Download or read book Assessing the Ability of Using Multi angular CHRIS PROBA Data for Estimating Snow Cover and Snow Properties in an Alpine Area written by Jan Martijn Roetman and published by . This book was released on 2009 with total page 89 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Assessing the Ability of Using Multi angular CHRIS PROBA Data for Estimating Snow Cover and Snow Properties in an Alpine Area written by Jan Martijn Roetman and published by . This book was released on 2009 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Snow is an important aspect of hydrologic and climatic cycle.

Download or read book Fractional Snow Cover Estimation in Complex Alpine forested Environments Using Remotely Sensed Data and Artificial Neural Networks written by Elzbieta Halina Czyzowska-Wisniewski and published by . This book was released on 2014 with total page 258 pages. Available in PDF, EPUB and Kindle. Book excerpt: There is an undisputed need to increase accuracy of snow cover estimation in regions comprised of complex terrain, especially in areas dependent on winter snow accumulation for a substantial portion of their annual water supply, such as the Western United States, Central Asia, and the Andes. Presently, the most pertinent monitoring and research needs related to alpine snow cover area (SCA) are: (1) to improve SCA monitoring by providing detailed fractional snow cover (FSC) products which perform well in temporal/spatial heterogeneous forested and/or alpine terrains; and (2) to provide accurate measurements of FSC at the watershed scale for use in snow water equivalent (SWE) estimation for regional water management. To address the above, the presented research approach is based on Landsat Fractional Snow Cover (Landsat-FSC), as a measure of the temporal/spatial distribution of alpine SCA. A fusion methodology between remotely sensed multispectral input data from Landsat TM/ETM+, terrain information, and IKONOS are utilized at their highest respective spatial resolutions. Artificial Neural Networks (ANNs) are used to capture the multi-scale information content of the input data compositions by means of the ANN training process, followed by the ANN extracting FSC from all available information in the Landsat and terrain input data compositions. The ANN Landsat-FSC algorithm is validated (RMSE ̃0.09; mean error ̃0.001-0.01 FSC) in watersheds characterized by diverse environmental factors such as: terrain, slope, exposition, vegetation cover, and wide-ranging snow cover conditions. ANN input data selections are evaluated to determine the nominal data information requirements for FSC estimation. Snow/non-snow multispectral and terrain input data are found to have an important and multi-faced impact on FSC estimation. Constraining the ANN to linear modeling, as opposed to allowing unconstrained function shapes, results in a weak FSC estimation performance and therefore provides evidence of non-linear bio-geophysical and remote sensing interactions and phenomena in complex mountain terrains. The research results are presented for rugged areas located in the San Juan Mountains of Colorado, and the hilly regions of Black Hills of Wyoming, USA.

Download or read book Estimating Snow Depth of Alpine Snowpack Via Airborne Multifrequency Passive Microwave Radiance Observations written by Rhae Sung Kim and published by . This book was released on 2017 with total page 114 pages. Available in PDF, EPUB and Kindle. Book excerpt: Based on a study of Tb spectra, we proposed a new snow depth retrieval algorithm for mountainous deep snow using airborne multifrequency PM radiance observation. In contrast to previous snow depth estimations using satellite PM radiance assimilation, the newly- proposed method utilized a single flight observation and deployed the snow hydrologic models as a basis for a “snapshot” retrieval algorithm. This method is promising since the satellite-based retrieval methods have difficulties to estimate snow depth due to their coarse resolution and computational effort. Our approach consists of a particle filter using combinations of multiple PM frequencies and multi-layer snow physical model (i.e., Crocus) to resolve melt-refreeze crusts. Results showed that there was a significant improvement over the prior snow depth estimates and the capability to reduce the prior snow depth biases. When applying our snow depth retrieval algorithm using a combination of four PM frequencies (10.7-, 18.7-, 37.0-, and 89.0 GHz), the root mean square error (RMSE) values were reduced by 62% at the snow depth transects sites where forest density was less than 5% despite deep snow conditions. This method displayed a higher sensitivity to different combinations of frequencies, model stratigraphy (i.e. different number of layering scheme for snow physical model) and estimation methods (particle filter and Kalman filter) except the forest cover density and precipitation bias. The prior RMSE values at the forest-covered areas were reduced by 27 - 41% even in the presence of forest cover.

