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  • View in gallery

    Measurement sites over the SGP. The red rectangle (30°–39°N, 102.25°–93.25°W) represents the region over which some statistics are conducted.

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    Model domain and topography (elevation: m). The red rectangle, as in Fig. 1, represents the region over which some statistics are conducted for Figs. 9, 11, and 15.

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    (top) MODIS potential irrigation area fraction (%) and (bottom) Noah land cover map of the study domain.

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    (top) Spatial distribution of irrigation rate (mm day−1) for JJA of 2006 and (bottom) monthly mean irrigation rate [mm (4 h)−1] averaged over 30°–39°N, 102.25°–93.25°W for 2006 and 2007.

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    WRF (12 km) simulated and observed (PRISM/UW ⅛°, NLDAS-2 ⅛°, and ABRFC 4 km) monthly mean precipitation (mm day−1) in June 2007.

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    Comparison of (upper) WRF-simulated and (lower) NLDAS-2 (left) latent heat (LH), (middle) sensible heat (SH), and (right) skin temperature (Ts) for (a) JJA 2006 and (b) JJA 2007.

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    Comparison between WRF simulations and observations of latent heat (LH), sensible heat (SH), fractional wetness index (FWI), surface air temperature (T), and precipitation (P) at pixels where observations are available for JJA of 2006 and 2007. Three color bars respectively represent observation (obs), control simulation with irrigation off (con), and simulation with irrigation on (irreg).

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    Irrigation-induced changes in LH flux (W m−2), SH flux (W m−2), T2m (K), and Q2m (g kg−1) for JJA 2006.

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    Irrigation-induced monthly-mean and JJA diurnal cycle changes in LH (W m−2), SH (W m−2), T2m (K), Q2m (g kg−1), total precipitation Ptot (mm day−1), and convective precipitation Pcon (mm day−1) averaged over 30°–39°N, 102.25°–93.25°W.

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    Irrigation-induced PBL height change (m), LCL change (m), and difference of PBL height decrease (ΔZi) and LCL decrease (ΔZLCl).

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    Scatterplots of 5-day mean LCL with PBL height, SH, LH, and LH/(SH+LH) (averaged over 30°–39°N, 102.25°–93.25°W).

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    Vertical profiles of (a) potential temperature (K), (b) specific humidity (g kg−1), (c) moist static energy (103 J kg−1), and (d) relative humidity (%) averaged over the domain 34°–43°N, 104.5°–95.5°W for 1300–1500 LT for JJA 2006 for the control (solid) and irrigation (dashed) simulation.

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    As in Fig. 12, but for nighttime.

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    Irrigation-induced total soil moisture content (layer from 0 to 2 m) changes (mm) from May to September 2006. The depths of four soil layers in the Noah land surface model are 0.1 m, 0.4 m, 0.6 m, and 1.0 m.

  • View in gallery

    Scatterplots of 5-day mean soil moisture (first soil layer) with PBL height, SH, LH, and LCL (averaged over 30°–39°N, 102.25°–93.25°W).

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A Modeling Study of Irrigation Effects on Surface Fluxes and Land–Air–Cloud Interactions in the Southern Great Plains

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  • 1 Pacific Northwest National Laboratory, Richland, Washington
  • | 2 Department of Atmospheric Sciences, Nanjing University, Nanjing, China
  • | 3 Pacific Northwest National Laboratory, Richland, Washington
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Abstract

In this study, the authors incorporate an operational-like irrigation scheme into the Noah land surface model as part of the Weather Research and Forecasting Model (WRF). A series of simulations, with and without irrigation, is conducted over the Southern Great Plains (SGP) for an extremely dry (2006) and wet (2007) year. The results show that including irrigation reduces model bias in soil moisture and surface latent heat (LH) and sensible heat (SH) fluxes, especially during a dry year. Irrigation adds additional water to the surface, leading to changes in the planetary boundary layer. The increase in soil moisture leads to increases in the surface evapotranspiration and near-surface specific humidity but decreases in the SH and surface temperature. Those changes are local and occur during daytime. There is an irrigation-induced decrease in both the lifting condensation level (ZLCL) and mixed-layer depth. The decrease in ZLCL is larger than the decrease in mixed-layer depth, suggesting an increasing probability of shallow clouds. The simulated changes in precipitation induced by irrigation are highly variable in space, and the average precipitation over the SGP region only slightly increases. A high correlation is found among soil moisture, SH, and ZLCL. Larger values of soil moisture in the irrigated simulation due to irrigation in late spring and summer persist into the early fall, suggesting that irrigation-induced soil memory could last a few weeks to months. The results demonstrate the importance of irrigation parameterization for climate studies and improve the process-level understanding on the role of human activity in modulating land–air–cloud interactions.

Corresponding author address: Yun Qian, PNNL, 3200 Q Ave., Richland, WA 99352. E-mail: yun.qian@pnnl.gov

Abstract

In this study, the authors incorporate an operational-like irrigation scheme into the Noah land surface model as part of the Weather Research and Forecasting Model (WRF). A series of simulations, with and without irrigation, is conducted over the Southern Great Plains (SGP) for an extremely dry (2006) and wet (2007) year. The results show that including irrigation reduces model bias in soil moisture and surface latent heat (LH) and sensible heat (SH) fluxes, especially during a dry year. Irrigation adds additional water to the surface, leading to changes in the planetary boundary layer. The increase in soil moisture leads to increases in the surface evapotranspiration and near-surface specific humidity but decreases in the SH and surface temperature. Those changes are local and occur during daytime. There is an irrigation-induced decrease in both the lifting condensation level (ZLCL) and mixed-layer depth. The decrease in ZLCL is larger than the decrease in mixed-layer depth, suggesting an increasing probability of shallow clouds. The simulated changes in precipitation induced by irrigation are highly variable in space, and the average precipitation over the SGP region only slightly increases. A high correlation is found among soil moisture, SH, and ZLCL. Larger values of soil moisture in the irrigated simulation due to irrigation in late spring and summer persist into the early fall, suggesting that irrigation-induced soil memory could last a few weeks to months. The results demonstrate the importance of irrigation parameterization for climate studies and improve the process-level understanding on the role of human activity in modulating land–air–cloud interactions.

Corresponding author address: Yun Qian, PNNL, 3200 Q Ave., Richland, WA 99352. E-mail: yun.qian@pnnl.gov

1. Introduction

Anthropogenic impacts on climate have received tremendous attention in the climate research community and the public during the past several decades (Solomon et al. 2007). Human activities influence the local and global climate via two ways: changing the atmospheric composition (e.g., greenhouse gases and aerosol) and land surface (e.g., urbanization, irrigation, and deforestation; e.g., Pielke and Avissar 1990; Kanamaru and Kanamitsu 2008). Land use and land cover change (LULCC) induced by human activities could modify biogeophysical, biogeochemical, and biogeographic properties of the terrestrial surface, which can in turn affect atmospheric processes (Pielke et al. 2007, 2011). The representations of LULCC in climate models used in previous Intergovernmental Panel on Climate Change assessments were not sufficient, partially because the contribution of LULCC to direct biogeophysical radiative forcing was treated as small relative to other forcing terms. However, as pointed out by Pielke et al. (2011), LULCC is a highly regionalized phenomenon with regional-scale climate impacts. Its role on climate and climate change should not be limited to radiative forcing and cannot be assessed adequately in a globally averaged sense.

