Potential impacts of climate change and water resource management on fish catch in the Lower Mekong River

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The Mekong River basin supports the world’s largest inland fishery, with the Tonle Sap system playing a central role in sustaining migratory fish populations through its seasonal flood pulse. However, the effects of climate change and dam operations on fish productivity remain poorly quantified. This study investigates how the shifts in hydrology and temperature may affect migratory fish catch in the Tonle Sap River throughout the 21st century. Empirical models were developed linking climatological variables, reservoir development, and fishery data. The hydrological models reproduced seasonal dynamics with high accuracy (R² > 0.95), while fish catch models explained up to 87% of observed variability. These models were forced with climate projections from four CMIP6 models under SSP2-4.5 and SSP5-8.5 scenarios, combined with increasing dam capacity. Scenario analysis indicates that increasing water temperature and altered flood regimes will substantially reduce fish catch. By the end of the century, migratory fish catch is projected to decline by 67–85% under climate change alone and by up to 95% when combined with intensified dam operations. Rising temperatures during spawning and refuge seasons and reduced flood rise rates were key drivers of this decline. The results highlight the vulnerability of the Tonle Sap fishery to interacting climatic and hydrological pressures and underscore the importance of adaptive water resources management that maintains functional flood regimes and the ecosystem services they support. Fish catch water resource management climate change projections ecosystem services Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction Globally, freshwater fisheries are vital for food security, livelihoods, and cultural identity (McIntyre et al., 2016 ). They provide employment opportunities and support regional trade, sustaining local economies. However, these ecosystem services are increasingly threatened by anthropogenic pressures, such as pollution, water resources development, and climate change (Malik et al., 2020 ; Peluso et al., 2022 ). As a result, approximately one third of freshwater fish species around the world face the threat of extinction (Vardakas et al., 2025 ). For example, the Aral Sea fishery in Central Asia has collapsed due to unsustainable water resource development, such as dam operation and irrigation (Alieva et al., 2023 ), and the Yangtze River in China has experienced a significant decline in biodiversity, especially in migratory species, due to the similar regional development (Du et al., 2025 ). To make the situation worse, climate change is projected to further reduce fish populations and consequently fish catch (Intergovernmental Panel on Climate Change (IPCC), 2022 ). Research and development over the past two decades have significantly advanced our understanding of these influences of changing habitats on fish. For instance, the responses to shifting hydrological patterns were found to differ among species, causing a potential dominance of some limited species while other populations decline (Van Zuiden et al., 2016 ). Further, raising water temperatures expand spatial distributions of some fish species towards high latitude regions and such invasions potentially displace native species (Elías Ilosvay et al., 2024 ). Additionally, climate change not only shifts their distributions but also decreases the growth rate of some fish, resulting in reduced fishery yields (M. Huang et al., 2021 ). These understandings in fish dynamics have been further enriched by recent technological developments. For instance, the development of high-resolution climate models in the Coupled Model Intercomparison Project Phase 6 (CMIP6) climate projections has enhanced the modelling accuracy (Czaja et al., 2023 ). Similarly, the increasing remote sensing data and high-resolution digital elevation models have enabled us to model habitat conditions more precisely (Kuiper et al., 2023 ). Particularly in Southeast Asia, freshwater systems are vulnerable to changes in the hydrological processes, as rapid population growth combined with economic development drives increasing water demands (FAO & Australian Water Partnership (AWP), 2023). Among these systems, the Mekong River basin is unique in its hydrological processes, characterised by the seasonal reversal flow in its lower reaches (hereafter, the Lower Mekong). This seasonal flow expands the surface area of Tonle Sap Lake during rainy season by nearly fivefold and inundates the vast floodplains, which sustains fish spawning, feeding, and migration (Sithirith & Grundy-Warr, 2025 ). Owing to such unique ecological processes, the Mekong River is one of major rivers holding the highest biodiversity and productivities in the world (Winemiller et al., 2016 ). Consequently, it is home to one of the world’s largest freshwater fishery, contributing to approximately 18% of Cambodia’s gross domestic product (GDP) and providing livelihoods for millions of people (Nam et al., 2015 ). Particularly in the Lower Mekong, fish is the primary source of animal protein intake, accounting for 76% of the animal protein consumption in Cambodia (Vilain et al., 2016 ). However, the Living Planet Index (LPI), indicating trends in population sizes, has decreased by 87.7% in the Lower Mekong from 2003 to 2019 (Chevalier et al., 2023 ). This reduction was attributed mainly to interventions of the natural system. Specifically, dam constructions throughout the Mekong catchment have been altering the natural flow regime, changing flood timing and magnitude, and fragmenting migratory pathways (Arias et al., 2012 ), while climate change altered precipitation patterns and temperatures. Those environmental influences have been reducing the flood extent of the Tonle Sap and thus fish catches and community incomes (Eyler, Basist, et al., 2024 ). While considerable research has examined the individual effects of either dams or climate change on the Tonle Sap ecosystem (Eyler, Kwan, et al., 2024 ; Semmler et al., 2020 ), only a single study has attempted to model their combined impacts on fish in the Mekong River (Nuon et al., 2024 ), although it has analysed the changes in fish distribution without considering population sizes. This technical gap is critical for the implementation of policies on sustainable resource management because understanding potential future trends of fish catch requires not only the knowledge of shifts of their spatial distribution but also the changes in abundance and productivity. The primary objective of this study is to determine the potential impacts of climate change and water resource management on the catch of migratory fish in the Tonle Sap River by focusing on the shifts in flow regime and temperature. To this end, empirical models were built based on the historical hydrological, climatological, and fishery data to describe water levels, water temperature, and the catch of migratory fish in the Lower Mekong. Then, the hydrology and the fish catch there were projected under two emission scenarios considering climate change and water resource management in the basin. 2. Study area and methods 2.1. Study area and targeted period This study focused on the migration routes of major migratory fish in the Lower Mekong, which covers Tonle Sap Lake, Tonle Sap River, and the sections of the Lower Mekong between Stung Treng and Phnom Penh (Ly et al., 2024 ) (Fig. 1 ). For modelling purpose, the basin was divided into 10 sub-basins according to Lehner et al. ( 2008 ). Climate data were averaged within these sub-basins and the data of water level and temperature were observed at five sites in Cambodia. Fish catch was monitored in the Tonle Sap River between Phnom Penh and Prek Kdam. The study analysed long-term hydrological and climatological data from 1950 to 2025, which cover natural flow regimes as well as the recent regimes under major infrastructure developments and climate change. While dam constructions started in 1992, major infrastructure developments were initiated by the construction of the Xiaowan and Nuozhadu dams in 2010 and 2014 respectively, causing major anthropogenic changes to the downstream hydrology (Hecht et al., 2019 ). The period from 2025 to 2099 was investigated in the scenario analysis for water levels, temperatures, and fish catch based on projected climates. 2.2. Data Historical fish catch, hydrological, and climatological data were collected for building the empirical models of water levels, water temperature, and fish catch (Figure S1 ). Hydrological and climatological variables were collected as monthly representative values. To model future water levels and water temperatures, projected climatological data were additionally collected on monthly basis. As for the fish data, this study utilised the catch weight data of Dai fishery for the hydrological years 2000/2001 to 2014/2015 (Ngor et al., 2018 ). Specifically, corresponding data of the species classified as migratory fish were collected and analysed, as those are the target species of Dai fishery (Ly et al., 2024 ). The Dai fishery operates seasonally during the period of falling water levels in Tonle Sap Lake from approximately October to March (Ngor et al., 2018 ). It uses bag nets in the reach between Phnom Penh and Prek Kdam (see Fig. 1 ) and has not experienced major changes in fishing effort throughout the analysed time frame (Ngor et al., 2018 ). The fish catch data were used as annual data to analyse long-term fishing trends. Water levels and temperature were collected at five sites in the Lower Mekong (Fig. 1 and Table 1 ), which are important locations for modelling migratory fish in the Tonle Sap River (Ly et al., 2024 ). Water level data at each site were extracted from the data portal of the Mekong River Commission (Mekong River Commission, 2024 ). Water level data were available for different time periods depending on the site, with the longest time series from 1950 to 2022 and the shortest series from 1997 to 2022 (Table S1 ). The series data of water temperature at these sites were obtained as the estimate of mixed layer water temperature from the land dataset of the fifth generation of European reanalysis (ERA5-Land) (Copernicus Climate Change Service (C3S), 2019 ). ERA5-Land is a downscaled grid of atmospheric and hydrological data based on the fifth generation of European reanalysis by the European Centre for Medium Range Weather Forecasts (ERA5) (Muñoz-Sabater et al., 2021 ). Water temperature data covered the period from 1950 to June 2025. In addition, climatological variables (precipitation (mm), air temperature (°C), surface solar radiation downwards (J/m2), surface thermal radiation downwards (J/m2), and wind speed (m/s)) were obtained from ERA5-Land on the platform of Google Earth Engines. They were extracted as monthly mean values of the sub-basins (Fig. 1 ), covering the period from 1950 to 2025. Projections of the same climatological variables were extracted from four different climate models for the period from 2025 to 2099 as well as from their historical simulations from 1950 to 2014. Those climate models were chosen among CMIP6 models, which are the latest generation of climate models under standardised data frames (Eyring et al., 2016 ). The models were NorESM2-MM by the Norwegian Climate Centre, CNRM-CM6-1 by the French National Centre for Meteorological Research, EC-Earth3 by the ECMWF, and MPI-ESM1-2-HR by the Max Planck Institute. All four models have been shown to describe precipitation and air temperature in the Mekong basin well compared to other CMIP6 models (Trinh-Tuan et al., 2025 ). From those model outputs, surface downwelling shortwave radiation (W/m2) was used to estimate the surface solar radiation downwards (J/m2), while surface downwelling longwave radiation (W/m2) was used for calculating the surface thermal radiation downwards (J/m2). These data from the climate models were extracted from the NASA earth exchange global daily downscaled climate projections (NEX-GDDP-CMIP6) in Google Earth Engines (Thrasher et al., 2012 ). To account for different future developments of climate change, data from projections of the Shared Socioeconomic Pathways SSP2-4.5 and SSP5-8.5 (IPCC, 2023 ) were collected. They account for an intermediate and very high greenhouse gas emission scenario with global warming being limited to 3°C and exceeding 4°C, respectively. 