Download or read book A Snow Water Equivalent Reanalysis Approach to Explore Spatial and Temporal Variability of the Sierra Nevada Snowpack written by Manuela Girotto and published by . This book was released on 2014 with total page 143 pages. Available in PDF, EPUB and Kindle. Book excerpt: The availability and variability of snowmelt has become a serious concern because of increased water demand, and because of the high degree of uncertainty related to climate variability posing a threat to the magnitude and timing of this precious resource. Understanding the geophysical controls and interannual variability of the spatial patterns of seasonal montane snowpacks are critical for understanding the effects of a warmer climate on the snowpack water storage. To explicitly resolve snow hydrological controls in complex montane environments, it is necessary to provide high resolution spatially and temporally distributed estimates of snow water equivalent, while also taking into consideration the uncertainties in the system. Toward this end, this dissertation developed a retrospective data assimilation technique (SWE reanalysis) that aimed to optimally merge VIS-NIR remote sensing data into a snow prediction model, and at the same time, account for the limitations of measurements, forcings, and model errors. The SWE reanalysis was: first developed and implemented over a small region, in order to investigate the performance of the methods under their nominal scenarios; second implemented for the full Landsat-5 record (27 year) over a regional scale domain in order to test accuracy and gain insight on the spatial and interannual controls on the SWE patterns; third extended to the entire Sierra Nevada in order to benchmark the reanalysis for its application to the full Sierra Nevada and to preliminarly [i.e. preliminarily] understand what are the spatial controls on SWE patterns. The key findings of this dissertation can be summarized as follows: 1) The SWE reanalysis approach provided accurate spatially and continuous estimates of SWE and of its uncertainties due to measurement, forcings, and model errors. 2) The methods were found to be robust to input errors such as biases in solar radiation and precipitation, and robust to the number of available VIS-NIR observations. 3) The application of the methods over the Kern watershed for the full Landsat-5 record suggested that SWE accumulation patterns were in general not interannually consistent and that the interannual variability was dependent on whether a dry or wet year was analyzed. 4) The trend test analysis showed that peak-SWE and day-of-peak have not drastically changed over the analyzed 27 years for the Kern River watershed, but suggested that the lower elevations may be more susceptible to climate variability and change. 5) Elevation was found to be the primary control on spatial patterns of peak-SWE and day-of-peak for the entire Sierra Nevada range; however different patterns were found across the watersheds of the Sierra Nevada depending on their location. Ultimately, the methods can be applied to the full Sierra Nevada and other montane regions over the modern remote sensing record to generate a dataset that should be useful to scientists and practitioners not only in hydrology, but other fields where seasonal snow processes are a key driver such as biogeochemistry, mountain meteorology, and water resource management.
Download or read book Improving the Understanding of the Spatiotemporal Variability of Hydrometeorology Across the Sierra Nevada Using a Novel Remote Sensing Reanalysis Approach written by Laurie Susan Huning and published by . This book was released on 2017 with total page 163 pages. Available in PDF, EPUB and Kindle. Book excerpt: While large populations worldwide depend on water derived from the seasonal snowpack, a detailed picture of the spatiotemporal variability of snowfall and snow water equivalent (SWE) across high-elevation mountain ranges remains a knowledge gap in understanding the hydrologic cycle. Previous studies relying on point-scale in situ measurements often yielded spatially incomplete characterizations of montane snow accumulation processes (e.g. orographic snowfall). These limitations were overcome in this dissertation by using a novel, high-resolution distributed snow reanalysis over Sierra Nevada, USA from 1985-2015. Across the 20 basins examined, over 50% of the integrated cumulative snowfall (CS) accumulated rapidly in less than or equal to six days or three snowstorms, on average, and the largest snowstorms yielded an average 27% of the seasonal CS. Results suggest that misrepresentation of a single snowstorm could lead to significant biases in CS. The hydroclimatology of the Sierra Nevada was found to be driven by extremes as manifested in the high inter-annual variability of its seasonally-integrated CS, 4.4-41.3 km3, over the record. Seasonal orographic CS gradients were shown to be highly variable, ranging from over 15 cm SWE/100 m to under 1 cm/100 m. Hence, the seasonal/elevational distribution of water storage can greatly vary with the western Sierra Nevada experiencing about twice as much orographic enhancement during wet years as in dry years. Among the largest winter snowstorms, moisture-rich atmospheric rivers (ARs) significantly contribute to the seasonal CS. Using both satellite-based integrated water vapor and reanalysis-based integrated vapor transport methods, AR-derived CS was found to be more orographically enhanced than non-AR derived CS above ~2200 m in the western Sierra Nevada; however, the understanding of the AR-derived CS distribution and enhancement is tightly coupled to the AR detection method applied. ARs were shown to contribute from ~33-56% of the seasonal CS, on average from 1998-2015, depending on the AR detection method utilized. Overall, more robust characterizations of the spatiotemporal variability and climatology of snowfall distributions, atmospheric drivers of snowfall, and accumulation rates than previously existed were provided. The resulting insight could be used for improving water resources management and hydrologic analysis as well as evaluating climate model snowpack estimates and improving their representation of subgrid snow processes (e.g. orographic snowfall).
