Hydrological responses to the synergistic climate and land-use changes in the upper Lancang river basin

Climate change trends in the upper Lancang river basin (LRB), a high-mountain area, are prominent on a global scale, and climate-induced land use change with increasing cropland and migration has been observed in the past decades and is expected to expand in the future. We assessed land use and sediment yield from the basin in the past and future under the synergistic impact of projected climate change and associated land use change. We found that the transition from grassland and forest to cropland under climatic change favorable to agriculture can be the topmost contributor to the sediment yield increase from the upper LRB, with an increaisng rate of 40.6% from the entire area and as high as 118% in some sub-areas. As the baisn serves as the source area of the Lancang-Mekong River Basin (LMRB), we call for coordinated management throughout the entire LMRB, given the complex sediment dynamics crossing scales, affected by both climate change and socioeconomic development in trans-boundary basin.


Introduction
Environmental changes, including climate change and land-use change, have been identified as principal drivers of hydrological alteration.Specifically, the hydrology regime in high-mountain Asia, which has the temperature-limited environment extremely sensitive to greenhouse warming [1,2], is among the most sensitive and prone to environmental changes [3,4].Understanding hydrology and its evolution with climatic and anthropogenic conditions remains a critical issue for sustainable river basin management in high-mountain areas, which are the world's major source areas of freshwater.Originating from the Tibetan Plateau, the Lancang-Mekong River is the largest transboundary river in Asia (figure S1 in supplementary information-SI); the upper Lancang river basin (LRB) contributes a significant amount of water and sediment to the entire Lancang-Mekong River Basin (LMRB).The ecosystems of the area are exceptionally fragile and susceptible to the stresses of climate change, land use change, and population expansion; especially, the changes in soil erosion and sediment discharges have affected the downstream receiving waterway [5].
The warming trend has favored vegetation growth in Tibetan Plateau over the past decades [6].Land and climate continuously interact, ending with changes in complex biophysical and biogeochemical processes and human activities in the context of coupled nature-human system [7][8][9][10] Specifically, climate change has triggered land use changes, especially an upward expansion of cropland in the mountain regions [11].During 1992-2015, the grassland, the primary vegetation cover, in the region declined by 22 621 km 2 ; while the cropland increased by 15 015 km 2 .As a result, climate-induced migration has occurred in recent years and is expected to increase in the future [12,13].The changes from grassland to cropland can make the soils more vulnerable to erosion, resulting in increasing sediment discharge [14,15].Thus, the natural and human changes have brought joint effects on watershed hydrological processing in the upper LRB, ultimately affecting flow and sediment discharges to downstream of the basin.The changing climate becomes more favor for agriculture, which along with private profit-seeking and government policies (incentives for socioeconomic development and regulations for environmental conservation), has caused increased cropland and decreased grassland and the trend may continue to the future.Numerous studies have examined climate change effects and land use effects on hydrologic processes in a separate way, which may ignore the joint effects caused by climate-land interactions.To address this issue, in this paper we assess the synergetic effects of climate and land changes and provide implications for land-based adaptation options to combat climate change [16].
Furthermore, our results add to a pressing debate on the difference of sediment and nutrient flows between pre-and post-dam period, and their effects on downstream ecosystems.The LMR transports substantial sediment and nutrients, which are important for biodiversity, ecosystem functioning, and human wellbeing in the downstream areas [17].Over the past two decades, the reduction of sediment with organic matters has led to undesirable consequences, including the coastal erosion in Mekong floodplains and the Vietnam Delta; the deterioration in critical ecosystems such as Cambodia's Tonle Sap lake [18][19][20][21].However, controversies have arisen in research communities regarding the causes of the sediment dynamics and its consequences, which have received plenty of public attention [22,23].In particular, some previous studies [23,24] concluded that climate change could introduce high uncertainties in sediment yields and discharges, which might then complicate the effects coming from other factors such as damming.Our study will add to the debate with new insights by examining the joint effect of climate change and land use change that can go along with climate change, i.e. the possible increase of sediment discharge to downstream due to grassland decrease and cropland increase in the upper LRB.
We propose a holistic framework to connect climate change, land use change, and hydrologic response in the upper LRB via historical data analysis and modeling projection of future occurrence.The modeling results, particularly the sediment discharges under the synergized climate and land use change, will contribute to the understanding of the existing climate-land-sediment relationship in the past and possible trends in the future in the baisn.The results will also provide scientific insights into the controversial hydro-sedimentary budget and implications for sustainable planning and management in the entire LMRB facing the climate change challenge.

