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Abstract
Landslide event inventories are a vital resource for landslide susceptibility and forecasting applications. However, landslide inventories can vary in accuracy, availability, and timeliness as a result of varying detection methods, reporting, and data availability. This study presents an approach to use publicly available satellite data and open-source software to automate a landslide detection process called the Sudden Landslide Identification Product (SLIP). SLIP utilizes optical data from the Landsat-8 Operational Land Imager sensor, elevation data from the Shuttle Radar Topography Mission, and precipitation data from the Global Precipitation Measurement mission to create a reproducible and spatially customizable landslide identification product. The SLIP software applies change-detection algorithms to identify areas of new bare-earth exposures that may be landslide events. The study also presents a precipitation monitoring tool that runs alongside SLIP called the Detecting Real-Time Increased Precipitation (DRIP) model that helps to identify the timing of potential landslide events detected by SLIP. Using SLIP and DRIP together, landslide detection is improved by reducing problems related to accuracy, availability, and timeliness that are prevalent in the state of the art for landslide detection. A case study and validation exercise in Nepal were performed for images acquired between 2014 and 2015. Preliminary validation results suggest 56% model accuracy, with errors of commission often resulting from newly cleared agricultural areas. These results suggest that SLIP is an important first attempt in an automated framework that can be used for medium-resolution regional landslide detection, although it requires refinement before being fully realized as an operational tool.
Abstract
Landslide event inventories are a vital resource for landslide susceptibility and forecasting applications. However, landslide inventories can vary in accuracy, availability, and timeliness as a result of varying detection methods, reporting, and data availability. This study presents an approach to use publicly available satellite data and open-source software to automate a landslide detection process called the Sudden Landslide Identification Product (SLIP). SLIP utilizes optical data from the Landsat-8 Operational Land Imager sensor, elevation data from the Shuttle Radar Topography Mission, and precipitation data from the Global Precipitation Measurement mission to create a reproducible and spatially customizable landslide identification product. The SLIP software applies change-detection algorithms to identify areas of new bare-earth exposures that may be landslide events. The study also presents a precipitation monitoring tool that runs alongside SLIP called the Detecting Real-Time Increased Precipitation (DRIP) model that helps to identify the timing of potential landslide events detected by SLIP. Using SLIP and DRIP together, landslide detection is improved by reducing problems related to accuracy, availability, and timeliness that are prevalent in the state of the art for landslide detection. A case study and validation exercise in Nepal were performed for images acquired between 2014 and 2015. Preliminary validation results suggest 56% model accuracy, with errors of commission often resulting from newly cleared agricultural areas. These results suggest that SLIP is an important first attempt in an automated framework that can be used for medium-resolution regional landslide detection, although it requires refinement before being fully realized as an operational tool.
Abstract
Trends and transitions in the growing-season normalized difference vegetation index (NDVI) from the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite sensor at 250-m resolution were analyzed for the period from 2000 to 2018 to understand recent patterns of vegetation change in ecosystems of the Arctic National Wildlife Refuge (ANWR) in Alaska. Statistical analysis of changes in the NDVI time series was conducted using the breaks for additive seasonal and trend method (BFAST). This structural change analysis indicated that NDVI breakpoints and negative 18-yr trends in vegetation greenness over the years since 2000 could be explained in large part by the impacts of severe wildfires. At least one NDVI breakpoint was detected in around 20% of the MODIS pixels within both the Porcupine River and Coleen River basins of the study area. The vast majority of vegetation cover in the ANWR Brooks Range and coastal plain ecoregions was detected with no (positive or negative) growing-season NDVI trends since the year 2000. Results suggested that most negative NDVI anomalies in the 18-yr MODIS record have been associated with early spring thawing and elevated levels of surface moisture in low-elevation drainages of the northern ANWR ecoregions.