Download or read book Estimating Snow Water Resources from Space written by Dongyue Li and published by . This book was released on 2016 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Improving the estimation of snow water equivalent (SWE) in the Sierra Nevada is critical for the water resources management in California. In this study, we carried out an experiment to estimate SWE in the Upper Kern Basin, Sierra Nevada, by assimilating AMSR-E observed brightness temperatures (Tb) into a coupled hydrology and radiative transfer model using an ensemble Kalman batch reanalysis. The data assimilation framework merges the complementary SWE information from modeling and observations to improve SWE estimates. The novelty of this assimilation study is that both the modeling and the radiance data processing were specifically improved to provide more information about SWE. With the enhanced SWE signals in both simulations and observations, the batch reanalysis stands a better chance of successfully improving the SWE estimates. The modeling was at a very high resolution (90m) and spanned a range of mountain environmental factors to better characterize the effects of the mountain environment on snow distribution and radiance emission. We have developed a dynamic snow grain size module to improve the radiance modeling during the intense snowfall events. The AMSR-E 37GHz V-pol observed Tb was processed at its native footprint resolution at ~100 square km. In the batch assimilation, the model predicted the prior SWE and Tb; the prior estimate of an entire year was then updated by the dry-season observations at one time. One advantage of this is that the prior SWE of a certain period is updated using the observations both before and after this period, which takes advantage of the temporally continuous signal of the seasonal snow accumulation in the observations. We found the posterior SWE estimates showed improved accuracy and robustness. During the study period of 2003 to 2008, at point-scale, the average bias of the six-year April 1st SWE was reduced from -0.17 m to -0.02m, the average temporal SWE RMSE of the snow accumulation season decreased by 51.2%. The basin-scale results showed that the April 1st SWE bias reduced from -0.17m to -0.11m, and the temporal SWE RMSE of the accumulation season decreased by 23.6%.
Download or read book Towards Large scale Implementation of a High Resolution Snow Reanalysis Over Midlatitude Montane Ranges written by Elisabeth Baldo and published by . This book was released on 2017 with total page 137 pages. Available in PDF, EPUB and Kindle. Book excerpt: Accurately representing the spatial variability of montane snowpack is challenging due to the high degree of complexity in the terrain's topography, and the lack of good quality in-situ data. To explicitly resolve snow processes in montane environment while taking into account the uncertainties in the system's high spatial and temporal resolution snow reanalyses are required. Ensemble-based approaches assimilating Landsat VIS-NIR remote sensing data at spatial resolutions of 100 m or less are, however, prohibitively expensive to run at large scales, and sub-optimal given that only the most complex parts of a montane range require such fine resolution. In addition, the assimilation of remote sensing data from a single source can also be unsatisfactory due to a lack of global coverage, hardware malfunction etc. In order to optimize computational needs while preserving the accuracy of ~ 100 m reanalyses, a raster-based multi-resolution approach was first developed and successfully implemented for a headwater catchment in the Colorado River Basin over the full length of Landsat record (30+ years). The potential use of MODIS-derived snow cover information in addition to Landsat in snow reanalyses was then investigated over three different regions in the Western U.S. and High Mountain Asia in order to make up for Landsat's shortcomings over midlatitude snowpacks. The key findings of this dissertation can be summarized as follows: 1) The physiographic complexity of a terrain can be characterized by its standard deviations of elevation, northness index and forested fraction. Using such a complexity metric to discretize the terrain into different spatial resolutions via a multi-resolution approach can significantly reduce computational needs, while mitigating errors in snow processes representation. 2) The multi-resolution approach did not significantly impact the remote sensing observations assimilated, and the posterior snow water equivalent (SWE) ensemble median and standard deviation matched the 90 m reanalysis, thus leading to a robust implementation in the context of a data assimilation framework. 3) A MODIS-derived snow cover product was found to be a useful complementary source of remote sensing data to be simultaneously assimilated with Landsat. Off-nadir-looking observations first had to be screened out of the reanalysis due to the distorting and snow obscuring effects of the sensor viewing geometry at high zenith angles. Ultimately, the methods developed in this work can be applied to all midlatitude montane ranges over the full lengths of Landsat and MODIS records to generate a fine spatial resolution SWE reanalysis dataset that will be useful to snow hydrologists to solve many unanswered science questions over such challenging regions.
