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Entropy-Based Temporal Downscaling of Precipitation as Tool for Sediment Delivery Ratio Assessment.

ABSTRACT: Many regions around the globe are subjected to precipitation-data scarcity that often hinders the capacity of hydrological modeling. The entropy theory and the principle of maximum entropy can help hydrologists to extract useful information from the scarce data available. In this work, we propose a new method to assess sub-daily precipitation features such as duration and intensity based on daily precipitation using the principle of maximum entropy. Particularly in arid and semiarid regions, such sub-daily features are of central importance for modeling sediment transport and deposition. The obtained features were used as input to the SYPoME model (sediment yield using the principle of maximum entropy). The combined method was implemented in seven catchments in Northeast Brazil with drainage areas ranging from 10^-3 to 10⁺² km² in assessing sediment yield and delivery ratio. The results show significant improvement when compared with conventional deterministic modeling, with Nash-Sutcliffe efficiency (NSE) of 0.96 and absolute error of 21% for our method against NSE of -4.49 and absolute error of 105% for the deterministic approach.

SUBMITTER: Alencar PHL

PROVIDER: S-EPMC8700339 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Abstract Despite continuous improvements, precipitation forecasts are still not as accurate and reliable as those of other meteorological variables. A major contributing factor to this is that several key processes affecting precipitation distribution and intensity occur below the resolved scale of global weather models. Generative adversarial networks (GANs) have been demonstrated by the computer vision community to be successful at super‐resolution problems, that is, learning to add fine‐scale structure to coarse images. Leinonen et al. (2020, https://doi.org/10.1109/TGRS.2020.3032790) previously applied a GAN to produce ensembles of reconstructed high‐resolution atmospheric fields, given coarsened input data. In this paper, we demonstrate this approach can be extended to the more challenging problem of increasing the accuracy and resolution of comparatively low‐resolution input from a weather forecasting model, using high‐resolution radar measurements as a “ground truth.” The neural network must learn to add resolution and structure whilst accounting for non‐negligible forecast error. We show that GANs and VAE‐GANs can match the statistical properties of state‐of‐the‐art pointwise post‐processing methods whilst creating high‐resolution, spatially coherent precipitation maps. Our model compares favorably to the best existing downscaling methods in both pixel‐wise and pooled CRPS scores, power spectrum information and rank histograms (used to assess calibration). We test our models and show that they perform in a range of scenarios, including heavy rainfall. Key Points We use generative adversarial neural networks to post‐process global weather forecast model output over the UK We produce more realistic precipitation forecasts than the input forecast data, at 10X resolution, with excellent statistical properties We match or outperform a state‐of‐the‐art pointwise downscaling scheme, while also producing spatially coherent images

Dataset Information

Entropy-Based Temporal Downscaling of Precipitation as Tool for Sediment Delivery Ratio Assessment.

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