Monitoring Snow Cover and Snowmelt Dynamics and Assessing their Influences on Inland Water Resources

Snow is one of the most vital cryospheric components owing to its wide coverage as well as its unique physical characteristics. It not only affects the balance of numerous natural systems but also influences various socio-economic activities of human beings. Notably, the importance of snowmelt water to global water resources is outstanding, as millions of populations rely on snowmelt water for daily consumption and agricultural use. Nevertheless, due to the unprecedented temperature rise resulting from the deterioration of climate change, global snow cover extent (SCE) has been shrinking significantly, which endangers the sustainability and availability of inland water resources. Therefore, in order to understand cryo-hydrosphere interactions under a warming climate, (1) monitoring SCE dynamics and snowmelt conditions, (2) tracking the dynamics of snowmelt-influenced waterbodies, and (3) assessing the causal effect of snowmelt conditions on inland water resources are indispensable. However, for each point, there exist many research questions that need to be answered. Consequently, in this thesis, five objectives are proposed accordingly. Objective 1: Reviewing the characteristics of SAR and its interactions with snow, and exploring the trends, difficulties, and opportunities of existing SAR-based SCE mapping studies; Objective 2: Proposing a novel total and wet SCE mapping strategy based on freely accessible SAR imagery with all land cover classes applicability and global transferability; Objective 3: Enhancing total SCE mapping accuracy by fusing SAR- and multi-spectral sensor-based information, and providing total SCE mapping reliability map information; Objective 4: Proposing a cloud-free and illumination-independent inland waterbody dynamics tracking strategy using freely accessible datasets and services; Objective 5: Assessing the influence of snowmelt conditions on inland water resources

Remote Sensing of Precipitation: Volume 2

Precipitation is a well-recognized pillar in global water and energy balances. An accurate and timely understanding of its characteristics at the global, regional, and local scales is indispensable for a clearer understanding of the mechanisms underlying the Earth’s atmosphere–ocean complex system. Precipitation is one of the elements that is documented to be greatly affected by climate change. In its various forms, precipitation comprises a primary source of freshwater, which is vital for the sustainability of almost all human activities. Its socio-economic significance is fundamental in managing this natural resource effectively, in applications ranging from irrigation to industrial and household usage. Remote sensing of precipitation is pursued through a broad spectrum of continuously enriched and upgraded instrumentation, embracing sensors which can be ground-based (e.g., weather radars), satellite-borne (e.g., passive or active space-borne sensors), underwater (e.g., hydrophones), aerial, or ship-borne

Water Resource Variability and Climate Change

Climate change affects global and regional water cycling, as well as surficial and subsurface water availability. These changes have increased the vulnerabilities of ecosystems and of human society. Understanding how climate change has affected water resource variability in the past and how climate change is leading to rapid changes in contemporary systems is of critical importance for sustainable development in different parts of the world. This Special Issue focuses on “Water Resource Variability and Climate Change” and aims to present a collection of articles addressing various aspects of water resource variability as well as how such variabilities are affected by changing climates. Potential topics include the reconstruction of historic moisture fluctuations, based on various proxies (such as tree rings, sediment cores, and landform features), the empirical monitoring of water variability based on field survey and remote sensing techniques, and the projection of future water cycling using numerical model simulations

Modeling and dating glacier fluctuations and their relation to Pacific Ocean climate

This thesis presents the results of an investigation into the interactions between the present-day South Cascade Glacier and the former Mauna Kea ice cap at short (annual to centennial) and long (millennial and multimillennial) time scales. To quantify the response of South Cascade Glacier to atmospheric conditions, a surface energy balance model has been developed. This model has been applied to annual simulations of the mass balance of South Cascade Glacier and is shown to faithfully simulate ablation on all time scales from daily to seasonal. An investigation into the sensitivity of this model to uncertainties in the physical parameters and input data is conducted and provides a comprehensive indication of the uncertainty associated with surface energy balance model estimates of mass balance. These uncertainties are of the order of 10% of the annual mass flux of the glacier. The model is then used in conjunction with a regional model downscaling of climate data and a high resolution (0.5°) gridded observational data set to compute the long-term mass balance history of South Cascade Glacier. Our simulations show that the greatest rate of volume loss in the history of the glacier was in the late 1930s through the mid 1940s. However, present day mass loss is equivalent despite the more climatologically favorable position of the glacier today. Simulated mass balance is compared with Pacific climate indexes and show that the glacier’s relationship to oceanic conditions peaked in the middle part of the 20th century and currently shows a sharp decline. Finally, we present an investigation of the deglacial chronology of Mauna Kea. Our results establish the age of the local last glacial maximum at an age of 22.1 ± 2.1 kyr BP and complete deglaciation was underway by 14.7 ± 1.4 kyr BP. We present strong evidence that retreat after the LGM was followed by a readvance at 16.1 to 16.8 kyr BP. The timing of this readvance is comparable to that of Heinrich event 1 in the North Atlantic. The connection between the North Atlantic and Hawaii climate is discussed in terms of atmospheric modeling results and proxy evidence

