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Within the context of the Belt and Road Initiative (BRI) and the China-Myanmar Economic Corridor (CMEC), the Dulong-Irrawaddy (Ayeyarwady) River, an international river among China, India and Myanmar, plays a significant role as both a valuable hydro-power resource and an essential ecological passageway. However, the water resources and security exhibit a high degree of vulnerability to climate change impacts. This research evaluates climate impacts on the hydrology of the Dulong-Irrawaddy River Basin (DIRB) by using a physical-based hydrologic model. We crafted future climate scenarios using the three latest global climate models (GCMs) from Coupled Model Intercomparison Project 6 (CMIP6) under two shared socioeconomic pathways (SSP2-4.5 and SSP5-8.5) for the near (2025–2049), mid (2050–2074), and far future (2075–2099). The regional model using MIKE SHE based on historical hydrologic processes was developed to further project future streamflow, demonstrating reliable performance in streamflow simulations with a validation Nash-Sutcliffe Efficiency (NSE) of 0.72. Results showed that climate change projections showed increases in the annual precipitation and potential evapotranspiration (PET), with precipitation increasing by 11.3% and 26.1%, and PET increasing by 3.2% and 4.9%, respectively, by the end of the century under SSP2-4.5 and SSP5-8.5. These changes are projected to result in increased annual streamflow at all stations, notably at the basin's outlet (Pyay station) compared to the baseline period (with an increase of 16.1% and 37.0% at the end of the 21st century under SSP2-4.5 and SSP5-8.5, respectively). Seasonal analysis for Pyay station forecasts an increase in dry-season streamflow by 31.3%–48.9% and 22.5%–76.3% under SSP2-4.5 and SSP5-8.5, respectively, and an increase in wet-season streamflow by 5.8%–12.6% and 2.8%–33.3%, respectively. Moreover, the magnitude and frequency of flood events are predicted to escalate, potentially impacting hydropower production and food security significantly. This research outlines the hydrological response to future climate change during the 21st century and offers a scientific basis for the water resource management strategies by decision-makers. [ABSTRACT FROM AUTHOR]

Construction of river infrastructure, such as dams and weirs, is a global issue for ecosystem protection due to the fragmentation of river habitat and hydrological alteration it causes. Accurate river barrier databases, increasingly used to determine river fragmentation for ecologically sensitive management, are challenging to generate. This is especially so in large, poorly mapped basins where only large dams tend to be recorded. The Mekong is one of the world's most biodiverse river basins but, like many large rivers, impacts on habitat fragmentation from river infrastructure are poorly documented. To demonstrate a solution to this, and enable more sensitive basin management, we generated a whole‐basin barrier database for the Mekong, by training Convolutional Neural Network (CNN)–based object detection models, the best of which was used to identify 10,561 previously unrecorded barriers. Combining manual revision and merged with the existing barrier database, our new barrier database for the Mekong Basin contains 13,054 barriers. Existing databases for the Lower Mekong documented under ∼3% of the barriers recorded by CNN combined with manual checking. The Nam Chi/Nam Mun region, eastern Thailand, is the most fragmented area within the basin, with a median [95% CI] barrier density of 15.53 [0.00–49.30] per 100 km, and Catchment Area‐based Fragmentation Index value, calculated in an upstream direction, of 1,178.67 [0.00–6,418.46], due to the construction of dams and sluice gates. The CNN‐based object detection framework is effective and potentially can transform our ability to identify river barriers across many large river basins and facilitate ecologically‐sensitive management. Key Points: Convolutional Neural Networks applied to satellite images to measure river fragmentationMore than ten thousand unrecorded barriers identified in Mekong Basin, SE Asia's largest river71.7% of sub‐basins in the Mekong Basin are impacted by river barriers, the Chi‐Mun Basin is the most fragmented region [ABSTRACT FROM AUTHOR]

