Ensemble rainfall forecasting with numerical weather prediction and radar‐based nowcasting models

The overall objective of this study is to improve the forecasting accuracy of the precipitation in the Singapore region by means of both rainfall forecasting and nowcasting. Numerical Weather Predication (NWP) and radar‐based rainfall nowcasting are two important sources for quantitative precipitation forecast. In this paper, an attempt to combine rainfall prediction from a high‐resolution mesoscale weather model and a radar‐based rainfall model was performed. Two rainfall forecasting methods were selected and examined: (i) the weather research and forecasting model (WRF); and (ii) a translation model (TM). The WRF model, at a high spatial resolution, was run over the domain of interest using the Global Forecast System data as initializing fields. Some heavy rainfall events were selected from data record and used to test the forecast capability of WRF and TM. Results obtained from TM and WRF were then combined together to form an ensemble rainfall forecasting model, by assigning weights of 0.7 and 0.3 weights to TM and WRF, respectively. This paper presented results from WRF and TM, and the resulting ensemble rainfall forecasting; comparisons with station data were conducted as well. It was shown that results from WRF are very useful as advisory of anticipated heavy rainfall events, whereas those from TM, which used information of rain cells already appearing on the radar screen, were more accurate for rainfall nowcasting as expected. The ensemble rainfall forecasting compares reasonably well with the station observation data. Copyright © 2012 John Wiley & Sons, Ltd.     

Regional frequency analysis of extreme rainfall events in Jakarta

Natural Hazards - Jakarta is vulnerable to flooding and extreme rainfall events are always the main cause of the occurrence of heavy flood events with loss of life and property. The flood in...     

Are satellite products good proxies for gauge precipitation over Singapore?

The uncertainties in two high-resolution satellite precipitation products (TRMM 3B42 v7.0 and GSMaP v5.222) were investigated by comparing them against rain gauge observations over Singapore on sub-daily scales. The satellite-borne precipitation products are assessed in terms of seasonal, monthly and daily variations, the diurnal cycle, and extreme precipitation over a 10-year period (2000–2010). Results indicate that the uncertainties in extreme precipitation is higher in GSMaP than in TRMM, possibly due to the issues such as satellite merging algorithm, the finer spatio-temporal scale of high intensity precipitation, and the swath time of satellite. Such discrepancies between satellite-borne and gauge-based precipitations at sub-daily scale can possibly lead to distorting analysis of precipitation characteristics and/or application model results. Overall, both satellite products are unable to capture the observed extremes and provide a good agreement with observations only at coarse time scales. Also, the satellite products agree well on the late afternoon maximum and heavier rainfall of gauge-based data in winter season when the Intertropical Convergence Zone (ITCZ) is located over Singapore. However, they do not reproduce the gauge-observed diurnal cycle in summer. The disagreement in summer could be attributed to the dominant satellite overpass time (about 14:00 SGT) later than the diurnal peak time (about 09:00 SGT) of gauge precipitation. From the analyses of extreme precipitation indices, it is inferred that both satellite datasets tend to overestimate the light rain and frequency but underestimate high intensity precipitation and the length of dry spells. This study on quantification of their uncertainty is useful in many aspects especially that these satellite products stand scrutiny over places where there are no good ground data to be compared against. This has serious implications on climate studies as in model evaluations and in particular, climate model simulated future projections, when information on precipitation extremes need to be reliable as they are highly crucial for adaptation and mitigation.     

Possible climate change/variability and human impacts,vulnerability of drought-prone regions,water resources and capacity building for Africa

ABSTRACT

This review article discusses the climate, water resources and historical droughts of Africa, drought indices, vulnerability, impact of global warming and land use for drought-prone regions in West, southern and the Greater Horn of Africa, which have suffered recurrent severe droughts in the past. Recent studies detected warming and drying trends in Africa since the mid 20th century. Based on the Fourth Assessment Report of the Intergovernmental Panel on Climate Change and the Coupled Model Intercomparison Project Phase 5 (CMIP5), both northern and southern Africa are projected to experience drying, such as decreasing precipitation, runoff and soil moisture in the 21st century and could become more vulnerable to the impact of droughts. The daily maximum temperature is projected to increase by up to 8°C (RCP8.5 of CMIP5), precipitation indices such as total wet day precipitation (PRCPTOT) and heavy precipitation days (R10 mm) could decrease, while warm spell duration (WSDI) and consecutive dry days (CDD) could increase. Uncertainties of the above long-term projections, teleconnections to climate anomalies such as ENSO and the Madden-Julian Oscillation, which could also affect the water resources of Africa, and capacity building in terms of physical infrastructure and non-structural solutions are also discussed. Given that traditional climate and hydrological data observed in Africa are generally limited, satellite data should also be exploited to fill the data gap for Africa in the future.

Editor D. Koutsoyiannis; Associate editor N. Ilich     

Hydro-meteorological drought assessment under climate change impact over the Vu Gia–Thu Bon river basin,Vietnam

Hydro-meteorological drought was assessed with respect to climate change over an estuary catchment Vu Gia-Thu Bon in Central Vietnam, which economy is dependent on agriculture. The fully-distributed hydrological model MIKE SHE was used to simulate river flow over the study region for the period 1991–2010. Drought were assessed using the Standardized Precipitation Index and the Standardized Runoff Index. The future climate was studied using the regional climate model Weather Research and Forecasting by downscaling an ensemble of three global climate models – CCSM3.0, ECHAM5 and MIROC-medium resolution over current (1961–1990) and future climates (2011–2040), under the A2 emissions scenario. The results suggest that, despite hotter and wetter future climate, the area is likely to suffer more from severe and extreme droughts, increasing about 100% in the median range for drought characteristics. Thus, there is a need for proper adaptation and planning for water resources management in this region.     

