Estimation of design flood is a crucial task in water resources engineering. Regional Flood Frequency Analysis is one of the widely used approaches for estimating design flood in ungauged basin. In the present research, we develop an eXtreme Gradient Boost based ML model for RFFA. The proposed approach relies on developing a regression model between flood quantiles and the commonly available catchment descriptors. In this study, the CAMELs data for 671 catchments from USA was used to study the efficiency of the approach. Further, the results were compared with the traditional methods such Multiple Linear Regression (MLR) and Artificial Neural Networks (ANN). XGB is a decision-tree-based ensemble machine learning algorithm that uses gradient boosting as a framework. The results revealed that the XGB based approach resulted in estimates with highest accuracy when using all the available catchment descriptors (i.e., mean annual rainfall(MAR), drainage area, fraction forest, mean annual potential evapotranspiration (MAPET), mean annual temperature, rainfall intensity, slope, fraction snow, soil porosity, and soil conductivity) both during training and validation. Four distinct models consisting of three to ten descriptors were examined for 2-, 5-, 10-, 25-, 50-, and 100-year return periods, all of the models exhibit smaller mean absolute error values and root mean square error values with percentage bias ranging from -10 to +10. A model with three predictor variables has comparable performance to other models. Drainage area, rainfall intensity, MAR, and fraction snow are the most efficient predictor variables, while MAPET, Slope, Temperature, Fraction Forest, Soil Porosity, and Soil Conductivity have low significance in predicting design flood for an ungauged catchment. The XGB modeling approach that has been proposed can be applied to different places throughout the world.

Nitrous oxide (N2O) is one of the important greenhouse gases (GHGs), with its global warming potential 265 times greater than that of carbon dioxide (CO2). About 60% of the anthropogenic N2O emission is from agriculture production. To date, estimating N2O emissions from cropland remains a challenging task because the related microbial origin processes (e.g. incomplete nitrification and denitrification) are controlled by a diverse factors of climate, soil, plant and human activities. In this study, we developed a ML model with physical/biogeochemical domain knowledge, namely knowledge guided machine learning (KGML), for simulating daily N2O fluxes from the agriculture ecosystem. The Gated Recurrent Unit (GRU) was used as the basis to build the model structure. A range of ideas have been implemented to optimize the model performance, including 1) hierarchical structure based on variable causal relations, 2) intermediate variable (IMV) prediction and transfer, 3) inputting IMV initials for constraints, 4) model pretrain/retrain, and 5) multitask learning. The developed KGML was pre-trained by millions of synthetic data generated by an advanced PB model, ecosys, and then re-trained by observations from six mesocosm chambers during three growing seasons. Six other pure ML models were developed using the same data from mesocosm chambers to serve as the benchmark for the KGML model. The results show that KGML can always outperform the PB model in efficiency and ML models in prediction accuracy of capturing N2O flux magnitude and dynamics. Besides, the reasonable predictions of IMVs increase the interpretability of KGML. We believe the footprint of KGML development in this study will stimulate a new body of research on interpretable machine learning for biogeochemistry and other related geoscience processes.

We perform a statistical study of 3-s ultra-low frequency (ULF) waves using MMS observations in the Earth’s foreshock region. The average phase velocity in the plasma rest frame is determined to be anti-sunward, and the intrinsic polarization is right-handed. We further examine the linear instability conditions based on drift-bi-Maxwellian distribution functions according to observed plasma conditions. The resulting instability is a solution to the common dispersion equation of the ion/ion right-hand non-resonant and left-hand resonant instabilities. The predicted wave propagation is also predominantly anti-sunward. The cyclotron resonant conditions of the solar wind and backstreaming beam ions are evaluated, and we find that in some cases, the anti-sunward propagating waves can be resonant with beam ions, which was overlooked in previous studies. The result suggests that the dispersion equation provides the 3-s ULF waves a fundamental explanation that unifies a rich variety of resonant conditions. In the later stage, the 3s ULF waves could further develop into Short Large Amplitude Magnetic Structures, contributing to the turbulence in the foreshock region.