2.3. Empirical models The study modelled water levels by linking upstream precipitation and temperature to downstream conditions using generalised additive model (GAM) and multiple linear regression (MLR). Water temperatures were estimated from key energy balance components and calibrated under near-natural conditions (Text S1). Using these hydrological variables, fish catch was modelled using a lifecycle-based approach capturing stage-specific responses via regression models (Text S2). All models and statistical analysis in this section were conducted by using version 4.5.0 (2025-04-11) of R (R Core Team, 2025 ). 2.4. Scenario analysis To project the effects of climate change and infrastructure development on the fish catch in Tonle Sap in the period from 2025 to 2099, we set up six scenarios based on two climatic projections of intermediate (SSP2-4.5) and very high (SSP5-8.5) greenhouse gas emission scenarios (Table 1 ). Climatic changes in those projections were used to project changes in hydrology throughout the 21st century. The effects of dam operations on water levels were added to the projected data and then used as input variables of the selected fish catch models to project the fish catch from 2025 to 2098. Table 1 Scenarios for the long-term projections. Code Scenario Climate Change Dam Operation SSP2-Dam0 Intermediate emissions without dam operation SSP2-4.5 No SSP2-Dam1 Intermediate emissions with moderate dam operations SSP2-4.5 Moderate SSP2-Dam2 Intermediate emissions with intense dam operations SSP2-4.5 Intense SSP5-Dam0 Very high emissions without dam operation SSP5-8.5 No SSP5-Dam1 Very high emissions with moderate dam operations SSP5-8.5 Moderate SSP5-Dam2 Very high emissions with intense dam operations SSP5-8.5 Intense The time series data of the projected climatological data were prepared using Linear Scaling (LS), as a bias correction method, to match both historical observed and projected monthly data. For temperature data, the scaling factor in the LS was treated as a summand, while it was utilised as a factor the remaining variables, following Dinh and Aires ( 2023 ). The period from 1961 to 1990 was used as baseline period for the bias correction, as it is defined as the standard reference period for long-term climate change assessments (World Meteorological Organization, 2017 ). The bias-corrected meteorological projections from the four CMIP6 models were averaged and used to project hydrological variables and then the fish catch in Tonle Sap. To avoid overestimation of snow melt due to extrapolation in these projections, the downstream river flows were estimated by capping air temperature in the Upper Lancang at 10°C. Consequently, if air temperature was higher than 10°C, it was treated as 10°C in the river flow estimation. Otherwise, original temperature from climate models were used in the estimation. This further assumed that the snow and ice storage there under climate projections will be the same as in the past. To simulate the effects of dams, the created model for water levels was first used to estimate the natural water level at Stung Treng for the period of 2010–2022 solely based on the observed climatological data. Then, the monthly difference between these modelled and the observed water levels was calculated and considered as the effect of dam operations. The relative changes were averaged over the period from 2010 to 2022 to represent the recent effects of dam operations and build the Dam1 scenarios. The hydrological change in the Lower Mekong was assumed to be proportional to the increase in the total hydropower capacity of all reservoirs in the basin. Thus, based on the total capacity of all operating and planned dams up to 2099 (Ang et al., 2024 ), a scaling factor was introduced to describe the future hydrological impacts of reservoirs for the scenarios of SSP2-Dam2 and SSP5-Dam2. Specifically, the total capacity of reservoirs in the Mekong River basin in 2022 was 36,059 MW, which is the total capacity used for the Dam1 scenarios. This is expected to increase by 27.7% until 2040 according to ongoing and planned constructions. When adding the planned dams with no specific envisioned year of completion, the total capacity of dams will increase by 33116.7 MW, corresponding to a capacity increase of 91.8%. This value was assumed to be reached by the end of the 21st century by applying a linear increase from the year 2040 to 2099. This scaling factor (%) was applied to determine the relative impact of dam operations on the water levels in Stung Treng and then project water level at Stung Treng in the scenarios of SSP2-Dam2 and SSP5-Dam2. Based on the projected water levels, hydrological variables at the remaining sites were also projected and used as input variables for the fish catch model. As the models for water temperatures did not show any significance correlation with water levels in rivers, the effect of dams on downstream water temperatures was omitted. 3. Results 3.1. Models of water levels and water temperatures The cross-validations revealed good fits for all tested models of water level and temperature during the calibration and validation period (1950/60–1992), with cross-validated R 2 > 0.95, NRMSE 0.95 (Figure S2, Table S3). Thus, the seasonality of water level and temperature was well described by those models (Figs. 2 and S2). The models for water temperatures at all sites performed slightly better with R 2 > 0.95, NRMSE 0.95. The model of the water level in Stung Treng (Table S4), revealed relatively large time lags observed with the precipitations in the Upper and Lower Lancang, as well as the Nam Ou Basin, where correlations with the water level in Stung Treng exceeded 0.75 at time lags of one to two months. For the remaining basins, time lags with the water level in Stung Treng were one month or shorter. As for the water levels at the downstream sites, they showed time lags from zero to two months to the water levels in Stung Treng, with higher time lags corresponding to sites located further downstream. During the post-dam construction period from 2010 to 2022, the observed water level at Stung Treng increased in dry season (December–June) and decreased during rainy season (July–November) compared to the natural hydrological processes, which were estimated by GAM model (Fig. 2 ). The relative difference in water level between the observation and the model estimation was the highest in April (53.4%) while its reduction was most significant in October (− 8.02%). In terms of the absolute difference, the water level in the dry season increased by up to 0.95 m in April and decreased by up to 0.51 m in September compared to the estimated natural levels. The Pearson correlation coefficients between these relative water level differences and volume changes in the upstream reservoirs were − 0.51 and − 0.44 for the Nuozhadu and Xiaowan reservoirs, respectively. Shortly after the reservoir fillings start to increase in June, the observed water level was lower than the natural level (June–July) while the increase in reservoir fillings in November was followed by a period where water levels exceed the natural level (November–December). Despite the distance of more than 2,000 km (Eyler, Basist, et al., 2024 ), the comparison to the reservoir filling data provided by the Stimson Center showed a clear negative correlation between changes in reservoir fillings and the estimated shift in water level (Fig. 2 c). As for water temperatures, the modelled and observed water temperatures did not differ significantly and the observed small differences did not show any significant correlations with the dam operation data (Figure S3). 3.2. Modelled fish catch Regarding the total catch of migratory fish, the selected models showed high cross-validated R 2 values between 0.8 and 0.9 and NRMSE values below 0.15 (Tables 2 and S5). The three best models of each structure were sorted according to their AICc, classifying them as the best (MLR1, GLM1), second (MLR2, GLM2) and third (MLR3, GLM3) suitable for the modelling of fish catch. Among them, MLR1 performed the best, while GLM1 showed the lowest cross-validated R 2 , indicating a lower suitability for the application in projections (Figure S4 and S5). Based on those results, MLR1 was selected for the scenario analysis. All the selected models used similar variable sets (Table S6). They showed high significance of the rate of flood rise (4RFR), as well as of the water temperature during spawning season (2WT) with a negative correlation. Other variables included in those models were the rate of drawdown (4RDD), fish catch in the previous year (5BP), water level in the spawning (2WL) and migration season (3WL), duration of flood (4DF), and water temperature in refugee (1WT) and migration (5WT) season. As for the species-specific models, all models had lower performance parameters than the models for total fish catch. The models for H. lobatus showed cross-validated R 2 between 0.55 and 0.65 with NRMSE values between 0.2 and 0.4. Among them, GLM1 showed the best performance of these models with a cross-validated R 2 of 0.65 and NRMSE of 0.32 (Figure S5c). According to this model, water temperatures during refugee (1WT), spawning (2WT), and second migration season (5WT) showed a negative impact on the catch (Table 2 ). The maximum water level during feeding season (4WLMax) showed the strongest standardised impact, affecting the catch positively, while the water level during the second migration season (5WL) showed negative correlations with the catch. The models for L. lineatus , on the other hand, performed nearly as reliable as the ones for the total fish catch with cross-validated R 2 between 0.65 and 0.8 and NRMSE values below 0.25. Out of these models, GLM2 had a cross-validated R 2 of 0.80 and NRMSE of 0.16 (Figure S5e). The water temperatures during refugee (1WT), and spawning season (2WT) showed negative effects on catch of this species. The maximum water level during feeding season (4WLMax) again showed a positive correlation with catch, yet less pronounced than that for H. lobatus. The water level during refugee season (1WL) had a positive impact on the catch, and the day of flood start (4FS) had the strongest positive effect, indicating that a later flood start corresponds to a higher catch. Table 2 Selected environmental variables and performance of MLR and GLM models for catch of all migratory fish species and two individual major species (n = 15). Significance level: ** p < 0.01 and *** p < 0.001. All species Henicorhynchus lobatus Labiobarbus lineatus Range (kg/year) 7.2×10 6 – 37.9×10 6 0.8×10 6 – 11.2×10 6 0.8×10 6 – 5.8×10 6 Link function none log log Cross-validated R 2 0.87 0.65 0.80 NRMSE 0.12 (MLR1) 0.32 (GLM1) 0.16 (GLM2) Standardised Coefficient 1WL Feb. – Apr. (Refugee) 0.37*** 1WT Feb. – Apr. (Refugee) –0.37** –0.28*** −0.31*** 2WT May – Jul. (Spawning) −0.41** −0.19*** −0.20*** 4WLMax Jul. – Nov. (Feeding) 0.93*** 0.39*** 4FS Jul. – Nov. (Feeding) 0.82*** 4RFR Jul. – Nov. (Feeding) 0.80*** 5WL Dec. – Feb. (Migration) −0.79*** 5WT Dec. – Feb. (Migration) −0.27*** 3.3. Scenario analysis Air temperature data for the Tonle Sap Basin have shown a significant increase from 1950 to 2025 by 0.168°C/decade. The projection suggests a further rise of 0.24°C/decade and 0.59°C/decade for the 21st century under SSP2-4.5 and SSP5-8.5, respectively. Water temperature data mirror this trend (Fig. 3 a). From 1950 until 2025 they show a significantly positive trend by 0.092°C/decade, which is projected to continue under SSP2-4.5 by 0.118°C/decade, while SSP5-8.5 leads to an increase of 0.27°C/decade. As