ORCID Profile
0000-0002-3578-3565
Current Organisation
University of California, Irvine
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Publisher: American Geophysical Union (AGU)
Date: 05-2022
DOI: 10.1029/2022WR032117
Abstract: Accurate and reliable near‐real‐time satellite precipitation estimation is of great importance for operational large‐scale flood forecasting and drought monitoring. The state‐of‐the‐art precipitation post‐processing model is based on a deterministic approach to construct relationships between satellites estimates and ground observations. We propose a probabilistic postprocessor, the P robabilistic P ost‐ P rocessing of N ear‐ R eal‐ T ime Satellite P recipitation Estimates using Q uantile R egression F orests (QRF4P‐NRT), based on quantile modeling, yielding both deterministic and probabilistic predictions. The experimental design incorporates different solutions of near‐real‐time predictors to further improve the model performance. Using the Integrated Multi‐satellitE Retrievals Early Run for Global Precipitation Measurement Mission (IMERG‐E) product as an ex le, we illustrate that the proposed method significantly improves the overall quality of the raw IMERG‐E and is also superior to the bias‐corrected product (IMERG Final Run, IMERG‐F) at daily scale in a complex mountain basin. Evaluations of the corrected IMERG‐E, raw IMERG‐E, and IMERG‐F using ground observation show that the corrected IMERG‐E improves correlation coefficients (0.7), mean error (−0.14 mm/day) and root mean square error (3.3 mm/day) relative to the raw IMERG‐E (0.31, −0.72 and 5.5 mm/day) and IMERG‐F (0.34, −0.09 and 6.0 mm/day). The error decomposition further confirms that the QRF4P‐NRT improves on the various deficiencies of the raw IMERG‐E product. The ensemble assessment also demonstrates that the quantile outputs provide reliable prediction spread and sharp prediction intervals. The promising results indicate the great potential of the proposed method for probabilistic post‐processing for near‐real‐time satellite precipitation estimates, and for further applications such as hydrological ensemble forecasting.
Publisher: Springer Science and Business Media LLC
Date: 12-05-2021
DOI: 10.1038/S41598-021-89576-8
Abstract: Larval source management has gained renewed interest as a malaria control strategy in Africa but the widespread and transient nature of larval breeding sites poses a challenge to its implementation. To address this problem, we propose combining an integrated high resolution (50 m) distributed hydrological model and remotely sensed data to simulate potential malaria vector aquatic habitats. The novelty of our approach lies in its consideration of irrigation practices and its ability to resolve complex ponding processes that contribute to potential larval habitats. The simulation was performed for the year of 2018 using ParFlow-Common Land Model (CLM) in a sugarcane plantation in the Oromia region, Ethiopia to examine the effects of rainfall and irrigation. The model was calibrated using field observations of larval habitats to successfully predict ponding at all surveyed locations from the validation dataset. Results show that without irrigation, at least half of the area inside the farms had a 40% probability of potential larval habitat occurrence. With irrigation, the probability increased to 56%. Irrigation d ened the seasonality of the potential larval habitats such that the peak larval habitat occurrence window during the rainy season was extended into the dry season. Furthermore, the stability of the habitats was prolonged, with a significant shift from semi-permanent to permanent habitats. Our study provides a hydrological perspective on the impact of environmental modification on malaria vector ecology, which can potentially inform malaria control strategies through better water management.
Publisher: American Meteorological Society
Date: 02-2006
DOI: 10.1175/JHM479.1
Abstract: Data assimilation in the field of predictive land surface modeling is generally limited to using observational data to estimate optimal model states or restrict model parameter ranges. To date, very little work has attempted to systematically define and quantify error resulting from a model's inherent inability to simulate the natural system. This paper introduces a data assimilation technique that moves toward this goal by accounting for those deficiencies in the model itself that lead to systematic errors in model output. This is done using a supervised artificial neural network to “learn” and simulate systematic trends in the model output error. These simulations in turn are used to correct the model's output each time step. The technique is applied in two case studies, using fluxes of latent heat flux at one site and net ecosystem exchange (NEE) of carbon dioxide at another. Root-mean-square error (rmse) in latent heat flux per time step was reduced from 27.5 to 18.6 W m−2 (32%) and monthly from 9.91 to 3.08 W m−2 (68%). For NEE, rmse per time step was reduced from 3.71 to 2.70 μmol m−2 s−1 (27%) and annually from 2.24 to 0.11 μmol m−2 s−1 (95%). In both cases the correction provided significantly greater gains than single criteria parameter estimation on the same flux.
