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  • View in gallery

    Study domain. The red boxes denote a combined local and downwind area of CCV and CB-SRP, the blue boxes denote the local heavily irrigated HP and MRV, and the green boxes denote the remote downwind areas in the Midwest and Southeast. Regions outside the United States and over the ocean are not taken into account.

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    Irrigated fraction from (a) WRF Model default USGS land-use type 3 (irrigated cropland and pasture) and (b) the 2012 MODIS irrigation map.

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    Flowchart of the WRF-Noah-Mosaic-Irrigation framework.

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    PET ratio of JJA to the growing season over the CONUS.

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    Comparison of observed and simulated JJA irrigation fraction and rate: (a) observed 2010 USGS irrigated fraction, (b) 2012 MODIS irrigated fraction, (c) differences between (b) and (a), (d) observed 2010 USGS irrigation rate (mm day−1), (e) simulated 2012 irrigation rate (mm day−1), and (f) differences between (e) and (d).

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    Changes (IRRI − CTL) in (left to right) the monthly mean and JJA-averaged soil moisture from the top to the bottom soil layer: (a) 0–10, (b) 10–40, and (c) 40–100 cm.

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    Changes (IRRI − CTL) in (left to right) the monthly mean and JJA-averaged (a) latent heat flux (W m−2) and (b) sensible heat flux (W m−2), (c) surface skin temperature bias from CTL (CTL − NARR, °C), and (d) surface skin temperature difference between IRRI and CTL (IRRI − CTL, °C).

  • View in gallery

    Wind fields (vectors, m s−1), geopotential height (blue lines, gpm), and air temperature (red lines, °C) at (top to bottom) 850–200 hPa from left to right by CTL, IRRI, and IRRI − CTL. Shaded areas denote the relative humidity (%) at 850 and 700 hPa.

  • View in gallery

    Changes (IRRI − CTL) in horizontal moisture flux (g Kg−1 m s−1, eastward is positive) along vertical cross sections at (a) 110° and (b) 95°W.

  • View in gallery

    (top) PBL height (m) and (bottom) CAPE (J Kg−1) from left to right by CTL, IRRI, and IRRI − CTL.

  • View in gallery

    JJA mean daily precipitation (mm day−1) by (a) CTL, (b) observations (NLDAS-2), (c) IRRI, and the corresponding bias (mm day−1) in (d) CTL − OBS and (f) IRRI − OBS. Daily precipitation changes induced by irrigation are shown in (e) IRRI − CTL (mm day−1).

  • View in gallery

    Probability density function of the JJA daily precipitation changes (mm day−1) over the heavily irrigated lands (irrigation fraction 0.1, blue line) and the absolute bias changes (mm day−1, black line) over the entire outlined subregions denoted in Fig. 1.

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Effects of Irrigation on Summer Precipitation over the United States

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  • 1 State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, and University of Chinese Academy of Sciences, Beijing, China, and Department of Geography and Center for Global Change and Earth Observations, Michigan State University, East Lansing, Michigan
  • 2 Department of Geography and Center for Global Change and Earth Observations, Michigan State University, East Lansing, Michigan
  • 3 Department of Geological Sciences, Michigan State University, East Lansing, Michigan
  • 4 State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
  • 5 Department of Geological Sciences, Michigan State University, East Lansing, Michigan
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Abstract

Irrigation’s effects on precipitation during an exceptionally dry summer (June–August 2012) in the United States were quantified by incorporating a novel dynamic irrigation scheme into the Weather Research and Forecasting (WRF) Model. The scheme is designed to represent a typical application strategy for farmlands across the conterminous United States (CONUS) and a satellite-derived irrigation map was incorporated into the WRF-Noah-Mosaic module to realistically trigger the irrigation. Results show that this new irrigation approach can dynamically generate irrigation water amounts that are in close agreement with the actual irrigation water amounts across the high plains (HP), where the prescribed scheme best matches real-world irrigation practices. Surface energy and water budgets have been substantially altered by irrigation, leading to modified large-scale atmospheric circulations. In the studied dry summer, irrigation was found to strengthen the dominant interior high pressure system over the southern and central United States and deepen the trough over the upper Midwest. For the HP and central United States, the rainfall amount is slightly reduced over irrigated areas, likely as a result of a reduction in both local convection and large-scale moisture convergence resulting from interactions and feedbacks between the land surface and atmosphere. In areas downwind of heavily irrigated regions, precipitation is enhanced, resulting in a 20%–100% reduction in the dry biases (relative to the observations) simulated over a large portion of the downwind areas without irrigation in the model. The introduction of irrigation reduces the overall mean biases and root-mean-square errors in the simulated daily precipitation over the CONUS.

Current affiliation: University Corporation for Atmospheric Research, Boulder, Colorado, and NOAA/Great Lakes Environmental Research Laboratory, Ann Arbor, Michigan.

