The Panola Mountain Research Watershed (PMRW) is a 41-hectare, forested research catchment within the Piedmont Province of the United States (U.S.), located about 25 km southeast of Atlanta Georgia (33˚ 37’ 54” N, 84˚ 10’ 20” W). Annual precipitation (P) averages 1,250 mm (<1% as snow) and annual runoff averages 358 mm, resulting in a runoff ratio of 0.29 (based on water years 1986–2015; annual range 0.13–0.50). The PMRW is seasonally water limited which results in water deficits and long-term actual evapotranspiration (ET) is about 75% of potential ET. Recharge of storage occurs predominantly in September–March when ET is lower. The U.S. Geological Survey (USGS) initiated research at the PMRW in 1985. Early focus on the effects of acid deposition transitioned to investigating processes affecting streamflow generation and water quality as part of a network of five diverse U.S. watersheds in the USGS Water, Energy and Biogeochemical Budgets Program (1991–2016). Current research is funded by the USGS Ecosystems’ Climate Research and Development Program and is focused on the effects of droughts and climatic change on ET, soil moisture, groundwater recharge, and streamflow generation. Collaboration with many Universities have occurred throughout the study. Long-term monitoring includes P, streamflow, groundwater, soil moisture, and meteorological parameters and water quality sampling of P, streamwater, and soil water. Thirty years of monthly water budgets (including actual ET and watershed storage components) and 31 years of atmospheric deposition and streamwater solute fluxes are published in USGS ScienceBase and include supporting data. The PMRW has long water residence times despite its small size, with a volume-weighted mean streamwater transit-time of ~4.7 years but can be >10 years during dry years. The PMRW has a large dynamic (>500 mm) and total (~1,000 mm) watershed storage with a hydrologic persistence of 19-months, which is evident from the water budget response to recurring hydrologic droughts. The dominant flowpath of hillslope recharge to the riparian area and stream is through bedrock. Storm-streamwater quality response was controlled by riparian (not hillslope) groundwater. We welcome opportunities for collaborative studies, cross-site comparisons, and data sharing.

Each state and district within the Chesapeake Bay watershed has cooperated with the Chesapeake Bay Program (CBP) to develop local Watershed Implementation Plans (WIPs) that identify the type and quantity of best management practices (BMPs) that, if implemented, are estimated to meet 2025 Total Maximum Daily Load (TMDL) goals for Bay water quality. However, top-down management of large regions, such as the 167,000-km2 Bay catchment, is often necessarily limited by the feasibility of providing implementation plans that are customized by watershed hydro-physiographic characteristics and socio-political considerations. The Bay simulation model divides the catchment into watersheds of approximately 350 km2 each; these watersheds become the Bay model’s smallest overland management unit. We used Bay WIP plans, local information, and a hydrologic model called Topo-SWAT to model three of these smallest-unit watersheds in more local detail. Our smallest management unit became contiguous, similarly managed, cropland areas (i.e., one or several neighboring agricultural fields) and these management units were further divided by the topographic wetness index. Our watersheds represent three distinct hydrological and geochemical regions within the Chesapeake Bay catchment, namely Appalachian Valley and Ridge – karst, Appalachian Valley and Ridge – nonkarst, and Appalachian Piedmont. We modeled three scenarios for each watershed: baseline (pre-WIP), WIP implementation, and “smarter” WIP placement where we targeted BMP placements for cost-effectiveness. We then compared results among scenarios as well as across watersheds. We are interested to see how well the models agree at the watershed outlet, discover cost-effective BMP placements within each watershed that meet WIP goals, and compare our findings across the physiographic regions to determine how they can guide regional planning.

