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L. Ruby Leung
and
Yun Qian

Abstract

This paper examines the sensitivity of regional climate simulations to increasing spatial resolution via nesting by means of a 20-yr simulation of the western United States at 40-km resolution and a 5-yr simulation at 13-km resolution for the Pacific Northwest and California. The regional simulation at 40-km resolution shows a lack of precipitation along coastal hills, good agreement with observations on the windward slopes of the Cascades and Sierra Nevada, but overprediction on the leeside and the basins beyond. Snowpack is grossly underpredicted throughout the western United States when compared against snowpack telemetry (snotel) observations. During winter, higher spatial resolution mainly improves the precipitation simulation in the coastal hills and basins. Along the Cascades and the Sierra Nevada range, precipitation is strongly amplified at the higher spatial resolution. Higher resolution generally improves the spatial distribution of precipitation to yield a higher spatial correlation between simulations and observations. During summer, higher resolution improves not only the spatial distribution but also the regional mean precipitation.

In the Olympic Mountains and along the Coastal Range, increased precipitation at higher resolution reflects mainly a shift from light to heavy precipitation events. In the Cascades and Sierra Nevada, increased precipitation is mainly associated with more frequent heavy precipitation at higher resolution. Changes in precipitation from 40- to 13-km resolution depend on synoptic conditions such as wind direction and moisture transport. The use of higher spatial resolution improves snowpack more than precipitation. However, results presented in this paper suggest that accuracy in the snow simulation is also limited by factors such as deficiencies in the land surface model or biases in other model variables.

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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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Huancui Hu
,
L. Ruby Leung
, and
Zhe Feng

ABSTRACT

Warm-season rainfall associated with mesoscale convective systems (MCSs) in the central United States is characterized by higher intensity and nocturnal timing compared to rainfall from non-MCS systems, suggesting their potentially different footprints on the land surface. To differentiate the impacts of MCS and non-MCS rainfall on the surface water balance, a water tracer tool embedded in the Noah land surface model with multiparameterization options (WT-Noah-MP) is used to numerically “tag” water from MCS and non-MCS rainfall separately during April–August (1997–2018) and track their transit in the terrestrial system. From the water-tagging results, over 50% of warm-season rainfall leaves the surface–subsurface system through evapotranspiration by the end of August, but non-MCS rainfall contributes a larger fraction. However, MCS rainfall plays a more important role in generating surface runoff. These differences are mostly attributed to the rainfall intensity differences. The higher-intensity MCS rainfall tends to produce more surface runoff through infiltration excess flow and drives a deeper penetration of the rainwater into the soil. Over 70% of the top 10th percentile runoff is contributed by MCS rainfall, demonstrating its important contribution to local flooding. In contrast, lower-intensity non-MCS rainfall resides mostly in the top layer and contributes more to evapotranspiration through soil evaporation. Diurnal timing of rainfall has negligible effects on the flux partitioning for both MCS and non-MCS rainfall. Differences in soil moisture profiles for MCS and non-MCS rainfall and the resultant evapotranspiration suggest differences in their roles in soil moisture–precipitation feedbacks and ecohydrology.

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Huancui Hu
,
Zhe Feng
, and
L. Ruby Leung

Abstract

Mesoscale convective systems (MCSs) that are clustered in time and space can have a broader impact on flooding because they have larger area coverage than that of individual MCSs. The goal of this study is to understand the flood likelihood associated with MCS clusters. To achieve this, floods in the Storm Events Database in April–August of 2007–17 are matched with clustered MCSs identified from a high-resolution MCS dataset and terrestrial conditions in a land surface dataset over the central-eastern United States. Our analysis indicates that clustered MCSs preferentially occurring in April–June are more effective at producing floods, which also last longer due to the greater rainfall per area and wetter initial soil conditions and, hence, produce greater runoff per area than nonclustered MCSs. Similar increases of flood occurrence with cluster-total rainfall size and wetter soils are also observed for each MCS cluster, especially for the overlapping rainfall areas within each cluster. These areas receive rainfall from multiple MCSs that progressively wet the soils and are therefore associated with higher flood likelihood. This study underscores the importance to understand clustered MCSs to better understand flood risks and their future changes.

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Hoang Tran
,
Yilin Fang
,
Zeli Tan
,
Tian Zhou
, and
L. Ruby Leung

Abstract

The Lower Mississippi River basin (LMRB) has experienced significant changes in land cover and is one of the most vulnerable regions to hurricanes in the United States. Here, we study the impacts of land-cover change on the hydrologic response to Hurricane Ida in LMRB. By using an integrated surface–subsurface hydrologic model, Energy Exascale Earth System Model (E3SM) Land Model coupled with the three-dimensional ParFlow subsurface flow model (ELM-ParFlow), we simulate the effects of land-cover change on the flood volume and peak timing induced by rainfall from Hurricane Ida. The results show that land-cover changes from 1850 to 2015, which resulted in a smoother surface and less vegetation, exacerbated both flood peak time and volume induced by Hurricane Ida. The effects of land-cover changes can be decomposed into two mechanisms: a smoother surface routes more water faster to a watershed outlet and less vegetation allows more water to contribute to surface runoff. By comparing scenarios in which the two mechanisms were isolated, we found that changes in soil moisture due to vegetation cover change have more dominant effects on floods in the southern part and changes in Manning’s coefficient have the largest effect on floods in the northern part of the LMRB. The study provides important insights into the complex relationship between land-use, land-cover, and hydrologic processes in coastal regions.

