Mapping the daily rainfall over an ungauged tropical micro-watershed: a downscaling algorithm using gpm data

In this study, half-hourly Global Precipitation Mission (GPM) satellite precipitation data were downscaled to produce high-resolution daily rainfall data for tropical coastal micro-watersheds (100-1000 ha) without gauges or with rainfall data conflicts. Currently, daily-scale satellite rainfall downscaling techniques rely on rain gauge data as corrective and controlling factors, making them impractical for ungauged watersheds or watersheds with rainfall data conflicts. Therefore, we used high-resolution local orographic and vertical velocity data as proxies to downscale half-hourly GPM precipitation data (0.1°) to high-resolution daily rainfall data (0.02°). The overall quality of the downscaled product was similar to or better than the quality of the raw GPM data. The downscaled rainfall dataset improved the accuracy of rainfall estimates on the ground, with lower error relative to measured rain gauge data. The average error was reduced from 41 to 27 mm/d and from 16 to 12 mm/d during the wet and dry seasons, respectively. Estimates of localized rainfall patterns were improved from 38% to 73%. The results of this study will be useful for production of high-resolution satellite precipitation data in ungauged tropical micro-watersheds

Spatial downscaling of satellite precipitation data in humid tropics using a site-specific seasonal coefficient

This paper described the development of a spatial downscaling algorithm to produce finer grid resolution for satellite precipitation data (0.05°) in humid tropics. The grid resolution provided by satellite precipitation data (>0.25°) was unsuitable for practical hydrology and meteorology applications in the high hydrometeorological dynamics of Southeast Asia. Many downscaling algorithms have been developed based on significant seasonal relationships, without vegetation and climate conditions, which were inapplicable in humid, equatorial, and tropical regions. Therefore, we exploited the potential of the low variability of rainfall and monsoon characteristics (period, location, and intensity) on a local scale, as a proxy to downscale the satellite precipitation grid and its corresponding rainfall estimates. This study hypothesized that the ratio between the satellite precipitation and ground rainfall in the low-variance spatial rainfall pattern and seasonality region of humid tropics can be used as a coefficient (constant value) to spatially downscale future satellite precipitation datasets. The spatial downscaling process has two major phases: the first is the derivation of the high-resolution coefficient (0.05°), and the second is applying the coefficient to produce the high-resolution precipitation map. The first phase utilized the long-term bias records (1998-2008) between the high-resolution areal precipitation (0.05°) that was derived from dense network of ground precipitation data and re-gridded satellite precipitation data (0.05°) from the Tropical Rainfall Measuring Mission (TRMM) to produce the site-specific coefficient (SSC) for each individual pixel. The outcome of the spatial downscaling process managed to produce a higher resolution of the TRMM data from 0.25° to 0.05° with a lower bias (average: 18%). The trade-off for the process was a small decline in the correlation between TRMM and ground rainfall. Our results indicate that the SSC downscaled method can be used to spatially downscale satellite precipitation data in humid, tropical regions, where the seasonal rainfall is consistent

Remote sensing based evaluation of uncertainties on modelling of streamflow affected by climate change

Assessment of the impacts of land-use and climate change on streamflow is vital to develop climate adaptation strategies. However, uncertainties in the climate impact study framework could lead to changes on streamflow impact. The aim of this study is to assess the uncertainties on Digital Elevation Model (DEM), Satellite Precipitation Product (SPP) and climate projection on the modelling of streamflow affected by climate changes. These uncertainties are evaluated and reduced independently. The climate projection uncertainty is addressed through the modification of the Quantifying and Understanding the Earth System - Global Scale Impacts (QUEST-GSI) methodology. Twenty-six modified QUEST-GSI climate scenarios were used as climate inputs into the calibrated Soil and Water Assessment Tool (SWAT) model to evaluate the impacts and uncertainties of climate change on streamflow for three future periods (2015-2034, 2045-2064 and 2075-2094). The selected study areas are the Johor River Basin (JRB) and Kelantan River Basin (KRB), Malaysia. The Shuttle Radar Topography Mission (SRTM) version 4.1 (90m resolution) DEM and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks – Climate Data Record (PERSIANN-CDR) SPP which show a better performance were selected for the SWAT model modification, calibration and validation. The results indicated that the modified SWAT model could simulate the monthly streamflow well for both basins. Land-use and climate changes from 1985 to 2012 reduced annual streamflow of the JRB and KRB by 5% and 4.2%, respectively. In future, the annual precipitation and temperature of the JRB / KRB are projected to increase by -0.4-10.3% / 0.1-11.2% and 0.6-3.2oC / 0.8-3.3oC, respectively, and that this will lead to an increase of annual streamflow by 0.5-13.3% / 4.4-18.5%. This study showed that satellite data play an important role in providing input data to hydrological models

