Flash droughts have recently gained significant attention due to their severe economic and ecological impacts. Despite extensive and growing research on flash drought processes, predictability, and trends, there is still no standard quantitative definition that encompasses all flash drought characteristics and pathways. This has motivated efforts to define, inventory, monitor, and forecast flash drought events. In our recent studies of flash droughts over the United States, we have introduced the Soil Moisture Volatility Index definition (SMVI) to inventory the onset dates and severity of flash across the Contiguous United States (CONUS) for the period 1979-2018. Post to an extended evaluation and comparison to other flash drought definitions and independent vegetation and crop datasets for seminal flash drought events, the SMVI has proved effectiveness in capturing flash drought onset in both humid and semi-arid regions. Using our SMVI inventory of flash droughts, we examine and classify flash droughts events based on multiple land surface and atmospheric conditions that may represent predictable drivers using a K-means-based clustering methodology. We found that there are three distinct classes of flash drought that can be diagnosed in our inventory. The first defined class of events are the “dry and demanding” droughts, showing high anomalies of evaporative demand and low soil moisture levels; The second are “evaporative” events, which develop under conditions of high demand and when elevated evapotranspiration accelerates soil drying, and a third class that we refer to as “stealth” events, which may be challenging to predict based on precursor atmospheric conditions due to the lack of a clear atmospheric signal with the observed modest anomalies. The contrasting meteorological and surface process signatures of the three classes do, however, indicate that events identified as “flash drought” using a reasonable definition, including events that have been widely reported as seminal flash droughts, represent a diversity of onset and intensification processes. Our results suggest that recognizing this diversity is critical to advance our understanding and ability to predict these events.

Water is scarce in semi-arid and arid areas where urban irrigation consumes a large portion of city water. It is important to manage and conserve water properly to meet the growing demand, especially in the summer season. The purpose of this research is to identify the major contributors to streamflow in the semi-arid Denver metropolitan area, CO, USA, and analyze the temporal variation of streamflow sources using two-year data. In this study, water-stable isotopes (δ18O and δ2H) were used as the tracer to identify the contribution of different sources such as tap water, precipitation, or irrigation water in Denver urban streamflow. Stream and tap water were sampled every other week and precipitation samples were collected once a month. There were 13 urban and 6 grassland streams, and tap water was collected from 6 different water providers. The USGS real-time streamflow data and BaseflowSeparation function in R package ‘EcoHydRology’ were used to select the baseflow condition in the streams for sampling. Picarro L2130i Laser Water Isotope Analyzer was used for oxygen and hydrogen isotope (δ18O and δ2H) analysis of stream, tap, and precipitation water. Results showed that precipitation samples were heavier in earlier summertime than the late summer and 2019 showed greater variability than 2021. Tap samples showed temporal and spatial variability in δ18O and δ2H values. Less variability in tap isotopic data could be observed in 2021 than in 2019. Centennial Water and Sanitation District showed a decreasing trend. In the δ18O vs. δ2H plot, stream and tap water followed local meteoric water line (LMWL) and global meteoric water line (GMWL) well. But precipitation sample exhibited a slight deviation from the LMWL and GMWL. The similar isotopic range in tap water and stream water supported that tap water was the main source of water during the summertime. In the future, the percent contribution of different sources will be evaluated. Furthermore, the effect of reduced urban irrigation by using efficient irrigation, landscape, or conservation techniques will be analyzed to achieve water security for sustainable urban development.

Most remote sensing-based surface energy balance (SEB) models are limited by data availability and physical constraints to fully capture the non-linear and temporally varying nature of atmospheric, biophysical, and environmental controls on evapotranspiration (ET). As such, currently, no single SEB model is considered to work best under all conditions particularly in irrigated croplands where surface moisture conditions could change dramatically in a short amount of time. Hence, irrigation water management based on a single remotely sensed ET model is often required to cope with model limitations and data latency issues, which could lead to unsustainable and unreliable accounting of water use over time. The recent inception of ensemble-based ET modeling takes the advantage of the strengths of the several SEB models under different conditions and is found to perform better as compared to an Individual model. Yet, challenges remain in how high-temporal ET outputs from different models are accurately assembled in a way that yields the most reliable estimates of ET across any environmental and surface conditions. Specifically, existing simple or Bayesian average and machine learning-based ensemble approaches have not been able to optimally utilize the comprehensive suite of existing SEB models and the availability of multiple remotely sensed datasets. Here, we discuss the utility of convolutional neural networks (CNNs) to assemble the outputs from a host of SEB models that can robustly capture the non-linear dynamics of ET under all conditions. We will also discuss the advantage and potential limitations of using the CNN-based ensemble ET modeling framework with respect to the individual, simple or Bayesian average, and other machine learning approaches and their implications for use in allocating water use across critically dry regions. Several ensemble models will be trained using eddy covariance flux data globally and will be evaluated based on their ability to estimate ET from MODIS and Landsat sensors with both individual and fused products and minimal weather inputs. The results can provide useful insights into how multiple datasets and SEB models could be optimally utilized to accurately monitor crop water status and support sustainable water resource management in drylands.

