Assessing Seasonal Biogeochemical Variations in the Mun River Watershed Using Water Quality Data and the Geochemical Mass Balance Method

Abstract

The Mun River watershed, a vital water resource in Northeastern Thailand and a major tributary of the Mekong River, faces significant water quality challenges driven by climate change and human activities. This study examines seasonal biogeochemical variations in the watershed, with a focus on how climate fluctuations affect water quality and geochemical processes. Water samples were collected from 19 surface sites during the dry and wet seasons of 2024 and analyzed for major dissolved ions. Using the geochemical mass balance method, we quantified rates of mineral weathering and biomass degradation. Our findings reveal a notable shift in hydrochemical facies from Na-Cl dominance in the dry season to Ca-HCO₃ dominance in the wet season, indicating reduced salinity and changes in geochemical conditions. Wet season mineral weathering rates averaged 300–700 µmol m⁻² d⁻¹, approximately 10–20 times higher than those in the dry season. The highest weathering and biomass degradation rates, ranging from 900 to 1200 µmol m⁻² d⁻¹, were observed in the northern subwatersheds, likely due to intensified agricultural practices and underlying geological conditions. These results highlight the urgent need for adaptive watershed management strategies to address the growing impact of climate change on regional water quality.

1. Introduction

The Mun River watershed, a major tributary of the Mekong River, faces significant challenges in water quality and quantity, driven by documented deterioration [1] and climate variability [2,3]. These changes pose serious threats to local ecosystems and livelihoods, particularly in downstream Mekong regions [4,5,6]. Infrastructure projects, such as hydropower, exacerbate these issues by reducing biodiversity and fish productivity across the watershed [7,8], while deforestation increases runoff and seasonal fluctuations in surface water [9].

Agriculture dominates land use in the Mun River watershed, which is Thailand’s largest cassava-producing region and one of its most water-stressed areas [10]. Intense rainfall, tropical cyclones, and the interplay of floods and droughts directly impact agricultural activities, particularly rice cultivation [11,12]. This agricultural reliance, combined with extreme precipitation events and limited water availability, intensifies pressure on the watershed’s resources and compounds challenges in maintaining water quality and quantity. Seasonal precipitation patterns further influence hydrological dynamics, with rainfall predominantly occurring during the rainy season and increasing downstream, while the dry season sees reversed patterns with reduced flow [13]. Climate models project that high-emission scenarios could increase temperature variability (1.45–1.54 °C) and alter rainfall distribution, with wetter rainy seasons and drier pre-rainy periods [14]. Such shifts disrupt the hydrological cycle, exacerbate nutrient depletion through soil erosion, and amplify seasonal fluctuations in surface water quality, particularly under the tropical monsoonal climate [15,16,17,18].

Biogeochemical cycles, essential for watershed health, reflect interactions among climate patterns, geology, and anthropogenic influences. Seasonal shifts in these cycles are key indicators of watershed resilience, particularly between dry and wet seasons. The chemical weathering of silicate and carbonate minerals drives carbon cycling, influencing atmospheric CO₂ sequestration and global climate regulation [19,20,21,22,23]. Understanding mineral weathering rates within the Mun River watershed is critical for both local water quality management and broader insights into carbon cycling under changing land use and climate conditions. Anthropogenic drivers, such as agriculture [24], industrialization [25], land-use changes [26,27], deforestation, and urbanization [28], significantly impact biogeochemical processes. For example, iron isotope geochemistry reveals that iron sources derive from both natural weathering and urban activities [29], while seasonal contributions of rare earth elements (REEs) vary, with soil materials dominating in the dry season and rock weathering becoming prominent during the rainy season [30].

Expanding agricultural irrigation and hazardous industries, particularly upstream, have disrupted flow and sediment transport, contributing to water quality degradation [31,32,33]. Additionally, rice cultivation in downstream areas has increased nutrient discharge, while poorly consolidated sandy soils amplify sediment load and water quality issues during intense monsoonal rainfall [34,35,36]. While geographic information system (GIS) and remote sensing methods are effective for mapping surface-level changes, they cannot capture subsurface flows that significantly influence biogeochemical cycles [37,38,39]. Supplementary techniques are thus necessary to fully understand these dynamics.

The geochemical mass balance (GMB) method offers an integrated framework for assessing biogeochemical processes by incorporating biomass, geology, and hydrology [40]. Though widely applied elsewhere, the GMB method has not been utilized in the Mun River watershed, leaving gaps in understanding the spatiotemporal variations in mineral weathering, biomass degradation, and water type classifications. Weathering rates are particularly relevant as they contribute to terrestrial CO₂ sequestration, mitigating climate change impacts. Silicate weathering, for instance, plays a substantial role in CO₂ consumption [41]. This study employs the GMB method to quantify seasonal variations in mineral weathering and biomass degradation in the Mun River watershed, aiming to address knowledge gaps and provide actionable insights. By analyzing water samples from dry and wet seasons, this study seeks to inform adaptive watershed management strategies, enhancing climate resilience and sustainability for the watershed and its downstream ecosystems [42].

2. Materials and Methods

2.1. Study Area

The Mun River watershed (Figure 1), located in Northeastern Thailand, spans approximately 71,059 square kilometers and serves as a key tributary of the Mekong River. It plays a crucial role in regional hydrology, extending over 900 km and encompassing 11 provinces [43]. Land use in the watershed is predominantly agricultural, with about 80% of the area dedicated to cultivation, supported by widespread fertilizer and pesticide use. Annual fertilizer use reaches 20,000 tons, with pesticides contributing several thousand tons [44,45].

Population density is highest in the middle Mun region, averaging 150 people per square kilometer, with densities ranging from 37 to 601 people per square kilometer [46]. The watershed experiences a tropical monsoon climate, characterized by a dry season (November to April) and a wet season (May to October). Rainfall increases from the upper western watershed, receiving around 870 mm annually, to the lower eastern region, where precipitation averages 1758 mm [17]. The Asian monsoon system primarily drives precipitation, with wet season rainfall impacting the middle to lower reaches [47].

Geologically, the watershed is dominated by sedimentary formations such as sandstone, siltstone, and evaporites [46], which heavily influence water chemistry. The northern watershed features quartzitic sandstone, siltstone, and evaporite-rich Maha Sarakham formations, interspersed with Pre-Jurassic volcanic rocks and granites [48]. In contrast, basaltic formations are prevalent in the southern watershed, particularly along the right tributaries of the Mun River. The Chi River, the largest tributary, adds hydrological complexity [49]. These geological and climatic factors (Figure 2) establish the Mun River watershed as an ideal site for studying biogeochemical processes [49].

2.2. Sample Collection and Analysis

Water samples were collected from 19 surface water sites in the Mun River watershed during mid-March (dry season) and mid-August (wet season) of 2024 to capture peak hydrological and geochemical contrasts. One sample per site was collected each season, totaling two samples per site. Sampling locations, selected to represent individual subwatersheds, were delineated using a digital elevation model (DEM) from the Shuttle Radar Topography Mission (SRTM) using the USGS EarthExplorer website (https://earthexplorer.usgs.gov, accessed on 15 September 2024), dividing the watershed into 19 subwatersheds with unique hydrological and topographical conditions. These sites, shown in Figure 1, reflect diverse land-use types, ensure accessibility, and are located near stream gaging stations for discharge data. Site coordinates and surrounding environmental descriptions (e.g., land use, geology) are provided in

Table 1. Subwatershed sampling sites and environmental context.

Subwatershed	UTM-E	UTM-N	Zone	Surrounding Environment
1	817584	1652994	47	Agricultural (rice fields), sandstone geology
2	199802	1631988	48	Agricultural (cassava), sandstone geology
3	205436	1680176	48	Intensive agriculture, evaporite-rich Maha Sarakham Formation
4	202911	1656643	48	Agricultural (rice), siltstone geology
5	223737	1684280	48	Mixed agriculture/urban, basalt formations
6	220760	1667281	48	Agricultural (rice), sandstone geology
7	265275	1659400	48	Agricultural (cassava), sedimentary formations
8	286745	1707469	48	Mixed agriculture/forest, basalt geology
9	381400	1709662	48	Intensive agriculture, evaporite-rich formations
10	331063	1670577	48	Agricultural (rice), siltstone geology
11	373466	1721910	48	Urban influence, sedimentary geology
12	394768	1663902	48	Agricultural (cassava), sandstone geology
13	426793	1671492	48	Agricultural (rice), evaporite-rich formations
14	484804	1683050	48	Forested, basalt geology
15	460599	1659065	48	Agricultural (rice), sedimentary geology
16	517251	1684212	48	Mixed agriculture/urban, basalt formations
17	559367	1693668	48	Forested, sandstone geology
18	496344	1714089	48	Agricultural (cassava), siltstone geology
19	456597	1714007	48	Intensive agriculture, evaporite-rich formations

to contextualize water quality influences. Sampling at subwatershed outlets enabled accurate dissolved species export calculations. Monthly sampling was not feasible due to resource constraints. Regional meteorological data from the Thai Meteorological Department indicate dry season (November–April) conditions with minimal rainfall (<50 mm/month) and temperatures of 25–32 °C, contrasting with wet season (May–October) rainfall of 150–300 mm/month and temperatures of 25–33 °C.

