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We develop a space‐time Bayesian hierarchical modeling (BHM) framework for two flood risk attributes—seasonal daily maximum flow and the number of events that exceed a threshold during a season (NEETM)—at a suite of gauge locations on a river network. The model uses generalized extreme value (GEV) and Poisson distributions as marginals for these flood attributes with non‐stationary parameters. The rate parameters of the Poisson distribution and location, scale, and shape parameters of the GEV are modeled as linear functions of suitable covariates. Gaussian copulas are applied to capture the spatial dependence. The best covariates are selected using the Watanabe‐Akaike information criterion (WAIC). The modeling framework results in the posterior distribution of the flood attributes at all the gauges and various lead times. We demonstrate the utility of this modeling framework to forecast the flood risk attributes during the summer peak monsoon season (July‐August) at five gauges in the Narmada River basin (NRB) of West‐Central India for several lead times (0–3 months). 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 antecedent seasons, which have shown strong teleconnections with the Indian monsoon. We alsomore »include new indices related to the East Pacific and West Indian Ocean regions depending on the lead times. We show useful long lead skill from this modeling approach which has a strong potential to enable robust risk‐based flood mitigation and adaptation strategies 3 months before flood occurrences.

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Abstract The timing of melt onset in the Arctic plays a key role in the evolution of sea ice throughout Spring, Summer and Autumn. A major catalyst of early melt onset is increased downwelling longwave radiation, associated with increased levels of moisture in the atmosphere. Determining the atmospheric moisture pathways that are tied to increased downwelling longwave radiation and melt onset is therefore of keen interest. We employed Self Organizing Maps (SOM) on the daily sea level pressure for the period 1979–2018 over the Arctic during the melt season (April–July) and identified distinct circulation patterns. Melt onset dates were mapped on to these SOM patterns. The dominant moisture transport to much of the Arctic is enabled by a broad low pressure region stretching over Siberia and a high pressure over northern North America and Greenland. This configuration, which is reminiscent of the North American-Eurasian Arctic dipole pattern, funnels moisture from lower latitudes and through the Bering and Chukchi Seas. Other leading patterns are variations of this which transport moisture from North America and the Atlantic to the Central Arctic and Canadian Arctic Archipelago. Our analysis further indicates that most of the early and late melt onset timings in the Arcticmore »are strongly related to the strong and weak emergence of these preferred circulation patterns, respectively.« less

In the Colorado River Basin (CRB), ensemble streamflow prediction (ESP) forecasts drive operational planning models that project future reservoir system conditions. CRB operational seasonal streamflow forecasts are produced using ESP, which represents climate using an ensemble of meteorological sequences of historical temperature and precipitation, but do not typically leverage additional real‐time subseasonal‐to‐seasonal climate forecasts. Any improvements to streamflow forecasts would help stakeholders who depend on operational projections for decision making. We explore incorporating climate forecasts into ESP through variations on an ESP trace weighting approach, focusing on Colorado River unregulated inflows forecasts to Lake Powell. The k‐nearest neighbors (kNN) technique is employed using North American Multi‐Model Ensemble one‐ and three‐month temperature and precipitation forecasts, and preceding three‐month historical streamflow, as weighting factors. The benefit of disaggregated climate forecast information is assessed through the comparison of two kNN weighting strategies; a basin‐wide kNN uses the same ESP weights over the entire basin, and a disaggregated‐basin kNN applies ESP weights separately to four subbasins. We find in general that climate‐informed forecasts add greater marginal skill in late winter and early spring, and that more spatially granular disaggregated‐basin use of climate forecasts slightly improves skill over the basin‐wide method at most leadmore »times.

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Summer rainfall in the southeast Prairie Pothole Region (SEPPR) is an important part of a vital wetland ecosystem that various species use as their habitat. We examine sources and pathways for summer rainfall moisture, large‐scale features influencing moisture delivery, and large‐scale connections related to summer moisture using the Hybrid Single‐Particle Lagrangian Integrated Trajectory (HYSPLIT) model. Analysis of HYSPLIT back trajectories shows that land is the primary moisture source for summer rainfall events indicating moisture recycling plays an important role in precipitation generation. The Great Plains Low‐Level Jet/Maya Express is the most prominent moisture pathway. It impacts events sourced by land and the Gulf of Mexico (GoM), the secondary moisture source. There is a coupling between land, atmosphere, and ocean conveyed by large‐scale climate connections between rainfall events and sea surface temperature (SST), Palmer Drought Severity Index, and 850‐mb heights. Land‐sourced events have a connection to the northern Pacific and northwest Atlantic Oceans, soil moisture over the central U.S., and low‐pressure systems over the SEPPR. GoM‐sourced events share the connection to soil moisture over the central U.S. but also show connections to SSTs in the North Pacific and Atlantic Oceans and the GoM, soil moisture in northern Mexico, and 850‐mbmore »heights in the eastern Pacific Ocean. Both types of events show connections to high 850‐mb heights in the Caribbean which may reflect a connection to Bermuda High. These insights into moisture sources and pathways can improve skill in SEPPR summer rainfall predictions and benefit natural resource managers in the region.