Abstract
Trends and transitions in the growing-season normalized difference vegetation index (NDVI) from the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite sensor at 250-m resolution were analyzed for the period from 2000 to 2018 to understand recent patterns of vegetation change in ecosystems of the Arctic National Wildlife Refuge (ANWR) in Alaska. Statistical analysis of changes in the NDVI time series was conducted using the breaks for additive seasonal and trend method (BFAST). This structural change analysis indicated that NDVI breakpoints and negative 18-yr trends in vegetation greenness over the years since 2000 could be explained in large part by the impacts of severe wildfires. At least one NDVI breakpoint was detected in around 20% of the MODIS pixels within both the Porcupine River and Coleen River basins of the study area. The vast majority of vegetation cover in the ANWR Brooks Range and coastal plain ecoregions was detected with no (positive or negative) growing-season NDVI trends since the year 2000. Results suggested that most negative NDVI anomalies in the 18-yr MODIS record have been associated with early spring thawing and elevated levels of surface moisture in low-elevation drainages of the northern ANWR ecoregions.
Abstract
The Iowa Atmospheric Observatory was established to better understand the unique microclimate characteristics of a wind farm. The facility consists of a pair of 120-m towers identically instrumented to observe basic landscape–atmosphere interactions in a highly managed agricultural landscape. The towers, one within and one outside of a utility-scale low-density-array wind farm, are equipped to measure vertical profiles of temperature, wind, moisture, and pressure and can host specialized sensors for a wide range of environmental conditions. Tower measurements during the 2016 growing season demonstrate the ability to distinguish microclimate differences created by single or multiple turbines from natural conditions over homogeneous agricultural fields. Microclimate differences between the two towers are reported as contrasts in normalized wind speed, normalized turbulence intensity, potential temperature, and water vapor mixing ratio. Differences are analyzed according to conditions of no wind farm influence (i.e., no wake) versus wind farm influence (i.e., waked flow) with distance downwind from a single wind turbine or a large group of turbines. Differences are also determined for more specific atmospheric conditions according to thermal stratification. Results demonstrate agreement with most, but not all, currently available numerical flow-field simulations of large wind farm arrays and of individual turbines. In particular, the well-documented higher nighttime surface temperature in wind farms is examined in vertical profiles that confirm this effect to be a “suppression of cooling” rather than a warming process. A summary is provided of how the wind farm boundary layer differs from the natural boundary layer derived from concurrent measurements over the summer of 2016.
Abstract
The Iowa Atmospheric Observatory was established to better understand the unique microclimate characteristics of a wind farm. The facility consists of a pair of 120-m towers identically instrumented to observe basic landscape–atmosphere interactions in a highly managed agricultural landscape. The towers, one within and one outside of a utility-scale low-density-array wind farm, are equipped to measure vertical profiles of temperature, wind, moisture, and pressure and can host specialized sensors for a wide range of environmental conditions. Tower measurements during the 2016 growing season demonstrate the ability to distinguish microclimate differences created by single or multiple turbines from natural conditions over homogeneous agricultural fields. Microclimate differences between the two towers are reported as contrasts in normalized wind speed, normalized turbulence intensity, potential temperature, and water vapor mixing ratio. Differences are analyzed according to conditions of no wind farm influence (i.e., no wake) versus wind farm influence (i.e., waked flow) with distance downwind from a single wind turbine or a large group of turbines. Differences are also determined for more specific atmospheric conditions according to thermal stratification. Results demonstrate agreement with most, but not all, currently available numerical flow-field simulations of large wind farm arrays and of individual turbines. In particular, the well-documented higher nighttime surface temperature in wind farms is examined in vertical profiles that confirm this effect to be a “suppression of cooling” rather than a warming process. A summary is provided of how the wind farm boundary layer differs from the natural boundary layer derived from concurrent measurements over the summer of 2016.
