Evaluation of Satellite-Based Rainfall Estimates in the Lower Mekong River Basin (Southeast Asia)

Satellite-based precipitation is an essential tool for regional water resource applications that requires frequent observations of meteorological forcing, particularly in areas that have sparse rain gauge networks. To fully realize the utility of remotely sensed precipitation products in watershed modeling and decision-making, a thorough evaluation of the accuracy of satellite-based rainfall and regional gauge network estimates is needed. In this study, Tropical Rainfall Measuring Mission (TRMM) Multi-Satellite Precipitation Analysis (TMPA) 3B42 v.7 and Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) daily rainfall estimates were compared with daily rain gauge observations from 2000 to 2014 in the Lower Mekong River Basin (LMRB) in Southeast Asia. Monthly, seasonal, and annual comparisons were performed, which included the calculations of correlation coefficient, coefficient of determination, bias, root mean square error (RMSE), and mean absolute error (MAE). Our validation test showed TMPA to correctly detect precipitation or no-precipitation 64.9% of all days and CHIRPS 66.8% of all days, compared to daily in-situ rainfall measurements. The accuracy of the satellite-based products varied greatly between the wet and dry seasons. Both TMPA and CHIRPS showed higher correlation with in-situ data during the wet season (JuneSeptember) as compared to the dry season (NovemberJanuary). Additionally, both performed better on a monthly than an annual time-scale when compared to in-situ data. The satellite-based products showed wet biases during months that received higher cumulative precipitation. Based on a spatial correlation analysis, the average r-value of CHIRPS was much higher than TMPA across the basin. CHIRPS correlated better than TMPA at lower elevations and for monthly rainfall accumulation less than 500 mm. While both satellite-based products performed well, as compared to rain gauge measurements, the present research shows that CHIRPS might be better at representing precipitation over the LMRB than TMPA

Meteorological drought analysis in the Lower Mekong Basin using satellite-based long-term CHIRPS product

Lower Mekong Basin (LMB) experiences a recurrent drought phenomenon. However, few studies have focused on drought monitoring in this region due to lack of ground observations. The newly released Climate Hazards Group Infrared Precipitation with Station data (CHIRPS) with a long-term record and high resolution has a great potential for drought monitoring. Based on the assessment of CHIRPS for capturing precipitation and monitoring drought, this study aims to evaluate the drought condition in LMB by using satellite-based CHIRPS from January 1981 to July 2016. The Standardized Precipitation Index (SPI) at various time scales (1-12-month) is computed to identify and describe drought events. Results suggest that CHIRPS can properly capture the drought characteristics at various time scales with the best performance at three-month time scale. Based on high-resolution long-term CHIRPS, it is found that LMB experienced four severe droughts during the last three decades with the longest one in 1991-1994 for 38 months and the driest one in 2015-2016 with drought affected area up to 75.6%. Droughts tend to occur over the north and south part of LMB with higher frequency, and Mekong Delta seems to experience more long-term and extreme drought events. Severe droughts have significant impacts on vegetation condition

Comparison of Gridded Precipitation Datasets for Rainfall-Runoff and Inundation Modeling in the Mekong River Basin

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

Applications of TRMM-based Multi-Satellite Precipitation Estimation for Global Runoff Simulation: Prototyping a Global Flood Monitoring System

Advances in flood monitoring/forecasting have been constrained by the difficulty in estimating rainfall continuously over space (catchment-, national-, continental-, or even global-scale areas) and flood-relevant time scale. With the recent availability of satellite rainfall estimates at fine time and space resolution, this paper describes a prototype research framework for global flood monitoring by combining real-time satellite observations with a database of global terrestrial characteristics through a hydrologically relevant modeling scheme. Four major components included in the framework are (1) real-time precipitation input from NASA TRMM-based Multi-satellite Precipitation Analysis (TMPA); (2) a central geospatial database to preprocess the land surface characteristics: water divides, slopes, soils, land use, flow directions, flow accumulation, drainage network etc.; (3) a modified distributed hydrological model to convert rainfall to runoff and route the flow through the stream network in order to predict the timing and severity of the flood wave, and (4) an open-access web interface to quickly disseminate flood alerts for potential decision-making. Retrospective simulations for 1998-2006 demonstrate that the Global Flood Monitor (GFM) system performs consistently at both station and catchment levels. The GFM website (experimental version) has been running at near real-time in an effort to offer a cost-effective solution to the ultimate challenge of building natural disaster early warning systems for the data-sparse regions of the world. The interactive GFM website shows close-up maps of the flood risks overlaid on topography/population or integrated with the Google-Earth visualization tool. One additional capability, which extends forecast lead-time by assimilating QPF into the GFM, also will be implemented in the future

