Irrigation Impact on Water and Energy Cycle During Dry Years Over the United States Using Convection‐Permitting WRF and a Dynamical Recycling Model

Yang, Zhao; Qian, Yun; Liu, Ying; Berg, Larry K.; Hu, Huancui; Domínguez, Francina; Yang, Ben; Feng, Zhe; Gustafson, William I.; Huang, Maoyi; Tang, Qi

doi:10.1029/2019jd030524

Cited by 49 publications

(47 citation statements)

References 60 publications

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“…For example, an underestimation in evaporative fraction (EF, defined as the ratio of LH to the sum of LH and SH) has been attributed as the dominant source of error in models with a large warming bias. Handling of anthropogenic impacts (or lack thereof), such as neglecting irrigation in the LSMs, has also been attributed to the warming bias (Pei et al., 2016; Qian et al., 2020; Z. Yang et al., 2019). Here, we show that increased LH and decreased SH occur when lateral flow is considered, suggesting that lateral flow might also play an important role in alleviating the warming bias over the SGP.…”

Section: Discussionmentioning

confidence: 99%

Impact of Lateral Flow on Surface Water and Energy Budgets Over the Southern Great Plains—A Modeling Study

et al. 2021

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As the horizontal grid spacing decreases, treatment of hydrologic processes in land surface models (LSMs), such as the lateral flow of surface and subsurface flow, need to be explicitly represented. Unlike previous studies that mainly focused on the mountainous regions, in this study, the offline Weather Research and Forecasting (WRF)‐Hydro model is employed to study the impact of lateral flow on soil moisture and energy fluxes over the relatively flat southern Great Plains (SGP). The vast amount of measurements over the SGP provide a unique opportunity to assess the model behavior. In addition, newly developed land surface properties and input forcing are ingested into the model, in an attempt to reduce uncertainties associated with the initial and boundary forcing and help to identify model deficiencies. Our results show that the more realistic inputs (parameters, soil types, forcing) lead to larger underestimation of latent heat flux and dry bias, indicating the existence of model structural uncertainty (embedded errors) in WRF‐Hydro that need to be characterized to inform future model development efforts. Including lateral flow processes partly mitigates the model deficiencies in representing hydrologic processes and alleviates the dry bias. In particular, both surface and subsurface lateral flow increase soil moisture mainly over the lower elevations, except that subsurface flow also affects soil moisture over steeper terrains. Additional simulations are performed to assess the effect of routing resolution on model results. When LSM resolution is high, noticeable differences in soil moisture are produced between different routing resolutions especially over steep terrain. Whereas when LSM resolution is coarse, differences between routing resolutions become negligible, especially over flat terrain.

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Section: Discussionmentioning

confidence: 99%

Impact of Lateral Flow on Surface Water and Energy Budgets Over the Southern Great Plains—A Modeling Study

et al. 2021

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“…Irrigation water is added as precipitation to mimic the sprinkler system. Further details on the irrigation scheme can be obtained from the previous studies (Ozdogan et al., 2010; Qian et al., 2013; Yang et al., 2019).…”

Section: Methodsmentioning

confidence: 99%

“…Irrigation has immensely contributed to ensuring the food security of about 1.4 billion people in India. A massive expansion of 40 million hectares (mha) in the net irrigated area occurred between 1950 and 2008 (Ambika et al., 2016), which had profound impacts on groundwater abstraction, construction of reservoirs, and water and energy cycles (Asoka et al., 2017; Boucher et al., 2004; Shah et al., 2019; Yang et al., 2019). Previous studies identified a substantial imprint of irrigation on local climate, land surface temperature (LST), and atmospheric aridity (Ambika & Mishra, 2019; Shah et al., 2019; Thiery et al., 2020).…”

Section: Introductionmentioning

confidence: 99%

Modulation of Compound Extremes of Low Soil Moisture and High Vapor Pressure Deficit by Irrigation in India