Abstract
Changes in vegetation are known to have an impact on climate via biogeophysical effects such as changes in albedo and heat fluxes. Here, the effects of maximum afforestation and deforestation are studied over Europe. This is done by comparing three regional climate model simulations—one with present-day vegetation, one with maximum afforestation, and one with maximum deforestation. In general, afforestation leads to more evapotranspiration (ET), which leads to decreased near-surface temperature, whereas deforestation leads to less ET, which leads to increased temperature. There are exceptions, mainly in regions with little water available for ET. In such regions, changes in albedo are relatively more important for temperature. The simulated biogeophysical effect on seasonal mean temperature varies between 0.5° and 3°C across Europe. The effect on minimum and maximum temperature is larger than that on mean temperature. Increased (decreased) mean temperature is associated with an even larger increase (decrease) in maximum summer (minimum winter) temperature. The effect on precipitation is found to be small. Two additional simulations in which vegetation is changed in only one-half of the domain were also performed. These simulations show that the climatic effects from changed vegetation in Europe are local. The results imply that vegetation changes have had, and will have, a significant impact on local climate in Europe; the climatic response is comparable to climate change under RCP2.6. Therefore, effects from vegetation change should be taken into account when simulating past, present, and future climate for this region. The results also imply that vegetation changes could be used to mitigate local climate change.
Abstract
Changes in vegetation are known to have an impact on climate via biogeophysical effects such as changes in albedo and heat fluxes. Here, the effects of maximum afforestation and deforestation are studied over Europe. This is done by comparing three regional climate model simulations—one with present-day vegetation, one with maximum afforestation, and one with maximum deforestation. In general, afforestation leads to more evapotranspiration (ET), which leads to decreased near-surface temperature, whereas deforestation leads to less ET, which leads to increased temperature. There are exceptions, mainly in regions with little water available for ET. In such regions, changes in albedo are relatively more important for temperature. The simulated biogeophysical effect on seasonal mean temperature varies between 0.5° and 3°C across Europe. The effect on minimum and maximum temperature is larger than that on mean temperature. Increased (decreased) mean temperature is associated with an even larger increase (decrease) in maximum summer (minimum winter) temperature. The effect on precipitation is found to be small. Two additional simulations in which vegetation is changed in only one-half of the domain were also performed. These simulations show that the climatic effects from changed vegetation in Europe are local. The results imply that vegetation changes have had, and will have, a significant impact on local climate in Europe; the climatic response is comparable to climate change under RCP2.6. Therefore, effects from vegetation change should be taken into account when simulating past, present, and future climate for this region. The results also imply that vegetation changes could be used to mitigate local climate change.
Abstract
In this article, the effect of different weather parameters on the mean height and the variability of the protein content in winter wheat is investigated. The analysis is based on the proteins of 148 800 wheat deliveries in Mecklenburg-Western Pomerania during 2004–15. From April to July, the forecast model was estimated with the following weather parameters: temperature sum, daily temperature range, precipitation, and sunshine duration. A Just and Pope function was estimated as a random intercept model. In addition to the weather parameters, a dummy variable is integrated into the forecast model to record differences in quality between A and B wheat varieties. The results show that 76.5% of the annual variability of the mean protein content can be explained on the basis of these weather parameters. In contrast, weather variables can only explain a small part of the variance in protein content per se.
Abstract
In this article, the effect of different weather parameters on the mean height and the variability of the protein content in winter wheat is investigated. The analysis is based on the proteins of 148 800 wheat deliveries in Mecklenburg-Western Pomerania during 2004–15. From April to July, the forecast model was estimated with the following weather parameters: temperature sum, daily temperature range, precipitation, and sunshine duration. A Just and Pope function was estimated as a random intercept model. In addition to the weather parameters, a dummy variable is integrated into the forecast model to record differences in quality between A and B wheat varieties. The results show that 76.5% of the annual variability of the mean protein content can be explained on the basis of these weather parameters. In contrast, weather variables can only explain a small part of the variance in protein content per se.