The Lancang-Mekong River, the largest river in Southeast Asia, supports high biodiversity of fish but has also suffered from intensive human- and nature-induced stressors for decades, and it is feared that this trend will continue in coming decades. Although the river has received wide attention from researchers, spatiotemporal trends in fish diversity and production and of environmental stressors within the catchment remain unclear. Also, an overview of current knowledge for catchment-scale sustainable management is lacking. This study analysed research publications related to Mekong fishes and threats, and identified knowledge gaps and limitations in current research. We found an increasing trend in studies on fish and/or environmental stressors, but that some research themes, including dam construction, pollution, infectious disease, and salinization, vary across regions. In general, research foci were determined by geographical location, fish diversity, and environmental stressors in each region. For fish-related studies, research in the Upper and Middle Lancang-Mekong regions was mostly related to the conservation of wild fish populations and communities, while in the Mekong Delta, a large proportion of studies were related to fish farming. For studies related to environmental stressors, current research foci were mainly on persistent threats, including flow modification, pollution, habitat degradation, and overexploitation. However, coverage of some newly emerging threats, such as microplastic pollution and harmful algal blooms were lacking. This study helps provide a better understanding of factors impacting the Lancang-Mekong River and its fish communities, and provides a basis for future research, management, and conservation actions. [ABSTRACT FROM AUTHOR]

• Novel DL framework with time-lag info provides upstream data-driven streamflow prediction for large-scale basins. • Time-lag info's efficacy proven in enhancing flow prediction and flood early warning. • Optimal model in the framework provides a 15-day lead time for flood forecasting. • The framework reduces data dependency and bolsters readiness for flood management. Constrained by the sparsity of observational streamflow data, large-scale catchments face pressing challenges in streamflow prediction and flood management amid climate change. Deep learning excels in simulation performance while flow lag information in data-driven approaches is barely highlighted. In this study, we introduce a time-lag informed deep learning framework for large-scale catchments. Central to this framework is the utilization of flow time-lag information between upstream and downstream subbasins, enabling precise flood forecasting at outlet driven by upstream data. Taking the monsoon-influenced large-scale Dulong-Irrawaddy River Basin (DIRB) as study area, we determined peak flow lag (PFL) days and relative annual flow scale (RAFS) for defined subbasins. By incorporating this time-lag information with historical flow data at different time intervals, we developed the optimal model for DIRB. This model was then applied to evaluate the flood processes in 2008 and 2009, using selected flood indicators. The results indicate that the time-lag information led to significant performance improvements, notably in the LSTM_PFL_RAFS model driven by upstream Hkamti sub-basin data, which achieved a Kling-Gupta Efficiency (KGE) of 0.891 (Nash-Sutcliffe efficiency coefficient, NSE, 0.904), surpassing LSTM's 0.683 (NSE, 0.785). Further integration of historical flow data with specific interval, the optimal model, H(15)_PFL utilizes Hkamti sub-basin data, reached an impressive KGE of 0.948 (NSE, 0.940). This model outperformed standard LSTM in accurately simulating key flood characteristics, including peak flows, initiation times, and durations for the 2008 and 2009 flood events. Notably, H(15)_PFL provides a valuable 15-day lead time for flood forecasting, extending the window for emergency response preparations. Future research that incorporates additional essential catchment features into the framework holds great potential in unraveling the complex mechanisms of hydrological responses to human activities and climate change. [ABSTRACT FROM AUTHOR]

The great rivers that flow from the southern Tibetan Plateau (TP) affect billions of people in the downstream countries of Asia. Understanding of the hydrological variability of these rivers is still limited, however, because of the lack of long-term streamflow records. Tree-ring width chronologies from six sites are applied to reconstruct annual streamflow of the Salween River, the last remaining large free-flowing transboundary river draining the southern TP, and a critical water source for countries of Southeast Asia. Response function analysis shows that precipitation is the main factor limiting the radial growth of the sampled trees. Linear regression of annual (September–June) Salween River streamflow on the first principal component of tree-ring chronologies explains 53.4% of the streamflow variance, 1958–2011, and yields a reconstruction for the interval 1500–2011 CE. A tally of droughts and wet periods emphasizes the severity of droughts before the start of the gauged records, and a tendency toward wetter conditions in recent decades. Regional temperature is negatively associated with the reconstructed streamflow. Cold wet summers controlled by the Asian summer monsoon are responsible for an increasing trend in streamflow over the last decades. Reconstructed hydrological change is linked to the history of mainland Southeast Asia through the impact of water shortages on Burma society. In particular, prolonged periods of low flow of the Salween River coincide with the falls of the Toungoo Empires and the First Anglo-Burmese War. This tree-ring reconstruction provides a long-term perspective on hydrological changes in the Upper Salween River Basin that can give insight for sustainable water management on the TP and in Myanmar. [ABSTRACT FROM AUTHOR]

