Search Results (23)

Investigations into the impacts of climate change on water conservation capacity in the upper Mekong River Basin (UMRB) are important for the region’s sustainability. However, quantitative studies on isolating the individual contribution of climate change to water conservation capacity are lacking. In this study, various data-driven SWAT models were developed to quantitatively analyze the unique impact of climate change on water conservation capacity in the UMRB. The results reveal the following: (1) From 1981 to 2020, the annual water conservation capacity ranged from 191.6 to 392.9 mm, showing significant seasonal differences with the values in the rainy season (218.6–420.3 mm) significantly higher than that in the dry season (−57.0–53.2 mm). (2) The contribution of climate change to water conservation capacity is generally negative, with the highest contribution (−65.2%) in the dry season, followed by the annual (−8.7%) and the rainy season (−8.1%). (3) Precipitation, followed by evaporation and surface runoff, emerged as the critical factor affecting water conservation capacity changes in the UMRB. This study can provide insights for water resources management and climate change adaptations in the UMRB and other similar regions in the world. Full article

Terrestrial water storage (TWS) is of great importance to the global water and energy budget, which modulates the hydrological cycle and then determines the spatiotemporal distributions of water resources availability. The Tibetan Plateau is the birthplace of the Yangtze, Yellow, and Lancang–Mekong River, where the water resources are directly related to the life of the Eastern and Southeastern Asian people. Based on multi-source datasets during the period 1981–2015, the long-term spatiotemporal variabilities of the TWS over the Tibetan Plateau were investigated by the Sen’s slope and Mann–Kendall test trend analysis methods; the changing mechanisms were explored from two perspectives of components analysis and the hydrological cycle. The water conservation capacity of vegetation in the alpine mountainous areas was also discussed by geostatistical methods such as correlation analysis, extracted by attributes and zonal statistics. The results show that the TWS of the Tibetan Plateau increased with the speed of 0.7 mm/yr as the precipitation accumulated and the glaciers melted during the period 1981–2015. The TWS values were low and generally present a trend of obvious accumulation over the northern Tibetan Plateau, while the high and decreasing values were distributed in the south of Tibetan Plateau. The results of the components analysis indicate that the TWS mainly consisted of soil moisture at one-fourth layers, which are 0–200 cm underground in most areas of the Tibetan Plateau. The precipitation is mainly lost through evapotranspiration over the northern Tibetan Plateau, while in the northwestern corner of the Tibetan Plateau, the Himalayas, and northeastern Yarlung Zangbo River basin, the runoff coefficients were larger than 1.0 due to the influence of snow melting. In the alpine mountains, different climate and vegetation conditions have complex effects on water resources. The results are helpful for understanding the changing mechanism of water storage over the Tibetan Plateau and have scientific meaning for the development, utilization, and protection of regional water resources. Full article

Drought is a natural hazard that stresses ecosystems, agricultural production, food security, and local economies. Given ongoing hydropower dam development in the Sesan and Srepok Basins, the two most dammed tributaries in the Lower Mekong Basin, characterizing baseline drought events and understanding how dams modify downstream flow is needed to manage water resources and mitigate drought effects. We used the Soil & Water Assessment Tool (SWAT) to estimate streamflow data from 2001 to 2019. For both rivers, we found that runoff varied, but contributed about 75% of streamflow, followed by shallow and deep groundwater, which contributed up to 25%. We used the Standardized Runoff Index to characterize drought and detected frequent, severe, and prolonged drought events in the two basins. Severe and prolonged droughts in the 2009–2011 and 2015–2016 periods corresponded to the occurrence of Typhoon Ketsana and the El Niño-Southern Oscillation. Streamflow alteration can be caused by climatic conditions and anthropogenic activities such as hydropower dam development and operations (e.g., the timing and magnitude of water releases). Results from this study can be used as a baseline to gauge potential future droughts and design appropriate drought management plans to preserve ecosystems and food security in the Lower Mekong Basin and its tributaries. Full article

