A synthesis of studies on Colorado River streamflow projections that examines methodological and model differences and their implications for water management.

This study compares methods to incorporate climate information into the National Weather Service River Forecast System (NWSRFS). Three small-to-medium river subbasins following roughly along a longitude in the Colorado River basin with different El Niño-Southern Oscillation signals were chosen as test basins. Historical ensemble forecasts of the spring runoff for each basin were generated using modeled hydrologic states and historical precipitation and temperature observations using the Ensemble Streamflow Prediction (ESP) component of the NWSRFS.Two general methods for using a climate index (e.g., Niño-3.4) are presented. The first method, post-ESP, uses the climate index to weight ensemble members from ESP. Four different post-ESP weighting schemes are presented. The second method, preadjustment, uses the climate index to modify the temperature and precipitation ensembles used in ESP. Two preadjustment methods are presented. This study shows the distance-sensitive nearest-neighbor post-ESP to be superior to the other post-ESP weighting schemes. Further, for the basins studied, forecasts based on post-ESP techniques outperformed those based on preadjustment techniques.

To understand the sources of temporal and spatial variability of atmospheric evaporative demand across the conterminous United States (CONUS), a mean-value, second-moment uncertainty analysis is applied to a spatially distributed dataset of daily synthetic pan evaporation for 1980–2009. This evaporative demand measure is from the “PenPan” model, which is a combination equation calibrated to mimic observations from U.S. class-A evaporation pans and here driven by six North American Land Data Assimilation System variables: temperature, specific humidity, station pressure, wind speed, and downwelling shortwave and longwave radiation. The variability of evaporative demand is decomposed across various time scales into contributions from these drivers. Contrary to popular expectation and much hydrologic practice, temperature is not always the most significant driver of temporal variability in evaporative demand, particularly at subannual time scales. Instead, depending on the season, one of four drivers (temperature, specific humidity, downwelling shortwave radiation, and wind speed) dominates across different regions of CONUS. Temperature generally dominates in the northern continental interior. This analysis assists land surface modelers in balancing parameter parsimony and physical representativeness. Patterns of dominant drivers are shown to cycle seasonally, with clear implications for modeling evaporative demand in operational hydrology or as a metric of climate change and variability. Depending on the region and season, temperature, specific humidity, downwelling shortwave radiation, and wind speed must together be examined, with downwelling longwave radiation as a secondary input. If any variable may be ignored, it is atmospheric pressure. Parameterizations of evaporative demand based solely on temperature should be avoided at all time scales.

Within the realm of climate and environmental sciences, stakeholder engagement has traditionally been given a relative low priority in favor of generating tools, products, and services following the longstanding practice of pushing out information in the hopes users will pull it into their decision toolkits. However, the landscape is gradually shifting away from that paradigm and toward one in which the stakeholder community is more directly involved in the production of products and services with the scientific organization. This mutual learning arrangement, referred to as the coproduction of knowledge, has been applied to two user engagement activities within the National Oceanic and Atmospheric Administration (NOAA) National Centers for Environmental Information (NCEI) and the NOAA Office of Coastal Management (OCM) Coral Reef Conservation Program (CRCP). The iterative nature of such dialogues helped scientists within NCEI and OCM to better understand user requirements and as a result generate climate information that was locally relevant and regionally applicable. The recent engagement activities exemplified the benefits of a robust and sustained relationship between climate scientists and the user community. They demonstrate that the interactions between the two led to the empowerment of the local community to shape and mold climate information products as well as further enhancing user buy in of these products and services with which local agriculture and food security, disaster risk reduction, energy, health, and water decisions are being made. This coproduction of knowledge model for user engagement activities also serves to build trust between the scientific and user communities.

Many fisheries and marine science organizations are working to determine how to meet their missions in the midst of the COVID-19 outbreak. As such, it seems prudent to exchange ideas, share knowledge, and initiate a discussion amongst us. As the scientific leadership team for NOAA Fisheries, we wanted to offer some perspectives. Others are also evaluating the impacts of COVID-19 but from the perspective of addressing tactical, day-to-day concerns of restarting operations for various marine and fisheries-oriented organizations. Thus, it seemed appropriate to us to explore the potential challenges posed by COVID-19, and to purposefully ascertain if there are strategic opportunities for improving how we conduct our operations. We need to find ways to mitigate the effects of COVID-19 on our mission and also to glean information from our responses while in the midst of the crisis. We offer some recommendations to that end, and offer these thoughts not as having solved every problem, but to learn from each other, compare across organizations, and engage in dialogue within our discipline to advance much-needed changes.