Estimation of Initial Abstraction for Hydrological Modeling Based on Global Land Data Assimilation System–Simulated Datasets

Abstract

Initial abstraction (Ia) is a sensitive parameter in hydrological models, and its value directly determines the amount of runoff. Ia, which is influenced by many factors related to antecedent watershed condition (AWC), is difficult to estimate due to lack of observed data. In the Soil Conservation Service curve number (SCS-CN) method, it is often assumed that Ia is 0.2 times the potential maximum retention S. Yet this assumption has frequently been questioned. In this paper, Ia/S and factors potentially influencing Ia were collected from rainfall–runoff events. Soil moisture and evaporation data were extracted from GLDAS-Noah datasets to represent AWC. Based on the driving factors of Ia, identified using the Pearson correlation coefficient and maximal information coefficient, artificial neural network (ANN)-estimated Ia was applied to simulate the selected flood events in the Hydrologic Engineering Center Hydrologic Modeling System (HEC-HMS) model. The results indicated that Ia/S varies over different events and different watersheds. Over 75% of the Ia/S values are less than 0.2 in the two study areas. The driving factors affecting Ia vary over different watersheds, and the antecedent precipitation index appears to be the most influential factor. Flood simulation by the HEC-HMS model using statistical Ia gives the best fitness, whereas applying ANN-estimated Ia outperforms the simulation with median Ia/S. For over 60% of the flood events, ANN-estimated Ia provided better fitness in flood peak and depth, with an average Nash–Sutcliffe efficiency coefficient of 0.76 compared to 0.71 for median Ia/S. The proposed ANN-estimated Ia is physically based and can be applied without calibration, saving time in constructing hydrological models.

Estimation and Validation Study of Soil Moisture Using GPS-IR Technique Over a Tropical Region: Variability of SM With Rainfall and Energy Fluxes

“…These vegetation effects are quantified by considering the values of A norm . An electrodynamic model was proposed in [30], which showed that an increase in SM content by 0.4 cm 3 cm −3 , which is the difference between the residual and saturated moisture contents for a typical soil, results in A norm decreasing from 1.0 to 0.78. Therefore, in the case of negligible vegetation growth, A norm would be above 0.78.…”

“…Hence fluxes data of ERA-5 is considered to study the variability of SM with the fluxes. For the purpose of comparison, energy fluxes from GLDAS data [30] are used and its variability with rainfall is shown in Fig. 6(a) and (b).…”

“…This method is immune to errors due to its advantage of uniformity over several sensors. Comparison and validation study has been done using in situ probe data [28], SMOS satellite data [5] and model datasets of ERA-5 [29], and Global Land Data Assimilation System (GLDAS) [30], [31]. Annual and seasonal variabilities of SM with rainfall and energy fluxes have been reported using automatic weather station (AWS) rainfall, ERA-5, and GLDAS energy fluxes data.…”

Analysis of Flood Discharge due to Impact of Tropical Cyclone

“…The initial abstraction is a sensitive parameter in hydrological models because its value directly determines the amount of runoff [21]. This is due to its ability to show the amount of precipitation required to fall to have an excess yield on the soil surface.…”

Evaluation of reanalysis soil moisture products using cosmic ray neutron sensor observations across the globe

“…Reanalysis products provide soil moisture data over long time periods (Li et al, 2005;Baatz et al, 2021) and typically merge soil moisture observations and land surface model output by adopting data assimilation techniques, which often results in better soil moisture estimation than satellite products (Naz et al, 2020;Beck et al, 2021;Mahto and Mishra, 2019). At present, reanalysis products are employed in a wide range of fields such as hydrological model initialisation (Zheng et al, 2020), flood modelling (McClean et al, 2023;El Khalki et al, 2020;Zheng et al, 2023), drought monitoring (Chen et al, 2019;El Khalki et al, 2020) and climatology research (Miralles et al, 2014). Currently, many reanalysis products exist including ERA5-Land (Muñoz Sabater 2019;Muñoz-Sabater et al, 2021), CFSv2 (Saha et al, 2011(Saha et al, , 2014, MERRA2 (GMAO, 2015;Gelaro et al, 2017), JRA55 (JMA, 2013;Kobayashi et al, 2015), GLDAS-Noah (Rodell et al, 2004;Beaudoing and Rodell, 2020), CRA40 (Liu et al, 2017;Li et al, 2021), GLEAM (Miralles et al, 2011;Martens et al, 2017) datasets and SMAP Level 4 datasets (Reichle et al, 2019(Reichle et al, , 2017a (one should note that technically speaking, GLDAS-Noah and GLEAM datasets are global land model-based products; we termed them "reanalysis products" in this paper for consistency).…”