Abstract
Abstract. The phase of precipitation when it reaches the ground is a first-order driver of hydrologic processes in a watershed. The presence of snow, rain, or mixed-phase precipitation affects the initial and boundary conditions that drive hydrological models. Despite their foundational importance to terrestrial hydrology, typical phase partitioning methods (PPMs) specify the phase based on near-surface air temperature only. Our review conveys the diversity of tools available for PPMs in hydrological modeling and the advancements needed to improve predictions in complex terrain with large spatiotemporal variations in precipitation phase. Initially, we review the processes and physics that control precipitation phase as relevant to hydrologists, focusing on the importance of processes occurring aloft. There is a wide range of options for field observations of precipitation phase, but there is a lack of a robust observation networks in complex terrain. New remote sensing observations have the potential to increase PPM fidelity, but generally require assumptions typical of other PPMs and field validation before they are operational. We review common PPMs and find that accuracy is generally increased at finer measurement intervals and by including humidity information. One important tool for PPM development is atmospheric modeling, which includes microphysical schemes that have not been effectively linked to hydrological models or validated against near-surface precipitation-phase observations. The review concludes by describing key research gaps and recommendations to improve PPMs, including better incorporation of atmospheric information, improved validation datasets, and regional-scale gridded data products. Two key points emerge from this synthesis for the hydrologic community: (1) current PPMs are too simple to capture important processes and are not well validated for most locations, (2) lack of sophisticated PPMs increases the uncertainty in estimation of hydrological sensitivity to changes in precipitation phase at local to regional scales. The advancement of PPMs is a critical research frontier in hydrology that requires scientific cooperation between hydrological and atmospheric modelers and field scientists.
Highlights
Introduction and motivationAs climate warms, a major hydrologic shift in precipitation phase from snow to rain is expected to occur across temperate regions that are reliant on mountain snowpacks for water resource provisioning (Bales et al, 2006; Barnett et al, 2005)
Our goals are to demonstrate that major research gaps in our ability to develop phase partitioning methods (PPMs) are contributing to errors and reducing the predictive skill of hydrological models
By highlighting the research gaps that could advance the science of PPMs, we provide a road map for future advances (Fig. 4)
Summary
A major hydrologic shift in precipitation phase from snow to rain is expected to occur across temperate regions that are reliant on mountain snowpacks for water resource provisioning (Bales et al, 2006; Barnett et al, 2005). Most operational models used by the National Weather Service River Forecast Centers in the United States use some type of temperature-based precipitation phase partitioning method (PPM) (Pagano et al, 2014). Forcing datasets for hydrological models are rapidly being developed for a suite of meteorological variables, to date no gridded precipitationphase product has been developed over regional to global scales Widespread advances in both simulation of terrestrial hydrological processes and computational capabilities may have limited improvements on water resources forecasts without commensurate advances in PPMs. Recent advances in PPMs incorporate effects of humidity (Harder and Pomeroy, 2013; Marks et al, 2013), atmospheric temperature profiles (Froidurot et al, 2014), and remote sensing of phase in the atmosphere (Minder, 2010; Lundquist et al, 2008). The overall objective is to convey a clear understanding of the diversity of tools available for PPMs in hydrological modeling and the advancements needed to improve predictions in complex terrain characterized by large spatiotemporal variations in precipitation phase
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