Precipitation is one of the most important climate variables for runoff simulation. The main objective of this study is to reveal uncertainties in precipitation products and demonstrate how those uncertainties affect runoff estimations in Southeast Asia. Through comparing precipitation products and performing hydrological simulations, the authors have reached the following main conclusions. In Southeast Asia, gauge-based precipitation products have similar monthly precipitation patterns and small differences in precipitation amount, except for Myanmar and Sumatra Island where few gauging stations are used for the precipitation products. Conversely, the uncertainties in seasonal and annual precipitation are large in Southeast Asia if few gauging stations are used for the precipitation products. Since most of the difference in precipitation translates to difference in runoff in wet region, small errors in precipitation can easily result in large errors in runoff in Southeast Asia. Even if located in wet Southeast Asia, impacts of precipitation errors on runoff estimates are different depending on precipitation amount and runoff ratio. In Chao Phraya Basin, relatively low precipitation and runoff ratio cause large percentage differences in simulated runoff. In Irrawaddy Basin, relatively high precipitation and runoff ratio cause large difference in simulated runoff volume.

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The performance of Global Satellite Mapping of Precipitation data (GSMaP_MVK, version 5.222.1) over the VuGia–ThuBon River basin and surrounding areas in central Vietnam was examined on a monthly basis in comparison with rainfall gauged at eight meteorological stations and a gridded rainfall product of the Asian Precipitation—Highly-Resolved Observational Data Integration Towards Evaluation of Water Resources project (APHRODITE, V1003R1). APHRODITE represented in situ observations well, whereas GSMaP had very low performance over the study area for the period 2001–2007. Particularly, GSMaP exhibited large negative rainfall biases for the winter monsoon period from October to December and the biases tended to increase as the elevation decreased. A correction method using an artificial neural network (ANN) was implemented for the GSMaP rainfall over the VuGia–ThuBon River basin. Validation showed that the ANN correction method significantly improved the GSMaP quality in terms of spatial correlation, rainfall amplitude, and Nash–Sutcliffe efficiency coefficient for both the dependent period 2001–2005 and the independent period 2006–2007.

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Canopy interception (I) was measured using artificial Christmas trees that were set on three trays under natural rainfall. Tree heights were 65 cm, 110 cm and 240 cm, with two of the higher stands thinned after three months. Gross rainfall (P_G) and water storage on a single tree of 65 cm high and 240 cm were measured, which enabled calculation of I not only on a per rain event basis but also over shorter time periods. Canopy interception rate (I/P_G) was comparable with that in the actual forest. The value of I/P_G tended to increase with tree height, while it increased or decreased after thinning depending on the forest structure. Evaporation during rainfall (I_R), during storm break time (I_Sbt) and after the cessation of rainfall (I_Aft) was calculated on a sub rain event basis at a resolution of 5 minutes. A sub rain event was defined when rainfall broke for more than 20 minutes during the rain event. Among the three evaporation components, I_R constituted nearly all of the total I, with I_Sbt close to zero and only a small contribution from I_Aft. The model forest appears useful for studying the mechanisms of I that are unexplainable using conventional approaches.

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The present study demonstrates and suggests a methodology for estimating watershed-scale storage changes from hourly discharge data in mountainous watersheds under a humid climate in Japan as the basis for watershed characterizations by combining a hydrograph separation and a storage-discharge formulizing method. Firstly, we separated hydrographs into different sub-components with respect to the time constants of the flow recession periods of the hydrograph by the filter-separation autoregressive method. Then we applied a storage-discharge formulizing method to the relationship between watershed-scale storage and discharge sub-components independently. As the result, we obtained a realistic estimate of annual watershed-scale storage change in the upper Abukuma River watershed. In addition, we compared our methodology with the non-linear reservoir modeling approach to explore the potential for extensive applications such as dominant process modeling, watershed classifications, and practical watershed management.

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The steady-state assumption for catchment transit time is a controversial issue in catchment hydrology. In this study, we propose a new approach to estimate the time-variant mean transit time (MTT) and transit time distribution (TTD) using a five-layer tank model with isotopic tracers and test it in the Fuefuki River catchment, central Japan. Model parameters were optimized during the calibration phase based on hydrometric and isotopic observations and then validated in a separate validation phase. The long-term (2003–2012) average MTT was estimated to be 23.7 years. However, the daily MTT was highly variable, ranging from 1.2 to 37.0 years. Instantaneous TTD also varied markedly. Precipitation alters TTD by increasing younger components and shortens the MTT. Thus, a steady-state assumption is inappropriate, with the relationship between monthly MTT and precipitation amount most closely approximated by an exponential function. The dependence of MTT on precipitation is an important descriptor for characterizing catchments. Although optimized model-parameters have some uncertainties, potential errors in estimating the MTT are relatively small (e.g., < ±3 years). Therefore, the tracer-aided tank model is useful for estimating time variations in MTT and TTD with high reliability.

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