Irrigation has been the main method of meeting the water demand and ensuring high crop yields within regions that lack sufficient precipitation. Irrigation accounts for about 70% of the global freshwater withdrawals and 90% of consumptive water uses (Siebert et al. 2010). Irrigation practice, which is identified as one of major human interferences on land surface, has both direct and indirect impacts on local, regional, and even global climate (e.g., Ozdogan et al. 2010; Sorooshian et al. 2011). Based on a simple soil moisture model, Mahmood and Hubbard (2002) find that irrigated croplands transport much more water to the atmosphere via evapotranspiration than natural grasslands in unaltered landscapes. Many studies have quantitatively investigated the impact of irrigation on weather, climate, and hydrology at local, regional, and continental scales (e.g., Puma and Cook 2010). Based on the modeling results (Sorooshian et al. 2011), irrigation could lead to a 3°–7°C surface air cooling over the California Central Valley in summer (June–August). While the behavior of the results from different regional models used in their studies [e.g., the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5), the Regional Atmospheric Modeling System (RAMS), and Regional Climate Model version 3 (RegCM3)] varied depending on the irrigation configurations, model physics, and spatial resolutions, many of the reported studies have been able to simulate some aspects of irrigation-related feedback mechanisms (Segal et al. 1998; Adegoke et al. 2003; de Rosnay et al. 2003; Haddeland et al. 2006; Kueppers et al. 2007).

In most of the studies cited above, the amount of water that should be realistically added into the model soil through irrigation processes was not dynamically determined, so the simulations may overestimate or underestimate the impact of irrigation. As Sorooshian et al. (2011) suggested, applying a realistic irrigation scheme in the climate model is important in order to obtain a more reliable and quantitative estimate on the effect of irrigation on climate. Ozdogan et al. (2010) and Sacks et al. (2009) implemented a more realistic dynamic irrigation scheme to the Noah land surface model and the Community Land Model, respectively. They demonstrated that irrigation could affect local to regional climate through modulating land surface states and fluxes. Lee et al. (2011) further illustrated that irrigation-induced changes in surface fluxes could lead to cooling of both the surface and lower troposphere over the irrigated regions, which in turn could result in a weakening of the strong upper-level westerly jet over eastern Europe, the Middle East, and central Asia.

The U.S. southern Great Plains (SGP) are characterized climatologically by a significant atmospheric moisture gradient increasing from southwest to northeast, related to the geographic proximity of the Gulf of Mexico moisture source and the Rocky Mountains moisture barrier (Rasmusson 1971). The amount of moisture transported from the Gulf of Mexico to the SGP region depends largely on the nature of the atmospheric boundary layer development along the trajectory. Different types of landscape variability such as topography, soil moisture, albedo, and vegetation, as well as sea surface temperature (e.g., Mei and Wang 2011) and tropical cyclones (e.g., Evans et al. 2011), could significantly affect boundary layer and cloud structures along this trajectory by altering the partitioning of sensible and latent heat fluxes from the surface to the top of the boundary layer (Lamb et al. 2012). Also, additional moisture may be added to the boundary layer through the evaporation of surface water and plant transpiration of soil moisture, further altering the state of the boundary layer.

The SGP region is representative of the interior regions of many continents where shallow cumuli form regularly during quiet synoptic conditions with strong surface forcing and well-developed boundary layers. Recent studies have shown, however, that climate models tend to have difficulty in accurately representing the moisture transport and occurrence of convective clouds over the SGP region, leading to errors in prediction of cloud cover, radiative fluxes, and the water cycle (e.g., Cheinet et al. 2005; Klein et al. 2006). These errors may be related to the poor representation of the convective triggering and/or boundary layer structure, both of which are susceptible to improper characterization of land–atmospheric exchanges.

On the other hand, remarkable changes in cultivation (e.g., crop types and farming techniques) of farmland together with insufficient precipitation in the warm season require extensive irrigation to sustain crop growth over large areas within the SGP region. For example, Barnston and Schickendanz (1984) investigated the effect of irrigation on warm-season precipitation in the SGP based on the observational data and found that irrigation increased precipitation in the Texas Panhandle when the synoptic condition provided low-level convergence and uplift, such that the additional moisture produced by irrigation was allowed to ascend to cloud base. The irrigation-induced modification of the surface water balance is apparent, but its impacts on the cloud and water cycle under different climate regimes are largely unknown. Few modeling studies have been done in this region to investigate the impact of irrigation on boundary layer, convective cloud, and regional climate.

In this study, we incorporated an operational-like irrigation scheme, which is informed by satellite-measured potential irrigation area data, into the Noah land surface model utilized as part of the Weather Research and Forecasting community model (WRF). Daily irrigation is triggered in early morning when root-zone soil moisture availability is below a specific threshold over croplands or pastures during the growing season. We conducted two sets of simulations using WRF, with and without irrigation included, over the SGP for an extreme dry (2006) and wet (2007) summer. The SGP also has a number of meteorological networks that 1) provide a rich dataset at the surface, subsurface, and in the atmosphere; 2) offer an exciting opportunity to evaluate the model performance in simulating the surface heat and water exchanges; and 3) provide the potential to examine the influences of land surface change on the thermodynamic structure of the atmospheric boundary layer, cloud formation, and water cycle. The objectives of our study are 1) to evaluate the WRF performance in simulating the regional climate and surface water and energy budgets over the SGP region; 2) to investigate whether the use of a more realistic irrigation scheme will improve the simulation of surface energy and heat fluxes and state variables (e.g., temperature and humidity); and 3) to investigate the impact of irrigation on boundary layer structure, convective clouds, and local climate in a regional modeling framework. The accomplishment of these objectives will help us to better understand and more accurately represent the interactions between the boundary layer, cumulus convection, soil moisture, and biosphere. The observational datasets and regional climate model, including the irrigation scheme, and simulation setup are described in section 2. Section 3 presents the model evaluation against the observations and details on how irrigation-added water affects the surface fluxes, cloud development, and precipitation cycle. Conclusions and discussion are provided in section 4.

