EVALUATION OF SOLAR RADIATION ESTIMATED FROM HIMAWARI-8 SATELLITE OVER VIETNAM REGION

The development of Solar energy system is growing rapidly in Vietnam in recent years by encouragement of the Government in renewable energy. Requirement for accurate knowledge of the solar radiation reaching the surface is increasingly important in the successful deployment of Solar photovoltaic plants. However, measurements of different components of solar resources including direct normal irradiance (DNI) and global horizontal irradiance (GHI) are limited to few stations over whole country. Satellite imagery provides an ability to monitor the surface radiation over large areas at high spatial and temporal resolution as alternatives at low cost. Observations from the new Japanese geostationary satellite Himawari-8 produce imagery covering Asia-Pacific region, permitting estimation of GHI and DNI over Vietnam at 10-minute temporal resolution. However, accurate comparisons with ground observations are essential to assess their uncertainty. In this study, we evaluated the Himawari-8 radiation product AMATERASS provided by JST/CREST TEEDDA using observations recorded at 5 stations in different regions of Vietnam. The result shows good agreement between satellite estimation and observed data with high correlation of range 0.92-0.94, but better in clear-sky episodes.Because of AMATERASS outperform, we used it for validating ERA-Interim reanalysis in the spatial scale. The comparison was made dividedly for 7 climate zones and 4 seasons. The conclusion is that ERA-Interim is also well associated with satellite-based estimates in seasonal trend for all season, but in average the reanalysis has negative bias towards satellite estimates. This underestimation is more pronounced in the months of JJA and SON periods and in the north part of Vietnam because of unpredicted cloud in the ERA reanalysis

Meteorological drought analysis in the Lower Mekong Basin using satellite-based long-term CHIRPS product

Lower Mekong Basin (LMB) experiences a recurrent drought phenomenon. However, few studies have focused on drought monitoring in this region due to lack of ground observations. The newly released Climate Hazards Group Infrared Precipitation with Station data (CHIRPS) with a long-term record and high resolution has a great potential for drought monitoring. Based on the assessment of CHIRPS for capturing precipitation and monitoring drought, this study aims to evaluate the drought condition in LMB by using satellite-based CHIRPS from January 1981 to July 2016. The Standardized Precipitation Index (SPI) at various time scales (1-12-month) is computed to identify and describe drought events. Results suggest that CHIRPS can properly capture the drought characteristics at various time scales with the best performance at three-month time scale. Based on high-resolution long-term CHIRPS, it is found that LMB experienced four severe droughts during the last three decades with the longest one in 1991-1994 for 38 months and the driest one in 2015-2016 with drought affected area up to 75.6%. Droughts tend to occur over the north and south part of LMB with higher frequency, and Mekong Delta seems to experience more long-term and extreme drought events. Severe droughts have significant impacts on vegetation condition

Désagrégation de l'humidité du sol issue des produits satellitaires micro-ondes passives et exploration de son utilisation pour l'amélioration de la modélisation et la prévision hydrologique