Corresponding author address: Lisi Pei, NOAA/Great Lakes Environmental Research Laboratory, 4840 S. State Rd., Ann Arbor, MI 48108. E-mail: lisipei@msu.edu

Abstract

Irrigation’s effects on precipitation during an exceptionally dry summer (June–August 2012) in the United States were quantified by incorporating a novel dynamic irrigation scheme into the Weather Research and Forecasting (WRF) Model. The scheme is designed to represent a typical application strategy for farmlands across the conterminous United States (CONUS) and a satellite-derived irrigation map was incorporated into the WRF-Noah-Mosaic module to realistically trigger the irrigation. Results show that this new irrigation approach can dynamically generate irrigation water amounts that are in close agreement with the actual irrigation water amounts across the high plains (HP), where the prescribed scheme best matches real-world irrigation practices. Surface energy and water budgets have been substantially altered by irrigation, leading to modified large-scale atmospheric circulations. In the studied dry summer, irrigation was found to strengthen the dominant interior high pressure system over the southern and central United States and deepen the trough over the upper Midwest. For the HP and central United States, the rainfall amount is slightly reduced over irrigated areas, likely as a result of a reduction in both local convection and large-scale moisture convergence resulting from interactions and feedbacks between the land surface and atmosphere. In areas downwind of heavily irrigated regions, precipitation is enhanced, resulting in a 20%–100% reduction in the dry biases (relative to the observations) simulated over a large portion of the downwind areas without irrigation in the model. The introduction of irrigation reduces the overall mean biases and root-mean-square errors in the simulated daily precipitation over the CONUS.

Current affiliation: University Corporation for Atmospheric Research, Boulder, Colorado, and NOAA/Great Lakes Environmental Research Laboratory, Ann Arbor, Michigan.

Corresponding author address: Lisi Pei, NOAA/Great Lakes Environmental Research Laboratory, 4840 S. State Rd., Ann Arbor, MI 48108. E-mail: lisipei@msu.edu

1. Introduction

Interactions between land and the atmosphere are critical drivers of Earth’s climate system over continental regions, as they modulate the energy and mass transport across this interface (Koster et al. 2004; Jimenez et al. 2014). Numerous observational and modeling studies have demonstrated the importance of these interactions on regional climate, predominantly through surface latent and sensible heat fluxes (e.g., Pielke 2001; Adegoke et al. 2003; Erlingis and Barros 2014). As anthropogenic activities designed to meet growing human socioeconomic development needs (e.g., deforestation, grazing, irrigation, urbanization) significantly modify the land surface and soil properties (Eastman et al. 2001; Ozdogan et al. 2010; Snyder 2010; Bagley et al. 2014; Medvigy et al. 2013; Qian et al. 2013; Huber et al. 2014; Li et al. 2015), a critical need arises to quantify the resultant effects of these modifications on the coupled climate system. In the United States, for instance, because of consistent agricultural and economic demand (Basso et al. 2013; McGuire 2013), the area of irrigated land and the volume of irrigation water applied over the conterminous United States (CONUS) has remained relatively steady throughout the past decade (USGS 2000; Kenny et al. 2009; Maupin et al. 2014), despite declining water resources, but its role in modulating the regional climate over the CONUS remains unclear.

Many studies have been conducted to examine the effects of irrigation on the local hydrometeorology and regional climate (e.g., Barnston and Schickedanz 1984; Moore and Rojstaczer 2001, 2002; Lobell et al. 2009; Saeed et al. 2009; Ozdogan et al. 2010; Pielke et al. 2011; Sorooshian et al. 2011; Harding and Snyder 2012a,b; Leng et al. 2014). Irrigation’s local effects on surface energy and water budgets are found to be mainly twofold: they enhance evapotranspiration (ET) and reduce surface temperatures (Pielke 2001; Adegoke et al. 2003; Boucher et al. 2004; Mahmood and Hubbard 2004; Gordon et al. 2005; Haddeland et al. 2006; Mahmood et al. 2006; Bonfils and Lobell 2007; Kueppers et al. 2007; Kawase et al. 2008; Lobell and Bonfils 2008; Lobell et al. 2008; Sacks et al. 2009; Ozdogan et al. 2010; Qian et al. 2013). But the effects on regional climate are more complicated, since soil moisture alters precipitation via multiscale processes including local secondary atmospheric circulations driven by the heterogeneous arrangement of irrigated and nonirrigated land (Zhong and Doran 1995; Kawase et al. 2008), long-distance ET water vapor transport (Kanamitsu and Mo 2003; Lo and Famiglietti 2013), and the effects of long-term (seasonal to interannual) memories (Koster and Suarez 2001; Seneviratne et al. 2006). Consequently, the effects of irrigation on regional climate are less certain. In some cases, irrigation enhances precipitation as a result of increased moisture from ET (Segal et al. 1998; Boucher et al. 2004). In other cases, irrigation may inhibit precipitation by reducing local sensible heating that is critical to initiate convection or by stabilizing a moist boundary layer and inhibiting deep convection (Lohar and Pal 1995; Paegle et al. 1996; Pielke 2001; Ek and Holtslag 2004).

Previous studies have demonstrated that the effects of local irrigation can be expanded to much larger downwind areas, through moisture transport, boundary layer evolution, and cloud formation (Segal et al. 1998; DeAngelis et al. 2010; Puma and Cook 2010; Lo and Famiglietti 2013; Wei et al. 2013). There is also observational evidence that the Great Plains irrigation contributes to enhanced summer rainfall over the Midwest during last century (Alter et al. 2015). But because of insufficient or inaccurate irrigated area information and difficulties in addressing the spatial variations in real-world irrigation practices, to date, most modeling attempts are still confined to relatively small regions or quantify the effects of a small portion of the irrigated lands.

In this study, a coupled atmosphere–land surface model is used to investigate the effects of nationwide irrigation on the June–August (JJA) summer precipitation over the CONUS. During this summer, drought was strong to severe in many of the heavily irrigated regions (NCDC 2012); the central Great Plains experienced the most severe drought since at least 1895 (Hoerling et al. 2014). This period was selected because irrigation water applications greatly exceeded or were comparable to precipitation in drought-stricken regions, providing a strong signal to examine irrigation-induced land–atmosphere interactions.