2. Methodology and data

To evaluate the impacts of irrigation on surface fluxes and land–air interactions, a set of numerical experiments are conducted, using WRF over the SGP region (Fig. 1), in which intensive observations on surface fluxes and state variables are available. The year 2006 (2007) is selected to represent the dry (wet) summer conditions for the numerical experiments. These years provide a unique opportunity to examine hydrological extremes in the central United States because there are no other examples of two such highly contrasting precipitation extremes occurring in consecutive years in the SGP in recorded history (Dong et al. 2011).

Fig. 1.
Fig. 1.

Measurement sites over the SGP. The red rectangle (30°–39°N, 102.25°–93.25°W) represents the region over which some statistics are conducted.

Citation: Journal of Hydrometeorology 14, 3; 10.1175/JHM-D-12-0134.1

a. Observations

The observational sites are illustrated in Fig. 1. The red rectangle in Fig. 1 indicates the region in which the atmospheric water budgets will be calculated and analyzed. The domain is instrumented with a number of observational networks from which land surface fluxes and state variables could be retrieved. These include networks deployed by the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) program and the state of Oklahoma. The ARM network consists of eddy correlation (EC) and energy balance Bowen ratio (EBBR) stations that measure land–atmosphere exchanges of latent heat (LH) and sensible heat (SH) fluxes (http://www.arm.gov/instruments/ecor; http://www.arm.gov/instruments/ebbr; Brotzge and Crawford 2003; Fischer et al. 2007; Gao et al. 1998; Riley et al. 2009); the Soil Water and Temperature System (SWATS), which measures shallow layer soil moisture and temperature (http://www.arm.gov/instruments/swats; Schneider et al. 2003) and is collocated with rain gauges at the SWATS locations (SWATSPCP); and the Surface Meteorological Observation System (SMOS; http://www.arm.gov/instruments/smos) for collecting surface meteorological conditions. The Oklahoma Mesonet (OKM; http://www.arm.gov/instruments/okm) covers all of Oklahoma and collects surface meteorological conditions as well as shallow layer soil moisture and temperature using an approach similar to the SMOS and SWATS stations (Illston et al. 2008).

For comparison with model simulations, LH and SH fluxes from the EC and EBBR stations, as well as soil moisture from SWATS and OKM, were processed and analyzed for the years 2006 and 2007. Additional processing of the SWATS and OKM soil moisture data is required before comparisons with WRF can be made. The soil moisture data were first converted to fractional wetness index (FWI; unitless) following Eq. (4) in Illston et al. (2008) as
e1
where ΔTref is the reference temperature difference measured by a given soil moisture sensor and ΔTd and ΔTw are constants determined through instrument-specific calibration. The FWI represents the relative soil moisture status with respect to field capacity and varies from 0 for very dry soils to 1 for saturated soils. For details on the FWI algorithm, interested readers are referred to Illston et al. (2008). FWI values were then filtered based on quality control flags provided by the ARM data team following a similar procedure described in appendix A of Xia et al. (2013b), in which a long-term hourly soil temperature dataset from OKM during the period of 1999–2010 was developed and used for validating phase 2 of the North American Land Data Assimilation System (NLDAS-2) simulations. Owing to the long-term memory in soil moisture (therefore FWI) and large number of OKM stations (Table 1), we did not fill the gaps in FWI. However, if for any station the number of missing values is more than 10 days (i.e., 480 points) in one given month, data in that month from the station are discarded when statistics were calculated.
Table 1.

Average number of stations with good data points over JJA for deriving monthly statistics in observational networks over SGP. The average number of stations with good data points (Ngood) is calculated as follows: the number of stations available at each time interval for each sensor is counted as Ns,t, where s stands for the sensor number and t stands for the time of measurements, so Ngood = (∑ts Ns,t)/nt, where nt represents all of the points available during the JJA each year. Although measurements of temperature and precipitation were provided from OKM, SWATSPCP, and SMOS stations, their statistics were not used in validation in this study. Rather, temperature and precipitation from the PRISM dataset were used as the validation dataset in Fig. 7 to provide a wall to wall comparison with the model.

Table 1.

The LH and SH fluxes from the EC and EBBR stations were provided as 30-min averages. For each 30-min value, a quality control flag is provided to indicate whether it is within the valid range and has passed the valid delta check (i.e., by comparing each data value to the one just prior to it in time). For this study, we kept “good” data points as suggested by the quality control flags for subsequent analysis.

Energy fluxes measured at the EBBR stations were derived based on energy closure principles and are therefore by definition balanced in energy (Stull 1988; also see http://science.arm.gov/vaps/baebbr.stm). For the EC stations used in this analysis, the measured fluxes could suffer from energy closure problems, as is true for any EC station (Baldocchi et al. 1998). As reported on the ARM website (http://www.arm.gov/instruments/ecor), energy closure at these stations typically ranges from 75% to 90%, with an uncertainty level of 5% (6%) for LH (SH). Therefore, the observed LH and SH fluxes reported in this study can be treated as reliable because there were 9 EC and 14 EBBR stations active in the study domain for the years 2006 and 2007, respectively. Nevertheless, we acknowledge that there are always uncertainties associated with instrument errors/failures and data processing procedures.

Using the data from the EC and EBBR stations, monthly mean diurnal cycles of fluxes at each location were derived, which serve as the basis for gap filling the missing and “bad” data points in the 30-min time series. Gap filling is a critical step to preserve the statistical moments before deriving temporal averages (e.g., monthly means) that could be biased by missing data (see discussions in Giambelluca et al. 2009). The data processing procedure to aggregate the 30-min values to monthly averages for validation is elaborated on in the appendix. Table 1 summarizes the statistics derived from the observational networks for deriving monthly values presented in section 3a. Furthermore, stations in ARM and OKM networks were selected so that they could fit the purpose of agricultural monitoring and therefore are suitable for validation purposes in this study (e.g., Brock et al. 1995). Nevertheless, because the observations from the ARM and OKM networks are essentially point measurements and subject to limitations such as spatial coverage and uncertainties associated instrumental errors and data processing procedures, we supplement the validation datasets with observations and simulations from the NLDAS to make the evaluation more robust, as will be discussed in section 2b.