The National Water Model (NWM) currently requires the post-processing of forecast discharges to produce forecast flood inundation maps (FIM) that support the National Weather Service’s mission of protecting life and property. Height Above Nearest Drainage (HAND) is a means of detrending digital elevation models (DEM) by normalizing elevations to the nearest, relevant drainage line (creeks, rivers, etc). It’s worthy of producing high-resolution FIMs at large spatial scales and frequent time steps using reach-averaged synthetic rating curves. Current operational capabilities support 10 meter (1/3 arc-second) spatial resolution DEMs sourced from the National Elevation Dataset (NED). The 3D Elevation Program (3DEP) managed by the United States Geological Survey (USGS) publishes a variety of gridded elevation datasets at 1 m, 3 m (1/3 arc-second), 5 m, and 10 m (1/9 arc-second) among others. While the 1/3 arc-second product provides seamless coverage across CONUS, the remaining products lack full spatial support with respect to that of the NWM. However, 3DEP is actively publishing additional data with national coverage scheduled for 2023. We seek to investigate the efficacy of assimilating higher resolution 1 m and 3 m (1/3 arc-second) data derived from light detection and ranging sensors (Lidar). These Lidar derived datasets not only represent higher horizontal resolution but also have improved vertical accuracy when compared to the NED. We seek to utilize Py3DEP from the HyRiver ecosystem of tools to retrieve 3DEP data. HAND derived FIMs will be evaluated against high-fidelity HEC-RAS 1D inundation maps for 100 year and 500 year events. Possible skill enhancements can be observed from having terrain information that better agrees with those of the benchmark HEC-RAS datasets. Lidar terrain data can better resolve fine scale features that flood inundation extents may be very sensitive to. Additionally, we would investigate mosaicing techniques to deal with processing units (hydrologic unit codes) of heterogeneous data availability. This can involve resampling DEM’s to create seamless rasters within units. Lastly, we can investigate the effect of Lidar data on synthetic rating curves as well as consider the latest hydro-conditioning techniques from GeoFlood for stream line delineation on Lidar data.

Compound event research has gained a lot of momentum over the past few years. Traditionally risk assessment studies used to consider only one climatic driver/ process at a time. However, it was then recognized that it is the combination of multiple drivers and their statistical dependencies that lead to aggravated, non-linear impacts. The present study investigated and quantified the preconditioning of precipitation extremes (P) by existing soil moisture (SM) anomalies. Event coincidence analysis (ECA) was employed to investigate the coupling nature between SM & P event series. The datasets used include GLDAS-2.2 CLSM model products for soil moisture and GPM IMERG V06 for gridded rainfall data. Using SM and P data from 2004-2020, we identified hot-spots of SM-P coupling over India. A statistical significance test (α = 0.05) was carried out to ensure that the observed coincidences are not by chance. Our observed results agree with the widely regarded hypothesis of stronger SM-P coupling in transitional regions between wet and dry climates. The temporal evolution of SM-P dependence over the past two decades is also investigated. Results obtained provides critical insights on the complex dynamical relationship between soil moisture and precipitation. The dependence nature unraveled has vast implications for directing future research on coupled hydro-meteorological phenomena.

Long-term soil moisture datasets at high spatial resolution are important in agricultural, hydrological, and climatic applications. The soil moisture estimates can be achieved using satellite remote sensing observations. However, the satellite soil moisture data are typically available at coarse spatial resolutions (~ several tens of km), therefore require further downscaling. Different satellite soil moisture products have to be conjointly employed in developing a consistent time-series of high resolution soil moisture, while the discrepancies amongst different satellite retrievals need to be resolved. This study aims to downscale three different satellite soil moisture products, the Soil Moisture and Ocean Salinity (SMOS, 25 km), the Soil Moisture Active Passive (SMAP, 36 km) and the SMAP-Enhanced (9 km), and to conduct an inter-comparison of the downscaled results. The downscaling approach is developed based on the relationship between the diurnal temperature difference and the daily mean soil moisture content. The approach is applied to two sub-catchments (Krui and Merriwa River) of the Goulburn River catchment in the Upper Hunter region (NSW, Australia) to estimate soil moisture at 1 km resolution for 2015. The three coarse spatial resolution soil moisture products and their downscaled results will be validated with the in-situ observations obtained from the Scaling and Assimilation of Soil Moisture and Streamflow (SASMAS) network. The spatial and temporal patterns of the downscaled results will also be analysed. This study will provide the necessary insights for data selection and bias corrections to maintain the consistency of a long-term high resolution soil moisture dataset. The results will assist in developing a time-series of high resolution soil moisture data over the south-eastern Australia.

