The unprecedented flood event in the central Chao Phraya basin in 2011 was a catastrophic natural disaster affecting Thai society and economic sectors. During this event massive amounts of overland flow played a major role in damaging properties and environmental systems, with typical drainage systems rather ineffective in low-lying flat lands. The capability to effectively handle flood problem under these conditions is indeed a great challenge. To achieve such a goal, hydrological management schemes need to be improved based on insightful knowledge of flood hydrodynamics. In this study, we aim to gain insight into the aforementioned issue by performing and analyzing simulation of overland flow in low-lying flat lands. The flood plain of Sam-Khok and Klong Luang districts was selected as the study area and the simulation performed with an unstructured grid shallow water model together with a high-fidelity topographic data (LIDAR). The simulation results have been validated with field data (water trace height). The major hydrodynamic mechanisms such as flow patterns, flow magnitudes are characterized and the effective discharge quantified.
Quantitative precipitation estimation and precipitation nowcasting are important components of systems that aim at minimizing or managing flash flooding. This study used the Short Term Ensemble Prediction System (STEPS), one of the most advanced Quantitative Precipitation Forecast (QPF) systems currently available. The Japan Meteorological Agency (JMA) radar rainfall data (1-km resolution) from the Kanto region, Japan, covering various periods, were used in STEPS to generate ensemble nowcasts of rainfall. Hour-long 30-member-ensemble rainfall nowcasts were generated for five separate rainfall events using 5-minute time steps. The ensemble nowcasts were verified using radar rainfall data, and the results showed that the STEPS forecasts are in good agreement with the observed data for forecast periods of <1 hour. To check the performance of the STEPS model output in more detail, it was compared with JMA precipitation nowcast data, and both nowcasting datasets were also compared separately with rain gauge data. The skill scores suggest that STEPS generates more accurate nowcasts, especially for higher-intensity rainfall events. Combining all members of the STEPS nowcasting results appears to improve the reliability of short-term rainfall prediction, and the output of such ensemble nowcasts could be used in hydrological models to generate probabilistic forecasts in the future.
This paper reports the implementation of high-performance computing using the K supercomputer in Kobe, Japan, for large-scale/high-resolution flood simulation. Supercomputer K was developed in 2012 by RIKEN and Fujitsu and ranked first in the list of Top 500 supercomputer sites in 2011 during its development stage. A two-dimensional inundation simulation model developed based on a shallow water equation using an existing numerical scheme was parallelized with the K supercomputer. Osaka and other cities along the Yodo River were chosen as application sites and the area discretized by 12824442 (= 3453 × 3714) nodes with a resolution of 10 m. The computational time for the five-hour flood simulation was measured by changing the number of 8-core CPUs of the K supercomputer. As a result, computational time was decreased to 9.3 min by using 128 × 64 = 8192 8-core CPUs. The computational time was 1423.7 min for one 8-core CPU. Thus, the simulation speed increased by a factor of 153.2 with the use of the K supercomputer.
Parameter calibration is fundamental for the implementation and operation of a hydrological model. Automatic calibration techniques have been widely studied. However, even the most modern optimization schemes cannot always help us to obtain an optimal parameter set due to high dimensionality of the parameter space and complex interactions between parameters. The main purpose of this study was to test our strategy for automatic parameter calibration: lowering the dimensionality. Our modified Xinanjiang model was selected for study. It consists of 15 parameters controlling data adjustment and representing hydrological processes. Morris’ global sensitivity analysis technique was used to get better understanding about the structure of the parameter space. Parameters were found to have significantly different sensitivities at yearly, monthly and daily temporal scales. Also strong interactions between the parameters were observed at all three scales. A multi-step optimization scheme was designed and tested based on these observations. In this scheme, the 15 parameters are divided into three groups and optimized group by group at the time scale they are most sensitive to by using the SCEM-UA algorithm, a global optimization algorithm. The newly developed scheme is shown to be very efficient and robust.
