Imagery from the GOES series has been a key element of U.S. operational weather forecasting for four decades. While GOES observations are used extensively by human forecasters for situational awareness, there has been limited usage of GOES imager data in numerical weather prediction (NWP), and operational data assimilation (DA) has ignored cloud and precipitating pixels. The motivation of this project is to bring the benefits of GOES-R Series enhanced capabilities to advance convective-scale DA for improving convective-scale forecasts. We have developed a convolutional neural network (CNN) prototype, dubbed “GOES Radar Estimation via Machine Learning to Inform NWP (GREMLIN)” that fuses GOES-R Advanced Baseline Imager (ABI) and Geostationary Lightning Mapper (GLM) information to produce maps of synthetic composite radar reflectivity. We find that the ability of CNNs to utilize spatial context is essential for this application and offers breakthrough improvement in skill compared to traditional pixel-by-pixel based approaches. Making use of ABI spatial information potentially provides benefits over radiance assimilation approaches that are limited by saturation of radiances in pixels with precipitation. This presentation will briefly describe the GREMLIN model and characterize its performance across meteorological regimes. We are developing a dense neural network (DNN) to produce vertical profiles of radar reflectivity and latent heating based on the two-dimensional fields output from GREMLIN. The resulting three-dimensional fields of latent heating will be used to initialize NWP simulations of convective-scale phenomena. Another DNN will be developed to produce uncertainty estimates of latent heating for each pixel. Our approach for data assimilation will be described and is innovative in being the first-time machine learning (ML) will be used for the nonlinear latent heating observation operator in the NOAA hybrid Gridpoint Statistical Interpolation (GSI) and/or JEDI systems. The approach will provide all the elements of the Jacobian needed for GSI DA and has the advantage of automatically maintaining the tangent linear and adjoint models through finite difference mathematics.

The east coast of India is prone to tropical cyclones. During pre-monsoon and post-monsoon seasons, almost every year, cyclones are developed over the Bay of Bengal and Andaman Sea. On 20 May 2020, tropical cyclone Amphan made initial landfall at Bakkhali in the West Bengal, India at 2:30 PM (local time) with wind velocity more than 175 km/h, which further intensified and moved into adjoining Bangladesh coastal areas on 21 May 2020 with highest wind speed 260 km/h. The cyclone formed on 16 May 2020 and dissipated finally on 21 May 2020. More than 22,000 houses in Coastal parts of West Bengal, and nearly 220,000 houses in Bangladesh coastal areas were damaged, and approximately 4.2 millions of people in both the countries were relocated to safe places. This cyclone affected many of the eastern states of India and 9 coastal districts of Bangladesh due to heavy rainfall that caused deadly flood. In West Bengal, Kolkata airport was flooded, and all the flights were closed. We have carried out analysis of meteorological and atmospheric parameters that helped in formation of the cyclone. Detailed analysis of multi satellite sensors, ground and Argo data have been carried out that show pronounced changes in ecological, atmospheric and ocean color parameters. The ocean parameters such as chlorophyll concentration, dissolved oxygen, salinity, sea surface and sub-surface temperature show strong changes in ocean ecology that have serious impacts on the marine life. Strong stratospheric-tropospheric exchange caused the rise in various trace gases close to the ground. A detailed damage analysis along the Eastern part of India and Bangladesh coast and major nearby cities and migration of the population from the affected region will be discussed.