Study site and data availability
As the largest transboundary river in Asia, the Lancang-Mekong River originates from the Qinghai-Tibetan Plateau and traverses six riparian countries including China, Laos, Thailand, Myanmar, Cambodia, and Vietnam.The focus on this paper is the upper LRB, which constitutes an area of 52 508 km 2 and serves as the source area of the LMR.The upper LRB is characterized by high altitudes ranging from 3000 to 6000 meters and topographic ruggedness.The basin information are obtained from multiple, openly accessible datasets (table S1).

Climate change projection
Detailed climate information encompassing multiple variables is imperative to evaluate the influence of climate change on watershed hydrology.With the release of the Coupled Model Intercomparison Project Phase 6 (CMIP6), three widely adopted General Circulation Models (GCMs): GFDL-CM4.0[25], MIP-ESM1.2-LR[26], and MIROC6 [27], are employed for climate change projections in the Tibetan Plateau.
The GCMs generate daily climate data (precipitation, maximum temperature, and minimum temperature) for the upper LRB across various horizontal resolutions under two emission scenarios: Shared Socioeconomic Pathways (SSP) 2.45 and SSP 585.These two scenarios respectively represent moderate and high levels of radiative forcing, reflecting future climate fluctuations under different intensities of human activities.To reduce the contingencies in basin-scale impact assessment, we propose a generalized joint bias correction (JBC) method (Note S1) that couples the Quantile Mapping method [28] and the JBC method [29] to correct the simulation datasets derived from GCMs projections.The corrected grid-scale corrected datasets are further subjected to statistical downscaling using the Empirical Bayesian Kriging geographic interpolation approach [30], reducing the resolution to 0.5 • × 0.5 • and facilitating the interpolation to meteorological stations in the upper LRB.

Climate-vegetation analysis: pixel-wise multivariate linear regression (MLR) model
The pixel-wise MLR model allows for a quantitative analysis to investigate the relationship between climate changes and vegetation cover dynamics at the pixel level in the upper LRB.In this study, the normalized difference vegetation index (NDVI) derived from satellite data is chosen as an indicator of live green vegetation growth in the high-mountain area, which is valuable in understanding the ongoing vegetation dynamics and the potential implications for land use.The terrestrial vegetation dynamics is influenced by both long-term changes in climate averages and extreme climate events.Here, five representative climatic variables (precipitation, maximum temperature, minimum temperature, RX1 day, and R10 mm) are employed as explanatory variables for NDVI estimation.R10 mm represents the number of days per year characterized by heavy precipitation (⩾10 mm d −1 ); RX1 day describes the annual maximum 1 day precipitation.The MLR model incorporates downscaled climatic variables as inputs and employs NDVI images as target outputs during the period 2000-2015: ( In equation (1), considering the observation i ∈ {1, . . ., n} for each year, the variable x ijx denotes the observed value of predictor j in pixel x, and NDVI ix represents the real-valued NDVI in pixel x.The parameters b 0x and b jx refer to the intercept and regression slope associated with predictor j in pixel x, respectively.Additionally, e ix represents a Gaussian error term.
To estimate the NDVI for the year 2050, we incorporate the predicted average climate variables within five-year intervals preceding and following 2050 (i.e.2048-2052) as inputs for the MLR model.These average climate variables are utilized as estimations for the anticipated climate conditions around the year 2050 (figure S2).