Download or read book Snow Cover Measurements and Areal Assessment of Precipitation and Soil Moisture written by Boris Sevruk and published by World Meteorological Organization. This book was released on 1992 with total page 316 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Assessment and Improvement of Snow Datasets Over the United States written by Nicholas Dawson and published by . This book was released on 2017 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Improved knowledge of the cryosphere state is paramount for continued model development and for accurate estimates of fresh water supply. This work focuses on evaluation and potential improvements of current snow datasets over the United States. Snow in mountainous terrain is most difficult to quantify due to the slope, aspect, and remote nature of the environment. Due to the difficulty of measuring snow quantities in the mountains, the initial study creates a new method to upscale point measurements to area averages for comparison to initial snow quantities in numerical weather prediction models. The new method is robust and cross validation of the method results in a relatively low mean absolute error of 18% for snow depth (SD). Operational models at the National Centers for Environmental Prediction which use Air Force Weather Agency (AFWA) snow depth data for initialization were found to underestimate snow depth by 77% on average. Larger error is observed in areas that are more mountainous. Additionally, SD data from the Canadian Meteorological Center, which is used for some model evaluations, performed similarly to models initialized with AFWA data. The use of constant snow density for snow water equivalent (SWE) initialization for models which utilize AFWA data exacerbates poor SD performance with dismal SWE estimates. A remedy for the constant snow density utilized in NCEP snow initializations is presented in the next study which creates a new snow density parameterization (SNODEN). SNODEN is evaluated against observations and performance is compared with offline land surface models from the National Land Data Assimilation System (NLDAS) as well as the Snow Data Assimilation System (SNODAS). SNODEN has less error overall and reproduces the temporal evolution of snow density better than all evaluated products. SNODEN is also able to estimate snow density for up to 10 snow layers which may be useful for land surface models as well as conversion of remotely-sensed SD to SWE. Due to the poor performance of previously evaluated snow products, the last study evaluates openly-available remotely-sensed snow datasets to better understand the strengths and weaknesses of current global SWE datasets. A new SWE dataset developed at the University of Arizona is used for evaluation. While the UA SWE data has already been stringently evaluated, confidence is further increased by favorable comparison of UA snow cover, created from UA SWE, with multiple snow cover extent products. Poor performance of remotely-sensed SWE is still evident even in products which combine ground observations with remotely-sensed data. Grid boxes that are predominantly tree covered have a mean absolute difference up to 87% of mean SWE and SWE less than 5 cm is routinely overestimated by 100% or more. Additionally, snow covered area derived from global SWE datasets have mean absolute errors of 20%-154% of mean snow covered area.

Download or read book Multivariate Land Snow Data Assimilation in the Northern Hemisphere written by Yongfei Zhang and published by . This book was released on 2015 with total page 116 pages. Available in PDF, EPUB and Kindle. Book excerpt: The past few decades have seen decreasing trends of snow-covered regions in the Northern Hemisphere. It remains unknown how these trends affect the spatial and temporal variability of snowpack water storage, a variable with significant implications for managing water resources to meet agricultural, municipal, and hydropower demands. To improve snowpack estimates, this dissertation developed a new snow data assimilation system (SNODAS) through multi-institutional collaborations. The new SNODAS consists of coupling of the Community Land Model version 4 (CLM4) and the Data Assimilation Research Testbed (DART), which is capable of assimilating multi-sensor satellite observations including the Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover fraction (SCF) and the Gravity Recovery and Climate Experiment (GRACE) terrestrial water storage (TWS) anomalies. This dissertation describes the new SNODAS, presents the results of the data assimilation of MODIS SCF and GRACE TWS observations, and assesses the influence of uncertainties from multiple sources on the SNODAS performance. The first two studies compared the open loop run and the assimilation runs to evaluate the data assimilation (DA) performance. Data assimilation results were also evaluated against other independent observation-based snow data on daily and monthly timescales. Both assimilations can improve the snowpack simulations in CLM4; their strengths and drawbacks were discussed. When only MODIS SCF is assimilated, the innovation (i.e. the difference between analysis and forecast) is marginal in the regions where the snow cover extent reaches 100% regardless of snow mass changes. Further assimilation of GRACE TWS anomalies, however, can adjust the modeled snowpack, resulting in noteworthy improvements over the MODIS-only run in high-latitude regions. The effectiveness of the assimilation was analyzed over several Arctic river basins and various land covers. The third study discussed the influences of atmospheric forcing, model structure, DA technique, and satellite remote sensing product within the framework of SNODAS. The atmospheric forcing uncertainty is found to be the largest among the various uncertainty sources examined, especially over the Tibetan Plateau and most of the mid- and high-latitudes. The uncertainty of model structure as represented by two different parameterizations of SCF is the second largest. DA methods and products of GRACE TWS data have relatively less impacts. This study also showed that CLM4.5 produces better TWS anomalies than CLM4, which would have implications for improving the performance of GRACE TWS data assimilation.

