GloH2O

PBCOR

Pre­cip­i­ta­tion Bias CORrection

Overview

State-of-the-art gauge-based cli­ma­tolo­gies — such as World­Clim, CHP­clim, and CHELSA — seri­ous­ly under­es­ti­mate pre­cip­i­ta­tion over most major moun­tain ranges. The Pre­cip­i­ta­tion Bias COR­rec­tion (PBCOR) dataset con­sists of glob­al gap-free bias cor­rec­tion maps derived using stream­flow obser­va­tions from 9372 sta­tions worldwide. 
For more infor­ma­tion, see the fol­low­ing open-access paper: 

Bias cor­rec­tion factor

Down­load

The lat­est ver­sion (1.0) of the PBCOR dataset, includ­ing a readme with infor­ma­tion about the files, can be down­loaded here. If the dataset forms a key com­po­nent of your research, we kind­ly ask that you give us the oppor­tu­ni­ty to com­ment on your results pri­or to pub­li­ca­tion. The dataset is released under the CC BY-NC 4.0 license and thus may not be used for com­mer­cial pur­pos­es. By using the dataset in any pub­li­ca­tion you agree to cite the cor­re­spond­ing paper. 

Acknowl­edge­ments

The fol­low­ing orga­ni­za­tions are thanked for pro­vid­ing stream­flow and/or catch­ment bound­ary data: the Unit­ed States Geo­log­i­cal Sur­vey (USGS), the Glob­al Runoff Data Cen­tre (GRDC), the Brazil­ian Agen­cia Nacional de Aguas, EURO-FRIEND-Water, the Euro­pean Com­mis­sion Joint Research Cen­tre (JRC), the Water Sur­vey of Cana­da (WSC), the Aus­tralian Bureau of Mete­o­rol­o­gy (BoM), and the Chilean Cen­ter for Cli­mate and Resilience Research (CR2, CONICYT/FONDAP/15110009).

Fre­quent­ly asked questions

The three pre­cip­i­ta­tion cli­ma­tolo­gies that we used as base­line (i) have a high res­o­lu­tion, (ii) incor­po­rate a large num­ber of gauge obser­va­tions, and (iii) explic­it­ly account for oro­graph­ic effects. They can there­fore be expect­ed to pro­vide more accu­rate cli­ma­to­log­i­cal pre­cip­i­ta­tion esti­mates than oth­er datasets. Oth­er datasets can be bias cor­rect­ed by rescal­ing them to match one of the bias-cor­rect­ed climatologies.

This is is not rec­om­mend­ed as each ver­sion exhibits unique bias pat­terns. Note that the orig­i­nal uncor­rect­ed pre­cip­i­ta­tion cli­ma­tolo­gies are also includ­ed in the dataset, in case they are no longer avail­able via the devel­op­ers’ website.

First, your gauge obser­va­tions may under­es­ti­mate pre­cip­i­ta­tion due to gauge under-catch. Sec­ond­ly, your (point scale) gauge obser­va­tions may not be rep­re­sen­ta­tive of 0.05° grid-cells if there is a high spa­tial vari­abil­i­ty in the region. Third­ly, the base­line cli­ma­tolo­gies may be wrong due to a lack of, or errors in, the gauge obser­va­tions used to pro­duce them. Final­ly, our bias cor­rec­tions may be wrong, due to (i) errors in the stream­flow, poten­tial evap­o­ra­tion, or catch­ment bound­ary data; (ii) uncer­tain­ty in Fu’s (1981) w para­me­ter used to infer pre­cip­i­ta­tion from stream­flow; and (iii) uncer­tain­ty in the ran­dom for­est regres­sion of the bias cor­rec­tion factors.

That depends on the region in ques­tion. In gen­er­al, one can assume that the cli­ma­tol­ogy that incor­po­rates the largest num­ber of gauge obser­va­tions for a par­tic­u­lar region is best. Maps of the gauge obser­va­tions incor­po­rat­ed in each cli­ma­tol­ogy can be found in the cor­re­spond­ing pub­li­ca­tions (Fick and Hij­mans, 2017, Karg­er et al., 2017, and Funk et al., 2015).

The GPCP and GPCC cli­ma­tolo­gies (i) have a fair­ly coarse 0.5° spa­tial res­o­lu­tion and (ii) were cor­rect­ed for gauge under-catch by inter­po­la­tion of cor­rec­tion fac­tors derived from sparse and uneven­ly dis­trib­uted sta­tion net­works. They may there­fore under­es­ti­mate pre­cip­i­ta­tion over moun­tain­ous areas, as demon­strat­ed in the PBCOR pub­li­ca­tion (Beck et al., 2019, Fig. 7).