Liyun Dai

Remote sensing for snow hydrology in China: challenges and perspectives

Abstract Snow is one of the most important components of the cryosphere. Remote sensing of snow focuses on the retrieval of snow parameters and monitoring of variations in snow using satellite data. These parameters are key inputs for hydrological and atmospheric models. Over the past 30 years, the field of snow remote sensing has grown dramatically in China. The 30-year achievements of research in different aspects of snow remote sensing in China, especially in (1) methods of retrieving snow cover, snow depth/snow water equivalent, and grain size and (2) applications to snowmelt runoff modeling, snow response on climate change, and remote sensing monitoring of snow-caused disasters are reviewed/summarized. The importance of the first remote sensing experiment on snow parameters at the upper reaches of the Heihe River Basin, in 2008, is also highlighted. A series of experiments, referred to as the Cooperative Observation Series for Snow (COSS), focus on some key topics on remote sensing of snow. COSS has been implemented for 3 years and will continue in different snow pattern regions of China. The snow assimilation system has been established in some regions using advanced ensemble Kalman filters. Finally, an outlook for the future of remote sensing of snow in China is given.

Bursty bulk flows at different magnetospheric activity levels: Dependence on IMF conditions

Based on concurrent observations of the ACE and Geotail satellites from 1998 to 2005, we statistically analyzed and compared the earthward bursty bulk flows (BBFs) with local positive Bz under different interplanetary magnetic field (IMF) conditions. Four different magnetospheric activity levels (MALs), including quiet times and substorm growth/expansion/recovery phases are considered. The properties of the BBFs, including their ion temperature (T), Vx component, X component of the energy flux density (Qx), and the solar wind dawn-dusk electric field Ey (observed at ~1 AU) are analyzed. Main observations include that: 1) BBF tends to have less penetration distance for northward IMF (NW-IMF) than for southward IMF (SW-IMF). Inward of 15 RE the BBFs for SW-IMF are dominant. Few BBFs for NW-IMF occur within 15 RE; 2) the occurrence probability of the BBFs at each MAL depend highly on the orientations of the IMF. During quiet times, the BBFs for NW-IMF are dominant. Reversely, during the growth and expansion phases of a substorm, the BBFs for SW-IMF are dominant; 3) the strengths of the BBF have significant evolution with substorm development. For SW-IMF condition, the strengths of the BBFs are the lowest for quiet times. The strength of the BBFs tends to increase during the growth phase, and reaches to the strongest value during the expansion phase, then, decays during the recovery phase. For NW-IMF condition, the strengths of the BBFs evolve with the substorm development in a similar way as for SW-IMF condition; 4) For SW-IMF, the solar wind Ey evolves with the substorm development in a similar way to the strength of the BBFs. However, no clear evolution is found for NW-IMF; 5) The strengths of the BBF Qx and solar wind Ey are closely related. Both tend to be stronger for growth phase than for quite time, reach the strongest for expansion phase, then decay for recovery phase. It appears that to trigger a substorm, the strength of the BBFs should achieve energy thresholds with values different for NW-IMF and SW-IMF.

No Consistent Evidence for Advancing or Delaying Trends in Spring Phenology on the Tibetan Plateau

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.

Characteristics of Carbon Storage and Density in Different Layers of Forest Ecosystems

Characteristics of carbon storage and density in different layers of forest ecosystems should be studied comprehensively and in more detail. Using forest inventory data in combination with field survey data, we explored the characteristics of carbon storage and density in different layers of forest ecosystems in Liaoning Province of China. Results showed that total carbon storage was 813.034 Tg C. The carbon storage of soil layer accounted for 81.0% of the total storage with 658.783 Tg C, followed by those of arbor, litter and shrub layers with 128.403 Tg C (15.8%), 22.723 Tg C (2.8%) and 3.125 Tg C (0.4%), respectively. The average carbon density for the forest ecosystems were 183.571 Mg C ha–1, with soil layer (148.744 Mg C ha–1) being the highest one, followed by arbor layer (28.992 Mg C ha–1), litter layer (5.131 Mg C ha–1) and shrub-grass layer (0.706 Mg C ha–1). Carbon storage in different forest ecosystems varied from 1.595 to 319.161 Tg C, while C density ranged from 165.067 to 235.947Mg C ha–1, with the highest and lowest values being observed in soil layer and shrub-grass layers, respectively, implying that soil is the main body of forest carbon storage. Young-aged forests accounted for a greater proportion of forests in the Province than forests in other age classes; and proper management of forests could increase the carbon sequestration in the forest ecosystems. The comparison with previous estimations of carbon storage for forest ecosystem implied that methods and data used for forest carbon storage estimation affected the results of estimates obviously.

Accuracy assessment of four cloud-free snow cover products over the Qinghai-Tibetan Plateau

ABSTRACT Four up-to-date daily cloud-free snow products – IMS (Interactive Multisensor Snow products), MOD-SSM/I (combination of the MODIS and SSM/I snow products), MOD-B (Blending method basing on the MODIS snow cover products) and TAI (Terra–Aqua–IMS) – with high-resolutions over the Qinghai-Tibetan Plateau (QTP) were comprehensively assessed. Comparisons of the IMS, MOD-SSM/I, MOD-B and TAI cloud-free snow products against meteorological stations observations over 10 snow seasons (2004–2013) over the QTP indicated overall accuracies of 76.0%, 89.3%, 92.0% and 92.0%, respectively. The Khat values of the IMS, MOD-SSM/I, MOD-B and TAI products were 0.084, 0.463, 0.428 and 0.526, respectively. The TAI products appear to have the best cloud-removal ability among the four snow products over the QTP. Based on the assessment, an I-TAI (Improvement of Terra–Aqua–IMS) snow product was proposed, which can improve the accuracy to some extent. However, the algorithms of the MODIS series products show instability when identifying wet snow and snow under forest cover over the QTP. The snow misclassification is an important limitation of MODIS snow cover products and requires additional improvements.