The effects of climate change have been observed in the Murrumbidgee River basin, which is one of the main river basins in the southeast region of Australia. The study area is the largest and most important agricultural production area within the Murray Darling Basin (MDB). It produces more than AUD 1.9 billion of agricultural products annually and accounts for about 46% of Australia’s total agricultural production. Since Australia’s economy largely depends on its natural resources, climate change adversely impacts the economy in various ways. According to the Intergovernmental Panel on Climate Change’s fifth assessment report (IPCC, AR5), the adaptive capacity and adaptation processes have increased in Australia. The country has implemented policies and management changes in both rural and urban water systems to adapt to future drought, unexpected floods, and other climatic changes. In this study, future catchment runoff has been estimated using the hydrological model, Simplified Hydrolog (SIMHYD), which is integrated with data from three different General Circulation Models (GCMs) and future emission scenarios. Two different representative concentration pathway (RCP) emission scenarios, RCP 4.5 and RCP 8.5, have been used to obtain downscaled future precipitation and evapotranspiration data for the period of 2016 to 2100. Modeling results from the two emission scenarios showed an anticipated warmer and drier climate for the Murrumbidgee River catchment. Runoff in the Murrumbidgee catchment is affected by various dams and weirs, which yields positive results in runoff even when the monthly rainfall trend decreases. The overall runoff simulation result indicated that the impact of climate change is short and intense. The result of the Simplified Hydrolog (SIMHYD) modeling tool used in this study under the RCP 4.5 scenario for the period 2016 to 2045 indicates a significant future impact from climate change on the volumes of runoff in the Murrumbidgee River catchment. For the same period, the climate change prediction showed a decrease in total annual rainfall within the range of 2% to 62%. This reduction in rainfall is projected to decrease river runoff in the upper catchments (e.g., Tharwa, and Yass) by 17% to 58% over the projected periods. However, the runoff trends in the lower sub-catchments (e.g., Borambola) have increased by 137% to 87% under RCP 4.5 and RCP 8.5, respectively. This increasing potential runoff trend in the lower Murrumbidgee catchments gives an indication to build irrigation dams for dry season irrigation management.

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.