Modelling Floodplain Vegetation Response to Groundwater Variability Using the ArcSWAT Hydrological Model, MODIS NDVI Data, and Machine Learning

Abstract

This study modelled the relationships between vegetation response and available water below the soil surface using Terra’s moderate resolution imaging spectroradiometer (MODIS), Normalised Difference Vegetation Index (NDVI), and soil water content (SWC). The Soil & Water Assessment Tool (SWAT) interface known as ArcSWAT was used in ArcGIS for the groundwater analysis. The SWAT model was calibrated and validated in SWAT-CUP software using 10 years (2001–2010) of monthly streamflow data. The average Nash-Sutcliffe efficiency during the calibration and validation was 0.54 and 0.51, respectively, indicating that the model performances were good. Nineteen years (2002–2020) of monthly MODIS NDVI data for three different types of vegetation (forest, shrub, and grass) and soil water content for 43 sub-basins were analysed using the WEKA, machine learning tool with a selection of two supervised machine learning algorithms, i.e., support vector machine (SVM) and random forest (RF). The modelling results show that different types of vegetation response and soil water content vary in the dry and wet seasons. For example, the model generated high positive relationships (r = 0.76, 0.73, and 0.81) between the measured and predicted NDVI values of all vegetation in the sub-basin against the groundwater flow (GW), soil water content (SWC), and combination of these two variables, respectively, during the dry season. However, these relationships were reduced by 36.8% (r = 0.48) and 13.6% (r = 0.63) against GW and SWC, respectively, in the wet season. Our models also predicted that vegetation in the top location (upper part) of the sub-basin is highly responsive to GW and SWC (r = 0.78, and 0.70) during the dry season. Although the rainfall pattern is highly variable in the study area, the summer rainfall is very effective for the growth of the grass vegetation type. The results predicted that the growth of vegetation in the top-point location is highly dependent on groundwater flow in both the dry and wet seasons, and any instability or long-term drought can negatively affect these floodplain vegetation communities. This study has enriched our knowledge of vegetation responses to groundwater in each season, which will facilitate better floodplain vegetation management.

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

“…The Murrumbidgee River begins its journey from Kosciuszko National Park with an altitude of 1500 m and flows around 316 km to enter the Burrinjuck Reservoir. We have considered this Burrinjuck sub-catchment as our area of interest because of plant diversity in this floodplain due to moderate slopes in its topography, where elevation varies from 370 m to 934 m [16]. The land use and land cover in this area are mostly controlled by forest and pasture; however, wheat and cereals also grow well in this area, contributing to the national agricultural production [32].…”

“…The datasets used in this study, including their source of availability, are listed in Table 1. A detailed description of SWAT input data was outlined by Muhury et al [16]. The LAI is widely used in research studies to assess the amount of leaf area in an ecosystem, which is a dimensionless and time-dependent vegetation parameter [74].…”

“…Previous studies also explained that temperature increases due to climate change can prolong the vegetation growth season and promote vegetation productivity [15]. However, in arid and semi-arid regions, increasing temperature may cause water stresses and adversely affect vegetation growth, especially in the floodplain areas [16]. Researchers have addressed the biophysical processes of vegetation impact on climate in numerous studies by investigating deforestation [17][18][19], changes in land use patterns and their vegetation condition [20][21][22], variations in the physiological characteristics of vegetation [23][24][25], and impact on monsoonal circulations [26][27][28][29].…”

“…By the application of precipitation data in the SWAT model to identify vegetation conditions for the new growth season, the simulation of vegetation dynamics in the SWAT model has been improved [49,50]. Previous studies have successfully applied the SWAT model globally for drought monitoring and vegetation growth predictions [16]. However, to date, there has been no study on vegetation greenness modeling by the SWAT hydrological model under climate scenarios based on projected minimum and maximum temperature and precipitation.…”

Study on the Annual Runoff Change and Its Relationship with Fractional Vegetation Cover and Climate Change in the Chinese Yellow River Basin

“…Forested areas, characterized by high vegetation cover, decrease annual runoff because wetter forests have a stronger runoff evapotranspiration capacity. In 1909, the results of the American-led Wagon Wheel Gap experiment indicated that deforestation of 81 ha of scrub willow and coniferous forests would increase annual water yield by approximately 30 mm [12]. Wang et al (2023) [13] summarized that the driving factor for runoff changes in the upper reach of the Yellow River basin is mainly precipitation, with a contribution rate of 39.31-54.70%.…”