Hydropower plants and large storage reservoirs upstream of water-dependent power plants represent physical assets with a substantial role in the function of both river and power grid systems. Hydropower has the potential to play an important role in decarbonization strategies as a means to achieve reliability and resilience in an increasingly complex grid; for example, as a means to offset variability in other renewable resources and provide long term energy storage. The power grid and river systems are interlinked through hydropower plants, resulting in hydropower operators needing to balance potentially competing interests of water management, as well as power demands. Despite this multisectoral dependency, the representation of hydropower in power system resource adequacy studies typically differs from the representation in water management adaptation studies, especially at the regional scale. The objective of this presentation is to clarify the concept of hydropower flexibility (and adaptation) in a way that can bridge gaps between the tools used by and expectations of water managers, hydropower operators and power system operators. The developed qualitative multisystem, multiscale approach to hydropower flexibility can be leveraged to highlight the value, facilitate the compatibility and complementarity, and inform on the generalization of technology innovation and climate change adaptation strategies.

Soil erosion is the most common type of land degradation, and it has become a global environmental issue that reduces soil productivity and water quality. LULC change related with climatic and geomorphologic states of the area affects land degradation. This study aims at estimating the annual average soil erosion for the study area, Nagavali River Basin, and also focuses to analyze the impact of land use land cover change on the annual average soil erosion rates of the Nagavali river basin. In this study, the soil properties, elevation, and topography of the study are considered to be constant. This study was done by using the Revised Universal Soil Loss Equation (RUSLE) model. This equation includes factors like rainfall, soil, land cover, cultivation practices, and slope, for the estimation of soil erosion. Each layer of the factors affecting soil erosion was prepared and integrated using GIS techniques. By using the RUSLE model, the present study identified that the soil erosion rates for the years 1990 and 2020 ranged from 0 - 2364.46 t/ha/yr and 0 - 7857.21 t/ha/yr respectively for the Nagavali river basin. The LULC change analysis for the years 1990 and 2020 revealed that the erosion rate increased from 2364.46 t/ha/yr to 7857.21 t/ha/yr in the Nagavali river basin. The results also depicted that the area under very severe erosion class increased drastically from 1990 to 2020 while the area under very slight erosion class decreased from 1990 to 2020. This shows that LULC change has a significant impact on increase of soil erosion rates in Nagavali River Basin.

Soil and Water Assessment Tool (SWAT) is one of the widely used hydrological models, especially it has been successfully applied for the assessment of the impact of land use land cover and best management practices scenarios. But it is less applicable for type of research that requires integration or optimization with other models since it cannot update the land-use and best management practice information efficiently and it should be run separately when results for the multiple watersheds are needed. These days, the attention on the water security has been growing and interdisciplinary works desires integration of the hydrological model with other models are being highlighted. Thus, there are needs of development of the surrogate model which is computationally efficient and applicable to multiple watersheds. In this research, we propose the surrogate model of the SWAT with novel machine learning techniques such as random forest model. As a first step, the models are trained with the SWAT data from one watershed, which is a Maumee River Watersheds. Models for flow, mineral phosphorus, total nitrogen, total phosphorus, and sediment transport are built separately, and the model performance was above satisfactory level based on R-squared value, Nash-Sutcliffe efficiency, and percent bias. In addition, the surrogate models were tested for the different best management practices adoption scenarios and were trained additional data to make the model valid for the wide range of the best management practices adoption ratios. Finally, the surrogate models were expanded to multiple watersheds, by training SWAT results from Huron River Watersheds and River Raisin and they evaluated with the R-squared value. High R-squared values indicated that the surrogate model could be used in place of SWAT.