Download or read book Validating Reconstruction of Snow Water Equivalent in California s Sierra Nevada Using Measurements from the NASAAirborne Snow Observatory written by Robert E. Davis and published by . This book was released on 2016 with total page 24 pages. Available in PDF, EPUB and Kindle. Book excerpt: Accurately estimating basin‐wide snow water equivalent (SWE) is the most important unsolved problem in mountain hydrology. Models that rely on remotely sensed inputs are especially needed in ranges with few surface measurements. The NASA Airborne Snow Observatory (ASO) provides estimates of SWE at 50 m spatial resolution in several basins across the Western U.S. during the melt season. Primarily, water managers use this information to forecast snowmelt runoff into reservoirs; another impactful use of ASO measurements lies in validating and improving satellite‐based snow estimates or models that can scale to whole mountain ranges, even those without ground‐based measurements. We compare ASO measurements from 2013 to 2015 to four methods that estimate spatially distributed SWE: two versions of a SWE reconstruction method, spatial interpolation from snow pillows and courses, and NOAA's Snow Data Assimilation System (SNODAS). SWE reconstruction downscales energy forcings to compute potential melt, then multiplies those values by satellite‐derived estimates of fractional snow‐covered area to calculate snowmelt. The snowpack is then built in reverse from the date the snow is observed to disappear. The two SWE reconstruction models tested include one that employs an energy balance calculation of snowmelt, and one that combines net radiation and degree‐day approaches to estimate melt. Our full energy balance model, without ground observations, performed slightly better than spatial interpolation from snow pillows, having no systematic bias and 26% mean absolute error when compared to SWE from ASO. Both reconstruction models and interpolation were more accurate than SNODAS.
Download or read book Multi spatial scale Observational Studies of the Sierra Nevada Snowpack Using Wireless sensor Networks and Multi platform Remote sensing Data written by Zeshi Zheng and published by . This book was released on 2018 with total page 121 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Sierra Nevada winter snowpack is the major water resource for the state of California. To better quantify the input of the water system, we deployed wireless-sensor networks across several basins in the Sierra Nevada. Together with operational and scientific research agencies, we also collected numerous scans of snow-on and snow-off lidar data over several basins in the high Sierra. We mined the lidar data and found how spatial patterns of snow depth are affected by topography and vegetation while elevation is the primary variable, other lidar-derived attributes slope, aspect, northness, canopy penetration fraction explained much of the remaining variance. By segmenting the vegetation into individual trees using lidar point clouds, we were able to extract tree wells from the high resolution snow-depth maps and we found the spatial snow distribution to be affected by the interactions of terrain and canopies. The snowpack is deeper at the downslope direction from the tree bole, however the snowpack at upslope direction being deeper. On sub-meter to meter scales, non-parametric machine-learning models, such as the extra-gradient boosting and the random-forest model, were found to be effective in predicting snow depth in both open and under-canopy areas. At spatial scales that are larger than 100 × 100 m2, we developed a novel approach of using the k-NN algorithm to combine the real-time wireless-sensor-network data with historical spatial products to estimate snow water equivalent spatially. The results suggest only a few historical snow-water-equivalent maps are needed if the historical maps can accurately represent the spatial distribution of snow water equivalent. The residual from the k-NN estimates can be distributed spatially using a Gaussian-process regression model. The entire estimation process can explain 90% of the variability of the spatial SWE.