Calibrating spatially distributed hydrological models in ungauged mountain basins is complicated due to the paucity of information and the uncertainty in representing the physical characteristics of a drainage area. In this study, an innovative method is proposed that incorporates the Budyko framework and water balance equation derived water yield (WYLD) in the calibration of the Soil and Water Assessment Tool (SWAT) with a monthly temporal resolution. The impact of vegetation dynamics (i.e., vegetation coverage) on Budyko curve shape parameter ω was considered to improve the Budyko calibration. The proposed approach is applied to the upstream Lancang-Mekong River (UL-MR), which is an ungauged mountain basin and among the world’s most important transboundary rivers. We compared the differences in SWAT model results between the different calibration approaches using percent bias (PBIAS), coefficient of determination (R²), and Nash–Sutcliffe efficiency (NSE) coefficient. The results demonstrated that the Budyko calibration approach exhibited a significant improvement against an unfitted priori parameter run (the non-calibration case) though it did not perform as good as fitting of the calibration by the observed streamflow. The NSE value increased by 44.59% (from 0.46 to 0.83), the R² value increased by 2.30% (from 0.87 to 0.89) and the PBIAS value decreased by 55.67% (from 39.7 to 17.6) during the validation period at the drainage outlet (Changdu) station. The outcomes of the analysis confirm the potential of the proposed Budyko calibration approach for runoff predictions in ungauged mountain basins. Full article

The Mekong River Basin comprises approximately 38% of Southeast Asia. Our study area comprises the right-bank tributaries, which drain a substantial portion of Northeast Thailand. This study aimed to estimate the evaporative losses from streams during the 2013–2015 period. The normal and warm El Niño–Southern Oscillation (ENSO) phases caused higher temperatures and low rainfall in the 2014–2015 period. The results show that the local meteoric water line for precipitation isotopes had seasonal variation due to variable precipitation. The enrichment of river isotopes indicated that streams lost an average of 4% of their water through evaporation. During the cooling ENSO phase, significant evaporation occurs due to the deep convection that typically occurs in tropical regions. In contrast, evaporation was low during the warm ENSO phase because of its geographic location. The El Niño year’s isotope values were significantly more enriched than the La Niña year’s, showing that precipitation and positive temperature anomalies affected the isotopic compositions in the continental basin. Furthermore, the deuterium excess helped distinguish the relative contributions of the wet and dry seasonal sources to the moisture origin, indicating that the predominant source of moisture is inland evaporation, with a small contribution from the ocean. Full article

The Tonle Sap Lake (TSL) Basins of the Lower Mekong are one of the world’s most productive ecosystems and have recently been disturbed by climate change. The SWAT (Soil & Water Assessment Tool) hydrological model is utilized to investigate the effect of future climate scenarios. This study focused on two climate scenarios (RCP2.6 and RCP8.5) with three GCMs (GFDL-CM3, GISS-E2-R-CC, and IPSL-CM5A-MR) and their impact on the hydrological process and extremes in the Sen River Basin, the largest tributary of the TSL basin. The annual precipitation, surface runoff, lateral flow, groundwater flow, and total water yield are projected to decrease in both the near-future (2020–2040) and mid-future period (2050–2070), while actual evapotranspiration is projected to increase by 3.3% and 5.3%. Monthly precipitation is projected to increase by 11.2% during the rainy season and decrease by 7.5% during the dry season. Two climate models (GISS and IPSL model) lead to decreases in 1-day, 3-day, 7-day, 30-day, and 90-day maximum flows and minimum flows flow. Thus, the prediction results depend on the climate model used. Full article

Basin water supply, planning, and its allocation requires runoff measurements near an estuary mouth. However, insufficient financial budget results in no further runoff measurements at critical in situ stations. This has recently promoted the runoff reconstruction via regression between the runoff and nearby remotely-sensed variables on a monthly scale. Nonetheless, reconstructing daily runoff from individual basin-upstream remotely-sensed climatic variables is yet to be explored. This study investigates standardized data regression approach to reconstruct daily runoff from the individual remotely-sensed climatic variables at the Mekong Basin’s upstream. Compared to simple linear regression, the daily runoff reconstructed and forecasted from the presented approach were improved by at most 5% and 10%, respectively. Reconstructed runoffs using neural network models yielded ~0.5% further improvement. The improvement was largely a function of the reduced discrepancy during dry and wet seasons. The best forecasted runoff obtained from the basin-upstream standardized precipitation index, yielded the lowest normalized root-mean-square error of 0.093. Full article