Downscaling satellite‐based precipitation to fine scales is crucial for deepening our understanding of global hydrologic cycles and water‐related issues. In this study, a novel approach that integrates precipitation zoning with random forest regression is proposed for the spatial downscaling of satellite‐based precipitation. Precipitation zoning is delineated through iterative rotated empirical orthogonal function (REOF) analyses of ground‐ and satellite‐based precipitation observations. Random forest regression is applied to link the satellite‐based precipitation to 1‐km‐resolution predictors such as latitude (Lat), longitude (Lon), elevation, aspect, slope, and the normalized difference vegetation index (NDVI). The accuracy of the resultant downscaled precipitation is evaluated based on five statistical evaluation indices. The performance of the proposed approach is exemplified in the Lancang–Mekong River basin, taking Tropical Rainfall Measuring Mission (TRMM) 3B43 and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks–Climate Data Record (PERSIANN–CDR) products acquired in 2001 (wet year), 2005 (normal year), and 2009 (dry year) as the databases. The results show that seven precipitation subregions can be roughly distinguished in the study basin. Zoning‐based downscaling outperforms non‐zoning‐based downscaling in terms of accuracy, resulting in statistically significant reductions in root mean square error (RMSE) and mean absolute error (MAE) of 1–17% across the entire basin. Among the selected predictors, the variables Lat and Lon are the most important for precipitation estimation, whereas the remaining variables have lesser and subregion‐dependent importance. The proposed approach is promising for generating high‐spatial‐resolution precipitation data in regions with sparse ground‐based observations and differentiated climatic regimes. [ABSTRACT FROM AUTHOR]

Satellite-based precipitation products have been widely used to estimate precipitation, especially over regions with sparse rain gauge networks. However, the low spatial resolution of these products has limited their application in localized regions and watersheds. This study investigated a spatial downscaling approach, Geographically Weighted Regression Kriging (GWRK), to downscale the Tropical Rainfall Measuring Mission (TRMM) 3B43 Version 7 over the Lancang River Basin (LRB) for 2001–2015. Downscaling was performed based on the relationships between the TRMM precipitation and the Normalized Difference Vegetation Index (NDVI), the Land Surface Temperature (LST), and the Digital Elevation Model (DEM). Geographical ratio analysis (GRA) was used to calibrate the annual downscaled precipitation data, and the monthly fractions derived from the original TRMM data were used to disaggregate annual downscaled and calibrated precipitation to monthly precipitation at 1 km resolution. The final downscaled precipitation datasets were validated against station-based observed precipitation in 2001–2015. Results showed that: 1) The TRMM 3B43 precipitation was highly accurate with slight overestimation at the basin scale (i.e., CC (correlation coefficient) = 0.91, Bias = 13.3%). Spatially, the accuracies of the upstream and downstream regions were higher than that of the midstream region. 2) The annual downscaled TRMM precipitation data at 1 km spatial resolution obtained by GWRK effectively captured the high spatial variability of precipitation over the LRB. 3) The annual downscaled TRMM precipitation with GRA calibration gave better accuracy compared with the original TRMM dataset. 4) The final downscaled and calibrated precipitation had significantly improved spatial resolution, and agreed well with data from the validated rain gauge stations, i.e., CC = 0.75, RMSE (root mean square error) = 182 mm, MAE (mean absolute error) = 142 mm, and Bias = 0.78% for annual precipitation and CC = 0.95, RMSE = 25 mm, MAE = 16 mm, and Bias = 0.67% for monthly precipitation. [ABSTRACT FROM AUTHOR]

Satellite-based products with high spatial and temporal resolution provide useful precipitation information for data-sparse or ungauged large-scale watersheds. In the Lower Lancang-Mekong River Basin, rainfall stations are sparse and unevenly distributed, and the transboundary characteristic makes the collection of precipitation data more difficult, which has restricted hydrological processes simulation. In this study, daily precipitation data from four datasets (gauge observations, inverse distance weighted (IDW) data, Tropical Rainfall Measuring Mission (TRMM) estimates, and Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS) estimates), were applied to drive the Soil and Water Assessment Tool (SWAT) model, and then their capability for hydrological simulation in the Lower Lancang-Mekong River Basin were examined. TRMM and CHIRPS data showed good performances on precipitation estimation in the Lower Lancang-Mekong River Basin, with the better performance for TRMM product. The Nash-Sutcliffe efficiency (NSE) values of gauge, IDW, TRMM, and CHIRPS simulations during the calibration period were 0.87, 0.86, 0.95, and 0.93 for monthly flow, respectively, and those for daily flow were 0.75, 0.77, 0.86, and 0.84, respectively. TRMM and CHIRPS data were superior to rain gauge and IDW data for driving the hydrological model, and TRMM data produced the best simulation performance. Satellite-based precipitation estimates could be suitable data sources when simulating hydrological processes for large data-poor or ungauged watersheds, especially in international river basins for which precipitation observations are difficult to collect. CHIRPS data provide long precipitation time series from 1981 to near present and thus could be used as an alternative precipitation input for hydrological simulation, especially for the period without TRMM data. For satellite-based precipitation products, the differences in the occurrence frequencies and amounts of precipitation with different intensities would affect simulation results of water balance components, which should be comprehensively considered in water resources estimation and planning. [ABSTRACT FROM AUTHOR]

