Quantification of global Digital Elevation Model (DEM) – A case study of the newly released NASADEM for a river basin in Central Vietnam

Tran, Thanh-Nhan-Duc; Nguyen, Binh X.; Vo, Ngoc Duong; Le, Manh-Hung; Dinh, Nguyen Quoc; Lakshmi, Venkataraman; Bolten, J. D.

doi:10.1016/j.ejrh.2022.101282

Cited by 28 publications

(14 citation statements)

References 64 publications

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“…Geo AI data and analysis can provide a strong basis for better and more informed decision-making in the face of disasters. Geo-AI can monitor and predict floods more accurately based on real-time satellite imagery and weather data, helping proactive and more timely weather data, helping proactive and more timely management [24]. The use of Geo-AI in combination with, GIS, and GEE to identify flood-prone areas provides enormous benefits in mitigating and limiting flood risks.…”

Section: Resultsmentioning

confidence: 99%

Mapping Flood-Prone Areas Using GIS Through as Geo-Artificial Intelligence (Geo-Ai) Approach in Bengkulu City

Apriana,

Al Ansory,

Agustina

et al. 2024

J. Technomaterial Phys.

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Bengkulu City is an area prone to flooding due to its proximity to the river estuary. Flooding in Bengkulu City usually occurs during the rainy season, when high rainfall and overflowing rivers can cause inundation in several areas around the city. This research aims to identify flood-prone areas in Bengkulu City through a Geographic Artificial Intelligence (Geo-AI) approach. Geo-AI is an artificial intelligent machine with geospatial data, including satellite images and weather data, whose data analysis is more accurate and efficient in identifying flood-prone areas. Geographic Information System (GIS) can present objects of flood-prone areas from the real world in digital form. Through Google Earth Engine (GEE), satellite imagery data and other geospatial data are processed and analyzed using artificial intelligence algorithms to identify flood patterns and frequently flooded areas. The final result of this research is a map of flood-prone areas in Bengkulu City. On the map, it can be concluded that the red-colored areas including Rawa Makmur, Tanjung Agung, Bentiring, Kebun Tebeng, Penurunan, Sukarami, Pekan Sabtu, and Air Sebakul are areas that are often flooded so that these areas can be confirmed as flood-prone.

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Section: Resultsmentioning

confidence: 99%

Mapping Flood-Prone Areas Using GIS Through as Geo-Artificial Intelligence (Geo-Ai) Approach in Bengkulu City

Apriana,

Al Ansory,

Agustina

et al. 2024

J. Technomaterial Phys.

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“…In recent years, this well-known watershed-scale model has been used more frequently in the United States, Europe, and other regions [70] due to its high capability to solve hydrological-related issues. Many studies have used SWAT to reveal the impacts of land-use and land cover (LULC), climate change [71], robustness of SPPs [13], and pesticide and agricultural chemical pollutions on streamflow or sediment loads [5,13,48,70,[72][73][74][75][76]. The first version of this model was developed for ArcGIS, as explained by [69,76].…”

Section: Swat Modelmentioning

confidence: 99%

Quantification of Gridded Precipitation Products for the Streamflow Simulation on the Mekong River Basin Using Rainfall Assessment Framework: A Case Study for the Srepok River Subbasin, Central Highland Vietnam