Sections 2 and 3 of this paper introduce modifications in the Noah-Mosaic land surface module of the Weather Research and Forecasting (WRF) Model based on satellite-derived irrigated areas and a realistic dynamic irrigation approach. Validation of the simulated irrigation rates is shown in section 4. With irrigation incorporated, the modified surface energy and water budget as well as changes in large-scale atmospheric features, are discussed in sections 5 and 6, respectively, and its consequent changes in summer precipitation of 2012 JJA are evaluated relative to observations in section 7. Finally, the discussion and conclusions are presented in section 8.

2. Model configuration

The WRF Model (v3.6) was applied in this study and configured as one mesh with 30-km grid spacing, centered at 38.83°N, 98.58°W with 125 × 190 grid points in the north–south and east–west directions, respectively. The study area is shown in Fig. 1 with six subregions outlined by the boxes used for summary statistical analysis. The simulations are driven by the North American Regional Reanalysis (NARR; Mesinger et al. 2006) datasets with 1 month of spinup. Physics parameterization schemes include the Kain–Fritsch scheme (Kain and Fritsch 1993; Kain 2004) for convective rainfall, the WSM6 graupel scheme for microphysics processes (Hong and Lim 2006), the Yonsei University (YSU) scheme (Hong et al. 2006) for planetary boundary layer (PBL) physics, the Dudhia shortwave radiation scheme (Dudhia 1989), and the RRTM longwave radiation scheme (Mlawer et al. 1997) for terrestrial radiation processes.

Fig. 1.
Fig. 1.

Study domain. The red boxes denote a combined local and downwind area of CCV and CB-SRP, the blue boxes denote the local heavily irrigated HP and MRV, and the green boxes denote the remote downwind areas in the Midwest and Southeast. Regions outside the United States and over the ocean are not taken into account.

Citation: Journal of Climate 29, 10; 10.1175/JCLI-D-15-0337.1

The Noah-Mosaic approach embedded in the WRF Noah land surface model was used in this study to incorporate subgrid-scale information, which can improve the accuracy of the simulated land surface processes in the regional climate model relative to the baseline Noah model (Li et al. 2013). In the Noah-Mosaic module, the 1D land surface model is applied independently to every subgrid fraction within each grid cell with the disaggregated forcing from the atmosphere based on the weight of that fraction in the grid cell. After the independent 1D land surface process has been completed for all fractions in the cell, the surface energy, mass, and momentum fluxes are aggregated and provided as feedback to the atmosphere. Eight dominant subgrid land-use types are configured within each 30-km WRF grid cell (Noah-Mosaic allow users to configure up to 15 subgrid land-use types but our research shows that in the 30-km resolution 5 types is nearly adequate and 8 is sufficient). With this mosaic approach, soil moisture is not shared between the subgrid fractions and it is thus possible to apply irrigation only to the irrigated crop fraction without affecting other land-use types within the same cell. We tested a 15-km grid spacing, but there was little difference in the results despite much higher computational costs. The mosaic approach thus helps maintain the computational benefits from relatively coarse model grid spacing without losing detailed subgrid land surface information.

An irrigation map created from the 2012 Moderate Resolution Imaging Spectroradiometer (MODIS) irrigated area dataset (Pervez and Brown 2010; Brown and Pervez 2014; accessed October 2014 at http://earlywarning.usgs.gov/USirrigation) for the CONUS (Fig. 2b) was introduced to estimate where irrigation is applied as it is the best available source of spatially explicit irrigated areas for the simulated period. The irrigated cropland and pasture (category 3 in the WRF default USGS-24 category land-use dataset in the Noah-Mosaic module) was adjusted to match the 2012 MODIS irrigation fraction (aggregated from the original 250-m resolution). Note that the current USGS-24 category land-use types in WRF have two categories related to irrigated land: categories 3 (irrigated cropland and pasture) and 4 (mixed dryland/irrigated cropland and pasture). Since no category 4 cells exist within the study domain, only category 3 was adjusted. The seven other dominant land-use type fractions within each 30-km grid point in the Noah-Mosaic module were either increased or decreased in magnitude proportional to their original weight. During the simulation process, the “irrigated cropland and pasture” land-use type (category 3) within each grid cell was irrigated when other triggering criteria (see section 3) were also satisfied.

Fig. 2.
Fig. 2.

Irrigated fraction from (a) WRF Model default USGS land-use type 3 (irrigated cropland and pasture) and (b) the 2012 MODIS irrigation map.

Citation: Journal of Climate 29, 10; 10.1175/JCLI-D-15-0337.1

3. Irrigation methodology

The process of incorporating the irrigation scheme into WRF needs to address three main questions: 1) When to trigger irrigation? 2) When to stop? 3) How to apply the irrigation—drip, flood, or sprinkler? Since actual irrigation practices vary considerably from place to place in the United States, strategies and assumptions are needed to optimize the irrigation scheme at this continental scale. The following section describes how we addressed these questions.

As shown in the 2012 MODIS irrigation map (Fig. 2b), the four most intensively irrigated areas over the CONUS are California’s Central Valley (CCV), the Columbia basin (CB) and the Snake River plain (SRP) in the Pacific Northwest, the high plains (HP), and the lower Mississippi River valley (MRV) in the Southeast. According to the United States Geological Survey (USGS) water-use report (Maupin et al. 2014), of the total 62.4 million acres irrigated nationwide in 2010, about 51% used the sprinkler method, 42% used flood irrigation, and the remainder used drip irrigation. Since the sprinkler method is dominant, we assume its adoption as the sole irrigation approach for this modeling work. The irrigation is thus applied in the form of added precipitation to mimic the sprinkler scenario when it is triggered. This could be refined if spatially explicit information about the irrigation methodology becomes available on this continental scale.