Distributed hydrological model using machine learning algorithm for assessing climate change impact

Rapid population growth, economic development, land-use modifications, and climate change are the major driving forces of growing hydrological disasters like floods and water stress. Reliable flood modelling is challenging due to the spatio-temporal changes in precipitation intensity, duration and frequency, heterogeneity in temperature rise and land-use changes. Reliable high-resolution precipitation data and distributed hydrological model can solve the problem. This study aims to develop a distributed hydrological model using Machine Learning (ML) algorithms to simulate streamflow extremes from satellite-based high-resolution climate data. An integrated statistical index coupled with a classification optimisation algorithm was used to select coupled model intercomparison project (CMIP6) global climate model (GCMs). Several bias-correction methods were evaluated to identify the best method for downscaling GCM simulations. The study also evaluated the performance of different Satellite-Based Products (SBPs) in replicating observed rainfall to select the best product. A novel two-stage bias correction method were used to correct the bias of the selected SBP. Besides, four widely used bias correction methods were compared to select the best method for downscaling GCM simulations at SBP grid locations. A novel ML-based distributed hydrological model was developed for modelling runoff from the corrected satellite rainfall data. Finally, the model was used to project future changes in runoff, and streamflow extremes from the downscaled GCM projected climate. The Johor River Basin (JRB) located at the south of Peninsular Malaysia was considered as the case study area. The results showed that three GCMs, namely EC-Earth, EC-Earth-Veg and MRI-ESM-2, were the best in replicating the precipitation climatology in mainland Southeast Asia. IMERG was the best among five SBPs with an R2 of 0.56 compared to SM2RAIN-ASCAT (0.15), GSMap (0.18), PERSIANN-CDR (0.14), PERSIANN-CSS (0.10) and CHIRPS (0.13). The two-step bias correction approach improved the performance of IMERG, which reduced the mean bias up to 140 % compared to the other conventional bias correction methods. The method also successfully simulates the historical high rainfall events that caused floods in Peninsular Malaysia. The distributed hydrological model developed using ML showed NSE values of 0.96 and 0.78 and RMSE of 4.01 and 5.64 during calibration and validation. The simulated flow analysis using the model showed that the river discharge would increase in the near future (2020 - 2059) and the far future (2060 - 2099) for different SSPs. The largest change in river discharge would be for SSP-585. The extreme rainfall indices, such as R95TOT, R99TOT, Rx1day, Rx5day and RI, were projected to increase from 5% for SSP-119 to 37% for SSP-585 in the future compared to the base period. The ML based distributed hydrological model developed using the novel two-step bias corrected SBP showed sufficient capability to simulate runoff from satellite rainfall. Application of the ML-based distributed model in JRB indicated that climate change and socio-economic development would cause an increase in the frequency streamflow extremes, causing larger flood events. The modelling framework developed in this study can be used for near-real time monitoring of flood through bias correction near-real time satellite rainfall

Rainfall intensity-duration-frequency curves at ungauged locations with uncertainties due to climate change