Quantitative Precipitation Estimates from space-based observations represent an important dataset for understanding the Earth’s atmospheric, hydrological and energy cycles. Precipitation retrieval algorithms have been developed and refined over the last few decades and their accuracy and reliability are becoming increasingly more important for Earth’s energy budget and human activities. In particular, snowfall represents a key component of water cycle and contributes significantly to the Earth’s radiative balance. Accurately quantifying global surface snowfall is especially important since snow comprises a large percentage of the annual surface precipitation in many regions. The complexity of snowflakes particles and the nonlinear relationships between observations and retrieved variables have moved scientists to continually develop and enhance retrieval techniques. The present work aims to improve snowfall retrievals from Passive Microwave (PMW) sensors. Within the Global Precipitation Measurement (GPM) mission, the Goddard PROFiling (GPROF) algorithm snowfall retrieval has been chosen as an example of PMW precipitation product. Since previous works have demonstrated that GPROF performance strongly depends on the snowfall type, we developed a Machine Learning technique to classify snowing regime. A combined CloudSat-GPM dataset has been used to build the training dataset in which the GPM Microwave Imager (GMI) brightness temperatures (TB) are associated with a snowfall type, classifying the snowfall into three classes (‘shallow convective’, ‘deep stratiform’, ‘other’). The snowfall classification was adopted from a CloudSat classifying technique, based on snowing profiles and cloud classification. The problem is posed as a supervised learning problem, using a fully connected deep learning architecture with TB textures serving as input features. After building, optimizing and applying the classification method to existing GMI data, an evaluation exercise was undertaken to assess the classification performance. Results show that using only 20,000 GMI-CloudSat collocated snowing scenes the accuracy in retrieving the three classes has exceeded 80%. Being able to classify snowfall mode will help develop a specific setup for GPROF to improve detection and retrieval performance.

Conceptual models are among the most frequently used type of models in watershed modelling studies, due to their low computational requirements and ease of interpretation. Model selection requires the comparison of model alternatives, which is complicated when the models present in the literature differ in many uncontrolled aspects, such as conceptualization, implementation, and source code availability. To overcome this limitation, several model-building frameworks have been introduced in the last decade, which facilitate model comparisons by enabling different model alternatives within the same software and numerical architecture. Building on the decennial experience with the development and usage of Superflex, a flexible modeling framework for conceptual model building, so far implemented in FORTRAN language and not available as open source, we propose SuperflexPy, an open source Python framework for building conceptual hydrological models. Compared to other existing models or flexible frameworks, SuperflexPy is designed to be extremely easy to modify or extend, allowing scientists to build models that reflect their processes understanding; thanks to its object-oriented architecture and its complete integration with the Python programming environment, SuperflexPy can be seen as a high-level programming language for constructing hydrological models that are extremely flexible both in terms of elements configuration (i.e. how the elements are connected into a structure) and spatial organization. By design, SuperflexPy is not limited to water quantity but can be extended to simulate transport processes (water quality). In this presentation, we will illustrate the principles behind the design of the framework and showcase some applications.

The climatology of atomic oxygen (O) in the MLT (mesosphere and lower thermosphere) is balanced by O production via photodissociation in the lower thermosphere and recombination in the upper mesosphere. The motivation here is to establish the intra-annual variation in the eddy diffusion coefficients and eddy velocity in the MLT based on the constituent climatology of the region. The analysis method, originally developed in the 60’s (Colegrove et al. 1965), was refined for a study of MLT global inter-annual variations in global mean values (Swenson et al. 2018, 2019, respectively) ,(S19). In the this study, the intra-annual cycle was divided into twenty-six (two-week) periods for each of three zones, the northern hemisphere (NH, 15 to 55 degrees ), southern hemisphere (SH, -15 to -55 degrees ), and the equatorial region (EQ, 15 to -15 degrees ). Sixteen years of SABER O density measurements (2002-2018) and MSIS 2.0 model N , O and T profiles (80-96 km) were determined for each of the periods and zones for determination of O eddy diffusion velocities and fluxes. Atomic oxygen diffusive fluxes ([O]*v, 80-96 km) are balanced by the continuity of chemical loss, but the intra-annual variation of k$_{zz}$ (determined from v ) and [O] are determined separately. The major findings include: 1) A dominant AO below 87 km in the NH and SH zones, with the largest variation in amplitude between winter and summer at 83 km. 2) A dominant SAO at all altitudes (80-96 km) in the EQ zone. 3) Intra-annual variability in the global average [O] and k$_{zz}$ contribute to variability of O eddy transport in the MLT.