The Southern Annular Mode (SAM) is the leading mode of atmospheric variability in the extratropical Southern Hemisphere, and its variations affect westerly winds, regional storm tracks, midlatitude wildfire activity, Antarctic and Southern Ocean dynamics, and surface mass balance. The SAM is therefore of high importance to both ecosystems and societies across the Southern Hemisphere. The behavior of the SAM has been extensively studied during the instrumental era, but there is substantially less confidence and considerable disagreement in its decadal to centennial-scale variability over the Common Era. Studying these longer time scales requires millennial-length reconstructions, but the sparsity of multi-century proxy records in the Southern Hemisphere has hindered the production of such reconstructions. Consequently, variability and trends in the SAM remain uncertain through most of the Common Era. Here, we use paleoclimate data assimilation to reconstruct the austral summer (DJF) SAM index (SAMI) over the entire Common Era. Our method integrates the South American Drought Atlas, Australia-New Zealand Drought Atlas, and the PAGES2k temperature-sensitive proxy network with a multi-model ensemble of last millennium GCM simulations using an offline ensemble Kalman Filter with a stationary prior. We use a novel nested variance adjustment to correct for the effect of changing proxy availability through time. Our reconstruction is not calibrated to the observed SAMI, yet exhibits a correlation coefficient greater than 0.6 over the instrumental era. Using superposed-epoch and wavelet analyses, we find the reconstruction exhibits minimal response to volcanic and solar forcings and is instead dominated by internal climate variability until the late 20th century. Our data assimilation framework also facilitates the use of optimal-sensor analysis, which we use to identify key proxy sites at different time periods in the reconstruction. Prior to 1400 CE, the reconstruction is strongly influenced by two tree-ring records (Mt. Read, Tasmania and Oroko, New Zealand) and two ice-cores (WDC05A and Plateau Remote). Finally, we examine the coherence of our results against existing reconstructions and compare reconstructed 20th century trends with the instrumental record.

Flare suprathermal ions with enhanced 3He and heavy-ion abundances are an essential component of the seed population accelerated by CME-driven shocks in gradual solar energetic particle (GSEP) events. However, the mechanisms through which CME-driven shocks gain access to flare suprathermals and produce spectral and abundance variations in GSEP events remain largely unexplored. We report two recent GSEP events: one observed by Solar Orbiter on 2020 Nov 24 (the first GSEP event on Solar Orbiter) and the other by ACE on 2021 May 29 (the most intense GOES proton event in the present solar cycle). The events were preceded by impulsive SEP (ISEP) events. Abundances and energy spectra are markedly different in the examined events at < 1 MeV/nucleon. For example, in the May event, Fe/O is typical of ISEP events, a factor of 100 to 10 higher than Fe/O in the November event. 3He abundance in the November event is high, typical of ISEP events, while in the May event, it is much lower, though finite. The May event shows a hard 4He spectrum with a power-law index of −1.6, and the November event a soft spectrum with an index of −3.5. The events were associated with halo CMEs with speeds around 900 km/s. The November event was also measured by Parker Solar Probe and the May event by STEREO-A and Solar Orbiter. This paper discusses the origin of vastly different abundances and spectral shapes in terms of variable remnant population from preceding ISEP events. Furthermore, we discuss a possible direct contribution from parent flares.

This study presents the application of broadband seismic recordings in improving the quality of tidal gravimetric recordings, especially during strong earthquakes. Four sites in Western and Central Europe, where broadband seismometer and tidal gravimeter are collocated, are selected. The data together with transfer functions of instruments from the Black Forest (Germany), Membach (Belgium), and Rochfort (Belgium) are downloaded from the IRIS database. The GWR iGrav-027 superconducting gravimeter is fully operational at the Borowa Gora Geodetic-Geophysical Observatory of the Institute of Geodesy and Cartography (Poland) since late April 2016. Later, the REF-TEK broadband seismometer was installed in the cooperation with the University of Warsaw. The Observatory, also temporally hosts a spring gravimeter, LCR ET-26 (owned by the Warsaw University of Technology). The seismometric and gravimetric signals of high coherence are selected for each site. The complex transfer functions in frequency domain are found by the least square minimization of the seismometric and seismic-corrected gravimetric signal. The transfer functions are calculated when the coherence is significant for two cases: with and without observed earthquake recordings. Next, the estimated transfer functions and the ongoing seismometric recordings are used to improve the quality of tidal gravimetric recordings. The presented method shows a significant reduction of the noise level of gravimetric recordings in a frequency range of seismometer’s operability. In order to obtain reliable models of the Earth’s structure based on analysis of surface waves recorded by tidal gravimeters, the careful estimation of gravimeters’ transfer functions in the seismic frequency range is necessary. The transfer functions of iGrav-027 and ET-26 were determined by the step response method during the experiments in 2018-2019. Now, thanks to the presented research, their quality can be verified. The limitation of this verification can be tested on recordings of Black Forest, Membach and Rochfort with very well-determined and documented transfer functions of tidal gravimeters. This work has been done in the research project No. 2017/27/B/ST10/01600 financed from the funds of the Polish National Science Centre.