Samples were collected in pre-cleaned polyethylene bottles, stored on ice during transport, and refrigerated at 4 °C (±2 °C) until analysis. Field measurements included pH, electrical conductivity (EC), oxidation-reduction potential (ORP), and alkalinity. In the laboratory, samples were filtered through 0.45-μm membrane filters (Whatman, Maidstone, UK). Major cations (${\mathrm{Na}}^{+}, {\mathrm{K}}^{+}, {\mathrm{Ca}}^{2+}, {\mathrm{Mg}}^{2+}, {\mathrm{SiO}}_2\left(\mathrm{aq}\right)$) were analyzed using inductively coupled plasma mass spectrometry (ICP-MS) (Agilent Technologies, Santa Clara, CA, USA). Anions $\left({\mathrm{Cl}}^{-}, {\mathrm{SO}}_4^{2-}, {\mathrm{NO}}_3^{-}\right)$ were analyzed using ion chromatography (Thermo Fisher Scientific, Waltham, MA, USA), carbonate concentrations, and hardness by titration. Total dissolved solids (TDSs) were assessed gravimetrically. Ion balance was verified using Aquachem software version 4.0 (Waterloo Hydrogeologic, Waterloo, ON, Canada), with charge balance errors below 5%.

2.3. Hydrochemical Facies

Hydrochemical facies were analyzed using Aquachem software, with Stiff diagrams employed to visualize the ionic composition of water samples (in meq L⁻¹) and map the spatial distribution of water types across the watershed during both seasons. Piper diagrams further illustrate seasonal shifts in hydrochemical characteristics, providing insights into patterns such as salinity increases and cation exchange. These visual tools facilitated the interpretation of seasonal variability in water chemistry.

2.4. Geochemical Mass Balance (GMB) Analysis

The geochemical mass balance (GMB) model was applied to individual subwatersheds to precisely quantify biogeochemical processes, minimizing groundwater influence. This model balances dissolved species inputs from weathering and biomass degradation against outputs through runoff and secondary mineral formation. Solute fluxes were calculated using a matrix system of linear equations, estimating weathering and biomass change rates in micromoles per square meter per day (µmol m⁻² d⁻¹). The analysis focused on minerals prone to weathering, such as halite, plagioclase feldspar, biotite, and garnet. Rainfall and evaporation rates were not included in this study. The mass balance equation used is as follows [50,51]:

$$\begin{array}{cc}{\displaystyle \sum _{j=1}^{\varphi }{\alpha }_j{\beta }_{c,j}=\Delta m_c},& \mathrm{for} c=1,\dots ,n\end{array}$$

(1)

where $\varphi$ is the number of unknown variables, j is an index for minerals or biomass undergoing weathering, $\alpha$ is the weathering rate of mineral or biomass j (moles per area per time), $\beta$ is the stoichiometric coefficient for ion c in mineral or biomass $j$, and $\Delta m_c$ is the flux change rate of ion c (moles per area per time).

3. Results

3.1. Physicochemical Parameters

Seasonal variations in temperature and pH were observed in the Mun River watershed. During the dry season, temperatures ranged from 25.60 °C to 31.80 °C (mean = 28.23 °C), while in the wet season, temperatures ranged from 25.00 °C to 33.00 °C (mean = 28.63 °C) (

Table 2. Seasonal variations in physicochemical parameters and major ion concentrations in the Mun River were analyzed. p-values were calculated using a two-sample t-test (or the Mann–Whitney U test for non-normal distributions) to assess seasonal differences. Statistically significant p-values (p < 0.05) are highlighted in red to emphasize notable seasonal variations.

Parameter	Season	Min	Max	Mean	Median	SD 1	p-Value
T (°C)	Dry	25.60	31.80	28.23	28.30	1.62	0.538
	Wet	25.00	33.00	28.63	29.00	2.78
pH	Dry	6.70	7.80	7.44	7.60	0.31	0.027
	Wet	6.40	7.70	6.97	6.90	0.45
Cond (µS cm−1)	Dry	78.00	2449.00	490.53	291.00	536.26	0.027
	Wet	32.00	2357.00	313.26	140.00	517.71
TDS (mg L−1)	Dry	56.34	1332.07	312.44	198.67	304.77	0.009
	Wet	26.19	1166.50	179.33	84.85	258.19
Alkalinity (mg L−1 CaCO3)	Dry	6.00	164.36	60.34	51.79	43.13	0.153
	Wet	4.50	150.94	39.51	21.48	39.54
DO (mg L−1)	Dry	4.00	8.20	6.40	6.50	1.13	<0.001
	Wet	3.00	5.90	4.63	4.70	0.76
Flow rate (m3 s−1)	Dry	0.00	101.42	14.27	0.75	29.65	<0.001
	Wet	1.60	471.70	100.99	44.60	132.64
${\mathrm{Na}}^{+}$ (mg L−1)	Dry	6.35	369.60	62.87	31.58	85.32	0.068
	Wet	2.12	326.23	35.87	13.65	71.55
${\mathrm{K}}^{+}$ (mg L−1)	Dry	1.32	14.05	5.21	5.12	3.29	0.153
	Wet	1.11	10.10	3.62	2.41	2.98
${\mathrm{Ca}}^{2+}$ (mg L−1)	Dry	1.29	62.50	19.24	17.48	15.18	0.153
	Wet	1.78	62.40	12.60	6.20	15.87
${\mathrm{Mg}}^{2+}$ (mg L−1)	Dry	0.84	16.18	5.43	4.49	3.80	0.068
	Wet	0.50	13.99	3.24	1.81	3.54
${\mathrm{SiO}}_2\left(\mathrm{aq}\right)$ (mg L−1)	Dry	4.59	13.47	9.65	9.98	2.65	0.009
	Wet	3.32	13.94	7.41	6.50	3.57
${\mathrm{Cl}}^{-}$ (mg L−1)	Dry	9.87	668.55	104.72	49.86	152.79	0.027
	Wet	3.77	603.01	57.11	13.75	132.29
${\mathrm{HCO}}_3^{-}$ (mg L−1)	Dry	7.32	200.39	74.05	63.14	52.73	0.306
	Wet	5.49	184.03	48.65	31.14	48.02
${\mathrm{SO}}_4^{2-}$ (mg L−1)	Dry	1.08	18.34	8.06	5.64	6.14	0.538
	Wet	1.30	28.36	6.39	4.03	6.73
${\mathrm{NO}}_3^{-}$ (mg L−1)	Dry	0.21	8.01	1.98	1.06	2.24	0.395
	Wet	0.30	2.71	1.07	0.62	0.90

Note: ¹ SD: Standard deviation.

). The difference in mean temperatures between seasons was statistically insignificant (p = 0.538). The dry season showed pH values ranging from 6.70 to 7.80 (mean = 7.44), while the wet season ranged from 6.40 to 7.70 (mean = 6.97), with a significant seasonal difference (p = 0.027) (Table 2).

Electrical conductivity (Cond) and total dissolved solids (TDSs) varied seasonally. Conductivity in the dry season ranged from 78.00 to 2449.00 µS cm⁻¹ (mean = 490.53 µS cm⁻¹) and from 32.00 to 2357.00 µS cm⁻¹ (mean = 313.26 µS cm⁻¹, p = 0.027) in the wet season. TDS ranged from 56.34 to 1332.07 mg L⁻¹ (mean = 312.44 mg L⁻¹) in the dry season and from 26.19 to 1166.50 mg L⁻¹ (mean = 179.33 mg L⁻¹) in the wet season (p = 0.009) (Table 2).

Alkalinity, dissolved oxygen (DO), and flow rate also showed seasonal patterns. Alkalinity averaged 60.34 mg L⁻¹ ${\mathrm{CaCO}}_3$ in the dry season and 39.51 mg L⁻¹ in the wet season (p = 0.153). DO levels were higher in the dry season (mean = 6.40 mg L⁻¹) compared to the wet season (mean = 4.63 mg L⁻¹, p < 0.001). Flow rates ranged from 0.00 to 101.42 m³ s⁻¹ (mean = 14.27 m³ s⁻¹) in the dry season and from 1.60 to 471.70 m³ s⁻¹ (mean = 100.99 m³ s⁻¹) in the wet season (p < 0.001) (Table 1).

3.2. Major Dissolved Species Chemistry

The concentrations of major dissolved species, including ${\mathrm{Na}}^{+}, {\mathrm{K}}^{+}, {\mathrm{Ca}}^{2+}, {\mathrm{Mg}}^{2+}$, ${\mathrm{SiO}}_2\left(\mathrm{aq}\right), {\mathrm{Cl}}^{-}, {\mathrm{HCO}}_3^{-}, {\mathrm{SO}}_4^{2-}, \mathrm{and} {\mathrm{NO}}_3^{-}$, were measured across the Mun River watershed (Table 2). Sodium (${\mathrm{Na}}^{+}$) concentrations ranged from 6.35 to 369.60 mg L⁻¹ (mean = 62.87 mg L⁻¹) in the dry season and from 2.12 to 326.23 mg L⁻¹ (mean = 35.87 mg L⁻¹) in the wet season (p = 0.068). Potassium (${\mathrm{K}}^{+}$) concentrations ranged from 1.32 to 14.05 mg L⁻¹ (mean = 5.21 mg L⁻¹) in the dry season and from 1.11 to 10.10 mg L⁻¹ (mean = 3.62 mg L⁻¹) in the wet season (p = 0.153). Calcium (${\mathrm{Ca}}^{2+}$) concentrations ranged from 1.29 to 62.50 mg L⁻¹ (mean = 19.24 mg L⁻¹) in the dry season and from 1.78 to 62.40 mg L⁻¹ (mean = 12.60 mg L⁻¹) in the wet season (p = 0.153). Magnesium (${\mathrm{Mg}}^{2+}$) concentrations ranged from 0.84 to 16.18 mg L⁻¹ (mean = 5.43 mg L⁻¹) in the dry season and from 0.50 to 13.99 mg L⁻¹ (mean = 3.24 mg L⁻¹) in the wet season (p = 0.068). Dissolved silica (SiO₂) concentrations averaged 9.65 mg L⁻¹ in the dry season and 7.41 mg L⁻¹ in the wet season (p = 0.009).

