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Abstract
The primary aim of this work is to study the response of the normalized difference vegetation index (NDVI) of landscapes in the lower Tigris basin to current global and regional climate variability presented, respectively, by the global circulation indices and monthly temperatures and precipitation extracted from five observational/reanalysis datasets. The second task is to find the dataset that best reflects the regional vegetation and climate conditions. Comparison of the Köppen–Trewartha bioclimatic landscapes with the positions of botanical districts, land-cover types, and streamflow estimates led to the conclusion that only two datasets correctly describe regional climatic zones. Therefore, searching for the NDVI response to regional climate variability requires the use of normalized analogs of temperatures and precipitations, as well as the Spearman rank correlation. We found that March/April NDVI, as proxies of the maximum biological productivity of the regional landscapes, are strongly correlated with October–March precipitation derived from three datasets and January–March temperatures derived from one dataset. We discovered the significant impact of autumn–winter El Niño–Southern Oscillation and winter Indian Oceanic dipole states on regional weather (e.g., all five recent severe droughts occurred during strong La Niña events). However, the strength of this impact on the vegetation was clearly linked to the zonal landscape type. By selecting pairs of the temperature/precipitation time series that best correlated with NDVI at a given landscape, we have built a synthetic climate dataset. The landscape approach presented in this work can be used to validate the viability of any dataset when assessing the impacts of climate change and variability on weather-dependent components of Earth’s surface.
Abstract
The primary aim of this work is to study the response of the normalized difference vegetation index (NDVI) of landscapes in the lower Tigris basin to current global and regional climate variability presented, respectively, by the global circulation indices and monthly temperatures and precipitation extracted from five observational/reanalysis datasets. The second task is to find the dataset that best reflects the regional vegetation and climate conditions. Comparison of the Köppen–Trewartha bioclimatic landscapes with the positions of botanical districts, land-cover types, and streamflow estimates led to the conclusion that only two datasets correctly describe regional climatic zones. Therefore, searching for the NDVI response to regional climate variability requires the use of normalized analogs of temperatures and precipitations, as well as the Spearman rank correlation. We found that March/April NDVI, as proxies of the maximum biological productivity of the regional landscapes, are strongly correlated with October–March precipitation derived from three datasets and January–March temperatures derived from one dataset. We discovered the significant impact of autumn–winter El Niño–Southern Oscillation and winter Indian Oceanic dipole states on regional weather (e.g., all five recent severe droughts occurred during strong La Niña events). However, the strength of this impact on the vegetation was clearly linked to the zonal landscape type. By selecting pairs of the temperature/precipitation time series that best correlated with NDVI at a given landscape, we have built a synthetic climate dataset. The landscape approach presented in this work can be used to validate the viability of any dataset when assessing the impacts of climate change and variability on weather-dependent components of Earth’s surface.
Abstract
The 2010 eruption of Eyjafjallajökull produced volcanic ash that was mostly deposited to the south and east of the volcano, with the thickest deposits closest to the eruption vents. For months following the eruption there were numerous reports of resuspended volcanic ash made by weather observers on the ground. A saltation sensor (SENSIT) and an optical particle counter (OPC) located on the southern side of Eyjafjallajökull measured posteruptive particulate matter (PM) saltation and suspension events, some of which were also observable by satellite imagery. During the autumn/winter following the eruption, visible satellite images and the SENSIT show that PM measured by the OPC was only detected when winds had a northerly component, making the source on the slopes of Eyjafjallajökull. During the largest observed events, particles >10 μm were suspended but measured in extremely low concentrations (<1 particle per centimeter cubed). The saltation measurements, however, show high concentrations of particles >100 μm in size during these events. During the largest events, winds were at least 5 m s−1 with a relative humidity < 70%. Ground conditions in Iceland change quickly from unfavorable to favorable for the suspension of particles. It is hypothesized that this is due to the porosity of the surface material allowing water to filter through quickly as well as the fast drying time of surface material. The high moisture content of the atmosphere and the ground do not appear to be a deterrent for large PM events to occur in Iceland.