Most climate change impacts are linked to terrestrial vegetation productivity, carbon stocks and land use change. Changes in land use and climate drive the dynamics of terrestrial carbon cycle. These carbon cycle dynamics operate at different spatial and temporal scales. Quantification of the spatial and temporal variability of carbon flux has been challenging because land-atmosphere-carbon exchange is influenced by many factors, including but not limited to, land use change and climate change and variability. The study of terrestrial carbon cycle, mainly gross primary product (GPP), net ecosystem exchange (NEE), soil organic carbon (SOC) and ecosystem respiration (Re) and their interactions with land use and climate change, are critical to understanding the terrestrial ecosystem. The main objective of this study was to examine the interactions among land use, climate change and terrestrial carbon cycling in the state of Texas using satellite measurements. We studied GPP, NEE, Re and SOC distributions for five selected major land covers and all ten climate zones in Texas using Soil Moisture Active Passive (SMAP) carbon products. SMAP Carbon products (Res=9 km) were compared with observed CO2 flux data measured at EC flux site on Prairie View A&M University Research Farm. Results showed the same land cover in different climate zones has significantly different carbon sequestration potentials. For example, cropland of the humid climate zone has higher (-228 g C/m2) carbon sequestration potentials than the semiarid climate zone (-36 g C/m2). Also, shrub land in the humid zone and in the semiarid zone showed high (-120 g C/m2) and low (-36 g C/m2) potentials of carbon sequestration, respectively, in the state. Overall, the analyses indicate CO2 storage and exchange respond differently to various land covers, and environments due to differences in water availability, root distribution and soil properties.

River networks are important features in surface hydrology. However, accurately representing river networks in spatially distributed hydrologic and Earth system models is often sensitive to the model’s spatial resolution. Specifically, river networks are often misrepresented because of the mismatch between the model’s spatial resolution and river network details, resulting in significant uncertainty in the projected flow direction. In this study, we developed a topological relationships-based river network representation method for spatially distributed hydrologic models. This novel method uses (1) graph theory algorithms to simplify real-world vector-based river networks and assist in mesh generation; and (2) a topological relationship-based method to reconstruct conceptual river networks. The main advantages of our method are that (1) it combines the strengths of vector-based and DEM raster-based river network extraction methods; and (2) it is mesh-independent and can be applied to both structured and unstructured meshes. This method paves a path for advanced terrain analysis and hydrologic modeling across different scales.

Following the radioactive fall out of the 2011 Fukushima Daiichi Nuclear Power Plant (FDNPP) accident, radiocesium (Cs-137) contaminated soils of forests, uplands, rice paddies and other land uses released contaminated sediments onto neighboring areas and into the creeks and rivers in Iitate Village, Japan. The study used conventional and Cs-137 fingerprinting techniques to determine runoff and suspended sediment discharges to assess the small and large-scale soil redistribution dynamics within contributing areas in two watersheds. Also, we attempted to use Cs-137 fingerprinting to identify spatial and temporal patterns of erosion, transport and sedimentation on hillslopes within those watersheds. Tributaries near the outlet of the 30 km2 Hiso watersheds were simulated at the hillslope and watersheds using the process-based Water Erosion Prediction Project (WEPP) model and the Geospatial Interface for WEPP (GeoWEPP). Besides the simulation of historic soil redistribution events, a particular emphasis was the identification and assessment of various land use and cover changes on the past soil redistribution. Results of the analysis in the post-fallout landscapes enables scientists and farmers as well as natural resources and disaster managers to investigate the consequences of active and passive land use and cover changes on the runoff and sediment dynamics at the plot, hillslope and watershed scales. Especially the behavior of Cs-137 contaminated clay particles in soils and sediments seem to be the key for the success of the measurement, modeling and management techniques. The result of this study has the potential to assist decision and policymaking for stakeholders not only in areas that were impacted by the contamination through radioactive fallout.