Chloride (${\mathrm{Cl}}^{-}$) concentrations ranged from 9.87 to 668.55 mg L⁻¹ (mean = 104.72 mg L⁻¹) in the dry season and from 3.77 to 603.01 mg L⁻¹ (mean = 57.11 mg L⁻¹) in the wet season (p = 0.027). Sulfate (${\mathrm{SO}}_4^{2-}$) concentrations ranged from 1.08 to 18.34 mg L⁻¹ (mean = 8.06 mg L⁻¹) in the dry season and from 1.30 to 28.36 mg L⁻¹ (mean = 6.39 mg L⁻¹) in the wet season (p = 0.538). Bicarbonate (${\mathrm{HCO}}_3^{-}$) concentrations averaged 74.05 mg L⁻¹ in the dry season and 48.65 mg L⁻¹ in the wet season (p = 0.306). Nitrate (${\mathrm{NO}}_3^{-}$) concentrations averaged 1.98 mg L⁻¹ in the dry season and 1.07 mg L⁻¹ in the wet season (p = 0.395). Seasonal variations in dissolved species concentrations are presented in Figure 3.

3.3. Hydrochemical Facies, Water Type Shifts, and Spatial Distribution

The Piper diagram (Figure 4) and Stiff diagrams (Figure 5) illustrate the hydrochemical facies across the Mun River watershed. Na-Cl water types were observed in the dry season, while Ca-HCO₃ facies predominated in the wet season (

Table 3. Percent error (Δ%) of chemical analyses and corresponding hydrochemical facies. The percent error (Δ%) represents the relative difference between total cations and anions in milliequivalents per liter (meq L⁻¹), with acceptable values generally within ±5%. Hydrochemical facies are shown for both wet and dry seasons.

Subwatershed	Δ%		Facies
	Wet	Dry	Wet	Dry
1	1.20	0.52	Ca-HCO3	Ca-HCO3
2	1.34	1.44	Na-HCO3	Na-Cl
3	1.70	2.73	Na-Cl	Na-Cl
4	1.02	1.21	Na-HCO3	Na-Cl
5	1.23	1.15	Na-Cl	Na-Cl
6	1.11	2.11	Ca-HCO3	Na-HCO3
7	1.01	0.23	Na-HCO3	Na-Cl
8	1.23	0.64	Na-Cl	Na-Cl
9	1.44	1.64	Na-Cl	Na-Cl
10	1.43	3.52	Na-HCO3	Ca-Cl
11	1.76	0.37	Na-Cl	Na-Cl
12	2.57	4.82	Na-HCO3	Na-Cl
13	1.75	0.41	Na-Cl	Na-Cl
14	1.17	2.48	Na-Cl	Na-HCO3
15	4.38	8.55	Na-HCO3	Ca-HCO3
16	4.23	1.33	Ca-Cl	Ca-HCO3
17	2.52	2.15	Ca-HCO3	Ca-HCO3
18	1.20	0.52	Ca-HCO3	Ca-HCO3
19	1.34	1.44	Na-HCO3	Na-Cl

). Table 3 presents the percent error (Δ%) of chemical analyses and corresponding hydrochemical facies for both seasons, with Δ% representing the relative difference between total cations and anions in milliequivalents per liter (meq L⁻¹).

The overall hydrochemical type of the Mun River was classified as Na-Ca-Cl-HCO₃. Figure 4 shows the facies of all collected samples across dry and wet seasons (left) and average seasonal shifts (right), with arrows indicating transitions between water types. Stiff diagrams (Figure 5) display the spatial distribution of water types, with larger polygons in the dry season (Figure 5a) and smaller polygons in the wet season (Figure 5b) across all subwatersheds.

3.4. Mineral Weathering and Biomass Degradation

Rock samples from key formations within the Mun River watershed, including the Phra Wihan and Sao Khua Formations, were analyzed using X-ray diffractometry (XRD) to determine their mineral composition (

Table 4. Rock-forming minerals and biomass for the geochemical mass balance calculations.

Mineral	Chemical Formula	Mineral Weathering Rate or Biomass Degradation Rate (µmol m−2 d−1)
Quartz	${\mathrm{SiO}}_2$	${\alpha }_1$
Halite	NaCl	${\alpha }_2$
Feldspar	${\mathrm{Na}}_{0.68}{\mathrm{Ca}}_{0.32}{\mathrm{Al}}_{1.32}{\mathrm{Si}}_{2.68}{\mathrm{O}}_8$	${\alpha }_3$
Garnet	${\mathrm{Ca}}_{0.2}{\mathrm{Mg}}_{0.5}{\mathrm{Mn}}_{0.2}{\mathrm{Fe}}_{2.1}{\mathrm{Al}}_2{\mathrm{Si}}_3{\mathrm{O}}_{12}$	${\alpha }_4$
Biotite	${\mathrm{K}}_{0.85}{\mathrm{Na}}_{0.02}\left({\mathrm{Mg}}_{1.2}{\mathrm{Fe}}_{1.3}{\mathrm{Al}}_{0.45}\left)\right({\mathrm{Al}}_{1.2}{\mathrm{Si}}_{2.8}\right){\mathrm{O}}_{10}{\left(\mathrm{OH}\right)}_2$	${\alpha }_5$
Vermiculite	${\mathrm{K}}_{0.25}{\mathrm{Na}}_{0.06}{\mathrm{Ca}}_{0.016}\left({\mathrm{Mg}}_{1.1}{\mathrm{Fe}}_{1.6}{\mathrm{Al}}_{0.45}\right)\left({\mathrm{Al}}_{1.2}{\mathrm{Si}}_{2.8}\right){\mathrm{O}}_{10}{\left(\mathrm{OH}\right)}_2$	${\alpha }_6$
Biomass 1	${\mathrm{Na}}_{\mathrm{x}}{\mathrm{Mg}}_{\mathrm{y}}{\mathrm{Ca}}_{\mathrm{z}}{\mathrm{K}}_{\mathrm{w}}$	${\alpha }_7$

Note: ¹ Variables x, y, z, and w in the biomass formula vary depending on the watershed and season and are determined using inverse modeling.

). The identified minerals include quartz, halite, feldspar, garnet, biotite, and vermiculite.

Weathering and biomass degradation rates were estimated using the geochemical mass balance (GMB) model through a system of linear equations incorporating observed mass fluxes and mineral stoichiometry. This system was solved using the PEST optimization software [53] with singular value decomposition (SVD). Results are presented in subsequent subsections.

3.5. Geochemical Mass Balance Calculations

3.5.1. Results from GMB Analysis

Geochemical mass balance (GMB) calculations for the Mun River watershed were conducted using water quality data, focusing on major ions such as ${\mathrm{Na}}^{+}, {\mathrm{Ca}}^{2+}, {\mathrm{Mg}}^{2+}, {\mathrm{K}}^{+}, \mathrm{and} {\mathrm{SiO}}_2\left(\mathrm{aq}\right)$. The elemental flux of each species, expressed in µmol m⁻² d⁻¹, was calculated using the following equation:

$$\Delta m_c={\displaystyle \frac{\left[c\right]\times Q}{A}},$$

(2)

where $\Delta m_c$ is the elemental flux of species c, $\left[c\right]$ is the concentration of species c in µmol m⁻³, Q is the stream discharge at the sampling point in m³ d⁻¹, and A is the watershed area in m².

The GMB calculations expanded upon this flux equation by incorporating the stoichiometric relationships of minerals and biomass into a linear system of equations:

$$\begin{gathered} \mathrm{A}\overrightarrow{x}=\overrightarrow{b} \\ \begin{array}{c}\mathrm{Na}:\\ \mathrm{Mg}: \\ \mathrm{Ca}: \\ \mathrm{K}: \\ {\mathrm{SiO}}_2:\end{array}{\left[\begin{array}{ccccccc}0& 1& 0.68& 0& 0.02& 0.06& \mathrm{x}\\ 0& 0& 0& 0.5& 1.2& 1.1& \mathrm{y}\\ 0& 0& 0.32& 0.2& 0& 0.016& \mathrm{z}\\ 0& 0& 0& 0& 0.85& 0.25& \mathrm{w}\\ 1& 0& 2.68& 3& 2.8& 2.8& 0\end{array}\right]}_{5\times 7}{\left[\begin{array}{c}{\alpha }_1\\ {\alpha }_2\\ {\alpha }_3\\ {\alpha }_4\\ {\alpha }_5\\ {\alpha }_6\\ {\alpha }_7\end{array}\right]}_{7\times 1}={\left[\begin{array}{l}\Delta {\mathrm{m}}_{\mathrm{Na}}\\ \Delta {\mathrm{m}}_{\mathrm{Mg}}\\ \Delta {\mathrm{m}}_{\mathrm{Ca}}\\ \Delta {\mathrm{m}}_{\mathrm{K}}\\ \Delta {\mathrm{m}}_{{\mathrm{SiO}}_2}\end{array}\right]}_{5\times 1} \end{gathered}$$

(3)

where matrix A contains stoichiometric coefficients derived from XRD analyses of minerals and biomass (Table 4); vector $\overrightarrow{x}$ includes unknown rates (${\alpha }_1$–${\alpha }_6$) for mineral weathering of quartz, halite, feldspar, garnet, biotite, and vermiculite and ${\alpha }_7$ for biomass degradation, and vector $\overrightarrow{b}$ represents observed flux values for dissolved ions (${\mathrm{Na}}^{+}, {\mathrm{Mg}}^{2+}, {\mathrm{Ca}}^{2+},{ \mathrm{K}}^{+}, \mathrm{and} {\mathrm{SiO}}_2\left(\mathrm{aq}\right)$) derived from field data.