Additional temperature and precipitation data are used in the analysis are from the University of Washington (UW) ⅛° gridded meteorological dataset, which includes daily precipitation, maximum and minimum 2-m temperature, and 10-m wind speed (Maurer et al. 2002). We also employ the Arkansas–Red Basin River Center (ABRFC) precipitation data in our evaluation. The hourly gridded ABRFC data are produced by multisensor precipitation estimate mosaics over a 4 km × 4 km grid based on the hourly digital precipitation product (HDP) computed by each Next Generation Weather Radar (NEXRAD) radar within the ABRFC area in combination with hourly rain gauge reports (usually at automated reporting sites). (More details about ABRFC data can be found at http://www.srh.noaa.gov/abrfc/?n=pcpn_methods.)

b. The reanalysis products NLDAS-2

The North American Land Data Assimilation System (http://www.emc.ncep.noaa.gov/mmb/nldas/; http://ldas.gsfc.nasa.gov/nldas) was established by the NOAA/National Centers for Environmental Prediction (NCEP)/Environmental Modeling Center (EMC), together with its NOAA/Climate Program Office (CPO) Climate Prediction Program of the Americas (CPPA) partners. Under the NLDAS system, several land surface models (LSMs), including the Noah model which is the land surface component in WRF, were driven by a common set meteorological forcings and land surface parameters over the continental United States to generate long-term hourly, ⅛° hydrometeorological products. The quality of NLDAS products has been evaluated by utilizing in situ observations and satellite retrievals (Xia et al. 2012a,b, 2013a,b). The NLDAS-2 meteorological forcing, including precipitation, shortwave and longwave radiation, air temperature, humidity and wind speed at a ⅛° resolution, is provided with an hourly time step for the period 1979–2007 (Xia et al. 2013a), derived from the 32-km resolution 3-hourly North American Regional Reanalysis (NARR) following the bias-correction algorithms detailed in Cosgrove et al. (2003). The precipitation fields in NLDAS-2 were produced by combining observations from field stations, level-4 precipitation retrievals from NEXRAD systems operated by the National Weather Service, and satellites. In this study, we used precipitation and land surface fluxes and states simulated by Noah (version 2.7.2) from NLDAS-2 as validation datasets to assess the skill of control simulations over the SGP domain, as detailed in section 2c.

c. Model

Version 3.2.1 of the Advanced Research WRF is used in this study (Skamarock et al. 2008). WRF is a fully compressible and nonhydrostatic model that uses a terrain-following hydrostatic-pressure vertical coordinate and an Arakawa C-grid staggering spatial discretization for variables. The simulation domain located within 25°–44°N, 112°–90°W covers the southern Rockies and southern Great Plains (see Fig. 2) with horizontal grid spacing 12 km and 36 sigma levels from the surface to 100 hPa. Wind, temperature, water vapor, pressure, and underlying surface variables used to generate initial and boundary conditions are derived from the NARR data with 32-km horizontal resolution and 3-h time intervals.

Fig. 2.
Fig. 2.

Model domain and topography (elevation: m). The red rectangle, as in Fig. 1, represents the region over which some statistics are conducted for Figs. 9, 11, and 15.

Citation: Journal of Hydrometeorology 14, 3; 10.1175/JHM-D-12-0134.1

To obtain a best simulation result for precipitation over the SGP region, we compared results using different radiation schemes, Rapid Radiative Transfer Model for GCMs (RRTMG; Barker et al. 2003; Pincus et al. 2003) versus the Community Atmosphere Model (CAM) version 3.0 (Collins et al. 2004), and different microphysics schemes, WRF Single-Moment 6-class (WSM6; Hong and Lim 2006) versus the Morrison two-moment (Morrison et al. 2005). We found that the RRTMG seemed to produce a more realistic magnitude and spatial pattern of precipitation for June–August (JJA) 2007, while the CAM radiation scheme tended to underestimate the summer mean precipitation over a major rainy belt. Simulations made using the Morrison scheme were slightly better (i.e., smaller precipitation bias) than those completed with WSM6 (not shown). Thus, the physics packages used in the this study include RRTMG radiation and Morrison microphysics schemes together with the Mellor–Yamada–Janjić (MYJ) planetary boundary layer (PBL) scheme (Janjić 2001), the Kain–Fritsch (KF) convective parameterization scheme (Kain and Fritsch 1993; Kain 2004; Yang et al. 2012), and the Noah LSM (version 3.2) (Chen and Dudhia 2001; Ek et al. 2003). We select the warm seasons of 2006 and 2007 for our simulations. All simulations start on 1 May and continue through 1 October.

d. Representation of irrigation in WRF

To investigate the impact of irrigation on shallow convection and land–air interactions, we implemented the irrigation algorithm of Ozdogan et al. (2010) in the Noah land surface model, which is a commonly used land surface model coupled with WRF. The implementation is briefly described as follows.

  1. The Moderate Resolution Imaging Spectroradiometer (MODIS)-based irrigated land map and area data (Ozdogan and Gutman 2008; Ozdogan et al. 2010) at 500-m resolution was obtained and aggregated to the same resolution of the model grid. Then we incorporated the potential irrigation fractional area in each grid cell into WRF. That is, we aggregated the 500-m MODIS-based products to irrigation fractions at 12-km resolution, consistent with the WRF domain described in section 2c, to represent the fraction of each grid cell that is equipped for irrigation and could, therefore, potentially be irrigated as needed. As shown in Fig. 3 (top), we can see a larger fraction of potential irrigation area located over the north-central portion of the simulation domain, with maximum values larger than 30% in eastern Nebraska.

  2. The land cover map of Noah (Fig. 3, bottom) suggests that a significant fraction of the study domain is covered by croplands and grasslands. According to Ozdogan et al. (2010), grasslands could be irrigated because most land classification systems, including the ones used in the Noah model, could not distinguish pasture from grassland. Therefore, we could potentially apply irrigation to any grid cell covered by cropland or grassland (i.e., pasture) as long as the aggregated irrigated fraction in Fig. 3 (top) is above 0 and the greenness fraction (GF) at the given grid cell is above a threshold given by
    e2
    where GFmin and GFmax are the annual minimum and maximum greenness fractions at a pixel and GFthresh is set as a threshold to represent the start and end of the growing season (i.e., irrigation could only be applied within the growing season). If the aforementioned conditions are satisfied in a given pixel, irrigation will be potentially applied to the fraction of the pixel with croplands/grasslands under appropriate conditions as discussed in step 3.
  3. If the GFthresh is met, irrigation is triggered when the root-zone soil moisture availability (MA) falls below a specific threshold (e.g., 50% in this study, same as in Ozdogan et al. 2010) of the field capacity, at 0600 LT on each day during the growing season (i.e., April–October). The maximum rooting depths for croplands and grasslands were set to be 1.5 m following Zeng (2001), and the depth of the root zone varies with time and is proportional to the greenness fraction to represent the dynamic phenological development of roots associated with the expansion of leaf area (represented by greenness fraction) during the growing season (Ozdogan et al. 2010). Moisture availability is defined as
    e3
    where SM is the current root-zone soil moisture and SMWP and SMFC are the soil wilting point and field capacity. In Eq. (3) SM, SMWP, and SMFC are aggregated properties of the entire root zone of the model grid cell, weighted by the active root length in each soil layer at a given time step. Once triggered, the amount of water to be irrigated is defined as the equivalent height of water needed to increase the soil moisture to the field capacity of each soil layer, again weighted by the active root length in each layer. The irrigation amount is administrated at a uniform rate between 0600 and 1000 LT, a time frame typically chosen by farmers to reduce evaporative losses (Ozdogan et al. 2010), in the same way as precipitation to mimic a sprinkler system. For more details of the irrigation module, interested readers are referred to Ozdogan et al. (2010).
Fig. 3.
Fig. 3.