Land use projection: random forest (RF) classifier
The RF approach is a robust supervised machine learning algorithm used for non-linear classification and regression tasks.RF builds and collects multiple decision trees via a bootstrap aggregating method on the training data to generate reliable predictions [31].In this study, we developed a RF classifier for land use classification and projection in the upper LRB.Given the limited coverage of barren land, urban areas, and water bodies in the study area (which collectively accounted for approximately 5% of the total area), these land use types are grouped as 'others' .Therefore, the land use types considered in the analysis include forest, grass, cropland, and others.The RF classifier utilizes annual NDVI data from 2000 to 2015, along with static geographic information (i.e.elevation and slope) as input variables to simulate the land use maps from 2000 to 2015. Figure S3 presents boxplots illustrating various explanatory variables in the RF classifier segregated by each land use class, which emphasize the impacts of environmental factors on the classification in the study area.
In this study area, the observed land use data is class-imbalanced, with grassland accounting for over 70% of the total samples.To mitigate the potential bias in classification, an oversampling procedure is implemented to balance the priors of land use classes.To evaluate the experimental performance, we conduct a comparison between the observed land use map and the projected land use map for the year 2015 (figure S4).Additionally, a confusion matrix and compile statistics on classification performance metrics are created for the validation of the RF classification (tables S2 and S3).The RF classifier achieves with an overall accuracy of 0.88, with a 95% confidence level, suggesting a satisfactory performance of the algorithm in the study region.With the RF classifier, we project the land use map in 2050, using NDVI projected in section 2.3, as well as elevation and slope, as input variables.

Hydrological simulation: soil and water assessment tool (SWAT) model
The SWAT model is a semi-distributed and physically based tool, which has been extensively used to tackle hydrological and environmental across various basin scales and geo-climatic conditions [32].In this study, the calibrated SWAT model [33] was used for simulating the various scenarios of climate and land use in the region.In the upper LRB, the SWAT model delineated the study area into 150 sub-basins, which were further divided into 958 hydrological response units (HRUs) based on land use, soil type, and slope.
To estimate soil erosion and sediment yield within each spatial unit (i.e.Hydrological Response Unit)-HRU, the SWAT model employs a Modified Universal Soil Loss Equation (MUSLE) [34].The MUSLE predicts annual soil loss as a result of six factors, including rainfall erosivity, soil erodibility, topography, land cover and management, and support practice.The rationale of the model for the study purpose and details of the model establishment can be seen in the Note S2.

Climate change
The complex topography and atmospheric circulation dominated by Asian monsoons and westerly perturbations jointly influence the climate characteristics in the region of upper LRB.As a result, climate change trends in the region are especially prominent on a global scale [35].Figures S5 and S6 illustrate the time series for climate variables throughout both the historical and future period, encompassing a range of climate projections.To examine the trends in climatic variables, the nonparametric Mann-Kendall (M-K) trend test (Note S3) was performed annually to the region, and the statistics results are shown in table S4.An overall increasing trend is found in the annual minimum and maximum temperature in the region.The existing change in the maximum and minimum temperature is 0.52 • C and 0.57 • C per decade during 1978-2020, respectively; the projected changes of the two variables are 0.69 • C and 0.51 • C per decade during 2021-2080, with a larger change in the maximum temperature than that of the minimum.Changes in precipitation, in terms of concentration, volume, and intensity, are also considerable.There was a non-significant downward trend in the annual precipitation during 1978-2020; while the projected annual precipitation during 2021-2080 shows a statistically upward trend.Furthermore, the frequency and intensity of extreme climate events had increased in the past and the trend will continue across much of the globe [36].In this study, the moderate and extreme precipitation events are indicated using two indicators: R10 mm and RX1day on an annual scale throughout the study period .Results of the M-K test indicate a rise in RX1 day by 6.4 mm and R10 mm by 9.6 d over the future period of 2021-2080.The Z values in the trend analysis of the two indicators are greater than 1.96, showing a prominent increase in the frequency of extreme climate variables at a high confidence level.

