ALBERT

Description: Pacific Islanders have been exposed to risks associated with climate change. Samoa, as one of the Pacific Islands, is prone to climatic hazards that will likely increase in the coming decades, affecting coastal communities and infrastructure around the islands. Climate models do not predict a reduction of such disaster events in the future in Samoa; indeed, most predict an increase. This paper identifies key infrastructure and their functions and status in order to provide an overall picture of relative vulnerability to climate-related stresses of such infrastructure on the island. By reviewing existing reports as well as holding a series of consultation meetings, a list of critical infrastructure was developed and shared with stakeholders for their consideration. An indicator-based vulnerability model (SIVM) was developed in collaboration with stakeholders to assess the vulnerability of selected infrastructure systems on the Samoan Islands. Damage costs were extracted from the Cyclone Evan recovery needs document. Additionally, data on criticality and capacity to repair damage were collected from stakeholders. Having stakeholder perspectives on these two issues was important because (a) criticality of a given infrastructure could be viewed differently among different stakeholders, and (b) stakeholders were the best available source (in this study) to estimate the capacity to repair non-physical damage to such infrastructure. Analysis of the results suggested a ranking of sectors from the most vulnerable to least vulnerable are: the transportation sector, the power sector, the water supply sector and the sewerage system.

Description: The present study develops a method called window correlation matching method (WCMM) to reduce collocation and timing errors in matching pairs of radar measured reflectivity, Ze, and gauge measured rainfall intensity, R, for improving the accuracy of the estimation of Ze−R relationships. This method was compared with the traditional matching method (TMM), the probability matching method (PMM) and the window probability matching method (WPMM). The calibrated relationship Ze=18.05 R1.45 obtained from 7×7 km of space window and both present and 5 min previous time of radar observation for time window (S77T5) produces the best results for radar rainfall estimates for orographic rain over the Mae Chaem Watershed in the north of Thailand. The comparison shows that the Ze−R relationship obtained from WCMM provide more accuracy in radar rainfall estimates as compared with the other three methods. The Ze−R relationships estimated using TMM and PMM provide large overestimation and underestimation, respectively, of mean areal rainfall whereas WPMM slightly underestimated the mean areal rainfall. Based on the overall results, it can be concluded that WCMM can reduce collocation and timing errors in Ze−R pairs matching and improve the estimation of Ze−R relationships for radar rainfall. WCMM is therefore a promising method for improved radar-measured rainfall, which is an important input for hydrological and environmental modeling and water resources management.

Description: This paper evaluates the impact of climate change on sediment yield in the Nam Ou basin located in northern Laos. Future climate (temperature and precipitation) from four general circulation models (GCMs) that are found to perform well in the Mekong region and a regional circulation model (PRECIS) are downscaled using a delta change approach. The Soil and Water Assessment Tool (SWAT) is used to assess future changes in sediment flux attributable to climate change. Results indicate up to 3.0 °C shift in seasonal temperature and 27% (decrease) to 41% (increase) in seasonal precipitation. The largest increase in temperature is observed in the dry season while the largest change in precipitation is observed in the wet season. In general, temperature shows increasing trends but changes in precipitation are not unidirectional and vary depending on the greenhouse gas emission scenarios (GHGES), climate models, prediction period and season. The simulation results show that the changes in annual stream discharges are likely to range from a 17% decrease to 66% increase in the future, which will lead to predicted changes in annual sediment yield ranging from a 27% decrease to about 160% increase. Changes in intra-annual (monthly) discharge as well as sediment yield are even greater (−62 to 105% in discharge and −88 to 243% in sediment yield). A higher discharge and sediment flux are expected during the wet seasons, although the highest relative changes are observed during the dry months. The results indicate high uncertainties in the direction and magnitude of changes of discharge as well as sediment yields due to climate change. As the projected climate change impact on sediment varies remarkably between the different climate models, the uncertainty should be taken into account in both sediment management and climate change adaptation.

