Rainfall estimation from surface soil moisture using SM2RAIN in cold mountainous areas

Lai, Yao; Tian, Jie; Kang, Weiming; Gao, Chao; Hong, Weijie; He, Chansheng

doi:10.1016/j.jhydrol.2022.127430

Cited by 12 publications

(3 citation statements)

References 75 publications

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“…The annual average temperature at the AGS is from −3.1 to 3.6 • C, and the annual average precipitation, most of which occurs from June to September, is about 300-600 mm [2,19,47]. Both observation sites have been used by a number of studies as good representatives of the study area [61][62][63][64]. The measurement of the CRNS observation reliability at the two observation sites has also been adequately verified by previous studies [48,64].…”

Section: Study Areasmentioning

confidence: 55%

Comparison of Data Fusion Methods in Fusing Satellite Products and Model Simulations for Estimating Soil Moisture on Semi-Arid Grasslands

Zhu,

Zhang,

et al. 2023

Remote Sensing

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In arid and semi-arid areas, soil moisture (SM) plays a crucial role in land-atmosphere interactions, hydrological processes, and ecosystem sustainability. SM data at large scales are critical for related climatic, hydrological, and ecohydrological research. Data fusion based on satellite products and model simulations is an important way to obtain SM data at large scales; however, little has been reported on the comparison of the data fusion methods in different categories. Here, we compared the performance of two widely used data fusion methods, the Ensemble Kalman Filter (EnKF) and the Back-Propagation Artificial Neural Network (BPANN), in the degraded grassland site (DGS) and the alpine grassland site (AGS). The SM data from the Community Land Model 5.0 (CLM5.0) and the Soil Moisture Active and Passive (SMAP) were fused and validated against the observations of the Cosmic-Ray Neutron Sensor (CRNS) to avoid the impacts of scale-mismatch. Results show that compared with the original data sets at both sites, the RMSE of the fused data by BPANN (FD-BPANN) and EnKF (FD-EnKF) had improved by more than 50% and 31%, respectively. Overall, the FD-BPANN performs better than the FD-EnKF because the BPANN method assigned higher weights to input data with better performance and the EnKF method is affected by the strong variabilities of both the fused CLM5.0 and SMAP data and the CRNS data. However, in terms of the percentile range, the FD-BPANN showed the worst performance, with overestimations in the low SM range of 25th percentile (<Q25), because the BPANN method tends to be trapped in a local minimum. The BPANN method performed better in humid areas, then followed by semi-humid areas, and finally arid and semi-arid areas. Moreover, compared with the previous studies in arid and semi-arid areas, the BPANN method in this study performed better.

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Section: Study Areasmentioning

confidence: 55%

Comparison of Data Fusion Methods in Fusing Satellite Products and Model Simulations for Estimating Soil Moisture on Semi-Arid Grasslands

Zhu,

Zhang,

et al. 2023

Remote Sensing

Self Cite

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“…The bottom-up approach has been applied at a global scale 23 , 24 and a local scale in Asia 25 – 28 , Australia 29 , 30 , Europe 31 , 32 , Africa 33 , 34 , and America 35 , 36 . More than 150 papers have been published in peer-reviewed journals related to SM2RAIN rainfall products, which have highlighted the high potential of this approach over different parts of the globe and also the effectiveness of them in many applications including flood prediction 37 , water resources management 38 , and prediction of landslide 39 , soil erosion 33 , and crop yield 40 .…”

Section: Background and Summarymentioning

confidence: 99%

SM2RAIN-Climate, a monthly global long-term rainfall dataset for climatological studies

Mosaffa,

Filippucci,

Massari

et al. 2023

Sci Data

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A reliable and accurate long-term rainfall dataset is an indispensable resource for climatological studies and crucial for application in water resource management, agriculture, and hydrology. SM2RAIN (Soil Moisture to Rain) derived datasets stand out as a unique and wholly independent global product that estimates rainfall from satellite soil moisture observations. Previous studies have demonstrated the SM2RAIN products’ high potential in estimating rainfall around the world. This manuscript describes the SM2RAIN-Climate rainfall product, which uses the European Space Agency (ESA) Climate Change Initiative (CCI) soil moisture v06.1 to provide monthly global rainfall for the 24-year period 1998–2021 at 1-degree spatial resolution. The assessment of the proposed rainfall dataset against different existing state-of-the-art rainfall products exhibits the robust performance of SM2RAIN-Climate in most regions of the world. This performance is indicated by correlation coefficients between SM2RAIN-Climate and state-of-the-art products, consistently exceeding 0.8. Moreover, evaluation results indicate the potential of SM2RAIN-Climate as an independent rainfall product from other satellite rainfall products in capturing the pattern of global rainfall trend.

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“…Once ET is calculated for a specific vegetation condition (ET0), the "crop coefficient" (Kc) can be applied to convert it for other vegetation types (Allen et al, 1998). This approach has been proven to be more cost-effective to estimate ET0 by utilizing other meteorological variables, although direct measuring of ET can be applied using lysimeters or the turbulent vortex covariance method (Lai et al, 2023). ETP and ET0 can be applied to different applications, where ETP is considered more general and suitable for larger areas, while ET0 is more specific and, therefore, particularly useful for project planning in small areas (Xiang et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Reference Evapotranspiration for Sparse Data and Data Gap Filling in Python: A Study Case at Urucuia River Basin, Brazil

Corrêa,

Silva,

Scariot

et al. 2024

Preprint

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Despite the several existing initiatives to obtain evapotranspiration data for several scientific analysis, it remains a challenge when dealing with data scarcity, especially when it is coupled with many data gaps of long-time climatological data series. In this study, we developed two Python scripts to perform reference evapotranspiration calculations in data scarcity conditions, filling potential data gaps, and testing them in the Urucuia River watershed.The first script (ETO_calc.py) is able to calculate evapotranspiration both by the Penman-Monteith method, adapting it according to user data availability, and by the Hargreaves-Samani method. The second script (Filling_gaps.py) fills out potential data gaps based on input from neighboring stations and tests the adequacy of the filling. The first script was tested by comparing the data generated by it with the online calculator 'ETCalc', while the second was tested by filling in a station's data from five neighboring stations, comparing the original data with the filled data. The output of the first script was practically identical to the online calculator used for comparison, which confirms the adequacy of the estimation made by ETO_calc.py. As for the second script, the regional weighting method and both linear and multiple regression methods were the methods that best filled in the missing data for the main station. This is due to their better fit to the historical variation of the data compared to the other tested methods. The results allow us to conclude that both scripts proved to be suitable tools for estimating evapotranspiration data.

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Rainfall estimation from surface soil moisture using SM2RAIN in cold mountainous areas

Cited by 12 publications

References 75 publications

Comparison of Data Fusion Methods in Fusing Satellite Products and Model Simulations for Estimating Soil Moisture on Semi-Arid Grasslands

Comparison of Data Fusion Methods in Fusing Satellite Products and Model Simulations for Estimating Soil Moisture on Semi-Arid Grasslands

SM2RAIN-Climate, a monthly global long-term rainfall dataset for climatological studies

Reference Evapotranspiration for Sparse Data and Data Gap Filling in Python: A Study Case at Urucuia River Basin, Brazil

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