De plus en plus de produits satellitaires en micro-ondes passives sont disponibles. Cependant, leur large résolution spatiale (25-50 km) n’en font pas un outil adéquat pour des applications hydrologiques à une échelle locale telles que la modélisation et la prévision hydrologiques. Dans de nombreuses études, une désagrégation d’échelle de l’humidité du sol des produits satellites micro-ondes est faite puis validée avec des mesures in-situ. Toutefois, l’utilisation de ces données issues d’une désagrégation d’échelle n’a pas encore été pleinement étudiée pour des applications en hydrologie. Ainsi, l’objectif de cette thèse est de proposer une méthode de désagrégation d’échelle de l’humidité du sol issue de données satellitaires en micro-ondes passives (Satellite Passive Microwave Active and Passive - SMAP) à différentes résolutions spatiales afin d’évaluer leur apport sur l’amélioration potentielle des modélisations et prévisions hydrologiques. À partir d’un modèle de forêt aléatoire, une désagrégation d’échelle de l’humidité du sol de SMAP l’amène de 36-km de résolution initialement à des produits finaux à 9-, 3- et 1-km de résolution. Les prédicteurs utilisés sont à haute résolution spatiale et de sources différentes telles que Sentinel-1A, MODIS et SRTM. L'humidité du sol issue de cette désagrégation d’échelle est ensuite assimilée dans un modèle hydrologique distribué à base physique pour tenter d’améliorer les sorties de débit. Ces expériences sont menées sur les bassins versants des rivières Susquehanna (de grande taille) et Upper-Susquehanna (en comparaison de petite taille), tous deux situés aux États-Unis. De plus, le modèle assimile aussi des données d’humidité du sol en profondeur issue d’une extrapolation verticale des données SMAP. Par ailleurs, les données d’humidité du sol SMAP et les mesures in-situ sont combinées par la technique de fusion conditionnelle. Ce produit de fusion SMAP/in-situ est assimilé dans le modèle hydrologique pour tenter d’améliorer la prévision hydrologique sur le bassin versant Au Saumon situé au Québec. Les résultats montrent que l'utilisation de l’humidité du sol à fine résolution spatiale issue de la désagrégation d’échelle améliore la représentation de la variabilité spatiale de l’humidité du sol. En effet, le produit à 1- km de résolution fournit plus de détails que les produits à 3- et 9-km ou que le produit SMAP de base à 36-km de résolution. De même, l’utilisation du produit de fusion SMAP/ in-situ améliore la qualité et la représentation spatiale de l’humidité du sol. Sur le bassin versant Susquehanna, la modélisation hydrologique s’améliore avec l’assimilation du produit de désagrégation d’échelle à 9-km, sans avoir recours à des résolutions plus fines. En revanche, sur le bassin versant Upper-Susquehanna, c’est le produit avec la résolution spatiale la plus fine à 1- km qui offre les meilleurs résultats de modélisation hydrologique. L’assimilation de l’humidité du sol en profondeur issue de l’extrapolation verticale des données SMAP n’améliore que peu la qualité du modèle hydrologique. Par contre, l’assimilation du produit de fusion SMAP/in-situ sur le bassin versant Au Saumon améliore la qualité de la prévision du débit, même si celle-ci n’est pas très significative.Abstract: The availability of satellite passive microwave soil moisture is increasing, yet its spatial resolution (i.e., 25-50 km) is too coarse to use for local scale hydrological applications such as streamflow simulation and forecasting. Many studies have attempted to downscale satellite passive microwave soil moisture products for their validation with in-situ soil moisture measurements. However, their use for hydrological applications has not yet been fully explored. Thus, the objective of this thesis is to downscale the satellite passive microwave soil moisture (i.e., Satellite Microwave Active and Passive - SMAP) to a range of spatial resolutions and explore its value in improving streamflow simulation and forecasting. The random forest machine learning technique was used to downscale the SMAP soil moisture from 36-km to 9-, 3- and 1-km spatial resolutions. A combination of host of high-resolution predictors derived from different sources including Sentinel-1A, MODIS and SRTM were used for downscaling. The downscaled SMAP soil moisture was then assimilated into a physically-based distributed hydrological model for improving streamflow simulation for Susquehanna (larger in size) and Upper Susquehanna (relatively smaller in size) watersheds, located in the United States. In addition, the vertically extrapolated SMAP soil moisture was assimilated into the model. On the other hand, the SMAP and in-situ soil moisture were merged using the conditional merging technique and the merged SMAP/in-situ soil moisture was then assimilated into the model to improve streamflow forecast over the au Saumon watershed. The results show that the downscaling improved the spatial variability of soil moisture. Indeed, the 1-km downscaled SMAP soil moisture presented a higher spatial detail of soil moisture than the 3-, 9- or original resolution (36-km) SMAP product. Similarly, the merging of SMAP and in-situ soil moisture improved the accuracy as well as spatial representation soil moisture. Interestingly, the assimilation of the 9-km downscaled SMAP soil moisture significantly improved the accuracy of streamflow simulation for the Susquehanna watershed without the need of going to higher spatial resolution, whereas for the Upper Susquehanna watershed the 1-km downscaled SMAP showed better results than the coarser resolutions. The assimilation of vertically extrapolated SMAP soil moisture only slightly further improved the accuracy of the streamflow simulation. On the other hand, the assimilation of merged SMAP/in-situ soil moisture for the au Saumon watershed improved the accuracy of streamflow forecast, yet the improvement was not that significant. Overall, this study demonstrated the potential of satellite passive microwave soil moisture for streamflow simulation and forecasting

Inter-comparison of high-resolution satellite precipitation products over Central Asia