The discipline of informatics, generically cast as the science and engineering of information system within a socio-technical framework, originating in the middle of last century has undergone generational adaptations as computer hardware, networks and software have evolved. Within the “eScience” era of the last two decades, discipline-specific fields of informatics have flourished, such as geoinformatics, mineral informatics and many more. In fact even in geosciences, there may be few fields of study that have not added an informatics sub-field. Over the same time, efforts at systematizing the common (or core, i.e. discipline neutral) aspects of informatics have been successful: use cases, human-centered design, iterative approaches, information models and more are some of the key elements. However new pressures are being placed on functional and non-functional requirements of information systems: with the now somewhat routine underlying data that are high dimensional, heterogeneous, sparse and with uncertain quality. However, demands have arisen from renewed attention to hub/ server/ cloud-based provision of the application of machine learning, neural networks and artificial intelligence in general. Those methods as implemented in software libraries producing results to be assessed and interpreted (often leading to decisions made) by “humans-in-the-loop”. Informatics, revisited is a possible answer. This presentation features some history of informatics, recent disruptions that need to be addressed, and offers ideas for new directions, with the goal of advancing geoscience in general.

The CYGNSS constellation of eight satellites was successfully launched in December 2016 into a low inclination (tropical) Earth orbit. Each satellite carries a four-channel bistatic radar receiver which measures signals transmitted by Global Positioning System (GPS) satellites and scattered back into space by the Earth surface. Over the ocean, surface roughness, near-surface wind speed and air-sea latent heat flux are estimated from the surface scattering cross section. Over the land, estimates of near-surface soil moisture and imaging of inland water bodies and flood inundation are derived from the surface reflectivity. The measurements are able to penetrate through all levels of precipitation and through most vegetation canopies due to the long radio wavelength at which GPS operates. The number of satellites in the constellation and their continuous data-taking operation produces high spatial sampling density and low temporal revisit times. Over ocean, this makes possible the reliable detection of tropical cyclone intensification and the resolution of diurnal cycles in tropical winds. Over land, diurnal soil moisture variability is resolved and rapidly changing flood inundation events are mapped. Engineering commissioning of the constellation was completed in March 2017 and the mission is currently in its science operations phase. Science data products are regularly produced over ocean for wind speed, surface roughness, and sensible and latent heat fluxes and over land for near surface volumetric soil moisture. Data products currently in development over ocean include tropical cyclone intensity (peak sustained winds) size (radius of maximum winds), extent (34, 50 and 64 knot wind radii), storm center location, and integrated kinetic energy. Over land, data products in development include refined versions of volumetric soil moisture content, flood inundation extent, time-varying inland water body maps, and riverine streamflow rate. An overview and the current status of the CYGNSS mission will be presented, together with updates on terrestrial science data products in development that are related to the terrestrial water cycle.

Wetlands represent an essential ecosystem, providing flood control, carbon storage, and supporting biodiversity. In particular, the Everglades is a Ramsar Wetland of International Importance, supporting several threatened and endangered species of flora and fauna, and is especially important for wintering birds. Understanding and monitoring wetlands like the Everglades requires the ability to accurately identify and measure wetland extent and change in extent on short time scales. However, in situ methods are difficult given the nature of the surrounding environment, and optical methods of remote sensing are unable to see through dense vegetation. NASA’s Cyclone Global Navigation Satellite System (CYGNSS) has shown promising results using GNSS Reflectometry to identify the presence and extent of inland water. Utilizing GNSS as a signal of opportunity in an L-band passive bistatic radar, it can penetrate rain, clouds, and vegetation. Its 8-satellite constellation exhibits daily or sub-daily revisit rates, enabling the observation of dynamic changes on short time scales. In this work, we utilize a combination of CYGNSS data, ancillary information, and simulations to understand the observability of inundation beneath vegetation. Simulations were used to predict the received power using a water mask derived from Landsat imagery over the Everglades. By analyzing the differences between expected and actual received power, we identified areas of flooded vegetation. These differences were then combined with ancillary data sets to measure seasonal changes and create a seasonal map of open water and inundated vegetation throughout the Everglades network. We also investigated the ability of CYGNSS to discern and measure different vegetation types. Results were then compared with optical and radar imagery and verified with truth data from the Everglades Depth Estimation Network (EDEN) and littoral vegetation maps from the South Florida Water Management District. By leveraging CYGNSS’s high temporal frequency of observations and ability to see under vegetation, measurements of inundated vegetation and its change can complement other remote sensing and in situ methods of wetland monitoring.

