**1. Introduction**

Precipitation is a key driving component for hydrological water cycle processes at regional and global scales. A catchment reacts very specifically to intense rainfall due to its steep slopes and shallow soils, and precipitation data with a high spatial and temporal distribution is critical for forecasting flash flooding events [1]. These events invariably have the characteristics of high intensity and sudden occurrence, and under climate change, the needs for high resolution and accurate rainfall data have increased, particularly because effective hydrological forecasting depends greatly on precipitation accuracy [2–4]. Rain gauges can measure precipitation very accurately at a point scale, but we would need a dense network of instruments to ascertain the rainfall intensity at local/regional scales because of its high variability. In a short-time flash flood simulation, the spatial representatives of the rain gauge and the accurate representation of spatial rainfall variability in the surrounding area need to be

considered [5,6]. High-resolution precipitation based on rain gauge data are usually geometrically interpolated from a limited number of observation points using geographic information systems (GIS).

Compared with a rain gauge, a quantitative precipitation estimation (QPE), based on the weather radar, has a primary advantage because it provides very high spatial and temporal resolution rainfall information, making it very suitable for hydrological modeling [7,8]. With the development of weather radar over the past 60 years, QPE, with its very high spatial and temporal resolutions, can accurately detect the location of precipitation, and can be applied to practical hydrological operations such as flood forecasting [9,10]. However, an error-free radar QPE is not possible due to various sources of error, such as indirect precipitation measurement, the Z-R relationship, being above the ground, beam shielding, and ground clutter, which result in range degradation [8,11,12]. Preserving the high spatial accuracy of rainfall in radar QPEs remains a challenge for meteorologists. This has been the case since the 1940s, when the potential for measuring precipitation with high spatial and temporal distributions based on weather radar was realized [13]. With the advantage of radar to estimate the spatial pattern and rain gauge data to obtain the correct point value, a combined product based on radar QPE and rain gauge data has significant potential for achieving superior rainfall estimations [14,15].

The concept of achieving high-resolution precipitation estimations by merging QPE and rain gauge data has resulted in proposals of numerous merging methods, and different ways of categorizing these methods have been applied [16]. An additional correction factor is the most commonly investigated and is currently being used by many national meteorological services due to its simplicity [17]. With the development of these interpolation methods, some studies have attempted to interpolate point rain gauge values with a variogram, which represents the spatial association of radar fields [18]. Sharon et al. (2015) found a clear difference between geostatistical and non-geostatistical methods, where the geostatistical methods attempt to use the variogram to represent the spatial bias and error variance of the rainfall field [19]. In a review, McKee (2015) adopted a viewpoint proposed by Wang (2013) that such merging methods generally achieve merging precipitation through either bias minimization methods or error variance reduction methods [20,21]. An integration method was recently proposed with the aim of minimizing data uncertainty [22]. When considering these merging methods, a better, application-oriented categorization is necessary.

Despite the research on this study, most of the studies have focused on evaluating the feasibility of the applied merging techniques and measuring the performance of the merged rainfall estimates against the rain gauge observation and radar estimates. Few studies have attempted to compare the results from various merging categories and have instead focused on large scale applications [23,24]. Although the impact of limited rain gauge data cannot be neglected in the merging performance when using rain gauge data for ground truthing, many studies have shown that more rain gauges across the catchment can increase the chances of capturing rainfall features, while fewer rain gauges may miss small convective cells [25]. To identify the commonly used merging techniques with better performances, many inter-comparison studies have focused on the performances of these methods, including the applied merging details of the type of method, spatiotemporal resolutions, and the better performance methods identified in previous work. Generally, the performances of different merging methods in most studies are assessed based on accuracy measures by comparing merged estimates against rain gauge observation through cross validation [26], but recently, some studies have attempted to evaluate the radar-rain gauge merging methods by comparing the hydrological performances resulting from these methods [27].

High-resolution precipitation data have been used in various types of hydrological studies, and the improvement of simulated hydrological dynamics using radar-based QPE has been highlighted [28–30]. It should be noted that in spite of the residual errors often remaining, these merging products have significant uses in hydrological applications, particularly when forecasting flash floods or extreme events [31]. When merging for flood forecasting, the application of high resolution and accurate precipitation at fine spatiotemporal scales presents some challenges, such as (1) preserving small-scale features (e.g., convective), (2) density of rain gauges across mountain basins, and (3) fitness of the

hydrology model for the local catchment. With regard to applying flood forecasting at piedmont plain scales, it is therefore critical to consider these factors when examining the performances of di fferent interpolated precipitation models and their ability to deal with challenges in flood forecasting [32].

In this study, the potential of flood forecasting with high-resolution precipitation was described, including its variability and uncertainty regarding less clarity. For hourly precipitation, few studies have focused on di fferent interpolations regarding possible covariates over the catchments in semi-humid and semi-arid climates. Evaluating the performance of both radar-based and rain-gauge-based precipitation produced in the hydrological model can thus not only help to understand its physical processes, but also its function as an indirect measure to assess the accuracy of the rainfall input.

Although many merging methods of di fferent categories are available, little research has been conducted to compare their performances and the applications driving hydrological models. In addition to choosing a reliable radar-rain gauge merging method to obtain high resolution and accurate precipitation data for the study area, the objective of this research was also to assess the detailed performances of di fferent quality merging data in flash flood forecasting. In this study, we aimed to assess how di fferent rain gauge observations, merged with radar data, leads to both better high-resolution precipitation resolutions and improved hydrological applications, thereby further enhancing the potential benefit of flash flood forecasting.
