Changes in Snow Phenology from 1979 to 2016 over the Tianshan Mountains, Central Asia

Snowmelt from the Tianshan Mountains (TS) is a major contributor to the water resources of the Central Asian region. Thus, changes in snow phenology over the TS have significant implications for regional water supplies and ecosystem services. However, the characteristics of changes in snow phenology and their influences on the climate are poorly understood throughout the entire TS due to the lack of in situ observations, limitations of optical remote sensing due to clouds, and decentralized political landscapes. Using passive microwave remote sensing snow data from 1979 to 2016 across the TS, this study investigates the spatiotemporal variations of snow phenology and their attributes and implications. The results show that the mean snow onset day (Do), snow end day (De), snow cover duration days (Dd), and maximum snow depth (SDmax) from 1979 to 2016 were the 78.2nd day of hydrological year (DOY), 222.4th DOY, 146.2 days, and 16.1 cm over the TS, respectively. Dd exhibited a spatial distribution of days with a temperature of \u3c0 \u3e°C derived from meteorological station observations. Anomalies of snow phenology displayed the regional diversities over the TS, with shortened Dd in high-altitude regions and the Fergana Valley but increased Dd in the Ili Valley and upper reaches of the Chu and Aksu Rivers. Increased SDmax was exhibited in the central part of the TS, and decreased SDmax was observed in the western and eastern parts of the TS. Changes in Dd were dominated by earlier De, which was caused by increased melt-season temperatures (Tm). Earlier De with increased accumulation of seasonal precipitation (Pa) influenced the hydrological processes in the snowmelt recharge basin, increasing runoff and earlier peak runoff in the spring, which intensified the regional water crisi

Evaluation of SMAP, SMOS-IC, FY3B, JAXA, and LPRM Soil Moisture Products over the Qinghai-Tibet Plateau and Its Surrounding Areas

© 2019 by the authors. High-quality and long time-series soil moisture (SM) data are increasingly required for the Qinghai-Tibet Plateau (QTP) to more accurately and effectively assess climate change. In this study, to evaluate the accuracy and effectiveness of SM data, five passive microwave remotely sensed SM products are collected over the QTP, including those from the soil moisture active passive (SMAP), soil moisture and ocean salinity INRA-CESBIO (SMOS-IC), Fengyun-3B microwave radiation image (FY3B), and two SM products derived from the advanced microwave scanning radiometer 2 (AMSR2). The two AMSR2 products are generated by the land parameter retrieval model (LPRM) and the Japan Aerospace Exploration Agency (JAXA) algorithm, respectively. The SM products are evaluated through a two-stage data comparison method. The first stage is direct validation at the grid scale. Five SM products are compared with corresponding in situ measurements at five in situ networks, including Heihe, Naqu, Pali, Maqu, and Ngari. Another stage is indirect validation at the regional scale, where the uncertainties of the data are quantified by using a three-cornered hat (TCH) method. The results at the regional scale indicate that soil moisture is underestimated by JAXA and overestimated by LPRM, some noise is contained in temporal variations in SMOS-IC, and FY3B has relatively low absolute accuracy. The uncertainty of SMAP is the lowest among the five products over the entire QTP. In the SM map composed by five SM products with the lowest pixel-level uncertainty, 66.64% of the area is covered by SMAP (JAXA: 19.39%, FY3B: 10.83%, LPRM: 2.11%, and SMOS-IC: 1.03%). This study reveals some of the reasons for the different performances of these five SM products, mainly from the perspective of the parameterization schemes of their corresponding retrieval algorithms. Specifically, the parameterization configurations and corresponding input datasets, including the land-surface temperature, the vegetation optical depth, and the soil dielectric mixing model are analyzed and discussed. This study provides quantitative evidence to better understand the uncertainties of SM products and explain errors that originate from the retrieval algorithms

Monitoring Water and Energy Cycles at Climate Scale in the Third Pole Environment (CLIMATE-TPE)

A better understanding of the water and energy cycles at climate scale in the Third Pole Environment is essential for assessing and understanding the causes of changes in the cryosphere and hydrosphere in relation to changes of plateau atmosphere in the Asian monsoon system and for predicting the possible changes in water resources in South and East Asia. This paper reports the following results: (1) A platform of in situ observation stations is briefly described for quantifying the interactions in hydrosphere-pedosphere-atmosphere-cryosphere-biosphere over the Tibetan Plateau. (2) A multiyear in situ L-Band microwave radiometry of land surface processes is used to develop a new microwave radiative transfer modeling system. This new system improves the modeling of brightness temperature in both horizontal and vertical polarization. (3) A multiyear (2001–2018) monthly terrestrial actual evapotranspiration and its spatial distribution on the Tibetan Plateau is generated using the surface energy balance system (SEBS) forced by a combination of meteorological and satellite data. (4) A comparison of four large scale soil moisture products to in situ measurements is presented. (5) The trajectory of water vapor transport in the canyon area of Southeast Tibet in different seasons is analyzed, and (6) the vertical water vapor exchange between the upper troposphere and the lower stratosphere in different seasons is presented

Hydrological Response of Alpine Wetlands to ClimateWarming in the Eastern Tibetan Plateau

