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

Evapotranspiration in the Nile Basin: Identifying Dynamics, Trends, and Drivers 2002-2011

Analysis of the relationship between evapotranspiration (ET) and its natural and anthropogenic drivers is critical in water-limited basins such as the Nile. The spatiotemporal relationships of ET with rainfall and vegetation dynamics in the Nile Basin during 2002–2011 were analyzed using satellite-derived data. Non-parametric statistics were used to quantify ET-rainfall interactions and trends across land cover types and subbasins. We found that 65% of the study area (2.5 million km2) showed significant (p \u3c 0.05) positive correlations between monthly ET and rainfall, whereas 7% showed significant negative correlations. As expected, positive ET-rainfall correlations were observed over natural vegetation, mixed croplands/natural vegetation, and croplands, with a few subbasin-specific exceptions. In particular, irrigated croplands, wetlands and some forests exhibited negative correlations. Trend tests revealed spatial clusters of statistically significant trends in ET (6% of study area was negative; 12% positive), vegetation greenness (24% negative; 12% positive) and rainfall (11% negative; 1% positive) during 2002–2011. The Nile Delta, Ethiopian highlands and central Uganda regions showed decline in ET while central parts of Sudan, South Sudan, southwestern Ethiopia and northeastern Uganda showed increases. Except for a decline in ET in central Uganda, the detected changes in ET (both positive and negative) were not associated with corresponding changes in rainfall. Detected declines in ET in the Nile delta and Ethiopian highlands were found to be attributable to anthropogenic land degradation, while the ET decline in central Uganda is likely caused by rainfall reduction

Pecora 16 "Global Priorities in Land Remote Sensing" October 23-27, 2005 * Sioux Falls, South Dakota ASSESSING LAND SURFACE DYNAMICS ACROSS THE NEBRASKA SAND HILLS USING ADVANCED MICROWAVE SCANNING RADIOMETER (AMSR-E) DATA PRODUCTS

ABSTRACT The Nebraska Sand Hills is the largest sand dune area in the Western hemisphere, and one of the largest grassstabilized dune regions in the world. It had been suggested that the Sand Hills dunes were active as recently as 900 years ago. To understand the persistence of dune stability, it is important to investigate land surface linkages with the hydrometeorological and biogeophysical processes. Synoptic views of surficial soil moisture and vegetation water content at 25 km spatial resolution have recently become available as standard data products from the multifrequency Advanced Microwave Scanning Radiometer (AMSR-E) on Aqua. In this paper we illustrate the diel, seasonal, and interannual patterns in AMSR-E vegetation water content product using data from 2003 and 2004. Using 14-day maximum AVHRR NDVI composites to capture land surface phenology, we find a strong spatiotemporal correspondence between the vegetation water content product and NDVI

Remittances and land change: A systematic review

Remittances—funds sent by migrants to family and friends back home—are an important source of global monetary flows, and they have implications for the maintenance and transformation of land systems. A number of published reviews have synthesized work on a variety of aspects of remittances (e.g., rural livelihoods, disasters, and economic development). To our knowledge, there are no reviews of work investigating the linkages between remittances and land change, broadly understood. This knowledge gap is important to address because researchers have recognized that remittances flows are a mechanism that helps to explain how migration can affect land change. Thus, understanding the specific roles remittances play in land system changes should help to clarify the multiple processes associated with migration and their independent and interactive effects. To address the state of knowledge about the connection between remittances and land systems, this paper conducts a systematic review. Our review of 51 journal articles finds that the linkages uncovered were commonly subtle and/or indirect. Very few studies looked at the direct connections between receipt of remittances and quantitative changes in land. Most commonly, the relationship between remittances and land change was found to occur through pathways from labor migration to household income to agricultural development and productivity. We find four non-exclusive pathways through which households spend remittances with consequent changes to land systems: (1) agricultural crops and livestock, (2) agricultural labor and technologies, (3) land purchases, and (4) non-agricultural purchases and consumables. In the papers reviewed, these expenditures are linked to various land system change outcomes, including land use change, soil degradation, pasture degradation, afforestation/deforestation/degradation, agricultural intensification/extensification/diversification, and no impact. These findings suggest four avenues for future research. One avenue is the use of the theoretical lens of telecoupling to understand how remittances may produce wider-scale changes in land systems. A second avenue is further examination of the impacts of shocks and disturbances to remittance flows on land change both in migrant sending and in remittance receiving areas. A third avenue is scholarship that examines the extent that household uses of remittances have a “ripple effect” on land uses in nearby interlinked systems. A fourth avenue for future work is the use of spatially explicit modeling that leverages land cover and land use data based on imagery and other geospatial information