(top) MODIS potential irrigation area fraction (%) and (bottom) Noah land cover map of the study domain.

Citation: Journal of Hydrometeorology 14, 3; 10.1175/JHM-D-12-0134.1

Figure 4 (top) shows the spatial distribution of calculated irrigation rate, that is, depth of water (mm day−1) being added over the pasture or croplands, averaged for JJA of 2006. Compared to the potential irrigation area map (Fig. 3, top), the spatial pattern of irrigation rate is similar but more smooth in space, with the maximum irrigation rate greater than 1 mm day−1 over Nebraska, Kansas, and the Texas Panhandle. The monthly-mean irrigation rates are greater than 0.5 mm day−1 (4 h) in May, June, and July for the dry year 2006 (Fig. 4, bottom). The maximum irrigation rate occurs at July in 2006 and dramatically drops in August and September. Because of abundant precipitation in 2007, the irrigation rate is much smaller in 2007 than in 2006 for the period May–July.

Fig. 4.
Fig. 4.

(top) Spatial distribution of irrigation rate (mm day−1) for JJA of 2006 and (bottom) monthly mean irrigation rate [mm (4 h)−1] averaged over 30°–39°N, 102.25°–93.25°W for 2006 and 2007.

Citation: Journal of Hydrometeorology 14, 3; 10.1175/JHM-D-12-0134.1

3. Results

a. Model evaluation

To simulate the impacts of irrigation on regional climate, it is important for the model to realistically capture precipitation, temperature, and surface energy and water fluxes. We first evaluate precipitation because it is a key physical variable and process linking atmosphere and land. Our simulations focus on the summers of 2006 and 2007, an extremely dry and wet year, respectively. According to Dong et al. (2011), 2006 is the second-driest year and the summer of 2007 is the second-wettest season in the record for the state of Oklahoma. Large-scale dynamics play a key role in these extreme events. Dong et al. found a circulation pattern associated with inhibited moisture transport from the Gulf of Mexico and strong subsidence over the SGP region, both contributing to the extreme dryness in 2006.

Figure 5 shows a comparison of the monthly-mean precipitation of June of 2007 for the control simulation against three sets of independent observations. We can see a strong precipitation band across central Texas, Oklahoma, and southeastern Kansas. The precipitation events during the extreme wet period are initially generated by active synoptic weather patterns, linked with moisture transport from the Gulf of Mexico by the northward low-level jet, and enhance the frequency of thunderstorms and their associated latent heat release (Dong et al. 2011). The summer months at the SGP region are characterized by the intermittent southerly flow from the Gulf of Mexico, which transports moisture from the gulf inland, thereby increasing the humidity in the lower levels of the atmosphere. The amount of moisture transported from the Gulf of Mexico depends largely on the nature of the atmospheric boundary layer development along the trajectory from the Gulf of Mexico (Dong et al. 2011). Precipitation is closely associated with cumulus clouds developed in the air mass en route.

Fig. 5.
Fig. 5.

WRF (12 km) simulated and observed (PRISM/UW ⅛°, NLDAS-2 ⅛°, and ABRFC 4 km) monthly mean precipitation (mm day−1) in June 2007.

Citation: Journal of Hydrometeorology 14, 3; 10.1175/JHM-D-12-0134.1

Figure 5 shows that the ⅛° Parameter-Elevation Regressions on Independent Slopes Model (PRISM)/UW and NLDAS-2 precipitation are very similar in both spatial pattern and magnitude, and both datasets match very well with the 4-km ABRFC data over the region for which the ABRFC data are available. The simulated monthly precipitation clearly has a spatial pattern similar to the observations. The WRF captures the precipitation belt and magnitude that stretches across Texas, Oklahoma, and Kansas. Figure 6 shows the comparison between LH, SH, and skin temperature (Ts) from the WRF control simulations and the NLDAS-2–Noah simulations. It can be seen that the WRF control simulations were able to capture the spatial patterns and magnitude of the key land surface variables reasonably well during the summer months of 2006 and 2007, compared to the NLDAS-2–Noah simulations that were driven by observed precipitation and NARR forcing fields during the same period. In addition, both the WRF control and NLDAS-2–Noah simulations show significant differences in the values of LH, SH, and Ts, as expected for the two contrasting years.

Fig. 6.
Fig. 6.

Comparison of (upper) WRF-simulated and (lower) NLDAS-2 (left) latent heat (LH), (middle) sensible heat (SH), and (right) skin temperature (Ts) for (a) JJA 2006 and (b) JJA 2007.

Citation: Journal of Hydrometeorology 14, 3; 10.1175/JHM-D-12-0134.1

Figure 7 summarizes the observed and simulated precipitation, temperature, soil wetness, and surface heat fluxes averaged over grid boxes where observations are available (see Fig. 1) for the summers (JJA) of 2006 and 2007. The model slightly underestimates the extreme precipitation that reaches 160 mm month−1 for JJA of 2007 and, consequently, underestimates soil wetness as represented by the FWI. While the simulated LH is very close to the observation, both SH and surface air temperature are slightly underestimated in 2007. The irrigation rate is relatively smaller in 2007 than in 2006 (see Fig. 4, bottom) because of an abnormally large amount of precipitation in early summer; the irrigation-induced changes in precipitation, temperature, soil wetness and surface heat fluxes are very minor for the wet year 2007.

Fig. 7.
Fig. 7.

Comparison between WRF simulations and observations of latent heat (LH), sensible heat (SH), fractional wetness index (FWI), surface air temperature (T), and precipitation (P) at pixels where observations are available for JJA of 2006 and 2007. Three color bars respectively represent observation (obs), control simulation with irrigation off (con), and simulation with irrigation on (irreg).

Citation: Journal of Hydrometeorology 14, 3; 10.1175/JHM-D-12-0134.1

In 2006, the LH is underestimated and the SH is overestimated during JJA, and the model bias in the SH and LH is significantly reduced when the irrigation scheme is turned on. The model overestimates the JJA rainfall, which accounts for less than half of that in 2007, indicating it is challenging to capture the precipitation magnitude in a dry season. The magnitude of simulated surface air temperature matches the observed one during the JJA. FWI is slightly underestimated, and the model dry bias is reduced as the irrigation scheme is on. Overall, irrigation reduces model bias in the dry year simulation of the soil moisture, surface latent, and sensible heat fluxes, suggesting it is important to include irrigation in modeling the land surface fluxes and land–air interactions.

b. Simulated irrigation effects

The difference between the sensitivity and control simulations (i.e., with irrigation on and off, respectively) can be used to determine the irrigation impact. The following analyses focus on the changes of variables such as surface air temperature and humidity, LH, SH, precipitation, soil moisture, and PBL properties. These variables are most relevant to the development of cumulus clouds and land–air interactions. Our analysis below focuses on 2006 because irrigation is much more intensive in the dry year (2006) than the wet year (2007); see Fig. 4.