Download or read book Assessment of ICESat 2 Level 3a Products for Snow Depth Estimation in Remote Mountainous Watersheds written by Colten Michael Elkin and published by . This book was released on 2021 with total page 67 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Seasonal snowpack accounts for ~70% of the water supply in the western United States, and measuring snow accumulation and ablation remotely has long been a stated goal of NASA. The 2018 launch of ICESat-2, a spaceborne Lidar system, has offered unparalleled spatial and temporal coverage of mountainous terrain with the potential for unprecedented vertical accuracy. Data from ICESat-2 are used to measure seasonal snow depths using the level-3A ATL08 (land and canopy elevation) product for the Reynolds Creek Experimental Watershed in southwest Idaho and the ATL06 (land ice elevation) product for Wolverine Creek in the Kenai Mountains of Alaska. The methodology for coregistering ICESat-2 transects to reference digital terrain models then estimating snow depths as the difference between the ICESat-2 and reference elevations is described. Median and MAD snow depths for transects from 2019 and 2020 are 3.1 +/- 6.7m at Reynolds Creek EW and are 5.5 +/- 2.1m at Wolverine glacier. Here we find that measuring snow depths using ICESat-2 is crude in variable, vegetated terrain covered by the ATL08 data product, and that there is not a strong relationship between the residual values reported at Reynolds Creek EW and terrain parameters such as slope, aspect, vegetative coverage, and elevation. We do find that the ATL06 analysis results in reasonable first-order estimates of snow depth but that the evolution of the glacier surface elevations must be more accurately constrained in order to ensure the snow depth estimates are unbiased."--Boise State University ScholarWorks.

Download or read book A Synergic Approach for Estimating Snow Cover Properties Using Optical and Active passive Microwave Observations written by M.J. Malik and published by . This book was released on 2009 with total page 60 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Fractional Snow cover Mapping Through Artificial Neural Network Analysis of MODIS Surface Reflectance written by Iliyana Dancheva Dobreva and published by . This book was released on 2010 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Accurate areal measurements of snow-cover extent are important for hydrological and climate modeling. The traditional method of mapping snow cover is binary where a pixel is approximated to either snow-covered or snow-free. Fractional snow cover (FSC) mapping achieves a more precise estimate of areal snow-cover extent by determining the fraction of a pixel that is snow-covered. The two most common FSC methods using Moderate Resolution Imaging Spectroradiometer (MODIS) images are linear spectral unmixing and the empirical Normalized Difference Snow Index (NDSI) method. Machine learning is an alternative to these approaches for estimating FSC, as Artificial Neural Networks (ANNs) have been used for estimating the subpixel abundances of other surfaces. The advantages of ANNs over the other approaches are that they can easily incorporate auxiliary information such as land-cover type and are capable of learning nonlinear relationships between surface reflectance and snow fraction. ANNs are especially applicable to mapping snow-cover extent in forested areas where spatial mixing of surface components is nonlinear. This study developed an ANN approach to snow-fraction mapping. A feed-forward ANN was trained with backpropagation to estimate FSC from MODIS surface reflectance, NDSI, Normalized Difference Vegetation Index (NDVI) and land cover as inputs. The ANN was trained and validated with high spatial-resolution FSC derived from Landsat Enhanced Thematic Mapper Plus (ETM+) binary snow-cover maps. ANN achieved best result in terms of extent of snow-covered area over evergreen forests, where the extent of snow cover was slightly overestimated. Scatter plot graphs of the ANN and reference FSC showed that the neural network tended to underestimate snow fraction in high FSC and overestimate it in low FSC. The developed ANN compared favorably to the standard MODIS FSC product with the two methods estimating the same amount of total snow-covered area in the test scenes.

