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  • Author or Editor: Liang Chen x
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Meixia Lv
,
Zhuguo Ma
,
Liang Chen
, and
Shaoming Peng

Abstract

The accurate estimation of evapotranspiration (ET) is essential for understanding the land surface–atmosphere interaction; however, current ET products have large uncertainties, and irrigation effects on ET are not well represented. In this study, the monthly ET was reconstructed (ETrecon) from GLDAS land surface models (LSMs) over the Yellow River basin of China, which was achieved by using observation-based precipitation, naturalized streamflow, and downscaled consumed irrigation water from the census annual data via an irrigation scheme. The results showed that the monthly ETrecon series were generally improved relative to the original LSM-based ET, with improvements in the correlation coefficient, Nash–Sutcliffe efficiency, mean absolute error, and root-mean-square error by 0.6%–1.8%, 1.2%–14.6%, 1.3%–21.0%, and 2.1%–20.4%, respectively. The ETrecon results were also superior to the collected ET synthesis products in terms of statistics, with generally higher peak values occurring in ETrecon. Regarding the annual time scale, the ETrecon values were close to the water balance ET values, which have been widely used as benchmark data. The interannual variability in ETrecon was good overall and was associated with the LSM precipitation variability and partitioning of precipitation into ET and runoff. The reconstruction method can provide an alternative ET estimate for other river basins. This study will also be valuable for studies and applications in climate change evaluation, drought assessment, and water resources management.

Full access
Liang Chen
,
Trent W. Ford
, and
Priyanka Yadav

Abstract

Flash droughts are noted by their unusually rapid rate of onset or intensification, which makes it difficult to anticipate and prepare for them, thus resulting in severe impacts. Although the development of flash drought can be associated with certain atmospheric conditions, vegetation also plays a role in propagating flash drought. This study examines the climatology of warm season (March–September) flash drought occurrence in the United States between 1979 and 2014, and quantifies the possible impacts of vegetation on flash drought based on a set of sensitivity experiments using the Community Earth System Model, version 2 (CESM2). With atmospheric nudging, CESM2 well captures historical flash drought. Compared with NASA’s Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), and National Climate Assessment–Land Data Assimilation System (NCA-LDAS), CESM2 shows agreement on the high flash drought frequency in the Great Plains and southeastern United States, but overestimates flash drought occurrence in the Midwest. The vegetation sensitivity experiments suggest that vegetation greening can significantly increase the flash drought frequency in the Great Plains and the western United States during the warm seasons through enhanced evapotranspiration. However, flash drought occurrence is not significantly affected by vegetation phenology in the eastern United States and Midwest due to weak land–atmosphere coupling. In response to vegetation greening, the extent of flash drought also increases, but the duration of flash drought is not sensitive to greening. This study highlights the importance of vegetation in flash drought development, and provides insights for improving flash drought monitoring and early warning.

Full access
Liang Chen
,
Paul A. Dirmeyer
,
Ahmed Tawfik
, and
David M. Lawrence

Abstract

The land surface state can be an important factor in the triggering of precipitation, whose depiction in Earth system models (ESMs) crucially relies on the representation of convective initiation. However, the sensitivity of land-cover change–precipitation feedbacks to different parameterized triggering criteria in ESMs has not been examined. In this study, a new triggering mechanism based on the heated condensation framework (HCF) is implemented in the Community Earth System Model (CESM). A set of land-cover change experiments with different convective triggering conditions are performed to evaluate the influence of convective triggering on land–atmosphere coupling strength and the response of summer afternoon precipitation to land-cover change over North America. Compared with the default parameterization, which depends on a CAPE threshold, the HCF trigger shows an improvement in the diurnal timing of summer precipitation but larger dry biases over much of the study area. With the HCF trigger, CESM exhibits weakened coupling strength between soil moisture and surface turbulent fluxes over the Great Plains. The surface temperature deficit, as an additional triggering criterion in HCF, is not significantly coupled with surface fluxes over the central Great Plains despite strong latent heat–CAPE coupling. In contrast to the CAPE-trigger simulations, which indicate increased precipitation over the Great Plains after agricultural expansion, the HCF-trigger simulations show significantly increased afternoon precipitation only over the northern plains, which is mainly associated with more frequent deep convection. The discrepancies suggest caveats when investigating the impacts of land-cover change on precipitation, because the magnitude and spatial patterns of precipitation change can be greatly affected by the treatment of convection in ESMs.