for the historical mean water levels at Kampong Luong, the Mann-Kendall test revealed a significant (p < 0.001) negative trend. In contrast, under SSP2-4.5 and SSP5-8.5, the results show significant increasing trends (0.003 m/decade and 0.01 m/decade, respectively). A similar trend is observed for maximum water levels at Kampong Luong. Further, Welch’s t-test showed significant differences between maximum water levels with and without dam operations for both climate change scenarios (p < 0.001). This behaviour is mirrored by the projected rate of flood rise (Fig. 3 d). Under the scenarios with increasing dam operations, the maximum water levels nor the rate of flood rise showed significant negative trends. The annual flood cycle in Stung Treng is dampened under the dam scenarios compared to the scenarios with no dam operation, with reductions during rainy season of up to 1.08 m and increases during dry season of up to 2.2 m (Fig. 4 b). Nonetheless, the maximum water level increases under both climate change scenarios, resulting in maximum water levels up to 1.38 m higher than before dam construction towards the end of the 21st century under all scenarios but SSP2-Dam2. As precipitation is not projected to change significantly compared to historic values (Fig. 3 a), this increase is attributed to increased snowmelt in upstream basins. The water level during dry season also shows significant increases of up to 2.59 m under scenarios with dam operations, while it is projected to be lower or the same as pre-dam levels with no dam operations. Similar trends are observed for the water level in Kampong Luong, where the water level is projected to increase by up to 2.61 m under SSP5-Dam2 in July, and the maximum water level is projected to increase by up to 1.02 m under SSP5-Dam0 (Fig. 3 d). The shifts in water levels in Stung Treng due to dam constructions translate downstream into an earlier onset of the seasonal flood pulse. However, the impacts of climate change and dam operations at Kampong Luong are expected to be weaker, as upstream snowmelt contributions and dam influences are more distant and overlaid with additional precipitation and tributary inflow. The applied scenarios project a significant decline of total migratory fish catch. While no significant trend could be observed for historical data (2000–2014), fish catch is projected to decrease by 75 tons/year under SSP2, with even higher decreases projected for SSP5 (106 tons/year) (Fig. 5 a). While projections with dam operations showed less significant trends, Welch’s t-tests revealed a significantly lower projected mean fish catch under Dam1 and Dam2 scenarios compared to Dam0 scenarios under both climate change scenarios. Taking the mean value of the observed fish catch in 2000–2014 as a baseline and comparing it to the mean projected fish catch in the last 10 years of the projection, fish catches are projected to decline by 67.0% for SSP2-4.5 and 84.8% for SSP5-8.5 in the decade from 2090 to 2099 (Figs. 6 and S7). The decreases are expected to reach 78.8% and 91.5% under the scenarios of unchanged dam operations. In combination with increasing dam operations, fish catches are projected to decline by 89.5% and 95.4%. The projections of total migratory fish catch show strong negative correlations with the air temperature in the Tonle Sap Basin as well as dam capacities (Fig. 6 ). These decreasing trends are mirrored by the two analysed individual species. Yet, the decrease of H. lobatus shows stronger effects of both climate change and dam operations. On the other hand, the catch of L. lineatus shows positive correlations with dam operations and less decreases under scenarios with dam operation compared to the scenarios without dam operations (Figure S6). This is attributed to the strong significance of the start date of flood (4FS) in modelling the catch of L. lineatus (Table 2 ). The sensitivity analysis further highlighted the direct effects of the predictor variables on the fish catch more detailed. Figure S7 illustrates these effects, plotting how the fish catch projection would change under variation of only one variable, while others are kept as the mean values during the observation period (2000–2014). Total fish catch appears to be most sensitive to temperature changes during the spawning and feeding seasons, showing steeper declines compared to the temperature during the refugee season. Additionally, water level during migration season and fish catch in the previous year show nearly linear relationships to fish catch in the following year for the observed range. 4. Discussion 4.1. Performance of empirical models All hydrological models captured seasonal variations well. For instance, seasonal patterns in Stung Treng were overall well represented (Fig. 2 a), although annual peak and low flows were not always accurately captured (Figure S2). While such limitation affects the accuracy of the projections for individual years, the cross-validation proves an overall good representation of general trends. Thus, comparing decadal mean projected values with mean historical values reduces the over- or underestimation of extreme values under unknown conditions. As for the water temperature, using ERA5-Land date with a coarse resolution does not allow the abstraction of site-specific data but rather utilises the mean temperature in the concerned grid (Muñoz-Sabater et al., 2021 ). Local circumstances and inflows might be underestimated in this estimation. However, as water temperature data by the Mekong River Commission are manually measured, ERA5-Land data are less vulnerable to inconsistent recording. The fish catch models achieved high predictive performances as well. The key predictors identified by the models align with established knowledge on fisheries in the Lower Mekong that emphasise the central role of hydrological seasonality for the productivity of the Tonle Sap fishery (Ly et al., 2024 ; Ngor et al., 2018 ). While water levels determine the extent and duration of floodplain inundation and thus nutrient availability, the vulnerability of fish catch to water temperatures during refugee and spawning season indicates that fish are especially vulnerable to thermal stress during these life stages, potentially affecting reproduction and migration-timing (Lema et al., 2024 ). For the total fish catch, the rate of flood rise was found to have a strong correlation with fish catch. This is consistent with the finding that the reversal of flow in the Tonle Sap River transports nutrients, larvae, and juvenile fish into Tonle Sap Lake (Holtgrieve et al., 2013 ). An increase of this rate increases the transport capacity. Further, this rate is strongly correlated with the maximum water level in Tonle Sap Lake, indicating the area of maximum inundation, which is crucial for nutrient supply during the feeding season and found to be significant for the two individual fish species. Interestingly, the model for H. lobatus indicated a negative effect of the water level during the second migration season (5WL) on catch. The timing of the migration of H. lobatus is strongly triggered by receding water levels in the Tonle Sap Lake, causing earlier out-migration compared to other species (Chan et al., 2019 ). Thus, fish passage through Dai nets is concentrated during the low-flow period (Dec–Feb), when low discharges optimise bagnet retention. The small sample size (n = 15) restricts the application of more complex modelling techniques, such as GAM and random forest and the integration of more variables. Further changed species compositions due to habitat alterations might alter prey-predator relationships and affect the capacity of fish populations to adapt to fluctuating conditions (Fujiwara et al., 2019 ). Despite these limitations, the models provide important insights into the primary hydrological and climatic drivers of fish catches. While the models cannot capture the full ecological and socio-economic complexity of the system, they establish a robust foundation for future research. 4.2. Impacts on river discharge in the Lower Mekong The historical annual mean water levels showed a slight decrease, consistent with already existing findings (Wang et al., 2024 ). In contrast, the models project an increase in annual runoff at Stung Treng, likely reflecting enhanced snowmelt contributions from the Upper Lancang under warming conditions. Reported studies have found that while snow cover in the upper Mekong is expected to decrease due to global warming, leading to a reduction in snowmelt, the share of rain to the total precipitation is increasing with rising temperatures (Cui et al., 2023 ). This increase of rain will offset the decrease in snowmelt, which is caused by the lower snow coverage, ultimately leading to an increased runoff. Empirical hydrological models excluding temperature and snowmelt effects project largely stable water levels, with only a slight increase under the Dam0 scenario (Figure S8). Regarding the impact of upstream water resource management on downstream water levels, the results showed a strong correlation between total upstream reservoir capacity and difference between modelled “natural” and observed water levels. Consistent with previous findings (Ly et al., 2021 ), maximum water levels (4WLMax) at sites in the Lower Mekong were found to be lower than under natural flow conditions, while minimum water levels increased. In addition to these amplitude shifts, the modelling revealed a slightly earlier offset of flood rise at Kampong Luong (4FS). The approach to assess the impacts of water resource management in this study was rather simple and widely applicable. Instead of directly including upstream dam operation data, it utilised statistical methods to identify observed changes. This enabled a broader interpretation of reservoir influences as part of wider water resource management activities. However, although the baseline period covered a time span without dams on the mainstream Mekong, water abstractions for irrigation have already altered the natural flow at that time (H. Huang et al., 2025 ). At the same time, we should note that water resource management is a matter of human operation, so despite being able to use averaged changes in water levels, the actual effects are dependent on the way that dams are operated. 4.3. Impacts on fish catch in the Lower Mekong The increased runoff due to climate change translates into increases in the maximum water level in Kampong Luong (4WLMax) of 1.2 m on average towards the end of the 21st century. Combined with no significant changes of the minimum water levels, these changes are reflected in an increased rate of flood rise (4RFR) by 10.0% and 16.1% under SSP2-4.5 and SSP5-8.5 respectively. This changed rate of flood rise leads to increases in the transport of nutrients, larvae, and juvenile fish into Tonle Sap Lake (Holtgrieve et al., 2013 ). These dynamics are fundamental to the provision of aquatic ecosystem services, as they sustain fish biomass and overall fishery productivity. Thus, the increase of maximum water levels ultimately leads to an increase in overall fish catch as well as both specific species. Water temperatures during refugee (February – April, 1WT), spawning (May – July, 2WT), and migration (December – February, 5WT) seasons showed negative correlations with fish catch of all migratory fish and individual fish species. Increasing temperatures at these life stages are particularly threatening to sustaining fish catch in the Lower Mekong. With temperatures increasing by between 0.86° and 1.99°C during these seasons, this thermal stress is resulting in a decline in fish catch. However, when combining the positive effect of rising maximum water levels and the rate of flood rise with the negative effect of increasing temperatures, the scenario analysis results suggest a decline in fish catches under future hydrological and climatic conditions, broadly consistent with observations reported in other studies (Ngor et al., 2018 ; Sor et al., 2024 ). Under dam scenarios, the decreases in maximum water levels (4WLMax) combined with the increases of minimum water levels lead to decreases in the rate of flood rise (4RFR), partially offsetting the positive impact of these variables under the Dam0 scenarios, and thus resulting in stronger declines of fish catch under increased dam construction. However, the water levels during the refugee season (February–April) play a significant role in modelling the catch of L. lineatus. Higher water levels during this season improve the floodplain connectivity and habitat conditions for this species, resulting in higher fish catch under dam operation scenarios compared to Dam0 scenarios. For further investigation, we should be aware that the construction of dams does not only affect downstream water levels. Dams in the Lower Mekong, such as in Laos, are known to have not implemented appropriate measures to ensure sediment transportation and fish migration (Roney, 2021 ). This might change habitat compositions and nutrient availability, as well as species distributions in the downstream areas. 