Download or read book On the Use of Modis Snow Cover Product for Assessing Snow Extension and Duration Over the Po River Basin written by Pierfrancesco Da Ronco and published by . This book was released on 2014 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Changes in climatic forcings on the Mediterranean area are foreseen by many global and regional models. Long-term temperature variations are expected to affect snow dynamics, thus impacting on the timing of the hydrologic response in Alpine catchments. Several authors agree in predicting a shift of the hydrologic regime in the major European rivers, due to a changed behavior of mountain valleys. In this context, digital snow maps are a powerful tool for reproducing large-scale snow distribution and extension. The use of such information for hydrological purposes is now considered an outlet of great practical interest. Here, we consider the Snow Covered Area (SCA) Product and develop a procedure for snow detection from MODIS data (MODerate resolution Imaging Spectroradiometeron on board Terra and Aqua satellites). MODIS snow maps have proven to be very reliable, despite the presence of cloudiness can mask the ground, thus preventing any snow detection. An application to Po river basin, in northern Italy, is here proposed. Daily maps of few years (2003-2007) have been analyzed and compared with the Digital Elevation Model (DEM) of the study area. By some clear-sky days we got complete maps of snow areas throughout the seasons, reproducing large-scale snow cover duration and its dependence on altitude. Focusing on the issue of cloudiness, we highlighted its increase at higher elevation. From our point of view, after this first attempt, MODIS Snow Products seem to have great applicative potentiality. However, many applications are conditioned to the possibility of providing a reliable estimation of ground conditions beneath the clouds.

Download or read book Large scale Snowpack Estimation Using Ensemble Data Assimilation Methodologies Satellite Observations and Synthetic Datasets written by Hua Su (Ph. D.) and published by . This book was released on 2009 with total page 274 pages. Available in PDF, EPUB and Kindle. Book excerpt: This work focuses on a series of studies that contribute to the development and test of advanced large-scale snow data assimilation methodologies. Compared to the existing snow data assimilation methods and strategies, which are limited in the domain size and landscape coverage, the number of satellite sensors, and the accuracy and reliability of the product, the present work covers the continental domain, compares single- and multi-sensor data assimilations, and explores uncertainties in parameter and model structure. In the first study a continental-scale snow water equivalent (SWE) data assimilation experiment is presented, which incorporates Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover fraction (SCF) data to Community Land Model (CLM) estimates via the ensemble Kalman filter (EnKF). The greatest improvements of the EnKF approach are centered in the mountainous West, the northern Great Plains, and the west and east coast regions, with the magnitude of corrections (compared to the use of model only) greater than one standard deviation (calculated from SWE climatology) at given areas. Relatively poor performance of the EnKF, however, is found in the boreal forest region. In the second study, snowpack related parameter and model structure errors were explicitly considered through a group of synthetic EnKF simulations which integrate synthetic datasets with model estimates. The inclusion of a new parameter estimation scheme augments the EnKF performance, for example, increasing the Nash-Sutcliffe efficiency of season-long SWE estimates from 0.22 (without parameter estimation) to 0.96. In this study, the model structure error is found to significantly impact the robustness of parameter estimation. In the third study, a multi-sensor snow data assimilation system over North America was developed and evaluated. It integrates both Gravity Recovery and Climate Experiment (GRACE) Terrestrial water storage (TWS) and MODIS SCF information into CLM using the ensemble Kalman filter (EnKF) and smoother (EnKS). This GRACE/MODIS data assimilation run achieves a significantly better performance over the MODIS only run in Saint Lawrence, Fraser, Mackenzie, Churchill & Nelson, and Yukon river basins. These improvements demonstrate the value of integrating complementary information for continental-scale snow estimation.

