonality and terrain, we investigated here how much more explanatory and predictive power information about climatic oscillation modes might add to explain variation in LSP. We focused on seasonal values of five climate oscillation indices that influence vegetation dynamics in Central Asia. We characterized the phenology in highland pastures with metrics derived from LSP modeling using Landsat NDVI time series together with MODIS land surface temperature (LST) data: Peak Height (PH), the maximum modeled NDVI and Thermal Time to Peak (TTP), the quantity of accumulated growing degree-days based on LST required to reach PH. Next, we calculated two metrics of snow cover seasonality from MODIS snow cover composites: last date of snow (LDoS), and the number of snow covered dates (SCD). For terrain features, we derived elevation, slope, and TRASP index as linearization of aspect. First, we used Spearman’s rank correlation to assess the geographical differentiation of land surface phenology metrics responses to environmental variables. PH showed weak correlations with TTP (positive in western but negative in eastern rayons), and moderate relationships with LDoS and SCD only in one northeastern rayon. Slope was weakly related to PH, while TRASP showed a consistent moderate negative correlation with PH. A significant but weak negative correlation was found between PH and SCAND JJA, and a significant weak positive correlation with MEI MAM. TTP showed consistently strong negative relationships with LDoS, SCD, and elevation. Very weak positive correlations with TTP were found for EAWR DJF, AMO DJF, and MEI DJF in western rayons only. Second, we used Partial Least Squares regression to investigate the role of oscillation modes altogether. PLS modelling of TTP showed that thermal time accumulation could be explained mostly by elevation and snow cover metrics, leading to reduced models explaining 55 to 70% of observed variation in TTP. Variable selection indicated that NAO JJA, AMO JJA and SCAND MAM had significant relationships with TTP, but their input of predictive power was neglible. PLS models were able to explain up to 29% of variability in PH. SCAND JJA and MEI MAM were shown to be significant predictors, but adding them into models did not influence modeling performance. We concluded the impacts of climate oscillation anomalies were not detectable or significant in mountain pastures using LSP metrics at fine spatial resolution. Rather, at a 30m resolution, the indirect effects of seasonal climatic oscillations are overridden by terrain influences (mostly elevation) and snow cover timing. Whether climate oscillation mode indices can provide some new and useful information about growing season conditions remains a provocative question, particularly in light of the multiple environmental challenges facing the agropastoralism livelihood in montane Central Asia

Land Surface Phenology in the Highland Pastures of Montane Central Asia: Interactions with Snow Cover Seasonality and Terrain Characteristics

Many studies have shown that high elevation environments are among very sensitive to climatic changes and where impacts are exacerbated. Across Central Asia, which is especially vulnerable to climate change due to aridity, the ability of global climate projections to capture the complex dynamics of mountainous environments is particularly limited. Over montane Central Asia, agropastoralism constitutes a major portion of the rural economy. Extensive herbaceous vegetation forms the basis of rural economies in Kyrgyzstan. Here we focus on snow cover seasonality and the effects of terrain on phenology in highland pastures using remote sensing data for 2001–2017. First, we describe the thermal regime of growing season using MODerate Resolution Imaging Spectrometer (MODIS) land surface temperature (LST) data, analyzing the modulation by elevation, slope, and aspect. We then characterized the phenology in highland pastures with metrics derived from modeling the land surface phenology using Landsat normalized difference vegetation index (NDVI) time series together with MODIS LST data. Using rank correlations, we then analyzed the influence of four metrics of snow cover seasonality calculated from MODIS snow cover composites—first date of snow, late date of snow, duration of snow season, and the number of snow-covered dates (SCD)—on two key metrics of land surface phenology in the subsequent growing season, specifically, peak height (PH; the maximum modeled NDVI) and thermal time to peak (TTP; the amount of growing degree-days accumulated during modeled green-up phase). We evaluated the role of terrain features in shaping the relationships between snow cover metrics and land surface phenology metrics using exact multinomial tests of equivalence. Key findings include (1) a positive relationship between SCD and PH occurred in over 1664 km2 at p \u3c 0.01 and 5793 km2 at p \u3c 0.05, which account for\u3e8% of 68,881 km2 of the pasturelands analyzed in Kyrgyzstan; (2) more negative than positive correlations were found between snow cover onset and PH, and more positive correlations were observed between snowmelt timing and PH, indicating that a longer snow season can positively influence PH; (3) significant negative correlations between TTP and SCD appeared in 1840 km2 at p \u3c 0.01 and 6208 km2 at p \u3c 0.05, and a comparable but smaller area showed negative correlations between TTP and last date of snow (1538 km2 at p \u3c 0.01 and 5188 km2 at p \u3c 0.05), indicating that under changing climatic conditions toward earlier spring warming, decreased duration of snow cover may lead to lower pasture productivity, thereby threatening the sustainability of montane agropastoralism; and (4) terrain had a stronger influence on the timing of last date of snow cover than on the number of snow-covered dates, with slope being more important than aspect, and the strongest effect appearing from the interaction of aspect and steeper slopes. In this study, we characterized the snow-phenology interactions in highland pastures and revealed strong dependencies of pasture phenology on timing of snowmelt and the number of snow-covered dates

