- ItemOpen AccessDataset associated with "An Improved Rescaling Algorithm for Estimating Groundwater Depletion Rates using the GRACE Satellite"(Colorado State University. Libraries, 2022) Ukasha, Muhammad; Ramirez, Jorge; Niemann, JeffreyThe Gravity Recovery And Climate Experiments (GRACE) satellite mission has been instrumental in estimating large-scale groundwater storage changes across the globe. GRACE observations include significant errors, so pre-processing is normally required before the data are used. In particular, the regional observations of terrestrial water storage anomalies (TWSA) are usually filtered to reduce the effects of measurement errors and then rescaled to reduce the unintended impacts of the filtering. The rescaling is typically selected to maximize the Nash-Sutcliffe Efficiency (NSE) between the rescaled filtered TWSA and original TWSA from large-scale hydrologic models that represent an incomplete water budget. The objectives of this study are (1) to evaluate the use of NSE in the current GRACE rescaling methodology, (2) develop an improved methodology that incorporates a complete regional water budget, and (3) examine the impacts of the rescaling methodology on regional assessments of groundwater depletion. To evaluate the use of NSE as a performance metric, we implement an analytical solution to improve the relative variability between the filtered and original TWSA series. Improved relative variability produces more reliable estimates when comparing the results to TWSA estimates from global positioning systems (GPS) for the Sacramento and San Joaquin River basins (containing Central Valley) in California. Rescaling with the complete regional water budget based on observed hydrological fluxes results in a larger scale factor (3.18) than the scale factor from the large-scale hydrologic model outputs (1.97), and the new TWSA results are more consistent with those from GPS. The larger scale factor also suggests that regional groundwater depletion is more severe than previously estimated.
- ItemOpen AccessDataset associated with "Flume investigation into mechanisms responsible for particle sorting in gravel-bed meandering channels"(Colorado State University. Libraries, 2022) White, Daniel; Nelson, PeterMeandering gravel-bed rivers tend to exhibit bed surface sorting patterns with coarse particles located in pools and fine particles on bar tops. The mechanism by which these patterns emerge has been explored in sand-bed reaches; however, for gravel-bed meandering channels it remains poorly understood. Here we present results from a flume experiment in which bed morphology, velocity, sediment sorting patterns, and bed load transport were intensively documented. The experimental channel is 1.35 meters wide, 15.2 meters long, and its centerline follows a sine-generated curve with a crossing angle of 20 degrees. Water and sediment input were held constant throughout the experiment and measurements were collected under quasi-equilibrium conditions. Boundary shear stress calculated from near-bed velocity measurements indicates that in a channel with mild sinuosity, deposition of fine particles on bars is a result of divergent shear stress at the inside bend of the channel, downstream of the apex. Boundary shear stress in the upstream half of the pool was below critical for coarse particles (>8 mm), leading to an armored pool. Inward directed selective transport was responsible for winnowing of fine particles in the pool. Fine and coarse sediment followed similar trajectories through the meander bend, which contrasts earlier studies of sand-bedded meanders where the loci of fine and coarse particles cross paths. This suggests a different sorting mechanism for gravel bends. This experiment shows that a complex interaction of quasi-equilibrium bed topography, selective sediment transport, and secondary currents are responsible for the sorting patterns seen in gravel-bed, meandering channels.
