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Abstract Accurate groundwater representation in land surface models (LSMs) is vital for water and energy cycle studies, water resource assessments, and climate projections. Yet, many LSMs do not consider key processes including lateral groundwater flow and aquifer pumping, especially at the global scale. This study simulates these processes using an enhanced version of the Community Land Model (CLM5) and evaluates their roles at three spatial resolutions (0.5°, 0.25°, 0.1°). Results show that lateral flow strongly modulates water table depth and capillary rise at all resolutions. The magnitude of mean lateral flow increases from 25 mm/year at 0.5° to 36 mm/year at 0.25°, and 52 mm/year at 0.1° resolution, with pumping inducing lateral flow even at 0.5° (∼50 km), a typical grid size in global LSMs. Further, lateral flow alters runoff in regions with high recharge and shallow water table (e.g., eastern North America and Amazon basin), and soil moisture and ET in regions with comparatively low recharge and deeper water table (e.g., western North America, central Asia, and Australia) through enhanced capillary rise. Runoff alteration by lateral flow increases substantially with resolution, from a maximum of 15 mm/month at 0.5° to 20 mm/month and 25 mm/month at 0.25° and 0.1°, respectively; the impact of resolution on soil moisture and ET is less pronounced. While the model does not fully capture deeper water tables—warranting further enhancements—it provides valuable insights on how lateral groundwater flow impacts land surface processes, highlighting the importance of lateral groundwater flow and pumping in global LSMs. 

Abstract Characterizing the impact of human actions on terrestrial water fluxes and storages at multi‐basin, continental, and global scales has long been on the agenda of scientists engaged in climate science, hydrology, and water resources systems analysis. This need has resulted in a variety of modeling efforts focused on the representation of water infrastructure operations. Yet, the representation of human‐water interactions in large‐scale hydrological models is still relatively crude, fragmented across models, and often achieved at coarse resolutions (10–100 km) that cannot capture local water management decisions. In this commentary, we argue that the concomitance of four drivers and innovations is poised to change the status quo: “hyper‐resolution” hydrological models (0.1–1 km), multi‐sector modeling, satellite missions able to monitor the outcome of human actions, and machine learning are creating a fertile environment for human‐water research to flourish. We then outline four challenges that chart future research in hydrological modeling: (a) creating hyper‐resolution global data sets of water management practices, (b) improving the characterization of anthropogenic interventions on water quantity, stream temperature, and sediment transport, (c) improving model calibration and diagnostic evaluation, and (d) reducing the computational requirements associated with the successful exploration of these challenges. Overcoming them will require addressing modeling, computational, and data development needs that cut across the hydrology community, thereby requiring a major communal effort. 

Abstract Global water models are increasingly used to understand past, present and future water cycles, but disagreements between simulated variables make model-based inferences uncertain. Although there is empirical evidence of different large-scale relationships in hydrology, these relationships are rarely considered in model evaluation. Here we evaluate global water models using functional relationships that capture the spatial co-variability of forcing variables (precipitation, net radiation) and key response variables (actual evapotranspiration, groundwater recharge, total runoff). Results show strong disagreement in both shape and strength of model-based functional relationships, especially for groundwater recharge. Empirical and theory-derived functional relationships show varying agreements with models, indicating that our process understanding is particularly uncertain for energy balance processes, groundwater recharge processes and in dry and/or cold regions. Functional relationships offer great potential for model evaluation and an opportunity for fundamental advances in global hydrology and Earth system research in general. 

Abstract The high‐mountain system, a storehouse of major waterways that support important ecosystem services to about 1.5 billion people in the Himalaya, is facing unprecedented challenges due to climate change during the 21st century. Intensified floods, accelerating glacial retreat, rapid permafrost degradation, and prolonged droughts are altering the natural hydrological balances and generating unpredictable spatial and temporal distributions of water availability. Anthropogenic activities are adding further pressure onto Himalayan waterways. The fundamental question of waterway management in this region is therefore how this hydro‐meteorological transformation, caused by climate change and anthropogenic perturbations, can be tackled to find avenues for sustainability. This requires a framework that can diagnose threats at a range of spatial and temporal scales and provide recommendations for strong adaptive measures for sustainable future waterways. This focus paper assesses the current literature base to bring together our understanding of how recent climatic changes have threatened waterways in the Asian Himalayas, how society has been responding to rapidly changing waterway conditions, and what adaptive options are available for the region. The study finds that Himalayan waterways are crucial in protecting nature and society. The implementation of integrated waterways management measures, the rapid advancement of waterway infrastructure technologies, and the improved governance of waterways are more critical than ever. This article is categorized under:Engineering Water > Sustainable Engineering of Water 

Abstract 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. 

Abstract 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. 

Abstract 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. 

Abstract 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. 

Abstract 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. 

Abstract 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.