# Evaluation of Grid-Based Rainfall Products and Water Balances over the Mekong River Basin
## 1 Introduction  
It is well recognized in the literature that precipitation is one of the key factors in hydrological application practices [1; 2; 3] Existing precipitation products generally include gauge observations, estimates inferred from satellite imagery, and outputs from various numerical models [4]. The strengths and weaknesses of different kinds of precipitation data vary greatly. While the data quality of insitu rain gauges is the most reliable, their spatial distribution is often sparse and uneven, as data availability in complex terrain regions is limited [5; 6; 7]. The quality of ground-based weather radars is also unreliable, as it can be affected by signal distortion [8; 9; 10]. Gridded precipitation products (GPPs; i.e., re-analysis, satellite, and model-based products) have high spatial and temporal resolutions, but they also have a high level of uncertainty, affected by retrieval algorithms, data sources [11], and gauge adjustment procedures [12; 13; 14]. Precipitation itself strongly varies in relation to different climate properties, altitudes, and surface conditions [15]. These strengths and weaknesses and the variability of precipitation thus affect hydrological simulation and water balance components. As a result, the selection of the best precipitation input for a hydrological model is basin-specific, especially in the case of large, complex basins stretching across different climatic zones and topographies, as with the Mekong River Basin (MRB) [16; 17]. Therefore, selection of the correct hydrological model [18] is crucial to ensure a high-accuracy assessment of water balance components, which constitutes an important issue for analyzing and developing strategies for current and future problems in sustainable water resource management. In a large number of hydrological models, the Soil and Water Assessment Tool (SWAT) [19] is widely used with little direct calibration being required to obtain good hydrologic predictions [20], and this method is helpful in identifying and calculating the components of water balance. Owing to its wide coverage and enhanced tempo-spatial resolutions, remote sensing contributes valuably to the monitoring of hydrological variables and hydrological predictions regionally and globally, particularly in areas with a limited capability of surface-based observations [2; 21; 22]. In typical satellite products, precipitation products are usually generated based on thermal infrared, passive microwave, and radar data, and then calibrated by using ground observations of rainfall to obtain better estimations [7]. Over the last few decades, multiple satellite projects have been launched. Various global GPPs are available online, such as the Climate Prediction Center (CPC) Unified Gauge-Based Analysis of Global Daily Precipitation datasets provided by National Oceanic and Atmospheric Administration (NOAA) [23], the Tropical Rainfall Measuring Mission (TRMM) precipitation data [24], the Climate Prediction Center Morphing (CMORPH) product [25], and the Global Satellite Mapping of Precipitation (GSMaP) product [26]. These GPPs have a large spatial and frequent temporal coverage and provide potential alternative data sources for hydrological studies and water resource management, especially in data-sparse and ungauged regions. At present, there is a demand for fuller awareness of the strengths and weaknesses of publicly available GPPs and rain gauge observations across different regions for more in-depth hydrological studies and improved water resources management. Over the Lower Mekong River Basin (from the place where the Mekong River leaves Yunnan province (China) and enters the Golden Triangle tripoint), Mohammed et al. [27] suggested that streamflow simulation by forcing TRMM data is able to capture the variability of observation, and is even better than using in situ precipitation data. In other research, Wang et al. [28] also confirmed that TRMM-forcing simulation performed well compared to observations for the regions with sparsely-distributed stations.