Climate Stations Research Articles

A Global Rain-Driven Soil Erosion Investigation Based on Simulated Breakpoint Precipitation

HighlightsAn international dataset of simulated breakpoint precipitation climate stations was used to overcome the limitations of fixed-interval precipitation in global soil erosion applications.The international simulated breakpoint dataset was validated against collocated high-quality, high-resolution precipitation data from a ground network and other data sources.The process-based Rangeland Hydrology and Erosion Model (RHEM) was used to predict erosion based on global climate classifications and soil properties.Critical precipitation factors in RHEM scenarios were identified based on their ability to predict erosion rates.Abstract. Recent research has highlighted problems with erosion modeling applications that use coarser fixed-interval precipitation data as opposed to breakpoint precipitation data, which better preserves precipitation characteristics such as intensity and duration. Due to their wider availability, erosion modeling applications and risk assessments are typically based on fixed-interval data. However, these applications could be subject to substantial erosion underestimation bias related to time-averaged precipitation. Alternatively, this manuscript presents a novel approach to global-scale erosion assessment based on simulated breakpoint precipitation data. A point-scale stochastic weather generator, CLIGEN, was used to generate precipitation events with characteristics more similar to breakpoint precipitation data than fixed-interval alternatives. An international CLIGEN dataset of climate parameters from more than 10,000 long-term climate stations in numerous countries was evaluated for use in parameterizing CLIGEN simulations. CLIGEN-simulated event characteristics and derived statistics such as annual rainfall erosivity were compared to high-quality, high-resolution NOAA-ASOS precipitation data where available. The Rangeland Hydrology and Erosion Model (RHEM) was used to predict runoff and soil loss based on the same CLIGEN inputs for simple bare soil scenarios with site-specific soil textures taken from the global 250m SoilGrids product. Erosion results were analyzed according to climate type, revealing that predicted distributions of sediment yield and runoff were statistically unique for most global climate types. A multivariate regression model was developed to explore and understand the importance of various precipitation input factors. Peak precipitation intensity was the most critical climate factor for determining sediment yield, and combined CLIGEN precipitation factors had approximately as much predictive power as soil texture. Average annual rainfall erosivity values were calculated for each location and were 25% and 20% greater than values from RUSLE2 and Panagos et al. (2017) estimates, respectively. This finding is in agreement with the latest research on the topic. Keywords: ASOS, CLIGEN, Erosivity, Global soil erosion, Machine learning, RHEM, Simulated breakpoint precipitation.

GNSS-R Snow Depth Inversion Based on Variational Mode Decomposition With Multi-GNSS Constellations

Snow depth monitoring is meaningful for climate analysis, hydrological research and snow disaster prevention. Global Navigation Satellite System-Reflectometry (GNSS-R) technology uses the relationship between the modulation frequency of the signal-to-noise ratio (SNR) and reflector height to monitor snow depth. Existing research on single constellation has made good progress and is gradually developing towards multi-constellation combined inversion. Aiming at the accuracy of snow depth inversion, this paper introduces the variational mode decomposition (VMD) algorithm with the characteristics of an adaptive high-pass filter to detrend the SNR data. The experimental results of KIRU station and P351 station show that VMD algorithm is suitable for different constellations and has better signal separation effect. The snow depth inversion results for both stations are in high agreement with the in-situ snow depths provided by the Swedish Meteorological and Hydrological Institute (SMHI) and the SNOTEL network, respectively. The root mean square error (RMSE) of the inversion results is reduced by 20-40% compared to the least squares fitting (LSF) algorithm, and the correlation coefficients are also greatly improved. Moreover, considering that there is no overlap between the climate station and the inversion area, this paper introduces the maximum spectral amplitude as another reference data source and obtains basically consistent experimental conclusions. On this basis, the maximum spectral amplitude is used as the input variable of the entropy method, and the feasibility of the combination strategy is studied. The results show that the combined strategy reduces a little inversion error and improves the temporal resolution of snow depth monitoring. It is of great significance for more accurate and rapid monitoring of snow depth changes and disaster warnings, and provides an important reference for further research on GNSS-R technology.