4.4. Policy implications The presented results clearly show that the total catch of migratory fish in the Lower Mekong, and the ecosystem services it provides, are facing threats by both climate change and hydropower development. Climate change is a main driver of change in catch numbers, yet its effects are highly dependent on the intensity of temperature rise. However, higher water levels could dampen the negative effects of rising water temperatures. Thus, designing flood cycles for different climate scenarios is critically important to sustain fish catch. For instance, according to the projections, water resource management should aim to design flood cycles with a rate of flood rise at Kampong Luong higher than 0.06 m/d under the SSP2-4.5 scenario to sustain fish catch at approximately one third of historical levels. Additionally, natural flood patterns might change due to changing precipitation patterns in the upstream basin. Earlier snowmelt might lead to an earlier offset of rising water levels, while stronger extreme events might induce a more variable flow compared to historical values. Dam operations could mitigate such hydrological extremes by storing water during high-flow events or periods of earlier snow depletion and model natural flow conditions by reintroducing this water at a later point. Meanwhile, we should be aware that this approach is subject to several limitations, including operational constraints, competing water demands, and uncertainties in predicting the timing and magnitude of future hydrological events. Declarations Author Contribution Hannah Plückhahn drafted the main manuscript under the supervision and methodological guidance of Chihiro Yoshimura. Sophanna Ly contributed methodological expertise and provided insights into the data. All authors critically reviewed and approved the final manuscript. Acknowledgement The authors acknowledge Stimson Center for providing information that supported this research. The first author further acknowledges the support from the Cusanuswerk scholarship awarded by the German government. Data Availability The data supporting the findings of this study are available from the corresponding author upon reasonable request. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9476830","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":637805796,"identity":"0e1c2ae0-62bf-417c-82fa-dc3536bac415","order_by":0,"name":"Hannah Plückhahn","email":"","orcid":"","institution":"RWTH Aachen University","correspondingAuthor":false,"prefix":"","firstName":"Hannah","middleName":"","lastName":"Plückhahn","suffix":""},{"id":637805797,"identity":"33d60915-15fb-44f7-aa62-3f9667ff8b5e","order_by":1,"name":"Sophanna Ly","email":"","orcid":"","institution":"Institute of Science Tokyo","correspondingAuthor":false,"prefix":"","firstName":"Sophanna","middleName":"","lastName":"Ly","suffix":""},{"id":637805798,"identity":"6d753c75-d0bd-4f1c-8d1f-a261a87da83d","order_by":2,"name":"Chihiro Yoshimura","email":"data:image/png;base64,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","orcid":"","institution":"Institute of Science Tokyo","correspondingAuthor":true,"prefix":"","firstName":"Chihiro","middleName":"","lastName":"Yoshimura","suffix":""}],"badges":[],"createdAt":"2026-04-20 23:23:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9476830/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9476830/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":109090394,"identity":"255b4394-0fa4-46d5-bb56-4d0517df455c","added_by":"auto","created_at":"2026-05-12 13:30:42","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":156636,"visible":true,"origin":"","legend":"\u003cp\u003eThe study area. The maps show the division of the Mekong catchment area into sub-basins and the locations of the major upstream dams (a). The inset of Cambodia (b) shows the Mekong River, Tonle Sap River, and Tonle Sap Lake (permanent water surface), indicating the monitoring sites (red points) for hydrological variables. Fish catch \u0026nbsp;was recorded in the reach of the Tonle Sap River between Phnom Penh and Prek Kdam.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-9476830/v1/4f049b30919ae532de4bd497.png"},{"id":109090351,"identity":"e324abb8-43a9-469c-9bf0-2cdc8256cff8","added_by":"auto","created_at":"2026-05-12 13:30:37","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":735504,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of modelled and observed water levels at Stung Treng during (a) the pre-dam and (b) the post-dam period. Panel (c) compares the differences between observed and modelled water levels in the post-dam period with the monthly volume change in upstream reservoirs. Panel (d) depicts the yearly variations of modelled and observed water levels. The seasons of spawning (\u003cem\u003e2-Spawning\u003c/em\u003e) and downstream migration (\u003cem\u003e3-Migration\u003c/em\u003e) overlap timewise, while the fish models targeted different sites during this overlapping season.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-9476830/v1/cf7578e88d20cb242a8a4121.png"},{"id":109092570,"identity":"711e810d-f49a-4c65-b08e-8292a89b2bd0","added_by":"auto","created_at":"2026-05-12 13:41:36","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":266408,"visible":true,"origin":"","legend":"\u003cp\u003eHistorical and projected variables under the considered scenarios (1950–2099). Panels (a, b) and (c, d) use the same legends.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-9476830/v1/9ab7c38e9dcc2e53913b0ecb.png"},{"id":109092201,"identity":"c7d21e91-ee4f-4547-99c8-5a02fe2c6f84","added_by":"auto","created_at":"2026-05-12 13:39:47","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":214985,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of the average monthly water level and upstream precipitation of Stung Treng (a and b) and Kampong Luong (c and d) before the dam construction (1950 – 1992) with the projections for the last decade of the 21st century (2090–2099). For the water level in Kampong Luong, historical data from 1997 to 2010 were used due to limited data availability.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-9476830/v1/512d162d0c33ad88322a27c2.png"},{"id":109204541,"identity":"1d1b5a9d-f3b8-4596-b965-26c4ff8240e7","added_by":"auto","created_at":"2026-05-13 15:00:55","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":278650,"visible":true,"origin":"","legend":"\u003cp\u003eHistorical and projected fish catch of all migratory fish and individual species under all scenarios (2000–2099).\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-9476830/v1/e374df1a03cefb09e21c2357.png"},{"id":109090255,"identity":"d94207db-0c91-4a46-a4f3-a0f2a03ec3fd","added_by":"auto","created_at":"2026-05-12 13:29:48","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":84901,"visible":true,"origin":"","legend":"\u003cp\u003eRelationship between mean air temperature in the Tonle Sap Basin and the proportion of projected total catch relative to the observed catch from 2001 to 2015 (a), and the relationship between the total dam capacity throughout the reservoir and the catch proportion (b). Data points are mean values of 2040–2049 and 2090–2099. The grey band visualises the 95% prediction interval of the decadal averages.\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-9476830/v1/22ee9d39df9e15208b6368df.png"},{"id":109207844,"identity":"c1d369b3-b864-496d-ab3a-61357f0ba69f","added_by":"auto","created_at":"2026-05-13 15:22:04","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2081133,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9476830/v1/9e77f273-f0d4-4581-8333-e3c36cd240da.pdf"},{"id":109090565,"identity":"1826c48e-2d62-4e77-87d7-3b99ece50b8f","added_by":"auto","created_at":"2026-05-12 13:32:29","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":3339183,"visible":true,"origin":"","legend":"","description":"","filename":"20260408Supplementarymaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-9476830/v1/10ed73ea28bc1d4cb23ef025.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Potential impacts of climate change and water resource management on fish catch in the Lower Mekong River","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eGlobally, freshwater fisheries are vital for food security, livelihoods, and cultural identity (McIntyre et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). They provide employment opportunities and support regional trade, sustaining local economies. However, these ecosystem services are increasingly threatened by anthropogenic pressures, such as pollution, water resources development, and climate change (Malik et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Peluso et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). As a result, approximately one third of freshwater fish species around the world face the threat of extinction (Vardakas et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). For example, the Aral Sea fishery in Central Asia has collapsed due to unsustainable water resource development, such as dam operation and irrigation (Alieva et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), and the Yangtze River in China has experienced a significant decline in biodiversity, especially in migratory species, due to the similar regional development (Du et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). To make the situation worse, climate change is projected to further reduce fish populations and consequently fish catch (Intergovernmental Panel on Climate Change (IPCC), \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eResearch and development over the past two decades have significantly advanced our understanding of these influences of changing habitats on fish. For instance, the responses to shifting hydrological patterns were found to differ among species, causing a potential dominance of some limited species while other populations decline (Van Zuiden et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Further, raising water temperatures expand spatial distributions of some fish species towards high latitude regions and such invasions potentially displace native species (El\u0026iacute;as Ilosvay et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Additionally, climate change not only shifts their distributions but also decreases the growth rate of some fish, resulting in reduced fishery yields (M. Huang et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThese understandings in fish dynamics have been further enriched by recent technological developments. For instance, the development of high-resolution climate models in the Coupled Model Intercomparison Project Phase 6 (CMIP6) climate projections has enhanced the modelling accuracy (Czaja et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Similarly, the increasing remote sensing data and high-resolution digital elevation models have enabled us to model habitat conditions more precisely (Kuiper et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eParticularly in Southeast Asia, freshwater