Search

Forecasting flooding hazards is a very effective non-engineering measure for flood control. Presently, the commonly used forecasting method in simulating flash flood events is through a watershed hydrological model. Many Asia-Pacific countries like the Philippines are prone to frequent hydrometeorological hazards such as tropical cyclones, resulting in frequent heavy rainfall events. However, most rivers in the many basins lack water flow observation data, which makes it challenging to use lumped and data-driven models for flash flood forecasting. With the continuous progress of remote sensing (RS) and geographic information system (GIS) techniques, the physically-based distributed hydrological model (PBDHMs) has rapidly advanced. PBDHMs can directly determine the model parameters according to the underlying surface characteristics from remotely-sensed data, which makes it possible for flood forecasting in areas with little to virtually no data. In this study, the Matina River basin in Davao City was selected as a case study in simulating a small data-poor basin in the region. The Liuxihe model was used to formulate a forecasting scheme and simulated the past flash flood events. The results show that there is a good correspondence between the past heavy rainfall events and their corresponding simulated river discharges. The results conform to the hydrological regularities, which can be used for flood forecasting and can serve as a baseline for the development of a flood forecasting system in the rivers of Davao City and elsewhere.

In this study, we examined the sensitivity of tropical cyclone (TC) characteristics and precipitation to the Cordillera Mountain Range (CMR) in Luzon, Philippines. Using the Weather Research and Forecasting (WRF) model, we simulated eight TCs with four different CMR orographic elevations: Control, Flat, Reduced, and Enhanced. We found that at significance level α=0.05, TC intensity significantly weakened as early as 21 h prior to landfall in the Enhanced experiment relative to the Control, whereas there was little change in the Flat and Reduced experiments. However, throughout the period when the TC crossed Luzon, we found no significant differences for TC movement speed and position in the different orographic elevations. When a TC made landfall, associated precipitation over the CMR increased as the mountain height increased. We further investigated the underpinning processes relevant to the effect of the CMR on precipitation by examining the effects of mountain slope, incoming perpendicular wind speed, and the moist Froude Number (Fw). Compared with other factors, TC precipitation was most strongly correlated with the strength of approaching winds multiplied by the mountain slope, i.e., stronger winds blowing up steeper mountain slopes caused higher amounts of precipitation. We also found that individually, Fw, mountain height, and upslope wind speeds were poor indicators of orographically induced precipitation. The effects of mountain range on TC rainfall varied with TC cases, highlighting the complexity of the mountain, wind, and rainfall relationship.

Abstract Metro Manila, Philippines and other urban areas have reached internationally known unacceptable levels of pollution where about 80% can be attributed to vehicular emissions. The Weather Research and Forecasting model coupled with Chemistry v.3.6.1 was used in the reanalysis of pollutant concentrations for the year 2013. Initial results from the planetary boundary layer study suggested that the Yonsei University scheme provides a good estimate of the atmosphere’s condition; hence, this setting was used for the succeeding simulations. The land coverage over Sangley point was not properly resolved by the model. This caused a cold bias for the station. Further evaluation of the model’s sea level pressure output for all sites returned high correlations showing that modeled values are in phase with the observed time series; however, wind speed values did not correlate well with the observed values and were all overestimated. The low correlations found were a result of the incapability of the model to detect the urban canopy layer over Metro Manila. Pollutant concentrations were overestimated. The pollutant time series suggests that the model overestimates concentration values for PM10, PM2.5, and SO2, while underestimating NO2 and O3 values. However, it does capture a significant 24-hourly cycle as seen in the time series’ spectra in the frequency domain. Furthermore, through a student’s t-test, the model also captures a significant difference in daytime and nighttime concentrations.