The system involved 11 unknowns and 5 known values, requiring advanced numerical methods for solutions. The PEST optimization tool [53] with singular value decomposition (SVD) was used to iteratively minimize errors between observed and modeled fluxes, enabling precise calculations of mineral weathering and biomass degradation rates for each subwatershed. Observed elemental fluxes of major ions, including ${\mathrm{Na}}^{+}, {\mathrm{Mg}}^{2+}, {\mathrm{Ca}}^{2+}, {\mathrm{K}}^{+}, \mathrm{and} {\mathrm{SiO}}_2\left(\mathrm{aq}\right)$, calculated from field-measured concentrations and discharge data are presented in

Table 5. Calculation of elemental mass flux for geochemical mass balance.

(a) Dry Season
Subwatershed	Area	Flow Rate	Concentration (mg L−1)					Elemental Flux (µmol m−2 d−1)
	(km2)	(m3 s−1)	Na	K	Ca	Mg	SiO2	Na	K	Ca	Mg	SiO2
1	3420	0.9223	38.82	7.65	46.92	8.07	7.78	39.33	4.56	27.28	7.73	3.02
2	2319	0.3968	39.52	10.6	10.93	5.29	10.51	25.4	4.01	4.03	3.22	2.59
3	2888	0.0373	369.6	6.9	62.5	16.18	13.47	17.95	0.2	1.74	0.74	0.25
4	2898	0.0037	26.81	8.23	10.4	4.49	12.23	0.13	0.02	0.03	0.02	0.02
5	1061	0.0373	134.8	14.05	29.99	11.6	8.02	17.82	1.09	2.28	1.45	0.41
6	1654	0.0373	26.29	5.12	22.05	7.55	11.25	2.23	0.26	1.07	0.61	0.37
7	5934	0.2240	26.6	5.21	18.64	5.5	11.94	3.77	0.43	1.52	0.74	0.65
8	3666	9.2113	213.7	5.08	43.3	8.44	13.26	2008.3	28.2	234.5	75.4	47.9
9	7869	11.4092	141.2	6.07	27.64	7.88	11.92	765.7	19.4	86.4	40.6	24.9
10	4966	0.0373	21.51	2.57	25.58	3.23	4.87	0.61	0.04	0.41	0.09	0.05
11	4443	0.0037	36.09	1.74	1.29	1.47	4.59	0.11	0	0	0	0.01
12	3803	3.2387	6.68	1.53	3.06	0.84	7.81	21.36	2.87	5.61	2.56	9.56
13	3376	47.0710	37.13	3.52	8.29	2.17	7.29	1944.82	108.49	249.18	107.6	146.17
14	2681	88.4194	-	-	-	-	-	-	-	-	-	-
15	3360	4.2742	8.37	6.75	9.94	3.21	12.59	39.98	18.97	27.26	14.53	23.03
16	4840	2.9523	6.35	6.31	7.95	1.75	7.93	14.54	8.51	10.45	3.8	6.96
17	4041	101.419	-	-	-	-	-	-	-	-	-	-
18	3493	0.6961	31.58	1.32	3.75	2.21	7.49	23.64	0.58	1.61	1.56	2.15
19	3878	0.7467	50.74	1.48	8.49	3	12.67	36.7	0.63	3.53	2.05	3.51
Mun 1	70,589	101.419	24.08	3.33	16.95	3.62	7.50	129.97	10.57	52.50	18.50	15.51
(b) Wet Season
Subwatershed	Area	Flow Rate	Concentration (mg L−1)					Elemental Flux (µmol m−2 d−1)
	(km2)	(m3 s−1)	Na	K	Ca	Mg	SiO2	Na	K	Ca	Mg	SiO2
1	3420	1.6	42.1	8.5	46.2	8.3	9.4	73.1	8.7	46.0	13.6	6.2
2	2319	4.3	27.8	8.1	14.0	5.8	13.1	195.3	33.3	56.2	38.6	35.3
3	2888	4.2	326.2	4.1	62.4	14.0	13.2	1777.2	13.0	195.1	72.1	27.5
4	2898	17.1	29.6	9.9	12.0	5.5	13.0	655.6	128.4	152.1	116.1	110.0
5	1061	24.6	89.1	10.1	28.4	8.1	13.9	7745.5	516.9	1415.1	667.9	464.2
6	1654	6.7	13.7	4.1	13.1	3.9	10.2	209.1	36.8	114.7	57.1	59.6
7	5934	35.3	8.7	2.5	5.6	1.8	7.6	194.2	32.4	72.4	38.2	65.0
8	3666	377.0	23.6	2.4	6.5	1.7	6.3	9077.4	545.4	1440.9	621.6	931.7
9	7869	471.7	46.5	2.45	10.31	2.52	6.5	10,425.6	324.5	1332.3	537.1	560.3
10	4966	66.8	6.3	1.6	4.0	0.9	5.8	316.1	47.8	115.3	41.0	111.3
11	4443	82.9	17.8	2.0	3.2	0.9	3.4	1248.2	82.5	128.1	56.7	91.0
12	3803	44.6	3.9	1.4	2.4	0.5	4.2	173.0	37.2	59.4	20.6	70.4
13	3376	55.2	3.7	1.7	1.9	0.5	3.9	226.0	60.7	68.0	30.6	91.0
14	2681	-	-	-	-	-	-	-	-	-	-	-
15	3360	128.8	2.9	1.6	1.8	0.7	5.3	411.6	133.0	146.7	89.9	294.5
16	4840	132.7	2.1	1.6	2.0	0.5	4.0	218.5	95.0	120.4	49.9	156.0
17	4041	-	-	-	-	-	-	-	-	-	-	-
18	3493	30.7	6.0	1.1	1.8	0.6	3.3	198.9	21.5	34.3	20.0	42.0
19	3878	232.6	6.5	1.4	2.6	0.7	3.8	1456.7	181.9	333.4	146.3	324.1
Mun 1	70,589	2543.1	9.0	1.9	5.0	1.1	4.6	1220.0	149.9	390.5	145.0	237.3

Note: ¹ The entire Mun River watershed data.

for both dry and wet seasons; these fluxes serve as inputs (vector $\overrightarrow{b}$) for the GMB model.

3.5.2. Geochemical Mass Balance Outputs and Mineral-Specific Contributions

The GMB results are presented in

Table 6. Geochemical mass balance results for seasonal mineral weathering and biomass degradation.

