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Reliable probabilistic hydrological prediction requires appropriate handling of residual errors, which can pose considerable complexity. This paper proposes a nonparametric residual error (NRE) model that effectively captures the statistical characteristics of raw residuals. The NRE model employs a local linear estimator with a robust bandwidth selector to estimate the regression and conditional volatility functions of raw residuals. Additionally, the AR(1) model and location-mixture Gaussian distribution are used to estimate the temporal correlation structure and innovation distribution. Through two case studies in South-East China, this research demonstrates the superiority of the NRE model over the benchmark Box-Cox transformation approach in terms of prediction reliability, precision, and bias correction capabilities. Simulation experiments further reveal that the NRE model can effectively fit the residual regression function, conditional volatility function, and innovation distribution under varying scenarios. The proposed residual error model is anticipated to promote the adoption of probabilistic predictions in hydrological modeling applications. • Residual Error Handling: This paper tackles complex residual errors in hydrological modeling. • Accuracy Improvement: The NRE model boosts accuracy with a local linear estimator. • Predictive Superiority: The NRE model excels in predicting reliability, precision, and bias correction. • Data-Driven Flexibility: The NRE model reduces reliance on assumptions for hydrological modelers. [ABSTRACT FROM AUTHOR]

• A framework is proposed to analyze compound floods with multiple driving factors. • Local rainfall has a larger impact on the flooding levels of the GBA, China. • Flooding area is about 50 km2 and 1.3 m flooded depth for the worst scenario. • Findings have important implications to GBA's flooding control and management. Low-lying coastal cities are vulnerable to compound floods caused by many factors including river flows, tides and local rainfall. Many previous studies focus on the impacts of rainfall and tidal levels (two driving factors) on estuaries or regions near the main single river, while research about the three influencing factors on the floods for complex urban river networks remain relatively scarce. This paper proposes a framework to simulate the coastal compound flooding process, with a series of scenarios designed to explore the main flooding drivers and their worst possible combination. The approach is used to quantify the induced flooding consequences, in which a two-dimensional model is employed to conduct a case study for a city with complex river networks. The Qianshan River basin in Zhuhai City, located in the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) of China is used as the case study. Application results show that: (i) the occurrence and properties of flooding in this coastal area are jointly affected by upstream river flows, downstream tidal levels and local rainfall intensity with different contribution levels, (ii) compared to tidal levels, local rainfall intensity has a larger impact on the flooding levels of the study region, and (iii) the worst scenario where a 50-year return period upstream river flow co-occurring with high tide and a 100-year return period rainfall can produce a flooding area up to 50 km2 for the study region, with an average of 1.3 m flooded depth. The proposed framework with its findings offers an in-depth understanding and quantification of coastal urban flood characteristics within the GBA of China. [ABSTRACT FROM AUTHOR]