Nitrate-N (NO3- - N) is one of the most pervasive contaminants in groundwater. Nitrate in groundwater exhibits long-term behavior due to complex interactions at multiple scales among various geophysical factors, such as sources of nitrate-N, characteristics of the vadose zone and aquifer attributes. To minimize contamination of nitrate-N in groundwater, it is important to estimate hot spots (>10 mg/L of NO3- - N), trends and persistence of nitrate-N in groundwater. To analyze the trends and persistence of nitrate-N in groundwater at multiple spatio-temporal scales, we developed and used an entropy-based method along with the Hurst exponent in two different hydrogeologic settings: The Trinity and Ogallala Aquifers in Texas at fine (2 km × 2 km), intermediate (10 km × 10 km) and coarse (100 km × 100 km) scales. Results show that nitrate-N exhibits long-term persistence at the intermediate and coarse scales. In the Trinity Aquifer, overall mean nitrate-N has declined with a slight increase in normalized marginal entropy (NME) over each decade from 1940 to 2008; however, the number of hot spots has increased over time. In the Ogallala Aquifer, overall mean nitrate-N has increased with slight moderation in NME since 1940; however, the number of hot spots has significantly decreased for the same period at all scales.

Science networks and their hosted applications require large and frequent data transfers, but these transfers are subject to network performance degradation, including queuing delays and packet drops. However, well known network dynamics along with limited instrumentation access complicate the creation of an accurate method that predicts different performance aspects of data transfers. In this study, we develop a lightweight machine learning tool to predict end-to-end packet retransmission in science flows of arbitrary size. We also identify the minimum set of necessary path and host measurements needed as input features in our predictor in order to achieve high accuracy. In our evaluation process our predictor demonstrated low training times and was able to provide accurate estimates (97%–99%) for packet retransmissions of data transfers of arbitrary sizes. The results also manifest that the our solution was able to predict retransmit behavior reasonably well (66%) even for previously unseen data if training and testing datasets had similar statistics.

In recent years, attacks such as the Stuxnet malware have demonstrated that cyberattacks against control systems cause extensive damage. These attacks can result in physical damage to the networked systems under their control. In this paper, we discuss our approach for detecting such attacks by distinguishing between programs running on a programmable logic controller (PLC) without having to monitor communications. Using power signatures generated by an attached, high-frequency power measurement device, we can identify what a PLC is doing and when an attack may have altered what the PLC should be doing. To accomplish this, we generated labeled data for testing our methods and applied feature engineering techniques and machine learning models. The results demonstrate that Random Forests and Convolutional Neural Networks classify programs with up to 98% accuracy for major program differences and 84% accuracy for minor differences. Our results can be used for both online and offline applications.

Abstract. High resolution gridded datasets of meteorological variables are needed in order to resolve fine-scale hydrological gradients in complex mountainous terrain. Across the United States, the highest available spatial resolution of gridded datasets of daily meteorological records is approximately 800 m. This work presents gridded datasets of daily precipitation and mean temperature for the East-Taylor subbasin (in western United States) covering a 12-year period (2008–2019) at a high spatial resolution (400 m). The datasets are generated using a downscaling framework that uses data-driven models to learn relationships between climate variables and topography. We observe that downscaled datasets of precipitation and mean temperature exhibit smoother spatial gradients compared to their coarser counterparts. Additionally, we also observe that when downscaled datasets are reaggregated to the original resolution (800 m), the mean residual error is almost zero, ensuring spatial consistency with the original data. Furthermore, the downscaled datasets are observed to be linearly related to elevation, which is consistent with the methodology underlying the original 800 m product. Finally, we validate the spatial patterns exhibited by downscaled datasets via an example use case that models lidar-derived estimates of snowpack. The presented dataset constitutes a valuable resource to resolve fine-sale hydrological gradients in the mountainous terrain of the East-Taylor subbasin, which is an important study area in the context of water security for southwestern United States and Mexico. The dataset is publicly available at https://doi.org/10.15485/1822259 (Mital et al., 2021).

Evaluation of long-term temporal and spatial climatic changes in mountainous areas is one of the most critical issues in the problem of global changes. A statistical framework to assess the long-term climatic water balance changes has been developed, including the following phases of the data analysis: (1) a time series analysis of daily, monthly, and yearly averaged meteorological parameters (temperature, relative humidity, precipitation, wind speed, etc.), (2) calculations of potential evapotranspiration, aridity index, and actuarial evapotranspiration, as well as the standard precipitation index (SPI) and standard precipitation-evapotranspiration index (SPEI), and (3) climatic zonation of the watershed area based on the temporal trends of ET and SPEI, performed by means of a combination of the hierarchical k-means clustering and Principal Component Analysis. The paper includes the results of the application of the developed statistical framework for 17 locations of meteorological stations at the East River watershed, using the meteorological datasets for the period from 1966 to 2020. Structural time series changes of meteorological variables and calculated water balance parameters are used to determine the time of climatic shifts. Calculations of the potential evapotranspiration are conducted using the Thornthwaite, Hargreaves, and Penman-Monteith equations, and evapotranspiration (ET) is calculated using the Budyko model. The time variations of the meteorological and calculated parameters before and after temporal structural breakpoints are evaluated using parametric and nonparametric statistics. The results of the hierarchical clustering are illustrated using the tree dendrograms and the PCA plots of clusters of the studied sites. A comparison of the results of clustering based on the ET and SPEI data showed no difference between these two types of the zonation. A significant shift in the cluster arrangements for the time periods before and after the temporal structural breakpoints indicate that zonation patterns are driven by dynamic climatic processes, which are variable through time and space. Therefore, the watershed zonation requires periodic re-evaluation based on the structural time series analysis of meteorological data.