systems are vulnerable to changes in the hydrological processes, as rapid population growth combined with economic development drives increasing water demands (FAO \u0026amp; Australian Water Partnership (AWP), 2023). Among these systems, the Mekong River basin is unique in its hydrological processes, characterised by the seasonal reversal flow in its lower reaches (hereafter, the Lower Mekong). This seasonal flow expands the surface area of Tonle Sap Lake during rainy season by nearly fivefold and inundates the vast floodplains, which sustains fish spawning, feeding, and migration (Sithirith \u0026amp; Grundy-Warr, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Owing to such unique ecological processes, the Mekong River is one of major rivers holding the highest biodiversity and productivities in the world (Winemiller et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Consequently, it is home to one of the world\u0026rsquo;s largest freshwater fishery, contributing to approximately 18% of Cambodia\u0026rsquo;s gross domestic product (GDP) and providing livelihoods for millions of people (Nam et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Particularly in the Lower Mekong, fish is the primary source of animal protein intake, accounting for 76% of the animal protein consumption in Cambodia (Vilain et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHowever, the Living Planet Index (LPI), indicating trends in population sizes, has decreased by 87.7% in the Lower Mekong from 2003 to 2019 (Chevalier et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This reduction was attributed mainly to interventions of the natural system. Specifically, dam constructions throughout the Mekong catchment have been altering the natural flow regime, changing flood timing and magnitude, and fragmenting migratory pathways (Arias et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), while climate change altered precipitation patterns and temperatures. Those environmental influences have been reducing the flood extent of the Tonle Sap and thus fish catches and community incomes (Eyler, Basist, et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). While considerable research has examined the individual effects of either dams or climate change on the Tonle Sap ecosystem (Eyler, Kwan, et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Semmler et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), only a single study has attempted to model their combined impacts on fish in the Mekong River (Nuon et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), although it has analysed the changes in fish distribution without considering population sizes. This technical gap is critical for the implementation of policies on sustainable resource management because understanding potential future trends of fish catch requires not only the knowledge of shifts of their spatial distribution but also the changes in abundance and productivity.\u003c/p\u003e \u003cp\u003eThe primary objective of this study is to determine the potential impacts of climate change and water resource management on the catch of migratory fish in the Tonle Sap River by focusing on the shifts in flow regime and temperature. To this end, empirical models were built based on the historical hydrological, climatological, and fishery data to describe water levels, water temperature, and the catch of migratory fish in the Lower Mekong. Then, the hydrology and the fish catch there were projected under two emission scenarios considering climate change and water resource management in the basin.\u003c/p\u003e"},{"header":"2. Study area and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Study area and targeted period\u003c/h2\u003e \u003cp\u003eThis study focused on the migration routes of major migratory fish in the Lower Mekong, which covers Tonle Sap Lake, Tonle Sap River, and the sections of the Lower Mekong between Stung Treng and Phnom Penh (Ly et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). For modelling purpose, the basin was divided into 10 sub-basins according to Lehner et al. (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Climate data were averaged within these sub-basins and the data of water level and temperature were observed at five sites in Cambodia. Fish catch was monitored in the Tonle Sap River between Phnom Penh and Prek Kdam.\u003c/p\u003e \u003cp\u003eThe study analysed long-term hydrological and climatological data from 1950 to 2025, which cover natural flow regimes as well as the recent regimes under major infrastructure developments and climate change. While dam constructions started in 1992, major infrastructure developments were initiated by the construction of the Xiaowan and Nuozhadu dams in 2010 and 2014 respectively, causing major anthropogenic changes to the downstream hydrology (Hecht et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The period from 2025 to 2099 was investigated in the scenario analysis for water levels, temperatures, and fish catch based on projected climates.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Data\u003c/h2\u003e \u003cp\u003eHistorical fish catch, hydrological, and climatological data were collected for building the empirical models of water levels, water temperature, and fish catch (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Hydrological and climatological variables were collected as monthly representative values. To model future water levels and water temperatures, projected climatological data were additionally collected on monthly basis.\u003c/p\u003e \u003cp\u003eAs for the fish data, this study utilised the catch weight data of Dai fishery for the hydrological years 2000/2001 to 2014/2015 (Ngor et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Specifically, corresponding data of the species classified as migratory fish were collected and analysed, as those are the target species of Dai fishery (Ly et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The Dai fishery operates seasonally during the period of falling water levels in Tonle Sap Lake from approximately October to March (Ngor et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). It uses bag nets in the reach between Phnom Penh and Prek Kdam (see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) and has not experienced major changes in fishing effort throughout the analysed time frame (Ngor et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The fish catch data were used as annual data to analyse long-term fishing trends.\u003c/p\u003e \u003cp\u003eWater levels and temperature were collected at five sites in the Lower Mekong (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), which are important locations for modelling migratory fish in the Tonle Sap River (Ly et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Water level data at each site were extracted from the data portal of the Mekong River Commission (Mekong River Commission, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Water level data were available for different time periods depending on the site, with the longest time series from 1950 to 2022 and the shortest series from 1997 to 2022 (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). The series data of water temperature at these sites were obtained as the estimate of mixed layer water temperature from the land dataset of the fifth generation of European reanalysis (ERA5-Land) (Copernicus Climate Change Service (C3S), \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). ERA5-Land is a downscaled grid of atmospheric and hydrological data based on the fifth generation of European reanalysis by the European Centre for Medium Range Weather Forecasts (ERA5) (Mu\u0026ntilde;oz-Sabater et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Water temperature data covered the period from 1950 to June 2025. In addition, climatological variables (precipitation (mm), air temperature (\u0026deg;C), surface solar radiation downwards (J/m2), surface thermal radiation downwards (J/m2), and wind speed (m/s)) were obtained from ERA5-Land on the platform of Google Earth Engines. They were extracted as monthly mean values of the sub-basins (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), covering the period from 1950 to 2025.\u003c/p\u003e \u003cp\u003eProjections of the same climatological variables were extracted from four different climate models for the period from 2025 to 2099 as well as from their historical simulations from 1950 to 2014. Those climate models were chosen among CMIP6 models, which are the latest generation of climate models under standardised data frames (Eyring et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The models were NorESM2-MM by the Norwegian Climate Centre, CNRM-CM6-1 by the French National Centre for Meteorological Research, EC-Earth3 by the ECMWF, and MPI-ESM1-2-HR by the Max Planck Institute. All four models have been shown to describe precipitation and air temperature in the Mekong basin well compared to other CMIP6 models (Trinh-Tuan et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). From those model outputs, surface downwelling shortwave radiation (W/m2) was used to estimate the surface solar radiation downwards (J/m2), while surface downwelling longwave radiation (W/m2) was used for calculating the surface thermal radiation downwards (J/m2). These data from the climate models were extracted from the NASA earth exchange global daily downscaled climate projections (NEX-GDDP-CMIP6) in Google Earth Engines (Thrasher et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). To account for different future developments of climate change, data from projections of the Shared Socioeconomic Pathways SSP2-4.5 and SSP5-8.5 (IPCC, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) were collected. They account for an intermediate and very high greenhouse gas emission scenario with global warming being limited to 3\u0026deg;C and exceeding 4\u0026deg;C, respectively.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Empirical models\u003c/h2\u003e \u003cp\u003eThe study modelled water levels by linking upstream precipitation and temperature to downstream conditions using generalised additive model (GAM) and multiple linear regression (MLR). Water temperatures were estimated from key energy balance components and calibrated under near-natural conditions (Text S1). Using these hydrological variables, fish catch was modelled using a lifecycle-based approach capturing stage-specific responses via regression models (Text S2). All models and statistical analysis in this section were conducted by using version 4.5.0 (2025-04-11) of R (R Core Team, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Scenario analysis\u003c/h2\u003e \u003cp\u003eTo project the effects of climate change and infrastructure development on the fish catch in Tonle Sap in the period from 2025 to 2099, we set up six scenarios based on two climatic projections of intermediate (SSP2-4.5) and very high (SSP5-8.5) greenhouse gas emission scenarios (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Climatic changes in those projections were used to project changes in hydrology throughout the 21st century. The effects of dam operations on water levels were added to the projected data and then used as input variables of the selected fish catch models to project the fish catch from 2025 to 2098.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eScenarios for the long-term projections.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCode\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eScenario\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eClimate Change\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDam Operation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSSP2-Dam0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIntermediate emissions without dam operation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSSP2-4.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSSP2-Dam1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIntermediate emissions with moderate dam operations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSSP2-4.