A D-vine copula-based quantile regression towards merging satellite precipitation products over rugged topography: a case study in the upper Tekeze–Atbara Basin
Mohammed Abdallah; Ke Zhang; Lijun Chao; Abubaker Omer; Khalid Hassaballah; Kidane Welde Reda; Linxin Liu; Tolossa Lemma Tola · 2024 · Hydrology and earth system sciences
WASTE classifies this as Negative / Null Result Report · AI classification, approximate
The study found no significant effect — useful as a negative control or null benchmark for your own design.
Abstract
Abstract. Precipitation is a vital key element in various studies of hydrology, flood prediction, drought monitoring, and water resource management. The main challenge in conducting studies over remote regions with rugged topography is that weather stations are usually scarce and unevenly distributed. However, open-source satellite-based precipitation products (SPPs) with a suitable resolution provide alternative options in these data-scarce regions, which are typically associated with high uncertainty. To reduce the uncertainty of individual satellite products, we have proposed a D-vine copul
Abstract by Mohammed Abdallah; Ke Zhang; Lijun Chao; Abubaker Omer; Khalid Hassaballah; Kidane Welde Reda; Linxin Liu; Tolossa Lemma Tola, Hydrology and earth system sciences (2024) — licensed CC BY 4.0.
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Metadata source: OpenAlex · DOI 10.5194/hess-28-1147-2024