Dataset Information

Comparison of gridded precipitation datasets for rainfall-runoff and inundation modeling in the Mekong River Basin.

ABSTRACT: Precipitation, as a primary hydrological variable in the water cycle plays an important role in hydrological modeling. The reliability of hydrological modeling is highly related to the quality of precipitation data. Accurate long-term gauged precipitation in the Mekong River Basin, however, is limited. Therefore, the main objective of this study is to assess the performances of various gridded precipitation datasets in rainfall-runoff and flood-inundation modeling of the whole basin. Firstly, the performance of the Rainfall-Runoff-Inundation (RRI) model in this basin was evaluated using the gauged rainfall. The calibration (2000-2003) and validation (2004-2007) results indicated that the RRI model had acceptable performance in the Mekong River Basin. In addition, five gridded precipitation datasets including APHRODITE, GPCC, PERSIANN-CDR, GSMaP (RNL), and TRMM (3B42V7) from 2000 to 2007 were applied as the input to the calibrated model. The results of the simulated river discharge indicated that TRMM, GPCC, and APHRODITE performed better than other datasets. The statistical index of the annual maximum inundated area indicated similar conclusions. Thus, APHRODITE, TRMM, and GPCC precipitation datasets were considered suitable for rainfall-runoff and flood inundation modeling in the Mekong River Basin. This study provides useful guidance for the application of gridded precipitation in hydrological modeling in the Mekong River basin.

SUBMITTER: Try S

PROVIDER: S-EPMC6948743 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Comparison of gridded precipitation datasets for rainfall-runoff and inundation modeling in the Mekong River Basin.

Try Sophal S Tanaka Shigenobu S Tanaka Kenji K Sayama Takahiro T Oeurng Chantha C Uk Sovannara S Takara Kaoru K Hu Maochuan M Han Dawei D

PloS one 20200108 1

Precipitation, as a primary hydrological variable in the water cycle plays an important role in hydrological modeling. The reliability of hydrological modeling is highly related to the quality of precipitation data. Accurate long-term gauged precipitation in the Mekong River Basin, however, is limited. Therefore, the main objective of this study is to assess the performances of various gridded precipitation datasets in rainfall-runoff and flood-inundation modeling of the whole basin. Firstly, th ...[more]

PMID: 31914161

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Project description:In this work, we have used the Soil & Water Assessment Tool (SWAT) to examine streamflow variability of the Lower Mekong River Basin (LMRB) associated with changes in the Upper Mekong River Basin (UMRB) inflows. Two hypothetical experiments were formulated and evaluated for the LMRB, where we conducted runoff simulations with multiple inflow changes that include upstream runoff yield increase and decrease scenarios. Streamflow variability of the LMRB was quantified by two streamflow metrics that explain flow variability and predictability, and high flow disturbance. The model experiments were performed for the Lower Mekong River Basin with identical climate, soil, and other watershed characteristics data. Remote sensing precipitation (Tropical Rainfall Measurement Mission, TRMM, and Global Precipitation Measurement mission, GPM), meteorological data as well as spatial data that include a digital elevation model, newly developed soil information (Harmonized World Soil Database, HWSD), and land use and land cover were processed as input to the LMRB model simulations. Observed daily streamflow data along the Lower Mekong River from Chiang Sean, Thailand to Kratie, Cambodia were used for calibration and validation. Our work results suggest that the Lower Mekong River streamflow is highly variable and has a low predictability (Colwell index of about 32%). We found that releasing more water from upstream Mekong during rainfall months by 30% would result in a reduction in the Lower Mekong streamflow predictability by about 21%. This reduction in predictability is mainly attributed to a decrease in the Contingency index. Our work shows that the ability to predict floods/droughts at the Lower Mekong River would be reduced if there is any anticipated change (i.e., increase/decrease) from UMRB releases. Our results also show that releasing more flows from the upstream Mekong would also affect flood duration and the frequency of flood occurrences downstream. The results of this work thus help to quantify the sensitivity of streamflow variability at the Lower Mekong River Basin to upstream anthropogenic changes.

Project description:Surface water dynamics play an important role in water, energy and carbon cycles of the Amazon Basin. A macro-scale inundation scheme was integrated with a surface-water transport model and the extended model was applied in this vast basin. We addressed the challenges of improving basin-wide geomorphological parameters and river flow representation for large-scale applications. Vegetation-caused biases embedded in the HydroSHEDS DEM data were alleviated by using a vegetation height map of about 1-km resolution and a land cover dataset of about 90-m resolution. The average elevation deduction from the DEM correction was about 13.2 m for the entire basin. Basin-wide empirical formulae for channel cross-sectional geometry were adjusted based on local information for the major portion of the basin, which could significantly reduce the cross-sectional area for the channels of some subregions. The Manning roughness coefficient of the channel varied with the channel depth to reflect the general rule that the relative importance of riverbed resistance in river flow declined with the increase of river size. The entire basin was discretized into 5395 subbasins (with an average area of 1091.7 km2), which were used as computation units. The model was driven by runoff estimates of 14 years (1994 - 2007) generated by the ISBA land surface model. The simulated results were evaluated against in situ streamflow records, and remotely sensed Envisat altimetry data and GIEMS inundation data. The hydrographs were reproduced fairly well for the majority of 13 major stream gauges. For the 11 subbasins containing or close to 11 of the 13 gauges, the timing of river stage fluctuations was captured; for most of the 11 subbasins, the magnitude of river stage fluctuations was represented well. The inundation estimates were comparable to the GIEMS observations. Sensitivity analyses demonstrated that refining floodplain topography, channel morphology and Manning roughness coefficients, as well as accounting for backwater effects could evidently affect local and upstream inundation, which consequently affected flood waves and inundation of the downstream area. It was also shown that the river stage was sensitive to local channel morphology and Manning roughness coefficients, as well as backwater effects. The understanding obtained in this study could be helpful to improving modeling of surface hydrology in basins with evident inundation, especially at regional or larger scales.

Dataset Information

Comparison of gridded precipitation datasets for rainfall-runoff and inundation modeling in the Mekong River Basin.

Publications

Comparison of gridded precipitation datasets for rainfall-runoff and inundation modeling in the Mekong River Basin.

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