A number of different methods have been used to dynamically trigger irrigation in previous works; the two main methods use either soil moisture conditions (Adegoke et al. 2003; Kanamaru and Kanamitsu 2008; Kawase et al. 2008; Saeed et al. 2009; Qian et al. 2013) or canopy evaporation status (Sacks et al. 2009; Lee et al. 2011). In our approach, we relied on the soil moisture conditions in the second soil layer (10–40 cm below the ground surface) of Noah-Mosaic as a trigger since it is the typical root zone for most irrigated crops. Irrigation was applied when soil moisture in this layer fell below the sum of the wilting point and 20% of the plant-available water (defined as the field capacity minus the wilting point):
e1
where SM2 refers to the soil moisture in layer 2 of the Noah-Mosaic module, SMW refers to the soil moisture wilting point, and SMF refers to the soil moisture field capacity.

Once the soil moisture satisfies the criteria in Eq. (1), irrigation was applied as precipitation in the Noah-Mosaic module for 2 h at a rate of 20 mm h−1 into the category 3 fraction of each grid cell. This irrigation rate and duration are based on the estimated pumping water supply and irrigation application duration and amount over the HP farmlands per irrigation event (by Kansas Geological Survey and personal communication with farmers in Kansas). Applying irrigation based on pre-event soil moisture, limited by typical well and system capacities, rather than the common modeling practice of arbitrarily setting the postevent soil moisture to some specified percent of soil saturation, may avoid induced biases and uncertainties. The flowchart of this WRF-Noah-Mosaic-Irrigation modeling framework is shown in Fig. 3. This approach makes it possible to dynamically simulate the irrigation water demand based on detected soil moisture deficiencies, without changing irrigation rates or durations to fit the observed data.

Fig. 3.
Fig. 3.

Flowchart of the WRF-Noah-Mosaic-Irrigation framework.

Citation: Journal of Climate 29, 10; 10.1175/JCLI-D-15-0337.1

4. Evaluation of simulated irrigation water

The simulated irrigation water amount is compared to the latest available (2010) observed county-level irrigation-water-use data from the USGS (water.usgs.gov/watuse/data/). These 2010 estimates are in units of millions of gallons per year for each county, while in practice this volume of water is applied only during the growing season of the corresponding year. To compare these water-use observations to the simulated irrigation water during JJA, a ratio of irrigation water demand during the JJA portion of the year to that of the entire growing season is needed. Here, we define the growing season as the period when the daily mean 2-m air temperature (data from NLDAS-2 forcing file A: http://ldas.gsfc.nasa.gov/nldas/NLDAS2forcing.php) is greater than 8°C (a typical threshold for crop growth), and introduced the potential evapotranspiration (PET, from NLDAS-2 forcing file A) ratio to represent the approximate plant evaporative demand of JJA over the growing season. The PET ratio is calculated by dividing the accumulated JJA PET value (when the daily mean 2-m air temperature is greater than 8°C) by that of the entire growing season. It is then applied as a weighting factor to determine the JJA irrigation portion of the USGS estimates for the whole growing season.

The spatial pattern of the PET ratio is consistent with expectations (Fig. 4). In the northern United States, where the growing season is relatively short, the JJA ratio is above 0.5, higher than in the southern part of the country, where the warm climate allows year-round growth (JJA ratio near 0.25). In the semiarid intermountain areas along the Rocky Mountains, where the agricultural yields are mainly supplied by irrigation, the growing season is mostly within JJA (ratio > 0.65). The PET ratio-weighted USGS county-level water-use estimates are then aggregated into the 30-km WRF resolution (Fig. 5d) to allow comparison with the simulated JJA period.

Fig. 4.
Fig. 4.

PET ratio of JJA to the growing season over the CONUS.

Citation: Journal of Climate 29, 10; 10.1175/JCLI-D-15-0337.1

Fig. 5.
Fig. 5.

Comparison of observed and simulated JJA irrigation fraction and rate: (a) observed 2010 USGS irrigated fraction, (b) 2012 MODIS irrigated fraction, (c) differences between (b) and (a), (d) observed 2010 USGS irrigation rate (mm day−1), (e) simulated 2012 irrigation rate (mm day−1), and (f) differences between (e) and (d).

Citation: Journal of Climate 29, 10; 10.1175/JCLI-D-15-0337.1

As shown in Fig. 5, the irrigation fraction incorporated into our model based on 2012 MODIS data (Fig. 5b) is close to the observed 2010 USGS irrigation fraction (Fig. 5a), with smaller magnitudes found in portions of CCV and the central and southern HP. The developers of the MODIS irrigation maps reported accuracies of 92% and 75% for the CCV and HP, respectively, in the 2002 irrigation map (Pervez and Brown 2010), while direct validation of the 2012 MODIS data is not yet available. Though uncertainties in the MODIS irrigation map remain since assumptions are inevitable within its modeling framework (Pervez and Brown 2010; Brown and Pervez 2014), the overall slight decline trend in the 2012 MODIS irrigated acres compared to the 2010 USGS records over the CONUS is consistent with the reported 0.4% decline from 55.54 million acres in 2008 to 55.32 million acres in 2013 (USDA 2013).