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSSP2-Dam2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIntermediate emissions with intense dam operations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSSP2-4.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIntense\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSSP5-Dam0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVery high emissions without dam operation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSSP5-8.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSSP5-Dam1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVery high emissions with moderate dam operations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSSP5-8.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSSP5-Dam2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVery high emissions with intense dam operations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSSP5-8.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIntense\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe time series data of the projected climatological data were prepared using Linear Scaling (LS), as a bias correction method, to match both historical observed and projected monthly data. For temperature data, the scaling factor in the LS was treated as a summand, while it was utilised as a factor the remaining variables, following Dinh and Aires (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The period from 1961 to 1990 was used as baseline period for the bias correction, as it is defined as the standard reference period for long-term climate change assessments (World Meteorological Organization, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The bias-corrected meteorological projections from the four CMIP6 models were averaged and used to project hydrological variables and then the fish catch in Tonle Sap. To avoid overestimation of snow melt due to extrapolation in these projections, the downstream river flows were estimated by capping air temperature in the Upper Lancang at 10\u0026deg;C. Consequently, if air temperature was higher than 10\u0026deg;C, it was treated as 10\u0026deg;C in the river flow estimation. Otherwise, original temperature from climate models were used in the estimation. This further assumed that the snow and ice storage there under climate projections will be the same as in the past.\u003c/p\u003e \u003cp\u003eTo simulate the effects of dams, the created model for water levels was first used to estimate the natural water level at Stung Treng for the period of 2010\u0026ndash;2022 solely based on the observed climatological data. Then, the monthly difference between these modelled and the observed water levels was calculated and considered as the effect of dam operations. The relative changes were averaged over the period from 2010 to 2022 to represent the recent effects of dam operations and build the Dam1 scenarios.\u003c/p\u003e \u003cp\u003eThe hydrological change in the Lower Mekong was assumed to be proportional to the increase in the total hydropower capacity of all reservoirs in the basin. Thus, based on the total capacity of all operating and planned dams up to 2099 (Ang et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), a scaling factor was introduced to describe the future hydrological impacts of reservoirs for the scenarios of SSP2-Dam2 and SSP5-Dam2. Specifically, the total capacity of reservoirs in the Mekong River basin in 2022 was 36,059 MW, which is the total capacity used for the Dam1 scenarios. This is expected to increase by 27.7% until 2040 according to ongoing and planned constructions. When adding the planned dams with no specific envisioned year of completion, the total capacity of dams will increase by 33116.7 MW, corresponding to a capacity increase of 91.8%. This value was assumed to be reached by the end of the 21st century by applying a linear increase from the year 2040 to 2099. This scaling factor (%) was applied to determine the relative impact of dam operations on the water levels in Stung Treng and then project water level at Stung Treng in the scenarios of SSP2-Dam2 and SSP5-Dam2. Based on the projected water levels, hydrological variables at the remaining sites were also projected and used as input variables for the fish catch model. As the models for water temperatures did not show any significance correlation with water levels in rivers, the effect of dams on downstream water temperatures was omitted.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Models of water levels and water temperatures\u003c/h2\u003e \u003cp\u003eThe cross-validations revealed good fits for all tested models of water level and temperature during the calibration and validation period (1950/60\u0026ndash;1992), with cross-validated R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.95, NRMSE\u0026thinsp;\u0026lt;\u0026thinsp;0.06, and NSE\u0026thinsp;\u0026gt;\u0026thinsp;0.95 (Figure S2, Table S3). Thus, the seasonality of water level and temperature was well described by those models (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and S2). The models for water temperatures at all sites performed slightly better with R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.95, NRMSE\u0026thinsp;\u0026lt;\u0026thinsp;0.04, and NSE\u0026thinsp;\u0026gt;\u0026thinsp;0.95.\u003c/p\u003e \u003cp\u003eThe model of the water level in Stung Treng (Table S4), revealed relatively large time lags observed with the precipitations in the Upper and Lower Lancang, as well as the Nam Ou Basin, where correlations with the water level in Stung Treng exceeded 0.75 at time lags of one to two months. For the remaining basins, time lags with the water level in Stung Treng were one month or shorter. As for the water levels at the downstream sites, they showed time lags from zero to two months to the water levels in Stung Treng, with higher time lags corresponding to sites located further downstream.\u003c/p\u003e \u003cp\u003eDuring the post-dam construction period from 2010 to 2022, the observed water level at Stung Treng increased in dry season (December\u0026ndash;June) and decreased during rainy season (July\u0026ndash;November) compared to the natural hydrological processes, which were estimated by GAM model (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The relative difference in water level between the observation and the model estimation was the highest in April (53.4%) while its reduction was most significant in October (\u0026minus;\u0026thinsp;8.02%). In terms of the absolute difference, the water level in the dry season increased by up to 0.95 m in April and decreased by up to 0.51 m in September compared to the estimated natural levels.\u003c/p\u003e \u003cp\u003eThe Pearson correlation coefficients between these relative water level differences and volume changes in the upstream reservoirs were \u0026minus;\u0026thinsp;0.51 and \u0026minus;\u0026thinsp;0.44 for the Nuozhadu and Xiaowan reservoirs, respectively. Shortly after the reservoir fillings start to increase in June, the observed water level was lower than the natural level (June\u0026ndash;July) while the increase in reservoir fillings in November was followed by a period where water levels exceed the natural level (November\u0026ndash;December). Despite the distance of more than 2,000 km (Eyler, Basist, et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), the comparison to the reservoir filling data provided by the Stimson Center showed a clear negative correlation between changes in reservoir fillings and the estimated shift in water level (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec). As for water temperatures, the modelled and observed water temperatures did not differ significantly and the observed small differences did not show any significant correlations with the dam operation data (Figure S3).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Modelled fish catch\u003c/h2\u003e \u003cp\u003eRegarding the total catch of migratory fish, the selected models showed high cross-validated R\u003csup\u003e2\u003c/sup\u003e values between 0.8 and 0.9 and NRMSE values below 0.15 (Tables\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and S5). The three best models of each structure were sorted according to their AICc, classifying them as the best (MLR1, GLM1), second (MLR2, GLM2) and third (MLR3, GLM3) suitable for the modelling of fish catch. Among them, MLR1 performed the best, while GLM1 showed the lowest cross-validated R\u003csup\u003e2\u003c/sup\u003e, indicating a lower suitability for the application in projections (Figure S4 and S5). Based on those results, MLR1 was selected for the scenario analysis. All the selected models used similar variable sets (Table S6). They showed high significance of the rate of flood rise (4RFR), as well as of the water temperature during spawning season (2WT) with a negative correlation. Other variables included in those models were the rate of drawdown (4RDD), fish catch in the previous year (5BP), water level in the spawning (2WL) and migration season (3WL), duration of flood (4DF), and water temperature in refugee (1WT) and migration (5WT) season.\u003c/p\u003e \u003cp\u003eAs for the species-specific models, all models had lower performance parameters than the models for total fish catch. The models for \u003cem\u003eH. lobatus\u003c/em\u003e showed cross-validated R\u003csup\u003e2\u003c/sup\u003e between 0.55 and 0.65 with NRMSE values between 0.2 and 0.4. Among them, GLM1 showed the best performance of these models with a cross-validated R\u003csup\u003e2\u003c/sup\u003e of 0.65 and NRMSE of 0.32 (Figure S5c). According to this model, water temperatures during refugee (1WT), spawning (2WT), and second migration season (5WT) showed a negative impact on the catch (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The maximum water level during feeding season (4WLMax) showed the strongest standardised impact, affecting the catch positively, while the water level during the second migration season (5WL) showed negative correlations with the catch.\u003c/p\u003e \u003cp\u003eThe models for \u003cem\u003eL. lineatus\u003c/em\u003e, on the other hand, performed nearly as reliable as the ones for the total fish catch with cross-validated R\u003csup\u003e2\u003c/sup\u003e between 0.65 and 0.8 and NRMSE values below 0.25. Out of these models, GLM2 had a cross-validated R\u003csup\u003e2\u003c/sup\u003e of 0.80 and NRMSE of 0.16 (Figure S5e). The water temperatures during refugee (1WT), and spawning season (2WT) showed negative effects on catch of this species. The maximum water level during feeding season (4WLMax) again showed a positive correlation with catch, yet less pronounced than that for \u003cem\u003eH. lobatus.\u003c/em\u003e The water level during refugee season (1WL) had a positive impact on the catch, and the day of flood start (4FS) had the strongest positive effect, indicating that a later flood start corresponds to a higher catch.