Multivariate assimilation of satellite-based leaf area index and ground-based river streamflow for hydrological modelling of irrigated watersheds using SWAT+

Mohammadi, Omid; Alizadeh, Hosein; Mojaradi, Barat; Bayat, Mehrad

doi:10.1016/j.jhydrol.2022.128012

Cited by 9 publications

(5 citation statements)

References 110 publications

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“…Future work will focus on joint assimilation of satellite soil moisture and in situ observations for multi-variable assimilation applications, optimization of the inflation factor, and machine learning methods within the chosen approach [ 89 ]. Additionally, leaf area index and streamflow data assimilation [ 18 ] will be considered, as well as coupling with WRF model extension.…”

Section: Discussionmentioning

confidence: 99%

“…If the last layer becomes saturated and there is still excess water, the model redistributes it back to the first layer. Therefore, soil moisture [ 16 ], evapotranspiration [ 17 ], streamflow [ 18 ], sediment, nutrient components [ 19 ], and crop yield can be estimated with SWAT [ 20 ]. Its significance and utility in Earth physical studies and water resource management cannot be overstated [ 15 ].…”

Section: Introductionmentioning

confidence: 99%

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Basin Scale Soil Moisture Estimation with Grid SWAT and LESTKF Based on WSN

Zhang,

Hou,

Huang

2023

Sensors

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This research utilized in situ soil moisture observations in a coupled grid Soil and Water Assessment Tool (SWAT) and Parallel Data Assimilation Framework (PDAF) data assimilation system, resulting in significant enhancements in soil moisture estimation. By incorporating Wireless Sensor Network (WSN) data (WATERNET), the method captured and integrated local soil moisture characteristics, thereby improving regional model state estimations. The use of varying observation search radii with the Local Error-subspace Transform Kalman Filter (LESTKF) resulted in improved spatial and temporal assimilation performance, while also considering the impact of observation data uncertainties. The best performance (improvement of 0.006 m3/m3) of LESTKF was achieved with a 20 km observation search radii and 0.01 m3/m3 observation standard error. This study assimilated wireless sensor network data into a distributed model, presenting a departure from traditional methods. The high accuracy and resolution capabilities of WATERNET’s regional soil moisture observations were crucial, and its provision of multi-layered soil temperature and moisture observations presented new opportunities for integration into the data assimilation framework, further enhancing hydrological state estimations. This study’s implications are broad and relevant to regional-scale water resource research and management, particularly for freshwater resource scheduling at small basin scales.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Basin Scale Soil Moisture Estimation with Grid SWAT and LESTKF Based on WSN

Zhang,

Hou,

Huang

2023

Sensors

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“…Furthermore, the assimilation of canopy parameters, including LAI), and/or soil moisture (SM), has led to improved model performance in several areas. These areas include the simulation of the carbon cycle [26][27][28][29], evapotranspiration (ET) [13,30], hydrological processes [31][32][33], agriculture dynamics [34][35][36], ecosystem functions [24,37], and seasonal temperature predictions [38].…”

Section: Introductionmentioning

confidence: 99%

Assimilation of Remotely Sensed Leaf Area Index for Improving Land Surface Simulation Performance at a Global Scale

Ling,

Gao,

Tang

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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The Community Land Model version 4 with carbon and nitrogen components (CLM4CN) is coupled with Data Assimilation Research Testbed (DART) to assimilate remotely sensed leaf area index (LAI), to analyze the improvement in model performance for simulating land surface variables and landatmospheric exchange fluxes. The results demonstrate that assimilation effectively addresses the issue of significant overestimation of LAI values, particularly noticeable in regions characterized by low latitudes and dense vegetation coverage. On a global scale, the disparities between simulated and assimilated LAI relative to observational data, are measured at 0.90 and -0.07, representing 54.1% and 3.9% of the observed values, respectively. The root mean square difference (RMSD) for assimilated LAI is 1.61 comparing with the simulated LAI of 1.85. Assimilating LAI globally leads to a noteworthy 1% reduction in the mean relative difference of the global average 2-meter air temperature (T2m) and a concurrent decrease of 0.15℃ in RMSD. However, at the global level, the assimilation of LAI does not yield a significant enhancement in the modeling capability of heat fluxes, although modeling capability of sensible heat (HS) slightly outperforms latent heat (LE). Improvements in land surface variables after assimilation show significant variations at regional scales due to factors such as vegetation coverage and climatic conditions. Overall, in regions characterized by periodic changes in vegetation, such as forested areas in Western Eurasian Continent (Region 5), the enhancements in T2m and HS after assimilating LAI are particularly notable, with mean relative difference reduced by 7% and 20%, respectively.

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“…The LAI provides information about the density and spatial arrangement of leaves within a vegetation canopy, which is essential for understanding various ecological processes and estimating primary productivity [41,42]. Moreover, the LAI is a measurement commonly used in ecology and remote sensing to describe the measurement of leaf area relative to ground area within a plant or vegetation canopy that represents the potential leaf surface area for photosynthesis [43].…”

Section: Introductionmentioning

confidence: 99%

Modelling Floodplain Vegetation Response to Climate Change, Using the Soil and Water Assessment Tool (SWAT) Model Simulated LAI, Applying Different GCM’s Future Climate Data and MODIS LAI Data

Muhury,

Apan,

Maraseni

2024

Remote Sensing

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Scientists widely agree that anthropogenically driven climate change significantly impacts vegetation growth, particularly in floodplain areas, by altering river flow and flood regimes. This impact will accelerate in the future, according to climate change projections. For example, in Australia, climate change has been attributed to a decrease in winter precipitation in the range of 56% to 72.9% and an increase in summer from 11% to 27%, according to different climate scenarios. This research attempts to understand vegetation responses to climate change variability at the floodplain level. Further, this study is an effort to enlighten our understanding of temporal climate change impacts under different climate scenarios. To achieve these aims, a semi-distributed hydrological model was applied at a sub-catchment level to simulate the Leaf Area Index (LAI). The model was simulated against future time series of climate data according to Global Climate Model (GCM) projections. The time series data underwent a non-parametric Mann–Kendall test to detect trends and assess the magnitude of change. To quantify the model’s performance, calibration and validation were conducted against the Moderate Resolution Imaging Spectroradiometer (MODIS) LAI. The calibration and validation results show Nash–Sutcliffe efficiency (NSE) values of 0.85 and 0.78, respectively, suggesting the model’s performance is very good. The modeling results reveal that the rainfall pattern fluctuates under climate projections within the study site, in which vegetation tends to be more vibrant during the warmer seasons. Moreover, the modeling results highlighted increases in the average projected future winter temperatures, which can help vegetation growth during winter. The results of this study may be employed for sustainable floodplain management, restoration, land-use planning, and policymaking, and help floodplain communities better prepare for and respond to changing flood patterns and related challenges under a future changing climate.

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Multivariate assimilation of satellite-based leaf area index and ground-based river streamflow for hydrological modelling of irrigated watersheds using SWAT+

Cited by 9 publications

References 110 publications

Basin Scale Soil Moisture Estimation with Grid SWAT and LESTKF Based on WSN

Basin Scale Soil Moisture Estimation with Grid SWAT and LESTKF Based on WSN

Assimilation of Remotely Sensed Leaf Area Index for Improving Land Surface Simulation Performance at a Global Scale

Modelling Floodplain Vegetation Response to Climate Change, Using the Soil and Water Assessment Tool (SWAT) Model Simulated LAI, Applying Different GCM’s Future Climate Data and MODIS LAI Data

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