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Maoyi Huang, Xu Liang, and L. Ruby Leung

Abstract

Subsurface flow is an important hydrologic process and a key component of the water budget. Through its direct impacts on soil moisture, it can affect water and energy fluxes at the land surface and influence the regional climate and water cycle. In this study, a new subsurface flow formulation is developed that incorporates the spatial variability of both topography and recharge. It is shown through theoretical derivation and case studies that the power-law and exponential subsurface flow parameterizations and the parameterization proposed by Woods et al. are all special cases of the new formulation. The subsurface flows calculated using the new formulation compare well with values derived from observations at Tulpehocken Creek, Pennsylvania, and Walnut Creek, Iowa. Sensitivity studies show that when the spatial variability of topography or recharge, or both is increased, the subsurface flows increase at the two aforementioned sites and at the Maimai hillslope, New Zealand. This is likely due to enhancement of interactions between the groundwater table and the land surface that reduce the flow path. An important conclusion of this study is that the spatial variability of recharge alone, and/or in combination with the spatial variability of topography can substantially alter the behaviors of subsurface flows. This suggests that in macroscale hydrologic models or land surface models, subgrid variations of recharge and topography can make significant contributions to the grid mean subsurface flow and must be accounted for in regions with large surface heterogeneity. This is particularly true for regions with humid climate and a relatively shallow groundwater table where the combined impacts of spatial variability of recharge and topography are shown to be more important. For regions with an arid climate and a relatively deep groundwater table, simpler formulations, for example, the power law, for subsurface flow can work well, and the impacts of subgrid variations of recharge and topography may be ignored.

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Maoyi Huang, Zhangshuan Hou, L. Ruby Leung, Yinghai Ke, Ying Liu, Zhufeng Fang, and Yu Sun

Abstract

In this study, the authors applied version 4 of the Community Land Model (CLM4) integrated with an uncertainty quantification (UQ) framework to 20 selected watersheds from the Model Parameter Estimation Experiment (MOPEX) spanning a wide range of climate and site conditions to investigate the sensitivity of runoff simulations to major hydrologic parameters and to assess the fidelity of CLM4, as the land component of the Community Earth System Model (CESM), in capturing realistic hydrological responses. They found that for runoff simulations, the most significant parameters are those related to the subsurface runoff parameterizations. Soil texture–related parameters and surface runoff parameters are of secondary significance. Moreover, climate and soil conditions play important roles in the parameter sensitivity. In general, water-limited hydrologic regime and finer soil texture result in stronger sensitivity of output variables, such as runoff and its surface and subsurface components, to the input parameters in CLM4. This study evaluated the parameter identifiability of hydrological parameters from streamflow observations at selected MOPEX basins and demonstrated the feasibility of parameter inversion/calibration for CLM4 to improve runoff simulations. The results suggest that in order to calibrate CLM4 hydrologic parameters, model reduction is needed to include only the identifiable parameters in the unknowns. With the reduced parameter set dimensionality, the inverse problem is less ill posed.

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Guoyong Leng, Maoyi Huang, Qiuhong Tang, Huilin Gao, and L. Ruby Leung

Abstract

Human alteration of the land surface hydrologic cycle is substantial. Recent studies suggest that local water management practices including groundwater pumping and irrigation could significantly alter the quantity and distribution of water in the terrestrial system, with potential impacts on weather and climate through land–atmosphere feedbacks. In this study, the authors incorporated a groundwater withdrawal scheme into the Community Land Model, version 4 (CLM4). To simulate the impact of irrigation realistically, they calibrated the CLM4 simulated irrigation amount against observations from agriculture censuses at the county scale over the conterminous United States. The water used for irrigation was then removed from the surface runoff and groundwater aquifer according to a ratio determined from the county-level agricultural census data. On the basis of the simulations, the impact of groundwater withdrawals for irrigation on land surface and subsurface fluxes were investigated. The results suggest that the impacts of irrigation on latent heat flux and potential recharge when water is withdrawn from surface water alone or from both surface and groundwater are comparable and local to the irrigation areas. However, when water is withdrawn from groundwater for irrigation, greater effects on the subsurface water balance are found, leading to significant depletion of groundwater storage in regions with low recharge rate and high groundwater exploitation rate. The results underscore the importance of local hydrologic feedbacks in governing hydrologic response to anthropogenic change in CLM4 and the need to more realistically simulate the two-way interactions among surface water, groundwater, and atmosphere to better understand the impacts of groundwater pumping on irrigation efficiency and climate.