Download or read book Estimating the Spatial and Temporal Distribution of Snow in Mountainous Terrain written by Keith Newton Musselman and published by . This book was released on 2012 with total page 164 pages. Available in PDF, EPUB and Kindle. Book excerpt: In-situ measurements and numerical models were used to quantify and improve understanding of the processes governing snowpack dynamics in mountainous terrain. Three studies were conducted in Sequoia National Park in the southern Sierra Nevada, California. The first two studies evaluated and simulated the variability of observed melt rates at the point-scale in a mixed conifer forest. The third study evaluated the accuracy of a distributed snow model run over 1800 km2; a 3600 m elevation gradient that includes ecosystems ranging from semi-arid grasslands to massive sequoia stands to alpine tundra. In the first study, a network of 24 automated snow depth sensors and repeated monthly snow density surveys in a conifer forest were used to measure snow ablation rates for three years. A model was developed to estimate the direct beam solar radiation beneath the forest canopy from upward-looking hemispherical photos and above-canopy measurements. Sub-canopy solar beam irradiance and the bulk canopy metric sky view factor explained the most (58% and 87%, respectively) of the observed ablation rates in years with the least and most cloud cover, respectively; no single metric could explain> 41% of the melt rate variability for all years. In the second study, the time-varying photo-derived direct beam canopy transmissivity and the sky view factor canopy parameter were incorporated into a one-dimensional physically based snowmelt model. Compared to a bulk parameterization of canopy radiative transfer, when the model was modified to accept the time-varying canopy transmissivity, errors in the simulated snow disappearance date were reduced by one week and errors in the timing of soil water fluxes were reduced by 11 days, on average. In the third study, a distributed land surface model was used to simulate snow depth and SWE dynamics for three years. The model was evaluated against data from regional automated SWE measurement stations, repeated catchment-scale depth and density surveys, and airborne LiDAR snow depth data. In general, the model accurately simulated the seasonal maximum snow depth and SWE at lower and middle elevation forested areas. The model tended to overestimate SWE at upper elevations where no precipitation measurements were available. The SWE errors could largely be explained (R2/super” 0.80, p0.01) by distance of the SWE measurement from the nearest precipitation gauge. The results suggest that precipitation uncertainty is a critical limitation on snow model accuracy. Finally, an analysis of seasonal and inter-annual snowmelt patterns highlighted distinct melt differences between lower, middle, and upper elevations. Snowmelt was generally most frequent (70% - 95% of the snow-covered season) at the lower elevations where snow cover was ephemeral and seasonal mean melt rates computed on days when melt was simulated were generally low (3 mm daysuper-1). At upper elevations, melt occurred during less than 65% of the snow-covered period, it occurred later in the season, and mean melt rates were the highest of the region ( 6 mm daysuper-1/super). Middle elevations remained continuously snow covered throughout the winter and early spring, were prone to frequent but intermittent melt, and provided the most sustained period of seasonal mean snowmelt (~ 5 mm day
Download or read book Assessing Seasonal Snowpack Distribution and Snow Storage Over High Mountain Asia written by Yufei Liu and published by . This book was released on 2022 with total page 169 pages. Available in PDF, EPUB and Kindle. Book excerpt: Seasonal snowpack is a vital water resource that impacts downstream water availability. However, accurately estimating snow storage and characterizing its spatiotemporal distribution remain challenging, in particular for data-scarce regions such as High Mountain Asia (HMA). In this dissertation, a newly developed snow reanalysis method is used to estimate snow water equivalent (SWE) over the HMA region, assessing its spatiotemporal distribution and quantifying the regional snow storage. The method assimilates fractional snow-covered area (fSCA) from the Landsat and MODIS platforms, over the joint Landsat-MODIS record (Water Year (WY) 2000 - 2017). A fine resolution (16 arc-second, ~480 m) and daily High Mountain Asia snow reanalysis (HMASR) dataset is derived and analyzed in the dissertation. The key conclusions are summarized as follows: 1) Snowfall precipitation is found underestimated in most precipitation products with sizeable uncertainty when evaluated in sub-domains of HMA. The research shows the potential of using satellite snow observations as a constraint, to infer biases and uncertainties in snowfall precipitation in remote regions and complex terrain where in-situ stations are very scarce. 2) Through examining the HMASR dataset, the domain-wide peak seasonal snow storage is quantified as 163 km3 when aggregated across the full HMA domain and averaged across WYs 2000-2017, with notable interannual variations between 114 km3 and 227 km3. 3) Existing global snow products over HMA on average underestimate the peak snow storage by 33% 52% over the entire HMA, and the uncertainty in peak snow storage estimates is primarily explained by accumulation season snowfall (88%) over HMA, partly due to a wide range (uncertainty) in precipitation (snowfall). Ultimately, the snow storage and its spatiotemporal variations characterized in this work can be used to understand the role of seasonal snowpack in the regional climate and water cycle over this region.