Intensity duration frequency (IDF) curves are important in designing and managing urban hydraulic structures for mitigation of floods. The objective of the study was to develop IDF curves at ungauged locations with associated uncertainties due to climate change. Peninsular Malaysia was considered as the case study area. The novelty of the study was to propose a new methodology for reliable estimation of IDF curves at any location with consideration of non-stationary behaviour of rainfall due to climate change which can be used for robust designing of climate change resilient urban hydraulic structures. Hourly observed rainfall data at 80 locations distributed over Peninsular Malaysia and four remote-sensing rainfall datasets namely, GSMaP_NRT, GSMaP_GC, PERSIANN and TRMM_3B42V7 were used for this purpose. Four widely used probability distribution functions (PDFs) and four methods for estimation of PDF parameters were compared to determine the most suitable PDF and its parameter estimation method in the study area. Subsequently, the estimated parameters of the selected PDF were used to generate IDF curves at all the observed locations. The performance of four remote sensing rainfall datasets in construction of IDF curves at observed locations was compared to find the best product. The bias in the IDF curve of the best rainfall product was corrected to generate the IDF curves at ungauged locations. To update the IDF curves for future climate change scenarios, high-resolution rainfall projections data were generated through selection of suitable global climate models (GCMs) of Coupled Model Intercomparison Project Phase 5 (CMIP5) and their downscaling at remote sensing rainfall grid locations. Climate change factor at each grid location was estimated through comparison of PDF of historical and future simulations of GCMs for different radiative concentration pathways (RCP) scenarios. The factors were used to perturb the historical IDF curves to generate IDF curves with associated uncertainties for future climate change scenarios. Results revealed general extreme value (GEV) as the best-fitted PDF and maximum likelihood as the best parameter estimation method at 62% of the stations. Performance assessment of remote sensing rainfall datasets revealed all datasets underestimated rainfall intensities for different durations and return periods. Comparative performance of the products revealed GSMaP_GC as the most suitable product for developing IDF curves at ungauged locations with least biases (8% to 27%). BCC-CSM1.1 (M), CCSM4, CSIRO-Mk3.6.0 and HadGEM2-ES were found as the most suitable GCMs models for the projection of daily rainfall in Peninsular Malaysia. The ensemble mean of projected rainfall showed a maximum increase in annual rainfall by 15.72% and an increase in variability by 26.15% during 2070-2099 compared to the base period (1971-2000) under RCP 8.5. The assessment of IDF curves with uncertainty revealed a maximum change in rainfall intensity for different durations under RCP 8.5 and the minimum for RCP 2.6. The rainfall intensity for different durations was found to increase with time. The highest increase was observed up to 96.8% for the period 2070-2099. The assessment of uncertainty in rainfall IDF for different RCP scenarios revealed higher uncertainty for higher return periods and vice versa. The IDF curves generated in this study can suitably be used for designing hydraulic structures at locations where observed rainfall data is not available. It can also be used for designing hydraulic structure for adaptation to climate change induced rainfall extremes and mitigation of urban flood

The climatology of Thailand and future climate change projections using the regional climate model precis

The climate of Thailand has not been studied in as much depth as in other parts of continental Southeast Asia. The baseline climate of Thailand during 1961-1990 is first analysed using daily observational data from five surface stations, each representing a different region of Thailand, supplemented by the high resolution 0.5° monthly gridded observational dataset, CRUTS2.1. The latter leads to a deeper understanding of the spatial variation in seasonal cycles of key climate variables in Thailand. Also revealed is an increase in the number of tropical depressions crossing Thailand during La Niña years. It was found that there is a statistically significant intensification (reduction) of precipitation during La Niña (El Niño) years at Surat Thani (Chiang Mai) in southern (northern) Thailand during ON (JJAS). This work facilitates the Regional Climate Model validation work which follows. The Providing REgional Climates for Impact Studies regional climate model, PRECIS, was run for the first time over Southeast Asia to specifically study the climate of Thailand. The first phase is model validation during the 1961-1990 baseline period. An ensemble of RCM runs is undertaken to study the sensitivity to the driving GCM. The added value provided by PRECIS in comparison to the coarser driving models is discussed. The possible causes of model bias are investigated. The model projections for the end of this century are undertaken based on high (SRESA2) and low (SRES-B2) emission scenarios which estimate the range of possible climate change in Thailand. These RCM simulations suggest trends in temperature that are broadly in line with those reported by IPCC. PRECIS A2 and B2 simulations mostly produce small precipitation increases in JJAS and small precipitation increases (decreases) during DJF under the A2 (B2) scenario. Wet season precipitation increases appear to be related to higher rain intensity on fewer rain days

Conceptualizing Spatial Heterogeneity of Urban Composition Impacts on Precipitation Within Tropics