Effect of baseline meteorological data selection on hydrological modelling of climate change scenarios

This study evaluates how differences in hydrological model parameterisation resulting from the choice of gridded global precipitation data sets and reference evapotranspiration (ETo) equations affects simulated climate change impacts, using the north western Himalayan Beas river catchment as a case study. Six combinations of baseline precipitation data (the Tropical Rainfall Measuring Mission (TRMM) and the Asian Precipitation – Highly Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE)) and Reference Evapotranspiration equations of differing complexity and data requirements (Penman-Monteith, Hargreaves –Samani and Priestley – Taylor) were used in the calibration of the HySim model. Although the six validated hydrological models had similar historical model performance (Nash–Sutcliffe model efficiency coefficient (NSE) from 0.64-0.70), impact response surfaces derived using a scenario neutral approach demonstrated significant deviations in the models’ responses to changes in future annual precipitation and temperature. For example, the change in Q10 varies between -6.5 % to -11.5% in the driest and coolest climate change simulation and +79% to +118% in the wettest and hottest climate change simulation among the six models. The results demonstrate that the baseline meteorological data choices made in model construction significantly condition the magnitude of simulated hydrological impacts of climate change, with important implications for impact study design.NER

Assessment of three long-term gridded climate products for hydro-climatic simulations in tropical river basins

10.3390/w9030229Water (Switzerland)9322

INTERCOMPARISON AND VALIDATION OF CONTINENTAL WATER LEVEL PRODUCTS DERIVED FROM SATELLITE RADAR ALTIMETRY, MODELING AND FORECASTING TROPICAL LAKE LEVELS

This dissertation focuses on validating the use of satellite radar altimetry products to observe and forecast water level in lakes and reservoirs. Satellite measurements of lake and reservoir water levels complement in situ observations by providing stage information for ungauged basins and by filling data gaps in gauge records. Yet different satellite radar altimeter-derived continental water level products may differ significantly due to choice of satellites, geophysical corrections, etc. To explore the impacts of these differences, in the first part of this dissertation a direct comparison between three different altimeter-based lake level estimates is presented and validated with lake level gauge time series for lakes of a variety of sizes and conditions (e.g. whether they freeze seasonally). This comparison provides quantitative estimates of the error in lake levels as well as advice on product choices to end users. The largest discrepancies among the altimeter products occur for the lakes that freeze. In the second part of this dissertation a simple water balance model is developed relating net freshwater flux on a catchment basin to lake level. The model is constructed with two empirical parameters: effective catchment to lake area ratio and time delay between freshwater flux and lake level response. This model allows comparison of observed net freshwater flux with the lake level estimates from altimetry for a series of 12 tropical lakes distributed across three continents. The results show encouraging agreement between these independent datasets. The third part of this dissertation uses the simple lake model, developed in the second part of this dissertation, and applies it to NOAA's Climate Forecast System (CFS) coupled model thus allowing us to produce seasonal lake level forecasts based on seasonal predictions of net freshwater flux. In the CFS net freshwater flux data bias with respect to the independent reanalysis is determined. One example of such a lake level model forecast is presented, showing promising significant results over most examined tropical lakes, but failing for reservoirs and smaller lakes. Model forecast bias with respect to altimeter observations is proposed to be further investigated for multiple lead times