The simulated irrigation water distribution (Fig. 5e) outlines the four heavily irrigated regions (irrigation fraction ≥ 0.1), with the HP area matching best with the observations. The simulated daily irrigation rate has small bias (±0.1 mm day−1; see Fig. 5f) over a large part of the HP, with a maximum difference of 0.5–1.0 mm day−1 over a small area near the border of Kansas and Nebraska (Fig. 5f), which is within the range of uncertainties among different model configurations compared by Tuinenburg et al. (2014). In the other heavily irrigated areas, the current irrigation model underestimates the irrigation rate by ~(0–3) mm day−1. This is partially due to the bias introduced by the irrigation fraction map (Fig. 5c), but is largely a result of discrepancies between the assumptions used in the current irrigation scheme (irrigation rate and duration) and the actual irrigation practice over these areas. In the semiarid regions in the West (CCV and CB-SRP), sprinkler irrigation is commonly applied in somewhat regular intervals to avoid inducing plant stresses rather than waiting for the soil to dry out to a specified level, as prescribed here. In the MRV where flood irrigation is dominant, the current scheme could be modified to better describe such irrigation water applications.

Biases in the simulated irrigation water amount are also subject to issues with the land–atmosphere coupling, the convection parameterization, and uncertainties in the large-scale forcing fields. Since the current irrigation scheme is designed to mainly be consistent with real-world irrigation practice (irrigation rate and duration per irrigation event) over the HP, the good agreement between the simulated and reported irrigation water use over this area lends confidence to the model’s capability to dynamically represent the deficiencies of soil water content and reasonably estimate the irrigation water demand within a regional climate modeling framework. It has thus laid the foundation for further investigations of the modified regional water cycle.

5. Effects of irrigation on the surface energy and water balance

Since irrigation has a direct impact on surface energy and water budget, we first examine the response of surface variables to irrigation before we examine how the energy and water budget interact with atmospheric processes to modify precipitation fields beyond the local scale. Based on the aforementioned modified land-use datasets, the results from the irrigation run (IRRI), described below, were compared with a control run (CTL) with no irrigation. The monthly averaged soil moisture change due to irrigation (Fig. 6) is a climate response rather than a direct measurement after each irrigation event. Over the HP, irrigation slightly increased the soil moisture in the top two soil layers (0–40 cm) during June and July (Figs. 6a,b), while in August slightly drier conditions in the top two soil layers arose because of decreased rainfall (discussed in section 7) as a climate feedback of the drier surface conditions earlier in the season. Replenishment of soil moisture due to irrigation over the HP occurred mainly in the third soil layer (40–100 cm; see Fig. 6c), and the total soil moisture change through the entire soil column (0–200 cm) during JJA caused by irrigation over the HP is not significant. Other than the negligibly small magnitude and mixed pattern of change over the HP, all three of the other heavily irrigated regions (CCV, CB-SRP, and MRV) show a consistent increase of soil moisture during JJA (Fig. 6).

Fig. 6.
Fig. 6.

Changes (IRRI − CTL) in (left to right) the monthly mean and JJA-averaged soil moisture from the top to the bottom soil layer: (a) 0–10, (b) 10–40, and (c) 40–100 cm.

Citation: Journal of Climate 29, 10; 10.1175/JCLI-D-15-0337.1

A careful examination of the irrigation-induced surface changes and the atmospheric feedback (particularly precipitation) suggests that this difference in the effects of irrigation on soil moisture between HP and other regions is likely due to the change in the affected precipitation as well as differences in the simulated irrigation amount. Over the four heavily irrigated areas, latent heat flux (Fig. 7a) and surface runoff (not shown) are all enhanced after irrigation is applied. In the semiarid mountainous CB-SRP, where the simulated irrigation rate is relatively small, enhanced precipitation by irrigation (see section 7) played an important role in increasing soil moisture. Over the MRV, where irrigation water applied is about 50% of the total precipitation (both in the observations and IRRI), irrigation is the main contributor to the soil moisture enhancement since precipitation is barely modified by irrigation in the IRRI. For the CCV, irrigation significantly exceeded precipitation, thus leading to enhanced soil moisture. But over the HP region and a majority of the central United States, a reduction in surface sensible heat flux (Fig. 7b) due to irrigation helped inhibit local convection and led to reduced rainfall in the early period of irrigation. Surface cooling occurred during this early period (June), but gradually the reduced rainfall led to a slightly drier (~0.02 decrease in top-layer soil moisture; see Fig. 6a) and warmer (0.1°–0.5°C warmer; see Fig. 7d) surface over the central United States from July to August, which strengthened the high pressure system over the region and in turn further inhibited rainfall as a climate feedback (see additional discussion in section 6). This subtle surface warming tendency over the central United States by IRRI corrected the original CTL cool bias (relative to NARR) in the simulated surface skin temperature over this region (Fig. 7c). Meanwhile, increased precipitation in the Midwest and eastern United States downwind of the heavily irrigated areas (see more discussions in section 7) helped reduce the corresponding CTL warm bias (Fig. 7c). As a result, the 2-m air temperature, as a diagnostic variable of the model outputs, showed a similar bias correction pattern with the magnitude of both warm and cool biases in CTL reduced by ~0.5°C across almost the entire CONUS. In short, irrigation suppressed or relocated precipitation in the HP enough to cause warmer surface conditions and thus strengthened the governing high pressure system, which in turn suppressed HP rainfall.

Fig. 7.
Fig. 7.