Hydrochemical constituents in streams may originate from currently active sources at the surface and/or legacy sources retained in soil, slow-flowing groundwater and sediments from earlier surface inputs, waste deposits, and land contamination. These source contributions need to be distinguished for effective pollution mitigation and water quality improvement. This study outlines a methodology for such distinction based on some general mechanistic differences in stream concentration and load behavior versus discharge between the contributions from these different types of sources. The methodology is applied to and tested on stream concentration data for chloride, nitrogen, phosphorous, copper, lead, and zinc, and corresponding data for water discharge, measured over recent decades (time series of 1-3 decades) in multiple Swedish hydrological catchments of different scales (10-37 catchments depending on substance, chloride/nutrient/metal, and its monitoring). Mixed sources are indicated in most (18 of 19) catchments for chloride (with average 19% active-source contribution to total load), but only in 3-4 (of 37) catchments for total nitrogen and total phosphorus (32-59% active), and 1-3 (of 11) catchments for copper, lead, and zinc (1-3% active). Only 1 catchment (of 37) is indicated to have dominant active sources for total nitrogen, and most catchments thus have dominant legacy source contributions for all substances. The legacy contributions correlate well with human activity indicators in the catchments (urban areas for chloride, agricultural land share for nitrogen, population density for phosphorus, historic mining and mine waste areas for the metals), indicating that they are largely anthropogenic. The developed and tested methodology is relatively simple and can be used to screen commonly available stream monitoring data for distinction of active and legacy contributions of any hydrochemical constituent in and across various hydrological catchment settings.

Understanding rock mechanical behavior, especially the onset and nature of yield (i.e. permanent deformation), is critical to ensure safe and sustainable operations in many geological engineering applications, including ‘low-carbon’ subsurface technologies to aid decarbonization. In porous sandstones, yield corresponds to onset of either permanent dilatation or compaction accompanied by microcracking and is defined by a ‘capped’ yield surface in differential stress vs effective mean stress (Q/P) space. Previous studies showed that compactive yield in porous sandstones corresponds to the onset of grain fragmentation and pore collapse, manifested by the deviation of the porosity reduction curve from the hydrostat. However, determining the onset of yield can be ambiguous. Here, we investigate the micromechanical basis for the compactive yield behavior of porous (25%) Hollington sandstone, which is comparable to common aquifers, hydrocarbon and geothermal reservoir rocks and other sandstone formations with potentials for underground storage of fluids (e.g. carbon dioxide and hydrogen). A suite of conventional triaxial compression experiments with concomitant pore volumometry were performed to characterize the mechanical behavior of the rock. Effective mean stress vs porosity reduction data show a slow but near-linear initial deviation from the hydrostat followed by a sharp increase in the rate of compaction for most samples that underwent inelastic porosity reduction below 243 MPa effective mean stress (P*). Synthesis of mechanical with microstructural data suggests that the initial deviation from the hydrostat is related to the collapse of local large pores while the sharp increase in the rate of compaction corresponds to the onset of grain fragmentation leading to widespread pore collapse. Plotting yield stresses in Q/P space reveal a near linear cap of negative slope, albeit with considerable scatter, for stresses related to the collapse of local large pores, evolving into an elliptical cap for stresses approximately corresponding to the onset of widespread grain fragmentation. The results highlight the enhancing influence of non-hydrostatic stresses acting around large pores on the compactive yield in rocks with porosity heterogeneities.