et al. 2023

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Many fields have identified an increasing need to use global satellite precipitation products for hydrological applications, especially in ungauged basins. In this study, we conduct a comprehensive evaluation of three Satellite-based Precipitation Products (SPPs): Integrated Multi–satellitE Retrievals for GPM (IMERG) Final run V6, Soil Moisture to Rain (SM2RAIN)-Advanced SCATterometer (ASCAT) V1.5, and Multi-Source Weighted-Ensemble Precipitation (MSWEP) V2.2 for a subbasin of the Mekong River Basin (MRB). The study area of the Srepok River basin (SRB) represents the Central Highland sub-climatic zone in Vietnam under the impacts of newly built reservoirs during 2001–2018. In this study, our evaluation was performed using the Rainfall Assessment Framework (RAF) with two separated parts: (1) an intercomparison of rainfall characteristics between rain gauges and SPPs; and (2) a hydrological comparison of simulated streamflow driven by SPPs and rain gauges. Several key findings are: (1) IMERGF-V6 shows the highest performance compared to other SPP products, followed by SM2RAIN-ASCAT V1.5 and MSWEP V2.2 over assessments in the RAF framework; (2) MSWEP V2.2 shows discrepancies during the dry and wet seasons, exhibiting very low correlation compared to rain gauges when the precipitation intensity is greater than 15 mm/day; (3) SM2RAIN–ASCAT V1.5 is ranked as the second best SPP, after IMERGF-V6, and shows good streamflow simulation, but overestimates the wet seasonal rainfall and underestimates the dry seasonal rainfall, especially when the precipitation intensity is greater than 20 mm/day, suggesting the need for a recalibration and validation of its algorithm; (4) SM2RAIN-ASCAT had the lowest bias score during the dry season, indicating the product’s usefulness for trend analysis and drought detection; and (5) RAF shows good performance to evaluate the performance of SPPs under the impacts of reservoirs, indicating a good framework for use in other similar studies. The results of this study are the first to reveal the performance of MSWEP V2.2 and SM2RAIN-ASCAT V1.5. Additionally, this study proposes a new rainfall assessment framework for a Vietnam basin which could support future studies when selecting suitable products for input into hydrological model simulations in similar regions.

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“…In recent years, the SWAT tool has become popular in modeling the river basins of the United States, Europe, and other regions [36,37,59] as the model has the capability to handle complex hydrological problems [60]. Other efforts have been made that employed the SWAT model regarding the effects of land use [4,61], climate change impacts [6,43,62], validating DEM products [63], or determining the robustness of SPPs [16,42].…”

Section: Swat Modelmentioning

confidence: 99%

Evaluation of Satellite-Derived Precipitation Products for Streamflow Simulation of a Mountainous Himalayan Watershed: A Study of Myagdi Khola in Kali Gandaki Basin, Nepal

Aryal,

Tran,

Kumar

et al. 2023

Remote Sensing

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This study assesses four Satellite-derived Precipitation Products (SPPs) that are corrected and validated against gauge data such as Soil Moisture to Rain—Advanced SCATterometer V1.5 (SM2RAIN-ASCAT), Multi-Source Weighted-Ensemble Precipitation V2.8 (MSWEP), Global Precipitation Measurement Integrated Multi-satellitE Retrievals for GPM Final run V6 (GPM IMERGF), and Climate Hazards Group InfraRed Precipitation with Station (CHIRPS). We evaluate the performance of these SPPs in Nepal’s Myagdi Khola watershed, located in the Kali Gandaki River basin, for the period 2009–2019. The SPPs are evaluated by validating the gridded precipitation products using the hydrological model, Soil and Water Assessment Tool (SWAT). The results of this study show that the SM2RAIN-ASCAT and GPM IMERGF performed better than MSWEP and CHIRPS in accurately simulating daily and monthly streamflow. GPM IMERGF and SM2RAIN-ASCAT are found to be the better-performing models, with higher NSE values (0.63 and 0.61, respectively) compared with CHIRPS and MSWEP (0.45 and 0.41, respectively) after calibrating the model with monthly data. Moreover, SM2RAIN-ASCAT demonstrated the best performance in simulating daily and monthly streamflow, with NSE values of 0.57 and 0.63, respectively, after validation. This study’s findings support the use of satellite-derived precipitation datasets as inputs for hydrological models to address the hydrological complexities of mountainous watersheds.

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Quantification of global Digital Elevation Model (DEM) – A case study of the newly released NASADEM for a river basin in Central Vietnam

Cited by 28 publications

References 64 publications

Mapping Flood-Prone Areas Using GIS Through as Geo-Artificial Intelligence (Geo-Ai) Approach in Bengkulu City

Mapping Flood-Prone Areas Using GIS Through as Geo-Artificial Intelligence (Geo-Ai) Approach in Bengkulu City

Quantification of Gridded Precipitation Products for the Streamflow Simulation on the Mekong River Basin Using Rainfall Assessment Framework: A Case Study for the Srepok River Subbasin, Central Highland Vietnam

Evaluation of Satellite-Derived Precipitation Products for Streamflow Simulation of a Mountainous Himalayan Watershed: A Study of Myagdi Khola in Kali Gandaki Basin, Nepal

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