Abstract
Climate warming is expected to disproportionately affect crop yields in the southern United States due to excessive heat stress, while presenting new farming opportunities through a longer growing season farther north. Few studies have investigated the impact of this warming on agro-climate indices that link meteorological data with important field dates in northern regions. This study employs regional dynamical downscaling using the Weather Research and Forecasting (WRF) Model to assess changes in growing season length (GSL), spring planting dates, and occurrences of plant heat stress (PHS) for five regions in Alaska. Differences between future representative concentration pathway 8.5 (RCP8.5; 2011–40, 2041–70, 2071–2100) and historical (1981–2010) periods are obtained using boundary forcing from the Geophysical Fluid Dynamics Laboratory Climate Model, version 3, and the NCAR Community Climate System Model, version 4. The model output is bias corrected using ERA-Interim. Median GSL shows increases of 48–87 days by 2071–2100, with the largest changes in northern Alaska. Similarly, by 2071–2100, planting dates advance 2–4 weeks, and PHS days increase from near 0 to 5–10 instances per summer in the hottest areas. The largest GSL changes occur in the mid- (2041–70) and late century (2071–2100), when a warming signal emerges from the historical interannual variability. These periods coincide with the greatest divergence of the RCPs, suggesting that near-term decision-making may affect substantial future changes. Early-century (2011–40) projections show median GSL increases of 8–27 days, which is close to the historical standard deviation of GSL. Thus, internal variability will remain an important source of uncertainty into the midcentury, despite a trend for longer growing seasons.
Abstract
Climate warming is expected to disproportionately affect crop yields in the southern United States due to excessive heat stress, while presenting new farming opportunities through a longer growing season farther north. Few studies have investigated the impact of this warming on agro-climate indices that link meteorological data with important field dates in northern regions. This study employs regional dynamical downscaling using the Weather Research and Forecasting (WRF) Model to assess changes in growing season length (GSL), spring planting dates, and occurrences of plant heat stress (PHS) for five regions in Alaska. Differences between future representative concentration pathway 8.5 (RCP8.5; 2011–40, 2041–70, 2071–2100) and historical (1981–2010) periods are obtained using boundary forcing from the Geophysical Fluid Dynamics Laboratory Climate Model, version 3, and the NCAR Community Climate System Model, version 4. The model output is bias corrected using ERA-Interim. Median GSL shows increases of 48–87 days by 2071–2100, with the largest changes in northern Alaska. Similarly, by 2071–2100, planting dates advance 2–4 weeks, and PHS days increase from near 0 to 5–10 instances per summer in the hottest areas. The largest GSL changes occur in the mid- (2041–70) and late century (2071–2100), when a warming signal emerges from the historical interannual variability. These periods coincide with the greatest divergence of the RCPs, suggesting that near-term decision-making may affect substantial future changes. Early-century (2011–40) projections show median GSL increases of 8–27 days, which is close to the historical standard deviation of GSL. Thus, internal variability will remain an important source of uncertainty into the midcentury, despite a trend for longer growing seasons.
Abstract
Land–atmosphere interactions play a critical role in the Earth system, and a better understanding of these interactions could improve weather and climate models. The interaction among drought, vegetation productivity, and land cover is of particular significance. In a semiarid environment, such as the U.S. Great Plains, droughts can have a large influence on the productivity of agriculture and grasslands, with serious environmental and economic impacts. Here, we used the vegetation drought response index (VegDRI) drought indicator to investigate the response of vegetation to weather and climate for land-cover types in the Great Plains in the United States from 1989 to 2012. We found that analysis that focused on land-cover types within ecoregion divisions provided substantially more and land-cover-based detail on the timing and intensity of drought than did summarizing across the entire Great Plains region. In the northern Great Plains, VegDRI measured more frequent drought impacts on vegetation in the western ecoregions than in the eastern ecoregions. Across the ecoregions of the Great Plains, drought impacts on vegetation were more commonly found in grassland than in cropland. For example, in the “Northwestern Great Plains” ecoregion (which encompasses areas of Montana, Wyoming, North Dakota, South Dakota, and Nebraska), grassland and nonirrigated cropland were observed in VegDRI to have historical fractional drought coverages in the growing season of 17% and 11%, respectively.