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The Prairie Pothole Region (PPR), located in central North America, is an important region hydrologically and ecologically. Millions of wetlands, many containing ponds, are located here, and they serve as habitats for various biota and breeding grounds for waterfowl. They also provide carbon sequestration, sediment and nutrient attenuation, and floodwater storage. Land modification and climate change are threatening the PPR, and water and wildlife managers face important conservation decisions due to these threats. We developed predictive, multisite forecasting models using canonical correlation analysis (CCA) for pond counts in the southeast PPR, the portion located within the United States, to aid in these important decisions. These forecast models predict spring (May) and summer (July) pond counts for each region (stratum) of the United States Fish and Wildlife Service’s pond and waterfowl surveys using a suite of antecedent, large-scale climate variables and indices including 500 millibar heights, sea surface temperatures (SSTs), and Palmer Drought Severity Index (PDSI). Models were developed to issue forecasts at the start of all preceding months beginning on March 1st. The models were evaluated for their performance in a predictive mode by leave-one-out cross-validation. The models exhibited good performance (Rvalues above 0.6 for May forecasts and 0.4more »for July forecasts), with performance increasing as lead time decreased. This simple and versatile modeling approach offers a robust tool for efficient management and sustainability of ecology and natural resources. It demonstrates the ability to use large-scale climate variables to predict a local variable in a skilful way and could serve as an example to develop similar models for use in management and conservation decisions in other regions and sectors of the environment.

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The Bureau of Reclamation (Reclamation) plays a central management role in the Colorado River Basin (CRB), with an increasing focus on meeting the needs of stakeholders during the current drought. One aspect of this role involves generating five‐year projections of reservoir operating conditions in the federal multi‐reservoir system. These projections are the basis for estimating the probability of shortage conditions, which are relied on by stakeholders, and are particularly important during drought. Currently, Ensemble Streamflow Prediction (ESP) forecasts drive Reclamation's Colorado River Mid‐term Modeling System to produce probabilistic reservoir projections to be used in risk‐based analysis and decision support for the first two years of the outlook period. The lack of significant forecast skill beyond the first year motivates interest in alternative forecasting approaches. The CRB Operational Prediction Testbed was created to provide a quantitative and consistent framework for assessing the skill of streamflow forecasts and their impact on associated reservoir system projections. Reservoir system projections are evaluated by analyzing Lakes Powell and Mead operations, including projected pool elevation and operating tiers. In an initial application of this testbed, ESP forecasts were compared to experimental streamflow forecasts to assess their skill impact on two‐year reservoir projections, which are criticalmore »information for managing drought.

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We describe a new effort to enhance climate forecast relevance and usability through the development of a system for evaluating and displaying real‐time subseasonal to seasonal (S2S) climate forecasts on a watershed scale. Water managers may not use climate forecasts to their full potential due to perceived low skill, mismatched spatial and temporal resolutions, or lack of knowledge or tools to ingest data. Most forecasts are disseminated as large‐domain maps or gridded datasets and may be systematically biased relative to watershed climatologies. Forecasts presented on a watershed scale allow water managers to view forecasts for their specific basins, thereby increasing the usability and relevance of climate forecasts. This paper describes the formulation of S2S climate forecast products based on the Climate Forecast System version 2 (CFSv2) and the North American Multi‐Model Ensemble (NMME). Forecast products include bi‐weekly CFSv2 forecasts, and monthly and seasonal NMME forecasts. Precipitation and temperature forecasts are aggregated spatially to a United States Geological Survey (USGS) hydrologic unit code 4 (HUC‐4) watershed scale. Forecast verification reveals appreciable skill in the first two bi‐weekly periods (Weeks 1–2 and 2–3) from CFSv2, and usable skill in NMME Month 1 forecast with varying skills at longer lead times dependentmore »on the season. Application of a bias‐correction technique (quantile mapping) eliminates forecast bias in the CFSv2 reforecasts, without adding significantly to correlation skill.

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