- ItemOpen AccessDataset associated with "Design and Testing of a Low-Cost Sensor and Sampling Platform for Indoor Air Quality"(Colorado State University. Libraries, 2021) Tryner, Jessica; Phillips, Mollie; Quinn, Casey W.; Neymark, Gabe; Wilson, Ander; Jather, Shantanu H.; Carter, Ellison; Volckens, JohnAmericans spend most of their time indoors at home, but comprehensive characterization of in-home air pollution is limited by the cost and size of reference-quality monitors. We assembled small "Home Health Boxes" (HHBs) to measure indoor PM2.5, PM10, CO2, CO, NO2, and O3 concentrations using filter samplers and low-cost sensors. Nine HHBs were collocated with reference monitors in the kitchen of an occupied home in Fort Collins, Colorado, USA for 168 h while wildfire smoke impacted local air quality. When HHB data were interpreted using gas sensor manufacturers' calibrations, HHBs and reference monitors (a) categorized the level of each gaseous pollutant similarly (as either low, elevated, or high relative to air quality standards) and (b) both indicated that gas cooking burners were the dominant source of CO and NO2 pollution; however, HHB and reference O3 data were not correlated. When HHB gas sensor data were interpreted using linear mixed calibration models derived via collocation with reference monitors, root-mean-square error decreased for CO2 (from 408 to 58 ppm), CO (645 to 572 ppb), NO2 (22 to 14 ppb), and O3 (21 to 7 ppb); additionally, correlation between HHB and reference O3 data improved (Pearson's r increased from 0.02 to 0.75). Mean 168-h PM2.5 and PM10 concentrations derived from nine filter samples were 19.4 micrograms per cubic meter (6.1% relative standard deviation [RSD]) and 40.1 micrograms per cubic meter (7.6% RSD). The 168-h PM2.5 concentration was overestimated by PMS5003 sensors (median sensor/filter ratio = 1.7) and underestimated slightly by SPS30 sensors (median sensor/filter ratio = 0.91).
- ItemOpen AccessDataset associated with "Temporal Variations of NDVI and LAI and Interactions with Hydroclimatic Variables in a Large and Agro-Ecologically Diverse Region"(Colorado State University. Libraries, 2021) Ukasha, Muhammad; Ramirez, Jorge A.; Niemann, Jeffrey D.Satellite based vegetation indices are increasingly used to characterize seasonal and interannual variations in vegetation as well as vegetation’s response to hydroclimatic variability. However, differences in the behavior of vegetation indices are not well understood over large spatial extents (e.g., 0.5° or larger). We hypothesize that normalized difference vegetation index (NDVI) and leaf area index (LAI) can exhibit different behaviors due to different relationships with hydroclimatic variables. To test this hypothesis, observations of monthly precipitation, discharge, temperature, vapor pressure deficit, evapotranspiration, and total water storage anomalies (TWSA) are processed for the combined Sacramento and San Joaquin river basins in California for 13 water years (October 2002-September 2015). Estimates of NDVI and LAI are obtained for the same period from MODerate resolution Imaging Spectroradiometer (MODIS). The seasonal cycle of NDVI peaks 2-3 months earlier than LAI. The seasonal variation in NDVI follows the seasonality of TWSA (i.e. water availability) whereas the seasonal cycle of LAI follows the seasonality in mean temperature and vapor pressure deficit (i.e. atmospheric water demand). Cross-correlation analyses of NDVI and LAI with the hydroclimatic variables show that LAI is more strongly correlated with most of the hydroclimatic variables considered.
- ItemOpen AccessDataset associated with "Estimation of the state-value function for optimal reservoir operations using continuous action deep reinforcement learning"(Colorado State University. Libraries, 2020) Peacock, Matthew E.; Labadie, John W.The state-value function of a reservoir system provides information about the long-term rewards that can be accrued from any state which the system can occupy. This function can be used to determine optimal decisions and is also key piece of information needed when reservoir operators wish to incorporate real-time forecast information. Dynamic programming is the most popular method for calculating the state-value function but has well-known limitations. The "curse of dimensionality,'' which can lead to computational intractability, arises from the discrete nature of the formulation and the backwards recursive solution process precluding consideration of delayed rewards. Continuous action deep reinforcement learning (CADRL) is a recent development for estimating the state-value function when delayed rewards are present and avoids the difficulties associated with use of discrete methods. Since application of this technique to reservoir operation problems is not without its own challenges, presented herein is a computational implementation with refinements needed to provide a stable and reliable learning process. CADRL is applied to development of optimal operational strategies for Lake Mendocino in the Russian River basin of Northern California using two single-objective reward functions, along with a multi-objective reward function for verification purposes. Performance of the optimal policy functions developed from the learning process is evaluated through simulation, with results showing that the system is able to learn far-sighted strategies that outperform idealized policies with foresight.