Download or read book Quantifying Spatial Variability of Snow Water Equivalent Snow Chemistry and Snow Water Isotopes Application to Snowpack Water Balance written by Joseph Rhodes Gustafson and published by . This book was released on 2008 with total page 302 pages. Available in PDF, EPUB and Kindle. Book excerpt: This study quantifies spatial and temporal patterns in snow water equivalent (SWE), chemistry, and water isotopes associated with snowpack shading due to aspect and vegetation in the Valles Caldera National Preserve, New Mexico. Depth, density, stratigraphy, temperature, and snow chemistry, isotope, and biogeochemical nutrient samples were collected and analyzed from five snowpit locations on approximate monthly intervals between January-April 2007. SWE showed little variability between sites in January (1̃0mm) but differences expanded to 84mm (30%) by max accumulation in open sites and 153mm (45%) between all sites. Sulfate varied by 22% (10.6-13.5 microeq/L), Cl- by 35% (17.4-26.9 microeq/L), and d18O by 17% ( -16.3 to -13.5), with SWE exhibiting inverse correlations with d18O (r2=0.96), SO42- (r2=0.75), and Cl- (r2=0.60) at max accumulation. Regression relationships suggest variability in SWE and solutes/water isotopes are primarily driven by sublimation. Mass balance techniques estimate sublimation ranges from 1-16% between topographically- and non-shaded open sites.
Download or read book Correlation and Prediction of Snow Water Equivalent from Snow Sensors written by Bruce J. McGurk and published by . This book was released on 1992 with total page 20 pages. Available in PDF, EPUB and Kindle. Book excerpt: Since 1982, under an agreement between the California Department of Water Resources and the USDA Forest Service, snow sensors have been installed and operated in Forest Service-administered wilderness areas in the Sierra Nevada of California. The sensors are to be removed by 2005 because of the premise that sufficient data will have been collected to allow "correlation" and, by implication, prediction of wilderness snow data by nonwilderness sensors that are typically at a lower elevation. Because analysis of snow water equivalent (SWE) data from these wilderness sensors would not be possible until just before they are due to be removed, "surrogate pairs" of high- and low-elevation snow sensors were selected to determine whether correlation and prediction might be achieved. Surrogate pairs of sensors with between 5 and 15 years of concurrent data were selected, and correlation and regression were used to examine the statistical feasibility of SWE prediction after "removal" of the wilderness sensors. Of the 10 pairs analyzed, two pairs achieved a correlation coefficient of 0.95 or greater. Four more had a correlation of 0.94 for the accumulation period after the snow season was split into accumulation and melt periods. Standard errors of estimate for the better fits ranged from 15 to 25 percent of the mean April 1 snow water equivalent at the high-elevation sensor. With the best sensor pairs, standard errors of 10 percent were achieved. If this prediction error is acceptable to water supply forecasters, sensor operation through 2005 in the wilderness may produce predictive relationships that are useful after the wilderness sensors are removed
Download or read book Examination of the Space time Variability and Uncertainty of Snow Water Storage Over the Western U S and Andes written by Yiwen Fang and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Seasonal snow water is a key component of the food-energy-water nexus in many regions, supporting one sixth of the global population. Given its importance, characterizing seasonal snow is critical to close terrestrial water budgets, especially for mountainous regions where as much as 70% of the water supply for humans originates from snowmelt. However, it is an ongoing challenge to characterize snow water equivalent (SWE) from existing snow products in snow-dominated mountain regions. In Chapter 2 of this thesis, a novel Western U.S. (WUS) snow reanalysis dataset (WUS-SR) that is continuous in space and time was developed at high-resolution (~ 500 m) over water years (WYs) 1985 to 2021. The snow dataset has been significantly verified with > 25,000 station-years of independent in situ and airborne data. Overall, WUS-SR peak SWE is well correlated against in situ peak SWE with correlation coefficient of 0.77, and against lidar-derived SWE taken near April 1st with correlation coefficients ranging from 0.75 to 0.91. In Chapter 3, the newly derived WUS-SR dataset wass used for examining the role of snow on streamflow drought. The analysis in this thesis shows that WY 2021 stands out as an unpredictable year with extremely low streamflow in the WUS, with only moderately low upstream snow conditions. Although snowmelt played a key role in the streamflow drought, the 2021 streamflow drought was a compound event modulated by contributors linking snow, soil moisture, and streamflow. In Chapter 4, the WUS-SR along with a previously derived Andes snow reanalysis were used as reference datasets in an intercomparison of other global products. Climatological snow storage is quantified as 269 km3 in the WUS and 29 km3 in the Andes from the reanalysis datasets. Existing high- and moderate-resolution products agree with the WUS-SR, whereas coarse-resolution products generally underestimate snow with large uncertainty in both WUS and Andes. Snow products with resolutions greater than 5 km did not resolve the orographic-rainshadow patterns that are important to downstream water resources. In addition to precipitation as the main driver of snow uncertainty, product spatial resolution, and LSM mechanisms such as rain-snow partitioning and snowmelt generation play important roles.