Reconstructions of river discharge and other hydrologic variables often exploit large available networks of tree-ring chronologies from multiple species and hydrologic settings. A common early step in such studies is screening to reduce the predictor data set and focus on chronologies with a strong hydrologic signal. A stepwise regression approach to screening is proposed and illustrated for reconstruction of April 1 snow-water equivalent (SWE) at three snow courses in the northern Sierra Nevada and Lake Tahoe region from a multi-species tree-ring network. SWE is regressed separately on each chronology lagged t-2 to t+2 years from the year of SWE. A chronology is accepted based on specified criteria for temporal stability of signal and skill of the lagged model in predicting SWE outside the calibration space. A cross-validation stepwise cutoff rule is applied to guard against over-fitting the lagged model. Illustration for a network of 23 chronologies of five snow-adapted species (Juniperus occidentalis, Pinus jeffreyi, Pinus ponderosa, Abies magnifica, and Tsuga mertensiana) underscores the critical importance of lags in the tree-ring response to SWE. For Abies and Tsuga, in particular, chronologies passing screening are characterized by lagged models with a positive coefficient on the year following the hydrologic anomaly (deep snowpack this year, wide ring the following year) and no dependence or a negative coefficient on the current year (deep snowpack, narrow current ring). The SWE signal is strongest for one particular Juniperus chronology whose regression explains 39% of the SWE variance at two snow courses. The strength of SWE signal varies greatly over sites within species. More than half of the Juniperus and Pinus chronologies were rejected by screening because of either weak or temporally unstable signal. A repeat of the regression screening using water-year precipitation (Global Historical Climate Network) instead of SWE suggests that different subsets of chronologies are optimal depending on the target hydologic variable. Few chronologies have a significant signal for the residual of SWE regressed on water-year precipitation. This suggests little snow-specific information on the moisture signal in annual ring width. Sub-annual ring measurements and quantitative wood anatomy are suggested as possible ways to help discriminate the rain and snow signals in tree rings from the region.

Recent proof-of-concept experiments have demonstrated the potential utility of Signals of Opportunity (SoOp) in remote sensing. SoOp methods involve the re-use of existing satellite transmissions as sources in bistatic radar, applying fundamental physical principles to estimate surface and scattered medium properties from reflectivity and phase observables in the reflected signal. Through utilizing signals intended for communications, SoOp methods can make these observables using frequencies that are not allocated or protected for scientific use. Two promising applications in hydrology have been studied: Sub-canopy root-zone soil moisture (RZSM) using satellite communications signals below 500 MHz and snow water equivalent (SWE) retrieval from the observed phase different through propagation through the snow layer. Signals of Opportunity P-band Investigation (SNOOPI) is a NASA Cubesat technology demonstration mission to test forward scattering models and validate a prototype instrument for SoOp reflectometry in 250-380 MHz range. Contribution to the panel discussion will focus on the expected contributions of the SoOp techniques validated in the SNOOPI mission and the existing challenges in the full utilization of SoOp methods for both RZSM and SWE remote sensing. Multiple frequencies are required in order to solve the inverse problem and estimating a sub-surface profile. In the case of SoOp, this may require combining observations with diverse geometry due to the different orbits of the potential sources. This presents new challenges in the development of retrieval algorithms and may possibly require the integration of additional data sources. Another important challenge for SWE retrieval is the need for repetitive coverage to extract phase differences between subsequent passes, coupled with orbit determination for the non-cooperative sources. In contrast to GNSS reflectometry (in which high-precision orbits are publicly available for use in positioning), communication satellite orbits are not known to the required meter-level accuracy. Even geostationary sources frequently have a small inclination which results in motion relative to the surface of the Earth. Finally, antenna calibration is a substantial contribution to the error budget.