High-resolution tropical rain-forest canopy climate data

Abstract Canopy habitats challenge researchers with their intrinsically difficult access. The current scarcity of climatic data from forest canopies limits our understanding of the conditions and environmental variability of these diverse and dynamic habitats. We present 307 days of climate records collected between 2019 and 2020 in the tropical rainforest canopy of the Yasuní National Park, Ecuador. We monitored climate with a 10-min temporal resolution in the middle crowns of eight canopy trees. The distance between canopy climate stations ranged from 700 m to 10 km. Apart from air temperature, relative humidity, leaf wetness, and photosynthetically active radiation (PAR), measured in each canopy climate station, global radiation, rainfall, and wind speed were measured in different subsets of them. We processed the eight data series to omit erroneous records resulting from sensor failures or lack of the solar-based power supply. In addition to the eight original data series, we present three derived data series, two aggregating canopy climate for valleys or for ridges (from four stations each), and one overall average (from the eight stations). This last derived data series contains 306 days, while the shortest of the original data series covers 22 days and the longest 296 days. In addition to the data, two open-source tools, developed in RStudio, are presented that facilitate data visualization (a dashboard) and data exploration (a filtering app) of the original and aggregated records.

The Influence of Weather on Fatal Accidents in Austrian Mountains

Abstract Projections of warmer global temperatures in fast-approaching time horizons warrant planning strategies for reducing impacts on human morbidity and mortality. This study sought to determine whether increases in temperature and other changes in weather indices had an impact on rates of fatal accidents occurring in the popular mountainous regions of Austria, with the purpose of improving prevention and accident-mitigation strategies in the mountains. The study was based on the merging of 3285 fatal outdoor accidents reported by the Austrian Alpine Safety Board for the period 2006 to 2018 with daily meteorological data from 43 nearby climate stations during the same period. Multivariable logistic regression was used to model the odds of one or more fatal accidents per station and day with weather indices as predictors, controlling for weekend effects bringing more visitors to the mountains. Separate prediction models were performed for summer and winter activities, as well as for specific disciplines. Even after adjustment for concomitant effects impacting mountain fatal accidents, the daily weather indices of temperature, relative humidity, global radiation, cloudiness, snow cover and precipitation were statistically significantly associated with fatal-accident risk. In particular, a 1° increase in temperature was associated with a 13% increase in odds of a mountain-biking accident in the summer and an 8% increase in odds of a mountain suicide in the winter. An increase in global radiation by 1 kW h m−2 was associated with an 11% and 28% increase in fatal-accident odds for mountaineering in the summer and touring in the winter, respectively.

Коррозионные испытания рабоче-консервационного масла в различных климатических зонах

The protection period of equipment with an analogue of the K-17 working-preservation oil calculated on the basis of laboratory tests according to GOST RV 9.513-97 correspond to the terms of protection of the K-17 working-preservation oil, established by GOST 9.014 -78. Corrosion tests of the developed preservation oil were carried out in comparison with the K–17 working preservation oil at test climatic stations corresponding to the conditions of subtropical, mountainous and continental climate. The results of climatic tests of the analogue of the K–17 preservation oil made it possible to establish that the developed sample has high protective properties and can be used as an effective means of anticorrosive protection of various metal products during transportation and storage.

Characteristics of Lake Sediment from Southwestern Mongolia and Comparison with Meteorological Data

To understand how the climate system works in the continental interior, sediment cores that are approximately 30-cm long were taken from Olgoy, Boontsagaan, and Orog lakes, Mongolia. These cores were analyzed and compared with meteorological data (air temperature, precipitation, and wind) from climate stations in the study area. Comparison of metrological data from four stations shows similar climate fluctuations. When the temperature was high, less precipitation occurred in general. The sedimentary features measured in this study were water content, organic matter, carbonate, amorphous silica contents, whole and mineral grain size, and grain density. Excess 210Pb measurements were used to estimate sedimentary ages. According to principal component analysis (PCA), temperature correlates well to sediment characteristics in Olgoy Lake. Whole and mineral grain sizes are coarser when the temperature is high, while the amorphous-silica concentration is lower. A coarse grain size is interpreted to reflect low lake levels due to evaporation under high temperature with less precipitation. Amorphous silica may be from surrounding plants and reflects less vegetation when the temperature is high. However, in the recent 30 years, after the social system changed and overgrazing became a problem, the amount of amorphous silica has positively correlated with temperature on a short time scale. In the past 30 years, with less vegetation, amorphous silica has mainly come from weathered mineral particles. High temperature caused a thick, weathered mantle for each mineral particle, resulting in high amorphous-silica concentration. In Boontsagaan Lake, whole and mineral grain sizes are coarser when the wind speed is increased. Low precipitation correlates with a decrease in organic matter and an increase in carbonate and amorphous silica. In Orog Lake, it is difficult to establish an age model due to dried-up events. Some fluctuations in sedimentary characteristics may correspond to extreme events, such as earthquakes, and natural hazards, such as dzuds (harsh winters).