Accurate and dynamic mapping of water and ice surfaces is directly useful to navigation and lake ice cover monitoring to study climate change. Water and ice maps are also useful for various scientific applications such as atmospheric correction of satellite imagery, remote sensing of water quality, and as input data for hydrological, weather and climate models. The existing literature shows that multi-spectral satellite imagery, as provided by Sentinel-2 and Landsat-8, provides a very effective means to discriminate between water, land, and ice. However, most studies focus either on very specific cases (a specific lake for instance), or on general cases but without complex and yet very frequent cases such as turbid waters and salt lakes which can be confused with snow and ice. The Copernicus High-Resolution Snow and Ice Monitoring Service provides an operational Sentinel-2 ice and water classification product at 20m resolution but with a lot of confusion on the aforementioned cases. Using a database of 31 fully hand-labelled Sentinel-2 L2A atmospherically corrected images, and machine learning SVM and RandomForest methods, the current study shows that the classification of land, water, ice, snow, turbid waters, salt lake categories can be achieved with an accuracy over 93%. It is also shown that the atmospheric correction has little to no impact on the results, as training and evaluating from L1C top of the atmosphere images instead of L2A images yields very similar results. This last find is very useful as it means that very accurate surface masks can now be provided to atmospheric processors and may therefore considerably improve the quality of atmospherically corrected images when compared to the current usage of static masks.

Forecasting of wastewater treatment plant inflow dynamics constitutes an enabler technology for wastewater treatment process optimization using model predictive control. However, accurate inflow prediction is still challenging, especially for strong rainfall events, where complex system dynamics and missing information on future rainfall represent limiting factors. We propose a seasonal probabilistic time series model for modelling the short-term wastewater inflow accurately while providing quantification of forecast uncertainty. To ensure suitability for practical implementation, the unconstrained parameters of the predictive distribution are modelled as linear functions of the input variables in the framework of Generalized Additive Models for Location Scale and Shape. Non-linear effects are approximated by Rectified Linear Units (ReLU), accounting for buffering within the sewer network and flow-dependent catchment response time. In addition to water level measurements from within the sewer network and rain rate measurements, rain forecasts are incorporated as exogenous regressors, where historical rain forecasts are used for model calibration. The model performance is evaluated on historical data from a German wastewater treatment plant using deterministic and probabilistic scoring rules. We benchmark against probabilistic time series models (SARX) and LSTMs. Our results show that the proposed model unites the benefits of high prediction accuracy of the LSTM and enhanced intelligibility of the SARX model, but accurate real-time rain forecasts are mandatory for successful real-world implementation.

The natural flow regime within a watershed can be considered as the expected temporal patterns of streamflow variation in the absence of human impacts. While ecosystems have evolved to function under these conditions, the natural flow regime of most rivers has been significantly altered by human activities. Land use change, including the development of agriculture and urbanization, is a primary cause of the loss of natural flow regimes. These changes have altered discharge volume, timing, and variability, and consequently affected the structure and functioning of river ecosystems. The Meramec River watershed is located in east central Missouri and changes in land use have been the primary factor impacting flow regimes across the watershed. In this study, a watershed model, the Soil and Water Assessment Tool (SWAT), was developed to simulate a long-term time series of streamflow (1978-2014) within the watershed. Model performance was evaluated using statistical metrics and graphical technique including R-squared, Nash-Sutcliffe efficiency, cumulative error, and 1:1-ratio comparison between observed and simulated variables. The calibrated and validated SWAT model was then used to quantify the responses of the watershed when it was a forested natural landscape. An Indicator of Hydrologic Alteration (IHA) approach was applied to characterize the flow regime under the current landcover conditions as well as the simulated natural flow regime under the no land use change scenario. Differences in intra- and inter-annual ecologically relevant flow metrics were then compared using SWAT model outputs in conjunction with the IHA approach based on model outputs from current and no land use change conditions. This study provides a watershed-scale understanding of effects of land use change on a river’s flow variability and provides a framework for the development of restoration plans for heavily altered watersheds.