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSelected environmental variables and performance of MLR and GLM models for catch of all migratory fish species and two individual major species (n\u0026thinsp;=\u0026thinsp;15). Significance level: ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01 and *** p\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAll species\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eHenicorhynchus lobatus\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eLabiobarbus lineatus\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eRange (kg/year)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.2\u0026times;10\u003csup\u003e6\u003c/sup\u003e \u0026ndash; 37.9\u0026times;10\u003csup\u003e6\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.8\u0026times;10\u003csup\u003e6\u003c/sup\u003e \u0026ndash; 11.2\u0026times;10\u003csup\u003e6\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.8\u0026times;10\u003csup\u003e6\u003c/sup\u003e \u0026ndash; 5.8\u0026times;10\u003csup\u003e6\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eLink function\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003enone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003elog\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003elog\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eCross-validated R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.80\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eNRMSE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.12 (MLR1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.32 (GLM1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.16 (GLM2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eStandardised Coefficient\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1WL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFeb. \u0026ndash; Apr. (Refugee)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.37***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1WT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFeb. \u0026ndash; Apr. (Refugee)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ndash;0.37**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;0.28***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;0.31***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2WT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMay \u0026ndash; Jul. (Spawning)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;0.41**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;0.19***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;0.20***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4WLMax\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJul. \u0026ndash; Nov. (Feeding)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.93***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.39***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4FS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJul. \u0026ndash; Nov. (Feeding)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.82***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4RFR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJul. \u0026ndash; Nov. (Feeding)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.80***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5WL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDec. \u0026ndash; Feb. (Migration)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;0.79***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5WT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDec. \u0026ndash; Feb. (Migration)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;0.27***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Scenario analysis\u003c/h2\u003e \u003cp\u003eAir temperature data for the Tonle Sap Basin have shown a significant increase from 1950 to 2025 by 0.168\u0026deg;C/decade. The projection suggests a further rise of 0.24\u0026deg;C/decade and 0.59\u0026deg;C/decade for the 21st century under SSP2-4.5 and SSP5-8.5, respectively. Water temperature data mirror this trend (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). From 1950 until 2025 they show a significantly positive trend by 0.092\u0026deg;C/decade, which is projected to continue under SSP2-4.5 by 0.118\u0026deg;C/decade, while SSP5-8.5 leads to an increase of 0.27\u0026deg;C/decade.\u003c/p\u003e \u003cp\u003eAs for the historical mean water levels at Kampong Luong, the Mann-Kendall test revealed a significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) negative trend. In contrast, under SSP2-4.5 and SSP5-8.5, the results show significant increasing trends (0.003 m/decade and 0.01 m/decade, respectively). A similar trend is observed for maximum water levels at Kampong Luong. Further, Welch\u0026rsquo;s t-test showed significant differences between maximum water levels with and without dam operations for both climate change scenarios (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). This behaviour is mirrored by the projected rate of flood rise (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ed). Under the scenarios with increasing dam operations, the maximum water levels nor the rate of flood rise showed significant negative trends.\u003c/p\u003e \u003cp\u003eThe annual flood cycle in Stung Treng is dampened under the dam scenarios compared to the scenarios with no dam operation, with reductions during rainy season of up to 1.08 m and increases during dry season of up to 2.2 m (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb). Nonetheless, the maximum water level increases under both climate change scenarios, resulting in maximum water levels up to 1.38 m higher than before dam construction towards the end of the 21st century under all scenarios but SSP2-Dam2. As precipitation is not projected to change significantly compared to historic values (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea), this increase is attributed to increased snowmelt in upstream basins. The water level during dry season also shows significant increases of up to 2.59 m under scenarios with dam operations, while it is projected to be lower or the same as pre-dam levels with no dam operations.\u003c/p\u003e \u003cp\u003eSimilar trends are observed for the water level in Kampong Luong, where the water level is projected to increase by up to 2.61 m under SSP5-Dam2 in July, and the maximum water level is projected to increase by up to 1.02 m under SSP5-Dam0 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ed). The shifts in water levels in Stung Treng due to dam constructions translate downstream into an earlier onset of the seasonal flood pulse. However, the impacts of climate change and dam operations at Kampong Luong are expected to be weaker, as upstream snowmelt contributions and dam influences are more distant and overlaid with additional precipitation and tributary inflow.\u003c/p\u003e \u003cp\u003eThe applied scenarios project a significant decline of total migratory fish catch. While no significant trend could be observed for historical data (2000\u0026ndash;2014), fish catch is projected to decrease by 75 tons/year under SSP2, with even higher decreases projected for SSP5 (106 tons/year) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea). While projections with dam operations showed less significant trends, Welch\u0026rsquo;s t-tests revealed a significantly lower projected mean fish catch under Dam1 and Dam2 scenarios compared to Dam0 scenarios under both climate change scenarios. Taking the mean value of the observed fish catch in 2000\u0026ndash;2014 as a baseline and comparing it to the mean projected fish catch in the last 10 years of the projection, fish catches are projected to decline by 67.0% for SSP2-4.5 and 84.8% for SSP5-8.5 in the decade from 2090 to 2099 (Figs.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e and S7). The decreases are expected to reach 78.8% and 91.5% under the scenarios of unchanged dam operations. In combination with increasing dam operations, fish catches are projected to decline by 89.5% and 95.4%.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe projections of total migratory fish catch show strong negative correlations with the air temperature in the Tonle Sap Basin as well as dam capacities (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). These decreasing trends are mirrored by the two analysed individual species. Yet, the decrease of \u003cem\u003eH. lobatus\u003c/em\u003e shows stronger effects of both climate change and dam operations. On the other hand, the catch of \u003cem\u003eL. lineatus\u003c/em\u003e shows positive correlations with dam operations and less decreases under scenarios with dam operation compared to the scenarios without dam operations (Figure S6). This is attributed to the strong significance of the start date of flood (4FS) in modelling the catch of \u003cem\u003eL. lineatus\u003c/em\u003e (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe sensitivity analysis further highlighted the direct effects of the predictor variables on the fish catch more detailed. Figure S7 illustrates these effects, plotting how the fish catch projection would change under variation of only one variable, while others are kept as the mean values during the observation period (2000\u0026ndash;2014). Total fish catch appears to be most sensitive to temperature changes during the spawning and feeding seasons, showing steeper declines compared to the temperature during the refugee season. Additionally, water level during migration season and fish catch in the previous year show nearly linear relationships to fish catch in the following year for the observed range.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e4.1. Performance of empirical models\u003c/h2\u003e \u003cp\u003eAll hydrological models captured seasonal variations well. For instance, seasonal patterns in Stung Treng were overall well represented (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea), although annual peak and low flows were not always accurately captured (Figure S2). While such limitation affects the accuracy of the projections for individual years, the cross-validation proves an overall good representation of general trends. Thus, comparing decadal mean projected values with mean historical values reduces the over- or underestimation of extreme values under unknown conditions. As for the water temperature, using ERA5-Land date with a coarse resolution does not allow the abstraction of site-specific data but rather utilises the mean temperature in the concerned grid (Mu\u0026ntilde;oz-Sabater et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Local circumstances and inflows might be underestimated in this estimation. However, as water temperature data by the Mekong River Commission are manually measured, ERA5-Land data are less vulnerable to inconsistent recording.\u003c/p\u003e \u003cp\u003eThe fish catch models achieved high predictive performances as well. The key predictors identified by the models align with established knowledge on fisheries in the Lower Mekong that emphasise the central role of hydrological seasonality for the productivity of the Tonle Sap fishery (Ly et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Ngor et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). While water levels determine the extent and duration of floodplain inundation and thus nutrient availability, the vulnerability of fish catch to water temperatures during refugee and spawning season indicates that fish are especially vulnerable to thermal stress during these life stages, potentially affecting reproduction and migration-timing (Lema et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFor the total fish catch, the rate of flood rise was found to have a strong correlation with fish catch. This is consistent with the finding that the reversal of flow in the Tonle Sap River transports nutrients, larvae, and juvenile fish into Tonle Sap Lake (Holtgrieve et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). An increase of this rate increases the transport capacity. Further, this rate is strongly correlated with the maximum water level in Tonle Sap Lake, indicating the area of maximum inundation, which is crucial for nutrient supply during the feeding season and found to be significant for the two individual fish species. Interestingly, the model for H. lobatus indicated a negative effect of the water level during the second migration season (5WL) on catch. The timing of the migration of H. lobatus is strongly triggered by receding water levels in the Tonle Sap Lake, causing earlier out-migration compared to other species (Chan et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Thus, fish passage through Dai nets is concentrated during the low-flow period (Dec\u0026ndash;Feb), when low discharges optimise bagnet retention.