1) Changes in surface variables

Figure 8 shows the spatial patterns of differences in LH, SH, 2-m air temperature (T2m) and specific humidity (Q2m) for JJA of 2006 between the irrigation and control simulations. Irrigation brings additional water onto the land surface, which potentially increases evapotranspiration over the croplands and pastures. It can be seen that LH overwhelmingly increases over the Great Plains, with the maximum increase greater than 10 W m−2 over southern Nebraska, western Kansas, and northern Texas. Meanwhile the specific humidity increases by 0.2–2.0 g kg−1, with a similar but more uniform spatial pattern than that of the LH because the additional evapotranspiration associated with irrigation brings more water from land surface and subsurface to the near-ground atmosphere. Because of the larger heat capacity of water stored in soil and vegetation under a wetter condition, the skin temperature decreases with irrigation (figure not shown). The SH decreases in magnitude similar to that of LH because the sum of SH and LH is generally dependent on the net solar flux reaching the surface. The spatial pattern of changes in SH is almost identical to that in LH, but opposite in sign. The surface air is cooled by 0.2°–1.5°C because of the cooled land surface and decreased SH. The strong spatial correlations for the variables shown in Fig. 8 indicate a tight physical linkage among the surface energy fluxes, temperature, and water vapor. The spatial similarity of the temperature and water vapor change with the irrigation rate, as shown at Fig. 4 (top), suggests that the irrigation-induced wet and cool effects occur mainly at the local scale and do not spread too far into nonirrigated regions.

Fig. 8.
Fig. 8.

Irrigation-induced changes in LH flux (W m−2), SH flux (W m−2), T2m (K), and Q2m (g kg−1) for JJA 2006.

Citation: Journal of Hydrometeorology 14, 3; 10.1175/JHM-D-12-0134.1

Figure 9 shows the seasonal and diurnal variations of the irrigation-induced changes for several key variables discussed above. The regional mean LH increases by more than 6 W m−2 for May–August, with a maximum increase of 14 W m−2 in July, which is consistent with the seasonal change of the irrigation rate (see Fig. 4). Like the change in the spatial pattern, the seasonal change for the SH (T2m) is almost identical with that for the LH (Q2m), but opposite in sign. Seasonal changes of surface air temperature and humidity share similar patterns to that of SH and the LH, respectively, with a maximum change occurring in July when the mean surface air temperature decreases by 0.5°C and specific humidity increases by 0.6 g kg−1. The spatial distribution of precipitation change is very inhomogeneous (not shown); that is, the areas with increased or decreased precipitation are mixed. As a result, the regionally averaged precipitation only slightly increases, mainly owing to changes in convective precipitation. For example, the mean precipitation only increases by 0.1 mm day−1 in July, much less than the irrigation rate of 0.75 mm day−1 in that month. Observational studies have not conclusively demonstrated the impact of irrigation on local deep convection and associated precipitation; some studies imply an increase in local precipitation amount or frequency (Barnston and Schickendanz 1984; Eltahir and Bras 1996; DeAngelis et al. 2010), while others have found no evidence for such effects (Segal et al. 1988). The decrease of SH and increase of LH induced by irrigation tend to enhance the characteristic moist static energy within the convective boundary layer and, consequently, make it thermodynamically conducive to a potential increase in precipitation (Segal et al. 1998). However, the impact of irrigation on other dynamical processes (e.g., the High Plains nocturnal low-level jet) might cause precipitation to decrease (Giorgi et al. 1996; Paegle et al. 1996).

Fig. 9.
Fig. 9.

Irrigation-induced monthly-mean and JJA diurnal cycle changes in LH (W m−2), SH (W m−2), T2m (K), Q2m (g kg−1), total precipitation Ptot (mm day−1), and convective precipitation Pcon (mm day−1) averaged over 30°–39°N, 102.25°–93.25°W.

Citation: Journal of Hydrometeorology 14, 3; 10.1175/JHM-D-12-0134.1

The irrigation modifies the partitioning of SH and LH rather than their sum because the net radiation reaching the surface is more affected by ambient atmospheric conditions and surface properties such as albedo (assuming that ground heat flux is small). The irrigation was only applied at a uniform rate between 0600 and 1000 local time (1200–1600 UTC). Consequently, the LH (SH) starts increasing (decreasing) from 1200 UTC. The change of LH and SH reaches a peak at around 1700 UTC, one hour after the irrigation is turned off, and then gradually decreases until 0200 UTC. The mean LH (SH) is increased (decreased) by approximately 33 W m−2 at 1700 UTC, while the change of SH and LH during the night (0200–1200 UTC) is negligible. The changes of air temperature and humidity follow a similar diurnal pattern as that of the SH and LH, respectively, but with less diurnal variation. Cooled and wetter air can be still seen at night, although the magnitude is much smaller than that during daytime. The surface air is cooled 0.6°–0.7°C from late morning to late afternoon.

2) Changes in mixing-level depth and lifting condensation level

The atmospheric mixed layer is a zone where the potential temperature and specific humidity are nearly constant with height. The depth of the mixed layer is known as the mixing height or the PBL depth. Turbulence typically plays a role in the formation of fluid mixed layers and the PBL is no exception. The atmospheric mixed layer results from the convective air motions, associated with heating of the surface. The amount of mixed layer moisture, in turn, is a key regulator of cloud formation and a critical mechanism through which cloud and land surface feedbacks may be manifest.

Generally, the height of the lifting condensation level (ZLCL) and the time of cumulus initiation are related to boundary layer structure, which is affected by the distribution of the surface fluxes (Berg and Kassianov 2008). Changes in the properties of shallow convection can be associated with the changes in low-altitude moisture and can be evaluated in a number of ways. Shallow clouds form whenever the mixed-layer depth (Zi) grows to be as large as ZLCL (e.g., Wilde et al. 1985). In general, increasing the low-level moisture will lead to a decrease in both ZLCL (because the lower atmosphere is more moist) and Zi (because the SH is reduced). The net impact on the probability of the formation of boundary layer clouds depends on the relative change of these two variables. If the difference of the absolute values of ΔZLCL and ΔZi is greater than 0, where Δ is the difference between moist (irrigation) and dry (control) conditions, then the probability of shallow cloud formation increases.

Figure 10 shows the irrigation-induced changes in mixed-layer depth and lifting condensation level. The values of Zi decrease by 20–200 m over irrigated areas and values of ZLCL decrease by 50–400 m over same region, both with a spatial pattern similar to that for near-ground humidity and SH/LH, shown in Fig. 8. The difference of absolute change of ZLCL and Zi is positive over the whole region. In another words, the irrigation-induced decrease in the lifting condensation level is more than the decrease in mixed layer depth, suggesting an increasing probability of shallow cloud formation.