A heteroskedastic error covariance matrix estimator using a first-order conditional autoregressive Markov simulation for deriving asympotical efficient estimates from ecological sampled Anopheles arabiensis aquatic habitat covariates

<p>Abstract</p> <p>Background</p> <p>Autoregressive regression coefficients for <it>Anopheles arabiensis </it>aquatic habitat models are usually assessed using global error techniques and are reported as error covariance matrices. A global statistic, however, will summarize error estimates from multiple habitat locations. This makes it difficult to identify where there are clusters of <it>An. arabiensis </it>aquatic habitats of acceptable prediction. It is therefore useful to conduct some form of spatial error analysis to detect clusters of <it>An. arabiensis </it>aquatic habitats based on uncertainty residuals from individual sampled habitats. In this research, a method of error estimation for spatial simulation models was demonstrated using autocorrelation indices and eigenfunction spatial filters to distinguish among the effects of parameter uncertainty on a stochastic simulation of ecological sampled <it>Anopheles </it>aquatic habitat covariates. A test for diagnostic checking error residuals in an <it>An. arabiensis </it>aquatic habitat model may enable intervention efforts targeting productive habitats clusters, based on larval/pupal productivity, by using the asymptotic distribution of parameter estimates from a residual autocovariance matrix. The models considered in this research extends a normal regression analysis previously considered in the literature.</p> <p>Methods</p> <p>Field and remote-sampled data were collected during July 2006 to December 2007 in Karima rice-village complex in Mwea, Kenya. SAS 9.1.4^®was used to explore univariate statistics, correlations, distributions, and to generate global autocorrelation statistics from the ecological sampled datasets. A local autocorrelation index was also generated using spatial covariance parameters (i.e., Moran's Indices) in a SAS/GIS^®database. The Moran's statistic was decomposed into orthogonal and uncorrelated synthetic map pattern components using a Poisson model with a gamma-distributed mean (i.e. negative binomial regression). The eigenfunction values from the spatial configuration matrices were then used to define expectations for prior distributions using a Markov chain Monte Carlo (MCMC) algorithm. A set of posterior means were defined in WinBUGS 1.4.3^®. After the model had converged, samples from the conditional distributions were used to summarize the posterior distribution of the parameters. Thereafter, a spatial residual trend analyses was used to evaluate variance uncertainty propagation in the model using an autocovariance error matrix.</p> <p>Results</p> <p>By specifying coefficient estimates in a Bayesian framework, the covariate number of tillers was found to be a significant predictor, positively associated with <it>An. arabiensis </it>aquatic habitats. The spatial filter models accounted for approximately 19% redundant locational information in the ecological sampled <it>An. arabiensis </it>aquatic habitat data. In the residual error estimation model there was significant positive autocorrelation (i.e., clustering of habitats in geographic space) based on log-transformed larval/pupal data and the sampled covariate depth of habitat.</p> <p>Conclusion</p> <p>An autocorrelation error covariance matrix and a spatial filter analyses can prioritize mosquito control strategies by providing a computationally attractive and feasible description of variance uncertainty estimates for correctly identifying clusters of prolific <it>An. arabiensis </it>aquatic habitats based on larval/pupal productivity.</p