Saturn’s moon Enceladus harbours a global subsurface ocean beneath its icy crust. Tidal dissipation within the moon’s core generates a substantial amount of heat which leads to ocean convection. Understanding this transport of mass and heat within the ocean is key to understand exchange processes between core, ocean and ice shell. Previous studies of ocean convection assume oceans of constant thickness and constant superadiabatic temperature gradients between the inner core and outer ice shell. However, observations indicate ocean thickness variations of up to ~20 km from equator to pole and heterogenous tidal dissipation within the core likely results in latitude-dependent temperature gradients. In this study, we analyse how heterogeneities affect circulation patterns and heat transfer in an Enceladan ocean using three-dimensional direct numerical simulations. We focus on a degree-2 meridional thickness profile with varying degree of thermal forcing to analyse the different rotating convection regimes and compare them with those of an ocean of constant thickness. Our results show that the non-uniform ocean gives rise to a latitudinally variable Rayleigh number, causing a potential decoupling of rotating convection regimes experienced locally within the ocean. As in the constant ocean thickness scenario, different regimes of convection exist, which depend on the relative influence of rotation. With increasing thermal forcing, convection moves from being restricted to equatorial regions to filling the whole fluid domain. Heat transport efficiency, as measured by the local Nusselt number, is different for the uniform and non-uniform cases and depends on the convection regime. Nusselt-Rayleigh relations are similar to those obtained for an ocean of uniform thickness, but the relevant Rayleigh number depends on the region of the domain that is convecting.

Cloud microphysics plays important role on the storm dynamics. To investigate the impact of advanced microphysics schemes, using single and double moment (WSM6/WDM6) schemes, numerical simulations are conducted for a severe convective system that formed over the Korean Peninsula. Spatial rainfall distribution and pattern correlation associated with the convective system are improved in WDM6. During developing stage of the system, the distribution of total hydrometeors is larger in WDM6 compared to WSM6. Along with mixing ratio of hydrometeors (cloud, rain, graupel, snow and ice),number concentration of cloud and rainwater are also predicted in WDM6. To understand the differences in vertical representation of cloud hydrometeors between the schemes, rain number concentration (Nr) from WSM6 is also computed using particle density to compare with Nr readily available in WDM6.Varied vertical distribution, and large differences in rain number concentration, rain particle mass are evident between the schemes. Inclusion of number concentration of rain and cloud, CCN along with mixing ratio of different hydrometers have improved the storm morphology in WDM6. Inorder to investigate the cloud-aerosol interactions, numerical simulation has been conducted using an increase in CCN(aerosol) in WDM6 which has shown an improved rainfall distribution with intense hydrometer distribution. The latent heating (LH) rates of different phase change processes (condensation, evaporation, freezing, melting, sublimation and deposition) are also computed using various transformation rate terms in the microphysics modules. It is inferred that the change in aerosol has increased the LH of evaporation and freezing and affected the warming and cooling processes, cloud vertical distribution and subsequent rainfall.

Despite recent advances in sensors, hydro-meteorological data remain scarce in urban watersheds. In the current, reactive approach to stormwater management, whether or not an urban flood event is documented, as well as where and how it actually occurred, is highly dependent on the level of monitoring. Even though there are many methods for observing flooding extent and predicting flooding vulnerability, issues with data availability and accuracy persist at the local level. Urban watersheds are spatially and temporally complex and flash floods, while of particular interest and importance to both hydrologists and communities, are hard to characterize, given that they are rare, spatially localized, short-lived, and often occur in locations without formal monitoring. On the other hand, identifying vulnerable areas in a large city using hydraulic/hydrological modeling would be very difficult, either because models have parameters that need to be calibrated against mostly non-existent data (in the case of conceptual models), or else we do not know all of the actual physical processes at work, or how to quantify them (in the case of physically-based models). Community-based monitoring activities can support the characterization of urban watersheds, as well as stormwater management, because they provide valuable spatial and temporal knowledge about the behavior of water flow and other related issues at a local level. An urban watershed study was conducted to demonstrate the value of community-based observations for understanding and characterizing urban pluvial flash flooding, by addressing the following questions: (i) How can communities feasibly monitor their local watershed using low-cost, straightforward approaches? (ii) How can local knowledge help us to better define and characterize urban pluvial flooding vulnerability? (iii) Are community-based data reliable and meaningful to urban watershed management and the decision-making process? To pursue these questions, participatory research was applied in a low-resourced community in the City of Paternò, in Sicily, Italy. We collected as unbiased as possible information about flash flooding events through 300 surveys. The locally-gathered data were compared to the results of advanced hydrodynamic models on smaller scales.