Alpine wetlands in the Tibetan Plateau (TP) play a crucial role in the regional hydrological cycle due to their strong influence on surface ecohydrological processes; therefore, understanding how TP wetlands respond to climate change is essential for projecting their future condition and potential vulnerability. We investigated the hydrological responses of a large TP wetland complex to recent climate change, by combining multiple satellite observations and in-situ hydro-meteorological records. We found different responses of runoff production to regional warming trends among three basins with similar climate, topography and vegetation cover but different wetland proportions. The basin with larger wetland proportion (40.1%) had a lower mean runoff coefficient (0.173 ± 0.006), and also showed increasingly lower runoff level (−3.9% year−1, p = 0.002) than the two adjacent basins. The satellite-based observations showed an increasing trend of annual non-frozen period, especially in the wetland-dominated region (2.64 day·year−1, p \u3c 0.10), and a strong extension of vegetation growing-season (0.26–0.41 day·year−1, p \u3c 0.10). Relatively strong increasing trends in evapotranspiration (ET) (~1.00 mm·year−1, p \u3c 0.01) and the vertical temperature gradient above ground surface (0.043 °C·year−1, p \u3c 0.05) in wetland-dominant areas were documented from satellite-based ET observations and weather station records. These results indicate recent surface drying and runoff reduction of alpine wetlands, and their potential vulnerability to degradation with continued climate warming

Evaluation of MERRA Land Surface Estimates in Preparation for the Soil Moisture Active Passive Mission

The authors evaluated several land surface variables from the Modern-Era Retrospective Analysis for Research and Applications (MERRA) product that are important for global ecological and hydrological studies, including daily maximum (Tmax) and minimum (Tmin) surface air temperatures, atmosphere vapor pressure deficit (VPD), incident solar radiation (SWrad), and surface soil moisture. The MERRA results were evaluated against in situ measurements, similar global products derived from satellite microwave [the Advanced Microwave Scanning Radiometer for Earth Observing System (EOS) (AMSR-E)] remote sensing and earlier generation atmospheric analysis [Goddard Earth Observing System version 4 (GEOS-4)] products. Relative to GEOS-4, MERRA is generally warmer (~0.5°C for Tmin and Tmax) and drier (~50 Pa for VPD) for low- and middle-latitude regions (\u3c50°N) associated with reduced cloudiness and increased SWrad. MERRA and AMSR-E temperatures show relatively large differences (\u3e3°C) in mountainous areas, tropical forest, and desert regions. Surface soil moisture estimates from MERRA (0–2-cm depth) and two AMSR-E products (~0–1-cm depth) are moderately correlated (R ~ 0.4) for middle-latitude regions with low to moderate vegetation biomass. The MERRA derived surface soil moisture also corresponds favorably with in situ observations (R = 0.53 ± 0.01, p \u3c 0.001) in the midlatitudes, where its accuracy is directly proportional to the quality of MERRA precipitation. In the high latitudes, MERRA shows inconsistent soil moisture seasonal dynamics relative to in situ observations. The study’s results suggest that satellite microwave remote sensing may contribute to improved reanalysis accuracy where surface meteorological observations are sparse and in cold land regions subject to seasonal freeze–thaw transitions. The upcoming NASA Soil Moisture Active Passive (SMAP) mission is expected to improve MERRA-type reanalysis accuracy by providing accurate global mapping of freeze–thaw state and surface soil moisture with 2–3-day temporal fidelity and enhanced (≤9 km) spatial resolution

Remote Sensing of Environmental Changes in Cold Regions

This Special Issue gathers papers reporting recent advances in the remote sensing of cold regions. It includes contributions presenting improvements in modeling microwave emissions from snow, assessment of satellite-based sea ice concentration products, satellite monitoring of ice jam and glacier lake outburst floods, satellite mapping of snow depth and soil freeze/thaw states, near-nadir interferometric imaging of surface water bodies, and remote sensing-based assessment of high arctic lake environment and vegetation recovery from wildfire disturbances in Alaska. A comprehensive review is presented to summarize the achievements, challenges, and opportunities of cold land remote sensing

Shrunken Locally Linear Embedding for Passive Microwave Retrieval of Precipitation

This paper introduces a new Bayesian approach to the inverse problem of passive microwave rainfall retrieval. The proposed methodology relies on a regularization technique and makes use of two joint dictionaries of coincidental rainfall profiles and their corresponding upwelling spectral radiative fluxes. A sequential detection-estimation strategy is adopted, which basically assumes that similar rainfall intensity values and their spectral radiances live close to some sufficiently smooth manifolds with analogous local geometry. The detection step employs a nearest neighborhood classification rule, while the estimation scheme is equipped with a constrained shrinkage estimator to ensure stability of retrieval and some physical consistency. The algorithm is examined using coincidental observations of the active precipitation radar (PR) and passive microwave imager (TMI) on board the Tropical Rainfall Measuring Mission (TRMM) satellite. We present promising results of instantaneous rainfall retrieval for some tropical storms and mesoscale convective systems over ocean, land, and coastal zones. We provide evidence that the algorithm is capable of properly capturing different storm morphologies including high intensity rain-cells and trailing light rainfall, especially over land and coastal areas. The algorithm is also validated at an annual scale for calendar year 2013 versus the standard (version 7) radar (2A25) and radiometer (2A12) rainfall products of the TRMM satellite