The instrumental data shows 7%-20% increase in annual discharge of the major Eurasian rivers like Ob, Yenisei and Lena between 1936 and 2018 (Wang et al. 2021). The trend has been attributed to increased precipitation and permafrost thawing due to the temperature warming (Walvoord, Kurylyk 2016). However, the instrumental data does not provide the longterm scope of the trend. We modeled seasonal discharge from tree rings for the Yenisei River upstream at Kyzyl gauge and found a remarkable 80% upsurge in winter ‘ow (Nov-Apr) over the last 25 years, which is unprecedented in the last 214 years since 1784 (Panyushkina et al. 2021). In contrast, the annual discharge (Oct-Sept) has only a 7% increase over the last 25 years and shows normal range of variability since 1700 (Fig. 1). Water balance modeling with CRU data at the Yenisei upstream indicates a significant discrepancy between decadal variability of the gauged ‘ow and climate data after 1960 (Fig. 1). The long-term buckhound of the changes in regional hydrology is successfully assessed with tree-ring methods. The tree-ring networks in Eurasian cold climates have a great potential to reconstruct spatial pattern of the seasonal runoff and quantify the long-term impact of permafrost degradation on the hydrological regimes in Siberia. We discuss the impact of melting permafrost on the base ‘ow and enrichment of the surface and groundwater interaction at the Yenisei River basin coupled with the warming temperature and, more importantly, forest fires. Recent increase in the frequency, size, and intensity of boreal fires scale s of its impact on hydrology and permafrost in Siberia. This study demonstrates the complexity of hydrological feedback in Siberia to the Arctic Ampli:cation (AA). The adverse impacts of AA have been and will remain the greatest for the health and socioeconomic of people living in the Pan-Arctic and the geopolitics and macroeconomics of the global society.

Polar regions are highly sensitive to climate change. In recent decades, the Arctic has warmed twice as fast as the world average, which has led to a significant loss of ice cover in the Arctic Ocean. The positive feedback from continental hydrology to Arctic warming amplifies perturbations in the climate system in response to changes in heat and freshwater fluxes. As the surface warms and storm paths change, precipitation and glacier melt have increased pan-Arctic runoff. Melting permafrost adds even more water to river systems. Our research aims to understand 1) whether this feedback impacts the river flow only in the warm season, 2) whether the runoff change in the cold (winter) season can amplify the initial warming, and 3) what is the spatiotemporal pattern of seasonal variations in streamflow across pan-Arctic watersheds. Tree rings serve as critical proxies for quantifying hydrological responses to climate change. We are exploring the potential of modeling seasonal flows, especially winter versus spring and summer flows, and water temperature using intra-seasonal aggregation of climate signals in subsets of ring chronologies derived from quantified tracheal cell parameters: cell area, tangential cell diameter, radial cell diameter, cell wall thickness. The preliminary results demonstrate significant correlation of summer water temperature with cell area and radial cell diameter, whereas the discharge correlates stronger with cell wall thickness and tangential cell diameter. We emphasize the importance of modeling intra-seasonal hydrological parameters versa the hydrological year average to analyze the contribution of continental hydrology to Arctic warming.

Percolation theory addresses the spatial and temporal scaling properties of disordered networks, both for topological and transport-related processes. The critical zone is, effectively, a random network. The ability to predict the dimensions of the critical zone as a function of time, as well as its carbon storage properties, are both critical to the understanding of its dynamics. In addition, combining the scaling relationships with the principal that represented ecosystems will tend to optimize net primary productivity, generates a basic understanding of the water cycle. When the future of hydrology is discussed, these facts should be of central importance. Furthermore, although the definition of complex systems is not necessarily generally agreed on, it is usually understood that phase transitions, such as described by percolation theory, are emergent phenomena within the range of topics encompassed. Thus, this topic is of fundamental relevance to the present conference.