This paper compared future runoff projections using a Budyko type equation with respect to projections by a global hydrological model (GHM). The comparison was made for the annual mean runoff projections for a future period (2060–2100) after the Budyko parameter was set based on hydrologic model outputs at a present period (1960–2000). The objective of this study was to investigate the performance of the Budyko equation with respect to the hydrologic model at different climate regions. To address the question this study used the spatial average of runoff for the 35 largest basins in the world. According to the comparison, the projections by the two approaches agreed well (R2 = 0.983), in particular in humid tropic region (R2 = 0.986), but with consistent underestimation of future runoff (Median Error, ME = –0.042) by the Budyko equation. In subarctic region the performance of the Budyko equation was low (R2 = 0.599) due to the overestimation of future runoff (ME = 0.110). The results in the dry and temperate regions also showed some discrepancy (R2 = 0.931 and 0.724) without apparent patterns in the errors. The paper discussed possible reasons for the errors with respect to water and energy seasonality and changes in storage component contributions.
Several studies have shown the change of future river flow projection in the Chao Phraya River basin; however, these researches focused on the natural river flow. In this study, to obtain a realistic river flow projection for the Chao Phraya River basin, bias corrected GCM outputs were given to a regional distributed hydrological model including dam operation and flood inundation components. The projected river flow data was analyzed to assess the change of drought and flood risk. The GCM outputs used were precipitation and evapotranspiration projected by MRI-AGCM3.2S, which is a 20 km spatial resolution general circulation model developed by the Meteorological Research Institute, Japan Meteorological Agency. The results obtained from the projected river flow at the Nakhon Sawan station are as follows: 1) mean monthly discharge tends to increase in both the near-future and far-future projection periods for all months; 2) low-flow exceeding 99% of a mean daily flow duration curve for the near-future and far-future periods tends to decrease; and 3) a flood frequency analysis using the annual maximum daily flow series indicates that the flood risk in the near-future and far-future projection periods becomes higher.
This study aimed at conducting both qualitative and quantitative analyses of the Central Asia South Asia Electricity Transmission and Trade Project (CASA-1000) to obtain risk management information. Consequently, real options were introduced in the form of risk hedges, and we attempted to estimate the hedge cost. Through qualitative analysis we identified the geopolitical risks. We also see that a certain degree of demand risk exists. Therefore, when evaluating project feasibility, it would be better to consider the hedge cost of these risks. Through quantitative analysis, when weighted average cost of capital is ≤10.0% and volatility is below a certain value, a hedge risk based on real options and the introduction of private funds may be possible. Thus, from a risk management viewpoint, it could be suggested that introducing private funds is possible in certain situations.
Flood inundation maps were generated in the Bago River Basin, Myanmar. Although the design of our study was not new, it is one of very few to have analyzed a flood inundation area in Myanmar. Nine flood events were applied to calibrate and validate the results. The flood-inundated area was validated with satellite image for the year 2006. The flood inundation maps with different return periods were delineated. Considering the 50- and 100-year return period flood scenario, the highest depth of inundation may affect the urban area of Bago. The information derived from this study can contribute to assessments of potential flood damage for the local region and for other locations where data is limited.
Natural disasters can have a damaging effect on human society. To understand the magnitude of risk of a natural disaster at the macro scale, basic socioeconomic parameters such as population or gross domestic product (GDP) are often used as proxies to evaluate value of specific asset classes (e.g., urban assets, agricultural land, etc.). However, such information is not always available and it becomes a challenge to perform cost-benefit analysis of tailored strategies to protect an asset class from natural disaster risk. Recent studies showed the prospects of relating GDP and population to produced capital representing urban assets. However, the methods used in earlier studies are unclear and resulted in different outcomes that need further clarification and generalization. This study aims to demonstrate the potential of developing a more generalized method to characterize the relation between produced capital and basic socioeconomic parameters at the global scale. We include purchasing power parity (PPP) into the country GDP and produced capital data, respectively. We develop a more generalized method that incorporates the uncertainty range to quantify the produced capital. This is an improvement from previous studies. The new approach might be useful for macro scale risk assessment within the context of climate change.
