3.1. Isotopic Compositions of River Water
The isotopic composition of river water within the Mun River basin, encompassing δ
18O, δ
2H, and
d-excess, has been meticulously analyzed across various locations, including the Upstream Mun River (UMR), Chi River (CR), and Mun River (MR). This comprehensive dataset, covering both wet and dry seasons, unveils profound insights into the basin’s hydrological dynamics (
Figure 3). Throughout the period under study, the isotopic values displayed substantial variability: δ
18O values ranged from -9.44‰ to 3.11‰, δ
2H spanned from -67.69‰ to 2.92‰, and
d-excess exhibited significant fluctuations (-22.50‰ to 12.36‰), highlighting the intricate interplay among evaporation, precipitation, and water source mixing that characterizes the basin’s hydrological processes.
To elaborate on the mean isotopic compositions for the entire period, the UMR, CR, and MR have revealed distinct isotopic signatures. The mean river δ
18O in the UMR was -4.17‰, in the CR was -4.56‰, and in the MR was slightly more enriched with a mean of -3.84‰ (
Figure 3a). Similarly, the mean δ
2H values were -36.85‰ for the UMR, -38.76‰ for the CR, and -34.04‰ for the MR, indicating a slight enrichment in the MR relative to the other locations (
Figure 3b). The mean river
d-excess values further elucidate the differences in hydrological processes across these locations: -3.50‰ for the UMR, -2.28‰ for the CR, and -3.33‰ for the MR (
Figure 3c).
These mean values underscore the spatial variability in isotopic compositions across the Mun River basin. The differences between locations can be attributed to a variety of factors, including local evaporation rates [
46], the mixing of water sources (e.g., groundwater inputs, tributary influx) [
47,
48], and the regional climate’s influence on precipitation patterns [
8]. The UMR and CR tend to show more depleted isotopic values, which could reflect the impact of higher elevation sources and more direct precipitation inputs, while the MR’s relative enrichment in isotopic values may indicate lower elevation sources, increased evaporation, or the mixing of different water sources as the river progresses downstream. This spatial variability provides essential clues to the hydrological connectivity and water cycle dynamics within the Mun River basin.
Seasonal variations in the isotopic composition of river water within the Mun River basin are marked, with distinct shifts observed between the wet and dry seasons. These shifts indicate the significant role of direct precipitation and the potential modulation of evaporation effects across different times of the year. For instance, the mean δ
18O value in the UMR during the wet season is notably more depleted at -4.36‰ compared to a less depleted mean of -3.97‰ during the dry season (
Figure 3a). This pattern of depletion in the wet season relative to the dry season is consistent across other locations within the basin, including the CR and the MR, underscoring the pronounced influence of seasonal precipitation on isotopic signatures [
11,
49].
In addition to δ
18O, the seasonal variations in
d-excess further illuminate the complexities of hydrological processes at play. The mean
d-excess values during the wet and dry seasons reveal differences in evaporation and moisture source conditions across the basin [
50,
51,
52]. For example, the UMR exhibits a mean d-excess of -2.79‰ in the wet season, which shifts to -4.23‰ in the dry season. (
Figure 3c) This change suggests that during the dry season, evaporation effects become more pronounced, or there might be a variation in the moisture sources contributing to river water [
53].
Comparing seasonal variations between locations, each river demonstrates unique patterns of isotopic changes that reflect local hydrological and meteorological influences. The CR and MR also show differences in their seasonal isotopic values, with generally more depleted δ
18O and δ
2H values during the wet season, indicative of enhanced precipitation input and potentially reduced evaporation. However, the extent of seasonal variation in
d-excess across these locations further points to the differential impact of evaporation and moisture source dynamics within the basin [
35,
54].
For precipitation, the isotopic values across the basin exhibit significant variability, with δ
18O and δ
2H reflecting seasonal influences, moisture sources, and atmospheric circulation patterns (
Figure 4). The mean δ
18O values in precipitation range from -6.53‰ to -5.85‰ across different locations (averaged -6.11‰ for all samples), demonstrating the influence of temperature, altitude, and rainfall amount on isotopic fractionation. δ
2H values (-136.01‰ to 26.75‰, averaged -39.65‰) and
d-excess in precipitation also vary, with
d-excess values (-19.82‰ to 20.85‰, averaged 9.25‰) providing insights into the evaporative conditions at the moisture source regions and during raindrop fall [
55,
56]. The range of
d-excess values, from negative to significantly positive, underscores the complex interplay between local and regional hydrological processes. The rainfall
d-excess values (averaged 8.48‰ for CR, 9.42‰ for UMR, and 9.86‰ for MR) suggest that, on average, precipitation in the basin tends to originate from moisture sources with similar characteristics [
57].
