1,720,990 research outputs found
Recommended from our members
Examination of the space-time variability and uncertainty of snow water storage over the Western U.S. and Andes
Get PDFSeasonal 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
Recommended from our members
Assessing seasonal snowpack distribution and snow storage over High Mountain Asia
Get PDFSeasonal 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
Recommended from our members
Enabling remote-sensing based streamflow estimation at the continental scale
Get PDFForecasting streamflow on a continental scale is a challenge, especially in ungauged basins where in-situ data is rare. We can combine numerical modeling with information from remotely-sensed data to make best-estimates of discharge. The body of this dissertation is split into three chapters. Chapter 2 presents a method for characterizing river channels, a key input for hydraulic models, using information purely from remote sensing data. Chapter 3 describes a high resolution, near-global dataset of inputs to the Variable Infiltration Capacity model, a land surface model that has been widely used for hydrologic forecasting, trend analysis, and coupling with climate models. This dataset, "VICGlobal," represents a major improvement on past land surface parameterizations for VIC, with higher resolution than existing VIC inputs, and greatly simplifies the process for running VIC anywhere in the world. Lastly, Chapter 4 focuses on a procedure called "Inverse Streamflow Routing (ISR)," a method for runoff estimation and discharge interpolation, originally proposed by researchers at Princeton in 2013. We build upon their method and use an update borrowed from the Ensemble Kalman smoother to reduce uncertainty in the a priori runoff estimate
Recommended from our members
Towards large-scale implementation of a high resolution snow reanalysis over midlatitude montane ranges
Get PDFAccurately 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
Recommended from our members
Mountain snowpacks in the Western U.S: improved estimation and understanding the impact on future water availability
Get PDFSeasonal snowpack serves as natural reservoir by storing winter precipitation and releasing it as snowmelt. In in the Western U.S., this is a crucial water supply that supports agriculture, hydropower, ecosystems, and millions of users. Rising temperatures are causing reduced snow storage, earlier melt, and increased drought risk. Future climate models predict these trends will continue and intensify, posing challenges for water management. Accurate snow water equivalent (SWE) estimates are essential for water managers in snowmelt-reliant regions, but characterizing the spatial distribution of snow is an ongoing challenge. In situ measurements are not always representative nor widespread and remote sensing of mountain SWE remains elusive. Modeling can fill space-time gaps in the observational record but is impacted by biases in forcings and uncertainties in model physics. To address the issue of uncertainties in model physics for simulating snow, we evaluate how altering the configurations of a land surface model (Noah-MP) affects its ability to recreate observed SWE across 199 stations in the Western U.S. The base case configuration, which matches that for the National Water Model, overestimates SWE at 90% of sites. Adjustments to model physics for precipitation partitioning, snow albedo formulation, and surface resistance cause significant changes in SWE predictions that vary by season and site climate and geography. No single configuration works best everywhere, but four alternatives outperform the base case at most sites.
To address the challenge of biases in mountain precipitation products, we leverage a historical snow reanalysis dataset to develop, apply, and test a novel precipitation bias correction and downscaling method towards modeling SWE in a real-time context. Over a test domain, this precipitation bias correction is effective in reducing error in April 1st SWE (-58%) and streamflow forecasts (-52%). Assimilating remotely-sensed snow depth observations further reduces errors.
To explore the impact of future shifts in snowpack on water resources, we apply hydrology projections driven by downscaled global climate models (GCMs) and a simple reservoir operations model to 13 major reservoirs in the California Sierra Nevada. Region wide, snowpack reductions (-44%) and earlier snowmelt (11 days) lead to earlier inflow and drops in water deliveries (-19%) and year-end storage (-18%). Reservoir storage and rainfall help offset the impact of snowpack losses, but the extent and mechanisms of this vary on the reservoir’s operations, characteristics, and upstream climate and hydrology. Current operating rules are not well-suited to let reservoirs store earlier inflow under future climate conditions
Climate Change Signature on Millions of Lakes
Get PDFLakes are unique in the land surface due to their well-known anomalous intrinsic properties – unusually large heat capacity, stark albedo contrast with water’s phase change and sun’s position. They also exhibit a wide spectrum of extrinsic properties – related to their occurrence, distribution and abundance. That they are ubiquitously besprinkled over most land surfaces with such intrinsic (extrinsic) anomalous (spectrum of) properties makes them a low-hanging fruit to observe from space and tease out in-land climate change signatures from local to global scales. In this dissertation, I explore four aspects in our current understanding of lakes and their connection to climate change, and I show that: 1) Long-term lake changes are multi-directional in nature as a rule and not exception; 2) Lake evaporation calculations using global data can be improved by ~5% at seasonal scales and ~50% (i.e. 5-10X better) in the energy gap to turbulence scales (i.e. ~30 minutes), compared to 5 other state-of-the-art mass-transfer methods, by virtue of a century-old misunderstood body of work by Robert E. Horton that is based on kinetic theory of gasses; 3) Long-term trends can be separated from correlation noise up to 1-sigma better than current practice in terms of both statistical power and confidence by combining a portfolio of 16 methods from two families of trend detection tools from Econometrics (parametric) and Hydrology (non-parametric); 4) Building upon the results of 1-3, a quasi-analytical water albedo model, and derived lake and climate variables from many sources (including Google Earth Engine datasets) can help us characterize lake changes up to sub-daily and sub-meter (micro-topography) scale, under the assumption of regional hydro-climate homogeneity at 0.25 degree spatial resolution (an unavoidable caveat governed by rain gauge density) for millions of Arctic Boreal Zone (ABZ) lakes. Collectively, these results I demonstrate at continental scale spanning whole of Canada and Alaska, ~10 million lakes, carve a pathway for a high-fidelity understanding of local-to-global scale climate change signatures on ~100 million lakes, which are, due to their intrinsic and extrinsic characteristics, our best in-land sentinels of climate change
