An Investigation of the Spatial and Temporal Characteristics of Extreme Dry and Wet Events across NLDAS-2 Models

Extreme hydrological events (including droughts and floods) produce severe social and economic impacts. Monitoring hydrological processes from remote sensing is necessary to improve understanding and preparedness for these events, with current missions focusing on a range of hydrological variables (i.e., SWOT, SMAP, and GRACE). This study uses output from three state-of-the-art land surface assimilation models and an event clustering algorithm to identify the characteristic spatial and temporal scales of large-scale extreme dry and wet events in the contiguous United States for three major hydrological processes: precipitation, runoff, and soil moisture. We also examine the sensitivity of extreme event characteristics to model resolution and assess intermodel differences. We find that models generally agree in terms of the mean characteristics of events: precipitation dry events are of shorter duration in comparison with soil moisture and runoff events, and more intense events tend to be smaller in area. We also find that mean spatial and temporal characteris-tics are highly dependent on model resolution-important in the context of detecting and monitoring these events. Results from this study can be used to inform land surface model development, extreme hydrology event detection, and sampling requirements of upcoming remote sensing missions in hydrology.SIGNIFICANCE STATEMENT: Understanding the fundamental characteristics of dry and wet extreme events (droughts and floods) is of importance for improving our preparedness and response to events, as well as for designing satellite observing systems that can adequately monitor them. Here we use output from land surface models to deter-mine the average size and duration of large-scale extreme events for the contiguous United States using fine temporal data. We find that events that are most extreme-the most severe floods and droughts-tend to be shorter in duration and smaller in size. We also present an assessment of how three commonly used land surface models detect extreme hy-drological events, which is important for assessments based on models. These findings are important for understanding the proportion of events that may be not adequately resolved by current hydrology remote sensing missions.