(a) Dry Season
Subwatershed	Biomass Formula: ${\mathrm{Na}}_{\mathrm{x}}{\mathrm{Mg}}_{\mathrm{y}}{\mathrm{Ca}}_{\mathrm{z}}{\mathrm{K}}_{\mathrm{w}}$				Mineral Weathering Rates (µmol m−2 d−1)						Total Mineral Weathering (µmol m−2 d−1)	Biomass Degradation (µmol m−2 d−1)
	x	y	z	w	α1	α2	α3	α4	α5	α6		α7
1	0.99	0.28	1.00	0.17	3.02	12.15	0.00	0.00	0.00	0.00	15.17	27.35
2	1.00	0.25	0.32	0.32	2.58	12.75	0.00	0.00	0.00	0.00	15.34	12.65
3	0.79	0.07	0.17	0.02	0.25	10.08	0.00	0.00	0.00	0.00	10.33	10.02
4	0.00	0.00	0.00	0.00	0.02	0.13	0.00	0.00	0.00	0.00	0.15	100.00
5	0.77	0.14	0.23	0.11	0.41	10.08	0.00	0.00	0.00	0.00	10.48	10.02
6	0.12	0.06	0.11	0.03	0.37	1.00	0.00	0.00	0.00	0.00	1.37	10.08
7	0.27	0.07	0.15	0.04	0.65	1.03	0.00	0.00	0.00	0.00	1.67	10.00
8	1.00	0.06	0.19	0.02	0.00	262.86	5.68	0.00	0.00	0.00	85.34	118.99
9	1.00	0.06	0.19	0.02	0.00	262.86	5.68	0.00	0.00	0.00	183.20	255.42
10	0.00	0.01	0.04	0.00	0.00	0.61	0.00	0.00	0.02	0.00	0.63	9.92
11	0.01	0.06	0.14	0.15	1.00	10.00	1.00	1.00	1.00	1.00	15.00	10.00
12	1.00	0.09	0.53	0.19	5.79	10.69	0.00	0.00	0.88	0.46	17.82	10.62
13	1.00	0.11	0.24	0.11	0.00	470.13	27.27	0.00	0.00	0.00	497.40	483.74
14	-	-	-	-	-	-	-	-	-	-	-	-
15	0.98	0.56	1.00	0.75	5.56	11.44	5.62	0.80	0.00	0.00	23.42	25.30
16	0.42	0.37	1.00	0.82	6.30	10.04	0.24	0.00	0.00	0.00	16.59	10.37
17	-	-	-	-	-	-	-	-	-	-	-	-
18	1.00	0.13	0.14	0.05	2.14	11.86	0.00	0.00	0.00	0.00	14.00	11.78
19	0.99	0.11	0.19	0.03	3.51	18.47	0.00	0.00	0.00	0.00	21.98	18.33
Mun 1	1.00	0.37	1.00	0.21	0.00	75.38	5.79	0.00	0.00	0.00	81.17	50.66
Average	0.70	0.18	0.44	0.18
(b) Wet Season
Subwatershed	Biomass Formula: ${\mathrm{Na}}_{\mathrm{x}}{\mathrm{Mg}}_{\mathrm{y}}{\mathrm{Ca}}_{\mathrm{z}}{\mathrm{K}}_{\mathrm{w}}$				Mineral Weathering Rates (µmol m−2 d−1)						Total Mineral Weathering (µmol m−2 d−1)	Biomass Degradation (µmol m−2 d−1)
	x	y	z	w	α1	α2	α3	α4	α5	α6		α7
1	0.00	0.14	0.46	0.09	6.2	73.1	0.0	0.0	0.0	0.0	79.3	100.0
2	0.97	0.39	0.57	0.34	35.3	100.1	0.0	0.0	0.0	0.0	135.4	98.0
3	1.00	0.08	0.22	0.01	0.0	884.9	10.3	0.0	0.0	0.0	895.2	885.3
4	1.00	0.37	0.45	0.41	20.3	316.4	33.5	0.0	0.0	0.0	370.2	316.4
5	1.00	0.11	0.22	0.08	178.2	1386.5	106.7	0.0	0.0	0.0	1671.4	6286.5
6	0.99	0.50	1.00	0.32	59.6	94.8	0.0	0.0	0.0	0.0	154.4	115.1
7	0.94	0.38	0.72	0.32	63.0	100.0	0.8	0.0	0.0	0.0	163.7	100.0
8	1.00	0.10	0.24	0.09	294.0	997.6	0.0	0.0	0.0	0.0	410.44	603.06
9	1.00	0.10	0.24	0.09	294.0	997.6	0.0	0.0	0.0	0.0	881.06	1294.54
10	1.00	0.38	1.00	0.44	56.7	193.5	20.4	0.0	0.0	0.0	270.6	108.7
11	1.00	0.09	0.19	0.13	0.0	612.6	34.0	0.0	0.0	0.0	646.5	612.6
12	0.72	0.21	0.59	0.37	64.4	99.8	2.2	0.0	0.0	0.0	166.4	99.6
13	1.00	0.28	0.59	0.55	64.0	109.6	10.1	0.0	0.0	0.0	183.6	109.6
14	-	-	-	-	-	-	-	-	-	-	-	-
15	1.00	0.38	0.69	0.68	66.3	182.9	66.2	0.0	7.4	10.7	333.6	182.9
16	0.99	0.28	1.00	0.76	66.8	94.4	13.8	4.5	6.9	7.0	193.3	115.0
17	-	-	-	-	-	-	-	-	-	-	-	-
18	0.99	0.20	0.34	0.21	42.0	100.0	0.0	0.0	0.0	0.0	142.0	100.0
19	1.00	0.21	0.43	0.26	0.0	687.2	120.9	0.0	0.0	0.0	808.2	687.2
Mun 1	1.00	0.25	0.62	0.26	0.0	579.9	88.5	0.0	0.0	0.0	668.4	579.9
Average	0.96	0.26	0.58	0.31

Note: ¹ The entire Mun River watershed data.

, showing mineral weathering rates (${\alpha }_1$ to ${\alpha }_6$) for quartz, halite, feldspar, garnet, biotite, and vermiculite and biomass degradation rates (${\alpha }_7$) for dry and wet seasons. The stoichiometric contributions of key cations (${\mathrm{Na}}^{+}, {\mathrm{Mg}}^{2+}, {\mathrm{Ca}}^{2+},{ \mathrm{K}}^{+})$ from biomass decomposition were expressed using the biomass formula ${\mathrm{Na}}_{\mathrm{x}}{\mathrm{Mg}}_{\mathrm{y}}{\mathrm{Ca}}_{\mathrm{z}}{\mathrm{K}}_{\mathrm{w}}$, with coefficients x, y, z, and w determined with the GMB method by integrating field-measured fluxes with stoichiometric data from XRD analyses, refined using PEST optimization. Total mineral weathering rates, calculated as the sum of rates (${\alpha }_1$ to ${\alpha }_6$) for major rock-forming minerals (quartz, halite, feldspar, garnet, biotite, and vermiculite), and biomass degradation rates (${\alpha }_7$) are provided for each subwatershed in Table 6.

3.5.3. Seasonal Trends in Total Weathering and Biomass Degradation Rates

Mineral weathering rates across the Mun River watershed ranged from 0.15 to 497.4 µmol m⁻² d⁻¹ in the dry season, with the highest rates in subwatersheds 13 and 9, and from 79.3 to 1671.40 µmol m⁻² d⁻¹ in the wet season, with rates exceeding 1000 µmol m⁻² d⁻¹ in subwatersheds 3, 5, and 9 (

Table 7. Seasonal mineral weathering and biomass degradation rates.

Subwatershed	Mineral Weathering Rate (µmol m−2 d−1)		Biomass Degradation Rate (µmol m−2 d−1)
	Dry	Wet	Dry	Wet
1	15.17	79.3	27.35	100
2	15.34	135.4	12.65	98
3	10.33	895.2	10.02	885.3
4	0.15	370.2	100	316.4
5	10.48	1671.40	10.02	6286.50
6	1.37	154.4	10.08	115.1
7	1.67	163.7	10	100
8	85.34	410.44	118.99	603.06
9	183.2	881.06	255.42	1294.54
10	0.63	270.6	9.92	108.7
11	15	646.5	10	612.6
12	17.82	166.4	10.62	99.6
13	497.4	183.6	483.74	109.6
14	-	-	-	-
15	23.42	333.6	25.3	182.9
16	16.59	193.3	10.37	115
17	-	-	-	-
18	14	142	11.78	100
19	21.98	808.2	18.33	687.2

, Figure 6). ANOVA results show a seasonal effect on mineral weathering rates (p = 0.0009).

Biomass degradation rates ranged from 9.92 to 483.74 µmol m⁻² d⁻¹ in the dry season, peaking in subwatershed 13, and from 98 to 6286.50 µmol m⁻² d⁻¹ in the wet season, exceeding 1000 µmol m⁻² d⁻¹ in subwatersheds 5 and 9, with a maximum in subwatershed 5 (Table 7, Figure 7). ANOVA results indicate a seasonal effect on biomass degradation rates (p = 0.0915).

3.6. Spatial and Flux Data Visualizations

Contour maps of dissolved species concentrations and physicochemical parameters (DO, conductivity, pH, temperature, flow rate, TDS) for the dry and wet seasons are presented in Figure 8 and Figure 9, respectively. Seasonal flux statistics of major dissolved ions are summarized in

Table 8. Seasonal flux statistics of major dissolved ions in the Mun River (µmol m⁻² d⁻¹). P-values were calculated using a two-sample t-test (or Mann–Whitney U test for non-normal distributions) to assess seasonal differences, with a significance threshold of p < 0.05.

Parameter	Season	Min	Max	Mean	Median	SD 1	p-Value
${\mathrm{Na}}^{+}$	Dry	0.11	2716.85	419.63	23.65	785.59	0.001
	Wet	0.00	17,971.18	2044.28	286.03	4378.72
${\mathrm{K}}^{+}$	Dry	0.00	252.15	30.80	2.88	65.64	0.003
	Wet	0.00	556.74	121.13	47.86	165.76
${\mathrm{Ca}}^{2+}$	Dry	0.00	1456.98	171.56	4.03	421.61	0.001
	Wet	0.00	2285.57	323.61	113.96	584.88
${\mathrm{Mg}}^{2+}$	Dry	0.00	788.52	77.12	2.54	198.56	0.003
	Wet	0.00	921.04	143.07	56.37	245.88
${\mathrm{Cl}}^{-}$	Dry	0.11	2206.43	404.12	22.71	712.29	0.003
	Wet	0.00	18,905.60	2069.60	222.37	4565.26
${\mathrm{HCO}}_3^{-}$	Dry	0.01	3433.52	435.96	19.51	1066.09	0.001
	Wet	0.00	4946.36	828.19	295.32	1360.18
${\mathrm{NO}}_3^{-}$	Dry	0.00	155.63	10.57	0.01	36.04	0.003
	Wet	0.00	84.98	17.26	7.00	22.83
${\mathrm{SO}}_4^{2-}$	Dry	0.00	400.91	37.88	0.83	97.68	0.001
	Wet	0.00	570.70	85.74	22.77	148.85
${\mathrm{SiO}}_2\left(\mathrm{aq}\right)$	Dry	0.01	402.66	54.24	3.02	120.32	0.001

Note: ¹ SD: Standard deviation; p-values show significant seasonal differences at p < 0.05.