Redefining homogeneous climate regions in Bangladesh using multivariate clustering approaches

The knowledge of the climate pattern for a particular region is important for taking appropriate actions to alleviate the impact of climate change. It is also equally important for water resource planning and management purposes. In this study, the regional disparities and similarities have been revealed among different climate stations in Bangladesh based on an adaptive clustering algorithms that include hierarchical clustering, partitioning around medoids, and k-means techniques under several validation measures to several important climatological factors including rainfall, maximum temperatures, and wind speed. \(H_{1}\) statistics based on the L-moment method were used to test the homogeneity of identified clusters by the algorithms. The results suggest that the climate stations of Bangladesh can be grouped into two prime clusters. In most cases, one cluster is located in the northern part of the country that includes drought-prone and vulnerable regions, whereas, the second cluster contains rain-prone and hilly regions that are found mostly in the southern part. In terms of cluster size and homogeneity, all clusters have been identified. In contrast, the clusters identified by the hierarchical method for all three factors are either homogeneous or reasonably homogeneous. The implementation of principal component analysis to climate station data further reveals that three latent factors play a vital role to address the total variations.

Regionalized dynamic climate series for ecological climate impact research in modern controlled environment facilities.

Modern controlled environment facilities (CEFs) enable the simulation of dynamic microclimates in controlled ecological experiments through their technical ability to precisely control multiple environmental parameters. However, few CEF studies exploit the technical possibilities of their facilities, as climate change treatments are frequently applied by static manipulation of an inadequate number of climate change drivers, ignoring intra‐annual variability and covariation of multiple meteorological variables. We present a method for generating regionalized climate series in high temporal resolution that was developed to force the TUMmesa Model EcoSystem Analyzer with dynamic climate simulations. The climate series represent annual cycles for a reference period (1987–2016) and the climate change scenarios RCP2.6 and RCP8.5 (2071–2100) regionalized for a climate station situated in a forested region of the German Spessart mountains. Based on the EURO‐CORDEX and ReKliEs‐DE model ensembles, typical annual courses of daily resolved climatologies for the reference period and the RCP scenarios were calculated from multimodel means of temperature (ta), relative humidity (rh), global radiation (Rg), air pressure (P), and ground‐level ozone and complemented by CO2. To account for intra‐annual variation and the covariability of multiple climate variables, daily values were substituted by hourly resolved data resampled from the historical record. The resulting present climate Test Reference Year (TRY) well represented a possible annual cycle within the reference period, and expected shifts in future mean values (e.g., higher ta) were reproduced within the RCP TRYs. The TRYs were executed in eight climate chambers of the TUMmesa facility and—accounting for the technical boundaries of the facility—reproduced with high precision. Especially, as an alternative to CEF simulations that reproduce mere day/night cycles and static manipulations of climate change drivers, the method presented here proved well suited for simulating regionalized and highly dynamic annual cycles for ecological CEF studies.