Extreme runoff modeling is hindered by the lack of sufficient and relevant ground information and the low reliability of physically-based models. The authors propose to combine precipitation Remote Sensing (RS) products, Machine Learning (ML) modeling, and hydrometeorological knowledge to improve extreme runoff modeling. The approach applied to improve the representation of precipitation is the object-based Connected Component Analysis (CCA), a method that enables classifying and associating precipitation with extreme runoff events. Random Forest (RF) is employed as a ML model. We used 2.5 years of nearly-real-time hourly RS precipitation from the PERSIANN-CCS and IMERG-early run databases (spatial resolutions of 0.04 o and 0.1 o , respectively), and runoff at the outlet of a 3391 km 2-basin located in the tropical Andes of Ecuador. The developed models show the ability to simulate extreme runoff for the cases of long-duration precipitation events regardless of the spatial extent, obtaining Nash-Sutcliffe efficiencies (NSE) above 0.72. On the contrary, we found an unacceptable model performance for a combination of short-duration and spatially-extensive precipitation events. The strengths/weaknesses of the developed ML models are attributed to the ability/difficulty to represents complex precipitation-runoff responses.

Snow and ice melt processes are a key in Earth’s energy-balance and hydrological cycle. Their quantification facilitates predictions of meltwater runoff as well as distribution and availability of fresh water. They control the balance of the Earth’s ice sheets and are acutely sensitive to climate change. These processes decrease the surface reflectance with unique spectral patterns due to the accumulation of liquid water and light absorbing particles (LAP), that require imaging spectroscopy to map and measure. Here we present a new method to retrieve snow grain size, liquid water fraction, and LAP mass mixing ratio from airborne and spaceborne imaging spectroscopy acquisitions. This methodology is based on a simultaneous retrieval of atmospheric and surface parameters using optimal estimation (OE), a retrieval technique which leverages prior knowledge and measurement noise in an inversion that also produces uncertainty estimates. We exploit statistical relationships between surface reflectance spectra and snow and ice properties to estimate their most probable quantities given the reflectance. To test this new algorithm we conducted a sensitivity analysis based on simulated top-of-atmosphere radiance spectra using the upcoming EnMAP orbital imaging spectroscopy mission, demonstrating an accurate estimation performance of snow and ice surface properties. A validation experiment using in-situ measurements of glacier algae mass mixing ratio and surface reflectance from the Greenland Ice Sheet gave uncertainties of ±16.4 μg/g_ice and less than 3%, respectively. Finally, we evaluated the retrieval capacity for all snow and ice properties with an AVIRIS-NG acquisition from the Greenland Ice Sheet demonstrating this approach’s potential and suitability for upcoming orbital imaging spectroscopy missions.

Lakes are traditionally classified based on their thermal regime and trophic status. While this classification adequately captures many lakes, it is not sufficient to understand seasonally ice-covered lakes, the most common lake type on Earth. Here, we propose an additional classification to differentiate under-ice stratification. When ice forms in smaller and deeper lakes, inverse stratification will form with a thin buoyant layer of cold water (near 0oC) below the ice, which remains above a deeper 4oC layer. In contrast, the entire water column can cool to ~0oC in larger and shallower lakes. We suggest these alternative conditions for dimictic lakes be termed “cryostratified” and “cryomictic.” We describe the inverse thermal stratification in 19 highly varying lakes and derive a model that predicts the temperature profile as a function of wind stress, area, and depth. The model opens up for a more precise prediction of lake responses to a warming climate.

Urbanization and climate change increase water pressure in dams and stress the stability of flood-control structures. Many of the existing dams are aging and have been classified as deficient or having potential for life-threatening hazards in the event of failure. Common mitigation measures include optimizing reservoir release rates and/or implementing additional large—scale infrastructure. Such decisions are typically investigated with drainage models that do not consider co-evolving variables, such as environmental effects or socio-economic impacts. Flood-control reservoirs form complex hydrologic systems that contain numerous interdependencies and intricate feedbacks that must be balanced to achieve optimal resiliency. A spatial multicriteria analysis (SMCA) framework is presented that integrates a suite of social and environmental vulnerabilities with reservoir modeling and decision-making weights. An implementation of adaptive flood control case study of the Addicks and Barker Reservoirs in Houston, Texas, USA during Hurricane Harvey is used to illustrate the proposed technique and to highlight the complexities involved in reservoir decision-making. Hydrologic synergies that would be realized from maintaining status quo operations, optimizing reservoir releases, or increasing storage capacity through engineered solutions are explored. The SMCA methodology is used to visualize how such relationships alter environmental and social vulnerabilities for improved decision-making. In this way, the decision-making process becomes an endogenous component of the integrated human-water-environment feedbacks, thus enabling adaptive management of flood-control reservoirs with comprehensive risk.