Description: Global Climate Models (GCMs) precipitation scenarios are often characterized by biases and coarse resolution that limit their direct application for basin level hydrological modeling. Bias-correction and spatial disaggregation methods are employed to improve the quality of ECHAM4/OPYC SRES A2 and B2 precipitation for the Ping River Basin in Thailand. Bias-correction method, based on gamma-gamma transformation, is applied to improve the frequency and amount of raw GCM precipitation at the grid nodes. Spatial disaggregation model parameters (β,σ2), based on multiplicative random cascade theory, are estimated using Mandelbrot-Kahane-Peyriere (MKP) function at q=1 for each month. Bias-correction method exhibits ability of reducing biases from the frequency and amount when compared with the computed frequency and amount at grid nodes based on spatially interpolated observed rainfall data. Spatial disaggregation model satisfactorily reproduces the observed trend and variation of average rainfall amount except during heavy rainfall events with certain degree of spatial and temporal variations. Finally, the hydrologic model, HEC-HMS, is applied to simulate the observed runoff for upper Ping River Basin based on the modified GCM precipitation scenarios and the raw GCM precipitation. Precipitation scenario developed with bias-correction and disaggregation provides an improved reproduction of basin level runoff observations.

Description: This paper presents a new approach using an Artificial Neural Network technique to improve rainfall forecast performance. A real world case study was set up in Bangkok; 4 years of hourly data from 75 rain gauge stations in the area were used to develop the ANN model. The developed ANN model is being applied for real time rainfall forecasting and flood management in Bangkok, Thailand. Aimed at providing forecasts in a near real time schedule, different network types were tested with different kinds of input information. Preliminary tests showed that a generalized feedforward ANN model using hyperbolic tangent transfer function achieved the best generalization of rainfall. Especially, the use of a combination of meteorological parameters (relative humidity, air pressure, wet bulb temperature and cloudiness), the rainfall at the point of forecasting and rainfall at the surrounding stations, as an input data, advanced ANN model to apply with continuous data containing rainy and non-rainy period, allowed model to issue forecast at any moment. Additionally, forecasts by ANN model were compared to the convenient approach namely simple persistent method. Results show that ANN forecasts have superiority over the ones obtained by the persistent model. Rainfall forecasts for Bangkok from 1 to 3 h ahead were highly satisfactory. Sensitivity analysis indicated that the most important input parameter besides rainfall itself is the wet bulb temperature in forecasting rainfall.

Description: The local hydroclimates get impacts from the large-scale atmospheric variables via atmospheric circulation. The developing of their relationships could enhance the understanding of hydroclimate variability. This study focuses on the Upper Chao Phraya River Basin in Thailand in which rainfall is influenced by the Indian Ocean and tropical Pacific Ocean atmospheric circulation. The Southwest monsoon from the Indian Ocean to Thailand is strengthened by the temperature gradient between land and ocean. Thus, the anomalous sea surface temperature (SST) is systematically correlated with the monthly rainfall and identified as the best predictor based on the significant relationships revealed by cross-correlation analysis. It is found that rainfall, especially during the monsoon season in the different zones of study basin, corresponds to the different SST indices. This suggests that the region over the ocean which develops the temperature gradient plays a role in strengthening the monsoon. The enhanced gradient with the SST over the South China Sea is related to rainfall in High Rainfall Zone (HRZ); however, the anomalous SST over the Indian Ocean and the equatorial Pacific Ocean are associated with rainfall in Normal and Low Rainfall Zone (NRZ and LRZ) in the study area. Moreover, the identified predictors are related to the rainfall with lead periods of 1–4 months for the pre-monsoon rainfall and 6–12 months for the monsoon and dry season rainfall. The study results are very useful in developing rainfall forecasting models and consequently in the management of water resources and extreme events.

Description: This paper evaluates the impact of climate change on sediment yield in the Nam Ou Basin located in Northern Laos. The Soil and Water Assessment Tool (SWAT) is used to assess future changes in sediment flux attributable to climate change. Future precipitation and temperature series are constructed through a delta change approach. As per the results, in general, temperature as well as precipitation show increasing trends in both scenarios, A2 and B2. However, monthly precipitation shows both increasing and decreasing trends. The simulation results exhibit that the wet and dry seasonal and annual stream discharges are likely to increase (by up to 15, 17 and 14% under scenario A2; and 11, 5 and 10% under scenario B2 respectively) in the future, which will lead to increased wet and dry seasonal and annual sediment yields (by up to 39, 28 and 36% under scenario A2; and 23, 12 and 22% under scenario B2 respectively). A higher discharge and more sediment flux are expected during the wet seasons, although the changes, percentage-wise, are observed to be higher during the dry months. In conclusion, the sediment yield from the Nam Ou Basin is likely to increase with climate change, which strongly suggests the need for basin-wide sediment management strategies in order to reduce the negative impact of this change.