Degree of connectivity in reconstructed precipitation dynamics and extremes for semiarid regions across South Siberia

Tree rings from forest-steppes of temperate continental Asia are useful proxies for the moisture regime reconstructions, encompassing environmental variations such as warming climate, changing frequency and intensity of droughts. Heterogeneity of precipitation leaves open the question of the probability of spatially large-scale droughts in this macro-region. Theoretically, such events could be driven by global tele-connections and/or common astronomic cycles. We have attempted the precipitation reconstructions of two distant (~1000 km) intermountain valleys in South Siberia, based on the tree ring width of Pinus sylvestris L. To enhance the quality of the precipitation reconstruction models, networks of existing tree-ring data were expanded and daily precision of instrumental precipitation series was implemented for calibration. Within-region (150–200 km) common signal between local chronologies r = 0.37–0.90 (p < 0.05) allowed obtaining regional ones, registering precipitation up to annual temporal scale. High correlations of both regional chronologies with annual precipitation were found for period from previous July 22 to current July 21 (r = 0.71–0.72). These precipitation series were further reconstructed. Reconstruction models explaining 50–52% of variation were developed for the years 1753–2015 and 1798–2015. Although both valleys do not record many concurrent extreme precipitation events, some common and opposite extremes have been revealed. For both regions, an 11-year and 26–29-year cycles were commonly observed. These were probably associated with the solar activity and Pacific Decadal Oscillation (PDO). However, phase shifts of these cycles were recorded between the regions and with PDO. Stronger impact of oceanic air masses was observed in the eastern one of the two considered territories. Whereas higher significance of frequencies associated with astronomic cycles (solar and lunar-nodal) was found in the western one. Data availabilityTemperature and precipitation series of climatic stations were obtained from the website of All-Russia Research Institute of Hydrometeorological Information, World Data Centre (RIHMI-WDC, http://meteo.ru/data). Other climatic time series and solar activity series were obtained from the website of The Royal Netherlands Meteorological Institute (KNMI) Climate Explorer (https://climexp.knmi.nl). Used in the study tree-ring width measurements will be submitted to the International Tree-Ring Data Bank (ITRDB; https://www.ncei.noaa.gov/products/paleoclimatology/tree-ring) upon publication of the manuscript and with reference to it.

A streamflow-oriented ranking-based methodological framework to combine multiple precipitation datasets across large river basins

Hydrologic-Land Surface Models (H-LSMs) are subject to input uncertainties arising from climate forcing data, especially precipitation. For better streamflow simulations and predictions, the generation of a hybrid dataset by combining existing precipitation products has attracted considerable interest in recent years. To assess the accuracy of the hybrid dataset, in-situ precipitation-gauge stations are used as a reference point. However, the robustness of the hybrid dataset in representing spatial details can be problematic when the evaluation uses only a sparse network of in-situ observations at regional or basin scales. This study aims to develop a methodological framework to generate hybrid precipitation datasets based on the model performance of streamflow simulations that are spatially representative across large river basins. The framework is illustrated using a Canadian H-LSM known as MESH (Modélisation Environmentale communautaire – Surface Hydrology) in the Saskatchewan River basin, Canada, for the period 2002–2010. Five regional and global precipitation products (Global Meteorological Forcing Dataset at Princeton University (Princeton); the WATCH Forcing Data methodology applied to the ERA-Interim (WFDEI) augmented by Climatic Research Unit (WFDEI [CRU]) and Global Precipitation Climatology Centre (WFDEI [GPCC]); North American Regional Reanalysis (NARR); and Canadian Precipitation Analysis (CaPA)) were included as candidates in this study. Results indicate that the generation of a hybrid dataset based on hydrological evaluation was useful for improving H-LSM modelling skills. Hybrid datasets showed a similar or better model performance compared to that of the best basin-wide precipitation product in the headwaters and gradually performed better downstream and at the basin outlet. When multiple products are combined model performance can be further enhanced by considering seasonality with respect to the hydrological regime of the river basin. This study demonstrates the usefulness of hybrid datasets in a large-scale river basin with low climate station network density.