Rapid population growth in Bangladesh has led to an increase in the rate of urbanization across the country. Runoff is increased due to an increase in impervious surfaces as a result of urbanization. Sylhet City Corporation (SCC) is a metropolitan city in northeastern Bangladesh that is frequently afflicted by urban flooding and waterlogging as a result of storm runoff due to heavy rainfall. A substantial portion of the city suffers from severe drainage congestion, which is caused by the inadequate drainage system, lack of maintenance of drainage system, garbage dumping in the drains and canals, and illegal encroachment of drainage canals. The review of the existing drainage system and the causes of the drainage congestion in the city area have been assessed in this study. This study also focuses on the stormwater management of Goali chhara sub-system of eastern part of Sylhet City Corporation which is responsible for waterlogging in the study area. The Goali chhara sub-system has been modeled using GeoSWMM to analyze the stormwater runoff and to prepare a masterplan of drainage network considering the climate change issues. The design water level of the Surma river has been estimated from historical river water level data using the Gumbel distribution method. Moreover, analyzing the historical rainfall data of Sylhet Station, the IDF curve for short-duration rainfall has been developed and the design rainfall intensity of 2-hour and 5-year return period has been estimated. These hydrological parameters have been used in the model and prepare the drainage network masterplan. The model has been calibrated using the estimated runoff coefficients from the land use map for the sub-catchments. The stream cross-sections for the 10-year return period were found to be larger than that obtained for the 5-year return period. This research will help the local authorities of Sylhet City Corporation by allowing them to make drainage capacity expansion decisions that will tackle this critical challenge for the next 30-40 years.

The significance of groundwater is likely to grow in the coming years because of the need to mitigate the negative consequences of climate change. Adaptive capacity becomes important as more frequent and intense rainfall variability would quickly increase the possibility of large reduction in the amount of sustainable surface water supplies while aquifers will be affected considerably more slowly by anticipated climate change. On the other hand, the combined effects of population growth, irrigation demand, and climate change will continue to put pressure on the groundwater resource in the future. Against this backdrop, the abstraction-induced recharge (AI-R) concept should be regarded as a future adaptation option for some likely sites in Bangladesh to solve unmet water concerns. The inherent concept of the AI-R is that the sub-surface storage can rapidly be filled during the wet season and consume during the following dry season. Variable thickness in subsurface geology, aquifer system and inconsistent response to monsoon rainfall as well as flooding made Bangladesh incompatible for large basin wide AI-R concept. As a result, this research thoroughly observed the reaction to rainfall events, aquifer condition, historical water level and other factors in two case studies that have the potential to be used in AI-R operations. High-resolution (hourly) in-situ recordings of co-located groundwater level and rainfall data are compared to low-frequency (daily rainfall and weekly groundwater level) observations in this study. Historical groundwater conditions, as well as rapid groundwater response to both diffuse and concentrated recharge, were found to be favorable to AI-R at one place (Bhuapur, Ishwarganj). Nearby ponds which store huge monsoon water and have low aquitard thickness can play potential role in induced recharge in this place. On the other place (Bochaganj, Dinajpur), groundwater response to rainfall is complicated and groundwater level shows decreasing trend in recent years which can get worsen if AI-R concept is implemented here. So, this type of fine resolution field inquiry is at the very least need to be observed on possible AI-R piloting locations. Multi-isotope studies are also advised for assessing long-term effects.

Water and carbon exchanges between the land and atmosphere reflect key ecohydrologic processes, from global climate change to local watershed dynamics. Environmental stable isotope ratios of H2O and CO2 fluxes have been used to study these processes, yet measurement constraints have limited macroscale surface-atmosphere isotope flux evaluations. Across North American biomes within the US National Ecological Observation Network (NEON), we have worked as a team to translate raw measurements of carbon and water stable isotopes into calibrated daily surface-atmosphere flux isotope ratios for precipitation, evapotranspiration, and net ecosystem carbon exchange. Using information theory metrics, we demonstrate that these isotope observations contain meaningful information about the bulk water and carbon fluxes, with isotope measurements carrying about the same amount of information as wind speed measurements. Decomposition of this multivariate mutual information further shows that: (1) this information is unique, i.e. not carried by other traditional ecosystem measurements; and (2) the information added by isotopes is larger in more arid and cool ecosystems. Combining these isotope fluxes with bulk hydrologic fluxes drawn from a suite of land surface models in a first-order mass balance framework also allows for evaluation of hydrologic model structure and estimated uncertainties in partitioning of fluxes into transpiration, evaporation, overland, and subsurface water fluxes. An inter-model comparison suggests distinct patterns in isotope flux composition associated with disparities in the relative contributions of partitioned fluxes. Our results show that conservative isotope tracers provide novel validation metrics for evaluation of land surface model performance across ecosystems at a continental scale. Broadly, this compilation of datasets - combined with both empirical and process-based isotope modeling - suggests NEON stable isotope observations can improve general understanding of land-surface processes influencing the water and carbon cycles from regional to global scales.

