A laboratory-scale experiment measuring the infiltration of Escherichia coli into saturated soils was performed under constant temperatures. Three soil columns were prepared with loamy clay soil using the wet packing method: Column A consisted of a 15-cm soil column with a water head of 5 cm above the soil surface, whereas Columns B and C had a soil column of 10 cm with a water head of 10 cm above the soil surface. Columns A and B were inoculated with autoclaved ultrapure water containing 7.4 × 107 colony-forming units/mL of E. coli K12 for 450 and 180 h respectively. Column C was inoculated only with autoclaved ultrapure water and served as an experimental control. Column A presented a steady decrease in infiltration rate, which showed a strong correlation (correlation coefficient = –0.93) with the amount of E. coli accumulated in the soil (clogging). Column B first presented similar results to Column A; however, after eight pore volumes were flushed, the infiltration rate increased rapidly, doubling the initial infiltration rate prior to E. coli inoculation. It is proposed that heterogeneous accumulation and growth of E. coli in the soil led to increased infiltration rate. In Column C, the infiltration rate decreased from 27.7 to 24.0 mL/h over the duration of the experiment, despite not having any other input than autoclaved ultrapure water. Additionally, the measurements of E. coli at the output of the soil columns were compared using spectrometry, plate counts, and quantitative polymerase chain reaction measurements. The results indicated that spectrometry was the most suitable method for determining breakthrough curves in soil infiltration experiments.
Precipitation would be one of the most important inputs for rainfall-runoff (RR) simulations. Therefore, quantifying the Areal Mean Precipitation (AMP) error is important to give the guidance for improving the accuracy and robustness of RR models. Instead of using limited number of rain gauge measurements, satellite based precipitation data would be a surrogate data source. In this study, the capability of remote sensing precipitation including satellite-only GSMaP-MVK and satellite-gauge merged data to evaluate AMP uncertainty is investigated. The adjusted precipitation performances depend upon the number of blended rain gauges, which would not always give superior results than the original data GSMaP-MVK. In addition GSMaP-MVK would be one of the choices for precipitation data source to compute AMP error. Therefore, a map of potential AMP uncertainty in major rivers in Vietnam is produced and utilized for improvement of rain gauge network. In order to ensure the fidelity of stream-flow simulation, rain gauge networks in 10 river basins in Vietnam are suggested to be upgraded for a total of 18 rain gauges. The rain gauge network in three river basins in Central of Vietnam Thach Han, Tam Ky and Lai Giang should be given the highest priority, due to their relatively high AMP uncertainty.
The Integrated Flood Analysis Model (IFAS), a distributed hydrological model, developed by the International Center for Water Hazard and Risk Management (ICHARM) was utilized to assess runoff from a flood event using two satellite-based rainfall products: Global Satellite of Mapping Precipitation (GSMaP): Near Real Time (NRT) and Tropical Rainfall Measuring Mission (TRMM):3B42RT V7. The devastating Thailand flood of 2011 in the Upper Nan river basin (13,000 km2) was selected as a case study. The temporal and spatial distribution of the satellite rainfall products were statistically evaluated using volume bias, peak bias, root mean square error (RMSE), correlation coefficients (CCs), and the coefficient of determination (R2). The statistical performance of simulated flood runoff using the GSMaP NRT and 3B42RT rainfall products were also analyzed by the Nash–Sutcliffe efficiency index (NSE), CCs, and the RMSE. This study found that both satellite-based rainfall products demonstrated weak CCs and R2 values at most ground-based rain gauges with respect to daily rainfall intensity. Runoff simulation results from the IFAS model demonstrated better performance from the 3B42RT than the GSMaP NRT product (NSE: 0.79, CCs: 0.90, and RMSE: 18.03 mcm), despite the smaller pixel resolution of 3B42RT.
