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Significant resources have been devoted to increasing the supply of data and information products for wildland fire management. There has been comparatively less emphasis on understanding the demand for these products. There are large differences in the number of information sources that fire managers use in decision making. We developed a value-of-information model for wildland fire managers to formulate hypotheses about what factors drive these differences. Data from a comprehensive internet survey targeting a well-defined population of the Southwest wildland fire managers are used to test these hypotheses. Results are generally consistent with hypotheses generated from the value-of-information model. Multiple regression results suggest information use increases with the number of decisions that managers make and is greater during fire season than before. Information use is affected by a manager’s level of education, age, experience, job type, the agencies they work for, and the multi-agency dispatch centers where they work. Agency and dispatch center effects explain more of the variation in information use than differences in the respondents’ personal characteristics. To better understand fire manager demand for information, future research could explore in more detail what specific attributes of agencies and dispatch centers affect use of information for wildland fire management.

Monsoon precipitation and severe flooding is highly variable and often unpredictable, with a range of flood conditions and impacts across metropolitan regions or a county. County and storm specific watches or warnings issued by the National Weather Service (NWS) alert the public to current flood conditions and risks, but floods are not limited to the area that is under alert and these zones can be relatively coarse depending on the data these warnings are based on. Research done by the Arizona Institute for Resilient Environments and Societies (AIRES) has produced an Application Programming Interface (API) accessible data warehouse of time series precipitation totals across the state of Arizona which consists of higher resolution geographically disperse data that helped create improved characterizations of monsoon precipitation variability. There is an opportunity to leverage these data to address flood risk particularly where advanced Computer Science methodologies and Machine Learning techniques may offer additional spatial and temporal insight into flood events. This can be especially useful during rainfall events where precipitation station reporting frequencies are increased and near real-time totals are accessible via the AIRES API. A Machine-Learning-ready dataset structured to train ML models facilitates an anticipatory approach to predicting/characterizing flood risk. This presents an opportunity for new inputs into management and decision making opportunities, in addition to describing precipitation and flood patterns after an event. In this paper we will be the first to make use of the AIRES API by taking the initial step of the Machine Learning process and assembling the precipitation data into a ML-ready dataset. We then look closer at the dataset assembled and call attention to characteristics of the dataset that can be further explored through machine learning processes. Finally, we will summarize future directions for research and climate services using this dataset and API.