Globally, water scarcity is on the rise. South Africa has a semi-arid to arid climate, and lack of water resources is resulting in physical water scarcity. Eastern Cape is the second most affected and under-serviced province with 26.1% of its population having no access to tap water. Winnie Madikizela Mandela Local Municipality of Nomlacu currently has 73.6% municipal water supply backlog. South Africa’s primary source of water is surface water which is of questionable quality. This gives Rooftop Rainwater Harvesting (RRWH) systems as a water resource, great opportunity to potentially alleviate water scarcity. RRWH collects rainwater runoff from impermeable roof surfaces, using temporal or permanent facilities such as buckets and storage tanks respectively. This study aims to assess water quality obtained from a rainwater harvesting system for potable use in Nomlacu, South Africa. Water samples were collected from houses with zinc metal and tile roofs in accordance to the South African National Standard (SANS 241-1:2015 Edition 2) for drinking water. According to the results obtained, Aluminium, Colour, Conductivity @25°C, Iron, Odour, pH, Lead, Suspended Solids, Total Dissolved Solids @105°C from both roof types remained under allowable limits. Several studies found steel roofs to present good water quality pertaining Colour and Turbidity. However, in this study, Turbidity and E.coli produced non-compliant results (0.8 – 2.6 NTU) and (4 - >2420 MPN /100mL) respectively. Although within limits, Aluminium, Colour, and Zinc present higher values on zinc metal roof compared to tiled roofing material. This is due to the high radiation and good heat conducting capacity of the metal. Results also showed higher pH levels on tiled roofs (7.15 – 7.25) compared to zinc roofs (6.27 – 6.8), which is in line with the nature of concrete. Usually, the roof can clean itself through first flush runoff, however, manually cleaning the roof before it starts raining and after dry season is recommended. Trimming overhanging tree branches may minimize bird droppings and leaves landing onto the roof. Once the water is collected, it is imperative that disinfection solutions such as chlorine tablets be used and that water is boiled before consumption.

Climate change is modifying the conditions of agricultural production. In particular, precipitations are redistributed in time and space. In the Rhine Valley, this results in prolonged and intensified dry and warm periods in summer on the one side, and wetter winters and heavy rain events on the other side (Riach et al., 2019). In agriculture, dry and warm periods can lead to severe loss in yields and revenue (Fuhrer & Jasper, 2009), while heavy rains can cause erosion and mudslides (Heitz, 2009) and excessive humidity can damage soils (Falloon & Betts, 2010) and favors fungal diseases (Rosenzweig et al., 2001). These evolutions of the water cycle will probably get worse as climate change go forth, and cannot anymore be totally prevented (Averbeck et al., 2019). Adaptation is therefore becoming a vital necessity (Darnhofer et al., 2010). Nevertheless, adoption or not of adaptation measures is a choice which depends on several factors: geographical (accessibility of a water resource; spatial, pedological and topographic situation of the farm); technical (equipment, workforce, know-how); economical (financial capacities, possible subsidies); geolegal (according to the rules in place in different territories). But, it can also depend on the perceptions a farmer has of climate change and of the benefits of adaptation, which are partially constructed through networks (interactions with colleagues, customers or agricultural organizations), leading to various trajectories of adaptation. Moreover, the adaptation measures shall not only be considered through their determinants, but also through their consequences, especially in terms of maladaptation, spatial inequalities but also synergies with mitigation and other issues. We base on semi-structured interviews conducted with crops and wine growing actors in the Rhine Valley (shared between France, Germany and Switzerland). Consequently, we can operate an innovative double comparison, between sectors and between countries, which sheds light on the most influential factors. We also observe that some measures are controversial, and promoted or rejected according to the actors, their perceptions and interests, resulting in a heterogeneous landscape where the role of consumers and borders remains significant. And, sometimes, hinders adaptation.