Abstract
The 2010 eruption of Eyjafjallajökull produced volcanic ash that was mostly deposited to the south and east of the volcano, with the thickest deposits closest to the eruption vents. For months following the eruption there were numerous reports of resuspended volcanic ash made by weather observers on the ground. A saltation sensor (SENSIT) and an optical particle counter (OPC) located on the southern side of Eyjafjallajökull measured posteruptive particulate matter (PM) saltation and suspension events, some of which were also observable by satellite imagery. During the autumn/winter following the eruption, visible satellite images and the SENSIT show that PM measured by the OPC was only detected when winds had a northerly component, making the source on the slopes of Eyjafjallajökull. During the largest observed events, particles >10 μm were suspended but measured in extremely low concentrations (<1 particle per centimeter cubed). The saltation measurements, however, show high concentrations of particles >100 μm in size during these events. During the largest events, winds were at least 5 m s−1 with a relative humidity < 70%. Ground conditions in Iceland change quickly from unfavorable to favorable for the suspension of particles. It is hypothesized that this is due to the porosity of the surface material allowing water to filter through quickly as well as the fast drying time of surface material. The high moisture content of the atmosphere and the ground do not appear to be a deterrent for large PM events to occur in Iceland.
Abstract
The occurrence of water deficit is intensified in lowland soils. Generating information with regard to its risk of occurrence is essential to avoid seed yield losses. The objective of this study was to determine the probability of water deficit in soybean cultivated in lowlands of the Vacacaí and Piratini River basins in the southern portion of Rio Grande do Sul in Brazil as a function of the sowing date. Soybean development was simulated considering three sets of cultivars of relative maturity groups (RMG) delimited by 5.9–6.8, 6.9–7.3, and 7.4–8.0, with a 10-day interval between the sowing dates making up the period between 21 September and 31 December. Daily meteorological data were used from 1971 to 2017 obtained from the Pelotas meteorological station and from 1968 to 2017 from the Santa Maria meteorological station. Water deficit (mm) in the subperiods and soybean development cycle was obtained from the calculation of evapotranspiration and daily sequential crop water balance. Data of water deficit were subjected to a probability distribution analysis, in which the exponential, gamma, lognormal, normal, and Weibull probability density function (pdf) adjustments were tested using chi-square and Kolmogorov–Smirnov adhesion tests, with a 10% significance level. The water deficit is lower in the Pelotas region than in Santa Maria. Sowings performed from 11 and 1 November present the lowest risk of occurrence of water deficit throughout the soybean cycle in Santa Maria and Pelotas, respectively. Risk of water deficit decreases for the beginning of flowering–beginning of seed (R1–R5) subperiod when soybean sowing occurs from the beginning of November.
Abstract
The occurrence of water deficit is intensified in lowland soils. Generating information with regard to its risk of occurrence is essential to avoid seed yield losses. The objective of this study was to determine the probability of water deficit in soybean cultivated in lowlands of the Vacacaí and Piratini River basins in the southern portion of Rio Grande do Sul in Brazil as a function of the sowing date. Soybean development was simulated considering three sets of cultivars of relative maturity groups (RMG) delimited by 5.9–6.8, 6.9–7.3, and 7.4–8.0, with a 10-day interval between the sowing dates making up the period between 21 September and 31 December. Daily meteorological data were used from 1971 to 2017 obtained from the Pelotas meteorological station and from 1968 to 2017 from the Santa Maria meteorological station. Water deficit (mm) in the subperiods and soybean development cycle was obtained from the calculation of evapotranspiration and daily sequential crop water balance. Data of water deficit were subjected to a probability distribution analysis, in which the exponential, gamma, lognormal, normal, and Weibull probability density function (pdf) adjustments were tested using chi-square and Kolmogorov–Smirnov adhesion tests, with a 10% significance level. The water deficit is lower in the Pelotas region than in Santa Maria. Sowings performed from 11 and 1 November present the lowest risk of occurrence of water deficit throughout the soybean cycle in Santa Maria and Pelotas, respectively. Risk of water deficit decreases for the beginning of flowering–beginning of seed (R1–R5) subperiod when soybean sowing occurs from the beginning of November.