Land use change
Land use in the upper LRB has undergone considerable transformations, in the forms of vegetation type alterations, land use transitions, and the spatial distribution of both vegetation and land use.Climate change was proved as the key factor driving the longterm change in vegetation cover [37].In particular, the vegetation cover evolution under climate change has greatly impacted the spatial and temporal distribution of land use in this region.The results of a pixelwise MLR model, as depicted in figures S7/8, show how vegetation cover dynamics are affected by the selected climate variables.The increase in NDVI can be attributed to favorable climatic conditions, specifically the rise in minimum temperature and precipitation.Using the MLR model and climate change projections, we estimate the NDVI in 2050 as well as its statistical distribution (figure S9).
These changes facilitate the necessary moisture and warmth required for vegetation growth, ultimately resulting in increased vegetation cover and land use change, along with socioeconomic drivers.Figure 1 shows the reclassified land use maps derived from the European Space Agency [38] for 1992(A) and 2015(B).The earliest (1992) and latest (2015) years of the historical period with data availability are chosen to represent the past and current land use conditions.The increase of cropland and decrease of grassland (as shown in figure 1(D)) reflect the joint impact of climate change, natural landscape, and the associated land use decisions over the past decades.The land use transition matrix depicted in table S5 delves into the land use transition processes between 1992 and 2015.In the study area, the primary transitions involved changes from grasslands to both forests and croplands in places with higher elevation and substantial transition from forest to cropland in places with lower elevation, with forests acting as an intermediary role, under both natural and socioeconomic driving forces [39].Thus, in an overarching view, statistical observation shows a primary loss with grassland and a primary gain with cropland, and a complicated change with forest (see more in the discussion section).The forest area increased in size at the end of last century due to more favorite climate condition but diminished after the year of 2000 due to dramatic human land development.The forest area increased in size at the end of last century due to more favorite climate condition, but diminished after the year of 2000 due to dramatic human land development.
To assess the spatiotemporal dynamics in land use change and predict the future land use under both climate change and human driving forces, we predict the future land use map (figure 1  and agriculture as the primary driving force, along with socioeconomic development as the secondary driving force, the cultivated land area is to be expanded by 13 052 km 2 , and the forest area is to increase by 237 km 2 in 2050; meanwhile, grassland area is to decrease by 15 538 km 2 , compared to the land use in 1992 (figure 1(A)).Overall, the human influences on the land will be rising through the transformation from herding to cultivation in the upper LRB.The interactions between climate, vegetation, and land use can cause unintended environmental consequences such as increased sediment discharge, as to be described in the following.

Hydrological responses
The spatiotemporal variation of sediment yield is assessed via three SWAT modeling scenarios: one baseline and two benchmark scenarios (BMS) of alternative climate and land use conditions.The baseline scenario (BAS) assumes future climate (2021-2080) projected as a recurring cyclic pattern and uses the observed land use map of 2015 to represent the future land use, i.e. no change with neither climate nor land use from now to the future.BAS is used as a basis for comparison even though the assumptions are not likely realistic.BMS1 assumes future climate (following the GCMs projections), as shown in section 3.1, and still uses the historical land use map in 2015.This scenario represents a situation that despite future climate change, there will be no change in land use due to mandatory land use control policies.BMS2 assumes future climate change and and land use change, i.e. without land use controls as enforced for BMS1.The land use modeling was based on the historical dataset, with land use as the result of both climate change and human intervention.Therefore, when we simulate future land use, the ongoing human intervention will persist into the future.In this way, the land use change will be the result of both future climate change and human intervention.In the SWAT model.The land use projections are periodically revised at 20 yr intervals to incorporate the changing landscape dynamics.
Figure 2 displays the 5-yr moving average of the simulated annual sediment yield under the three scenarios.The detailed 10-yr moving average of sediment yield under three scenarios across various climate change projections is presented in figure S10.The sediment yield increase under BMS1 compared to the BAS is estimated as 29.8% reflecting the individual impact of future climate change; and 40.6% under BMS2, indicating the synergistic impacts of climate change and associated land use continuing the historical changing trend.An estimated 6.9% increase in sediment yield is observed when comparing BMS2 to BMS1 under the same future climate conditions but differing in land use conditions (i.e.without or with land use control).Thus, the land use change is anticipated to result in a notable increase in sediment yield, corresponding to the projected climate change and land use change in the area.It is interesting to notice that BMS2 results in larger sediment yield increase than the sum of the increase between BMS1 and BAS and that between BMS2 and BMS1, i.e. 40.6% > 36.7% (=29.8%+ 6.9%).This shows the synergetic effect of climate change and land use change, as observed in the past and possibly continuing to the future.That is to say, climate change and land use change associated with climate change (if not controlled) will have the most significant impact on sediment yield increase in the area (figure 3(C)), with major contribution from cropland and barren land.The conversion from grassland and forest to cropland usually destroys dense root networks, leading to sparser vegetation cover and land surface vulnerable to erosion.In addition, more intense precipitation (i.e.floods) caused by climate change will result in a great magnitude of soil erosion.The M-K test confirms that the sediment discharge is likely to increase at the highest rate under BMS2 involving the synergistic climate and land use changes (table S6).Such a synergistic effect indicates that it may not be appropriate to separately assess the impact of climate change and land use, the two independent environmental variables, on the hydrological processes.
Besides the increasing sediment yield trend of the entire basin as shown in figures 2 and 3 portrays the spatial distribution of sediment yield change under the BAS and BMS1 and BMS2.An overall increasing trend in sediment yield is found for most subareas with increased cropland and decreased grassland and forestland.Exceptions are observed for a small number of sub-areas where there is increased vegetation cover intensity due to warmer and wetter climate.For example, forest expansion is found in the central sub-areas, with relatively low elevation and slope, which benefits soil erosion control under changing environmental conditions.However, since the increase of cropland is far more than the increase of forest, the net sediment yield from the area will increase significantly under the project climate change and associated land use change without control.
The heterogeneity of sediment yield across the sub-areas highlights the necessity for distributed land management and adaptation planning.Areas exposed to the reductions of forest or grassland may require prioritization for land acquisition and protection efforts.The mid-south sub-basins and eastern sub-basins differ significantly in their hydrological responses to climatic and land use scenarios; the mid-south sub-basins require adaptation strategies to prevent high rates of soil losses.