Evaluation of hydrological modeling using climatic station and gridded precipitation dataset

The conventional rainfall data estimates are relatively accurate at some points of the region. The interpolation of such type of data approximates the actual rainfield however in data scarce regions; the resulted rainfield is the rough estimate of the actual rainfall events. In data scarce regions like Indus basin Pakistan, the data obtained through remote sensing can be very useful. This research evaluates two types of gridded data i.e., European Reanalysis (ERA) interim and Japanese Reanalysis 55 years (JRA-55) along with the climatic station data for three small dams in Pakistan. Since no measured flow data is available at these dams, the nearest possible catchments where flow data is available are calibrated and the calibrated parameters of these catchments are then used in actual dams for simulating the flow from all the three types of data using Soil and Water Assessment Tool (SWAT). The results of the comparison of gridded and rainguage precipitation shows that gridded data highly overestimates the climatic station data. Similar results were observed in the comparison of flow simulated by SWAT model. The Peak flood calculated from JRA-55 overestimates while the Era-Interim peak floods are comparable to that of climatic stations in two of the three catchments.

Statistical Modelling of Drought Indicators and Evaporation/Transpiration and Their Impact on Determining the Erosion Ability of Winds in Iraq

Geographical studies are concerned with analyzing and interpreting its most important phenomena, especially those related to climate, which in one way or another affect the provision of harsh environmental conditions such as drought, desertification, and others, as well as being one of its prominent manifestations. As these studies are interested in one way or another in determining the mechanism of action in life, as they are interested in determining the various human activities, and in this research, the researchers were interested in adopting (10) Iraqi climatic stations to find and calculate the values of the phenomena referred to from drought, evaporation/transpiration, wind erosion and the strength of wind pressure in Iraq, using an applied quantitative-statistical method to determine the monthly and annual ranges of wind erosion for the period (1986-2018), and by applying the correlation, regression and determination coefficients. As well as cartographic representation using Excel techniques and geographic information systems (GIS).

Analisis Kondisi Iklim dan Pemanfaatannya untuk Penetapan Musim Tanam di Daerah Batabual Kabupaten Buru

The determination of the growing season is closely related with the climate change that occurs in a region The study aims to determine the trend of changing the growing season in the Batabual Area, changes in the growing season that occur due to changes in rainfall, and alternative planting patterns in Batabual region based on changes in the growing season that occur. Data analysis consisted of (1) determining the climatic conditions of the region using the Algebraic average method from monthly rainfall data from the Buru meteorological climate station for the last 30 years 1991-2020 and (2) determining the planting season using the FAO method (1978). The results showed that based on average rainfall data, the growing season lasted for 10 months and 19 days (November to September 19 of the following year). However, if you use rainfall with a 75% chance of being exceeded, the growing season period only lasts for 8 months and 21 days (December – July 21 of the following year).

Bayesian model averaging of the RegCM temperature projections: a Canadian case study

Abstract The choices of physical schemes coupled in the Regional Climate Model version 4 (RegCM4), the input general circulation model (GCM) results, and the emission scenarios may cause considerable uncertainties in future temperature projections. Therefore, the ensemble approach, which can be used to reflect these uncertainties, is highly desired. In this study, the probabilistic projections for future temperature are generated at 88 Canadian climate stations based on the developed RegCM4 ensemble and obtained Bayesian model averaging (BMA) weights. The BMA weights indicate that the RegCM4 coupled with the holtslag PBL scheme driven by the HadGEM can provide relatively reliable temperature projections at most climate stations. It is also suggested that the BMA approach is effective in simulating temperature over middle and eastern Canada through taking advantage of each ensemble member. However, the effectiveness of the BMA method is limited when all the models in the ensemble cannot simulate the temperature robustly. The projected results demonstrate that the temperature will increase continuously in the future, while the temperature increase under RCP8.5 will be significantly larger than that under RCP4.5.

Analysis of PCA based AdaBoost Machine Learning Model for Predict Mid-Term Weather Forecasting

In general, weather forecasting is done with the use of enormously complicated physical science models that use a variety of environmental circumstances over a long period of time. Because of the annoyances of the climatic framework, these criteria are frequently fragile, causing the models to produce inaccurate forecasts. The models are mostly run on multiple hubs in a massive High-Performance Computing (HPC) environment that uses a lot of energy. In this research, we offer a climate expectation approach that uses historical data from various climate stations to create basic AI models that may provide meaningful forecasts for specific climatic conditions in the not-too-distant future within a given time frame. In this paper, we offer a climate expectation approach that uses historical data from several climate stations to create basic AI models that can anticipate certain climatic conditions in the not-too-distant future within a given time frame. Overall research performed into two stages; in first stage Principle component Analysis has been used to extract the irrelevant attributes from the datasets. In second stage five different machines learning algorithm used to predict temperature condition for midterm span &amp; finally four performance indicators along with training time used to identify the best fitted model. From the result analysis it is seen that PCA based AdaBoost model is the fittest model with acquired the best outcome of R2, RMSE, MAE &amp; MSE are 0.992, 0.539, 0.398 &amp; 0.209 respectively. Beside of this present model also outperformed than the other state of art model proposed for midterm weather forecasting purpose. Keyword : Weather Forecasting, PCA, Machine learning, Performance indicator