Fig. 10.
Fig. 10.

Irrigation-induced PBL height change (m), LCL change (m), and difference of PBL height decrease (ΔZi) and LCL decrease (ΔZLCl).

Citation: Journal of Hydrometeorology 14, 3; 10.1175/JHM-D-12-0134.1

Figure 11 demonstrates how the ZLCL varies with Zi and the surface fluxes. As discussed above, it is not surprising to see a large positive correlation between Zi and ZLCL (top left panel). A tight correlation can also be seen between ZLCL and SH (Pearson correlation coefficient is 0.85) because the larger SH indicates a warmer and dryer boundary layer. We find no statistically significant correlation between ZLCL and LH. This is probably because the LH is interacting with multiple factors. For example, increasing water vapor inputs from land to atmosphere associated with a larger LH increases the humidity in the lower PBL, which favors a decrease of ZLCL. Meanwhile, increasing the amount of shallow cloud may reduce solar radiation reaching the surface, which in turn limits the energy available for both SH and LH production. At a longer time scale, however, a stronger negative correlation can be found between LH and ZLCL (see Table 2). Such a negative correlation suggests that ZLCL is a function of the amount of energy reaching the land surface, as well as its partitioning between latent and sensible heat fluxes, in which the former is associated with the cloud formation as a clear indication of the feedback between land and atmosphere. Indeed, there is a strong negative correlation between ZLCL and the ratio of the LH to the sum of SH and LH (Fig. 11). Generally, cloud formation is linked closely to the partitioning of the surface fluxes and soil moisture, and clouds are more likely to initially form in regions where large sensible heat fluxes coincide with adequate moisture.

Fig. 11.
Fig. 11.

Scatterplots of 5-day mean LCL with PBL height, SH, LH, and LH/(SH+LH) (averaged over 30°–39°N, 102.25°–93.25°W).

Citation: Journal of Hydrometeorology 14, 3; 10.1175/JHM-D-12-0134.1

Table 2.

Correlation coefficients for soil moisture and lifting condensation level correlated to surface fluxes of SH and LH, PBL change, and LCL change as a function of averaging time. Variables are averaged over 30°–39°N, 102.25°–93.25°W.

Table 2.

3) Changes in vertical profiles

Changes in the amount of irrigation and associated changes in evapotranspiration can have an impact on the thermodynamic properties of the PBL. To investigate further, domain-average profiles of several key thermodynamic parameters have been created for times between 1300 and 1500 LST and during the night (1900–0600 LST). During the afternoon, well-mixed boundary layers are expected with nearly constant values of potential temperature and specific humidity within the lower boundary layer (Fig. 12). In the control simulations the top of the well-mixed boundary layer is approximately 2.0 km above ground. The mixed-layer depth is slightly less for the irrigation simulations (about 1.8 km AGL), consistent with a reduced SH and increased LH associated with enhanced soil moisture. The overall structure of the profiles is similar for both control and irrigation simulations. There are, however, systematic differences in some aspects of the profiles of potential temperature, specific humidity, moist static energy, and relative humidity between the control and irrigation simulations. During the afternoon, the irrigation simulation is found to be approximately 1 K cooler and 1 g kg−1 moister than the control simulation. These differences were of limited vertical extent and confined to the PBL. The moist static energy in the irrigation simulation was also greater within the middle and low boundary layer. Above that layer, the moist static energy in the irrigation simulation is actually slightly less than that found for the control simulation. This is likely due to small differences in temperature and humidity within the free troposphere, and it is not clear if they are significant. The relative humidity is approximately 5% greater in the irrigation simulations than in the control simulations and reaches a peak in the middle level of the boundary layer. The maximum relative humidity also occurs at lower altitude in the irrigation simulations, consistent with a shallower boundary layer in these simulations.

Fig. 12.
Fig. 12.

Vertical profiles of (a) potential temperature (K), (b) specific humidity (g kg−1), (c) moist static energy (103 J kg−1), and (d) relative humidity (%) averaged over the domain 34°–43°N, 104.5°–95.5°W for 1300–1500 LT for JJA 2006 for the control (solid) and irrigation (dashed) simulation.

Citation: Journal of Hydrometeorology 14, 3; 10.1175/JHM-D-12-0134.1

Domain-average profiles have been created for nighttime conditions (Fig. 13). In these cases, the boundary layer usually is stable, and the maximum relative humidity is found adjacent to the surface because of radiative cooling. The irrigation simulation is slightly cooler and moister than the control simulation. There are also differences in the potential temperature and specific humidity profiles, but they are much smaller than those found during daytime. This is likely due to the small amount of nighttime evapotranspiration. The irrigation simulations have a larger moist static energy near the surface, and the maximum value is found a few hundred meters above the surface in both simulations. Below 2 km, the irrigation simulations have a larger relative humidity than the control simulations, although the difference is about half of that seen during daytime. The dryer conditions above 2 km are likely related to the suppressed daytime boundary layer heights and generally thinner residual layers.

Fig. 13.
Fig. 13.

As in Fig. 12, but for nighttime.

Citation: Journal of Hydrometeorology 14, 3; 10.1175/JHM-D-12-0134.1

4) Role of soil moisture

Figure 14 shows the irrigation-induced changes in total soil moisture content (up to 2 m in depth) for May to September 2006. The larger increase of soil moisture content occurs in July–September, although the irrigation rate is much larger from May to July in 2006 (Fig. 4, bottom). For example, the total soil moisture content increases by 0.05–0.2 mm over the major irrigated areas. The large soil moisture increase in September when the model irrigation is very minor (10% of July’s) suggests that irrigation-induced soil memory and the associated wetter and cooler surface from the previous period can last for a few weeks to months, implying a lag effect of irrigation at the scale, potentially, from intraseasonal to seasonal.

Fig. 14.
Fig. 14.

Irrigation-induced total soil moisture content (layer from 0 to 2 m) changes (mm) from May to September 2006. The depths of four soil layers in the Noah land surface model are 0.1 m, 0.4 m, 0.6 m, and 1.0 m.

Citation: Journal of Hydrometeorology 14, 3; 10.1175/JHM-D-12-0134.1

Figure 15 shows the role of soil moisture in affecting the surface heat fluxes, Zi, and ZLCL (Ferguson et al. 2012). That is, wetter soil favors a lower SH, a higher evaporative fraction, a lower LCL, and a less stable boundary layer (Betts 2004). Looking at 2006 and 2007 separately in Fig. 15, we can find that land–atmospheric interactions behave differently under wet and dry conditions. Stronger relationships between soil moisture and sensible heat flux, evaporative fraction, Zi, and ZLCL are found in the dry year (i.e., 2006). This suggests that, in the dry year, which falls within a transitional regime as defined in Seneviratne et al. (2010), the land–atmospheric interactions likely play a more important role in the water cycle. In the wet year, however, we find weaker correlations between soil moisture and surface fluxes. Land–atmosphere interactions are likely weak in the season when the evaporation is not constrained by soil moisture, but rather influenced by large-scale atmospheric conditions including winds and atmospheric moisture.