Urban composition has exacerbated precipitation patterns. Rapid urbanization with dynamic composition and anthropogenic activities lead to the change of physical environment, especially land-use and land cover which subsequently magnifies the environmental effects such as flash floods, extreme lightning, and landslides. Due to extreme and elevated temperature trends with exacerbated rainfall patterns, these environmental effects become major issues in tropics. Albeit several studies pointed out that rapid urbanization induced precipitation, studies about the heterogeneity of urban composition on precipitation variables are still limited. Thus, this paper review studies about precipitation pattern in relation to the heterogeneity of urban composition that successfully integrates geographical information system (GIS) and remote sensing techniques to enhance the understanding of interactions between precipitation patterns against heterogeneity of urban composition. This article also addressed the current state of uncertainties and scarcity of data concerning remote sensing techniques. Evidently, with a comprehensive investigation and probing of the precipitation variables in the context of urbanization models fused with remote sensing and GIS, they put forward powerful set tools for geographic cognition and understand how its influence on spatial variation. Hence, this study indicated a great research opportunity to set the course of action in determining the magnitude of spatial heterogeneity of an urban composition towards the pattern of precipitation

湿潤熱帯地域における降雨観測衛星データのダウンスケーリング

首都大学東京, 2015-09-30, 博士（観光科学）, 甲第588号首都大学東

Distribution of Accuracy of TRMM Daily Rainfall in Makassar Strait

This research aims to evaluate rainfall estimates of satellite products in regions that have high variations of rainfall pattern. The surrounding area of Makassar Strait have chosen because of its distinctive rainfall pattern between the eastern and western parts of the Makassar Strait. For this purpose, spatial distribution of Pearson’s coefficient correlation and Root Mean Square Error (RMSE) is used to evaluate accuracy of rainfall in the eastern part of Kalimantan Island and the western part of Sulawesi Island. Moreover, we also used the contingency table to complete the parameter accuracy of the TRMM rainfall estimates. The results show that the performance of TRMM rainfall estimates varies depending on space and time. Overall, the coefficient correlation between TRMM and rain observed from no correlation was -0.06 and 0.78 from strong correlation. The best correlation is on the eastern coast of South West Sulawesi located in line with the Java Sea. While, no variation in the correlation was related to flatland such as Kalimantan Island. On the other hand, in the mountain region, the correlation of TRMM rainfall estimates and observed rainfall tend to decrease. The RMSE distribution in this region depends on the accumulation of daily rainfall. RMSE tends to be high where there are higher fluctuations of fluctuating rainfall in a location. From contingency indicators, we found that the TRMM rainfall estimates were overestimate. Generally, the absence of rainfall during the dry season contributes to improving TRMM rainfall estimates by raising accuracy (ACC) in the contingency table

Quantifying Uncertainties of Multi-Model Climate Change Scenarios on the Water Crisis in Malaysia

Malaysia has a relatively limited capacity to deal with the effects of climate change while being one of the most vulnerable nations to its effects. As a developing country, the lack of a consistent temporal and spatial data source has always been an issue, and the region is also considered data-scarce. This study’s primary goal is to evaluate the effects of climate change on Malaysia’s water resources, particularly the Selangor River Basin (SRB). Instead of using a single source input dataset, cross-combined datasets from multiple sources were used in order to optimise the hydrological model. Five input variables, including precipitation, temperature, solar radiation, relative humidity, and wind speed, were used to define seven scenarios using single and cross- combined method. To improve the hydrological model multi-site calibration method is employed. Finally, climate change prediction data from several Global Climate Models (GCMs) is utilised to assess the effects of climate change on SRB water supplies. The CFSR and CMADS global reanalysis datasets show a highly significant relationship on precipitation, with an r-value of 0.81 for both datasets. However, for temperature data, CMADS surpasses CFSR on maximum and minimum temperatures, with 0.6 and 0.7, respectively. In the SWAT model, most of the scenarios achieved a ‘good’ performance range on the calibration and validation processes. However, SWAT model with CFSR as input data achieved an ‘unsatisfactory’ range with R2 of 0.35, NSE of 0.16, Pbias of 0.00, KGE of 0.50, and RSR of 0.92. For a cross-combined approach, the result shows the combination of the observed and CMADS datasets performed better than the combination of the observed and CFSR datasets. The sequential technique outperformed the simultaneous and basin-by-basin techniques by achieving ‘satisfactory’ range at all outlets. The SRB’s assessment of climate change predicted an increase in precipitation and temperature from 2030 to 2050. Climate data from ‘ensemble average’ realisation predicted SRB would receive a huge amount of precipitation in November and April every year, and high temperatures from February to June. Additionally, a few sub-basins are expected to have water availability greater than 5 m3/s for three consecutive years