Recommended from our members
Improving the understanding of the spatiotemporal variability of hydrometeorology across the Sierra Nevada using a novel remote sensing reanalysis approach
Get PDFWhile 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)
Recommended from our members
Gaining insight into Andean snowpack climatology and change using a snow reanalysis approach applied over the Landsat satellite record
Get PDFThis dissertation presents the results of a snowpack estimation system based on the integration of remotely sensed data with snow modeling over the extratropical Andes domain (27?S to 37?S). The framework is based on the Bayesian principles of data assimilation: by assimilating Landsat fractional snow cover imagery in an ensemble snow model, the snow model estimates are conditioned by the observed depletion as sensed by the remote sensing platform during 1984 to present. The snow model is forced using the MERRA atmospheric reanalysis data set. Uncertainty of MERRA was characterized using in-situ precipitation and temperature observations. The results of the framework are daily ensemble of estimates of snowpack states with a spatial resolution of 180m, distributed throughout the domain. Verification of the estimates was performed using in-situ snow survey data taken over several headwaters of the domain during the 2009-2014 winter and spring months. Results of the in-situ verification showed that the posterior estimates of the framework are in general much more accurate than the prior estimates, with significant reductions in mean error, root mean square error and increases in correlation. The error metrics were invariant to the different physiographic characteristics of each the snow survey sites and the fSCA imagery availability over each of the surveyed data points. An analysis of runoff and the SWE estimates over a large headwater basin of the Aconcagua river showed a strong correlation between surface streamflow and peak SWE estimates. The framework was implemented for the regional domain and the SWE volume for each of the watersheds was analyzed. This constituted the first analysis of the impact of El Ni?o in the water stored as snow for the extratropical Andes region. The effect of El Ni?o is particularly important for the northern watersheds (north of 32?S) of the domain. El Ni?o was related to the wettest year observed, which presented SWE volumes an order of magnitude larger than normal years. For Southern watersheds, the effect of El Ni?o is diminished, with SWE volumes showed a marked reduction in interannual variability with respect to the northern domain. Longitudinal SWE transects were analyzed in order to quantify the effect of the Andes barrier in orographic enhancement and suppression, which affects SWE accumulation directly. The analysis showed significant variability of the effect of the barrier in SWE, with varying enhancing and suppression rates throughout the domain. The strongest magnitude of the effect were seen over the southern extent of the domain (35?S), with reductions in the long-term SWE average of an order of magnitude between windward and leeward slopes
Recommended from our members
Science-based approaches to water resources management: Studies in remote sensing, groundwater and California's Central Valley
Get PDFThis dissertation is motivated by the principle that data availability and scientific analysis are fundamental for effective natural resources management. The research in this thesis presents approaches that can enhance water management institutions’ ability to more comprehensively measure and manage groundwater resources. This research draws from a diverse scientific body of work in numerical modeling, remote sensing science, hydrology and public policy. A robust, artificial neural network model is presented that downscales GRACE gridded land datasets (~150,000km2) to higher-resolution (~16km2) groundwater storage change estimates, a 100-fold higher resolution. This modeling approach uses minimum input data - five key data sets and minimally processed GRACE data - and thus has applicability to data scarce regions. For California’s Central Valley, downscaled groundwater storage change maps can be used to inform groundwater management as they point to specific sub-regional patterns in groundwater storage change. This dissertation also presents a framework intended to strengthen the scientific underpinnings of groundwater management in California. A methodology is developed to calculate sustainable yield under California's new Sustainable Groundwater Management Act (SGMA) that is flexible to varying input data as well as to a given region’s local socio-economic and environmental dynamics. The long-term implications of three different determinations of sustainable yield are assessed through an empirical groundwater balance that is projected to 2040. The results of these three scenarios show that there are tradeoffs to be had between groundwater availability, future climate uncertainty and socio-economic preferences that must be carefully weighed. Finally, research is presented that addresses the future of remote sensing. A novel approach to quantify the value of geospatial data for decision makers is presented, along with an unbiased assessment of a rapidly developing branch of remote sensing – privately-owned and privately-funded small satellites. Overall, groundwater management in California is a critical example of the need for robust management strategies in the face of increasing resource scarcity and rising climate variability. California is not alone in this task. The lessons and takeaways presented in this dissertation can be applied to address similar natural resource management challenges across the globe
- …