The surface of ice in contact with water contains sites that undergo deprotonation 6 and protonation, and can act as adsorption sites for aqueous ions. Therefore, an electrical double layer should form at this interface, and existing models for describing the electrical double layer at metal oxide-water interfaces should be able to be modified to describe the surface charge, surface potential, and ionic occupancy at the ice-water interface. I used a surface complexation model along with literature measurements of zeta potential of ice in brines of various strength and pH to constrain equilibrium constants. I then made predictions of ion site occupancy, surface charge density, and partitioning of counterions between the Stern and diffuse layers. The equilibrium constant for cation adsorption is more than 5 orders of magnitude larger than the others constants, indicating that this reaction dominates even at low salinity. Deprotonated OH sites are predicted to be slightly more abundant than dangling O sites, consistent with previous work. Surface charge densities are on the order of ±0.001 C/m^2 and are always negative at the moderate pH values of interest to atmospheric and geophysical applications (6-9). In this pH range, over 99% of the counterions are contained in the Stern layer. This suggests that diffuse layer polarization will not occur because the ionic concentrations in the diffuse layer are nearly identical to those in the bulk electrolyte, and that electrical conduction and polarization in the Stern layer will be negligible due to reduced ion mobility.

Fluvio-lacustrine features on the martian surface attest to a climate that was radically different in the past. Since climate models have difficulty sustaining a liquid hydrosphere at the surface, multiple cycles of runoff episodes may have characterized the ancient Mars climate. A fundamental question thus remains: what was the duration of these runoff-producing episodes? Here we use morphometric measurements from newly identified coupled lake systems (containing both an open- and a closed-basin lake). We combined hydrological balances with precipitation outputs from climate models, and found that breaching runoff episodes likely lasted 10^2–10^5 yr; other episodes may have been shorter but could not be longer. Runoff episode durations are model-dependent and spatially variable, and no climate model scenario can satisfy a unique duration for all coupled systems. In the near future, these quantitative constraints on early Mars lake persistence may be tested through in situ observations from Perseverance rover.

Southern Africa is facing unprecedented strains on its agriculture, including a rapidly increasing population and demand for cereals. The global issues of climate change, water scarcity, and soil erosion are also affecting southern Africa, which expects a drier climate in the future. A promising tool in the fight for food security is Conservation Agriculture (CA), a technique based on minimum soil disturbance, mulching using crop residues, and crop rotation and/or intercrops. CA is promoted by organisations including the United Nations due to its potential to increase crop yields in arid/semi-arid climates; increase drought resilience; and increase infiltration of rainwater, reducing flooding and erosion. Despite its benefits and promotion, little is understood of the hydrodynamics of soils under CA cultivation. In order to investigate these hydrological processes, we installed Electrical Resistivity Tomography (ERT) monitoring systems (PRIME, developed by BGS) at three agricultural research sites in southern Africa (Zambia, Malawi, & Zimbabwe) under CA and conventional tillage systems. The sites are also instrumented with soil temperature, moisture, and matric potential sensors, as well as monitored groundwater boreholes, enabling comparison between monitoring techniques and the tracking of water from the ground surface to depth. ERT deployments for the respective sites include surface 2D, shallow cross-borehole 3D, and surface 3D electrode arrays. Each PRIME system is configured for twice daily data collection, and uses data telemetry for remote data retrieval. ERT monitoring allows us to monitor the hydrodynamics from the root zone, through the soil profile and vadose zone, to the aquifer. Initial results show variability between the sites, and heterogeneous nature of the vadose zone within the sites. This heterogeneity has been shown to influence preferential fluid flow pathways in the vadose zone. Monitoring over rainfall events has shown a strong, rapid response of pronounced, shallow wetting fronts, with limited changes at depth. We are beginning the process of comparing the hydrodynamics between CA and conventional plots, and the procedure of optimising data processing to enable better imaging of soil moisture changes at depth in the presence of rapid near surface changes.