Discussion
As the source area, the upper LRB plays a significant role in the hydro-sedimentary budget of the LMRB.The climate in the baisn is likely to be warmer and wetter, with a much higher rate than the global average.The rising trend predicted with extreme precipitation events is also more obvious than what occurred during the historical period.The warmer and wetter climate, along with the complex natural landscape and socioeconomic development driving forces had intricate effects on land use by converting grassland and forestland to crop cultivation, and this effect can be expedited in the future.In high mountain regions, land elevation is a pivotal factor for land cover.Forests usually exist with relatively low elevations, while grassland with relatively high elevations.As climate conditions become more favorable, high mountain areas could become more suitable for vegetation growth.Specifically, grassland at high elevation may extend to even higher areas; grassland at low elevation may switch to forestland, typically ending with the expansion of forestland [40].Meanwhile, due to socioeconomic factors, both grassland and forecast may transit to cropland especially in lowelevation areas [41].Human agricultural activities tend to initiate in low-elevation areas in a high mountain region, typically ending with the conversion of forests in areas with relatively low elevation, and may expand into grasslands at higher elevation for agricultural development purposes.Such grassland-to-forest and grassland & forestland to crop land transitions are driven by both climate and human factors.
Statistical observations suggest the expansion of cropland in the past (1992-2015), and it is likely that the expansion continues to the future if no land use control.The results of our study show that the complex interactions between climate, vegetation, and land use give rise to synergistic effects (beyond individual effects from climate or land use) on hydrological responses, i.e. sediment discharge analyzed in this study.The projected climate and land use change, which is assumed to follow the past trend, can accelerate the rate of soil erosion and sediment yield, i.e. due to the interactions among climate, vegetation and land use, the joint effect of climate and land use change is stronger than the sum of the individual effects.This can be explained in the context of human-environment interactions, which characterize how society depends on, adapts, and modifies the environment and how they co-exist and coevolve.Land use changes such as the forest-grass-crop land transitions present examples of human-environment interaction.The state of a human-environment system is determined by the balance of two counterpart driving forces, i.e. environment's ability to control and limit society (environmental determinism [42]) and humans' ability to overcome the limitations in the physical environment (possibilism [43]).Positive interactions between humans and the environment include subtle and adaptive changes that maintain the equilibrium of resources for human needs and the natural variability of ecosystems; negative interactions include the demise of ecosystems and marginal systems for agriculture [44].The synergistic effects of climate change and humans on land use change and sediment discharge in the upper LRB can be the results of humans' strong desire of society growth with no or less attention on the environmental consequences, as what BMS2 scenario shows).While if society wishes to avoid environmental degradation (i.e.extra sediment discharge in this case), regulation on land use might be needed, as what BMS1 scenario shows.The loss of soil can result in low land productivity, poor biological diversity, and eventually negative socio-economic consequences in the upper LRB.
Given the projected sediment yields under the climate and human driving forces, how will the changes affect the pressing debate on the difference of sediment flows between pre-and post-dam period?Currently, limited observation and methodological bias have posed serious difficulty in ascertaining to what extent the sediment evolution is affected by certain factors, including climatic changes, damming, and land use changes crossing different regions and countries in the basin; in particular, inconsistent arguments regarding the ecological and environmental impacts of the Lancang cascade dams have been reported, associated with uncertainties and knowledge gaps [45].Researchers have questioned some missing drivers and interventions and the extent of influence that have not been considered.For instance, a report released by the Mekong River Commission attributed the changed hydrological regimes in the LMRB to prolonged climate change in the region [46].In the future, exacerbated climate change, ongoing land use changes, and existing and additional dam construction may make the issue even more intricate.