Summer monsoon brings over 70% of total annual precipitation to Vietnam northern mountainous region. The large amount of rainfall concentrated in a short time resulted in various flood-related disasters in many regions, especially in Cau-Thuong-Luc Nam (CTLN) watershed. Under the global warming, frequency and intensity of flood occurrence in CTLN watershed have been gradually increasing. There is an urgent need to establish the countermeasures for this key economic region based on the deep understanding of the hydro-meteorological characteristics of the watershed. In this study, we investigated the rainfall-runoff and inundation characteristics of the CTLN watershed in connection with the correspondence climate condition of the present (2000-2009) and future (2060-2069). The Rainfall-Runoff and Inundation (RRI) model was used for the simulation of watershed hydrological characteristics. The essential future precipitation inputs for RRI were achieved by using the Weather Research and Forecasting (WRF) model nested inside GFDL-CM3, and MIROC-5 models. Results of this study suggest the severe flood and inundation condition of the CTLN watershed in the mid-21st century. We have found the increasing trend in total rainfall during the rainy season throughout the watershed. Compared to the present climate, both GFDL-CM3 and MIROC-5 models show the significantly stronger flood intensity with extended inundation radius.
Projections of river discharge at the Indochinese Peninsula under climate change were analyzed by comparing the empirical distributions of annual maximum daily river discharge for the present and future climate experiments. The river discharge was simulated by a kinematic-wave flow routing model, 1K-FRM using the runoff generation data stored in the database for Policy Decision Making for Future climate change, d4PDF. To fully utilize the multi-initial and boundary condition datasets, the differences between each couple of the empirical distributions of the future annual maximum river discharge produced by different groups of SST patterns were investigated firstly using the non-parametric two-sample KS-test and AD-tests. The analysis results indicated that the differences in the distributions were significant for much of the study area except parts of the Mekong Delta and southern Indochina Region. Thus, the total number of samples was limited within the same SST pattern, which is equivalent to 900-year period data. Then, the changes of river discharge in the future period for each SST pattern and its statistical significance were assessed using the Mann-Whitney U-test. The outcome demonstrated that despite the various degrees of changes according to locations, the detected changes at the Mekong Delta, southern Indochinese Peninsula and at the mouth of the Red River were statistically meaningful with the 95% confidence level.
A systematic method to project the future distribution of population in megacities is introduced. Two general steps were discussed: (1) estimation of urban sprawl by an urban growth model, SLEUTH; (2) estimation of population distribution by a logistic model with variable empirical coefficients. Predicting the annual change from 2014 to 2050, Jakarta megacity was used as a benchmark urban agglomeration. The key inputs are historical land cover and geographic information, transportation networks, high-spatial resolution population density, and country-level projection of population as defined by various shared socio-economic pathways (SSP). Coefficients were modified in SLEUTH to predict urban sprawling (and auxiliary probability map) compatible with a suitable SSP faced by the encompassing country. Utilizing the predicted annual probability of urbanization and the key inputs into a discrete logistic model with empirical coefficients fitted to minimize the difference of total predicted population with that provided by SSP, population distribution of the target urban agglomeration, Jakarta, was obtained.
This research aims to investigate the growth stage of cumulus cloud before the occurrence of Guerilla-heavy rainfall (GHR) using the rapid scan observation of Himawari-8, which has fine temporal and spatial resolutions. For our goal, we utilized Rapid Development Cumulus Area (RDCA) index for earlier detection of first echo aloft (baby-rain cell). As our research focuses on the development of small-scale cloud, we eliminated parallax problem in Himawari-8 observation. We overlaid 16 cases of brightness temperature (TB; Band 13) of Himawari-8 and composite radar observational data. Based on the distance between cloud and rain cell centers in two images, we retrieved the linear equation to solve the parallax effect. The correctness of relocated cloud was confirmed by comparing the estimated top heights of cloud and precipitation. The displacement vector of TB was applied to solve parallax effect for Band 03 visible channel and RDCA index. In this research, we modified the usage of RDCA index from lightning prediction to the baby-rain cell prediction of GHR. The original RDCA index, ranging from 0.1 to 0.9 was used to detect the early signal in cloud development process to predict the occurrence of baby-rain cell. By analyzing some case studies we confirmed that detailed RDCA index can predict the occurrence of baby-rain cell of GHR.