Northern Eurasia Future Initiative (NEFI): facing the challenges and pathways of global change in the twenty-first century

© 2017, The Author(s). During the past several decades, the Earth system has changed significantly, especially across Northern Eurasia. Changes in the socio-economic conditions of the larger countries in the region have also resulted in a variety of regional environmental changes that can have global consequences. The Northern Eurasia Future Initiative (NEFI) has been designed as an essential continuation of the Northern Eurasia Earth Science Partnership Initiative (NEESPI), which was launched in 2004. NEESPI sought to elucidate all aspects of ongoing environmental change, to inform societies and, thus, to better prepare societies for future developments. A key principle of NEFI is that these developments must now be secured through science-based strategies co-designed with regional decision-makers to lead their societies to prosperity in the face of environmental and institutional challenges. NEESPI scientific research, data, and models have created a solid knowledge base to support the NEFI program. This paper presents the NEFI research vision consensus based on that knowledge. It provides the reader with samples of recent accomplishments in regional studies and formulates new NEFI science questions. To address these questions, nine research foci are identified and their selections are briefly justified. These foci include warming of the Arctic; changing frequency, pattern, and intensity of extreme and inclement environmental conditions; retreat of the cryosphere; changes in terrestrial water cycles; changes in the biosphere; pressures on land use; changes in infrastructure; societal actions in response to environmental change; and quantification of Northern Eurasia’s role in the global Earth system. Powerful feedbacks between the Earth and human systems in Northern Eurasia (e.g., mega-fires, droughts, depletion of the cryosphere essential for water supply, retreat of sea ice) result from past and current human activities (e.g., large-scale water withdrawals, land use, and governance change) and potentially restrict or provide new opportunities for future human activities. Therefore, we propose that integrated assessment models are needed as the final stage of global change assessment. The overarching goal of this NEFI modeling effort will enable evaluation of economic decisions in response to changing environmental conditions and justification of mitigation and adaptation efforts

Northern Eurasia Future Initiative (NEFI): facing the challenges and pathways of global change in the 21st century

During the past several decades, the Earth system has changed significantly, especially across Northern Eurasia. Changes in the socio-economic conditions of the larger countries in the region have also resulted in a variety of regional environmental changes that can have global consequences. The Northern Eurasia Future Initiative (NEFI) has been designed as an essential continuation of the Northern Eurasia Earth Science Partnership Initiative (NEESPI), which was launched in 2004. NEESPI sought to elucidate all aspects of ongoing environmental change, to inform societies and, thus, to better prepare societies for future developments. A key principle of NEFI is that these developments must now be secured through science-based strategies co-designed with regional decision makers to lead their societies to prosperity in the face of environmental and institutional challenges. NEESPI scientific research, data, and models have created a solid knowledge base to support the NEFI program. This paper presents the NEFI research vision consensus based on that knowledge. It provides the reader with samples of recent accomplishments in regional studies and formulates new NEFI science questions. To address these questions, nine research foci are identified and their selections are briefly justified. These foci include: warming of the Arctic; changing frequency, pattern, and intensity of extreme and inclement environmental conditions; retreat of the cryosphere; changes in terrestrial water cycles; changes in the biosphere; pressures on land-use; changes in infrastructure; societal actions in response to environmental change; and quantification of Northern Eurasia's role in the global Earth system. Powerful feedbacks between the Earth and human systems in Northern Eurasia (e.g., mega-fires, droughts, depletion of the cryosphere essential for water supply, retreat of sea ice) result from past and current human activities (e.g., large scale water withdrawals, land use and governance change) and potentially restrict or provide new opportunities for future human activities. Therefore, we propose that Integrated Assessment Models are needed as the final stage of global change assessment. The overarching goal of this NEFI modeling effort will enable evaluation of economic decisions in response to changing environmental conditions and justification of mitigation and adaptation efforts