How to construct future IDF curves,under changing climate,for sites with scarce rainfall records?

Optimal designs of stormwater systems rely very much on the rainfall Intensity–Duration–Frequency (IDF) curves. As climate has shown significant changes in rainfall characteristics in many regions, the adequacy of the existing IDF curves is called for particularly when the rainfall are much more intense. For data sparse sites/regions, developing IDF curves for the future climate is even challenging. The current practice for such regions is, for example, to ‘borrow’ or ‘interpolate’ data from regions of climatologically similar characteristics. A novel (3‐step) Downscaling‐Comparison‐Derivation (DCD) approach was presented in the earlier study to derive IDF curves for present climate using the extracted Dynamically Downscaled data an ungauged site, Darmaga Station in Java Island, Indonesia and the approach works extremely well. In this study, a well validated (3‐step) DCD approach was applied to develop present‐day IDF curves at stations with short or no rainfall record. This paper presents a new approach in which data are extracted from a high spatial resolution Regional Climate Model (RCM; 30 × 30 km over the study domain) driven by Reanalysis data. A site in Java, Indonesia, is selected to demonstrate the application of this approach. Extremes from projected rainfall (6‐hourly results; ERA40 Reanalysis) are first used to derive IDF curves for three sites (meteorological stations) where IDF curves exist; biases observed resulting from these sites are captured and serve as very useful information in the derivation of present‐day IDF curves for sites with short or no rainfall record. The final product of the present‐day climate‐derived IDF curves fall within a specific range, +38% to +45%. This range allows designers to decide on a value within the lower and upper bounds, normally subjected to engineering, economic, social and environmental concerns. Deriving future IDF curves for Stations with existing IDF curves and ungauged sites with simulation data from RCM driven by global climate model (GCM ECHAM5) (6‐hourly results; A2 emission scenario) have also been presented. The proposed approach can be extended to other emission scenarios so that a bandwidth of uncertainties can be assessed to create appropriate and effective adaptation strategies/measures to address climate change and its impacts. Copyright © 2013 John Wiley & Sons, Ltd.     

Summary of IAEA-MEL's investigation of Kara Sea radioactivity and radiological assessment

IAEA-MEL participated in five expeditions to the Kara Sea with the aim of assessing the radiological consequences of dumped radioactive wastes in the Novaya Zemlya Bays and Trough. The programme included sampling, in-situ underwater investigations, laboratory analyses of water, sediment and biota samples, the development of a marine radioactivity database, modelling and radiological assessment, the organization of intercomparison exercises and the evaluation of distribution coefficients. Radiometric investigations have shown that no radiologically significant environmental contamination has occurred. Leakages which have led to locally increased levels of radionuclides in sediment have only been observed in Stepovoy and Abrosimov Bays. Computer modelling results suggest that only radiological effects on local and regional scales may be of importance. The global radiological impact of the disposals in the Arctic Seas will be negligible.     

Strain localization analysis by elasto-viscoplastic softening model for frozen sand

The aim of the present paper is to numerically analyse the behaviour of frozen sand by using a viscoplastic constitutive model with strain softening. A constitutive model has been developed introducing the stress history tensor which is a functional of the stress history, with respect to a generalized time measure. It is shown that Adachi and Oka's model is applicable to the results of triaxial tests on a frozen Toyoura sand at different strain rates. First, the instability of the model is discussed within the framework of bifurcation theory. The model is then implemented into a FEM code to numerically simulate the behaviour under plane strain conditions. From the numerical results, it is revealed that the formation of shear bands is possible and the characteristics of strain localization, such as shear banding, depend on the strain rates.     

An ANN application for water quality forecasting

Rapid urban and coastal developments often witness deterioration of regional seawater quality. As part of the management process, it is important to assess the baseline characteristics of the marine environment so that sustainable development can be pursued. In this study, artificial neural networks (ANNs) were used to predict and forecast quantitative characteristics of water bodies. The true power and advantage of this method lie in its ability to (1) represent both linear and non-linear relationships and (2) learn these relationships directly from the data being modeled. The study focuses on Singapore coastal waters. The ANN model is built for quick assessment and forecasting of selected water quality variables at any location in the domain of interest. Respective variables measured at other locations serve as the input parameters. The variables of interest are salinity, temperature, dissolved oxygen, and chlorophyll-a. A time lag up to 2Deltat appeared to suffice to yield good simulation results. To validate the performance of the trained ANN, it was applied to an unseen data set from a station in the region. The results show the ANN's great potential to simulate water quality variables. Simulation accuracy, measured in the Nash-Sutcliffe coefficient of efficiency (R(2)), ranged from 0.8 to 0.9 for the training and overfitting test data. Thus, a trained ANN model may potentially provide simulated values for desired locations at which measured data are unavailable yet required for water quality models.     

Assessment of future stream flow over the Sesan catchment of the Lower Mekong Basin in Vietnam

This study used a regional climate model, driven at a resolution of 30 km, to derive climate estimates that were used as input to a hydrological model to determine stream flow in a changing climate. This regional climate model output was derived using the Weather Research and Forecasting model, which was used to downscale the general circulation model ECHAM5 T63 under the A2 greenhouse gas emission scenario for the future. Two river basins, Dakbla and Poko, over the Sesan catchment of the Lower Mekong region were considered for runoff modeling. A 10‐year climatology of the recent past, 1991–2000, was used as the baseline for the present‐day climate, and another 10‐year climate over the period 2091–2100 was chosen for the future time slice. The results from the simulation of future stream flow indicate that, over both Dakbla and Poko river basins, the stream flow is likely to increase, especially during the peak rainfall season. The Dakbla River Basin shows a substantial increase in stream flow when compared with the Poko River Basin. Copyright © 2011 John Wiley & Sons, Ltd.