Abstract
Sustainable management of biodiversity requires a thorough understanding of local climate and weather, particularly in areas where ecosystems have been degraded and where life is highly adapted to or dependent on narrow ecological niches. Furthermore, society, economy, and culture of urban agglomerations are directly affected by the quality and quantity of services provided by adjacent ecosystems, which makes knowledge of regional characteristics and impact of climate variability crucial. Here, we present precipitation data from six meteorological stations spread across several orographic zones of the eastern Andes in the surroundings of Bogotá, Colombia’s biggest urban agglomeration. The time series of rainfall data are analyzed statistically, examined regarding the occurrence of cyclicity in relation to ENSO, and correlated to the multivariate El Niño–Southern Oscillation index (MEI). Results offer no conclusive ENSO-related cycles but show that data of most of the stations are marked by annual or semestral cyclicity. There is no straightforward correlation between MEI and monthly precipitation values, and neither filtered nor lagged values showed any conclusive and significant correlation. Stations within the same orographic zones do not necessarily bring forth comparable statistical results. Temporal and spatial properties of precipitation appear to result from micro- and mesoscale topoclimates rather than from ENSO variability.
Abstract
Sustainable management of biodiversity requires a thorough understanding of local climate and weather, particularly in areas where ecosystems have been degraded and where life is highly adapted to or dependent on narrow ecological niches. Furthermore, society, economy, and culture of urban agglomerations are directly affected by the quality and quantity of services provided by adjacent ecosystems, which makes knowledge of regional characteristics and impact of climate variability crucial. Here, we present precipitation data from six meteorological stations spread across several orographic zones of the eastern Andes in the surroundings of Bogotá, Colombia’s biggest urban agglomeration. The time series of rainfall data are analyzed statistically, examined regarding the occurrence of cyclicity in relation to ENSO, and correlated to the multivariate El Niño–Southern Oscillation index (MEI). Results offer no conclusive ENSO-related cycles but show that data of most of the stations are marked by annual or semestral cyclicity. There is no straightforward correlation between MEI and monthly precipitation values, and neither filtered nor lagged values showed any conclusive and significant correlation. Stations within the same orographic zones do not necessarily bring forth comparable statistical results. Temporal and spatial properties of precipitation appear to result from micro- and mesoscale topoclimates rather than from ENSO variability.
Abstract
The Colorado River basin (CRB) supplies water to approximately 40 million people and is essential to hydropower generation, agriculture, and industry. In this study, a monthly water balance model is used to compute hydroclimatic water balance components (i.e., potential evapotranspiration, actual evapotranspiration, and runoff) for the period 1901–2014 across the entire CRB. The time series of monthly runoff is aggregated to compute water-year runoff and then used to identify drought periods in the basin. For the 1901–2014 period, eight basinwide drought periods were identified. The driest drought period spanned years 1901–04, whereas the longest drought period occurred during 1943–56. The eight droughts were primarily driven by winter precipitation deficits rather than warm temperature anomalies. In addition, an analysis of prehistoric drought for the CRB—computed using tree-ring-based reconstructions of the Palmer drought severity index—indicates that during some past centuries drought frequency was higher than during the twentieth century and that some centuries experienced droughts that were much longer than those during the twentieth century. More frequent or longer droughts than those that occurred during the twentieth century, combined with continued warming associated with climate change, may lead to substantial future water deficits in the CRB.
Abstract
The Colorado River basin (CRB) supplies water to approximately 40 million people and is essential to hydropower generation, agriculture, and industry. In this study, a monthly water balance model is used to compute hydroclimatic water balance components (i.e., potential evapotranspiration, actual evapotranspiration, and runoff) for the period 1901–2014 across the entire CRB. The time series of monthly runoff is aggregated to compute water-year runoff and then used to identify drought periods in the basin. For the 1901–2014 period, eight basinwide drought periods were identified. The driest drought period spanned years 1901–04, whereas the longest drought period occurred during 1943–56. The eight droughts were primarily driven by winter precipitation deficits rather than warm temperature anomalies. In addition, an analysis of prehistoric drought for the CRB—computed using tree-ring-based reconstructions of the Palmer drought severity index—indicates that during some past centuries drought frequency was higher than during the twentieth century and that some centuries experienced droughts that were much longer than those during the twentieth century. More frequent or longer droughts than those that occurred during the twentieth century, combined with continued warming associated with climate change, may lead to substantial future water deficits in the CRB.