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J. L. Zhang, Y. P. Li, G. H. Huang, C. X. Wang, and G. H. Cheng

Abstract

In this study, a Bayesian framework is proposed for investigating uncertainties in input data (i.e., temperature and precipitation) and parameters in a distributed hydrological model as well as their effects on the runoff response in the Kaidu watershed (a snowmelt–precipitation-driven watershed). In the Bayesian framework, the Soil and Water Assessment Tool (SWAT) is used for providing the basic hydrologic protocols. The Delayed Rejection Adaptive Metropolis (DRAM) algorithm is employed for the inference of uncertainties in input data and model parameters with global and local adaptive strategies. The advanced Bayesian framework can help facilitate the exploration of variation of model parameters due to input data errors, as well as propagation from uncertainties in data and parameters to model outputs in both snow-melting and nonmelting periods. A series of calibration cases corresponding to data errors under different periods are examined. Results show that 1) input data errors can affect the distributions of model parameters as well as parameters’ correlation, implying that data errors could influence the related hydrologic processes as well as their relations; 2) considering input data errors could improve the hydrologic simulation ability for peak streamflows; 3) considering errors of temperature and precipitation data as well as uncertainties of model parameters can provide the best modeling simulation performance in the snow-melting period; and 4) accounting for uncertainties in precipitation data and model parameters can provide the best modeling performance during the nonmelting period. The findings will help enhance hydrological model’s capability for simulating/predicting water resources during different seasons for snowmelt–precipitation-driven watersheds.

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Hongyi Li, Mark S. Wigmosta, Huan Wu, Maoyi Huang, Yinghai Ke, André M. Coleman, and L. Ruby Leung

Abstract

A new physically based runoff routing model, called the Model for Scale Adaptive River Transport (MOSART), has been developed to be applicable across local, regional, and global scales. Within each spatial unit, surface runoff is first routed across hillslopes and then discharged along with subsurface runoff into a “tributary subnetwork” before entering the main channel. The spatial units are thus linked via routing through the main channel network, which is constructed in a scale-consistent way across different spatial resolutions. All model parameters are physically based, and only a small subset requires calibration. MOSART has been applied to the Columbia River basin at ⅙°, ⅛°, ¼°, and ½° spatial resolutions and was evaluated using naturalized or observed streamflow at a number of gauge stations. MOSART is compared to two other routing models widely used with land surface models, the River Transport Model (RTM) in the Community Land Model (CLM) and the Lohmann routing model, included as a postprocessor in the Variable Infiltration Capacity (VIC) model package, yielding consistent performance at multiple resolutions. MOSART is further evaluated using the channel velocities derived from field measurements or a hydraulic model at various locations and is shown to be capable of producing the seasonal variation and magnitude of channel velocities reasonably well at different resolutions. Moreover, the impacts of spatial resolution on model simulations are systematically examined at local and regional scales. Finally, the limitations of MOSART and future directions for improvements are discussed.