Discutindo a educação ambiental no cotidiano escolar: desenvolvimento de projetos na escola formação inicial e continuada de professores

A presente pesquisa buscou discutir como a Educação Ambiental (EA) vem sendo trabalhada, no Ensino Fundamental e como os docentes desta escola compreendem e vem inserindo a EA no cotidiano escolar., em uma escola estadual do município de Tangará da Serra/MT, Brasil. Para tanto, realizou-se entrevistas com os professores que fazem parte de um projeto interdisciplinar de EA na escola pesquisada. Verificou-se que o projeto da escola não vem conseguindo alcançar os objetivos propostos por: desconhecimento do mesmo, pelos professores; formação deficiente dos professores, não entendimento da EA como processo de ensino-aprendizagem, falta de recursos didáticos, planejamento inadequado das atividades. A partir dessa constatação, procurou-se debater a impossibilidade de tratar do tema fora do trabalho interdisciplinar, bem como, e principalmente, a importância de um estudo mais aprofundado de EA, vinculando teoria e prática, tanto na formação docente, como em projetos escolares, a fim de fugir do tradicional vínculo “EA e ecologia, lixo e horta”.Facultad de Humanidades y Ciencias de la Educació

Spatial analysis of air masses backward trajectories in order to identify distant sources of fine particulate matter emission / Analiza przestrzenna wstecznych trajektorii mas powietrza w celu rozpoznania odległych źródeł emisji pyłu drobnego

W pracy zaprezentowano metodę rozpoznawania odległych źródeł emisji pyłu drobnego PM2.5, polegającą na przestrzennej analizie łącznej informacji o imisji PM2.5 oraz o wstecznych trajektoriach mas powietrza, obliczonych za pomocą modelu HYSPLIT. Trajektorie wsteczne obliczono startując z wysokości 30, 50, 100 i 150 m n.p.g., dla trzech lokalizacji stacji pomiarowych stężeń PM2.5 (Diabla Góra, Gdańsk, Katowice), reprezentujących różne warunki środowiskowe. Wszystkim punktom pojedynczej trajektorii wstecznej przyporządkowano stężenie PM2.5 odpowiadające dacie startu obliczeń tej trajektorii. Użyto dobowych średnich stężeń PM2.5, a w przypadku Gdańska dodatkowo także godzinnych średnich, co umożliwiło ocenę skuteczności przedstawionej metody. Położenie odległych źródeł emisji pyłu drobnego zostało określone poprzez interpolację danych punktowych trajektorii do regularnej siatki przy zastosowaniu metody metryki danych. Każdemu węzłowi siatki przypisano wartość średnią obliczoną ze stężeń PM2.5 przyporządkowanych punktom trajektorii znajdujących się w obrębie tzw. elipsy wyszukiwania. Przed obliczeniem wartości średniej ukryto część danych, eliminując w ten sposób bliskie źródła emisji pyłu drobnego. Analizy objęły okres styczeń-marzec 2010 roku. Wyniki wskazały na odmienne pochodzenie mas powietrza w północnej i południowej Polsce. W Diablej Górze i Gdańsku odległe źródła emisji pyłu drobnego rozpoznano głównie w Białorusi i Rosji. W Katowicach również zaznaczył się wpływ źródeł białoruskich, ale jako najbardziej istotne odległe źródła emisji PM2.5 uznano te zlokalizowane na obszarze Rumunii, Węgier, Słowacji i Ukrainy

Remote Sensing of Pasture Degradation in the Highlands of the Kyrgyz Republic: Finer-Scale Analysis Reveals Complicating Factors

Degradation in the highland pastures of the Kyrgyz Republic, a small country in Central Asia, has been reported in several studies relying on coarse spatial resolution imagery, primarily MODIS. We used the results of land surface phenology modeling at higher spatial resolution to characterize spatial and temporal patterns of phenometrics indicative of the seasonal peak in herbaceous vegetation. In particular, we explored whether proximity to villages was associated with substantial decreases in the seasonal peak values. We found that terrain features—elevation and aspect—modulated the strength of the influence of village proximity on the phenometrics. Moreover, using contrasting hotter/drier and cooler/wetter years, we discovered that the growing season weather can interact with aspect to attenuate the negative influences of dry conditions on seasonal peak values. As these multiple contingent and interactive factors that shape the land surface phenology of the highland pastures may be blurred and obscured in coarser spatial resolution imagery, we discuss some limitations with prior and recent studies of pasture degradation