On the selection of precipitation products for the regionalisation of hydrological model parameters
<p>Over the past decades, novel parameter regionalisation techniques have been developed to predict streamflow in data-scarce regions. In this paper, we examined how the choice of gridded daily precipitation (<span class="inline-formula"><i>P</i></span>) produ...
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| Autores principales: | , , , , , , , , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
Copernicus Publications
2021
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/02077e1090724f7f8872672abbcacf08 |
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| Sumario: | <p>Over the past decades, novel parameter regionalisation techniques have been developed to predict streamflow in data-scarce regions. In this paper, we examined how the choice of gridded daily precipitation (<span class="inline-formula"><i>P</i></span>) products affects the relative performance of three well-known parameter regionalisation techniques (spatial proximity, feature similarity, and parameter regression) over 100 near-natural catchments with diverse hydrological regimes across Chile. We set up and calibrated a conceptual semi-distributed HBV-like hydrological model (TUWmodel) for each catchment, using four <span class="inline-formula"><i>P</i></span> products (CR2MET, RF-MEP, ERA5, and MSWEPv2.8). We assessed the ability of these regionalisation techniques to transfer the parameters of a rainfall-runoff model, implementing a leave-one-out cross-validation procedure for each <span class="inline-formula"><i>P</i></span> product. Despite differences in the spatio-temporal distribution of <span class="inline-formula"><i>P</i></span>, all products provided good performance during calibration (median Kling–Gupta efficiencies (KGE<span class="inline-formula"><sup>′</sup></span>s) <span class="inline-formula">></span> 0.77), two independent verification periods (median KGE<span class="inline-formula"><sup>′</sup></span>s <span class="inline-formula">>0.70</span> and 0.61, for near-normal and dry conditions, respectively), and regionalisation (median KGE<span class="inline-formula"><sup>′</sup></span>s for the best method ranging from 0.56 to 0.63). We show how model calibration is able to compensate, to some extent, differences between <span class="inline-formula"><i>P</i></span> forcings by adjusting model parameters and thus the water balance components. Overall, feature similarity provided the best results, followed by spatial proximity, while parameter regression resulted in the worst performance, reinforcing the importance of transferring complete model parameter sets to ungauged catchments. Our results suggest that (i) merging <span class="inline-formula"><i>P</i></span> products and ground-based measurements does not necessarily translate into an improved hydrologic model performance; (ii) the spatial resolution of <span class="inline-formula"><i>P</i></span> products does not substantially affect the regionalisation performance; (iii) a <span class="inline-formula"><i>P</i></span> product that provides the best individual model performance during calibration and verification does not necessarily yield the best performance in terms of parameter regionalisation; and (iv) the model parameters and the performance of regionalisation methods are affected by the hydrological regime, with the best results for spatial proximity and feature similarity obtained for rain-dominated catchments with a minor snowmelt component.</p> |
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