Abstract Introduction: Nowadays it is nebulous how much matter is delivered by the largest rivers into the Arctic ocean and how much sediments are eroded by bank and watershed erosion. Thus, these investigations were made for the two biggest watersheds of the Arctic Ocean (the Ob’ and Yenisei) and their components of the sediment budget estimated. Methods and results: 1.The fieldwork campaign allowed us to obtain characteristic models of the distribution of suspended and bed sediments in the estuaries of the Yenisei and Ob’ river 2.The method for estimating the instantaneous sediment yield is developed based on the author’s program for extrapolating ADCP measurement data (language R), water velocity and backscatter intensity. The average annual sediment runoff for the Ob’ is 63.5 Mt/year, for the Yenisei – 32.5 MT/year; 3.The watershed component of sediment runoff was estimated by the RUSLE, taking into account the trapping of sediments by reservoirs and lakes. For the Ob’ potential watershed is 85 MT/year, and 53.6 MT/year for the Yenisei 4.The Bank erosion was calculated based on GSWE, Arctic-DEM, and HYDROATLAS and GRWL databases for the downstream of rivers, which was 35.0 MT/year for the Ob and 21.9 MT/year for the Yenisei. 5.The total deposition of sediments was calculated as a difference between total sediment yield and total erosion in catchments. For the Ob’ total deposition was 56.5 MT/year, for the Yenisei is 43 MT/year. Discussion and conclusion The sediments deposited in the catchment area or in the bedforms can be eroded again by snowmelt and rainfall erosion in the catchment area or directly due to the erosion of the banks. The products of erosion gradually move to the end of the river, with the exception of the part of sediment trapped by reservoirs. For instance, for the Ob’, it is only 10% of the watershed erosion, and for the Yenisei, it is 17% of eroded soil on the catchment. According to the calculations, bank erosion for both downstream is less than the watershed component of sediment runoff. The fact that these components are comparable and it gives some progress in solving the still unexplored question of the role of the bank and watershed components in the origin of sediment runoff of large rivers. Acknowledgments This work powered by the grant №18-05-60219 of the Russian Foundation for Basic Research(RFBR)

Synthetic aperture radar (SAR) multi-temporal techniques have been proposed to improve image resolution, persistent-scatterer detection, and damage-map generation (Bujor et al., 2004; Hooper, 2008; Yun et al., 2012). The usage of multiple scenes diminishes the speckle noise commonly present in a SAR image and increases the overall signal-to-noise ratio (SNR) of dominant radar scatterers. Following the same idea, we propose a new SAR processing workflow based on the back-projection algorithm (Cafforio et al., 1991). We employ the back-projection method’s flexibility to focus the radar pulses at multiple depths shifted with respect to the reference digital elevation model (DEM), yielding a volume of SLCs discretely sampled over a range of elevations/depths. These scenes are then combined to produce a single-look complex (SLC) image, which presents increased SNR and effective resolution. Similar image improvements can be achieved by spatially averaging a SLC image generated at high spatial sampling. However, the proposed method requires a fraction of the processing time of the fine-spatial SLC formation. The proposed approach retains the same benefits of any back-projection algorithm and enables the formation of SLC images corresponding to large Earth surface extents.