Abstract
Irrigation has the potential to modify local weather and regional climate through a repartitioning of water among the surface, soil, and atmosphere with the potential to drastically change the terrestrial energy budget in agricultural areas. This study uses local observations, satellite remote sensing, and numerical modeling to 1) explore whether irrigation has historically impacted summer maximum temperatures in the Columbia Plateau, 2) characterize the current extent of irrigation impacts to soil moisture (SM) and land surface temperature (LST), and 3) better understand the downstream extent of irrigation’s influence on near-surface temperature, humidity, and boundary layer development. Analysis of historical daily maximum temperature (TMAX) observations showed that the three Global Historical Climate Network (GHCN) sites downwind of Columbia Basin Project (CBP) irrigation experienced statistically significant cooling of the mean summer TMAX by 0.8°–1.6°C in the post-CBP (1968–98) as compared to pre-CBP expansion (1908–38) period, opposite the background climate signal. Remote sensing observations of soil moisture and land surface temperatures in more recent years show wetter soil (~18%–25%) and cooler land surface temperatures over the irrigated areas. Simulations using NASA’s Land Information System (LIS) coupled to the Weather Research and Forecasting (WRF) Model support the historical analysis, confirming that under the most common summer wind flow regime, irrigation cooling can extend as far downwind as the locations of these stations. Taken together, these results suggest that irrigation expansion may have contributed to a reduction in summertime temperatures and heat extremes within and downwind of the CBP area. This supports a regional impact of irrigation across the study area.
Abstract
Irrigation has the potential to modify local weather and regional climate through a repartitioning of water among the surface, soil, and atmosphere with the potential to drastically change the terrestrial energy budget in agricultural areas. This study uses local observations, satellite remote sensing, and numerical modeling to 1) explore whether irrigation has historically impacted summer maximum temperatures in the Columbia Plateau, 2) characterize the current extent of irrigation impacts to soil moisture (SM) and land surface temperature (LST), and 3) better understand the downstream extent of irrigation’s influence on near-surface temperature, humidity, and boundary layer development. Analysis of historical daily maximum temperature (TMAX) observations showed that the three Global Historical Climate Network (GHCN) sites downwind of Columbia Basin Project (CBP) irrigation experienced statistically significant cooling of the mean summer TMAX by 0.8°–1.6°C in the post-CBP (1968–98) as compared to pre-CBP expansion (1908–38) period, opposite the background climate signal. Remote sensing observations of soil moisture and land surface temperatures in more recent years show wetter soil (~18%–25%) and cooler land surface temperatures over the irrigated areas. Simulations using NASA’s Land Information System (LIS) coupled to the Weather Research and Forecasting (WRF) Model support the historical analysis, confirming that under the most common summer wind flow regime, irrigation cooling can extend as far downwind as the locations of these stations. Taken together, these results suggest that irrigation expansion may have contributed to a reduction in summertime temperatures and heat extremes within and downwind of the CBP area. This supports a regional impact of irrigation across the study area.
Abstract
Woody plant cover, the area of the vertical projection of woody plants (trees, shrubs, and bushes), plays an important role in the structure and function of savanna ecosystems and is needed by the savanna modeling community. Recent problems facing savanna ecosystems such as woody plant encroachment and subsequent habitat fragmentation further underscore the relevance of regional-scale and even larger-scale woody plant cover mapping. The mixture of woody plants and herbaceous vegetation in savanna landscapes lends woody plant cover mapping to fractional representation. This study endeavors to develop a simple and reliable approach for fractional woody plant cover mapping in savanna ecosystems. It was tested in the savanna of central Texas, which features a wide woody plant density gradation. A multiple linear regression model was calibrated between orthophoto-based fractional woody plant cover and metrics derived from time series MODIS products of surface reflectance (MOD09A1) and fraction of photosynthetically active radiation (MOD15A2H). By applying this model, woody plant cover was extrapolated to Texas savanna at MODIS scale (500 m). Validation suggests a mean absolute error of 0.098 and an R-squared value of 0.60. This study demonstrates a potential approach for woody plant cover mapping in other savanna ecosystems of the world. It also highlights the utility of time series MODIS products in savanna woody plant cover estimation.