We developed an improved method to detect rice cultivated areas in semi-arid regions by combining the usage of Landsat imagery for detecting rice fields and MODIS for spatial and temporal upscaling of rice cultivation areas. We selected Haryana State in northwestern India as a case study area, where average farm plot size is small (~4,000 m2) and cultivated areas largely fluctuate from year to year due to water availability. Firstly, rice cultivated areas were detected by unsupervised classification of Landsat images of a specific year. Secondly, two conditional parameters for time-series EVI and LSWI were optimized by Powell’s method to best match the rice cultivated areas detected by MODIS to the areas detected by Landsat. Thirdly, calculated rice cultivated areas were compared with statistical data. Until results showed reasonable agreement, rice cultivated areas were reclassified in Landsat images and the following procedures were repeated. A good coefficient of determination (R2 = 0.82) was obtained between the estimated rice area and the statistical data at the district (sub-state) level in the study area. This demonstrates the potential and increased accuracy of the developed methodology to detect and map harvested rice areas in water scarce arid and semi-arid regions.
Penman-type evapotranspiration (ET) methods are widely used in irrigation water management and water resource planning. To determine the daily reference ET using Penman-type methods, net longwave radiation must be estimated through an empirical net longwave radiation equation based on meteorological data. This paper presents the coefficients of the net longwave radiation equation calibrated using highly accurate data observed at the Tateno observatory, which belongs to the Baseline Surface Radiation Network (BSRN). The daily net longwave radiation calculated with these calibrated coefficients is in better agreement with the observed data as compared with those of the original Penman–Monteith equation in the Food and Agriculture Organization (FAO) Irrigation and Drainage Paper no. 56 (FAO56-PM). In particular, the FAO56-PM cloudiness factor coefficients are more effectively calibrated in this study. The annual mean reference ET calculated through the calibrated FAO56-PM corresponds to approximately 90% of the values calculated using the original coefficients.
Precipitation isotope maps over mountains are critical for water resource assessment, yet isoscape modeling has been minimally investigated for complex terrain with high relief. Here we show that multiannual (2011–2015) mean precipitation isoscapes across the Japanese Alps region can be represented by simple multiple-regression models with strong goodness of fit (R2 = 0.928 for δ2H and 0.944 for δ18O). Reliability of the models was further confirmed by agreement with previously reported independent data throughout a wider range of elevation (7–3,730 m asl). Modeled precipitation isoscapes were consistent with observations of soil water (20 sites) and river water (50 sites) when considering evaporative enrichment and unclosed water balance. Uncertainties of modeled δ values for precipitation were greater in lowlands near the coast than over the mountains. Unexpectedly, spatially uniform isotopic lapse rates (–11.66‰ km–1 for δ2H and –1.724‰ km–1 for δ18O), which are divorced from continental effects, gave substantially good approximation on a regional (e.g., a few hundred kilometers) scale. This allows for convenient and reliable modeling of precipitation isoscapes over mountainous regions for hydrological/ecological/interdisciplinary applications.
A newly developed web application, Climates of Global Lake Basins (CGLB), combines existing datasets and interactively displays geographical, hydrological, and climatological information for hundreds of lakes around the world. CGLB also provides photographs containing vegetation information as well as quasi-real time monitoring of lake water levels. CGLB can interactively create and animate time series of climatological data in a one-dimensional or two-dimensional (geographical) form. These functions are useful for education, expedition planning, and scientific research. As an example of the application’s use, links between water levels in Kenya’s Lake Turkana and sea surface temperature (SST) and regional precipitation were tested by time-lag correlation analysis. Precipitation showed no significant correlation with the lake water level for time lags of 0, 1, and 2 months. This suggests that variation in land-surface hydrological processes are key to the interannual variability in water levels in the lake. In contrast, SST in the central tropical Pacific was strongly correlated with the lake water level for all time lags. No significant correlation was found between the lake water level and SST in the Indian Ocean adjacent to Kenya, although significant correlations were found between regional precipitation and SST in the Indian Ocean.