Estuarine freshwater transport has a substantial impact on the near-shore ecosystem and coastal ocean environment away from the estuary. This paper introduces two independent methods to track the Mekong freshwater-induced mass transport by calculating the time lag (or equivalently, the phase) between in situ Mekong basin runoff and the equivalent water height (EWH) time series over the western South China Sea from a gravity recovery and climate experiment (GRACE). The first method is the harmonic analysis that determines the phase difference between annual components of the two time series (called the P-method), and the other is the cross-correlation analysis that directly obtains the time lag by shifting the lagged time series forward to attain the highest cross-correlation between the two time series (called the C-method). Using a three-year rolling window, the time lag variations in three versions of GRACE between 2005 and 2012 are computed for demonstrating the consistency of the results. We found that the time lag derived from the P-method is, on average, slightly larger and more variable than that from the C-method. A comparison of our gridded time lag against the age determined via radium isotopes in September, 2007 by Chen et al. (2010) revealed that our gridded time lag results were in good agreement with most isotope-derived ages, with the largest difference less than 6 days. Among the three versions of the GRACE time series, CSR Release 05 performed the best. The lowest standard deviation of time lag was ~1.6 days, calculated by the C-method, whereas the mean difference for all the time lags from the isotope-derived ages is ~1 day by P-method. This study demonstrates the potential of monitoring Mekong estuarine freshwater transport over the western South China Sea by GRACE. Full article

Basin runoff is a quantity of river discharge per unit basin area monitored close to an estuary mouth, essential for providing information on the flooding and drought conditions of an entire river basin. Owing to a decreasing number of in situ monitoring stations since the late 1970s, basin runoff estimates using remote sensing have been advocated. Previous runoff estimates of the entire Mekong Basin calculated from the water balance equation were achieved through the hybrid use of remotely sensed and model-predicted data products. Nonetheless, these basin runoff estimates revealed a weak consistency with the in situ ones. To address this issue, we provide a newly improved estimate of the monthly Mekong Basin runoff by using the terrestrial water balance equation, purely based on remotely sensed water balance component data products. The remotely sensed water balance component data products used in this study included the satellite precipitation from the Tropical Rainfall Measuring Mission (TRMM), the satellite evapotranspiration from the Moderate Resolution Imaging Spectroradiometer (MODIS), and the inferred terrestrial water storage from the Gravity Recovery and Climate Experiment (GRACE). A comparison of our new estimate and previously published result against the in situ runoff indicated a marked improvement in terms of the Pearson’s correlation coefficient (PCC), reaching 0.836 (the new estimate) instead of 0.621 (the previously published result). When a three-month moving-average process was applied to each data product, our new estimate further reached a PCC of 0.932, along with the consistent improvement revealed from other evaluation metrics. Conducting an error analysis of the estimated mean monthly runoff for the entire data timespan, we found that the usage of different evapotranspiration data products had a substantial influence on the estimated runoff. This indicates that the choice of evapotranspiration data product is critical in the remotely sensed runoff estimation. Full article

The Mekong River Basin (MRB) in Southeast Asia is among the world’s ten largest rivers, both in terms of its discharge and sediment load. The spatial and temporal resolution to accurately determine the sediment load/yield from tributaries and sub-basin that enters the Mekong mainstream still lacks from the large-scale model. In this study, the SWAT model was applied to the MRB to assess long-term basin hydrology and to quantify the sediment load and spatial sediment yield in the MRB. The model was calibrated and validated (1985–2016) at a monthly time step. The overall proportions of streamflow in the Mekong River were 34% from surface runoff, 21% from lateral flow, 45% from groundwater contribution. The average annual sediments yield presented 1295 t/km²/year in the upper part of the basin, 218 t/km²/year in the middle, 78 t/km²/year in the intensive agricultural area and 138 t/km²/year in the highland area in the lower part. The annual average sediment yield for the Mekong River was 310 t/km²/year from upper 80% of the total MRB before entering the delta. The derived sediment yield and a spatial soil erosion map can explicitly illustrate the identification and prioritization of the critical soil erosion-prone areas of the MR sub-basins. Full article

