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Hydropower dams have received increased global attention due to their detrimental socioenvironmental ramifications. Such attention has led to an increase in studies on the impacts of reservoir operation on river flow; however, a holistic understanding of the compounded effects of hydropower dams on different hydrological characteristics is lacking, especially for large river basins such as the Amazon where hydropower development is on the rise. Here, we mechanistically quantify the historical impacts of existing dams and the potential impacts of the collective operation of existing and planned dams on a basin‐wide scale in the Amazon for the 1981–2019 period. We build on the recently developed high‐resolution (3‐arcmin; ∼5 km) river‐floodplain‐reservoir model, the CaMa‐Flood‐Dam, which is enhanced to realistically simulate hydropower dam operation considering maximized power production. Flood simulations are further downscaled to 3 arc‐seconds (∼90 m) resolution to investigate the impacts of dams on fine‐scale flood dynamics across the basin. Results indicate that existing dams have substantially altered downstream river flow and flooding patterns across the Amazon River basin. Specifically, large dams in the Amazonian subbasins, including the Xingu, Madeira, and Tocantins, have altered downstream river flow amplitude by up to 3 orders of magnitude. Further, the collective operation of existing and planned dams could increasingly alter river flow patterns, causing ∼10% decrease in flood duration in many parts of the Amazon mainstem. Our results provide quantitative evidence on the severity of the hydrologic impacts of large hydropower dams and have important implications for sustainable hydropower operation and development in the Amazon and worldwide.

 

Numerous studies have examined the reliability of various precipitation products over the Mekong River Basin (MRB) and modeled its basin hydrology. However, there is a lack of comprehensive studies on precipitation‐induced uncertainties in hydrological simulations using process‐based land surface models. This study examines the propagation of precipitation uncertainty into hydrological simulations over the entire MRB using the Community Land Model version 5 (CLM5) at a high spatial resolution of 0.05° (∼5 km) and without any parameter calibration. Simulations conducted using different precipitation datasets are compared to investigate the discrepancies in streamflow, terrestrial water storage (TWS), soil moisture, and evapotranspiration (ET) caused by precipitation uncertainty. Results indicate that precipitation is a key determinant of simulated streamflow in the MRB; peak flow and soil moisture are particularly sensitive to precipitation input. Further, precipitation data with a higher spatial resolution did not improve the simulations, contrary to the common perception that using meteorological forcing with higher spatial resolution would improve hydrological simulations. In addition, since high flow indicators are particularly influenced by precipitation data, the choice of precipitation data could directly impact flood pulse simulations in the MRB. Notable differences are also found among TWS, soil moisture, and ET simulated using different precipitation products. Moreover, TWS, soil moisture, and ET exhibit a varying degree of sensitivity to precipitation uncertainty. This study provides crucial insights on precipitation‐induced uncertainties in process‐based hydrological modeling and uncovers these uncertainties in the MRB.

 

Daily floods including event, characteristic, extreme and inundation in the Lancang‐Mekong River Basin (LMRB), crucial for flood projection and forecasting, have not been adequately modeled. An improved hydrological‐hydrodynamic model (VIC and CaMa‐Flood) considering regional parameterization was developed to simulate the flood dynamics over the basin from 1967 to 2015. The flood elements were extracted from daily time series and evaluated at both local and regional scales using the data collected from in‐situ observations and remote sensing. The results show that the daily discharge and water level are both well simulated at selected stations with relative error (RE) less than 10% and Nash‐Sutcliffe efficiency coefficient (NSE) over 0.90. Half of the flood events haveNSEexceeding 0.76. The peak time and flood volume are well reproduced while both peak discharge and water level are slightly underestimated. The results tend to worsen when the characteristics of flood events are extended to annual extremes. These extremes are generally underestimated withNSEless than 0.5 butREis within 20%. The simulated rainy season inundation area generally agrees with observations from remote sensing, with about 86.8% inundation occurrence frequency captured within the model capacity. Ignoring the regional parameterization and reservoir regulation can both deteriorate flood simulation performance at the local scale, resulting in lowerNSE. Specifically, systematically higher water levels and up to 27% overestimation of peak discharge are found when ignoring regional parameterization, while ignoring reservoir regulation would cause up to 23% overestimation for flood extremes. It is expected that these findings would contribute to the regional flood forecasting and flood management.

 

Numerous studies have examined the changes in streamflow in the Mekong River Basin (MRB) using observations and hydrological modeling; however, there is a lack of integrated modeling studies that explicitly simulate the natural and human‐induced changes in flood dynamics over the entire basin. Here we simulate the river‐floodplain‐reservoir inundation dynamics over the MRB for 1979–2016 period using a newly integrated, high‐resolution (~5 km) river hydrodynamics‐reservoir operation model. The framework is based on the river‐floodplain hydrodynamic model CaMa‐Flood in which a new reservoir operation scheme is incorporated by including 86 existing MRB dams. The simulated flood extent is downscaled to a higher resolution (~90 m) to investigate fine‐scale inundation dynamics, and results are validated with ground‐ and satellite‐based observations. It is found that the historical variations in surface water storage have been governed primarily by climate variability; the impacts of dams on river‐floodplain hydrodynamics were marginal until 2009. However, results indicate that the dam impacts increased noticeably in 2010 when the basin‐wide storage capacity doubled due to the construction of new mega dams. Further, results suggest that the future flood dynamics in the MRB would be considerably different than in the past even without climate change and additional dams. However, it is also found that the impacts of dams can largely vary depending on reservoir operation strategies. This study is expected to provide the basis for high‐resolution river‐floodplain‐reservoir modeling for a holistic assessment of the impacts of dams and climate change on the floodpulse‐dependent hydro‐ecological systems in the MRB and other global regions.