Unpaved roads are ubiquitous features of anthropogenic landscapes that facilitate the use and extraction of coveted resources. Until recently, unpaved roads had been overlooked as significant drivers of erosion and sediment yields in the Northeastern Caribbean. This paper summarizes findings of two decades of work documenting the role human disturbance on surface erosion and sediment yields. Project objectives have been to: (1) quantify the role of roads on surface erosion and watershed sediment yields; (2) identify controlling factors; (3) evaluate the effectiveness of mitigating measures; and (4) assess road hydrological and sediment connectivity. In the small, dry-tropical coastal watersheds of the U.S. Virgin Islands and Puerto Rico, land use change is driven by low-density urban development associated to tourism. In this setting, ephemeral streams deliver runoff to coral-bearing waters only ~4–5 times every year. However, unpaved roads covering only ~0.1–3% of the land surface can increase runoff delivery up to ~40 times per year. In this setting, road erosion may be up to four orders of magnitude above background and they contribute 80–99% of sediment yields. Mitigation strategies have included road drainage improvements, road paving, and sediment detention ponds. In the sun-grown coffee growing region of the wet tropical highlands of Puerto Rico, roads are a key source of the sediment affecting downstream water resources. A high erosion potential exists due to its steep relief, copious rainfall (1.6-2.1 m y-1), and considerable soil exposure. Here, watershed-scale sediment yields are 3–30 Mg ha-1 y-1, yet surface erosion rates under its natural forested cover are only ~0.25 Mg ha-1 y-1. Unpaved road erosion on these settings is 14–780 times faster than on forested hillslopes. Gravel reduces road erosion rates by 66 – 90%, but the effect seems to fade after one to two years. Cultivated lands contribute 5–63% of farm-scale surface erosion, and roads are responsible for the remainder—even though they cover 8–15% of the total farm area. In the 45-km2 Lucchetti Watershed, surface erosion from coffee farms equals 1–18% of its long-term sediment yield. The residual sediment is believed to originate from sources being quantified presently; these include road cutslopes, gullies, and landslides.

Hydroclimate extreme events, especially precipitation and streamflow extremes during wet seasons, pose severe threats to life, livelihoods, and infrastructure. Therefore, timely and skillful projections of attributes of seasonal streamflow extremes are imperative to plan mitigation strategies. In particular, the number of ‘events’ – i.e., exceedances of flow thresholds that result in flooding and the magnitude of such extremes during the season, will be of immense use to policymakers for early planning and implementation of flood risk mitigation and adaptation strategies. However, predicting seasonal extremes is challenging, particularly under spatial and temporal non-stationarity. To address this need, we develop a space-time model to project seasonal flow risk attributes using a Bayesian hierarchical modeling (BHM) framework in this study. In this model, the number of events exceeding a threshold during a season at a suite of gauge locations on a river network are modeled as Poisson margins. The seasonal daily maximum flows are modeled as a generalized extreme value (GEV). The rate parameters of the Poisson distribution and scale and shape parameters of the GEV are modeled as a linear function of suitable covariates. Gaussian Elliptical Copulas are applied to capture the spatial dependence. The best set of covariates is selected using the leave-one-out cross-validation information criteria (LOOIC). The modeling framework results in the posterior distribution of the risk attributes for each season and, thus, the uncertainties. We demonstrate the utility of this modeling framework to project the flood risk attributes during the summer peak monsoon season (July-August) at five gauges in the Narmada River basin of West-Central India. As potential covariates, we consider climate indices such as El Niño–Southern Oscillation (ENSO), the Indian Ocean Dipole (IOD), and the Pacific Warm Pool Region (PWPR) from the precedent season, which have shown strong teleconnections with the Indian monsoon. This spatiotemporal modeling framework helps in the planning of seasonal adaptation and preparedness measures as predictions of monsoon high flow risk occurrence become available up to 3 months before actual flood occurrence.