Download or read book Snow Weather and Avalanches written by and published by . This book was released on 2004 with total page 154 pages. Available in PDF, EPUB and Kindle. Book excerpt: laminated front and back cover with plastic spiral binding

Download or read book The Mechanical Properties of Sea Ice written by W. F. Weeks and published by . This book was released on 1967 with total page 100 pages. Available in PDF, EPUB and Kindle. Book excerpt: The review discusses the state of thinking of each of the main national groups investigating sea ice and gives an overall appraisal of the field as a whole. Emphasis is placed on (1) the physical basis for interpreting sea ice strength (phase relations, air volume, and structural considerations), (2) theoretical considerations (strength models, air bubbles and salt reinforcement, and interrelations between growth conditions and strength), (3) experimental results (tensile, flexural, shear, and compressive strength, elastic modulus, shear modulus and Poisson's ratio, time dependent effects, and creep), and (4) plate characteristics. The paper includes a review of problems in sea ice investigations, relates the chemical, crystallographic, mechanical, and physical aspects involved, and concludes by showing how to utilize this knowledge to solve practical problems. (Author).

Download or read book Quaternary Dating Methods written by Mike Walker and published by John Wiley & Sons. This book was released on 2013-04-30 with total page 311 pages. Available in PDF, EPUB and Kindle. Book excerpt: This introductory textbook introduces the basics of dating, the range of techniques available and the strengths and limitations of each of the principal methods. Coverage includes: the concept of time in Quaternary Science and related fields the history of dating from lithostratigraphy and biostratigraphy the development and application of radiometric methods different methods in dating: radiometric dating, incremental dating, relative dating and age equivalence Presented in a clear and straightforward manner with the minimum of technical detail, this text is a great introduction for both students and practitioners in the Earth, Environmental and Archaeological Sciences. Praise from the reviews: "This book is a must for any Quaternary scientist." SOUTH AFRICAN GEOGRAPHICAL JOURNAL, September 2006 “...very well organized, clearly and straightforwardly written and provides a good overview on the wide field of Quaternary dating methods...” JOURNAL OF QUATERNARY SCIENCE, January 2007

Download or read book Climate Change 2014 written by Groupe d'experts intergouvernemental sur l'évolution du climat and published by . This book was released on 2015 with total page 151 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Handbook of Environmental Engineering written by Myer Kutz and published by John Wiley & Sons. This book was released on 2018-10-16 with total page 768 pages. Available in PDF, EPUB and Kindle. Book excerpt: A comprehensive guide for both fundamentals and real-world applications of environmental engineering Written by noted experts, Handbook of Environmental Engineering offers a comprehensive guide to environmental engineers who desire to contribute to mitigating problems, such as flooding, caused by extreme weather events, protecting populations in coastal areas threatened by rising sea levels, reducing illnesses caused by polluted air, soil, and water from improperly regulated industrial and transportation activities, promoting the safety of the food supply. Contributors not only cover such timely environmental topics related to soils, water, and air, minimizing pollution created by industrial plants and processes, and managing wastewater, hazardous, solid, and other industrial wastes, but also treat such vital topics as porous pavement design, aerosol measurements, noise pollution control, and industrial waste auditing. This important handbook: Enables environmental engineers to treat problems in systematic ways Discusses climate issues in ways useful for environmental engineers Covers up-to-date measurement techniques important in environmental engineering Reviews current developments in environmental law for environmental engineers Includes information on water quality and wastewater engineering Informs environmental engineers about methods of dealing with industrial and municipal waste, including hazardous waste Designed for use by practitioners, students, and researchers, Handbook of Environmental Engineering contains the most recent information to enable a clear understanding of major environmental issues.