 

The Mekong River Basin (MRB) is undergoing unprecedented changes due to the recent acceleration in large-scale dam construction. While the hydrology of the MRB is well understood and the effects of some of the existing dams have been studied, the potential effects of the planned dams on flood pulse dynamics over the entire Lower Mekong remains unexamined. Here, using hydrodynamic model simulations, we show that the effects of flow regulation on downstream river-floodplain dynamics are relatively predictable along the mainstream Mekong, but flow regulations could potentially disrupt the flood dynamics in the Tonle Sap River (TSR) and small distributaries in the Mekong Delta. Results suggest that TSR flow reversal could cease if the Mekong flood pulse is dampened by 50% and delayed by one-month. While flood occurrence in the vicinity of the Tonle Sap Lake and middle reach of the delta could increase due to enhanced low flow, it could decrease by up to five months in other areas due to dampened high flow, particularly during dry years. Further, areas flooded for less than five months and over six months are likely to be impacted significantly by flow regulations, but those flooded for 5–6 months could be impacted the least.

 

Irrigation representation in land surface models has been advanced over the past decade, but the soil moisture (SM) data from SMAP satellite have not yet been utilized in large‐scale irrigation modeling. Here we investigate the potential of improving irrigation representation in the Community Land Model version‐4.5 (CLM4.5) by assimilating SMAP data. Simulations are conducted over the heavily irrigated central U.S. region. We find that constraining the target SM in CLM4.5 using SMAP data assimilation with 1‐D Kalman filter reduces the root‐mean‐square error of simulated irrigation water requirement by 50% on average (for Nebraska, Kansas, and Texas) and significantly improves irrigation simulations by reducing the bias in irrigation water requirement by up to 60%. An a priori bias correction of SMAP data further improves these results in some regions but incrementally. Data assimilation also enhances SM simulations in CLM4.5. These results could provide a basis for improved modeling of irrigation and land‐atmosphere interactions.

 

Manmade reservoirs are important components of the terrestrial water balance. Thus, considering the hydro‐climatic effects of reservoirs is important in water cycle studies at a river basin to global scales; yet, reservoirs are represented poorly in large‐scale hydrological and climate models. Here we present a high‐resolution (5 km) continental‐scale reservoir storage dynamics and release scheme by enhancing existing schemes and adding critical novel parameterizations to improve reservoir storage and release simulations. The new scheme simulates river‐floodplain‐reservoir storages in an integrated manner considering their spatial and temporal variations. A new calibration scheme is also incorporated to better simulate reservoir dynamics considering cascade‐reservoir effects. Further, since no reservoir bathymetry data are available over large domains, we use a state‐of‐the‐art digital elevation model and reservoir extent data to derive reservoir bed elevation. The new scheme is integrated within the river‐floodplain routing scheme of a continental hydrological model LEAF‐Hydro‐Flood. Results from the simulation of ~1,900 reservoirs within the contiguous United States suggest that the model well captures the observed reservoir storage‐release dynamics. Comparison of our results with those from the existing schemes suggest a significant improvement; importantly, the new scheme reduces the excessive and frequent reservoir overfilling and underfilling. Comparison of results with satellite‐based surface water data shows that the model accurately reproduces the large‐scale patterns of reservoir‐floodplain inundation extents. It is expected that the results of this study will inform the incorporation of reservoirs in hyper‐resolution models to improve simulations of terrestrial water storage and flow and examine reservoir‐atmosphere interactions over large domains.

 

Abstract The Mekong River basin (MRB) is a transboundary basin that supports livelihoods of over 70 million inhabitants and diverse terrestrial-aquatic ecosystems. This critical lifeline for people and ecosystems is under transformation due to climatic stressors and human activities (e.g., land use change and dam construction). Thus, there is an urgent need to better understand the changing hydrological and ecological systems in the MRB and develop improved adaptation strategies. This, however, is hampered partly by lack of sufficient, reliable, and accessible observational data across the basin. Here, we fill this long-standing gap for MRB by synthesizing climate, hydrological, ecological, and socioeconomic data from various disparate sources. The data— including groundwater records digitized from the literature—provide crucial insights into surface water systems, groundwater dynamics, land use patterns, and socioeconomic changes. The analyses presented also shed light on uncertainties associated with various datasets and the most appropriate choices. These datasets are expected to advance socio-hydrological research and inform science-based management decisions and policymaking for sustainable food-energy-water, livelihood, and ecological systems in the MRB. 

Abstract Water resources sustainability in High Mountain Asia (HMA) surrounding the Tibetan Plateau (TP)—known as Asia’s water tower—has triggered widespread concerns because HMA protects millions of people against water stress 1,2 . However, the mechanisms behind the heterogeneous trends observed in terrestrial water storage (TWS) over the TP remain poorly understood. Here we use a Lagrangian particle dispersion model and satellite observations to attribute about 1 Gt of monthly TWS decline in the southern TP during 2003–2016 to westerlies-carried deficit in precipitation minus evaporation (PME) from the southeast North Atlantic. We further show that HMA blocks the propagation of PME deficit into the central TP, causing a monthly TWS increase by about 0.5 Gt. Furthermore, warming-induced snow and glacial melt as well as drying-induced TWS depletion in HMA weaken the blocking of HMA’s mountains, causing persistent northward expansion of the TP’s TWS deficit since 2009. Future projections under two emissions scenarios verified by satellite observations during 2020–2021 indicate that, by the end of the twenty-first century, up to 84% (for scenario SSP245) and 97% (for scenario SSP585) of the TP could be afflicted by TWS deficits. Our findings indicate a trajectory towards unsustainable water systems in HMA that could exacerbate downstream water stress.