Daily Precipitation Frequency Distributions Impacts on Land-Surface Simulations of CONUS

Daniel P Sarmiento,Pete Peterson,Jossy P Jacob,Kimberly Slinski,Amy Mcnally,Christa D Peters-Lidard,Chris Funk

doi:10.3389/frwa.2021.640736

Abstract

Many precipitation-driven data products from land data assimilation systems support assessments of droughts, floods, and other societally-relevant land-surface processes. The accumulated precipitation used as input to these products has a significant impact on water budgets; however, the effects of daily distribution of precipitation on these products are not well known. A comparison of the Integrated Multi-satellite Retrievals for GPM (IMERG) and Climate Hazards Group InfraRed Precipitation with Stations version 2 (CHIRPS2) rainfall products over the continental United States (CONUS) was performed to quantify the impacts of the daily distribution of precipitation on biases and errors in soil moisture, runoff, and evapotranspiration (ET). Since the total accumulated precipitation between the IMERG and CHIRPS product differed, a third precipitation product, CHIRPS-to-IMERG (CHtoIM), was produced that used CHIRPS2 accumulated precipitation totals and the daily precipitation frequency distribution of IMERG. This new product supported a controlled analysis of the impact of precipitation frequency distribution on simulated hydrological fields. The CHtoIM had higher occurrences of precipitation in the 0–5 mm day−1 range, with a lower occurrence of dry days, which decreased soil moisture and surface runoff in the land-surface model. The surface soil layer had a tendency to reach saturation more often in the CHIRPS2 simulations, where the number of moderate to heavy precipitation days (&gt;5 mm day−1) was increased. Using the blended CHtoIM product as input reduced errors in surface soil moisture by 5–15% when compared to Soil Moisture Active/Passive (SMAP) data. Similarly, ET errors were also slightly decreased (~2%) when compared to SSEBop data. Moderate changes in daily precipitation distributions had a quantifiable impact on soil moisture, runoff, and ET. These changes usually improved the model when compared to other modeled and observational datasets, but the magnitude of the improvements varied by region and time of year.

Highlights

Accurate precipitation data is critical for simulations of land-surface processes and hydrometeorological phenomena
In CHIRPS2, 11.23% of the days were within the >0 mm per day of rain but
It should be noted that the subgroupings were chosen based on the differences in the daily precipitation frequency and not an official designation provided by a meteorological or hydrological organization

Summary

Introduction

Accurate precipitation data is critical for simulations of land-surface processes and hydrometeorological phenomena. While in some regions increases in the accuracy and number of observations have allowed these precipitation forcing datasets to more accurately represent precipitation patterns (e.g., Fekete et al, 2004; Feidas, 2010; Huffman et al, 2010), large differences between the products still occur, especially when looking at precipitation rates on shorter time scales or over certain regions (e.g., Feidas, 2010; Manz et al, 2017; Iqbal and Athar, 2018). The potential for non-linear sensitivity to errors in precipitation makes quantification of errors in precipitation forcing products important when trying to understand the potential hydrological field errors that can occur in land-surface models, such as impacts on simulated soil moisture (e.g., Gebregiorgis et al, 2012)

Objectives

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Discussion

Conclusion