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Huancui Hu
,
L. Ruby Leung
,
Zhe Feng
, and
James Marquis

Abstract

Moisture recycling, the contribution of local evapotranspiration (ET) to precipitation, has been studied using bulk models assuming a well-mixed atmosphere. The latter is inconsistent with a climatologically stratified atmosphere that slants across latitudes. Reconciling the two views requires an understanding of overturning associated with different weather systems. In this study, we aim to better understand moisture recycling associated with mesoscale convective systems (MCSs). Using a convection-permitting WRF simulation equipped with water vapor tracers (WRF-WVT), we tag moisture from terrestrial ET in the U.S. Southern Great Plains during May 2015, when more than 20 MCS events occurred and produced significant precipitation and flooding. Water budget analysis reveals that approximately 76% of terrestrial ET is advected away from the region while the remaining 24% of terrestrial ET is “pumped” upward within the region, accounting for 12% of precipitation. Moisture recycling peaks during early night hours (1800–2400 LT) due to the mixing of the daytime accumulated ET by active convection. By focusing on five “diurnally driven” MCSs with less large-scale circulation influence than other MCSs during the same period, we find an upright pumping of terrestrial ET at the MCS initiation and development stages, which diverges into two branches during the MCS mature and decaying stages. One branch in the upper level advects the ET-sourced moisture downstream, while the other branch in the mid-to-upper level contributes to the trailing precipitation upstream. Overall, our analysis depicts a pumping mechanism associated with MCSs that mixes local ET vertically, highlighting its specific contributions to enhancing convective precipitation processes.

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L. Ruby Leung
,
Yun Qian
,
Jongil Han
, and
John O. Roads

Abstract

Estimating water budgets of river basins in the western United States is a challenge because of the effects of complex terrain and lack of comprehensive observational datasets. This study aims at comparing different estimates of cold season water budgets of the Columbia River (CRB) and Sacramento–San Joaquin River (SSJ) basins. An intercomparison was performed based on the NCEP–NCAR reanalysis I (NRA1), NCEP–Department of Energy (DOE) reanalysis II (NRA2), ECMWF reanalyses (ERA), regional climate simulations produced by the fifth-generation Pennsylvania State University–NCAR Mesoscale Model (MM5) and NCEP Regional Spectral Model (RSM) driven by the reanalyses, and two precipitation datasets gridded at 2.5° and ⅛° for 7 yr between 1986 and 1993. The purpose of the intercomparison was to understand the effects of spatial resolution, model configuration and associated parameterizations, and large-scale conditions on basin-scale water budgets.

Overall, the regional simulations were superior to the global reanalyses in terms of the spatial distribution of mean precipitation and precipitation anomalies. However, cold season precipitation was generally amplified in the regional models. Basin mean precipitation was typically higher than observed in the regional models and less than observed in the reanalyses. The amplification was the largest in the RSM simulation driven by NRA2, which had the biggest difference between the reanalyzed and regional simulation of basin mean precipitation. ERA and the MM5 simulations driven by ERA provided the best basin mean precipitation estimates when compared to the ⅛° observational dataset.

Large differences remain in estimating the water budgets of western river basins, such as CRB and SSJ. In terms of atmospheric moisture flux, there was a 15%–20% difference between the global reanalyses. In terms of basin mean precipitation, differences among the reanalyses, regional simulations, and observations were as large as 100% of the overall mean. There were large differences in spatial distribution of precipitation between the RSM and MM5 simulations because of terrain representations and other factors. Runoff and snowpack showed the most sensitivity to model differences in spatial resolution, physics parameterizations, and model representations. Better simulations of basin mean precipitation did not necessarily imply superior simulations of runoff or snowpack.

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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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Xiaodong Chen
,
L. Ruby Leung
,
Yang Gao
, and
Ying Liu

Abstract

Sea surface temperature (SST) significantly modulates the precipitation and temperature over land, with important consequences on land surface processes such as snowpack. Compared to the impact of remote SST, the effect of nearshore/local SST is less well understood. In this study, the impact of local SST on the mountain snowpack of the U.S. West Coast is investigated using two 6-km regional climate simulations driven by the same lateral boundary conditions but with time-varying versus time-invariant and warmer local SSTs during 2003–15. Results show that local SST warming leads to warmer winter with more precipitation over the mountains. Meanwhile, the removal of SST temporal variability results in reduced temperature variability but increased precipitation variability. As a result, winter snow accumulation decreases by ~200 mm per season in the Cascade Mountains in the north but increases by ~100 mm per season in the Sierra Nevada in the south. Such a dipole response results from the competing effects of precipitation and temperature change at different elevations and are amplified by the enhanced atmospheric river moisture transport. To further delineate the relative contributions of different meteorological factors to the snowpack response, two neural network models were developed to predict the snow behaviors at seasonal and monthly scales. These models reveal the dominant influence of the total amount and the average temperature of precipitation on the snowpack response. These findings highlight the sensitivity of mountain snowpack to local SST in the western United States and underscore the importance of local SST and atmospheric rives to accurate snowpack estimations for water management.

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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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