Correction: Koutsouris et al. Utilization of Global Precipitation Datasets in Data Limited Regions: A Case Study of Kilombero Valley, Tanzania. Atmosphere, 2017, 8, 246

Alexander J. Koutsouris; Jan Seibert; Steve W. Lyon

doi:10.3390/atmos9040148

,

and

¹

Department of Physical Geography, Stockholm University, 10691 Stockholm, Sweden

²

Department of Geography, Hydrology and Climate, University of Zurich, 8006 Zurich, Switzerland

³

The Nature Conservancy, Delmont, NJ 08314, USA

^*

Author to whom correspondence should be addressed.

Atmosphere2018, 9(4), 148;https://doi.org/10.3390/atmos9040148

This article belongs to the Special Issue Precipitation Variability and Change in Africa

Version Notes

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The authors would like to correct the published article [1], following the detection of editorial mistakes by the main author, as explained below. Table 3 has been replaced with a new Table to show the streamflow simulation results for Mpanga Catchment. A missing table has been inserted as Table 5, showing the streamflow simulation results for Kiburubutu Catchment. The table previously labeled and referenced as Table 5 should now be considered as Table 6. All sentences that were referenced to Table 5 refer to the new Table 5, except for the sentence “Simulations based on QM bias corrected GPD products performances were in general worse compared to their non-bias corrected counterparts (Table 5)”, which refers to Table 6. Table 3, Table 5, and Table 6 should read:

Table 3. Performance scores for MC discharge simulations. Performance is shown as R_eff_,log, R², V_e, and RMSE values for each bias correction technique considered.

Table 5. Performance scores for KC discharge simulations. Performance is shown as R_eff_,log, R², Volume Error (Vol. Err.), and RMSE values for each bias correction technique considered.

Table 6. Performance scores for KC discharge simulations. Performance is shown as R_eff_,log values for each bias correction technique considered.

The authors apologize for any inconvenience this has caused to the readers. The changes do not affect the scientific results of this paper. The manuscript will be updated, and the original version will remain online on the article webpage, with a reference to this Correction.

Reference

Koutsouris, A.J.; Seibert, J.; Lyon, S.W. Utilization of Global Precipitation Datasets in Data Limited Regions: A Case Study of Kilombero Valley, Tanzania. Atmosphere. 2017, 8, 246. [Google Scholar] [CrossRef]

Table 3. Performance scores for MC discharge simulations. Performance is shown as R_eff_,log, R², V_e, and RMSE values for each bias correction technique considered.

Mpanga Catchment
GDP			R_eff_,log	R²	V_e	RMSE			R_eff_,log	R²	V_e	RMSE
CFSR	Non-	bias corrected	0.33	0.23	0.96	0.28	ModB	bias corrected	0.43	0.40	1.00	0.26
ERAi			0.53	0.48	0.96	0.23			0.53	0.50	0.96	0.23
MERRA			0.38	0.44	0.94	0.29			0.61	0.58	0.99	0.22
CMORPH			0.41	0.32	0.95	0.27			0.41	0.33	0.95	0.27
TRMMv7			0.45	0.36	1.00	0.27			0.51	0.42	0.99	0.24
Ensemble			0.55	0.49	0.97	0.23			0.56	0.53	0.99	0.23
Rain gauge			0.12	0.14	0.94	0.36			0.47	0.34	0.97	0.25
CRU *			0.27	0.17	0.98	0.30			0.30	0.20	0.96	0.29
GPCC *			0.29	0.23	0.99	0.30			0.32	0.24	0.97	0.29
UDEL *			0.35	0.25	0.99	0.28			0.45	0.37	0.99	0.25
CFSR	QM	bias corrected	0.22	0.18	0.96	0.33	QM + ModB	bias corrected	0.53	0.40	0.98	0.23
ERAi			0.18	0.16	0.91	0.35			0.54	0.41	0.98	0.23
MERRA			0.05	0.08	1.00	0.33			0.52	0.45	0.98	0.23
CMORPH			0.35	0.22	1.00	0.28			0.51	0.40	0.98	0.23
TRMMv7			0.19	0.15	0.91	0.35			0.51	0.42	0.98	0.24
Ensemble			0.19	0.16	0.91	0.35			0.50	0.36	0.98	0.24
GPCC *	DP	bias corrected	0.34	0.19	0.99	0.29	DP + ModB	bias corrected	0.37	0.23	0.97	0.28
CRU *			0.36	0.24	0.97	0.28			0.38	0.27	0.95	0.28
UDEL *			0.46	0.32	0.96	0.27			0.52	0.44	1.00	0.24

* Monthly products.

Table 5. Performance scores for KC discharge simulations. Performance is shown as R_eff_,log, R², Volume Error (Vol. Err.), and RMSE values for each bias correction technique considered.

Kiburubutu Catchment
GDP			R_eff_,log	R²	Vol. Err.	RMSE			R_eff_,log	R²	Vol. Err.	RMSE
CFSR	Non-	bias corrected	0.38	0.01	0.78	0.26	ModB	bias corrected	0.41	0.01	0.74	0.25
ERAi			0.63	0.26	0.78	0.18			0.66	0.24	0.73	0.18
MERRA			0.47	0.25	0.86	0.20			0.68	0.24	0.72	0.18
CMORPH			0.56	0.08	0.79	0.23			0.62	0.10	0.68	0.20
TRMMv7			0.55	0.07	0.87	0.23			0.59	0.11	0.70	0.20
Ensemble			0.63	0.19	0.80	0.19			0.67	0.22	0.73	0.19
Rain gauge			0.52	0.25	0.96	0.23			0.59	0.21	0.78	0.20
CRU *			0.42	0.09	0.97	0.29			0.57	0.12	0.70	0.21
GPCC *			0.38	0.14	0.79	0.24			0.59	0.14	0.69	0.21
UDEL *			0.47	0.12	0.86	0.23			0.64	0.16	0.71	0.19
CFSR	QM	bias corrected	0.41	0.07	0.92	0.27	QM + ModB	bias corrected	0.53	0.12	0.71	0.20
ERAi			0.49	0.14	0.91	0.27			0.59	0.20	0.79	0.20
MERRA			0.40	0.07	0.96	0.29			0.53	0.12	0.75	0.21
CMORPH			0.44	0.05	0.92	0.27			0.51	0.08	0.74	0.22
TRMMv7			0.45	0.04	0.98	0.29			0.52	0.04	0.77	0.23
Ensemble			0.48	0.07	0.91	0.29			0.56	0.06	0.83	0.23
GPCC *	DP	bias corrected	0.51	0.20	0.96	0.24	DP + ModB	bias corrected	0.59	0.12	0.82	0.22
CRU *			0.51	0.13	0.99	0.26			0.57	0.18	0.83	0.21
UDEL *			0.56	0.24	0.99	0.23			0.63	0.23	0.82	0.19

* Monthly products.

Table 6. Performance scores for KC discharge simulations. Performance is shown as R_eff_,log values for each bias correction technique considered.

© 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

Correction: Koutsouris et al. Utilization of Global Precipitation Datasets in Data Limited Regions: A Case Study of Kilombero Valley, Tanzania. Atmosphere, 2017, 8, 246

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