Abstract
Land–atmosphere interactions play a critical role in the Earth system, and a better understanding of these interactions could improve weather and climate models. The interaction among drought, vegetation productivity, and land cover is of particular significance. In a semiarid environment, such as the U.S. Great Plains, droughts can have a large influence on the productivity of agriculture and grasslands, with serious environmental and economic impacts. Here, we used the vegetation drought response index (VegDRI) drought indicator to investigate the response of vegetation to weather and climate for land-cover types in the Great Plains in the United States from 1989 to 2012. We found that analysis that focused on land-cover types within ecoregion divisions provided substantially more and land-cover-based detail on the timing and intensity of drought than did summarizing across the entire Great Plains region. In the northern Great Plains, VegDRI measured more frequent drought impacts on vegetation in the western ecoregions than in the eastern ecoregions. Across the ecoregions of the Great Plains, drought impacts on vegetation were more commonly found in grassland than in cropland. For example, in the “Northwestern Great Plains” ecoregion (which encompasses areas of Montana, Wyoming, North Dakota, South Dakota, and Nebraska), grassland and nonirrigated cropland were observed in VegDRI to have historical fractional drought coverages in the growing season of 17% and 11%, respectively.
Abstract
Classifying “urban” and “rural” environments is a challenge in understanding urban climate, specifically urban heat islands (UHIs). Stewart and Oke developed the “local climate zone” (LCZ) classification system to clarify these distinctions using 17 unique groups. This system has been applied to many areas around the world, but few studies have attempted to utilize them to detect UHI effects in smaller cities. Our aim was to use the LCZ classification system 1) to detect UHI in two small cities in Alabama and 2) to determine whether similar zones experienced similar intensity or magnitude of UHIs. For 1 week, we monitored hourly temperature in two cities, in four zones: compact low-rise, open low-rise, dense forests, and water. We found that urban zones were often warmer for overall, daytime, and nighttime temperatures relative to rural zones (from −0.1° to 2.8°C). In addition, we found that temperatures between cities in similar zones were not very similar, indicating that the LCZ system does not predict UHI intensity equally in places with similar background climates. We found that the LCZ classification system was easy to use, and we recognize its potential as a tool for urban ecologists and urban planners.
Abstract
Classifying “urban” and “rural” environments is a challenge in understanding urban climate, specifically urban heat islands (UHIs). Stewart and Oke developed the “local climate zone” (LCZ) classification system to clarify these distinctions using 17 unique groups. This system has been applied to many areas around the world, but few studies have attempted to utilize them to detect UHI effects in smaller cities. Our aim was to use the LCZ classification system 1) to detect UHI in two small cities in Alabama and 2) to determine whether similar zones experienced similar intensity or magnitude of UHIs. For 1 week, we monitored hourly temperature in two cities, in four zones: compact low-rise, open low-rise, dense forests, and water. We found that urban zones were often warmer for overall, daytime, and nighttime temperatures relative to rural zones (from −0.1° to 2.8°C). In addition, we found that temperatures between cities in similar zones were not very similar, indicating that the LCZ system does not predict UHI intensity equally in places with similar background climates. We found that the LCZ classification system was easy to use, and we recognize its potential as a tool for urban ecologists and urban planners.
Abstract
California’s winter storms produce intense rainfall capable of triggering shallow landslides, threatening lives and infrastructure. This study explores where hourly rainfall in the state meets or exceeds published values thought to trigger landslides after crossing a seasonal antecedent precipitation threshold. We answer the following questions: 1) Where in California are overthreshold events most common? 2) How are events distributed within the cool season (October–May) and interannually? 3) Are these events related to atmospheric rivers? To do this, we compile and quality control hourly precipitation data over a 22-yr period for 147 Remote Automated Weather Stations (RAWS). Stations in the Transverse and Coast Ranges and portions of the northwestern Sierra Nevada have the greatest number of rainfall events exceeding thresholds. Atmospheric rivers coincide with 60%–90% of these events. Overthreshold events tend to occur in the climatological wettest month of the year, and they commonly occur multiple times within a storm. These statewide maps depict where to expect intense rainfalls that have historically triggered shallow landslides. They predict that some areas of California are less susceptible to storm-driven landslides solely because high-intensity rainfall is unlikely.