This work reports on the design and implementation of advanced geospatial simulations using an Agent-Based Model (ABM) integrated with an augmented reality solution for interactive and immersive modeling exploration. The multi-scenario modeling framework allows for emergent phenomena and provides flexible representation of biological and physical environmental factors associated with natural and man-made systems. Augmented reality is provided by a sandbox running Tangible Landscape, based on a customization of GRASS GIS. An integrated Microsoft Kinect sensor mounted over the sandbox captures real-time topography produced by physical interactions with sand and resulting digital elevation models are ingested into the Recursive Porous Agent Simulation Toolkit (Repast) as landscape definition input. We illustrate the implementation by presenting a model system that includes a classic predator-prey relationship over a grassland habitat where sheep and wolves coexist as agents. Food sources for sheep are scattered over the landscape and are consumed as agents forage. Wolves control sheep population by actively searching for sheep and chasing individuals when their presence is detected. We simulate natural conditions by defining that the presence and movement of agents over the landscape is controlled by elevation provided by the sandbox. For instance, the presence of agents and resources can be limited to specific elevation ranges and slope is used to incorporate movement cost (energy loss) while individual agents travel over the landscape. Ecological conditions are further simulated by the consumption and regrowth of food resources. Users interact with the sandbox and the modeling effort by manually moving sand and altering landforms. This effort brings together multiple technologies and data manipulation/visualization strategies and allows for feature-rich experimentation by supporting multiple co-located and georeferenced layers (e.g., land use/land cover, soil, hydrography).

Soil moisture plays an essential role in the complex eco-hydrologic processes, such as infiltration, rainfall-evapotranspiration-runoff circulation, photosynthesis, and groundwater recharge. However, the accurate estimation of soil moisture (SM) at regional or larger scale is difficult because SM varies highly over space and time due to heterogeneous land cover and soil properties, and ground measurements are often time-consuming and expensive. Currently, Bangladesh Meteorological Department (BMD) measures SM only at twelve stations which is quite inadequate for assessing large-scale spatial and temporal variation of SM. Thus, satellite-derived soil moisture data products or Global Land Data Assimilation System simulated (GLDAS-2.2) soil moisture dataset with the Gravity Recovery and Climate Experiment Data Assimilation (GRACE-DA) can be promising alternatives to the in-situ measurement for this data-scarce region. In this study, the spatial and temporal variations of SM from GLDAS and Soil Moisture Active Passive (SMAP) satellite were compared against the in-situ measurements from seven agrometeorological stations of Bangladesh. The GLDAS and SMAP products overpredicted the in-situ SM for most of the stations and could capture the temporal dynamics of observed SM with correlation coefficient (R) of 0.36 and 0.17, respectively. Later an Artificial Neural Network model was developed based on soil moisture from both sources (SMAP and GLDAS) and terrestrial water storage from GLDAS to obtain more accurate estimation of SM for this data-scarce region. The ANN model shows an improvement in estimation and predicted SM with R = 0.63 (considering all stations). The results were more promising when separate model is developed for each study site. Incorporating additional climate data (such as precipitation with different lag times) as input improved the accuracy marginally. This study suggests that the release of daily GRACE gravity field solutions in near-real-time may provide a reasonable and continuous estimate of soil moisture in this data-scarce region.

Studies have long reported the existence of pronounced diurnal and semi-diurnal variations in near-surface winds and divergence over the tropical oceans. Diurnal cycles of convective precipitation and cloudiness in the tropics are also well recognized from in-situ and satellite observations. However, the linkages between diurnal variations in tropospheric circulation, cloudiness and precipitation over the tropical oceans remain to be fully documented and understood. Recently, global storm-resolving models, which do not require convective parameterizations, have created an unprecedented opportunity to investigate the full three-dimensional structure of the diurnal cycle over the tropical oceans. In this study, we used one such model – the Model for Prediction Across Scales (MPAS) – for two main purposes: first, to evaluate the model’s representation of semi-diurnal and diurnal variations in near-surface winds, precipitation, and cloudiness over the tropical oceans; and second, to extend the analyses to provide a full three-dimensional picture of the daily variations in tropospheric circulation and their linkage with the hydrological cycle. A 40-day MPAS simulation (the same as used for DYAMOND-1 global storm-resolving models inter-comparison project) was utilized in this study to examine the large-scale geographical patterns and vertical structures of mean daily variations of zonal and meridional wind components, wind divergence, vertical velocity, cloudiness, water vapor mixing ratio and precipitation. The model shows generally good agreement with the previously reported observational results for near-surface winds and divergence. In particular, MPAS exhibits a pronounced large-scale diurnal cycle in the local Hadley Circulation over the Tropical Pacific Ocean, with lower tropospheric divergence (convergence) relative to the daily mean, maximizing around 1000 (2200) LT. The amplitude of the diurnal variation in near-surface wind divergence at the equator is approximately 0.8×10-6 s-1, or approximately 44% of the daily mean. The vertical structure of this diurnal circulation, along with its signature in vertical velocity and its association with water vapor, cloudiness and precipitation, will be presented.

