Deriving intensity–duration–frequency (IDF) curves using downscaled in situ rainfall assimilated with remote sensing data

The rainfall intensity–duration–frequency (IDF) curves play an important role in water resources engineering and management. The applications of IDF curves range from assessing rainfall events, classifying climatic regimes, to deriving design storms and assisting in designing urban drainage systems, etc. The deriving procedure of IDF curves, however, requires long-term historical rainfall observations, whereas lack of fine-timescale rainfall records (e.g. sub-daily) often results in less reliable IDF curves. This paper presents the utilization of remote sensing sub-daily rainfall, i.e. Global Satellite Mapping of Precipitation (GSMaP), integrated with the Bartlett-Lewis rectangular pulses (BLRP) model, to disaggregate the daily in situ rainfall, which is then further used to derive more reliable IDF curves. Application of the proposed method in Singapore indicates that the disaggregated hourly rainfall, preserving both the hourly and daily statistic characteristics, produces IDF curves with significantly improved accuracy; on average over 70% of RMSE is reduced as compared to the IDF curves derived from daily rainfall observations. © 2019, The Author(s)

NDVI With Artificial Neural Networks For SRTM Elevation Model Improvement – Hydrological Model Application

Digital elevation model (DEM) plays a substantial role in hydrological study, from understanding the catchment characteristics, setting up a hydrological model to mapping the flood risk in the region. Depending on the nature of study and its objectives, high resolution and reliable DEM is often desired to set up a sound hydrological model. However, such source of good DEM is not always available and it is generally high-priced. Obtained through radar based remote sensing, Shuttle Radar Topography Mission (SRTM) is a publicly available DEM with resolution of 92m outside US. It is a great source of DEM where no surveyed DEM is available. However, apart from the coarse resolution, SRTM suffers from inaccuracy especially on area with dense vegetation coverage due to the limitation of radar signals not penetrating through canopy. This will lead to the improper setup of the model as well as the erroneous mapping of flood risk. This paper attempts on improving SRTM dataset, using Normalised Difference Vegetation Index (NDVI), derived from Visible Red and Near Infra-Red band obtained from Landsat with resolution of 30m, and Artificial Neural Networks (ANN). The assessment of the improvement and the applicability of this method in hydrology would be highlighted and discussed

Would Bangkok Be More Vulnerable To The Anticipated Changing Climate?

The severe flooding in Thailand in 2011 was triggered by the tropical storm Nock-ten at end of July along the Mekong and Chao Phraya river basin. There are 4 additional storms that caused medium to heavy rainfall from June to October in the north and north-east of Thailand. Due to limited capacity of the Chao Phraya river and also Pasak river, several overbank flows occurred and also dikes along the river were broken causing excessive flow to many communities beside the river and downstream. The consequence was a total of 815 deaths with 13.6 million people affected and over 20,000 km2 farmland devastated, the inundation remained until mid-January 2012. Total estimated cost of economic lost was about 45.7 billion US$ with respect to manufacturing industry as seven major industrial estate in the northern provinces of Bangkok were submerged 2-3 m during high flood. This caused interruption to supply chain to car parts regionally and world-wide, e.g. electronic components and hard disk drives. Will Bangkok experience more intense rainfall under the changing climate? The Artificial Neural Network (ANN) technique is adopted in this study to shed some lights on this issue. ANN is an established technique with a flexible mathematical structure that is capable of identifying complex non-linear relationships between input and output data. The present study utilizes ANN to statistically downscale global climate models (GCMs) at some meteorology stations in Bangkok. The study illustrates the applications of the feed forward back propagation using large-scale predictor variables derived from the ERA-Interim reanalysis data, meteorological station data (predictand), present and future GCM data of certain emission scenarios. The findings will certainly be useful to the policy makers in pondering, e.g. whether the current drainage network system is sufficient to meet the changing climate, and a range of flood adaptation and mitigation measures

Combining Satellite And Gauge Precipitation Data With Co-Kriging Method For Jakarta Region

Jakarta is vulnerable to flooding mainly caused by prolonged and heavy rainfall and thus a robust hydrological modeling is called for. A good quality of spatial precipitation data is therefore desired so that a good hydrological model could be achieved. Two types of rainfall sources are available: satellite and gauge station observations. At-site rainfall is considered to be a reliable and accurate source of rainfall. However, the limited number of stations makes the spatial interpolation not very much appealing. On the other hand, the gridded rainfall nowadays has high spatial resolution and improved accuracy, but still, relatively less accurate than its counterpart. To achieve a better precipitation data set, the study proposes cokriging method, a blending algorithm, to yield the blended satellite-gauge gridded rainfall at approximately 10-km resolution. The Global Satellite Mapping of Precipitation (GSMaP, 0.1⁰×0.1⁰) and daily rainfall observations from gauge stations are used. The blended product is compared with satellite data by cross-validation method. The newly-yield blended product is then utilized to re-calibrate the hydrological model. Several scenarios are simulated by the hydrological models calibrated by gauge observations alone and blended product. The performance of two calibrated hydrological models is then assessed and compared based on simulated and observed runoff

Calibration strategy for urban catchment parameters

Journal of Hydraulic Engineering118111550-1570JHEN

Estimation of peak flow and runoff volume with response surface method

Journal of Water Resources Planning & Management - ASCE1202161-17

Intelligent decision support for water resources management

The hydrological basis for water resources management. Proc. symposium, Beijing, 1990305-31

IRRIGATION WATER REQUIREMENT MODEL.

13. 23-13. 3