. Seasonal box plots and contour maps of these fluxes are shown in Figure 10 and Figure 11, respectively.

4. Discussion

4.1. Seasonal Biogeochemical Dynamics

The observed seasonal variations in physicochemical parameters reflect complex interactions between hydrological and geochemical processes. The significant pH difference between seasons (p = 0.027), with a shift from slightly alkaline conditions in the dry season (mean = 7.44) to near neutral in the wet season (mean = 6.97), is attributed to increased runoff and dilution during the wet season, introducing organic acids and rainwater. These pH fluctuations can influence mineral solubility and biological processes, underscoring their role in geochemical and ecosystem health, as supported by hydrological modeling studies in the Mun River basin [45].

The reduction in electrical conductivity and total dissolved solids from dry to wet seasons (p = 0.027 and p = 0.009, respectively) highlights the role of monsoonal rains in diluting dissolved ions, a pattern consistent with long-term streamflow forecasts under climate change scenarios [17]. This dilution is particularly pronounced in agricultural areas, where elevated dry season salinity may affect soil and crop health, necessitating targeted management strategies, especially in drought-prone regions [2]. Higher dry season alkalinity (mean = 60.34 mg L⁻¹ CaCO₃) compared to the wet season (mean = 39.51 mg L⁻¹) suggests a greater concentration of dissolved carbonate species due to lower flow rates and evaporation. The significant seasonal variation in dissolved oxygen (p < 0.001), higher in the dry season (mean = 6.40 mg L⁻¹) than the wet season (mean = 4.63 mg L⁻¹), is likely due to increased photosynthesis in clearer waters during the dry season and higher organic decomposition during the wet season. Similarly, the dramatic increase in flow rates during the wet season (mean = 100.99 m³ s⁻¹ vs. 14.27 m³ s⁻¹ in dry season, p < 0.001) emphasizes the impact of monsoonal rainfall on hydrological dynamics, aligning with hydrological simulations of the watershed [45].

4.2. Major Dissolved Species and Their Implications

The concentrations of major dissolved species were generally higher in the dry season, reflecting reduced river flow and increased evaporation that concentrate dissolved components. This trend aligns with previous studies in the region, where agricultural, industrial, and domestic discharges also contributed to elevated ion concentrations during low-flow periods [27]. For sodium (Na⁺), the seasonal increase (p = 0.068) is attributed to the weathering of sodium-rich minerals like halite and plagioclase feldspar, abundant in northern subwatersheds, and evaporative concentration during the dry season. Elevated Na⁺ levels may contribute to long-term soil salinity issues, emphasizing the need for targeted salinity management, particularly in northern agricultural regions where soil nutrient dynamics are heavily influenced by seasonal variations [25].

Potassium (K⁺) showed a similar pattern, though the difference was not statistically significant (p = 0.153), reflecting contributions from feldspar and mica weathering. Higher dry season concentrations of calcium (Ca⁺) and magnesium (Mg²⁺) (p = 0.153 and p = 0.068, respectively) suggest intensified carbonate and silicate weathering under lower flow conditions. Dissolved silica (SiO₂(aq)) followed a comparable trend (p = 0.009), underscoring the role of hydrological variability in regulating weathering processes.

Among anions, the significant increase in chloride (Cl⁻) during the dry season (p = 0.027) is primarily linked to evaporite dissolution and influenced by anthropogenic inputs like agricultural runoff [27]. Many dry season samples exceeded the World Health Organization (WHO) guideline for chloride (250 mg L⁻¹) [52], highlighting salinity issues in areas dominated by evaporite-rich formations. Sulfate (${\mathrm{SO}}_4^{2-}$) levels, though not significantly different between seasons (p = 0.538), are influenced by natural sulfide oxidation and anthropogenic sources, including gypsum dissolution and agricultural runoff. Bicarbonate (${\mathrm{HCO}}_3^{-}$) stability across seasons (p = 0.306) reflects the buffering effect of carbonate weathering, sustaining alkalinity. Nitrate (${\mathrm{NO}}_3^{-}$) remained low and stable (p = 0.395), well below the WHO guideline of 50 mg L⁻¹ [52], suggesting limited agricultural nitrate input, possibly mitigated by wet season dilution. These seasonal variations, illustrated in Figure 3, provide a comprehensive overview of the biogeochemical processes shaping water quality within the Mun River watershed.

4.3. Hydrochemical Facies and Spatial Patterns

The Piper and Stiff diagrams (Figure 4 and Figure 5) reveal distinct seasonal shifts in hydrochemical facies across the Mun River watershed. During the dry season, Na-Cl water types dominate, reflecting the concentration of dissolved ions due to reduced flow rates and increased evaporation. In contrast, the wet season is characterized by a predominance of Ca-HCO₃ facies (Table 2), driven by the dilution effects of higher precipitation and enhanced weathering of carbonate minerals. This seasonal variability aligns with previous findings, where ${\mathrm{Na}}^{+}$ and ${\mathrm{Cl}}^{-}$ were identified as dominant ions, following the cation order ${\mathrm{Na}}^{+} > {\mathrm{Ca}}^{2+} > {\mathrm{Mg}}^{2+} >{ \mathrm{K}}^{+}$ and the anion order ${\mathrm{Cl}}^{-} > {\mathrm{HCO}}_{3 }^{-}> {\mathrm{SO}}_4^{2-} > {\mathrm{NO}}_3^{-}$, indicating a natural prevalence of Na-Cl facies [1].

The overall hydrochemical classification of the Mun River as Na-Ca-Cl-HCO₃ is influenced by both natural geochemical processes, primarily evaporite dissolution and silicate weathering, and anthropogenic inputs such as agricultural runoff and wastewater discharge. These factors result in elevated ion concentrations during the dry season, while wet season flow rates dilute these ions, leading to the dominance of Ca-HCO₃ facies. In the dry season (Figure 5a), larger Stiff polygons indicate higher dissolved ion concentrations due to geological factors like evaporite dissolution and silicate weathering, as well as agricultural runoff, a pattern corroborated by studies on dissolved heavy metals in the region [54]. Conversely, in the wet season (Figure 5b), smaller Stiff diagrams reflect dilution from increased precipitation and runoff, particularly in southern subwatersheds. These regions, shifting from Ca-HCO₃ to Na-Cl water types in the dry season, are especially vulnerable to anthropogenic influences, including fertilizer application and wastewater inputs [55]. These findings underscore the need for best management practices to mitigate nutrient pollution and maintain water quality, particularly in agriculturally intensive areas [55].

4.4. Mineral Weathering and Biomass Degradation Processes

The key minerals identified in the Mun River watershed—quartz, halite, feldspar, garnet, biotite, and vermiculite—significantly contribute to the weathering processes that influence water chemistry. These minerals, characterized by using XRD analysis from formations like Phra Wihan and Sao Khua (Table 4), were used as input data for the geochemical mass balance (GMB) model to calculate weathering rates based on stoichiometric relationships and mass flux data, consistent with established methodologies for small watersheds [40].

The GMB approach, employing a system of linear equations solved with PEST optimization software [53], enabled precise quantification of mineral weathering and biomass degradation rates across the watershed. Biomass degradation rates offer valuable insights into nutrient cycling: a positive rate indicates ion release from decaying plant matter, while a negative rate reflects nutrient absorption by growing vegetation, highlighting its role in nutrient uptake and retention. These processes underscore the interplay between geological composition and biological activity in shaping the watershed’s biogeochemical dynamics. Furthermore, the weathering of silicate minerals, such as feldspar and biotite, contributes to atmospheric CO₂ consumption, a critical process for long-term carbon sequestration that aligns with observations in North American watersheds [23]. This dual role of mineral weathering in influencing both local water chemistry and global carbon cycles emphasizes the broader significance of these findings for watershed management under changing climatic conditions.

4.5. Geochemical Mass Balance Insights

The GMB analysis focused on major ions (${\mathrm{Na}}^{+}, {\mathrm{Mg}}^{2+}, {\mathrm{Ca}}^{2+}, {\mathrm{K}}^{+}, \mathrm{and} {\mathrm{SiO}}_2\left(\mathrm{aq}\right)$), classified as conservative species due to their low likelihood of reprecipitation or loss. These ions originate from the weathering of rock-forming minerals and biomass degradation within the Mun River watershed, as modeled using stoichiometric relationships and field-derived fluxes.

The results, summarized in Table 5 and Table 6, highlight the spatial variability of geochemical processes across the watershed. The mass fluxes of major ions (${\mathrm{Na}}^{+}, {\mathrm{Mg}}^{2+}, {\mathrm{Ca}}^{2+}, {\mathrm{K}}^{+}, \mathrm{and} {\mathrm{SiO}}_2\left(\mathrm{aq}\right)$) exhibited clear seasonal distinctions, influenced by both hydrological and geological factors. Minerals such as feldspar, halite, and garnet, identified as highly susceptible to chemical weathering, were key contributors to these fluxes. These findings underscore the notable interplay between geology and hydrology in shaping weathering and biomass processes, emphasizing the watershed’s heterogeneous response to seasonal changes. Additionally, isotopic studies in the Mun River have traced sulfate sources to both natural weathering and agricultural inputs [56], complementing the GMB results by identifying anthropogenic contributions to ion fluxes. Seasonal variations in the stable carbon isotopic composition values further indicate that carbonate weathering dominates during the wet season, enhancing ion fluxes in northern subwatersheds [57]. The presence of dissolved rare earth elements (REEs), primarily linked to agricultural activities in the middle reaches, also aligns with elevated ion fluxes in these areas [58], reinforcing the role of land use in modulating geochemical processes across the watershed.