Statistical Comparison of IMERG Precipitation Products with Optical Rain Gauge Observations over Kototabang, Indonesia

Satellite-based precipitation estimates play a crucial role in many hydrological and numerical weather models, especially to overcome the scarcity of rain gauge data. Globally gridded rainfall product from Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (GPM) (IMERG) has been used in a wide range of hydrological applications. However, the IMERG is inherently prone to errors and biases. This study evaluated the performance of the IMERG-Final run (IMERG-F) product to estimate rainfall in a mountainous area of Sumatra. Validation was carried out using optical rain gauge (ORG) data for 15 years (2002-2016), at Kototabang, West Sumatra, Indonesia. In general, IMERG-F overestimated rainfall in all time scales. The longer the time scale was, the better the performance of IMERG-F we obtained. This feature was indicated by all quantities of continuous and categorical statistical matrices used. The performance of IMERG-F was lower than in other areas of the Maritime Continent, except for the probability of detection (POD) value. IMERG-F could detect rain very well, including for daily and hourly data, but the false alarm rate (FAR) was also relatively high. Such high FAR value may indicate a significant small-scale spatial rainfall variability in mountainous area of Sumatra

Satellite-based precipitation datasets evaluation using gauge observation and hydrological modeling in a typical arid land watershed of Central Asia

Hydrological modeling has always been a challenge in the data-scarce watershed, especially in the areas with complex terrain conditions like the inland river basin in Central Asia. Taking Bosten Lake Basin in Northwest China as an example, the accuracy and the hydrological applicability of satellite-based precipitation datasets were evaluated. The gauge-adjusted version of six widely used datasets was adopted; namely, Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (CDR), Climate Hazards Group Infrared Precipitation with Stations (CHIRPS), Global Precipitation Measurement Ground Validation National Oceanic and Atmospheric Administration Climate Prediction Center (NOAA CPC) Morphing Technique (CMORPH), Integrated Multi-Satellite Retrievals for GPM (GPM), Global Satellite Mapping of Precipitation (GSMaP), the Tropical Rainfall Measuring Mission (TRMM) and Multi-satellite Precipitation Analysis (TMPA). Seven evaluation indexes were used to compare the station data and satellite datasets, the soil and water assessment tool (SWAT) model, and four indexes were used to evaluate the hydrological performance. The main results were as follows: 1) The GPM and CDR were the best datasets for the daily scale and monthly scale rainfall accuracy evaluations, respectively. 2) The performance of CDR and GPM was more stable than others at different locations in a watershed, and all datasets tended to perform better in the humid regions. 3) All datasets tended to perform better in the summer of a year, while the CDR and CHIRPS performed well in winter compare to other datasets. 4) The raw data of CDR and CMORPH performed better than others in monthly runoff simulations, especially CDR. 5) Integrating the hydrological performance of the uncorrected and corrected data, all datasets have the potential to provide valuable input data in hydrological modeling. This study is expected to provide a reference for the hydrological and meteorological application of satellite precipitation datasets in Central Asia or even the whole temperate zone

Application of HEC-HMS model and satellite precipitation products to restore runoff in Laigiang river basin in Vietnam

The Laigiang river basin in the South Central Coast of Vietnam plays an important role in the socio-economic development of Binhdinh Province. In recent decades, the region has experienced commonly flooding in vast areas. This research aims to simulate event-based rainfall-runoff modelling, a historical flood event in December 2016, by applying the HEC-HMS model and rainfall data from CHIRPS. The CHIRPS data is an acceptable potential data input of the hydrology model. These have been confirmed through reliable validation indexes: The peak flood flow rate of 2,542.6 m3/s corresponds to the flood frequency of 5%; NSE with the value at 0.95; R2 coefficient reached 0.87; PBIAS being around 0.45, and PFC being at 0.89. It shows better performance in the rainy season than in the dry season. Inclusive, the CHIRPS rainfall data set and the HEC model could be used for some operational purposes in weather forecasting, especially for flood warnings in river basins in the South Central Coast, Vietnam