The phenomenon of freezing point depression in frozen soils results in the co-existence of ice and liquid water in soil pores at temperatures below 273.15 K, and is thought to have two causes: i) capillary effects, where the phase transition relationship is modified due to soil-air-water-ice interactions, and ii) solute effects, where the presence of salts lowers the freezing temperature. The soil freezing characteristic curve (SFC) characterizes the relationship between liquid water content and temperature in frozen soils. Most hydrological models represent the SFC using only capillary effects with a relationship known as the Generalized Clapeyron Equation (GCE). In this study, we develop and test a salt exclusion model for characterizing the SFC, comparing this with the GCE-based model and a combined capillary-solute effect model. We test these models against measured SFCs in laboratory and field experiments with diverse soil textures and salinities. We consistently found that the GCE-based models under-predicted freezing-point depression. We were able to match the observations with the salt exclusion model and the combined model, suggesting that salinity is a dominant control on the SFC in real soils that always contain solutes. In modelling applications where the salinity is unknown, the soil bulk solute concentration can be treated as a single fitting parameter. Improved characterization of the SFC may result in improvements in coupled mass-heat transport models for simulating hydrological processes in cold regions, particularly the hydraulic properties of frozen soils and the hydraulic head in frozen soils that drives cryosuction.

COSMOS soil moisture sensors provide meso-scale area-averaged soil moisture estimates, presenting a unique opportunity for validating remotely sensed soil moisture data from satellite sensing platforms such as SMAP. New, roving COSMOS sensors can provide greater spatial coverage than their stationary counterparts. However, COSMOS sensors require careful site-specific calibrations, which are not available for roving sensors. As such, it is critically important for researchers to monitor roving COSMOS collection campaigns in near-real-time. However, specialized user interfaces are needed for rapid analysis. Moreover, harmonizing remotely sensed data (such as Landsat, SSURGO, MODIS, SMAP, and SRTM) with a roving COSMOS sensor is non-trivial and requires great care that cannot be accomplished on-the-fly in the field. To address these problems, we are developing the open source SoilMAP (Soil Moisture Analysis and Processing) software, which is a specialized analysis application for COSMOS and SMAP soil moisture data. We are developing this application using PODPAC (https://podpac.org/), a cloud-ready open source Python library for large-scale analysis and on-demand processing of raw earth science data. Our soil moisture analysis application aims to provide (1) customizable, rapid, near-real-time visualization and analysis of COSMOS and SMAP data; (2) unified data access and automated data wrangling to harmonize roving COSMOS measurements and SMAP L3 data; and (3) a streamlined workflow for developing roving COSMOS sensor calibrations with uncertainty estimates. We will demonstrate on-demand processing of raw soil moisture data retrieved from COSMOS sensors and SMAP L3 data using our SoilMAP software framework. We will also show our user workflows specialized for (1) staging data from various remotely-sensed and in-situ sensors, (2) monitoring a COSMOS data collection campaign in near-real-time, and (3) analyzing the resultant data with comparison to SMAP soil moisture. We will outline the steps required to build and customize this application. SoilMAP greatly reduces the burden of analyzing, comparing, and validating soil moisture data using measurements from roving COSMOS sensors.

We present an upscaled model to predict the plume evolution in highly heterogeneous alluvial aquifers. The model is parameterized exclusively by the mean, variance and correlation length of the logarithm of hydraulic conductivity, porosity and the mean hydraulic gradient. It can be conditioned on the tracer and conductivity data at the injection region. The model predicts the evolution of the longitudinal mass distribution observed at the MADE site, which is characterized by strongly non-Gaussian plume shapes with a localized peak and pronounced forward tail. The proposed model explains these features by the conductivity heterogeneity at the injection region, and tracer propagation due to a broad distribution of spatially persistent Eulerian flow speeds.