A suitable routing model for predicting future monthly water discharge (WD) is essential for operational hydrology, including water supply, and hydrological extreme management, to mention but a few. This is particularly important for a remote area without a sufficient number of in-situ data, promoting the usage of remotely sensed surface variables. Direct correlation analysis between ground-observed WD and localized passive remotely-sensed surface variables (e.g., indices and geometric variables) has been studied extensively over the past two decades. Most of these related studies focused on the usage of constructed correlative relationships for estimating WD at ungauged locations. Nevertheless, temporal prediction performance of monthly runoff (R) (being an average representation of WD of a catchment) at the river delta reconstructed from the basin’s upstream remotely-sensed water balance variables via a standardization approach has not been explored. This study examined the standardization approach via linear regression using the remotely-sensed water balance variables from upstream of the Mekong Basin to reconstruct and predict monthly R time series at the Mekong Delta. This was subsequently compared to that based on artificial intelligence (AI) models. Accounting for less than 1% improvement via the AI-based models over that of a direct linear regression, our results showed that both the reconstructed and predicted Rs based on the proposed approach yielded a 2–6% further improvement, in particular the reduction of discrepancy in the peak and trough of WD, over those reconstructed and predicted from the remotely-sensed water balance variables without standardization. This further indicated the advantage of the proposed standardization approach to mitigate potential environmental influences. The best R, predicted from standardized water storage over the whole upstream area, attained the highest Pearson correlation coefficient of 0.978 and Nash–Sutcliffe efficiency of 0.947, and the lowest normalized root-mean-square error of 0.072. Full article

While in-situ estuarine discharge has been correlated and reconstructed well with localized remotely-sensed data and hydraulic variables since the 1990s, its correlation and reconstruction using averaged GPS-inferred water storage from satellite gravimetry (i.e., GRACE) at the basin upstream based on the water balance standardization (WBS) approach remains unexplored. This study aims to illustrate the WBS approach for reconstructing monthly estuarine discharge (in the form of runoff (R)) at Mekong River Delta, by correlating the averaged GPS-inferred water storage from GRACE of the upstream Mekong Basin with the in-situ R at the Mekong River Delta estuary. The resulting R based on GPS-inferred water storage is comparable to that inferred from GRACE, regardless of in-situ stations within Mekong River Delta being used for the R reconstruction. The resulting R from the WBS approach with GPS water storage converted by GRACE mascon solution attains the lowest normalized root-mean-square error of 0.066, and the highest Pearson correlation coefficient of 0.974 and Nash-Sutcliffe efficiency of 0.950. Regardless of using either GPS-inferred or GRACE-inferred water storage, the WBS approach shows an increase of 1–4% in accuracy when compared to those reconstructed from remotely-sensed water balance variables. An external assessment also exhibits similar accuracies when examining the R estimated at another station location. By comparing the reconstructed and estimated Rs between the entrance and the estuary mouth, a relative error of 1–4% is found, which accounts for the remaining effect of tidal backwater on the estimated R. Additional errors might be caused by the accumulated errors from the proposed approach, the unknown signals in the remotely-sensed water balance variables, and the variable time shift across different years between the Mekong Basin at the upstream and the estuary at the downstream. Full article