The Lancang-Mekong River has attracted much attention from researchers, but the cooperation on water issues in this river basin has been limited, even after the establishment of the Mekong River Commission (MRC). Cooperation on water resources has been determined as one of the key priority areas in the Lancang-Mekong Cooperation Mechanism, but there are no details of targets. In order to establish the priorities of water cooperation under the mechanism, we adopted nine categories to classify the objectives of 87 water cooperation events based on the 'Lancang-Mekong Water Cooperative Events Database' from 1995 to 2015. Based on the occurrence of cooperative events, cooperative objectives, cooperative scales, and approaches to cooperation, we conducted statistical, correlation, and text analyses. Our analyses indicated the following results: under the impact of economic conditions inside and outside the river basin, full cooperation appeared more difficult than bilateral and multilateral cooperation. Each of the partners adopted different preferences for cooperation targets. Cooperation with more definite objectives was easier to establish than cooperation with broader and more complex objectives. The potential objectives for water cooperation were navigation, hydropower, joint management, data sharing, flood control and water use. Because hydropower development is controversial, and because water cooperation is avoided by most existing regional cooperation mechanisms due to its complexity, we suggest the following priority areas for water cooperation in the Lancang- Mekong River Basin. 1) Navigation and flood control/drought relief are attractive objectives for all the riparian countries across the whole watershed. 2) Data sharing should be a priority for cooperation in the watershed due to its laying the foundation for the equitable and reasonable utilization of transboundary waters. 3) Hydropower is an objective best implemented mainly through bilateral cooperation, and on tributaries. [ABSTRACT FROM AUTHOR]

The water stored and sediment trapped by the reservoirs result in physical, chemical and ecological effects on downstream watercourse environment. Quantitative research on the reservoir sedimentation is the key to assess its environment impacts. The sediment load data obtained over 39 years at the gauging stations located in the upstream and downstream of the Manwan Reservoir were used to estimate the trapping efficiency (TE) of the Manwan Dam. The TE value was then compared with the estimates using the modified Brune and Siyam models. The results indicate that: (1) The TE of the Manwan Dam estimated by the gauging data was 60.48%, and both the TE values calculated by the modified Brune and Siyam models were 60.03%. The approximate TE values indicated that the modified Brune and Siyam models could be used to forecast the TEs of other reservoirs with similar storage capacities as the Manwan Reservoir in the mainstream of the Lancang River; (2) The TE values of Gongguoqiao, Dachaoshan and Jinghong Dams were about 30.23%, 66.05% and 63.50% estimated by the modified Brune model, and 38.81%, 63.97% and 62.30% by the modified Siyam model. The results and the methodologies adopted in the paper could also be used to estimate the TE in other river basins in southwestern China. [ABSTRACT FROM AUTHOR]

Based on analyses of key issues concerning watershed ecological security assessment including the subject, content, and methodology of assessment, this paper points out that ecological security should be assessed dynamically at different levels (e.g. watershed and ecosystem levels) with human being as the main assessment subject. Both the functional security and the structural security must be considered in watershed ecological security assessment. In order to reflect the overall and timely security status of the watershed, the PSR (pressure-state-response) method should be employed. Longitudinal Range-Gorge Region (LRGR) was researched as an example. An index system of watershed ecological security assessment has been proposed, which includes structural index, functional index, pressure and response index, and the state of ecological security is analyzed for LRGR in Yunnan Province. [ABSTRACT FROM AUTHOR]

