Srivatsan V. Raghavan

Statistical downscaling rainfall using artificial neural network: significantly wetter Bangkok?

Artificial neural network (ANN) is an established technique with a flexible mathematical structure that is capable of identifying complex nonlinear relationships between input and output data. The present study utilizes ANN as a method of statistically downscaling global climate models (GCMs) during the rainy season at meteorological site locations in Bangkok, Thailand. The study illustrates the applications of the feed forward back propagation using large-scale predictor variables derived from both the ERA-Interim reanalyses data and present day/future GCM data. The predictors are first selected over different grid boxes surrounding Bangkok region and then screened by using principal component analysis (PCA) to filter the best correlated predictors for ANN training. The reanalyses downscaled results of the present day climate show good agreement against station precipitation with a correlation coefficient of 0.8 and a Nash-Sutcliffe efficiency of 0.65. The final downscaled results for four GCMs show an increasing trend of precipitation for rainy season over Bangkok by the end of the twenty-first century. The extreme values of precipitation determined using statistical indices show strong increases of wetness. These findings will be useful for policy makers in pondering adaptation measures due to flooding such as whether the current drainage network system is sufficient to meet the changing climate and to plan for a range of related adaptation/mitigation measures.

Development of Intensity-Duration-Frequency curves at ungauged sites: risk management under changing climate

The impact of a changing climate is already being felt on several hydrological systems both on a regional and sub-regional scale of the globe. Southeast Asia is one of the regions strongly affected by climate change. With climate change, one of the anticipated impacts is an increase in the intensity and frequency of extreme rainfall which further increase the region’s flood catastrophes, human casualties and economic loss. Optimal mitigation measures can be undertaken only when stormwater systems are designed using rainfall Intensity-Duration-Frequency (IDF) curves derived from a long and good quality rainfall data.Developing IDF curves for the future climate can be even more challenging especially for ungauged sites. The current practice to derive current climate’s IDF curves for ungauged sites is, for example, to ‘borrow’ or ‘interpolate’ data from regions of climatologically similar characteristics. Recent measures to derive IDF curves for present climate was performed by extracting rainfall data from a high spatial resolution Regional Climate Model driven by ERA-40 reanalysis dataset. This approach has been demonstrated on an ungauged site (Java, Indonesia) and the results were quite promising.In this paper, the authors extend the application of the approach to other ungauged sites particularly in Peninsular Malaysia. The results of the study undoubtedly have significance contribution in terms of local and regional hydrology (Malaysia and Southeast Asian countries). The anticipated impacts of climate change especially increase in rainfall intensity and its frequency appreciates the derivation of future IDF curves in this study. It also provides policy makers better information on the adequacy of storm drainage design, for the current climate at the ungauged sites, and the adequacy of the existing storm drainage to cope with the impacts of climate change.

Assessment of CMIP5 historical simulations of rainfall over Southeast Asia

We present preliminary analyses of the historical (1986–2005) climate simulations of a ten-member subset of the Coupled Model Inter-comparison Project Phase 5 (CMIP5) global climate models over Southeast Asia. The objective of this study was to evaluate the general circulation models’ performance in simulating the mean state of climate over this less-studied climate vulnerable region, with a focus on precipitation. Results indicate that most of the models are unable to reproduce the observed state of climate over Southeast Asia. Though the multi-model ensemble mean is a better representation of the observations, the uncertainties in the individual models are far high. There is no particular model that performed well in simulating the historical climate of Southeast Asia. There seems to be no significant influence of the spatial resolutions of the models on the quality of simulation, despite the view that higher resolution models fare better. The study results emphasize on careful consideration of models for impact studies and the need to improve the next generation of models in their ability to simulate regional climates better.

Investigating the relationship between Aerosol Optical Depth and Precipitation over Southeast Asia with Relative Humidity as an influencing factor

Atmospheric aerosols influence precipitation by changing the earth’s energy budget and cloud properties. A number of studies have reported correlations between aerosol properties and precipitation data. Despite previous research, it is still hard to quantify the overall effects that aerosols have on precipitation as multiple influencing factors such as relative humidity (RH) can distort the observed relationship between aerosols and precipitation. Thus, in this study, both satellite-retrieved and reanalysis data were used to investigate the relationship between aerosols and precipitation in the Southeast Asia region from 2001 to 2015, with RH considered as a possible influencing factor. Different analyses in the study indicate that a positive correlation was present between Aerosol Optical Depth (AOD) and precipitation over northern Southeast Asia region during the autumn and the winter seasons, while a negative correlation was identified over the Maritime Continent during the autumn season. Subsequently, a partial correlation analysis revealed that while RH influences the long-term negative correlations between AOD and precipitation, it did not significantly affect the positive correlations seen in the winter season. The result of this study provides additional evidence with respect to the critical role of RH as an influencing factor in AOD-precipitation relationship over Southeast Asia.

Climate Change and Its Impacts on Streamflow: WRF and SCE-Optimized SWAT Models

It has been noted that global warming is likely to increase both the frequency and severity of weather events such as heat waves and heavy rainfall. These could lead to large scale effects such as melting of large ice sheets with major impacts on low-lying regions throughout the world (Intergovernmental Panel on Climate Change, IPCC 2007a). Since these projected climate changes will impact water resources, agriculture, bio-diversity and health, one of the key challenges of climate research is the application of climate models to quantify both future climate change and its impacts on the physical and biological environment. One of the widely studied impacts is on hydrology, right from large scale river basins, river deltas through to small scale urban reservoirs. In this context, this chapter discusses some hydrological impact studies and presents results of a study done over the Sesan catchment in Lower Mekong Basin (in Southeast Asia). Sensitivity analysis and an optimization calibration scheme, SCE-UA algorithm, are applied to the SWAT model.

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.

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

ABSTRACT 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.

Publisher Correction: Investigating the relationship between Aerosol Optical Depth and Precipitation over Southeast Asia with Relative Humidity as an influencing factor

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