Changes (IRRI − CTL) in (left to right) the monthly mean and JJA-averaged (a) latent heat flux (W m−2) and (b) sensible heat flux (W m−2), (c) surface skin temperature bias from CTL (CTL − NARR, °C), and (d) surface skin temperature difference between IRRI and CTL (IRRI − CTL, °C).

Citation: Journal of Climate 29, 10; 10.1175/JCLI-D-15-0337.1

6. Effects of irrigation on large-scale atmospheric circulations

In JJA 2012, the central and southern United States were governed by an abnormally strong interior high pressure system (geopotential height peaks around 5920 gpm at 500 hPa in IRRI; see Fig. 8), while shortwave troughs along the jet stream in the north were weaker than normal. This abnormally strong interior high pressure centered over New Mexico blocked moisture from entering the central United States, leading to the exceptionally dry summer over this region. The IRRI run represented this general synoptic pattern as did the CTL, though apparent modifications in the magnitudes of geopotential height and wind fields in IRRI relative to CTL were found from 850 to 200 hPa (Fig. 8c). A strengthening and expansion of the dominant interior high pressure system is evident in the IRRI run (Fig. 8c; 850–200 hPa) in correspondence with the warmer land surface conditions in IRRI over the central United States (Fig. 7d). The trough over the upper Midwest at some distance from the high pressure system deepened due at least partially to the enhanced moisture import into the region from the upwind irrigated lands (Fig. 9b, increased eastward moisture transport at the vertical cross section of 95°W between 35° and 45°N). At the continental scale, the long-distance transport of irrigation-enhanced ET is evident (Fig. 9; increased eastward moisture transport at the vertical cross section of 110° and 95°W between 35° and 45°N). This downwind moisture advection was also found in the irrigation study of Huber et al. (2014).

Fig. 8.
Fig. 8.

Wind fields (vectors, m s−1), geopotential height (blue lines, gpm), and air temperature (red lines, °C) at (top to bottom) 850–200 hPa from left to right by CTL, IRRI, and IRRI − CTL. Shaded areas denote the relative humidity (%) at 850 and 700 hPa.

Citation: Journal of Climate 29, 10; 10.1175/JCLI-D-15-0337.1

Fig. 9.
Fig. 9.

Changes (IRRI − CTL) in horizontal moisture flux (g Kg−1 m s−1, eastward is positive) along vertical cross sections at (a) 110° and (b) 95°W.

Citation: Journal of Climate 29, 10; 10.1175/JCLI-D-15-0337.1

The strengthened and expanded interior high pressure system in turn led to weakening of the southerly winds (Fig. 8c; 850 hPa) along the trajectory of the Great Plains low-level jet (e.g., Zhong et al. 1996; Whiteman et al. 1997; Weaver and Nigam 2011; Pu and Dickinson 2014), helping inhibit convective precipitation over the high pressure area; both of these factors contributed to reduced rainfall over the central United States as climate feedbacks (see section 7). This illustrates the coupled mechanism of irrigation contributing to sustainably dry conditions over the central United States under the 2012 dry climate background. On the other hand, the expanded interior high pressure system helped transport and distribute the evaporated irrigation water from the HP to more remote downwind regions—the lower Midwest and part of the Southeast (Fig. 8c; 850–700 hPa), thus increasing moisture in the lower atmosphere over those regions. As shown in Fig. 8c, the increase in relative humidity (5%–10% in absolute terms) across the Southeast at 850 hPa due to irrigation can mainly be attributed to three sources: water vapor transported from the HP irrigation ET, the MRV irrigation ET, and an irrigation-initiated secondary cyclonic vortex along the Gulf coast entraining more moisture from the ocean. The convective available potential energy (CAPE) is enhanced over this area (Fig. 10f), resulting in much more convective rainfall (Fig. 11e), which strongly alleviated the dry biases relative to the observations in the CTL by about 30%–100%.

Fig. 10.
Fig. 10.

(top) PBL height (m) and (bottom) CAPE (J Kg−1) from left to right by CTL, IRRI, and IRRI − CTL.

Citation: Journal of Climate 29, 10; 10.1175/JCLI-D-15-0337.1

Fig. 11.
Fig. 11.

JJA mean daily precipitation (mm day−1) by (a) CTL, (b) observations (NLDAS-2), (c) IRRI, and the corresponding bias (mm day−1) in (d) CTL − OBS and (f) IRRI − OBS. Daily precipitation changes induced by irrigation are shown in (e) IRRI − CTL (mm day−1).

Citation: Journal of Climate 29, 10; 10.1175/JCLI-D-15-0337.1

Irrigation modified the PBL heights (Fig. 10c) in both directions: decreased PBL height over most heavily irrigated land (because of increased moist static energy in the lower atmosphere by irrigation) and in areas with enhanced rainfall (drag effect of the rainfall subsidence) and increased PBL height along the irrigation-triggered upper-level trough areas where upward atmospheric motion is favored. The simulated decreases in the PBL height over the HP irrigated land are about 20–200 m, similar to what was found for the same region in the Qian et al. (2013) modeling study. With irrigation applied, the nationwide volumetric thermodynamic features of the atmosphere from the lower boundary layer to the upper levels were modified, altering the hydroclimate and energy cycles.

7. Effects of irrigation on summer precipitation

In this section, we evaluated the effects of irrigation on summer precipitation across the CONUS. The hourly precipitation fields in the aforementioned NLDAS-2 forcing file A are a product of temporal disaggregation (using Doppler radar observations) of the gauge-only Climate Prediction Center analysis of daily precipitation (Cosgrove et al. 2003; Luo et al. 2003) and were used as observations to validate the simulated rainfall fields in our study.