Based on the results of this study, on the one hand, the increased sediment load will affect effective reservoir storage and hydropower generation and riverine ecosystem stability.On the other hand, the rising sediment discharge from the source area may counteract a part of diminished sediment flows caused by hydropower dams built or under construction.Some researchers argued that the dam construction boom in the basin since the 1990s has trapped a portion of the incoming sediment load during the postdam period, and subsequently altered the sediment regimes [19,20,47].However, studies that cover multiple spatial scales showed that the sediment reduction from the cascade reservoirs had no significant impact on the lower streams.This is because the sediment flow affected by the dams can be counteracted by other local contributing factors [48,49].Land use changes associated with socioeconomic development in middle and downstream regions have increased sediment load [50].For example, the sediment variability observed at Nongkhai and Mukdahan in Thailand suggests that regional tributaries dominated by agricultural land and frequent anthropogenic activities may contribute significant amounts of sediment into the mainstream [51].In addition, soil and water conservation programs corresponding to the regional development in the upstream area of a dam can cause significant reduction of sediment load to downstream [52], though this usually occurred after the dam was built.In summary, limited monitoring data and diverse data sources processed with different methods and parameters from different areas hinder a consistent assessment of sediment load contribution changes among comparative studies.Especially, it is difficult to assess the difference of sediment load pre-and post-dam, which is complicated by climate change and associated land use change [53].Thus, the likely sediment yield increase in the upper LRB facing the future changes in climate and land use adds to the complexity of the basin-wide sediment budget of the entire LMRB.
In the future, continually shifting anthropogenic and natural factors will no doubt pose profound influences on sediment flux, which carries many implications to downstream terrestrial and coastal environments.With the progressive completion of ongoing hydropower dams in the six riparian countries where the Mekong River flows through, the longterm sediment trapping by the cascading reservoirs, along with the expansion of controlling watershed areas above these dams, will further reduce the sediment discharge to the mainstream and the downstream.On the other hand, changes in climate, along with land use change may accelerate soil erosion and sediment load in the source area, as shown in this study Agricultural development and other human activities in midstream and downstream regions can cause significant soil erosion and increase sediment load.These factors may at least partially counteract the diminished sediment flows caused by the dams.Thus, the LMRB will face a complex situation involving both conflicts among different purposes in the riparian countries and potential synergies that may counteract some negative effects.Coordinated management in the entire basin is critical for the sustainable use of land and water in the basin.

Figure 1 .
Figure 1.Land Use Changes in the Upper LRB.
(C)) using the RF algorithm.The midpoint year (2050) is chosen as a representative year for the future period during 2021-2080.The land use record (1992, 2015) and projection (2050) are used for subsequent comparative land use change analysis.According to the prediction, under more favorable climate to vegetation

Figure 3 .
Figure 3. Spatial distribution of sediment yield difference (%) among three scenarios-BAS, BMS1, and BMS2 in the upper LRB: (A) difference between BMS2 and BMS1 (future climate with/without land use control); (B) difference BMS1 and BAS (the impact of climate change only with controlled land use); (C) difference between BMS2 and BAS (the impact of both climate change and associated land use change).