ABSTRACT Partitioning evapotranspiration (ET) is challenging but essential for understanding the exchange of energy, water, and carbon between terrestrial ecosystems and the atmosphere. In this study, we applied the simple biosphere model (SiB2) to partition ET at a typical alpine grassland site on the Qinghai–Tibet Plateau (QTP). In addition, through process‐based model scenario experiments, we quantified the effects of four environmental factors on ET components and predicted their evolution under the two future carbon emission scenarios (ssp126 and ssp585). Our findings are summarized as follows: (1) The original version of SiB2, despite its simple structure, effectively simulates ET and its components. (2) The ratios of annual total transpiration ( T ), soil evaporation ( E s ), and canopy interception evaporation ( E i ) to ET in the alpine grassland ecosystem were 51%, 43%, and 6%, respectively. (3) Each 100 mm increase in annual precipitation results in a significant increase in soil evaporation (2.77%). A 1°C increase in air temperature leads to a significant increase in vegetation transpiration (5.22%) and canopy interception evaporation (5.63%). Each 100 ppm increase in CO 2 concentration causes a significant decrease in T (−5.43%) and ET (−2.97%). An increase in LAI (1 m 2 m −2 ) has the largest effect on canopy interception evaporation (4.67%). (4) Under the high carbon emission scenario (ssp585), all ET components in this ecosystem show a significant growth trend, particularly vegetation transpiration and canopy interception evaporation. These findings will facilitate more precise predictions of the water cycle dynamics, reveal land‐atmosphere interaction mechanisms, and aid in the protection of the ecological environment of the QTP.
Abstract Vegetation phenology is a sensitive indicator of climate change and has significant effects on the exchange of carbon, water, and energy between the terrestrial biosphere and the atmosphere. The Tibetan Plateau, the Earth's “third pole,” is a unique region for studying the long‐term trends in vegetation phenology in response to climate change because of the sensitivity of its alpine ecosystems to climate and its low‐level human disturbance. There has been a debate whether the trends in spring phenology over the Tibetan Plateau have been continuously advancing over the last two to three decades. In this study, we examine the trends in the start of growing season (SOS) for alpine meadow and steppe using the Global Inventory Modeling and Mapping Studies (GIMMS)3g normalized difference vegetation index (NDVI) data set (1982–2014), the GIMMS NDVI data set (1982–2006), the Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI data set (2001–2014), the Satellite Pour l'Observation de la Terre Vegetation (SPOT‐VEG) NDVI data set (1999–2013), and the Sea‐viewing Wide Field‐of‐View Sensor (SeaWiFS) NDVI data set (1998–2007). Both logistic and polynomial fitting methods are used to retrieve the SOS dates from the NDVI data sets. Our results show that the trends in spring phenology over the Tibetan Plateau depend on both the NDVI data set used and the method for retrieving the SOS date. There are large discrepancies in the SOS trends among the different NDVI data sets and between the two different retrieval methods. There is no consistent evidence that spring phenology (“green‐up” dates) has been advancing or delaying over the Tibetan Plateau during the last two to three decades. Ground‐based budburst data also indicate no consistent trends in spring phenology. The responses of SOS to environmental factors (air temperature, precipitation, soil temperature, and snow depth) also vary among NDVI data sets and phenology retrieval methods. The increases in winter and spring temperature had offsetting effects on spring phenology.
Latest satellite images have been utilized to update the inventories of glaciers and glacial lakes in the Pumqu river basin, Xizang (Tibet), in the study. Compared to the inventories in 1970s, the areas of glaciers are reduced by 19.05% while the areas of glacial lakes are increased by 26.76%. The magnitudes of glacier retreat rate and glacial lake increase rate during the period of 2001–2013 are more significant than those for the period of the 1970s–2001. The accelerated changes in areas of the glaciers and glacial lakes, as well as the increasing temperature and rising variability of precipitation, have resulted in an increased risk of glacial lake outburst floods (GLOFs) in the Pumqu river basin. Integrated criteria were established to identify potentially dangerous glacial lakes based on a bibliometric analysis method. It is found, in total, 19 glacial lakes were identified as dangerous. Such finding suggests that there is an immediate need to conduct field surveys not only to validate the findings, but also to acquire information for further use in order to assure the welfare of the humans.
The Nanwenghe Wetlands Reserve in the Yile'huli Mountains is a representative region of the Xing'an permafrost. The response of permafrost to climate change remains unclear due to limited field investigations. Thus, longer-term responses of the ground thermal state to climate change since 2011 have been monitored at four sites with varied surface characteristics: Carex tato wetland (P1) and shrub-C. tato wetland (P2) with a multi-year average temperatures at the depth of zero annual amplitude (TZAA) of −0.52 and −1.19 °C, respectively; Betula platyphylla-Larix gmelinii (Rupr.) Kuzen mixed forest (P3) with TZAA of 0.17 °C, and; the forest of L. gmelinii (Rupr.) Kuzen (P4) with TZAA of 1.65 °C. Continuous observations demonstrate that the ecosystem-protected Xing'an permafrost experienced a cooling under a warming climate. The temperature at the top of permafrost (TTOP) rose (1.8 °C per decade) but the TZAA declined (−0.14 °C per decade), while the active layer thickness (ALT) thinned from 0.9 m in 2012 to 0.8 m in 2014 at P1. Both the TTOP and TZAA increased (0.89 and 0.06 °C per decade, respectively), but the ALT thinned from 1.4 m in 2012 to 0.7 m in 2016 at P2. Vertically detached permafrost at P3 disappeared in summer 2012, with warming rates of +0.42 and + 0.17 °C per decade for TTOP and TZAA, respectively. However, up to date, the ground thermal state has remained stable at P4. We conclude that the thermal offset is crucial for the preservation and persistence of the Xing'an permafrost at the southern fringe.