Download or read book Estimating Snow Water Equivalent from Shallow Snowpack Depth Measurements in the Great Salt Lake Desert Basin written by Lance C. Kovel, P.E. and published by . This book was released on 2013-11-23 with total page 72 pages. Available in PDF, EPUB and Kindle. Book excerpt: An independent technical study evaluating the use of snowpack depth measurements to estimate snow water equivalent (SWE) of shallow and ephemeral snowpacks in the Great Salt Lake Desert Basin, located in Utah, Nevada, and Idaho. A parameterized bulk snow density model was combined with mean air temperature measurements to predict snow water equivalent in the Great Salt Lake Desert Basin using only snowpack depth measurements and prior 10-day average daily mean air temperatures. The model was developed using historic snowpack data obtained from a limited number of automated snowpack telemetry (SNOTEL) and weather stations within and near the Basin. Model results from lower-elevation, shallow and ephemeral snowpacks may be used to supplement data obtained from existing SNOTEL stations, sparsely located in the higher elevations of the Basin, to create a more-complete and accurate prediction of the Basin’s snow water equivalent, which may be used to better-manage the water demands of the Basin’s surrounding populations.
Download or read book Climate Change Impacts on Snowpack Heterogeneity written by Adrienne M. Marshall and published by . This book was released on 2019 with total page 330 pages. Available in PDF, EPUB and Kindle. Book excerpt: Throughout the western United States, seasonal snowpack is critical for water resources timing and availability and ecosystem function. Warming temperatures associated with climate change reduce snow accumulation and advance melt timing, with serious consequences for snow-dependent social and ecological systems. While many impacts of climate change on snowpack are well established, this dissertation investigates several elements of changing snowpack that have not been previously assessed. In particular, each chapter contributes to an improved understanding of the changing heterogeneity of snow under climate change. The first chapter tests the sensitivity of snow drifting to altered climate, using a physically-based hydrologic model and thirty years of hydroclimatological data at a site where aspen stands are subsidized by a wind-driven snow drift. We find a warming-induced reduction in snow drifting, increase in ecohydrologic homogeneity across the landscape, and altered interannual variability of hydrologic metrics. The second chapter assesses changes in interannual variability of snowpack magnitude and timing across the western United States, using downscaled global climate model data as forcing to the Variable Infiltration Capacity (VIC) model. We find that changes in interannual variability are spatially heterogeneous across the western U.S., but that interannual variability of annual maximum snow water equivalent (SWE[max]) decreases in regions transitioning from snow- to rain-dominated precipitation regimes. Changes in the date of SWE[max] are less spatially coherent, but agreement between general circulation models (GCMs) is most reliably found at relatively warm sites where the date of SWE[max] variability increases. The third chapter assesses another element of snow heterogeneity by testing the effect of snowfall intensity on winter ablation. Using a statistical modeling approach with observational snow data, we find that higher snowfall intensity is associated with reduced winter ablation; projected changes in snowfall intensity will likely exacerbate warming-induced increases in winter ablation in the maritime mountains of the western U.S. and mitigate it in the cooler continental regions. Finally, a fourth interdisciplinary, collaborative chapter synthesizes research on climate change in the mountainous headwaters of the Columbia River Basin. Findings show that research in this basin is focused on climate change impacts, rather than adaptation or mitigation, that social and biophysical sciences are not well integrated, and that research priorities differ across an international boundary. Cumulatively, this set of studies advances knowledge of how the spatial and temporal heterogeneity of snowpack will respond to climate change in the western United States, with implications for snow-dependent social and ecological systems.
