High‐resolution precipitation mapping in a mountainous watershed: ground truth for evaluating uncertainty in a national precipitation dataset

Abstract

A 69-station, densely spaced rain gauge network was maintained over the period [1951][1952][1953][1954][1955][1956][1957][1958] in the Coweeta Hydrologic Laboratory, located in the southern Appalachians in western North Carolina, USA. This unique dataset was used to develop the first digital seasonal and annual precipitation maps for the Coweeta basin, using elevation regression functions and residual interpolation. It was found that a 10-m elevation grid filtered to an approximately 7-km effective wavelength explained the most variance in precipitation (R 2 = 0.82-0.95). A 'dump zone' of locally high precipitation a short distance downwind from the mountain crest marking the southern border of the basin was the main feature that was not explained well by the precipitation-elevation relationship.These data and maps provided a rare 'ground-truth' for estimating uncertainty in the national-scale Parameter-elevation Relationships on Independent Slopes Model (PRISM) precipitation grids for this location and time period. Differences between PRISM and ground-truth were compared with uncertainty estimates produced by the PRISM model and cross-validation errors. Potential sources of uncertainty in the national PRISM grids were evaluated, including the effects of coarse grid resolution, limited station data, and imprecise station locations.The PRISM national grids matched closely (within 5%) with the Coweeta dataset. The PRISM regression prediction interval, which includes the influence of stations in an area of tens of kilometres around a given location, overestimated the local error at Coweeta (12-20%). Offsetting biases and generally low error rates made it difficult to isolate major sources of uncertainty in the PRISM grids, but station density and selection, and mislocation of stations were identified as likely sources of error. The methods used in this study can be repeated in other areas where high-density data exist to gain a more comprehensive picture of the uncertainties in national-level datasets, and can be used in network optimization exercises.

Evaluation of Gridded Precipitation Data Products for Hydrological Applications in Complex Topography

“…This can be confirmed also by the lower coefficients of variation in the winter months for both groups of data sets. Differences in the performance of the observational data sets is strongly linked to station density [2], which is extremely low in the E-OBS data set [21] and, especially for the southern part of the catchment, also for GPCC-FDD. This leads to underestimated precipitation for E-OBS in annual, as well as seasonal (summer), precipitation and an increased number of consecutive dry days for the southern parts.…”

“…Precipitation plays an important role in the hydrological cycle and is one of the most widely used climate variables [1,2]. While there is a clear link between the amount, intensity, and distribution of precipitation to various processes in the ecosystem [3], this relation is nonlinear, and heavy precipitation does not necessarily result in high river discharge [4,5].…”

Non‐linear quickflow response as indicators of runoff generation mechanisms

“…Rainstorms vary throughout the year in both study locations and tend to be more convective in the summer and more frontal in the winter (Laseter, Ford, Vose, & Swift, 2012; Miller, Miniat, Wooten, & Barros, 2019). At CHL, spatial patterns of rainfall are reinforced more strongly by elevation during the summer months (Daly, Slater, Roberti, Laseter, & Swift, 2017), likely due to convective storms generating over higher elevations.…”

Geographic patterns of the climate sensitivity of lakes

“…We used climate data that were derived from 800‐m resolution monthly PRISM climate grids (Daly et al. ) and extracted as 1981–2010 means for HUC 12s (Collins et al. ).…”