Water budget record from variable infiltration capacity (VIC) model

No abstract available

Validation of Current Moderate Resolution Imaging Spectroradiometer (MODIS) Daily Snow Albedo Product and Spatial Analysis Based on Multiple Sensors

Snow albedo is one of the most important factors for atmosphere-surface energy exchange in high latitude areas. Remote sensing provides continual observations of snow albedo. However, the reliability of snow albedos obtained from remotely sensed images can be problematic, especially when acquired over heterogeneous land surfaces. This research examines spatial variations in snow albedo observed under different conditions in order to assess how accurate an individual in situ observation of snow albedo is when compared to the Moderate Resolution Imaging Spectroradiometer (MODIS) daily snow albedo product (MOD10A1) and its relationship with land surface types. In addition to the field observations, albedos retrieved from two SURFRAD stations are also examined. The overall Root Mean Square Error (RMSE) between the in situ and MODIS albedos is 8%. Semivariogram analysis of Landsat ETM+ snow albedo retrievals on January 26th, 2010 over an ice and snow covered lake indicates spatial autocorrelation lengths of approximately 260 m, suggesting limited in situ observation can be considered fairly representative of albedos retrieved from MODIS images. To further reveal what parameters could influence the spatial representativeness, this research examined landscape metrics based on seven binary snow maps created from Landsat images for three areas of differing roughness and for different snow cover conditions. There are two Landscape metrics, Mean Shape Index (MSI) and Area Weighted Shape Index (AWMSI), were found to be correlated the spatial autocorrelation lengths of snow albedo as measured from the range distance of the modeled semivariograms. In addition, this research also introduced a method of using multi-angle mast to measure the surface bidirectional reflectance distribution function (BRDF). This method could be used for further research to build the BRDF library of the snow-covered canopies

Overview of NASA's MODIS and Visible Infrared Imaging Radiometer Suite (VIIRS) snow-cover Earth System Data Records

Knowledge of the distribution, extent, duration and timing of snowmelt is critical for characterizing the Earth's climate system and its changes. As a result, snow cover is one of the Global Climate Observing System (GCOS) essential climate variables (ECVs). Consistent, long-term datasets of snow cover are needed to study interannual variability and snow climatology. The NASA snow-cover datasets generated from the Moderate Resolution Imaging Spectroradiometer (MODIS) on the Terra and Aqua spacecraft and the Suomi National Polar-orbiting Partnership (S-NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) are NASA Earth System Data Records (ESDR). The objective of the snow-cover detection algorithms is to optimize the accuracy of mapping snow-cover extent (SCE) and to minimize snow-cover detection errors of omission and commission using automated, globally applied algorithms to produce SCE data products. Advancements in snow-cover mapping have been made with each of the four major reprocessings of the MODIS data record, which extends from 2000 to the present. MODIS Collection 6 (C6; https://nsidc.org/data/modis/data_summaries) and VIIRS Collection 1 (C1; https://doi.org/10.5067/VIIRS/VNP10.001) represent the state-of-the-art global snow-cover mapping algorithms and products for NASA Earth science. There were many revisions made in the C6 algorithms which improved snow-cover detection accuracy and information content of the data products. These improvements have also been incorporated into the NASA VIIRS snow-cover algorithms for C1. Both information content and usability were improved by including the Normalized Snow Difference Index (NDSI) and a quality assurance (QA) data array of algorithm processing flags in the data product, along with the SCE map. The increased data content allows flexibility in using the datasets for specific regions and end-user applications. Though there are important differences between the MODIS and VIIRS instruments (e.g., the VIIRS 375 m native resolution compared to MODIS 500 m), the snow detection algorithms and data products are designed to be as similar as possible so that the 16+ year MODIS ESDR of global SCE can be extended into the future with the S-NPP VIIRS snow products and with products from future Joint Polar Satellite System (JPSS) platforms. These NASA datasets are archived and accessible through the NASA Distributed Active Archive Center at the National Snow and Ice Data Center in Boulder, Colorado