The Physical Oceanography Distributed Active Archive Center (PO.DAAC) has traditionally hosted NASA’s Earth Observing System oceanography datasets, but is expanding its archive to include hydrology datasets from satellites like the upcoming Surface Water and Ocean Topography (SWOT) mission. The SWOT mission, expected to launch later this year (2022), will deliver approximately 20 TB of data per day! Though hydrologic and water resources applications will be enabled at a greater scale than ever before, an increase in data volume requires more efficient and scalable data management technologies. Cloud computing tools and services can help pave the way toward efficiency. By June 2022, PO.DAAC will have enabled all its data to be accessed in the NASA Earthdata Cloud hosted in Amazon Web Services (AWS). Other NASA DAACs are also in the process of migrating their Earth observations to the Earthdata Cloud, which will support seamless access across DAACs and disciplines. PO.DAAC desires to make data access, pre-processing, and analysis as seamless as possible for data users, supporting science and applications users alike with relevant tools and resources. In this presentation, after introducing the PO.DAAC, we highlight a new SWOT-specific data search mechanism (searching via the SWOT River Database (SWORD) pre-defined river reaches) and showcase a cloud computing workflow in the context of hydrologic applications by accessing and analyzing a proxy SWOT dataset, Pre-SWOT Making Earth System Data Records for Use in Research Environments (MEaSUREs) river heights. This cloud workflow can be easily adapted to other PO.DAAC datasets, or further developed with other DAAC data, offering effective guidance and support for a variety of science use cases and applications.

We present a high-resolution continental-scale compound flood modeling system. It aims to quantify inland flooding resulting from the composite effects of riverine discharge and surface runoff and storm surge, in the inland-coastal zone during significant riverine and coastal storm events. This is achieved by coupling three continental models: the National Water Model (NWM) for the hydrology component, the Advanced Circulation Ocean Model for the coastal storm surge component, and the WAVEWATCH III model for the surface wave component with a detailed inland-coastal inundation model as the mediator between coastal and inland hydrology module. The inundation model, Delft3D FM, D-Flow Flexible Mesh (D-Flow FM), uses a high quality 2D unstructured grid with high-resolution (~100 m) near coastal features and lower-resolution in other areas to resolve the geometry of the study area. The coastal features are collected from NWM streamlines, National Hydrography Dataset, US medium shorelines and bathymetric features from the United States Army Corps of Engineers . The D-Flow FM model is forced by time-varying water levels and riverine discharges applied at its offshore and inland boundaries, respectively, by spatially- and time-varying wind and pressure fields and incorporates the contributions of surface and subsurface runoff to the total discharge in rivers, channels and streams. We conducted model validations for the following four major flooding events across the US coast: Hurricanes Ike (2008), Sandy (2012), Irma (2017), and Florence (2018). The results highlight the importance of including composite effects of compound flooding to accurately predict water levels during combined river flooding and extreme storm surge events.

Groundwater contamination with geogenic arsenic poses a major health risk to millions of people throughout the world. Among various group of microbes, dissimilatory arsenate reducing bacteria (DARB) are considered to be primarily responsible for arsenic mobilization in anaerobic environments of deep underground aquifer sediments. This group of microbes carries out enzyme catalyzed conversion of more immobilized and less toxic arsenate [As (V)] to more soluble and more toxic arsenite [As (III)]. Aquifers are deep subsurface layers of rocks, sand or soil capable of storing and transmitting water. These are potential environments for arsenic mobilization by anaerobic dissimilatory arsenate reducing bacteria (DARB). Study of these bacteria has been restricted to culture based microcosm studies, which suffers from several drawbacks like inappropriate simulation of ecological factors, exclusion of unculturable members, inappropriate elucidation of community behavior etc. With the recent advent of culture independent molecular analysis, more wholesome analysis of microbial community in diverse ecological habitats has become possible. Anaerobic dissimilatory As(V) reduction is catalyzed by the periplasmic arsenate respiratory reductase (Arr) complex, which consists of a large catalytic subunit (ArrA) and a small subunit (ArrB). arrA gene encoding large subunit of the reductase can be used as a reliable marker for arsenate respiration. Our study is a preliminary attempt to isolate community DNA from aquifer sediments collected from various depths and study the differential distribution of arrA in community genome at various depths. We had successfully isolated humic contaminant free community DNA from aquifer sediments and subjected them to PCR amplification with arrA gene specific primers. The amplicons obtained from community DNA of various depths were subsequently sublected to RFLP analysis by HaeIII and the restriction patterns was compared. The study revealed differential distribution of arrA containing DARB population at various depths of aquifer sediments.