Abstract
California’s winter storms produce intense rainfall capable of triggering shallow landslides, threatening lives and infrastructure. This study explores where hourly rainfall in the state meets or exceeds published values thought to trigger landslides after crossing a seasonal antecedent precipitation threshold. We answer the following questions: 1) Where in California are overthreshold events most common? 2) How are events distributed within the cool season (October–May) and interannually? 3) Are these events related to atmospheric rivers? To do this, we compile and quality control hourly precipitation data over a 22-yr period for 147 Remote Automated Weather Stations (RAWS). Stations in the Transverse and Coast Ranges and portions of the northwestern Sierra Nevada have the greatest number of rainfall events exceeding thresholds. Atmospheric rivers coincide with 60%–90% of these events. Overthreshold events tend to occur in the climatological wettest month of the year, and they commonly occur multiple times within a storm. These statewide maps depict where to expect intense rainfalls that have historically triggered shallow landslides. They predict that some areas of California are less susceptible to storm-driven landslides solely because high-intensity rainfall is unlikely.
Abstract
Better quantification of the spatiotemporal distribution of soil moisture across different spatial scales contributes significantly to the understanding of land surface processes on the Earth as an integrated system. While observational data for root-zone soil moisture (RZSM) often have sparse spatial coverage, model-simulated soil moisture may provide a useful alternative. TOPMODEL-Based Land Surface–Atmosphere Transfer Scheme (TOPLATS) has been widely studied and actively modified in recent years, while a detailed regional application with evaluation currently is still lacking. Thus, TOPLATS was used to generate high-resolution (30 arc s) RZSM based on coarse-scale (0.125°) forcing data over part of the Arkansas–Red River basin. First, the simulated RZSM was resampled to coarse scale to compare with the results of Mosaic, Noah, and VIC from NLDAS. Second, TOPLATS performance was assessed based on the spatial absolute difference among the models. The comparison shows that TOPLATS performance is similar to VIC, but different from Mosaic and Noah. Last, the simulated RZSM was compared with in situ observations of 16 stations in the study area. The results suggest that the simulated spatial distribution of RZSM is largely consistent with the distribution of topographic index (TI) in most instances, as topography was traditionally considered a major, but not the only, factor in horizontal redistribution of soil moisture. In addition, the finer-resolution RZSM can reflect the in situ soil moisture change at most local sites to a certain degree. The evaluation confirms that TOPLATS is a useful tool to estimate high-resolution soil moisture and has great potential to provide regional soil moisture estimates.
Abstract
Better quantification of the spatiotemporal distribution of soil moisture across different spatial scales contributes significantly to the understanding of land surface processes on the Earth as an integrated system. While observational data for root-zone soil moisture (RZSM) often have sparse spatial coverage, model-simulated soil moisture may provide a useful alternative. TOPMODEL-Based Land Surface–Atmosphere Transfer Scheme (TOPLATS) has been widely studied and actively modified in recent years, while a detailed regional application with evaluation currently is still lacking. Thus, TOPLATS was used to generate high-resolution (30 arc s) RZSM based on coarse-scale (0.125°) forcing data over part of the Arkansas–Red River basin. First, the simulated RZSM was resampled to coarse scale to compare with the results of Mosaic, Noah, and VIC from NLDAS. Second, TOPLATS performance was assessed based on the spatial absolute difference among the models. The comparison shows that TOPLATS performance is similar to VIC, but different from Mosaic and Noah. Last, the simulated RZSM was compared with in situ observations of 16 stations in the study area. The results suggest that the simulated spatial distribution of RZSM is largely consistent with the distribution of topographic index (TI) in most instances, as topography was traditionally considered a major, but not the only, factor in horizontal redistribution of soil moisture. In addition, the finer-resolution RZSM can reflect the in situ soil moisture change at most local sites to a certain degree. The evaluation confirms that TOPLATS is a useful tool to estimate high-resolution soil moisture and has great potential to provide regional soil moisture estimates.