Fig. 15.
Fig. 15.

Scatterplots of 5-day mean soil moisture (first soil layer) with PBL height, SH, LH, and LCL (averaged over 30°–39°N, 102.25°–93.25°W).

Citation: Journal of Hydrometeorology 14, 3; 10.1175/JHM-D-12-0134.1

Table 2 summarizes the Pearson correlation coefficients between soil moisture and surface fluxes for different averaging times. We can see a clear increase of the correlation coefficients with averaging time. The correlation coefficients are usually larger for longer time periods on average because some fast processes (e.g., the passage of fronts) that occur at a synoptic time scale in the PBL are filtered out in monthly-mean data. Also, because changes in soil moisture change occur on relatively longer time scales compared to atmospheric processes (see Fig. 14), the increased correlation of soil moisture with surface fluxes over a longer time scale suggests that soil moisture memory plays an important role in modulating land surface water and energy budgets and, therefore, land–atmospheric interactions over the SGP region.

4. Summary and discussion

In this study, we incorporated an irrigation scheme informed by satellite-measured potential irrigation area data into the Noah land surface model coupled to the WRF. We conducted two sets of simulations using WRF, with and without irrigation included, over the SGP for an extremely dry (2006) and wet (2007) summer, respectively. The results show that WRF generally captures the precipitation belt and magnitude in a wet summer (2007). The irrigation reduces model bias in the dry year in simulating the soil moisture, surface latent heat (LH), and sensible heat (SH) fluxes, suggesting that it is important to include irrigation processes in modeling the land surface fluxes and land–air interaction.

Irrigation adds additional water to the land surface, leading to an increase of soil moisture. As a result, evapotranspiration and the corresponding LH increase at the land surface. Model results show that, during the simulation period from May to September 2006, irrigation causes a LH (SH) increase (decrease) by 5–15 W m−2, cooling of the surface by 0.3°–0.5°C, and an increase of surface air specific humidity by 0.3–0.6 g kg−1, shown as monthly averages over the domain highlighted in Figs. 1 and 2. Those changes mainly occur locally and during daytime. The simulated precipitation change induced by irrigation is very inhomogeneous spatially, and the total precipitation averaged over the SGP region only slightly increases. We acknowledge that this is a regional modeling study, and within the limited model domain we are not able to investigate possible precipitation effects downstream.

The irrigation-induced decrease in lifting condensation level is greater than the decrease in mixed-layer depth over the study period, suggesting an increasing probability of shallow cloud formation, which is linked closely to the partitioning of the surface fluxes and soil moisture. We found that land–atmosphere interactions behave differently under wet and dry conditions. In the dry year, the land–atmosphere interactions likely play a more important role in the regional water cycle. In the wet year, when the evaporation is not constrained by the soil moisture, however, we find weaker correlations between soil moisture and surface fluxes. The large soil moisture increase in early fall, when the modeled irrigation is very small, indicates that irrigation-induced soil memory, and the resulting wetter and cooler surface from the previous period, can last for a few weeks to months, implying a lag effect of irrigation at the scale, potentially, from intraseasonal to seasonal.

The irrigation map used in this study was derived based on 2001 MODIS observations. In other words, we assumed that the potential irrigated land area did not change significantly between 2001 and the time period used in this study. Given the complexity of deriving time-varying irrigation maps [see discussions in Ozdogan and Gutman (2008) and Ozdogan et al. (2010)] and the relatively slow pace of land use and land cover change over the SGP domain, the primary focus of this study is our first step to understanding the potential impacts of irrigation on land surface fluxes and states and the resultant triggering of convection and precipitation over the study domain; we leave LULCC and its impacts as a topic to be investigated in the future.

In the future, we would like to extend the work to longer periods and larger spatial domains to assess the impact of irrigation on regional climate and hydrologic cycles, including the hydrologic responses such as runoff generation and streamflow over land and atmospheric processes in the upper atmosphere. Observational datasets over the SGP and from other sources will be used to validate and improve process-level understanding of these impacts. We will also evaluate the impact of model resolution and model physics (including the options of land surface, boundary layer, and convective schemes) on the simulations, which have been identified as the biggest sources of uncertainties in the existing modeling studies on irrigation effects (Sorooshian et al. 2011).

Acknowledgments

The study was supported by the DOE Office of Science Biological and Environmental Research (BER) Atmospheric System Research (ASR) program. We thank the DOE Atmospheric Radiation Measurement and Oklahoma Mesonet Programs for providing the ARM/CART and Oklahoma Mesonet meteorological, heat flux, and soil data. The Oklahoma Mesonet Program is supported by the State of Oklahoma. We thank Dr. Youlong Xia for his assistance in providing the NLDAS-2 dataset. PNNL is operated for the U.S. DOE by Battelle Memorial Institute under Contract DE-AC06-76RLO1830.

APPENDIX A

Data Processing Procedure for the LH and SH Observations from ARM EC and EBBR Stations over SGP

The 30-min LH and SH observations from EC and EBBR stations were processed using the following the procedure, similar to the methods used by Giambelluca et al. (2009).

  1. Calculate monthly-mean diurnal cycles for LH and SH at each location by taking the average over all data from a given month within each 30-min interval.

  2. Apply a low-pass filter to each diurnal cycle to make it smooth:

    1. for missing points (i.e., points with missing values or absolute values of the coefficient of variance greater than 20), interpolate from the nearby points if the number of sequentially missing points is less than three;

    2. if there are more than three points missing sequentially in a diurnal cycle, use the corresponding values from the previous month;

    3. Apply a low-pass filter to obtain smooth diurnal cycles. A finite impulse response (FIR) low-pass filter (Oppenheim et al. 1999) is used to remove the high-frequency signals in the diurnal cycles. A FIR filter has an impulse response that reaches zero in a finite number of steps. The impulse response of the filter is computed based on Kaiser’s window, which in turn is based on a modified Bessel function. Then the mean diurnal cycle was convolved with the impulse response to obtain a “smoothed” mean diurnal cycle. The parameters of the impulse response are given as

      • the lower frequency of the filter as a fraction of the Nyquist frequency (i.e., 0.5 T, where T is the sample frequency): 0,

      • the upper frequency of the filter as a fraction of the Nyquist frequency: 0.25,

      • The filter power relative to the Gibbs phenomenon wiggles in decibels: 25, and

      • the number of terms used to construct the filter: 4.

  3. Fill gaps in the data using the filtered diurnal cycles.

  4. Compute daily and monthly fluxes using the gap-filled 30-min files to avoid any bias in statistics.

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