This presentation demonstrates a CT scanning and image analysis workflow to characterize wellbore cement degradation under corrosive geologic CO2 storage (GCS) conditions. The workflow includes 1) acquisition of raw CT images of the cement sample (before and after exposure to CO2); 2) application of rigid registration to align raw CT images; 3) acquisition of grayscale intensity difference images; 4) application of noise filtering technique to obtain images with good quality; 5) acquisition of 3D pore structure change of the cement sample after CO2 exposure from grayscale intensity difference images, showing degradation of wellbore cement. To demonstrate an application of the workflow, an experiment of reaction between CO2 and wellbore cement under corrosive GCS conditions was conducted and the wellbore cement samples used in the experiment went through aforementioned CT scanning and image analysis procedures. CT image analysis results demonstrate a region with increased porosity in the exterior of the cement sample (Zone 1) and a region with decreased porosity next to Zone 1 due to CaCO3 precipitation (Zone 2). Next to Zone 2, a region with increased porosity due to Ca(OH)2 and C-S-H dissolution (Zone 3) was observed. In summary, this study proves feasibility to use 3D CT scanning and CT image analysis techniques to investigate CO2-induced degradation of wellbore cement.

The rainfall reduction in the 1970s, less marked in Central Africa than in West Africa, still had a major impact on the hydrological regimes of the region’s large rivers. The study of the hydropluviometric behavior of the Ubangi at Mobaye has the advantage of studying a basin excluding anthropogenic impact. Forest cover and population density have not changed since at least 1970. Statistical analysis of the breaks in the long rainfall time series from Ubangi at Mobaye (1935-2015) confirms a long period of drought from 1969 to 2006 corresponding to a reduction of -8% in rainfall. And the study of the corresponding hydrological series indicates a second downward break in 1981, few years after the rainfall increase. This period points an exceptional hydrological drought period until 2013, which is the first year with an increase of flows. The statistical study of the annual rainfall/flow series of the upstream basins over the period 1951-1995 (the Kotto at Kembé and Bria, the Mbomu at Bangassou and Zémio, the Uélé + Bili hydrographic system) highlights different hydrological behaviors related to the vegetation cover. The savanna basins show a continuous hydrological deficit marked by a runoff coefficient (CE) that fell to 5% only from the 1990s. On the other hand, the basins under forest show a runoff increase since 1990 marked by CE above 10%. Under savannah, the part of the flow infiltrating to recharge the aquifer would have decreased faster than under forest, which results in a runoff coefficient CE very significantly negatively correlated with the savanna area present in the studied watershed.

We report the results of an extensive experimental campaign dedicated to the analysis of turbulent dispersion owing to the circulations in tide dominated estuaries, characterized by a compound cross section (a main channel and lateral tidal flats). Following the classification suggested by Toffolon et al. (2006), we concentrate our attention on weakly-convergent and weakly-dissipative estuaries, where the internal waters communicate with the open sea through an inlet mouth. Particle Image Velocimetry is employed to measure two-dimensional surface velocity. Large scale macro-vortices, generated by vortex shedding during the flood phase from the inlet barrier, tend to occupy the entire tidal flats width and, irrespective of the controlling parameters, they are completely flushed out during the ebb phase. Flow decomposition based on averaging over the tidal period enlightens the presence of an intense residual current, with shape influenced by the large-scale flood vortices. The measured Eulerian surface velocity fields form the basis for a thorough Lagrangian analysis, which yields a clear picture of the dispersion regimes. The presence of large-scale vortices and of an intense residual current strongly influences the Lagrangian auto-correlation functions and the corresponding absolute dispersion time evolution. Looping auto-correlations are the signature of both the periodic forcing and vortices, ultimately, leading to super diffusive regimes. Moreover, an asymptotic Brownian regime is always found for the investigated range of parameters allowing for an estimate of the horizontal dispersion coefficients. For the latter, we suggest a simplified algebraic formulation that well fits the experimental estimates.