Description: Global Climate Models (GCMs) precipitation scenarios are often characterized by biases and coarse resolution that limit their direct application for basin level hydrological modeling. Bias-correction and spatial disaggregation methods are employed to improve the quality of ECHAM4/OPYC SRES A2 and B2 precipitation for the Ping River Basin in Thailand. Bias-correction method, based on gamma-gamma transformation, is applied to improve the frequency and amount of raw GCM precipitation at the grid nodes. Spatial disaggregation model parameters (β,σ2), based on multiplicative random cascade theory, are estimated using Mandelbrot-Kahane-Peyriere (MKP) function at q=1 for each month. Bias-correction method exhibits ability of reducing biases from the frequency and amount when compared with the computed frequency and amount at grid nodes based on spatially interpolated observed rainfall data. Spatial disaggregation model satisfactorily reproduces the observed trend and variation of average rainfall amount except during heavy rainfall events with certain degree of spatial and temporal variations. Finally, the hydrologic model, HEC-HMS, is applied to simulate the observed runoff for upper Ping River Basin based on the modified GCM precipitation scenarios and the raw GCM precipitation. Precipitation scenario developed with bias-correction and disaggregation provides an improved reproduction of basin level runoff observations.

Description: This research evaluates different land management practices for the Nam Ou River Basin in Northern Laos for reducing vulnerability of the basin due to erosion and sediment yield under existing and future climate conditions. We use climate projection data (precipitation and temperature) from three general circulation models (GCMs) for three greenhouse gas emission scenarios (GHGES), namely B1, A1B and A2 and three future periods, namely 2011–2030, 2046–2065 and 2080–2099. These large resolution GCM data are downscaled using the Long Ashton Research Station-Weather Generator (LARS-WG). The Soil and Water Assessment Tool (SWAT), which is a process based hydrological model, is used to simulate discharge and sediment yield and a threshold value of annual sediment yield is applied to identify vulnerable sub-basins. Results show that the change in the annual precipitation is expected to be between −7.60 to 2.64% in 2011–2030, −8.98 to 11.85% in 2046–2065, and −11.04 to 25.84% in 2080–2099. In the meantime, the changes in mean monthly temperature vary from 0.3 to 1.3 °C in the 2011–2030, 1.3 to 2.9 °C in the 2046–2065 and 1.9 to 4.9 °C in the 2080–2099. Five sub-basins are identified vulnerable (critical) under the current climate. Our results show that terracing is the most effective land management practice to reduce sediment yield in these sub-basins followed by strip-cropping and filter strip. Appropriate land management practices applied under future climate scenarios show significant reduction in sediment yield (i.e. up to the tolerance limit) except for some sub-basins. In these exceptional sub-basins, designing an optimum combination of management practices is essential to reduce the vulnerability of the basin.

Description: The present study develops a method called window correlation matching method (WCMM) to reduce collocation and timing errors in matching pairs of radar measured reflectivity, Ze, and gauge measured rainfall intensity, R, for improving the accuracy of the estimation of Ze−R relationships. This method is compared with the traditional matching method (TMM) and the probability matching method (PMM). The relationship Ze=18.05 R1.45 obtained from 7×7 km of space window and both present and 5 min previous time of radar observation for time window (S77T5) produces the best results for radar rainfall estimates for orographic rain over the Mae Chaem Watershed in north of Thailand. The comparison shows that the Ze−R relationships obtained from WCMM provide more accuracy in radar rainfall estimates as compared with the other two methods. The Ze−R relationships estimated using TMM and PMM show large overestimation and underestimation, respectively, of mean areal rainfall. Based on the overall results, it can be concluded that WCMM can reduce collocation and timing errors in Ze−R pairs matching and improve the estimation of Ze−R relationships for radar rainfall. WCMM is therefore a promising method for improved radar-measured rainfall, which is an important input for hydrological and environmental modeling and water resources management.