\u003c/p\u003e \u003cp\u003eThe small sample size (n\u0026thinsp;=\u0026thinsp;15) restricts the application of more complex modelling techniques, such as GAM and random forest and the integration of more variables. Further changed species compositions due to habitat alterations might alter prey-predator relationships and affect the capacity of fish populations to adapt to fluctuating conditions (Fujiwara et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Despite these limitations, the models provide important insights into the primary hydrological and climatic drivers of fish catches. While the models cannot capture the full ecological and socio-economic complexity of the system, they establish a robust foundation for future research.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e4.2. Impacts on river discharge in the Lower Mekong\u003c/h2\u003e \u003cp\u003eThe historical annual mean water levels showed a slight decrease, consistent with already existing findings (Wang et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In contrast, the models project an increase in annual runoff at Stung Treng, likely reflecting enhanced snowmelt contributions from the Upper Lancang under warming conditions. Reported studies have found that while snow cover in the upper Mekong is expected to decrease due to global warming, leading to a reduction in snowmelt, the share of rain to the total precipitation is increasing with rising temperatures (Cui et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This increase of rain will offset the decrease in snowmelt, which is caused by the lower snow coverage, ultimately leading to an increased runoff. Empirical hydrological models excluding temperature and snowmelt effects project largely stable water levels, with only a slight increase under the Dam0 scenario (Figure S8). Regarding the impact of upstream water resource management on downstream water levels, the results showed a strong correlation between total upstream reservoir capacity and difference between modelled \u0026ldquo;natural\u0026rdquo; and observed water levels. Consistent with previous findings (Ly et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), maximum water levels (4WLMax) at sites in the Lower Mekong were found to be lower than under natural flow conditions, while minimum water levels increased. In addition to these amplitude shifts, the modelling revealed a slightly earlier offset of flood rise at Kampong Luong (4FS).\u003c/p\u003e \u003cp\u003eThe approach to assess the impacts of water resource management in this study was rather simple and widely applicable. Instead of directly including upstream dam operation data, it utilised statistical methods to identify observed changes. This enabled a broader interpretation of reservoir influences as part of wider water resource management activities. However, although the baseline period covered a time span without dams on the mainstream Mekong, water abstractions for irrigation have already altered the natural flow at that time (H. Huang et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). At the same time, we should note that water resource management is a matter of human operation, so despite being able to use averaged changes in water levels, the actual effects are dependent on the way that dams are operated.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e4.3. Impacts on fish catch in the Lower Mekong\u003c/h2\u003e \u003cp\u003eThe increased runoff due to climate change translates into increases in the maximum water level in Kampong Luong (4WLMax) of 1.2 m on average towards the end of the 21st century. Combined with no significant changes of the minimum water levels, these changes are reflected in an increased rate of flood rise (4RFR) by 10.0% and 16.1% under SSP2-4.5 and SSP5-8.5 respectively. This changed rate of flood rise leads to increases in the transport of nutrients, larvae, and juvenile fish into Tonle Sap Lake (Holtgrieve et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). These dynamics are fundamental to the provision of aquatic ecosystem services, as they sustain fish biomass and overall fishery productivity. Thus, the increase of maximum water levels ultimately leads to an increase in overall fish catch as well as both specific species. Water temperatures during refugee (February \u0026ndash; April, 1WT), spawning (May \u0026ndash; July, 2WT), and migration (December \u0026ndash; February, 5WT) seasons showed negative correlations with fish catch of all migratory fish and individual fish species. Increasing temperatures at these life stages are particularly threatening to sustaining fish catch in the Lower Mekong. With temperatures increasing by between 0.86\u0026deg; and 1.99\u0026deg;C during these seasons, this thermal stress is resulting in a decline in fish catch. However, when combining the positive effect of rising maximum water levels and the rate of flood rise with the negative effect of increasing temperatures, the scenario analysis results suggest a decline in fish catches under future hydrological and climatic conditions, broadly consistent with observations reported in other studies (Ngor et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Sor et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eUnder dam scenarios, the decreases in maximum water levels (4WLMax) combined with the increases of minimum water levels lead to decreases in the rate of flood rise (4RFR), partially offsetting the positive impact of these variables under the Dam0 scenarios, and thus resulting in stronger declines of fish catch under increased dam construction. However, the water levels during the refugee season (February\u0026ndash;April) play a significant role in modelling the catch of L. lineatus. Higher water levels during this season improve the floodplain connectivity and habitat conditions for this species, resulting in higher fish catch under dam operation scenarios compared to Dam0 scenarios. For further investigation, we should be aware that the construction of dams does not only affect downstream water levels. Dams in the Lower Mekong, such as in Laos, are known to have not implemented appropriate measures to ensure sediment transportation and fish migration (Roney, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This might change habitat compositions and nutrient availability, as well as species distributions in the downstream areas.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e4.4. Policy implications\u003c/h2\u003e \u003cp\u003eThe presented results clearly show that the total catch of migratory fish in the Lower Mekong, and the ecosystem services it provides, are facing threats by both climate change and hydropower development. Climate change is a main driver of change in catch numbers, yet its effects are highly dependent on the intensity of temperature rise. However, higher water levels could dampen the negative effects of rising water temperatures. Thus, designing flood cycles for different climate scenarios is critically important to sustain fish catch. For instance, according to the projections, water resource management should aim to design flood cycles with a rate of flood rise at Kampong Luong higher than 0.06 m/d under the SSP2-4.5 scenario to sustain fish catch at approximately one third of historical levels.\u003c/p\u003e \u003cp\u003eAdditionally, natural flood patterns might change due to changing precipitation patterns in the upstream basin. Earlier snowmelt might lead to an earlier offset of rising water levels, while stronger extreme events might induce a more variable flow compared to historical values. Dam operations could mitigate such hydrological extremes by storing water during high-flow events or periods of earlier snow depletion and model natural flow conditions by reintroducing this water at a later point. Meanwhile, we should be aware that this approach is subject to several limitations, including operational constraints, competing water demands, and uncertainties in predicting the timing and magnitude of future hydrological events.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eHannah Pl\u0026uuml;ckhahn drafted the main manuscript under the supervision and methodological guidance of Chihiro Yoshimura. Sophanna Ly contributed methodological expertise and provided insights into the data. All authors critically reviewed and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e \u003cp\u003eThe authors acknowledge Stimson Center for providing information that supported this research. The first author further acknowledges the support from the Cusanuswerk scholarship awarded by the German government.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data supporting the findings of this study are available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAkinwande MO, Dikko HG, Samson A (2015) Variance Inflation Factor: As a Condition for the Inclusion of Suppressor Variable(s) in Regression Analysis. 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World Meteorological Organization\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Fish catch, water resource management, climate change, projections, ecosystem services","lastPublishedDoi":"10.21203/rs.3.rs-9476830/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9476830/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eFreshwater fisheries provide essential ecosystem services, supporting food security and livelihoods worldwide, yet they are increasingly threatened by climate change and water resource development. The Mekong River basin supports the world\u0026rsquo;s largest inland fishery, with the Tonle Sap system playing a central role in sustaining migratory fish populations through its seasonal flood pulse. However, the effects of climate change and dam operations on fish productivity remain poorly quantified. This study investigates how the shifts in hydrology and temperature may affect migratory fish catch in the Tonle Sap River throughout the 21st century. Empirical models were developed linking climatological variables, reservoir development, and fishery data. The hydrological models reproduced seasonal dynamics with high accuracy (R\u0026sup2; \u0026gt; 0.95), while fish catch models explained up to 87% of observed variability. These models were forced with climate projections from four CMIP6 models under SSP2-4.5 and SSP5-8.5 scenarios, combined with increasing dam capacity. Scenario analysis indicates that increasing water temperature and altered flood regimes will substantially reduce fish catch. By the end of the century, migratory fish catch is projected to decline by 67\u0026ndash;85% under climate change alone and by up to 95% when combined with intensified dam operations. Rising temperatures during spawning and refuge seasons and reduced flood rise rates were key drivers of this decline. The results highlight the vulnerability of the Tonle Sap fishery to interacting climatic and hydrological pressures and underscore the importance of adaptive water resources management that maintains functional flood regimes and the ecosystem services they support.\u003c/p\u003e","manuscriptTitle":"Potential impacts of climate change and water resource management on fish catch in the Lower Mekong River","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-12 13:01:25","doi":"10.21203/rs.3.rs-9476830/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8fa475c4-191d-4d73-9198-9aa3b1ff153b","owner":[],"postedDate":"May 12th, 2026","published":true,"recentEditorialEvents":[{"type":"reviewerAgreed","content":"269842690440578035029051630512615964322","date":"2026-05-18T08:00:22+00:00","index":21,"fulltext":""},{"type":"reviewerAgreed","content":"97488681481591966654830959642217359349","date":"2026-05-05T03:39:49+00:00","index":12,"fulltext":""},{"type":"reviewersInvited","content":"10","date":"2026-05-04T14:48:57+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-05-01T13:27:27+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-12T13:01:26+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-12 13:01:25","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9476830","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9476830","identity":"rs-9476830","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}