Publisher: American Society of Tropical Medicine and Hygiene
Date: 11-10-2022
Abstract: Food insecurity, recurrent famine, and poverty threaten the health of millions of African residents. Construction of dams and rural irrigation schemes is key to solving these problems. The sub-Saharan Africa International Center of Excellence for Malaria Research addresses major knowledge gaps and challenges in Plasmodium falciparum and Plasmodium vivax malaria control and elimination in malaria-endemic areas of Kenya and Ethiopia where major investments in water resource development are taking place. This article highlights progress of the International Center of Excellence for Malaria Research in malaria vector ecology and behavior, epidemiology, and pathogenesis since its inception in 2017. Studies conducted in four field sites in Kenya and Ethiopia show that dams and irrigation increased the abundance, stability, and productivity of larval habitats, resulting in increased malaria transmission and a greater disease burden. These field studies, together with hydrological and malaria transmission modeling, enhance the ability to predict the impact of water resource development projects on vector larval ecology and malaria risks, thereby facilitating the development of optimal water and environmental management practices in the context of malaria control efforts. Intersectoral collaborations and community engagement are crucial to develop and implement cost-effective malaria control strategies that meet food security needs while controlling malaria burden in local communities.
Publisher: American Geophysical Union (AGU)
Date: 03-09-2021
DOI: 10.1029/2021GL094092
Abstract: It is very important to quantify errors of precipitation estimation products. However, the existing methods do not describe all error components and are therefore not comprehensive enough. In this study, we propose a four‐component error decomposition method (4CED) that decomposes the total errors of precipitation products into four independent parts: hit positive bias, hit negative bias, false bias, and missed bias. And we use it to evaluate the performance of the three latest satellite precipitation products in the eastern monsoon region of China. Our study reveals 4CED has apparent improvements compared with the previous method. Results also provide new insights for tracking error sources and quantifying the error magnitudes of precipitation products. Moreover, the proposed 4CED can be extended to different spatial and temporal scales. Our new method will not only contribute to product upgrades, but also provide guidance for potential applications.
Publisher: MDPI AG
Date: 04-08-2021
DOI: 10.3390/RS13163061
Abstract: Satellite precipitation estimates (SPEs) are promising alternatives to gauge observations for hydrological applications (e.g., streamflow simulation), especially in remote areas with sparse observation networks. However, the existing SPEs products are still biased due to imperfections in retrieval algorithms, data sources and post-processing, which makes the effective use of SPEs a challenge, especially at different spatial and temporal scales. In this study, we used a distributed hydrological model to evaluate the simulated discharge from eight quasi-global SPEs at different spatial scales and explored their potential scale effects of SPEs on a cascade of basins ranging from approximately 100 to 130,000 km2. The results indicate that, regardless of the difference in the accuracy of various SPEs, there is indeed a scale effect in their application in discharge simulation. Specifically, when the catchment area is larger than 20,000 km2, the overall performance of discharge simulation emerges an ascending trend with the increase of catchment area due to the river routing and spatial averaging. Whereas below 20,000 km2, the discharge simulation capability of the SPEs is more randomized and relies heavily on local precipitation accuracy. Our study also highlights the need to evaluate SPEs or other precipitation products (e.g., merge product or reanalysis data) not only at the limited station scale, but also at a finer scale depending on the practical application requirements. Here we have verified that the existing SPEs are scale-dependent in hydrological simulation, and they are not enough to be directly used in very fine scale distributed hydrological simulations (e.g., flash flood). More advanced retrieval algorithms, data sources and bias correction methods are needed to further improve the overall quality of SPEs.
No related grants have been discovered for KUOLIN HSU.