Morphodynamic evolution of coastal barriers and the way it is recorded in the internal structure are still revealing many uncertainties. Coastal spits have a high potential of preservation due to their fast elongation under littoral drift, and are thus suitable for this kind of study. Compared to sandy spits, gravel and mixed spits are understudied. Yet these coarse-grained coastal barriers are sensitive sedimentary bodies due to the high energy conditions they are usually associated with and to often scarce sediment supply. The present study focus on the relationships between multi-decadal morphological evolution of a mixed sand and gravel spit in the bay of Somme (Northern France) and its sedimentary architecture. A geophysical survey using a 400 MHz GPR GSSI antenna have been performed on the Molliere spit, a fast-growing secondary spit developing along the main 15 km-long Somme barrier spit. The development of this sedimentary body starts in the 1940s and allows a morpho-stratigraphical approach, thanks to the cross analysis of aerial photographs and GPR profiles. The spit is 5 km long and is composed of several ridges, sometimes with hooks at the tip. A synthetic depositional and stratigraphical model specific to gravel spits is proposed, based on radar profiles and aerial photographs analysis, differentiating three contrasting morpho-sedimentary units. The Unit 1, at the spit root, is characterized by mainly progradating structures that can be assimilated to a beach ridge. The Unit 2, in the central part of the spit, is characterized by individual ridges with a core structure reflecting a longshore development, topped by an aggradating unit associated to a cross-shore-dominated dynamics. The third unit, at the spit terminus, is also mainly characterized by longshore dynamics, but interfering with sand bank dynamics due to the proximity of the Somme estuary and the development of the spit platform. Larger-scale GPR prospecting, especially in older coast sections, will be necessary to understand how this fast secondary spit development (decadal to centennial time scale) is integrated into the longer-term (millennial) construction dynamics of the main Somme spit.

Gold mining has played a significant role in Ghana’s economy for centuries. Regulation of this industry has varied over time and while large-scale mining is prevalent in the country, prevalence of artisanal mining, or Galamsey has escalated throughout Ghana in recent years. These mines are not only harmful to human health due to the use of Mercury in the amalgamation process, but also leave a significant footprint on terrestrial ecosystems, degrading and destroying forested ecosystems in the region. This study used machine learning and Google Earth Engine to quantify the footprint of artisanal gold mines in Ghana and understand how conversion of forested regions to mining has changed from 2002-2019. We used Landsat imagery and a random forest classification to classify areas of anomalous NDVI loss during this time period and used WorldView image collections to assess the accuracy of the model. We then used a 3-year moving average to calculate the year of maximum derivative NDVI values. We used this calculation to identify the year of conversion to mining. Within the study area of Southwestern Ghana, our analysis showed that approximately 35,000 ha of vegetation were converted to mining. The majority of this mining occurred between 2014 and 2017. Additionally, around 700 ha ha of mining occurred within protected areas defined by the World Database on Protected Areas. Often, artisanal mining appears to be co-located with rivers such as the Orin and Ankobra Rivers, demonstrating the potential of these mines to affect access to clean drinking water. Through the process of gold extraction, these mines leave a distinct footprint with a series of ponds following these major rivers. However, while the footprints of these ponds are spatially distinct, our model does not distinguish between active and inactive ponds if no remediation actions are taken following inactivity. Future research should work towards distinguishing between active and inactive mining sites to better understand current levels of mining activity in Ghana.

Radiosonde observations are the gold-standard for quantifying vertical profiles of atmospheric state variables. Knowledge of which is critical for quantifying moisture and instability, two main ingredients for severe weather. Unfortunately, radiosondes are very sparse, averaging just one observation per 500 x 500 km area over CONUS, and most locations have only two observations per day. This creates uncertainty in the representation of short wavelength and rapidly evolving synoptic and mesoscale features in numerical weather prediction (NWP) and provides few points of comparison for human forecasters to interpret NWP in making forecasts. To fill this gap in our knowledge of the atmospheric state, human forecasters make use of satellite imagery to estimate airmass properties for incrementing NWP outputs. Data from geostationary satellites have been especially useful because of its high temporal resolution (5-minutes) and high spatial resolution (2 km). While the Advanced Baseline Imager (ABI) was not designed as a sounding sensor, the three water vapor bands and three infrared window bands do provide some sounding capabilities. Satellite data are particularly useful in assessing position and timing errors, the representation of short waves, and humidity. The key question addressed by this work is can the mental process used by human forecasters be translated into a machine learning (ML) algorithm to provide automated and objective estimates of airmass properties from ABI? Experiments with convolutional neural networks (CNNs) show that ML can indeed be used. Related research efforts, such as NOAA Unique Combined Atmospheric Processing System (NUCAPS) has explored use of dense neural networks (DNNs), which are essentially replacing a radiative transfer model with a ML model. However, we find more skill can be achieved by making use of the spatial information captured with CNNs. This more closely mimics the human imagery interpretation process: it is the spatial patterns in the features (as much as the pixel-wise values themselves) that carry the useful information content. We will present our latest results, focusing especially on relative humidity, compare against radiosondes, and discuss whether skill is enough to potentially make a positive impact on NWP analyses.

