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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Preprints
https://doi.org/10.5194/hess-2020-26
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-2020-26
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  24 Feb 2020

24 Feb 2020

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A revised version of this preprint is currently under review for the journal HESS.

Technical Note: Evaluation and bias correction of an observations-based global runoff dataset using historical streamflow observations from small tropical catchments in the Philippines

Daniel E. Ibarra1,2, Carlos Primo C. David3, and Pamela Louise M. Tolentino3 Daniel E. Ibarra et al.
  • 1Department of Earth and Planetary Science, University of California, Berkeley, California 94720, USA
  • 2Institute at Brown for Environment and Society and the Department of Earth, Environmental and Planetary Science, Brown University, Providence, Rhode Island 02912, USA
  • 3National Institute of Geological Sciences, University of the Philippines, Diliman, Quezon City, Philippines 1101

Abstract. The predictability of freshwater availability is one of the most important issues facing the world's population. Even in relatively wet tropical regions, seasonal fluctuations in the water cycle complicate the consistent and reliable supply of water to urban, industrial and agricultural demands. Importantly, historic streamflow monitoring datasets are crucial in assessing our ability to model and subsequently plan for future hydrologic changes. In this technical note we evaluate a new global product of monthly runoff (GRUN_v1; Ghiggi et al., 2019) using small tropical catchments in the Philippines. This observations-based monthly runoff product is evaluated using archived monthly streamflow data from 55 catchments with at least 10 years of data, extending back to 1946 in some cases. These catchments are completely independent of the GRUN gridded product as no catchments in the Philippines were of sufficient size to fulfil the original filtering criteria and databases of these data were either not digitized or difficult to compile. Using monthly runoff observations from catchments with more than 10 years of data between 1946 and 2014, we demonstrate across all observations significant but weak correlation (r2 = 0.372) and skilful prediction (Volumetric Efficiency = 0.363 and log(Nash–Sutcliff Efficiency) = 0.453) between the predicted values and the observations. At a regional scale we demonstrate that GRUN performs best among catchments located in Climate Types III (no pronounced maximum rainfall with short dry season) and IV (evenly distributed rainfall, no dry season). We also find a weak negative correlation between volumetric efficiency and catchment area, and a positive correlation between volumetric efficiency and mean observed runoff. Further, analysis of individual rivers demonstrates systematic biases (over and under) in baseflow during the dry season, and under-prediction of peak flow during some wet months among most catchments. These results demonstrate the potential utility of GRUN and future data products of this nature with due consideration and correction of systematic biases at the individual basin level to: (1) assess trends in regional scale runoff over the past century, (2) validate hydrologic models for un-monitored catchments in the Philippines, and (3) assess the impact of hydrometeorological phenomenon to seasonal water supply in this wet but drought prone archipelago. Finally, to correct for underprediction during wet months we perform a log-transform bias correction which greatly improves the nationwide Root Mean Square Error between GRUN and the observations by an order of magnitude (2.648 vs. 0.292 mm/day). This technical note demonstrates the importance of performing such corrections when accounting for the proportional contribution of catchments from smaller catchments in tropical land such as the Philippines to global tabulations of discharge.

Daniel E. Ibarra et al.

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Daniel E. Ibarra et al.

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Short summary
We evaluate a recently published global product of monthly runoff using streamflow data from small tropical catchments in the Philippines. Using monthly runoff observations from catchments we tested for correlation and prediction. We demonstrate the potential utility of this product in assessing trends in regional scale runoff, as well as look at the correlation of phenomenon such as the El Nino Southern Oscillation on streamflow in this wet but drought-prone archipelago.
We evaluate a recently published global product of monthly runoff using streamflow data from...
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