This paper examines the spatial error structures of eight precipitation estimates derived from four different satellite retrieval algorithms including TRMM Multi-satellite Precipitation Analysis (TMPA), Climate Prediction Center morphing technique (CMORPH), Global Satellite Mapping of Precipitation (GSMaP) and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN). All the original satellite and bias-corrected products of each algorithm (3B42RTV7 and 3B42V7, CMORPH_RAW and CMORPH_CRT, GSMaP_MVK and GSMaP_Gauge, PERSIANN_RAW and PERSIANN_CDR) are evaluated against ground-based Asian Precipitation-Highly Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE) over Central Asia for the period of 2004 to 2006. The analyses show that all products except PERSIANN exhibit overestimation over Aral Sea and its surrounding areas. The bias-correction improves the quality of the original satellite TMPA products and GSMaP significantly but slightly in CMORPH and PERSIANN over Central Asia. 3B42RTV7 overestimates precipitation significantly with large Relative Bias (RB) (128.17%) while GSMaP_Gauge shows consistent high correlation coefficient (CC) (>0.8) but RB fluctuates between -57.95% and 112.63%. The PERSIANN_CDR outperforms other products in winter with the highest CC (0.67). Both the satellite-only and gauge adjusted products have particularly poor performance in detecting rainfall events in terms of lower POD (less than 65%), CSI (less than 45%) and relatively high FAR (more than 35%)

Comprehensive evaluation of high-resolution satellite-based precipitation products over China

Characterizing the errors in satellite-based precipitation estimation products is crucial for understanding their effects in hydrological applications. Six precipitation products derived from three algorithms are comprehensively evaluated against gauge data over mainland China from December 2006 to November 2010. These products include three satellite-only estimates: the Global Satellite Mapping of Precipitation Microwave-IR Combined Product (GSMaP_MVK), the Climate Prediction Center (CPC) MORPHing (CMORPH), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), as well as their gauge-corrected counterparts: the GSMaP Gauge-calibrated Product (GSMaP_Gauge), bias-corrected CMORPH (CMORPH_CRT), and PERSIANN Climate Data Record (PERSIANN-CDR). Overall, the bias-correction procedures largely reduce various errors for the three groups of satellite-based precipitation products. GSMaP_Gauge produces better fractional coverage with the highest correlation (0.95) and the lowest RMSE (0.53 mm/day) but also high RB (15.77%). In general, CMORPH_CRT amounts are closer to the gauge reference. CMORPH shows better performance than GSMaP_MVK and PERSIANN with the highest CC (0.82) and the lowest RMSE (0.93 mm/day), but also presents a relatively high RB (-19.60%). In winter, all six satellite precipitation estimates have comparatively poor capability, with the IR-based PERSIANN_CDR exhibiting the closest performance to the gauge reference. Both satellite-only and gauge-corrected satellite products show poor capability in detecting occurrence of precipitation with a low POD (40%)

A Self-calibrating Runoff and Streamflow Remote Sensing Model for Ungauged Basins Using Open-access Earth Observation Data

Due to increasing pressures on water resources, there is a need to monitor regional water resource availability in a spatially and temporally explicit manner. However, for many parts of the world, there is insufficient data to quantify stream flow or ground water infiltration rates. We present the results of a pixel-based water balance formulation to partition rainfall into evapotranspiration, surface water runoff and potential ground water infiltration. The method leverages remote sensing derived estimates of precipitation, evapotranspiration, soil moisture, Leaf Area Index, and a single F coefficient to distinguish between runoff and storage changes. The study produced significant correlations between the remote sensing method and field based measurements of river flow in two Vietnamese river basins. For the Ca basin, we found R2 values ranging from 0.88–0.97 and Nash–Sutcliffe efficiency (NSE) values varying between 0.44–0.88. The R2 for the Red River varied between 0.87–0.93 and NSE values between 0.61 and 0.79. Based on these findings, we conclude that the method allows for a fast and cost-effective way to map water resource availability in basins with no gauges or monitoring infrastructure, without the need for application of sophisticated hydrological models or resource-intensive data

Remote Sensing of Precipitation: Volume 2

Precipitation is a well-recognized pillar in global water and energy balances. An accurate and timely understanding of its characteristics at the global, regional, and local scales is indispensable for a clearer understanding of the mechanisms underlying the Earth’s atmosphere–ocean complex system. Precipitation is one of the elements that is documented to be greatly affected by climate change. In its various forms, precipitation comprises a primary source of freshwater, which is vital for the sustainability of almost all human activities. Its socio-economic significance is fundamental in managing this natural resource effectively, in applications ranging from irrigation to industrial and household usage. Remote sensing of precipitation is pursued through a broad spectrum of continuously enriched and upgraded instrumentation, embracing sensors which can be ground-based (e.g., weather radars), satellite-borne (e.g., passive or active space-borne sensors), underwater (e.g., hydrophones), aerial, or ship-borne

Proceedings Of The 18th Annual Meeting Of The Asia Oceania Geosciences Society (Aogs 2021)

The 18th Annual Meeting of the Asia Oceania Geosciences Society (AOGS 2021) was held from 1st to 6th August 2021. This proceedings volume includes selected extended abstracts from a challenging array of presentations at this conference. The AOGS Annual Meeting is a leading venue for professional interaction among researchers and practitioners, covering diverse disciplines of geosciences