4.6. Seasonal Variability in Weathering and Biomass Degradation

The marked increase in mineral weathering rates from the dry season (0.15–497.4 µmol m⁻² d⁻¹) to the wet season (up to 1671.40 µmol m⁻² d⁻¹), particularly in subwatersheds 3, 5, and 9 (Table 6, Figure 6), is attributed to intensified hydrological processes and enhanced water-rock interactions driven by higher precipitation during the wet season. Geological factors, notably the presence of feldspar, garnet, and halite—minerals highly susceptible to chemical weathering—play a substantial role, with halite’s rapid dissolution significantly contributing to dissolved ion fluxes. Spatial variability reflects the watershed’s heterogeneous geological characteristics, with northern and central subwatersheds (subwatersheds 3, 5, and 9) showing particularly high rates. ANOVA confirms a significant seasonal effect on mineral weathering rates (p = 0.0009).

Biomass degradation rates also increased substantially from the dry season (9.92–483.74 µmol m⁻² d⁻¹) to the wet season (up to 6286.50 µmol m⁻² d⁻¹), especially in subwatersheds 5 and 9 (Table 6, Figure 7). This increase is driven by enhanced microbial activity and organic matter inputs from agricultural runoff and land-use changes during the monsoon period, a pattern consistent with nutrient transport during storm events in agricultural watersheds [59]. Higher flow rates and nutrient influx further promote biomass breakdown, aligning with findings that land use strongly influences nutrient exports in agricultural settings [60]. Subwatersheds with extensive agricultural practices (e.g., 5, 9, and 8) exhibited the most pronounced increases, reflecting the influence of human activities on watershed biogeochemistry, as observed in multi-year studies of nutrient fluxes from agricultural watersheds [61]. ANOVA indicates a modest seasonal effect on biomass degradation rates (p = 0.0915), suggesting that while hydrology drives significant changes, land-use impacts amplify these seasonal shifts.

4.7. Seasonal and Spatial Influence on Weathering and Biomass Degradation

The GMB results (Table 6) indicate that both mineral weathering and biomass degradation rates are significantly higher during the wet season compared to the dry season. Among the minerals, halite, quartz, and feldspar exhibited the highest weathering rates, with halite’s high solubility contributing prominently to the overall rate. The total mineral weathering rate, calculated as the sum of rates (${\alpha }_1$ to ${\alpha }_6$) for major rock-forming minerals (quartz, halite, feldspar, garnet, biotite, and vermiculite), underscores the influence of geological composition on dissolution processes. Similarly, the biomass degradation rate (${\alpha }_7$) quantifies the contribution of organic material breakdown to dissolved ion fluxes, reflecting the role of biological processes in ion dynamics.

Spatially, higher mineral weathering and biomass degradation rates were observed in the northern part of the Mun River watershed compared to the southern regions. This variability is primarily attributed to the easily weathered Maha Sarakham Formation and intensive agricultural land use in the north, which enhance water-rock interactions and biomass decomposition, consistent with soil nutrient distribution patterns influenced by agricultural practices [25]. The presence of dissolved rare earth elements (REEs) further highlights agricultural impacts in northern subwatersheds, where elevated weathering rates align with REE sources from fertilizers [58]. These findings highlight the critical influence of climatic variability, land use, and geological characteristics on biogeochemical processes within the watershed, illustrating the combined impact of natural and anthropogenic factors. Moreover, silicate weathering, particularly of quartz and feldspar, contributes to atmospheric$\mathrm{C}{\mathrm{O}}_2$consumption, a process with implications for climate mitigation that mirrors observations in other regions [23], emphasizing the watershed’s role in regional carbon cycling.

4.8. Spatial Patterns of Dissolved Species and Physicochemical Parameters

Contour maps (Figure 8) illustrate the spatial distribution of dissolved cations, anions, and key physicochemical parameters (DO, conductivity, pH, temperature, and flow rate) within the Mun River watershed across dry and wet seasons. These maps highlight significant and spatial variability in dissolved species concentrations and water quality, as evidenced by significant differences in concentrations (e.g., ${\mathrm{Cl}}^{-}$, p = 0.027, Table 2), shaped by hydrological conditions and geological settings.

Dissolved species, such as ${\mathrm{Na}}^{+}$, ${\mathrm{Cl}}^{-}$, and ${\mathrm{SO}}_4^{2-}$, show pronounced seasonal variability, with higher concentrations during the dry season due to evaporation and reduced discharge. In particular, ${\mathrm{Na}}^{+}$ and ${\mathrm{Cl}}^{-}$ levels are elevated in the northern subwatersheds, attributed to evaporite dissolution from the Maha Sarakham Formation, a major source of surface salt, a pattern consistent with spatiotemporal water quality assessments [27]. This geological influence is evident in areas underlain by evaporite-rich lithology, where localized high levels of dissolved species are observed. Meanwhile, the upstream and western parts of the watershed, with lower annual precipitation, are more drought prone, further concentrating dissolved species compared to downstream and eastern areas with higher rainfall. The contour maps also reveal elevated ${\mathrm{Ca}}^{2+}$ and ${\mathrm{HCO}}_3^{-}$ concentrations in upstream regions, resulting from carbonate rock dissolution. Calcite supersaturation in tributaries, such as the Takhong River, highlights localized carbonate influences. In contrast, the wet season’s monsoonal rains dilute dissolved species concentrations across the watershed. Southern subwatersheds experience notable reductions in dissolved ion levels due to precipitation and runoff. However, regions overlying carbonate formations, such as the Khok Kruat Formation, display stable ${\mathrm{HCO}}_3^{-}$ concentrations across seasons, reflecting the buffering capacity of carbonate minerals in moderating seasonal fluctuations.

${\mathrm{SO}}_4^{2-}$ and ${\mathrm{NO}}_3^{-}$ exhibit distinct spatial patterns. Elevated ${\mathrm{SO}}_4^{2-}$ levels in northern subwatersheds during both seasons suggest contributions from evaporite dissolution and agricultural runoff. Localized increases in ${\mathrm{NO}}_3^{-}$ in southern subwatersheds are linked to agricultural practices, underscoring the dual influence of geology and land use on water quality within the watershed.

Contour maps of DO, conductivity, pH, temperature, flow rate, and TDS (Figure 9) further reveal seasonal dynamics. Higher DO levels during the dry season, particularly in northern subwatersheds, are associated with organic material input and reduced flow. During the wet season, DO levels decline due to increased microbial activity and decomposition processes. Spatially, DO remains highest in upstream areas with natural aeration and lower nutrient runoff, while downstream areas, such as Ubon Ratchathani, show consistently reduced DO levels due to anthropogenic impacts and natural gradients, a trend also observed in dissolved heavy metal distributions [54]. Conductivity is higher during the dry season, reflecting concentrated dissolved ions from evaporation and low water flow. In the wet season, rainfall dilutes ion concentrations across the watershed. Spatially, conductivity remains highest in areas underlain by highly soluble bedrock, showing the interplay between geology and seasonal hydrology. Seasonal pH variation indicates higher alkalinity in the dry season due to reduced dilution and contributions from alkaline geological formations. During the wet season, pH shifts toward neutrality, especially in middle and lower reaches, due to dilution. Upstream tributaries draining carbonate formations, such as the Takhong River, sustain elevated pH levels across seasons, highlighting the buffering role of carbonate minerals. Water temperature shows minimal seasonal variation, with slightly higher averages during the dry season. Spatially, temperature is relatively uniform but marginally higher in open, less vegetated regions with increased solar exposure. Groundwater inflows help moderate temperature fluctuations. Wet season flow rates rise dramatically due to monsoonal rainfall, diluting dissolved substances and enhancing oxygen distribution, while dry season flow rates are lower, leading to ion concentration and higher temperatures in stagnant areas. TDS levels are higher during the dry season due to evaporation and reduced flow, with particularly elevated levels in northern subwatersheds influenced by geological formations and agricultural runoff. Wet season rains dilute TDS concentrations across the watershed, aligning with seasonal shifts in other physicochemical parameters.

4.9. Dissolved Species Fluxes and Hydrological Influences

Concentration contour maps alone do not fully capture the influence of processes like agricultural activities within the watershed, as they omit the role of flow rate. To address this, net export fluxes for dissolved solutes were calculated by incorporating flow rate, which is critical for accurately depicting spatial variations in solute transport. The resulting flux data (Table 8) and seasonal box plots (Figure 10) reveal distinct seasonal patterns, highlighting the hydrological control on biogeochemical processes.

The flux contour maps (Figure 11) illustrate seasonal and spatial distribution, with significant increases observed during the wet season compared to the dry season. Fluxes of dissolved species, including nitrate, are most elevated in middle to northern subwatersheds, corresponding with areas experiencing the highest flow rates (as shown in the flow rate contour map). This pattern underscores water flow as a primary driver of seasonal solute transport, influencing weathering and biomass degradation across the watershed. Seasonal climatic changes further amplify the fluxes of dissolved species, particularly nitrogen, which exhibits marked variability between wet and dry seasons. Increased fertilizer application during the wet season also contributes to nutrient loading, consistent with land use-nutrient export relationships in agricultural watersheds [60].