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Weijing Chen
,
Chunlin Huang
,
Zong-Liang Yang
, and
Ying Zhang

Abstract

Data assimilation provides a practical way to improve the accuracy of soil moisture simulation by integrating a land surface model and satellite data. This study establishes a multisource remote sensing data assimilation framework by incorporating a simultaneous state and parameter estimation method to acquire an accurate estimation of the soil moisture over the Tibetan Plateau. The brightness temperature of the Advanced Microwave Scanning Radiometer 2 (AMSR2) is directly assimilated into the coupled system of the Common Land Model (CoLM) and a microwave radiative transfer model (RTM) to improve the soil moisture simulation. The Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature product and the Beijing Normal University (BNU) leaf area index product are employed to not only improve the estimation of temperature and vegetation variables from the CoLM, but they also provide more accurate background information for the RTM during the brightness temperature assimilation. In situ measurements from the Naqu network are used to evaluate the results. The model simulation showed an obvious underestimation of soil moisture and overestimation of soil temperature, which was alleviated by the assimilation experiments, particularly in the shallow soil layers. The estimated parameters also showed advantages in the soil moisture simulation when compared with the default parameters. The assimilation experiment presents promising results in the combination of model and multisource remote sensing data for estimating soil moisture over the complex mountainous region in Tibet.

Full access
Trent W. Ford
,
Liang Chen
, and
Justin T. Schoof

Abstract

Monthly to seasonal precipitation extremes, both flood and drought, are important components of regional climates worldwide, and are the subjects of numerous investigations. However, variability in and transition between precipitation extremes, and associated impacts are the subject of far fewer studies. Recent such events in the Midwest region of the United States, such as the 2011–12 flood to drought transition in the upper Mississippi River basin and the flood to drought transition experienced in parts of Kentucky, Ohio, Indiana, and Illinois in 2019, have sparked concerns of increased variability and rapid transitions between precipitation extremes and compounded economic and environmental impacts. In response to these concerns, this study focuses on characterizing variability and change in Midwest precipitation extremes and transitions between extremes over the last 70 years. Overall we find that the Midwest as a region has gotten wetter over the last seven decades, and that in general the annual maximum and median wetness, defined using the standardized precipitation index (SPI), have increased at a larger magnitude than the annual minimum. We find large areas of the southern Midwest have experienced a significant increase in the annual SPI range and associated magnitude of transition between annual maximum and minimum SPI. We additionally find wet to dry transitions between extremes have largely increased in speed (i.e., less time between extremes), while long-term changes in transition frequency are more regional within the Midwest.

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Ren Wang
,
Pierre Gentine
,
Longhui Li
,
Jianyao Chen
,
Liang Ning
,
Linwang Yuan
, and
Guonian Lü

Abstract

Land–atmosphere interactions play an important role in the changes of extreme climates, especially in hot spots of land–atmosphere coupling. One of the linkages in land–atmosphere interactions is the coupling between air temperature and surface energy fluxes associated with soil moisture variability, vegetation change, and human water/land management. However, existing studies on the coupling between hot extreme and surface energy fluxes are mainly based on the parameterized solution of climate model, which might not dynamically reflect all changes in the surface energy partitioning due to the effects of vegetation physiological control and human water/land management. In this study, for the first time, we used daily weather observations to identify hot spots where the daily hot extreme (i.e., the 99th percentile of maximum temperature, Tq99th) rises faster than local mean temperature (Tmean) during 1975–2017. Furthermore, we analyzed the relationship between the trends in temperature hot extreme relative to local average (ΔTq99th/ΔTmean) and the trends in evaporative fraction (ΔEF), i.e., the ratio of latent heat flux to surface available energy, using long-term latent and sensible heat fluxes, which are informed by atmospheric boundary layer theory, machine learning, and ground-based observations of flux towers and weather stations. Hot spots of increase in ΔTq99th/ΔTmean are identified to be Europe, southwestern North America, northeast Asia, and southern Africa. The detected significant negative correlations between ΔEF and ΔTq99th/ΔTmean suggested that the hot spot regions are typically affected by annual/summer surface dryness. Our observation-driven findings have great implications in providing realistic observational evidence for the extreme climate change accelerated by surface energy partitioning.