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Hong-Yi Li, L. Ruby Leung, Augusto Getirana, Maoyi Huang, Huan Wu, Yubin Xu, Jiali Guo, and Nathalie Voisin

Abstract

Accurately simulating hydrological processes such as streamflow is important in land surface modeling because they can influence other land surface processes, such as carbon cycle dynamics, through various interaction pathways. This study aims to evaluate the global application of a recently developed Model for Scale Adaptive River Transport (MOSART) coupled with the Community Land Model, version 4 (CLM4). To support the global implementation of MOSART, a comprehensive global hydrography dataset has been derived at multiple resolutions from different sources. The simulated runoff fields are first evaluated against the composite runoff map from the Global Runoff Data Centre (GRDC). The simulated streamflow is then shown to reproduce reasonably well the observed daily and monthly streamflow at over 1600 of the world’s major river stations in terms of annual, seasonal, and daily flow statistics. The impacts of model structure complexity are evaluated, and results show that the spatial and temporal variability of river velocity simulated by MOSART is necessary for capturing streamflow seasonality and annual maximum flood. Other sources of the simulation bias include uncertainties in the atmospheric forcing, as revealed by simulations driven by four different climate datasets, and human influences, based on a classification framework that quantifies the impact levels of large dams on the streamflow worldwide.

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Paul A. Dirmeyer, Benjamin A. Cash, James L. Kinter III, Cristiana Stan, Thomas Jung, Lawrence Marx, Peter Towers, Nils Wedi, Jennifer M. Adams, Eric L. Altshuler, Bohua Huang, Emilia K. Jin, and Julia Manganello

Abstract

Global simulations have been conducted with the European Centre for Medium-Range Weather Forecasts operational model run at T1279 resolution for multiple decades representing climate from the late twentieth and late twenty-first centuries. Changes in key components of the water cycle are examined, focusing on variations at short time scales. Metrics of coupling and feedbacks between soil moisture and surface fluxes and between surface fluxes and properties of the planetary boundary layer (PBL) are inspected. Features of precipitation and other water cycle trends from coupled climate model consensus projections are well simulated. Extreme 6-hourly rainfall totals become more intense over much of the globe, suggesting an increased risk for flash floods. Seasonal-scale droughts are projected to escalate over much of the subtropics and midlatitudes during summer, while tropical and winter droughts become less likely. These changes are accompanied by an increase in the responsiveness of surface evapotranspiration to soil moisture variations. Even though daytime PBL depths increase over most locations in the next century, greater latent heat fluxes also occur over most land areas, contributing a larger energy effect per unit mass of air, except over some semiarid regions. This general increase in land–atmosphere coupling is represented in a combined metric as a “land coupling index” that incorporates the terrestrial and atmospheric effects together. The enhanced feedbacks are consistent with the precipitation changes, but a causal connection cannot be made without further sensitivity studies. Nevertheless, this approach could be applied to the output of traditional climate change simulations to assess changes in land–atmosphere feedbacks.

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N. Wanders, A. Bachas, X. G. He, H. Huang, A. Koppa, Z. T. Mekonnen, B. R. Pagán, L. Q. Peng, N. Vergopolan, K. J. Wang, M. Xiao, S. Zhan, D. P. Lettenmaier, and E. F. Wood

Abstract

Dry conditions in 2013–16 in much of the western United States were responsible for severe drought and led to an exceptional fire season in the Pacific Northwest in 2015. Winter 2015/16 was forecasted to relieve drought in the southern portion of the region as a result of increased precipitation due to a very strong El Niño signal. A student forecasting challenge is summarized in which forecasts of winter hydroclimate across the western United States were made on 1 January 2016 for the winter hydroclimate using several dynamical and statistical forecast methods. They show that the precipitation forecasts had a large spread and none were skillful, while anomalously high observed temperatures were forecasted with a higher skill and precision. The poor forecast performance, particularly for precipitation, is traceable to high uncertainty in the North American Multi-Model Ensemble (NMME) forecast, which appears to be related to the inability of the models to predict an atmospheric blocking pattern over the region. It is found that strong El Niño sensitivities in dynamical models resulted in an overprediction of precipitation in the southern part of the domain. The results suggest the need for a more detailed attribution study of the anomalous meteorological patterns of the 2015/16 El Niño event compared to previous major events.

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