The findings align with prior studies that Identified runoff, lithology, and land cover as major determinants of bicarbonate fluxes and atmospheric CO₂ consumption in North American watersheds [23]. Similarly, bicarbonate fluxes in the Mun River respond strongly to seasonal flow variations and geological characteristics. The mean ${\mathrm{HCO}}_3^{-}$ flux increases from 435.96 µmol m⁻² d⁻¹ in the dry season to 828.19 µmol m⁻² d⁻¹ in the wet season, highlighting the critical role of flow rate in enhancing carbonate weathering and atmospheric CO₂ consumption during high-flow periods.

Specific dissolved species such as ${\mathrm{K}}^{+}, {\mathrm{Na}}^{+}, {\mathrm{Cl}}^{-}, {\mathrm{NO}}_3^{-}, \mathrm{and} {\mathrm{SO}}_4^{2-}$ are closely linked to agricultural fertilizers, animal waste, and sewage. Sharp increases in ${\mathrm{Cl}}^{-}, {\mathrm{NO}}_3^{-}, \mathrm{and} {\mathrm{SO}}_4^{2-}$ fluxes are observed in midstream areas, likely due to fertilizer use. Similarly, elevated dissolved rare earth elements (REEs) in middle reaches, primarily attributed to agricultural fertilizers and sewage discharge, corroborate these patterns [58]. Although minimal, pesticide levels also peak during the wet season, reflecting increased agricultural runoff and its cumulative impact on water quality, as observed in studies of pesticide transport to surface waters [62]. These hotspots of dissolved species fluxes, particularly in midstream northern subwatersheds, underscore the strong influence of agricultural activities.

Land use patterns vary across the watershed, with the northern subwatersheds and upstream Mun River dominated by agriculture, while southern subwatersheds are more forested. Agricultural watersheds discharge significantly higher nutrient loads than forested ones [60]. Nitrate flux, often associated with runoff contaminants like pesticides, peaks during wet seasons, exacerbating water quality and aquatic ecosystem impacts. Statistical analysis of fluxes reveals significant seasonal differences for all measured dissolved species, in contrast to concentration data alone. This highlights the importance of incorporating flow rate—a critical hydrological factor—into flux calculations to accurately capture dissolved solute transport. Representing solute fluxes rather than concentration provides a clearer depiction of biogeochemical processes, particularly in areas influenced by agricultural activities.

4.10. Interplay of Biomass Degradation and Mineral Weathering

The biomass formula ${\mathrm{Na}}_{\mathrm{x}}{\mathrm{Mg}}_{\mathrm{y}}{\mathrm{Ca}}_{\mathrm{z}}{\mathrm{K}}_{\mathrm{w}}$ and mineral weathering rates (${\alpha }_1$–${\alpha }_6$) elucidate the interconnected biogeochemical processes in the Mun River watershed. The total mineral weathering rate, calculated as the sum of rates for quartz (${\alpha }_1$), halite (${\alpha }_2$), plagioclase/feldspar (${\alpha }_3$), garnet (${\alpha }_4$), biotite (${\alpha }_5$), and vermiculite (${\alpha }_6$), varies significantly across seasons and subwatersheds (Figure 12). In the dry season, rates range from 0.15 to 497.40 µmol m⁻² d⁻¹, with halite (${\alpha }_2$) dominating in northern subwatersheds (e.g., subwatershed 13: ${\alpha }_2$ = 470.13), reflecting evaporite dissolution under low flow conditions. Quartz (${\alpha }_1$) contributions are generally low (e.g., subwatershed 5: ${\alpha }_1$ = 0.41). In the wet season, rates increase markedly, ranging from 79.3 to 1671.4 µmol m⁻² d⁻¹, with northern subwatersheds showing pronounced halite (${\alpha }_2$) and quartz (${\alpha }_1$) weathering (e.g., subwatershed 5: ${\alpha }_2$ = 1386.5, ${\alpha }_1$ = 178.2), alongside feldspar (${\alpha }_3$ = 106.7), driven by monsoon-enhanced water-rock interactions.

The biomass formula complements these findings by detailing cation contributions from degradation, visualized in heatmaps (Figure 13a,b). Dry season coefficients are lower (e.g., subwatershed 5: x = 0.77, y = 0.14), indicating reduced microbial activity, while wet season increases (e.g., subwatershed 5: x = 1.00, y = 0.11) highlight enhanced ${\mathrm{Na}}^{+}$ release, aligning with high halite weathering (${\alpha }_2$). This synergy is evident in northern subwatersheds (e.g., subwatershed 9: x = 1.00, ${\alpha }_2$ = 997.6), where agricultural runoff and the Maha Sarakham Formation’s evaporites amplify ${\mathrm{Na}}^{+}$ and ${\mathrm{Cl}}^{-}$ fluxes, consistent with Na-Cl facies dominance (Section 3.3) and nutrient flux patterns from agricultural watersheds [61]. Southern subwatersheds (e.g., subwatershed 16: z = 1.00, ${\alpha }_3$ = 13.8, ${\alpha }_5$ = 6.9, ${\alpha }_6$ = 7.0) exhibit ${\mathrm{Ca}}^{2+}$ dominance and diverse weathering, reflecting basaltic geology and supporting Ca-HCO₃ facies with enhanced pH buffering.

Spatially, northern subwatersheds show higher biomass degradation and halite weathering rates, driven by intensive agriculture and evaporite-rich geology, while southern regions demonstrate more stable chemistry due to contributions from feldspar, biotite, and vermiculite. Seasonally, the wet season’s increased flow (Section 4.1) amplifies both processes, with quartz weathering (${\alpha }_1$) aiding CO₂ sequestration and halite (${\alpha }_2$) exacerbating salinity risks, particularly in drought-prone agricultural areas [2]. This interplay underscores the watershed’s vulnerability to climate change, as intensified monsoons may further increase ion loads.

Management strategies must address these patterns. Northern subwatersheds require salinity mitigation through buffer zones and reduced fertilizer use to curb ${\mathrm{Na}}^{+}$ inputs from biomass and halite weathering, as proposed in best management practices for nutrient pollution [55]. Southern areas can leverage silicate weathering (e.g., quartz, feldspar) for long-term carbon sequestration via reforestation. Integrated approaches, informed by these geochemical insights, are essential to enhance water quality and ecosystem resilience under future climate scenarios.

5. Conclusions

This study assessed seasonal biogeochemical variations in the Mun River watershed, Northeastern Thailand, using water quality data and the geochemical mass balance (GMB) method to quantify mineral weathering and biomass degradation rates across dry and wet seasons of 2024. The results reveal significant seasonal and spatial differences driven by hydrological dynamics, geological composition, and anthropogenic influences, particularly agriculture. Wet season mineral weathering rates (300–1671.4 µmol m⁻² d⁻¹) were 10–20 times higher than dry season rates (0.15–497.4 µmol m⁻² d⁻¹), with the highest rates (900–1200 µmol m⁻² d⁻¹) observed in northern subwatersheds, attributed to intensified water-rock interactions facilitated by monsoonal rainfall and the presence of easily weathered minerals like halite and feldspar. Biomass degradation rates similarly increased in the wet season (up to 6286.50 µmol m⁻² d⁻¹) compared to the dry season (9.92–483.74 µmol m⁻² d⁻¹), reflecting enhanced microbial activity and organic matter inputs from agricultural runoff. These processes underscore the watershed’s sensitivity to seasonal climatic shifts, with northern areas showing heightened vulnerability due to evaporite-rich geology and intensive land use.

Water quality analyses further highlight the impact of these dynamics. Dry season conditions exhibited higher concentrations of dissolved ions (e.g., ${\mathrm{Na}}^{+}$, ${\mathrm{Cl}}^{-}$) due to reduced flow and evaporation, often exceeding WHO guidelines (e.g., ${\mathrm{Cl}}^{-}$ > 250 mg L⁻¹), while wet season dilution shifted hydrochemical facies from Na-Cl to Ca-HCO₃ types. Spatial variability was pronounced, with northern subwatersheds displaying elevated ${\mathrm{Na}}^{+}$ and ${\mathrm{Cl}}^{-}$ fluxes linked to the Maha Sarakham Formation and agricultural practices, and southern regions showing more stable Ca-HCO₃ chemistry influenced by basaltic geology. The GMB approach provided critical insights into these patterns, linking ion fluxes to specific mineral weathering (e.g., halite, feldspar) and biomass degradation processes, with implications for carbon cycling through silicate weathering and CO₂ sequestration.

These findings emphasize the urgent need for adaptive watershed management strategies to mitigate the compounded effects of climate change and human activities on water quality and ecosystem health. In northern subwatersheds, salinity management through reduced fertilizer use and buffer zones is essential to address ${\mathrm{Na}}^{+}$ and ${\mathrm{Cl}}^{-}$ loading from halite weathering and biomass degradation. Conversely, southern areas could leverage silicate weathering for long-term carbon sequestration via reforestation. Future research should employ advanced techniques to further disentangle these dynamics. Principal component analysis (PCA) can identify key variables driving water quality variations, while isotopic analysis can trace water sources and quantify groundwater contributions to solute fluxes. Integrating these methods with long-term monitoring will support sustainable watershed management, ensuring resilience against ongoing climate change and land-use intensification in the Mun River watershed and its downstream Mekong ecosystems. As climate models predict intensified monsoons and temperature variability, such integrated approaches, informed by these biogeochemical insights, are vital to enhance sustainability and fill critical knowledge gaps in this region.