Ambika

Mishra

2021

JGR Atmospheres

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Compound extremes of low soil moisture and high vapor pressure deficit (VPD) have implications on terrestrial carbon sequestration, vegetation growth, and net primary productivity (NPP). Yet, the role of irrigation on the occurrence of compound extremes remains unexplored in India. Here, we use satellite observations, reanalysis data sets, and high‐resolution simulations from the Weather Research and Forecasting (WRF) model to show that irrigation significantly reduces the frequency of compound extremes of low soil moisture and high VPD in India. The Indo‐Gangetic plain witnessed a significant (p < 0.05) increase in mean annual soil moisture while a substantial reduction in VPD during 1979–2018. Moreover, irrigation caused considerable cooling over the Indo‐Gangetic plain during 1982–2018. The increase in soil moisture and the decline in VPD partly contributed to a substantial rise in Normalized Difference Vegetation Index (NDVI) and NPP. We conducted high‐resolution simulations using the WRF model to examine the role of irrigation in modulating the frequency of compound extremes during 1979–2018. The WRF simulations under the irrigation‐on show a considerably reduced frequency of the compound extremes compared to the irrigation‐off (control) simulations. Irrigation modulates compound extremes by changing the land surface energy budget and planetary boundary layer height. Therefore, there is a need to consider irrigation for reliable projections of compound extremes under a warming climate in intensively irrigated regions.

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“…Leng et al (2013) reported that the irrigation amount simulated by CLM4 can be improved by tuning model parameters (e.g., weighted factor related to target soil moisture) and implementing a more accurate representation of the spatial distribution and intensity of irrigated areas. Additional water from irrigation can lead to increases in the soil water content and lower surface temperature and sensible/latent heat fluxes, with the potential to change boundary layer dynamics and regional scale precipitation patterns (Devanand et al, 2019;Qian et al, 2013;Thiery et al, 2017;Yang et al, 2019). It is therefore imperative to improve the simulation of irrigation which can significantly affect the simulated effects of irrigation on land-atmosphere exchange of water, carbon, and energy fluxes as well as regional/local climates.…”

Section: Irrigationmentioning

confidence: 99%

Validation of the Community Land Model Version 5 Over the Contiguous United States (CONUS) Using In Situ and Remote Sensing Data Sets

et al. 2021

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The Community Land Model (CLM) is an effective tool to simulate the biophysical and biogeochemical processes and their interactions with the atmosphere. Although CLM version 5 (CLM5) constitutes various updates in these processes, its performance in simulating energy, water and carbon cycles over the contiguous United States (CONUS) at scales which land surface changes and hydrometeorological and hydroclimatological applications are more locally relevant is yet to be assessed. In this study, we conducted three simulations at 0.125° during 1979–2018 over the CONUS using different configurations of CLM, namely CLM5‐biogeochemistry (CLM5BGC), CLM4.5BGC, and CLM5‐satellite phenology (CLM5SP). We validated and compared their simulations against multiple remote‐sensed and in situ data sets. Overall, the parametric and structural updates (e.g., carbon cost for nitrogen uptake, variable soil thickness, dry surface layer) in CLM5 improve its ability in capturing terrestrial biogeochemical dynamics. The low evapotranspiration in CLM5BGC is associated with biases in simulating vegetation phenological characteristics rather than soil water limitations. The mismatch between CLM5BGC‐simulated peak leaf area index and reference data can be attributed to CLM5BGC's inability in simulating phenology of trees and grasses. The differences between CLM‐simulated irrigation and reference estimates can be attributed to differences between processes represented in models and in reality, and uncertainties in input and validation data sets. Evaluation against observations at small catchments suggest that hydrologic parameters needed to be calibrated to improve simulations of runoff, especially subsurface runoff. Additional efforts are needed to incorporate spatially distributed plant phenology and physiology parameters and regional‐specific agricultural management practices (e.g., planting and harvest).

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Irrigation Impact on Water and Energy Cycle During Dry Years Over the United States Using Convection‐Permitting WRF and a Dynamical Recycling Model

Cited by 49 publications

References 60 publications

Impact of Lateral Flow on Surface Water and Energy Budgets Over the Southern Great Plains—A Modeling Study

Impact of Lateral Flow on Surface Water and Energy Budgets Over the Southern Great Plains—A Modeling Study

Modulation of Compound Extremes of Low Soil Moisture and High Vapor Pressure Deficit by Irrigation in India

Validation of the Community Land Model Version 5 Over the Contiguous United States (CONUS) Using In Situ and Remote Sensing Data Sets

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