Reusing produced water for hydraulic fracturing simultaneously satisfies challenges of fresh water sourcing and the design/operation of an extensive disposal well infrastructure. This presentation provides an overview of a reuse program from concept through implementation including qualification of advanced water recycling technologies. We target the most prolific unconventional reservoir play in the United States – the Permian Basin. Sourcing water for full-field development therein represents a significant problem since it is in short supply in (semi)arid regions of West Texas and Southeastern New Mexico. We report results from a synergistic industry-academia collaboration wherein desalination pretreatment was first evaluated at lab scale to (i) systematically evaluate partial softening (i.e. “floc-and-drop”) versus neutral pH oxidation for iron removal (ii) investigate synergistic effects of FeCl3 and polymer addition to destabilize colloids (including particulate iron) and induce high-rate sedimentation and (iii) develop and implement robust techniques using video and image analysis to characterize process performance and floc properties (e.g. morphology, size, and settling velocity). Jar tests and associated measurements were completed in the range 4 - 44 ºC covering the range of temperatures measured in the Permian. FeCl3 in conjunction with an anionic polymer dramatically improved colloid destabilization and floc growth via enmeshment of primary colloids by amorphous iron precipitates and inter-particle bridging by the adsorbed polymer. Larger, stronger, and denser flocs thus formed settled extremely rapidly without breakage (i.e. high rate sedimentation). Bench-scale results were integrated in the design and testing a 5,000 BPD pilot scale high-rate clarifier. Pilot scale results show that the neutral pH method of clean-brine generation produced 5-10 times less sludge while achieving 15-20% higher throughput over the alternative floc-and-drop method. Both bench- and pilot-scale findings were incorporated in design and operation of a 50,000 BPD full-scale reuse facility in the Permian Basin. This presentation will share lessons learned from operating a large-scale reuse facility and how academic research can inform and be motivated by industrial practices (and vice versa).

Sunlight plays an important role in regulating the nutrient cycle in streams. Throughout the years, more streams are buried to accommodate for residential or commercial facilities. This results in severe impacts on the health of streams due to altered exposure to sunlight, air, and soil; it subsequently affects the growth of aquatic and riparian vegetation, thus impairing the water quality of streams. Although urbanization effects on urban surface streams are well understood, the adverse effects of burying streams on dissolved oxygen (DO), reaeration, photosynthesis and respiration processes have been mainly remained theoretical. This study evaluated the effect of stream piping on DO for two sections of Stroubles Creek in Blacksburg, VA through comparing the water quality prior to entering, and post exiting the buried sections of the Creek. Monitoring DO was conducted through manual readings and continuous measurement for a number of days in Fall 2015 and 2016. The results indicated that the water DO level decreased by approximately 11% as water flowed through the buried sections. The covered section of the stream also caused variability of the water temperature, resulting a possible disturbance to the downstream ecosystem. The buried channels-induced DO variations effects on the Brook Trout (Salvelinus sp.), a sensitive species present at the Stroubles Creek headwaters was also assessed. Further, the photosynthesis rate, reaeration coefficient, and daily respiration rate were computed prior and after one of buried channels through a series of mathematical and empirical approaches to assess the buried channels effect on the aforementioned parameters. The photosynthesis and respiration rates were reduced at the inlet compared to outlet, primarily due to less solar radiation and aquatic vegetation oxygen uptake, however, reaeration rate was increased at the outlet compared to inlet. This study can assist the decision makers and resource planners in taking the appropriate decisions regarding daylighting stream channels to improve water quality or/and accommodate increasing demand for urbanization.