Download or read book Collaborative Research to Address Changes in the Climate Hydrology and Cryosphere of High Mountain Asia written by Anthony Arendt and published by Frontiers Media SA. This book was released on 2021-01-06 with total page 433 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Thriving on Our Changing Planet A Decadal Strategy for Earth Observation from Space written by National Academies of Sciences, Engineering, and Medicine and published by National Academies Press. This book was released on 2019-06-18 with total page 29 pages. Available in PDF, EPUB and Kindle. Book excerpt: We live on a dynamic Earth shaped by both natural processes and the impacts of humans on their environment. It is in our collective interest to observe and understand our planet, and to predict future behavior to the extent possible, in order to effectively manage resources, successfully respond to threats from natural and human-induced environmental change, and capitalize on the opportunities â€" social, economic, security, and more â€" that such knowledge can bring. By continuously monitoring and exploring Earth, developing a deep understanding of its evolving behavior, and characterizing the processes that shape and reshape the environment in which we live, we not only advance knowledge and basic discovery about our planet, but we further develop the foundation upon which benefits to society are built. Thriving on Our Changing Planet: A Decadal Strategy for Earth Observation from Space (National Academies Press, 2018) provides detailed guidance on how relevant federal agencies can ensure that the United States receives the maximum benefit from its investments in Earth observations from space, while operating within realistic cost constraints. This short booklet, designed to be accessible to the general public, provides a summary of the key ideas and recommendations from the full decadal survey report.
Download or read book Predictability of Weather and Climate written by Tim Palmer and published by Cambridge University Press. This book was released on 2014-07-10 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The topic of predictability in weather and climate has advanced significantly in recent years, both in understanding the phenomena that affect weather and climate and in techniques used to model and forecast them. This book, first published in 2006, brings together some of the world's leading experts on predicting weather and climate. It addresses predictability from the theoretical to the practical, on timescales from days to decades. Topics such as the predictability of weather phenomena, coupled ocean-atmosphere systems and anthropogenic climate change are among those included. Ensemble systems for forecasting predictability are discussed extensively. Ed Lorenz, father of chaos theory, makes a contribution to theoretical analysis with a previously unpublished paper. This well-balanced volume will be a valuable resource for many years. High-calibre chapter authors and extensive subject coverage make it valuable to people with an interest in weather and climate forecasting and environmental science, from graduate students to researchers.
Download or read book Atmospheric Rivers written by F. Martin Ralph and published by Springer Nature. This book was released on 2020-07-10 with total page 284 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is the standard reference based on roughly 20 years of research on atmospheric rivers, emphasizing progress made on key research and applications questions and remaining knowledge gaps. The book presents the history of atmospheric-rivers research, the current state of scientific knowledge, tools, and policy-relevant (science-informed) problems that lend themselves to real-world application of the research—and how the topic fits into larger national and global contexts. This book is written by a global team of authors who have conducted and published the majority of critical research on atmospheric rivers over the past years. The book is intended to benefit practitioners in the fields of meteorology, hydrology and related disciplines, including students as well as senior researchers.