Open access
Zhenghui Xie
,
Fei Yuan
,
Qingyun Duan
,
Jing Zheng
,
Miaoling Liang
, and
Feng Chen

Abstract

This paper presents a methodology for regional parameter estimation of the three-layer Variable Infiltration Capacity (VIC-3L) land surface model with the goal of improving the streamflow simulation for river basins in China. This methodology is designed to obtain model parameter estimates from a limited number of calibrated basins and then regionalize them to uncalibrated basins based on climate characteristics and large river basin domains, and ultimately to continental China. Fourteen basins from different climatic zones and large river basins were chosen for model calibration. For each of these basins, seven runoff-related model parameters were calibrated using a systematic manual calibration approach. These calibrated parameters were then transferred within the climate and large river basin zones or climatic zones to the uncalibrated basins. To test the efficiency of the parameter regionalization method, a verification study was conducted on 19 independent river basins in China. Overall, the regionalized parameters, when evaluated against the a priori parameter estimates, were able to reduce the model bias by 0.4%–249.8% and relative root-mean-squared error by 0.2%–119.1% and increase the Nash–Sutcliffe efficiency of the streamflow simulation by 1.9%–31.7% for most of the tested basins. The transferred parameters were then used to perform a hydrological simulation over all of China so as to test the applicability of the regionalized parameters on a continental scale. The continental simulation results agree well with the observations at regional scales, indicating that the tested regionalization method is a promising scheme for parameter estimation for ungauged basins in China.

Full access
Yanping Li
,
Kit Szeto
,
Ronald E. Stewart
,
Julie M. Thériault
,
Liang Chen
,
Bohdan Kochtubajda
,
Anthony Liu
,
Sudesh Boodoo
,
Ron Goodson
,
Curtis Mooney
, and
Sopan Kurkute

Abstract

A devastating, flood-producing rainstorm occurred over southern Alberta, Canada, from 19 to 22 June 2013. The long-lived, heavy rainfall event was a result of complex interplays between topographic, synoptic, and convective processes that rendered an accurate simulation of this event a challenging task. In this study, the Weather Research and Forecasting (WRF) Model was used to simulate this event and was validated against several observation datasets. Both the timing and location of the model precipitation agree closely with the observations, indicating that the WRF Model is capable of reproducing this type of severe event. Sensitivity tests with different microphysics schemes were conducted and evaluated using equitable threat and bias frequency scores. The WRF double-moment 6-class microphysics scheme (WDM6) generally performed better when compared with other schemes. The application of a conventional convective/stratiform separation algorithm shows that convective activity was dominant during the early stages, then evolved into predominantly stratiform precipitation later in the event. The HYSPLIT back-trajectory analysis and regional water budget assessments using WRF simulation output suggest that the moisture for the precipitation was mainly from recycling antecedent soil moisture through evaporation and evapotranspiration over the Canadian Prairies and the U.S. Great Plains. This analysis also shows that a small fraction of the moisture can be traced back to the northeastern Pacific, and direct uptake from the Gulf of Mexico was not a significant source in this event.

Full access
Paul A. Dirmeyer
,
Liang Chen
,
Jiexia Wu
,
Chul-Su Shin
,
Bohua Huang
,
Benjamin A. Cash
,
Michael G. Bosilovich
,
Sarith Mahanama
,
Randal D. Koster
,
Joseph A. Santanello
,
Michael B. Ek
,
Gianpaolo Balsamo
,
Emanuel Dutra
, and
David M. Lawrence

Abstract

This study compares four model systems in three configurations (LSM, LSM + GCM, and reanalysis) with global flux tower observations to validate states, surface fluxes, and coupling indices between land and atmosphere. Models clearly underrepresent the feedback of surface fluxes on boundary layer properties (the atmospheric leg of land–atmosphere coupling) and may overrepresent the connection between soil moisture and surface fluxes (the terrestrial leg). Models generally underrepresent spatial and temporal variability relative to observations, which is at least partially an artifact of the differences in spatial scale between model grid boxes and flux tower footprints. All models bias high in near-surface humidity and downward shortwave radiation, struggle to represent precipitation accurately, and show serious problems in reproducing surface albedos. These errors create challenges for models to partition surface energy properly, and errors are traceable through the surface energy and water cycles. The spatial distribution of the amplitude and phase of annual cycles (first harmonic) are generally well reproduced, but the biases in means tend to reflect in these amplitudes. Interannual variability is also a challenge for models to reproduce. Although the models validate better against Bowen-ratio-corrected surface flux observations, which allow for closure of surface energy balances at flux tower sites, it is not clear whether the corrected fluxes are more representative of actual fluxes. The analysis illuminates targets for coupled land–atmosphere model development, as well as the value of long-term globally distributed observational monitoring.

Open access