Abstract
Woody plant cover, the area of the vertical projection of woody plants (trees, shrubs, and bushes), plays an important role in the structure and function of savanna ecosystems and is needed by the savanna modeling community. Recent problems facing savanna ecosystems such as woody plant encroachment and subsequent habitat fragmentation further underscore the relevance of regional-scale and even larger-scale woody plant cover mapping. The mixture of woody plants and herbaceous vegetation in savanna landscapes lends woody plant cover mapping to fractional representation. This study endeavors to develop a simple and reliable approach for fractional woody plant cover mapping in savanna ecosystems. It was tested in the savanna of central Texas, which features a wide woody plant density gradation. A multiple linear regression model was calibrated between orthophoto-based fractional woody plant cover and metrics derived from time series MODIS products of surface reflectance (MOD09A1) and fraction of photosynthetically active radiation (MOD15A2H). By applying this model, woody plant cover was extrapolated to Texas savanna at MODIS scale (500 m). Validation suggests a mean absolute error of 0.098 and an R-squared value of 0.60. This study demonstrates a potential approach for woody plant cover mapping in other savanna ecosystems of the world. It also highlights the utility of time series MODIS products in savanna woody plant cover estimation.
Abstract
Clouds can modify terrestrial productivity by reducing total surface radiation and increasing diffuse radiation, which may be more evenly distributed through plant canopies and increase ecosystem carbon uptake (the “diffuse fertilization effect”). Previous work at ecosystem-level observational towers demonstrated that diffuse photosynthetically active radiation (PAR; 400–700 nm) increases with cloud optical thickness (COT) until a COT of approximately 10, defined here as the “low-COT regime.” To identify whether the low-COT regime also influences carbon uptake on broader spatial and longer temporal time scales, we use global, monthly data to investigate the influence of COT on carbon uptake in three land-cover types: shrublands, forests, and croplands. While there are limitations in global gross primary production (GPP) products, global COT data derived from Moderate Resolution Imaging Spectroradiometer (MODIS) reveal that during the growing season tropical and subtropical regions more frequently experience a monthly low-COT regime (>20% of the time) than other regions of the globe. Contrary to ecosystem-level studies, comparisons of monthly COT with monthly satellite-derived solar-induced chlorophyll fluorescence and modeled GPP indicate that, although carbon uptake generally increases with COT under the low-COT regime, the correlations between COT and carbon uptake are insignificant (p > 0.05) in shrublands, forests, and croplands at regional scales. When scaled globally, vegetated regions under the low-COT regime account for only 4.9% of global mean annual GPP, suggesting that clouds and their diffuse fertilization effect become less significant drivers of terrestrial carbon uptake at broader spatial and temporal scales.
Abstract
Clouds can modify terrestrial productivity by reducing total surface radiation and increasing diffuse radiation, which may be more evenly distributed through plant canopies and increase ecosystem carbon uptake (the “diffuse fertilization effect”). Previous work at ecosystem-level observational towers demonstrated that diffuse photosynthetically active radiation (PAR; 400–700 nm) increases with cloud optical thickness (COT) until a COT of approximately 10, defined here as the “low-COT regime.” To identify whether the low-COT regime also influences carbon uptake on broader spatial and longer temporal time scales, we use global, monthly data to investigate the influence of COT on carbon uptake in three land-cover types: shrublands, forests, and croplands. While there are limitations in global gross primary production (GPP) products, global COT data derived from Moderate Resolution Imaging Spectroradiometer (MODIS) reveal that during the growing season tropical and subtropical regions more frequently experience a monthly low-COT regime (>20% of the time) than other regions of the globe. Contrary to ecosystem-level studies, comparisons of monthly COT with monthly satellite-derived solar-induced chlorophyll fluorescence and modeled GPP indicate that, although carbon uptake generally increases with COT under the low-COT regime, the correlations between COT and carbon uptake are insignificant (p > 0.05) in shrublands, forests, and croplands at regional scales. When scaled globally, vegetated regions under the low-COT regime account for only 4.9% of global mean annual GPP, suggesting that clouds and their diffuse fertilization effect become less significant drivers of terrestrial carbon uptake at broader spatial and temporal scales.