The spatial distribution of the observed 2012 JJA daily mean precipitation (Fig. 11b) shows that rainfall mainly occurred in the Southeast, mostly because the Gulf coast received precipitation from two landfalling hurricanes (Debby, 24–27 June, and Isaac, 27–31 August). Most parts of the western United States received very little rainfall in JJA, with an average of less than 1 mm day−1 across most of Intermountain West and California, while the Midwest and the HP region had average daily precipitation between 1 and 3 mm day−1. Both the CTL and IRRI captured the main features of the observed precipitation pattern (Figs. 11a and 11c), with generally dry biases over most of the central and eastern United States, except for the Appalachian Mountains and the lower Midwest (Figs. 11d and 11f).

Adding irrigation to the simulations resulted in notable changes in precipitation magnitude across the CONUS. Figure 12 shows the probability density function of the precipitation change over the heavily irrigated lands (irrigation fraction ≥ 0.1, blue line) along with the absolute bias change (black line) over the outlined regions shown in Fig. 1. There are complex features in the local precipitation changes over the four most intensively irrigated regions due to different precipitation regimes for these respective areas. The HP region shows an overall decrease of local daily rainfall in 0–0.5 mm day−1 (Fig. 11e) because of simulated irrigation, resulting from the irrigation–climate feedback mechanism discussed above. Previous modeling studies showed important links between the large-scale moisture convergence and the local rainfall change rather than through local ET enhancement over the HP and central United States (Paegle et al. 1996; Pei et al. 2014), where the recycling rate of ET into local precipitation is rather low (Qian et al. 2013). Our study affirmed this mechanism to some degree, implying that irrigation might have helped worsen the drought over the HP by reducing its rainfall through both surface cooling and modifications in the large-scale synoptic conditions. The majority of the CB-SRP region sees an increase in simulated rainfall of 0–1 mm day−1 (Fig. 12), favored by the enhanced southwesterly flow (Fig. 8c; 700 hPa) since complex terrain-induced intermountain convergence at the lower boundary layer under synoptic westerly flow is its dominant rainfall mechanism (Andretta and Hazen 1998; Steenburgh and Blazek 2001). No apparent local rainfall changes were found over the directly irrigated CCV, which is consistent with Kueppers et al.’s (2007) study in simulating this region’s irrigation effects on local precipitation and clouds. The MRV has subtle fluctuations (±0.5 mm day−1) in its local precipitation changes (Fig. 12), while the effects in its downwind areas are more profound.

Fig. 12.
Fig. 12.

Probability density function of the JJA daily precipitation changes (mm day−1) over the heavily irrigated lands (irrigation fraction 0.1, blue line) and the absolute bias changes (mm day−1, black line) over the entire outlined subregions denoted in Fig. 1.

Citation: Journal of Climate 29, 10; 10.1175/JCLI-D-15-0337.1

Nationwide, irrigation has stronger effects downwind rather than in the aforementioned four heavily irrigated regions. This is especially true over the lower Midwest, the Southeast, and the western United States (Fig. 12). The lower Midwest is downwind of the irrigated areas over the HP and the upper Midwest is remotely downwind of CB-SRP, as can be seen from the wind fields aloft (Fig. 8; 850–700 hPa), while the Southeast is influenced more by irrigation over MRV as a result of the winds traveling along the periphery of the anticyclone over that region. In the semiarid West, where there is little daily rainfall (0–0.5 mm day−1; see Fig. 11b), the enhancement of daily rainfall by irrigation in IRRI can exceed 200% in western Utah and Arizona on the windward side of the Rocky Mountains, and 20%–100% over Oregon, Idaho, northern California, and northern Nevada. This amplification strongly alleviated the dry biases in Nevada, western Utah, and western Arizona in the CTL run relative to the observations while increasing the wet biases in the northern part. In the southeast United States and parts of the Midwest, with irrigation applied the dry biases in the CTL are reduced, especially in the Southeast [dry bias reduced in around 0–2 mm day−1 (Fig. 12), or by 30%–100%]. These downwind effects in precipitation are both a result of increased moisture advection as well as modified synoptic conditions through land–atmosphere interaction in the seasonal scale as discussed in section 6.

Skill score analysis based on the contingency table [(hits + correct rejections)/total] for daily precipitation throughout the JJA period showed high scores for both CTL and IRRI, with the lowest score around 0.7 occurring in the southern Great Plains and the mountainous Pacific Northwest (figure not shown). Statistics (Table 1) show that with the current irrigation scheme the representation for the JJA nationwide spatially averaged daily precipitation has a broadly lower bias. The mean bias and root-mean-square error of the mean daily precipitation over the CONUS are both reduced by including irrigation, with the correlation between the observed and simulated daily precipitation increased from 0.41 (CTL) to 0.51 (IRRI). The improvements were mainly downwind of the irrigated areas, especially over the lower Midwest and the Southeast.

Table 1.

Statistics of the spatially averaged daily precipitation (mm day−1).

Table 1.

8. Discussion and conclusions

We have developed a novel irrigation approach for the WRF model to dynamically simulate the influence of irrigation on the hydroclimate cycle of the United States, and applied it to study landscape–atmosphere interactions during an exceptionally dry summer of 2012. This paper presents the validation of the irrigation method and an analysis of the modified hydroclimate cycles, focusing on evaluating the simulated irrigation water amount and the modified summer precipitation.