River water isotopes, by comparison, tend to be more enriched than those in precipitation. This depletion is generally attributed to the evaporative enrichment of heavy isotopes in open water bodies, which results in higher δ
18O and δ
2H values in river water compared to precipitation. The mean δ
18O values in river water for the entire period studied across the UMR, CR, and MR locations indicate this enrichment, alongside variations in
d-excess that underscore the differences in evaporation rates and moisture mixing processes between the river and atmospheric water [
11,
35,
44].
The deviations between river water and precipitation isotopic signatures across locations reveal the complex hydrological interactions within the basin (
Figure 4). These deviations are particularly pronounced when analyzing the slope and intercept of the regression between δ
18O and δ
2H of precipitation (forming the Local Meteoric Water Line or LMWL) compared to river water. The LMWL, defined by the linear relationship between δ
18O and δ
2H in precipitation, serves as a benchmark for understanding hydrological processes. Deviations of river water isotopes from the LMWL indicate evaporation, mixing with different water sources, or both [
11,
58]. With a slope of 7.66 and an intercept of 7.21, the LMWL represents the isotopic relationship in precipitation across the Mun River basin. These values are indicative of the general atmospheric conditions and moisture sources influencing precipitation isotopes in the region [
59].
The regression line for δ
18O vs. δ
2H in the Mun River has a slope of 5.44 and an intercept of -14.16 (
Figure 4). The shallower slope and negative intercept compared to the LMWL suggest significant evaporation effects, altering the isotopic composition of river water [
60]. For the Chi River, the slope is 6.23 with an intercept of -10.33. This closer alignment with the LMWL compared to the MR indicates less evaporation influence but still reflects a deviation from the isotopic composition expected from direct precipitation [
35]. The UMR features a slope of 5.85 and an intercept of -11.57. These values, while differing from the LMWL, suggest a moderate evaporation effect and possibly different mixing dynamics with groundwater or tributaries [
61]. The lower slopes for MR, CR, and UMR compared to the LMWL indicate evaporation’s role in enriching the heavy isotopes in river water. This evaporation effect is most pronounced in the MR, as evidenced by its significantly lower slope and more negative intercept. The intercept values, particularly the negative ones for river locations, hint at the mixing of river water with other water sources that have distinct isotopic signatures, such as groundwater, which may have undergone evaporation or fractionation processes before entering the river system [
62].
3.2. Performance of ANN Models
The application of the Artificial Neural Network (ANN) method for analyzing river flow components signifies an advanced approach in comprehending water dynamics within river systems. Focusing on the Main Mun River, this study examines contributions from its tributaries, the Upper Mun River (UMR) and the Chi River (CR), utilizing ANN to unravel the complexities of river flow variations and their contributing factors. The UMR’s contribution to the Main Mun River flow, denoted as F
UMR-ANN, showed significant variability (
Figure 5). Statistical analysis highlighted a maximum flow contribution rate of 0.78 and a minimum of 0.51, with an average contribution of 0.70. Analysis of the Chi River’s flow contributions, F
CR-ANN, revealed a maximum contribution of 0.49 and a minimum of 0.22, with an average contribution of 0.30, illustrating the variability in its contributions to the Main Mun River. The comparative analysis of trained and test results enhances understanding of the ANN model’s performance and reliability in predicting river flow contributions (
Figure 5b). Minor deviations between training and testing phases for both UMR (F
UMR-ANN) and CR (F
CR-ANN) suggest slight variances in model predictions across different datasets [
63].
Incorporating aspects of hydrology and tropical meteorology into the study of river flow components substantially enhances our understanding of water dynamics in river systems. The ANN analysis offers insights into river flow contributions during the wet and dry seasons, highlighting the seasonal dynamics of river flow. During the wet season, the analysis indicates that the UMR contributes an average fraction of 0.69 to the Main Mun River flow, while the CR contributes a slightly lower average fraction of 0.29. These findings illustrate the impact of increased precipitation during the wet season on river flows, where both tributaries significantly contribute to the Main Mun River, albeit with slight variations in their contributions [
64]. Conversely, in the dry season, the UMR shows a slight increase in contribution, averaging 0.70, suggesting a consistent or slightly enhanced flow to the Main Mun River despite reduced precipitation. In contrast, the CR shows a minor decrease in its contribution, averaging 0.29, indicating a more pronounced sensitivity to seasonal precipitation reductions [
34].