Abstract. Permafrost has great influences on the climatic, hydrological, and ecological systems on the QinghaiâTibet Plateau (QTP). The changing permafrost and its impact have been attracting great attention worldwide like never before. More observational and modeling approaches are needed to promote an understanding of permafrost thermal state and climatic conditions on the QTP. However, limited data on the permafrost thermal state and climate background have been sporadically reported in different pieces of literature due to the difficulties of accessing and working in this region where the weather is severe, environmental conditions are harsh, and the topographic and morphological features are complex. From the 1990s, we began to establish a permafrost monitoring network on the QTP. Meteorological variables were measured by automatic meteorological systems. The soil temperature and moisture data were collected from an integrated observation system in the active layer. Deep ground temperature (GT) was observed from boreholes. In this study, a comprehensive dataset consisting of long-term meteorological, GT, soil moisture, and soil temperature data was compiled after quality control from an integrated, distributed, and multiscale observation network in the permafrost regions of QTP. The dataset is helpful for scientists with multiple study fields (i.e., climate, cryospheric, ecology and hydrology, meteorology science), which will significantly promote the verification, development, and improvement of hydrological models, land surface process models, and climate models on the QTP. The datasets are available from the National Tibetan Plateau/Third Pole Environment Data Center (https://data.tpdc.ac.cn/en/disallow/789e838e-16ac-4539-bb7e-906217305a1d/, last access: 24 August 2021, https://doi.org/10.11888/Geocry.tpdc.271107, Lin et al., 2021).
Site selection for building solar farms in deserts is crucial and must consider the dune threats associated with sand flux, such as sand burial and dust contamination. Understanding the changes in sand flux can optimize the site selection of desert solar farms. Here we use the ERA5-Land hourly wind data with 0.1°×0.1° resolution to calculate the yearly sand flux from 1950 to 2022. The mean of sand flux is used to score the suitability of global deserts for building solar farms. We find that the majority of global deserts have low flux potential (≤40 m3 m-1 yr-1) and resultant flux potential (≤2.0 m3 m-1 yr-1) over the past 73 years. The scoring result demonstrates presents that global deserts have obvious patch distribution of site suitability for building solar farms. Our study optimizes the site selection of desert solar farms, which aligns with the United Nations sustainability development goals for achieving affordable and clean energy target by 2030.
A decision tree algorithm was developed to classify the freeze/thaw status of the surface soil based on the cluster analysis of samples such as frozen soil,thawed soil,desert and snow,along with microwave emission and scattering characteristics of the frozen/thawed soil.The algorithm included five SSM/I channels(19V,19H,22V,37V,85V)and three crucial indices including scattering index,37GHz vertical polarization brightness temperature and 19GHz polarization difference,and took into consideration the scattering effect of desert and precipitation.The pureness of samples is essential to the analysis of the microwave brightness temperature characteristics,which is prior to deciding the thresholds of each node of the decision tree.We have selected four types of samples,including frozen soil,thawed soil,desert and snow.The frozen soil has some special microwave emission and scattering characteristics different from the thawed soil:① lower thermodynamic temperature and brightness temperature;② higher emissivity;③ stronger volume scattering,and the brightness temperature decreased with increasing frequency.The threshold of each node of the decision tree can be determined by using cluster analysis of three vital indices,and calculating the average and standard differences of each type and each index.The 4cm-depth soil temperature on the Qinghai-Tibetan Plateau observed by Soil Moisture and Temperature Measuring System of GEWEX-Coordinated Enhanced Observing Period,were used to validate the classification results.The total accuracy can reach about 87%.A majority of misclassification occurred near the freezing point of soil,about 40% and 73% of the misclassified cases appeared when the surface soil temperature is between-0.5—0.5℃ and-2.0—2.0℃,respectively.Furthermore,the misclassification mainly occurred during the transition period between warm and cold seasons,namely April-May and September-October.Based on this decision tree,a map of the number of frozen days during Oct.2002 to Sep.2003 in China was produced by composing 5 days classification results due to the swath coverage of SSM/I.The accuracy assessment for pixels with more than 15 frozen days(less than 15 meaning the short time frozen soil)was carried out with the regions of permafrost and seasonally frozen ground in map of geocryological regionalization and classification in China as reference data(Zhou et al.,2000),and the total classification accuracy was 91.66%,while the Kappa coefficient was 80.5%.The boundary between frozen and thawed soil was well consistent with the southern limit of seasonally frozen ground.A long time series surface frozen/thawed dataset can be produced using this decision tree,which may provide indicating information for regional climate change studies,regional and global scale carbon cycle models,hydrologic model and land surface model so on.
Abstract Climate warming is altering historical patterns of snow accumulation and ablation, hence threatening natural water resources. We evaluated the impact of climate warming on snowmelt rates using the GlobSnow v2.0 and the second Modern‐Era Retrospective analysis for Research and Applications data sets over the Northern Hemisphere (NH) during the past 38 years (1980–2017). Higher ablation rates were found in the locations with deeper snow water equivalent (SWE) because high snow melt rates occurred in late spring and early summer in deep snowpack regions. In addition, due to the reduction of SWE in deep snowpack regions, moderate and high snow ablation rates showed a decreasing trend. Therefore, slower snowmelt rates were found over the entire NH in a warmer climate in general. Based on projections of SWE in Representative Concentration Pathways 2.6, 4.5, and 8.5 climate scenarios, slower snowmelt rates in the NH may continue to happen in the future.
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