In establishing adequate climate change policies regarding water resource development and management, the most essential step is performing a rainfall-runoff analysis. To this end, although several physical models have been developed and tested in many studies, they require a complex grid-based parameterization that uses climate, topography, land-use, and geology data to simulate spatiotemporal runoff. Furthermore, physical rainfall-runoff models also suffer from uncertainty originating from insufficient data quality and quantity, unreliable parameters, and imperfect model structures. As an alternative, this study proposes a rainfall-runoff analysis system for the Kratie station on the Mekong River mainstream using the long short-term memory (LSTM) model, a data-based black-box method. Future runoff variations were simulated by applying a climate change scenario. To assess the applicability of the LSTM model, its result was compared with a runoff analysis using the Soil and Water Assessment Tool (SWAT) model. The following steps (dataset periods in parentheses) were carried out within the SWAT approach: parameter correction (2000–2005), verification (2006–2007), and prediction (2008–2100), while the LSTM model went through the process of training (1980–2005), verification (2006–2007), and prediction (2008–2100). Globally available data were fed into the algorithms, with the exception of the observed discharge and temperature data, which could not be acquired. The bias-corrected Representative Concentration Pathways (RCPs) 4.5 and 8.5 climate change scenarios were used to predict future runoff. When the reproducibility at the Kratie station for the verification period of the two models (2006–2007) was evaluated, the SWAT model showed a Nash–Sutcliffe efficiency (NSE) value of 0.84, while the LSTM model showed a higher accuracy, NSE = 0.99. The trend analysis result of the runoff prediction for the Kratie station over the 2008–2100 period did not show a statistically significant trend for neither scenario nor model. However, both models found that the annual mean flow rate in the RCP 8.5 scenario showed greater variability than in the RCP 4.5 scenario. These findings confirm that the LSTM runoff prediction presents a higher reproducibility than that of the SWAT model in simulating runoff variation according to time-series changes. Therefore, the LSTM model, which derives relatively accurate results with a small amount of data, is an effective approach to large-scale hydrologic modeling when only runoff time-series are available. Full article

Population growth and economic development are driving changes in land use/land cover (LULC) of the transboundary Lower Mekong River Basin (LMB), posing a serious threat to the integrity of the river system. Using data collected on a monthly basis over 30 years (1985–2015) at 14 stations located along the Lower Mekong river, this study explores whether spatiotemporal relationships exist between LULC changes and instream concentrations of total suspended solids (TSS) and nitrate—as proxies of water quality. The results show seasonal influences where temporal patterns of instream TSS and nitrate concentrations mirror patterns detected for discharge. Changes in LULC influenced instream TSS and nitrate levels differently over time and space. The seasonal Mann–Kendall (SMK) confirmed significant reduction of instream TSS concentrations at six stations (p < 0.05), while nitrate levels increased at five stations (p < 0.05), predominantly in stations located in the upper section of the basin where forest areas and mountainous topography dominate the landscape. Temporal correlation analyses point to the conversion of grassland (r = −0.61, p < 0.01) to paddy fields (r = 0.63, p < 0.01) and urban areas (r = 0.44, p < 0.05) as the changes in LULC that mostly impact instream nitrate contents. The reduction of TSS appears influenced by increased forest land cover (r = −0.72, p < 0.01) and by the development and operation of hydropower projects in the upper Mekong River. Spatial correlation analyses showed positive associations between forest land cover and instream concentrations of TSS (r = 0.64, p = 0.01) and nitrate (r = 0.54, p < 0.05), indicating that this type of LULC was heavily disturbed and harvested, resulting in soil erosion and runoff of nitrate rich sediment during the Wet season. Our results show that enhanced understanding of how LULC changes influence instream water quality at spatial and temporal scales is vital for assessing potential impacts of future land and water resource development on freshwater resources of the LMB. Full article

Mun River is the largest tributary of the Mekong River in Thailand and provides abundant water resources not only for an important agricultural area in Thailand but also for the lower Mekong River. To understand how the runoff of Mun River responds to climate change and human activities in recent decades, this study performed a detailed examination of the characteristics of runoff variation based on measurements at two hydrological gauging stations on the main stem of Mun River during 1980–2018. Using the Mann-Kendall test, Morlet wavelet transform and Double Cumulative Curve methods, this study identifies that the variation of annual runoff of Mun River encountered an abruption in 1999/2000, with an increased trend taking place since then. Furthermore, a detailed assessment of the effects of the variations in rainfall, temperature, evaporation, and land use types extracted from remote sensing images at the basin scale reveals that a significant reduction in forest area and slight reductions in evaporation and farmland area taking place since 1999 can lead to an increase in the runoff of Mun River, while the dramatic increase in garden area since 1999 tends to make the runoff decrease. Full article