Tropical cyclone (TC) rainfall results in damages to properties and loss of lives. It is also a significant source of freshwater in the Philippines. This study describes a method in forecasting accumulated TC rainfall using analogous TCs from historical datasets. A TC rainfall database where precipitation within 5∘ of TC centers was created for all landfalling TCs from 1951 to 2015. To predict TC rainfall, the mean rainfall of all past TCs with similar tracks included in the database, referred to as analog TCs, is calculated. Landfalling TCs from 2016 to 2018 are used to optimize the selection of past analog TCs. Each past TC member was also adjusted according to a target TC's intensity and movement speed. The optimized analog method is then applied to landfalling TCs from 2019 to November 2020. Results show that the composite rainfall from past TCs within 1.8∘ of the forecast TC yields the best hit rate of intense rainfall. The analog TC rainfall forecast generally has a similar spatial distribution as the observed TC rain. However, this method tends to miss extreme rainfall values due to a “smoothing” effect caused by the variability of extreme rain locations of each TC member and constraints in the rainfall data used in the database. Nevertheless, forecast assessment results show that analog TC rainfall forecasting performed better than the WRF model in predicting intense and inland rainfall. In addition to it being computationally inexpensive, it can complement the inherent biases of dynamical models.

Tropical cyclone (TC) rainfall is both a resource and a hazard in the Philippines. Observation of its spatiotemporal distribution is necessary for water and disaster mitigation management. This study evaluated the performance of two high-resolution satellite precipitation datasets—the GSMaP and GPM-IMERG—in estimating accumulated TC rainfall in the Philippines from 2000 to 2021. TC rain is defined as rainfall within 5° of a TC center. Several estimation algorithms were included in the assessment. The uncalibrated near-real-time GSMaP_NRT and early version GPM_ER, the reanalysis GSMaP_RNL, and the gauge-calibrated GSMaP_G and GPM_G. The assessment shows the worst scores for the uncalibrated GSMaP_NRT and GSMaP_RNL, followed by GPM_ER with station correlation coefficient (CC) values of 0.63, 0.67, and 0.73, respectively, compared to GSMaP_G CC of 0.79 and GPM_G CC of 0.77. GSMaP_G and GPM_G also gave the least bias and error, with a consistently high (>0.6) probability of detection (POD) and Pierce skill score (PSS) up to rainfall of 300 mm. In addition to the evaluation, the GSMaP_G and GPM_G were used in the analog forecasting of TC rain. Analog forecasting is based on the principle that past weather conditions can occur again. In TC rain analog forecasting, past TCs with similar intensities and tracks are assumed to bring similar rainfall amounts and distribution as a current TC. Composite mean TC rainfall from historical satellite precipitation estimations was produced to create TC rain forecasts. Results show the analog TC rain forecasts generally captured the spatial distribution of TC rain and performed better than the uncalibrated GSMaP_NRT, with a mean station correlation of 0.62–0.67, POD greater than 0.7, and positive PSS indicating good skills. However, forecasts have a false alarm ratio greater than 0.8 for 150 mm rain and have difficulty producing extreme rainfall (>250 mm).

The Total Carbon Column Observing Network (TCCON) is a global network dedicated to the precise and accurate measurements of greenhouse gases (GHG) in the atmosphere. The TCCON station in Burgos, Ilocos Norte, Philippines was established with the primary purpose of validating the upcoming Greenhouse gases Observing SATellite-2 (GOSAT-2) mission and in general, to respond to the need for reliable ground-based validation data for satellite GHG observations in the region. Here, we present the first 4 months of data from the new TCCON site in Burgos, initial comparisons with satellite measurements of C O 2 and model simulations of C O . A nearest sounding from Japan’s GOSAT as well as target mode observations from NASA’s Orbiting Carbon Observatory 2 (OCO-2) showed very good consistency in the retrieved column-averaged dry air mole fractions of C O 2 , yielding TCCON - satellite differences of 0.86 ± 1.06 ppm for GOSAT and 0.83 ± 1.22 ppm for OCO-2. We also show measurements of enhanced C O , probably from East Asia. GEOS-Chem model simulations were used to study the observed C O variability. However, despite the model capturing the pattern of the C O variability, there is an obvious underestimation in the C O magnitude in the model. We conclude that more measurements and modeling are necessary to adequately sample the variability over different seasons and to determine the suitability of current inventories.