Against the background of global climate change, spatial-temporal variation in net primary productivity (NPP) has attracted much attention. To analyze NPP spatial-temporal variation within the context of changes in hydrothermal conditions, the Vegetation Photosynthesis Model (VPM) is used to elucidate the mathematical relationship between NPP and hydrothermal conditions. Based on this spatial-temporal pattern of NPP and hydrothermal conditions in the Lancang-Mekong River Basin, regression statistics, an empirical model of land evaporation, and the water and thermal product index (K) are used to evaluate correlations between NPP and hydrothermal conditions in terms of their distribution pattern and interaction. The results show the following. (1) From 2000 to 2014, NPP in the Lancang-Mekong River Basin was highest in the central region and gradually decreased toward the southwest and northwest, whereas the annual change rate in NPP showed no significant increasing trend. (2) In the Lancang Basin, the correlation between hydrothermal conditions and NPP was high with respect to their distribution patterns, though this correlation was low in the Mekong Basin. (3) Correlation between K and NPP is high in the region where the effects of water and thermal factors on vegetation growth are similar. (4) K is an effective complement to the correlation between a single hydrothermal factor (temperature or precipitation) and NPP, and the influence of hydrothermal conditions on NPP was positive in the Lancang River and negative in the Mekong River Basin. Our study quantitatively analyzes the spatial-temporal correlation between NPP and hydrothermal conditions. The findings can reflect the vegetation change tendency and provide scientific data for ecological environment development and protection in the study area. [ABSTRACT FROM AUTHOR]

Hydrological drought (HD) in dynamic environments, particularly complicated by climate change and human activity, challenges traditional stationary-based research approaches. These challenges are particularly pronounced in the Upper Red River Basin characterized by complex topography and extensive human activity. To these issues, this study introduces non-stationarity in determining drought propagation time and develops a Non-Stationary ImpactQuant Framework (NSIQF) to assess human activity impacts on HD's frequency and propagation processes. Implementing non-stationarity involves using the Generalized Additive Models for Location, Scale, and Shape to construct the Non-stationary Standardized Runoff Index (NSRI), which is employed for attribution analysis and determining drought propagation time. We systematically quantified human activity impacts by comparing NSRI with drought indices and propagation time derived from reconstructed natural runoff. Our findings indicate that the NSRI is more adept at capturing drought characteristics in changing environments than the traditional Standardized Runoff Index. The human activity impacts on HD in the study area exhibits distinct temporal characteristics. Human activity contributed to 42.64% of the HD at 12-month scales, and 29.28%, 17.68%,19.14%, and 24.82% in spring, summer, autumn, and winter, respectively. They prolonged the propagation time from meteorological drought to HD by 1–5 months. Land use change is a potential primary anthropogenic factor. Effective management and strategic interventions for predominantly agricultural activity constitute viable solutions to alleviate regional drought and enhance drought resistance. • The non-stationarity is incorporated into drought propagation analysis • A novel framework to assess human activity impacts on hydrological drought • Human activities delay the propagation from meteorological to hydrological drought • Agriculturally related management strategies may alleviate hydrological drought [ABSTRACT FROM AUTHOR]

Gridded precipitation products have the advantages of wide spatial coverage and high spatiotemporal resolution compared to conventional rain-gauge data, and have been extensively applied in hydrology-related research. As one of the most representative of the gridded precipitation products, APHRODITE performs well in most regions over Asia except around the Tibetan Plateau. This study implemented four bias correction methods including both mean-based (LS, LOCI) and distribution-based approaches (CDF, LS_CDF) for APHRODITE data over the Yarluzangbu-Brahmaputra River Basin based on different climate zones, and the correction methods were assessed according to the performance of both statistical indices and hydrological simulation. First, it was found that the APHRODITE data underestimated the precipitation amount and overestimated the number of raindays for the entire study region; and the bias is relatively small in upstream (Tibetan Plateau) and large in downstream (floodplains). Second, the result showed that all four correction methods could effectively improve the precipitation estimates of the APHRODITE data and the combined LS_CDF method performed the best because of its greater spatial consistency advantages of bias and closer matching of the wet-day event and cumulative frequency. Hydrological performance also supports that the LS_CDF method is the best due to the fine driving simulation result for all three hydrological stations during long periods and the ability to better force simulation of extreme runoff. Third, this study also found that even though distribution-based approaches performed well in precipitation correction, especially for extreme precipitation, their correction effects also depend on the spatial consistency of the bias, which is even more important than frequency matching. These findings have certain reference values for the evaluation, correction, and application of grid precipitation data, not only for APHRODITE data, but also satellite and reanalysis precipitation data. • Adjusted rain-gauge data were used to evaluate the performance of APHRODITE data. • Bias correction methods were implemented based on Köppen-Geiger climatic zones. • The combination of distribution-based and mean-based method perform well in extreme precipitation and peak flow simulation. [ABSTRACT FROM AUTHOR]