Benchmarking urban local weather with long-term monitoring compared with weather datasets from climate station and EnergyPlus weather (EPW) data

Weather is essential for building energy simulation and energy efficient retrofitting since building energy use is highly weather-dependent. Weather data provided by EnergyPlus weather (EPW) data or generated by urban climate stations are used in most simulation tools. However, weather varies temporally and spatially, and the gaps may exist in the typical-year weather, weather from peri-urban areas, and local actual weather. This paper compares three types of weather datasets, from EPW data, from an urban climate station (STATION), and from long-term measurement with a local micro-climate station at Southeast University (SEU) campus. Results show there are significant gaps between the three weather datasets and compared with the STATION and EPW data, the average temperature in the micro-climate (SEU) is higher by 1.2 °C and 2.2 °C, respectively. The cooling degree day in SEU weather data is 20.4% and 40.8% higher, with heating degree day 15.7% and 26.3% lower. Comparing average relative humidity shows a big difference of 18.3% and 22.1% between SEU, STATION and EPW, and the former is comparatively lower than the latter two and no clear correlated trend. The SEU weather data has 23.1% more solar radiation hours than STATION weather data. As for wind speed, the average wind speed in the SEU weather data is lower by 1.14m/s and 1.29m/s, respectively, when compared with the STATION and EPW data. This study could be the reference for different trials for energy performance dynamic simulation for building energy analysis.

Greenhouse localized heating powered by a polygeneration system

Energy consumption in greenhouse heating could reach up to 90% of the total energy requirement depending on the type of greenhouse, environmental control equipment and location of the greenhouse. The use of climate conditioning technologies that exploit renewable energy and the application of passive systems to improve the energy efficiency and the sustainability of the greenhouse sector are recommended. During winter 2020-2021, an experimental test was carried out at the University of Bari in a Mediterranean greenhouse heated by a polygeneration system, composed of a solar system and an air-water heat pump. Three localized heating systems were tested to transfer thermal energy close to plants of Roman lettuce. Heating pipes were placed inside the cultivation substrate in the underground pipe system and on the cultivation substrate in the laid pipe system. The third system consists of metal plates heated by steel tubes and placed in the aerial area of plants. A weather climatic station and a sensor system interfaced with a data logger for continuous data acquisition and storage were used. The plate system was the best for air temperature rising, as it allowed an increase of 3.6% compared to the set-up without any localised heating system. The underground pipe system was the best for the soil heating, as it achieved a temperature increase of 92%. Localized soil heating systems contributed significantly to an earlier harvest by almost 2 weeks.

Evaluation of bio-inspired optimization algorithms hybrid with artificial neural network for reference crop evapotranspiration estimation

Reference crop evapotranspiration (ETo) is a determinant factor in agricultural water resource management. Therefore, accurate ETo information is critical to quantify crop water requirements for precision agriculture management. This study coupled bio-inspired optimization algorithms with artificial neural network (ANN), i.e., ANN with bat algorithm (BA-ANN), ANN with cuckoo search algorithm (CSA-ANN), and ANN with whale optimization algorithm (WOA-ANN), and developed three hybrid ANN models for daily ETo modeling with limited inputs. The models were trained and evaluated using a k-fold test approach and long-term daily climatic data from 2001 to 2018 at six climatic stations in the Loess Plateau of north China. Three input scenarios were used, including temperature-based inputs, radiation-based inputs, and mass transfer-based inputs. The statistical comparison showed that the hybrid WOA-ANN offered better estimates than BA-ANN and CSA-ANN in all three input scenarios. In general, the radiation-based WOA-ANN provided the most accurate ETo estimations, with regional average relative root mean square error and Nash-Sutcliffe efficiency coefficient of 13.3% and 0.959, respectively. The temperature-based WOA-ANN offered acceptable and reasonable ETo estimates. Thus, it is a reliable tool for ETo modeling, given that air temperature is available in many regions. Overall, the bio-inspired optimization algorithms are robust tools for enhancing ANN performance in ETo simulation, and thus they are highly recommended to estimate ETo in the study region. Our study proposed powerful models for accurately estimating ETo with limited inputs, offering practical implications for the development of precision agriculture.

