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Extreme precipitation over a two-week period can cause significant impacts to life and property. Trustworthy and easy-to-understand forecasts of these extreme periods on the subseasonal-to-seasonal timeframe may provide additional time for planning. The Prediction of Rainfall Extremes at Subseasonal to Seasonal Periods (PRES²iP) project team conducted three workshops over six years to engage with stakeholders to learn what is needed for decision-making for subseasonal precipitation. In this study experimental subseasonal to seasonal (S2S) forecast products were designed, using knowledge gained from previous stakeholder workshops, and shown to decision-makers to evaluate the products for two 14-day extreme precipitation period scenarios. Our stakeholders preferred a combination of products that covered the spatial extent, regional daily values, with associated uncertainty, and text narratives with anticipated impacts for planning within the S2S timeframe. When targeting longer extremes, having information regarding timing of expected impacts was seen as crucial for planning. We found that there is increased uncertainty tolerance with stakeholders when using products at longer lead times that typical skill metrics, such as critical success index or anomaly correlation coefficient, do not capture. Therefore, the use of object-oriented verification, that allows for more flexibility in spatial uncertainty, might be beneficial for evaluating S2S forecasts. These results help to create a foundation for design, verification, and implementation of future operational forecast products with longer lead times, while also providing an example for future workshops that engage both researchers and decision-makers.

 

Extreme precipitation events can cause significant impacts to life, property, and the economy. As forecasting capabilities increase, the subseasonal-to-seasonal (S2S) time scale provides an opportunity for advanced notice of impactful precipitation events. Building on a previous workshop, the Prediction of Rainfall Extremes at Subseasonal to Seasonal Periods (PRES²iP) project team conducted a second workshop virtually in the fall of 2021. The workshop engaged a variety of practitioners, including emergency managers, water managers, tribal environmental professionals, and National Weather Service meteorologists. While the team’s first workshop examined the “big picture” in how practitioners define “extreme precipitation” and how precipitation events impact their jobs, this workshop focused on details of S2S precipitation products, both current and potential future decision tools. Discussions and activities in this workshop assessed how practitioners use existing forecast products to make decisions about extreme precipitation, how they interpret newly developed educational tools from the PRES²iP team, and how they manage uncertainty in forecasts. By collaborating with practitioners, the PRES²iP team plans to use knowledge gained going forward to create more educational and operational tools related to S2S extreme precipitation event prediction, helping practitioners to make more informed decisions.

 

As a result of climate change, extreme precipitation events are likely to become more common in Oklahoma, requiring cities and municipalities to plan for managing this extra water. There are multiple types of practitioners within communities who are responsible for overseeing planning for the future, including stormwater and floodplain management. These practitioners may be able to integrate weather and climate information into their decision-making to help them prepare for heavy precipitation events and their impacts. Floodplain managers from central and eastern Oklahoma were interviewed to learn what information they currently use and how it informs their decision-making. When making decisions in the short term, floodplain managers relied on weather forecasts; for long-term decisions, other factors, such as constrained budgets or the power of county officials, had more influence than specific climate predictions or projections. On all time scales, social networks and prior experience with flooding informed floodplain managers’ decisions and planning. Overall, information about weather and climate is just one component of floodplain managers’ decision-making processes. The atmospheric science community could work more collaboratively with practitioners so that information about weather and climate is more useful and, therefore, more relevant to the types of decisions that floodplain managers make.

 

Abstract Heavy precipitation events and their associated flooding can have major impacts on communities and stakeholders. There is a lack of knowledge, however, about how stakeholders make decisions at the subseasonal-to-seasonal (S2S) time scales (i.e., 2 weeks to 3 months). To understand how decisions are made and S2S predictions are or can be used, the project team for “Prediction of Rainfall Extremes at Subseasonal to Seasonal Periods” (PRES 2 iP) conducted a 2-day workshop in Norman, Oklahoma, during July 2018. The workshop engaged 21 professionals from environmental management and public safety communities across the contiguous United States in activities to understand their needs for S2S predictions of potential extended heavy precipitation events. Discussions and role-playing activities aimed to identify how workshop participants manage uncertainty and define extreme precipitation, the time scales over which they make key decisions, and the types of products they use currently. This collaboration with stakeholders has been an integral part of PRES 2 iP research and has aimed to foster actionable science. The PRES 2 iP team is using the information produced from this workshop to inform the development of predictive models for extended heavy precipitation events and to collaboratively design new forecast products with our stakeholders, empowering them to make more-informed decisions about potential extreme precipitation events. 

One of the benefits of training a process-based, land surface model is the capacity to use it in ungauged sites as a complement to standard weather stations for predicting energy fluxes, evapotranspiration, and surface and root-zone soil temperature and moisture. In this study, dynamic (i.e., time-evolving) vegetation parameters were derived from remotely sensed Moderate Resolution Imaging Spectroradiometer (MODIS) imagery and coupled with a physics-based land surface model (tin-based Real-time Integrated Basin Simulator (tRIBS)) at four eddy covariance (EC) sites in south-central U.S. to test the predictability of micro-meteorological, soil-related, and energy flux-related variables. One cropland and one grassland EC site in northern Oklahoma, USA, were used to tune the model with respect to energy fluxes, soil temperature, and moisture. Calibrated model parameters, mostly related to the soil, were then transferred to two other EC sites in Oklahoma with similar soil and vegetation types. New dynamic vegetation parameter time series were updated according to MODIS imagery at each site. Overall, the tRIBS model captured both seasonal and diurnal cycles of the energy partitioning and soil temperatures across all four stations, as indicated by the model assessment metrics, although large uncertainties appeared in the prediction of ground heat flux, surface, and root-zone soil moisture at some stations. The transferability of previously calibrated model parameters and the use of MODIS to derive dynamic vegetation parameters enabled rapid yet reasonable predictions. The model was proven to be a convenient complement to standard weather stations particularly for sites where eddy covariance or similar equipment is not available. 

Decision‐makers using climate projection information are often faced with the problem of data breadth, complexity, and uncertainty, which complicates the translation of climate science products in addressing management challenges. Recently, the concept of climate scenario planning attempts to simplify climate information by developing a series of plausible future “storylines.” In some cases, however, these storylines lack quantitative detail on extremes that may be useful to decision‐makers. Here, we analyse a large suite of statistically downscaled climate projections from two methods to develop quantitative projections for hydrologic extremes (heavy precipitation and drought) across Oklahoma and Texas in the United States. Downscaled projections are grouped into four specific temperature/precipitation scenarios, including “Warm/Wet,” “Hot/Dry,” “Central Tendency,” and the full multi‐model ensemble average. The region is split into three sub‐domains spanning the region's west–east precipitation gradient, and projections are examined throughout the mid‐ and late‐21st century, using two emissions scenarios (“mid‐range” and “high”). Most scenarios project increased frequency and duration of moderate or greater drought across the whole domain, with the high‐emissions Hot/Dry projections showing the most severe examples. The Warm/Wet scenario also increases the frequency of dry months, particularly in the Southern High Plains, but does not discernably alter duration, and retains a similar frequency of pluvial (wet) periods. The mid‐range projections generally retain similar evolutions among scenarios, but they reduce drought intensity and project no change in drought/pluvial frequency with the Warm/Wet scenario. Notably, the occurrence of intense precipitation increases across all scenarios and emissions categories and does not significantly differ between any of the scenarios, including Hot/Dry versus Warm/Wet. Some observed differences in extreme precipitation magnitudes between the two downscaled data sets are briefly discussed.