The Surface Biology and Geology (SBG) mission is one of the core missions of NASA’s Earth System Observatory (ESO). SBG will acquire high resolution solar-reflected spectroscopy and thermal infrared observations at a data rate of ~10 TB/day and generate products at ~75 TB/day. As the per-day volume is greater than NASA’s total extant airborne hyperspectral data collection, collecting, processing/re- processing, disseminating, and exploiting the SBG data presents new challenges. To address these challenges, we are developing a prototype science pipeline and a full-volume global hyperspectral synthetic data set to help prepare for SBG’s flight. Our science pipeline is based on the science processing operations technology developed for the Kepler and TESS planet-hunting missions. The pipeline infrastructure, Ziggy, provides a scalable architecture for robust, repeatable, and replicable science and application products that can be run on a range of systems from a laptop to the cloud or an on-site supercomputer. Our effort began by ingesting data and applying workflows from the EO- 1/Hyperion 17-year mission archive that provides globally sampled visible through shortwave infrared spectra that are representative of SBG data types and volumes. We have fully implemented the first stage of processing, from the raw data (Level 0) to top-of-the-atmosphere radiance (Level 1R). We plan to begin reprocessing the entire 55 TB Hyperion data set by the end of 2021. Work to implement an atmospheric correction module to convert the L1R data to surface reflectance (Level 2) is also underway. Additionally, an effort to develop a hybrid High Performance Computing (HPC)/cloud processing framework has been started to help optimize the cost, processing throughput and overall system resiliency for SBG’s science data system (SDS). Separately, we have developed a method for generating full-volume synthetic data sets for SBG based on MODIS data and have made the first version of this data set available to the community on the data portal of NASA’s Advanced Supercomputing Division at NASA Ames Research Center. The synthetic data will make it possible to test parts of the pipeline infrastructure and other software to be applied for product generation.

In recent decades, major growth is observed in wildfire incidents across the globe. These ecological disasters, triggered by natural and/or anthropogenic factors, can have long-lasting effects on the environment, ecosystems, and biodiversity. Advancements in remote sensing technology provided an impetus to forest fires research, enabling precise determination of the geographical locations susceptible to fire and assess fire risk. This study focuses on India, whose total forest cover (71.22 Mha) is about 21.67% of the country’s total geographical area. Around 90% of the forest fires in India are attributable to anthropogenic factors. Therefore, the generation of a forest fire risk map (FRM) is essential for devising strategies to mitigate/manage forest fires and avert their disastrous impacts. An attempt is made to develop FRM for the Budhabalanga river basin, which contains the Similipal national park, a part of the UNESCO World Network of Biosphere Reserves. For this purpose, covariates affecting forest fire are identified viz. fuel (vegetation), topographic features (elevation, aspect, and slope), and human activities. The covariates are assigned weights, depicting their relative importance in influencing the fire, byusing Analytical Hierarchical Process (AHP). The AHP is a widely used technique in multi-criteria decision-making models. The Similipal national park has experienced a prolonged dry spell and below-average monsoon in 2020. The consequent dry conditions led to a significant forest fire event in the last week of February 2021, which lasted nearly three weeks. Fine (30m) resolution satellite data (Landsat-8) are used to calculate the Normalized Difference Vegetation Index (NDVI) corresponding to the study area to assess vegetation conditions before the fire event. Furthermore, Cartosat-1 Digital Elevation Model (24m resolution) is used to extract topographic-related information. The FRM generated for the basin using the assigned weights was 80% accurate when validated with 375m resolution NASA’s VIIRS (Visible Infrared Imaging Radiometer Suite) fire point data for the analyzed fire event. Hence, the methodology considered for developing FRM appears promising. It can be extended to other river basins for identifying fire risk zones and devise timely strategies to mitigate fire risk.