We apply Cattaneo's relaxation approach to the one-dimensional coupled Boussinesq groundwater flow and advection-diffusion-reaction equations, commonly used in engineering applications to simulate contaminant transport in the subsurface. The diffusion-type governing equations are reformulated as a hyperbolic system, augmented by an equation that can be interpreted as a momentum balance. The hyperbolization enables an efficient unified computation of the primary variable and its gradients, for example piezometric head and unit discharge in the Boussinesq equation. An augmented Roe scheme is used to solve the hyperbolic system. The hyperbolized system of equations is studied in a set of steady state and transient test cases with idealized geometry. These test cases confirm the equivalence of the hyperbolic system to its original formulation. The larger time step size of the hyperbolic equation is verified theoretically by means of a stability analysis and numerically in the test cases. Finally, a reach-scale application of flow and transport across a river meander is considered. This application case shows that the performance of the hyperbolic relaxation approach holds for more realistic groundwater flow and transport problems, relevant to water resources management.

High-resolution gridded datasets of meteorological variables are needed in order to resolve fine-scale hydrological gradients in complex mountainous terrain. Across the United States, the highest available spatial resolution of gridded datasets of daily meteorological records is approximately 800 m. This work presents gridded datasets of daily precipitation and mean temperature for the East-Taylor subbasin (in the western United States) covering a 12-year period (2008-2019) at a high spatial resolution (400 m). The datasets are generated using a downscaling framework that uses data-driven models to learn relationships between climate variables and topography. We observe that downscaled datasets of precipitation and mean temperature exhibit smoother spatial gradients (while preserving the spatial variability) when compared to their coarser counterparts. Additionally, we also observe that when downscaled datasets are upscaled to the original resolution (800 m), the mean residual error is almost zero, ensuring no bias when compared with the original data. Furthermore, the downscaled datasets are observed to be linearly related to elevation, which is consistent with the methodology underlying the original 800 m product. Finally, we validate the spatial patterns exhibited by downscaled datasets via an example use case that models lidar-derived estimates of snowpack. The presented dataset constitutes a valuable resource to resolve fine-scale hydrological gradients in the mountainous terrain of the East-Taylor subbasin, which is an important study area in the context of water security for the southwestern United States and Mexico.

Escherichia coli (E. coli) contamination of groundwater (GW) and surface water (SW) occurs significantly through the subsurface from onsite wastewater treatment systems (OWTSs). However, E. coli transport in the subsurface remains inadequately characterized at the field scale, especially within the vadose zone. Therefore, the aim of this research is to investigate the impact of groundwater fluctuations (e.g., recharging, discharging conditions) and variable conditions in the vadose zone (e.g., pulses of E. coli flux) by characterizing E. coli fate and transport in a linked surface water-soil water-groundwater system (SW-SoW-GW). In particular, this study characterizes the impact of flow regimes on E. coli transport in the subsurface and evaluates the sensitivity of parameters that control the transport of E. coli in the SW-SoW-GW system. This study was conducted in Lake Granbury, which is an important water supply in north-central Texas providing water for over 250,000 people. Results showed that there was less removal of E. coli during groundwater recharge events as compared to GW discharge events. Also, groundwater and surface water systems largely control E. coli transport in the subsurface; however, temporal variability of E. coli can be explained by linking the SW-SoW-GW system. Moreover, sensitivity analysis revealed that saturated water content of the soil, total retention rate coefficient, and hydraulic conductivity are important parameters for E. coli transport in the subsurface.

Meteorological forcing plays a critical role in accurately simulating the watershed hydrological cycle. With the advancement of high-performance computing and the development of integrated watershed models, simulating the watershed hydrological cycle at high temporal (hourly to daily) and spatial resolution (tens of meters) has become efficient and computationally affordable. These hyperresolution watershed models require high resolution of meteorological forcing as model input to ensure the fidelity and accuracy of simulated responses. In this study, we utilized the Advanced Terrestrial Simulator (ATS), an integrated watershed model, to simulate surface and subsurface flow and land surface processes using unstructured meshes at the Coal Creek Watershed near Crested Butte (Colorado). We compared simulated watershed hydrologic responses including streamflow and distributed variables such as evapotranspiration, snow water equivalent (SWE), and groundwater table driven by three publicly available, gridded meteorological forcings (GMFs) - Daily Surface Weather and Climatological Summaries (Daymet), the Parameter-elevation Regressions on Independent Slopes Model (PRISM), and the North American Land Data Assimilation System (NLDAS). By comparing various spatial resolutions (ranging from 400ĝ€¯m to 4km) of PRISM, the simulated streamflow only becomes marginally worse when spatial resolution of meteorological forcing is coarsened to 4km (or 30% of the watershed area). However, the 4km-resolution has much worse performance than finer resolution in spatially distributed variables such as SWE. Using the temporally disaggregated PRISM, we compared models forced by different temporal resolutions (hourly to daily), and sub-daily resolution preserves the dynamic watershed responses (e.g., diurnal fluctuation of streamflow) that are absent in results forced by daily resolution. Conversely, the simulated streamflow shows better performance using daily resolution compared to that using sub-daily resolution. Our findings suggest that the choice of GMF and its spatiotemporal resolution depends on the quantity of interest and its spatial and temporal scale, which may have important implications for model calibration and watershed management decisions.