The observed seasonal variations in river flow contributions from the UMR and CR provide critical insights into the interaction between hydrological processes and tropical meteorology. The slight increase in the UMR’s contribution during the dry season may be attributed to the catchment area’s characteristics, potentially including its water retention capability during wet periods and consistent release throughout the year. This suggests resilience in the UMR’s flow contributions, possibly mitigating the impacts of seasonal precipitation variability [
2]. On the other hand, the slight decrease in the CR’s contribution during the dry season suggests its heightened sensitivity to precipitation patterns, reflecting the catchment’s hydrological responses to tropical meteorological variations [
22,
64].
Comparing ANN model predictions with observed river flow data for the UMR and CR, using the coefficient of determination (R²) and the Root Mean Square Error (RMSE) [
43], provides insight into the model’s precision and areas of uncertainty. For F
UMR-ANN versus F
UMR-Q, the R
2 of 0.33 and RMSE of 0.10 were observed, while for F
CR-ANN versus F
CR-Q, R
2 was 0.31 with an RMSE of 0.10 (
Figure 5b). These findings suggest a moderate positive correlation, indicating that as observed flow rates increase, the model’s predictions also tend to increase, although not strongly. This reveals the model’s partial success in capturing observed data trends and highlights areas for significant improvement. This comprehensive analysis underscores the efficacy and potential of ANN in hydrological studies, indicating both its strengths and limitations in capturing the complexities of river flow dynamics. Enhancing the model with more detailed data, such as isotopic signatures, could improve prediction accuracy and provide deeper insights into flow components.
3.3. Isotopic End-Member Mixing Analysis Results
Incorporating Oxygen-18 (δ
18O) isotopic signatures through Isotopic End-Member Mixing Analysis (IEMMA) significantly enhances the understanding of river flow dynamics within the Upper Mun River (UMR) and the Chi River (CR), contributing to the Main Mun River. This advanced analysis reveals the substantial contributions of these tributaries, providing a detailed narrative of hydrological interactions in tropical regions. The IEMMA outcomes for the UMR indicate a broad range of flow contributions, with the maximum nearing unity (0.99), suggesting instances where the UMR predominantly sustains the Main Mun River (
Figure 6). This variability—from a significant minimum contribution (0.50) to an important average (0.70)—highlights the UMR’s crucial role in the river system’s hydrology. In contrast, the CR’s contributions, while essential, are noticeably lower on average (0.30), with a maximum that coincides with UMR’s minimum and a minimal figure (0.01) emphasizing its intermittent influence [
36].
Seasonal variations add complexity to this hydrological narrative. The wet season’s dilution effects and runoff, driven by increased rainfall, might equalize contributions from both tributaries, whereas the dry season’s distinct isotopic signatures could emphasize UMR’s dominance due to its larger catchment area. These insights illustrate the dynamic relationship between hydro-meteorological conditions and river flow contributions in tropical settings [
54].
Despite IEMMA’s insightful nature, challenges such as isotopic data resolution, end-member selection, and the assumption of isotopic homogeneity within each end-member limit the analysis’s accuracy. These challenges highlight a complex matrix of factors influencing river flow dynamics, calling for refined methodologies in future research. Addressing these limitations involves enhanced spatial and temporal sampling, advanced end-member characterization, integration with hydrological models, and the application of machine learning techniques to improve isotopic analysis resolution and accuracy, offering a more nuanced understanding of hydrological processes in river systems.
The precision of IEMMA outcomes, influenced by factors like isotopic fractionation [
12,
65], irrigation return flows [
66], and groundwater contributions [
25], introduces uncertainties in interpreting isotopic data, crucial for advancing hydrological model accuracy. Isotopic fractionation, the separation of isotopes during physical or chemical processes, can significantly affect δ
18O signatures, potentially masking true source contributions [
25,
47,
54]. Irrigation return flows, with distinct isotopic signatures, can alter a river’s isotopic composition, skewing the analysis of tributary contributions [
66]. Groundwater contributions, with unique isotopic signatures, can significantly change a river’s isotopic composition, necessitating their inclusion as end-members in the mixing analysis [
54].