Time-lapse borehole nuclear magnetic resonance (bNMR) relaxation is a promising method for linking water content changes in the unsaturated region of the critical zone with pore-scale properties associated with bedrock weathering. The saturation-dependance of the NMR T2 distribution is strongly controlled by pore-scale material properties and can be linked to hydraulic properties (e.g. the water retention function and hydraulic conductivity). Here, we leverage NMR’s sensitivity to pore-scale properties to investigate material controls on plant-available water storage dynamics in weathered bedrock via time-lapse bNMR relaxation measurements. To overcome slow logging speed and poor signal-to-noise (SNR) ratio typically associated with bNMR measurements in the unsaturated zone, we focus on the sum of echos (SE). We show that the advantage of using SE to characterize NMR relaxation, rather than using the full T2 distribution or the logarithmic mean of the distribution, is that it is easy to calculate, does not require inversion, has enhanced SNR, and is sensitive to both volumetric water content (VWC) and mean T2. This leads to high contrast in SE between time-lapse measurements relative to other metrics of NMR relaxation. At our hillslope study site associated with the Eel River CZO, VWC changes in weathered bedrock driven by deeply-rooted trees allow us to create “NMR characteristic curves” for different regions of the weathering profile. Analogous to a water retention function, the NMR characteristic curves describe NMR relaxation times of a material at a given VWC, and can be used to identify differences in pore-scale properties. We show that mean T2 times are typically shorter in bedrock that is more weathered for the same VWC, which is consistent with smaller pore-sizes and higher surface relaxivities associated with weathering products such as secondary clays and oxides. Our well logging indicates that changes in pore structure associated with bedrock weathering control plant-available water supply within the bedrock weathering profile. While these results illustrate the utility of bNMR, further studies that quantitatively link NMR measurements to flow properties via pore or empirical models will benefit mechanistic understanding of plant available water in the critical zone.

Documented trends in the timing and intensity of precipitation events have been met with associated hydrologic changes in water-balance partitioning. Changes in land use, initiation and length of the growing season and associated irrigation practices, timing of autumn leaf senescence, snow accumulation and melt, flooding, and drought all affect the hydrological processes of infiltration, evapotranspiration, and runoff. This study is an investigation of trends in groundwater recharge over space and time in the temperate climate of the Midwest USA. It tests the hypothesis that increasingly intense precipitation events resulting in extreme runoff events might be short-circuiting processes of infiltration and groundwater recharge by lowering the residence time of water in upland landscapes, while increasing infiltration in lowlands and riparian corridors. The climate-driven changes in precipitation events can be more easily linked to the documented increasing stream discharge trends than trends in groundwater levels. In this ongoing study, multiple methods were utilized to calculate annual and monthly, or seasonal, potential aquifer recharge amounts and trends in unconsolidated aquifers in Indiana and surrounding watersheds. Because observation wells in Indiana are sparsely distributed, estimates of groundwater recharge for selected wells were used for calibration and validation of a 40-year spatially continuous water balance (U.S. Geological Survey Soil Water Balance v 2.0, SWB2) model that solved for potential groundwater recharge (net infiltration) at a daily time step and was analyzed at annual and monthly intervals. Using estimates of annual net infiltration from the SWB2 model, 40-year temporal trends calculated on space-time cubes suggest that over 50% of the model domain is experiencing statistically significant increases (p < 0.006) in potential groundwater recharge. Trend analyses of unconfined water levels from 1980-2019 were conducted, finding that water levels in 8 (out of 9) climate divisions in the state are increasing (rising) in 6 of the 8 climate divisions. The distribution of increasing water tables reflects the same spatial distribution as the annual estimates of recharge generated by the SWB2 model.