Our study shows that incorporating irrigation has substantially improved the simulated precipitation amounts over the downwind areas across the CONUS, suggesting the importance of integrating anthropogenic influences on the land surface in climate modeling. Over the irrigated areas, the added surface water by irrigation is redistributed within the regional climate system through complex land–atmosphere interactions, wind modifications, and upper-level moisture advection processes rather than simply through direct local recycling via the ET–convective rainfall link, which is commonly assumed. This simulated anthropogenic change in soil water content strongly affects atmospheric thermodynamic features nationwide from the lower atmosphere to the upper troposphere, modifying the entire water cycle.

A mixed pattern of changes in local precipitation was found in the studied four intensively irrigated agricultural regions, with the HP having slightly decreased rainfall, CB-SRP having slightly increased rainfall, and subtle fluctuations for both the CCV and MRV. This is due mainly to the regional differences in dominant precipitation mechanisms. As the central HP witnessed its most severe drought since 1895 (Hoerling et al. 2014), it is worth noting that the irrigation over the HP may have helped worsen the drought conditions by modifying large-scale atmospheric circulations that weakened the Great Plains low-level jet intensity and reduced moisture convergence over this region. Overall, the downwind precipitation was enhanced nationwide (though this is spatially complicated) through increased irrigation-induced water vapor advection as well as irrigation-triggered secondary atmospheric circulations favoring convection in these areas. This enhancement in downwind precipitation alleviated 20%–100% of the dry biases (relative to observations) otherwise present in the WRF simulation without irrigation. The appearance of downstream effects in the model far from the irrigated areas highlights two major issues: 1) global models do not parameterize these remote effects and 2) we do not have a clear understanding of the relative roles of albedo change and latent/sensible heat flux partitioning that accompanies irrigation; more work is needed in these areas.

Irrigation effects are expected in and around the irrigated regions. However, the existence of these long-distance telecoupling effects implies that there are significant excursions to expected atmospheric responses from land-use/cover changes compared to how they are currently understood, and how the models are generally used. Studies by Pitman et al. (2012) and de Noblet-Ducoudré et al. (2012) as part of the Land-Use and Climate, Identification of Robust Impacts (LUCID) project illustrate that land cover–atmospheric couplings can have large and far-reaching effects, but that these connections are poorly understood and usually not considered in global models. This illustrates a clear need for scientists to improve the knowledge of how land models and hydrology interact with micro- and mesoscale atmospheric circulations. The results presented here point to the much needed but absent inclusion of nonradiative forcing in studies about human modification of climate. This work also reinforces a key recommendation from the LUCID experiments that atmospheric feedbacks and their sensitivity to dynamic land use and land cover needs much more detailed exploration to meaningfully improve the skill of global models.

Recent irrigation modeling studies have highlighted the sensitivity of model results to chosen irrigation schemes (Sorooshian et al. 2012; Leng et al. 2013), which remain highly uncertain because of complex real-world practices. Our work has emphasized the importance of a realistically prescribed irrigation scheme for hydroclimate modeling. In this study, with the capability of applying irrigation to the subgrid scale in the Noah-Mosaic land surface module, our modeling framework showed reasonable skill in simulating the water applied into the irrigated lands over the HP, where the irrigation scheme was configured to match a typical real-world practice over this region, both in water supply rate and duration. No tuning or parameter estimation was done to match precipitation rates; rather, the irrigation routine uses common farmer practices associated with dry soils to determine when and how irrigation is applied. This approach links the climate simulation framework with corresponding agricultural water demand, providing insights for further interdisciplinary study of interactions between climate, hydrology, agriculture, and economics to improve our knowledge of climate change’s effects on variable irrigation water demand. It also provides a powerful tool to help quantify potential future irrigation water demands under climate change scenarios beyond what is currently available in crop models (Basso et al. 2015).

Because of the complex nature of real-world irrigation practices in different regions (e.g., variations in climate conditions, crop water demands, practice conventions, irrigation methods, yield expectations, and farmers’ personal preferences), it is impossible for one uniform irrigation routine to satisfy all application methods. Another limitation is the continuing problem of properly reproducing cloud physics and shallow convection, which are both significant sources of error in our CTL simulations. Challenges for irrigation modeling studies in the future include improving the realism of prescribed irrigation for multiple irrigation types as well as model optimizations and calibrations. Leng et al. (2013) found that after calibrating the irrigation water amount, smaller errors and higher correlations of the ET fields were evident across a majority of the United States in their offline model simulations. If the irrigation water amount is expected to be simulated dynamically, prescribing different irrigation schemes in different regions will likely help alleviate part of these simulated biases in the current study. Uncertainties in the model physics can lead to large deviations in the simulated irrigation water amount (Tuinenburg et al. 2014), and model ensembles might be adopted to decrease the uncertainties and biases (Posselt and Vukicevic 2010; Awan et al. 2011).

Acknowledgments

This research was supported primarily by the U.S. National Science Foundation (Water Sustainability and Climate 1039180). Partial support was also provided by the USDA NIFA Water CAP Award 2015-68007-23133, the National Program on Key Basic Research Project of China (973) under Grant 2012CB417203, and the AgBioResearch program at Michigan State University. We thank Dr. Keith Harding at University of Minnesota, Twin Cities, and Dr. Mutlu Ozdogan at University of Wisconsin–Madison for helpful discussions. We also acknowledge the UCAR/NCAR Computational and Information Systems Lab for the high-performance computing resources and the visualization support from the NCAR Command Language (NCAR 2012). Any opinions, findings, and conclusions or recommendations expressed are those of the authors and do not necessarily reflect the views of the funding agencies.

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