An evaluation of ECOSTRESS products of a temperate montane humid forest in a complex terrain environment

Plant water use is difficult to monitor and predict in complex terrain. NASA's ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) provides new data for understanding hydrologic cycling in these forested mountain areas. Evaluation of ECOSTRESS evapotranspiration (ET) has relied primarily on eddy flux towers, but most flux towers are located in flat terrain, sampling relatively homogeneous vegetation. To address this knowledge gap, the accuracy of the ECOSTRESS land surface temperature (L2_LST) and ET (L3_ET_PT-JPL) data were evaluated against data from the U.S. Department of Agriculture (USDA) Forest Service's Coweeta Hydrologic Laboratory: a humid temperate forest ecosystem in the southern Appalachian Mountains of the southeastern U.S. with extensive data from one eddy covariance tower and five climate stations varying in elevation. ECOSTRESS LST showed a strong linear relationship with the weather station near-surface air temperature (R2 = 0.85; offset = −2.5 °C), showing no difference across slope, aspect and elevation, but a higher correlation during the night than the day. ECOSTRESS tended to overestimate ET compared with our site eddy covariance measurements (R2 = 0.43; RMSE = 146 W m−2). To evaluate potential sources of error associated with remotely-sensed inputs to the ECOSTRESS Priestly-Taylor Jet Propulsion Laboratory (PT-JPL) ET model, we ran PT-JPL using ground-based data from our site. ET estimated with locally-collected data showed much better performance in capturing variability (R2 ~ 0.70), illustrating the uncertainty contributed to ECOSTRESS by the coarse scale meteorological inputs in areas of complex topography. Notably, both the ECOSTRESS LST and ET captured important topographic gradients and spatially-explicit diurnal variability. The valley floor of the Coweeta Basin was warmer than the higher elevations by 8 °C in the day, but cooler at night. On the south-facing aspects, LST and ET were higher, consistent with observations. This also highlights the ability of ECOSTRESS and PT-JPL to decouple ET from LST when and where appropriate, as they are normally inverse to one another. Our study provides the first detailed analysis of ECOSTRESS for forested ecosystems in complex terrain and offers insight for future evaluation.

Elevation-Dependent Changes to Plant Phenology in Canada’s Arctic Detected Using Long-Term Satellite Observations

Arctic temperatures have increased at almost twice the global average rate since the industrial revolution. Some studies also reported a further amplified rate of climate warming at high elevations; namely, the elevation dependency of climate change. This elevation-dependent climate change could have important implications for the fate of glaciers and ecosystems at high elevations under climate change. However, the lack of long-term climate data at high elevations, especially in the Arctic, has hindered the investigation of this question. Because of the linkage between climate warming and plant phenology changes and remote sensing’s ability to detect the latter, remote sensing provides an alternative way for investigating the elevation dependency of climate change over Arctic mountains. This study investigated the elevation-dependent changes to plant phenology using AVHRR (Advanced Very High Resolution Radiometer) time series from 1985 to 2013 over five study areas in Canada’s Arctic. We found that the start of the growing season (SOS) became earlier faster with an increasing elevation over mountainous study areas (i.e., Sirmilik, the Torngat Mountains, and Ivvavik National Parks). Similarly, the changes rates in the end of growing season (EOS) and the growing season length (GSL) were also higher at high elevations. One exception was SOS in the Ivvavik National Park: “no warming trend” with the May-June temperature at a nearby climate station decreased slightly during 1985–2013, and so no elevation-dependent amplification.