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.

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.

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.

A challenge to managing water resources is characterizing the scale-dependent heterogeneity created by the interactions among hydrological, ecological and anthropological processes. It is often difficult to collect sufficient empirical data over the range of scales required to construct mathematical models that facilitate robust bottom-up descriptions or predictions. An alternative is identifying emergent properties of complex systems, whose components self-organize into novel structures or processes via their collective interactions with each other and the environment. A new level of organization and complexity emerges that cannot be predicted from or attributed to the components alone. Emergence offers a number of perspectives from which to interpret, if not predict, the behavior of complex water resource systems. One of these is entropy, which maximizes the options for system components to alter their interactions and, thus, permits variability and adaptability. At the scale of watersheds, increased entropy is pertinent because of its relationship to information (as probability functions), which is transmitted through connected components of a watershed in a manner such that the accrued information gives rise to emergent properties. Hence, analyzing the behaviors of a system according to emergence introduces the possibility of evaluating the information content via its interconnected components. Connectivity then assumes an integral role in a hydrologic system’s response to natural or anthropogenic disturbances (e.g., climate change, land use). Replacing the details of multi-scale heterogeneity and causal mechanisms with the functions that watersheds perform allows processes such as stream flow rate/duration and flood frequency to be construed as emergent spatiotemporal patterns. A reductionist or bottom-up approach to assessing the behavior of aquatic systems shifts to a functional or top-down approach that does not depend upon an understanding of all the physical, chemical or biological mechanisms involved. This latter approach could supplement conventional water resource descriptions and predictions via more comprehensively characterizing watershed or aquatic ecosystem functions.

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.

Recent, record-breaking discharge in the Yenisei River, Siberia, is part of a larger trend of increasing river flow in the Arctic driven by Arctic amplification. These changes in magnitude and timing of discharge can lead to increased risk of extreme flood events, with implications for infrastructure, ecosystems, and climate. To better understand the changes taking place, it is useful to have records that help place recent hydrological changes in context. In addition to an existing network of river gauges, extreme flood events can be captured in the wood anatomical features of riparian trees, which help identify the most extreme flood events. Along the Yenisei River, Siberia we collected willow (Salix spp.) samples from a low terrace that occasionally floods when water levels are extremely high. Using these samples, we use an approach known as quantitative wood anatomy to measure variation in radial cell dimensions, including vessel area, wood fiber size and cell wall thickness. We then compare these measurements to observed records of flood stage. We hypothesize that (1) characteristic patterns of wood fiber size and cell wall thickness in Salix rings are present during flood years, (2) these patterns can be quantified by measuring wood fiber size and cell wall thickness, and (3) quantified variations in cell anatomical properties can be related to flood magnitude and duration. Understanding how riparian vegetation responds to extreme flood events can help us better manage riparian ecosystems and understand changes to the Arctic hydrological regime.

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.

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.

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.

Due to the effects of climate change, the USA Southwest is expected to experience increased temperatures and decreased water availability. Extended droughts will likely have important consequences for riparian trees whose growth and habitat are strongly limited by water availability. One consequence of extended water table declines on riparian trees is the development of wood anatomical anomalies. Using techniques developed for quantitative wood anatomy (QWA) it may be possible to quantify these anomalies and reconstruct water table variability through time. We explore this possibility using tree rings from netleaf hackberry (Celtis laevigata var. reticulata) collected along the Upper Santa Cruz River, Arizona. We suggest hackberry has the potential to be used 1) as an indicator of riparian ecosystem health, and 2) to provide improved reconstructions of water availability. Understanding how hackberry responds to periods of low and high flows can provide insight into how to better manage these ecosystems into the future.

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.

The interpretation and use of scientific models are relevant to public discourse and decision-making about future water scenarios. Tools to use these scientific models aim to facilitate understanding water systems; however, the information in these tools can be so vast, complex, and prone to uncertainties that users may find using some of these tools a cumbersome task. Users can be presented with numerous outputs, visualizations, and vocabulary that are irrelevant or do not align with their perspective (i.e., user role) in the water system under study. To address these issues, the SWIM platform extends the capabilities of traditional Web-based graphical interfaces to foster the use of scientific models, in particular, water sustainability models. With the integration of a recommender system and a dynamic interface, SWIM provides users with a list of prioritized outputs (i.e., modeling results) based on their perspective, to reduce the overload of data presented to users. Users are also provided with a range of output visualization graphs and narrative elements (i.e., contextual natural language descriptions) to support the interpretation of model results. The SWIM interface design provides a seamless high-level workflow for models developed in different modeling software tools and languages. The SWIM platform is designed to foster interoperability by providing open APIs and knowledge bases to access data, metadata, and models (i.e., Model-As-A-Service). This presentation highlights the key elements of SWIM that enable the interpretation of scientific model outputs from different perspectives and the interoperability features of this platform.

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.