Agriculture is one of the important sectors of California's economy and a major provider of agricultural products to the United States and the global markets. However, given the semiarid nature of California's agricultural region and lack of sufficient precipitation, irrigation has been the main method of meeting the water demand and ensuring high crop yields. Viewed in the context of California's overall water management, irrigation is the largest consumer of water. However, current water management decision-making models assume that the consumptive use of water for irrigation is fixed and ignores interannual variations (see review by Tang et al. ). Better estimation of irrigated crop's ET and other climate variables presumably could lead to more efficient use of water in arid and semiarid California areas.
 Crop water use depends on surface atmospheric conditions, crop characteristics, the type of irrigation, as well as soil moisture. The relationship between large-scale irrigation (i.e., water use) and land surface energy and mass flux exchanges (depending on atmospheric conditions) has also received much attention, especially in recent years (see the reviews of Pielke et al.  and Sacks et al. ). Qualitatively, irrigation practices have been identified as having both direct and indirect consequences on local and regional climate (e.g., see the review by Pielke et al. ) through land cover and land use changes (LCLUS). The types of irrigation schemes have also impacted the ecohydrological processes in the water-limited environments through modifying the soil water contents (e.g., see the review by Newman et al. ). Many studies have quantitatively investigated the impact of irrigation on weather, climate, and hydrology at different scales. Such studies have relied mainly on the use of physics-based numerical models [e.g., Segal et al., 1998; Adegoke et al., 2003; Kueppers et al., 2007, 2008; Kanamaru and Kanamitsu, 2008; Lobell et al., 2009]. However, such studies have shown results which disagree on the magnitude and spatial pattern [Sacks et al., 2009]. For example, most authors of these studies have usually fixed the root zone soil moisture within the models to either field capacity or saturation in the runs [e.g., Adegoke et al., 2003; Haddeland et al., 2006; Kueppers et al., 2007; Kanamaru and Kanamitsu, 2008]. Consequently, the reported results of previous studies may not accurately represent the effects of irrigation on surface fluxes and on the regional/local climate and surface hydrology.
 The question of how much water should be added into climate models' assumed soil zone in order to account for irrigation is still unresolved and has typically been decided based on some sensitivity studies. For example, Kanamaru and Kanamitsu  investigated the effects of irrigation on regional climate by prescribing the Oregon State University land surface model (LSM) root zone soil moisture to saturated and half-saturated conditions for each time step separately. Their results suggested that the soil moisture prescription is too high and, hence, causes cool bias. Sacks et al.  prescribed a specific amount of water into the model (CLM3.5) irrigation grid based on leaf area index (LAI) and mean estimated annual irrigation water amount to investigate the effect of irrigation on global climate. However, Sacks et al.  mentioned that their method assumes that the irrigation water usage has no seasonal and interannual variation as well as being independent of crop type. On the other hand, Lobell et al. , using the community atmospheric model (CAM3.3), which is coupled with CLM3 LSM, and through prescribing the top 30 cm soil moisture at the irrigation grid for 90%, 50%, 40%, and 30% of soil saturation, respectively, found that the impacts of irrigation on air temperature and latent heat fluxes (i.e., ET) are “extremely insensitive” to soil moisture increases beyond 30% saturation. The same results were done by Kueppers and Snyder , who also claimed that “irrigation to 50, 75, or 100% of field capacity did not result in detectably different effects on afternoon maximum temperatures in any month of the year in RegCM3,” which is coupled with BATS LSM. Most recently, using the Noah LSM offline, Ozdogan et al.  studied the ET variation at the Five Points site (grass farm) in California as well as the Mead (previous name of the station Soybean) Ameriflux site (bean crop) in Nebraska and found that ET is improved by setting the maximum allowable water depletion (SWm) of soil moisture at the fixed value of 50% for the two sites studied.
 While the study of Ozdogan et al.  showed improvement in ET estimates for a fixed SWm, there is a plausible explanation for their reported underestimation of ET at Five Points in California and overestimation at the Mead Ameriflux site in Nebraska [Ozdogan et al., 2010, Figure 7]. This is partly due to the fact that the recommended SWm values for bean crop are 0.45 and 0.50–0.55 for alfalfa or grass. According to Hanson et al. , the maximum allowable water depletion changes depending on a wide range of vegetation (crop) types. For example, SWm values as high as 0.90 for wheat (ripening) and as low as only 0.15 for strawberries have been recommended. In short, fixing the SWm to a specific value may still result in inaccurate capturing of the effect of irrigation on ET, etc.
 There are also a number of reported studies where the Noah LSM has been employed to study irrigation impact on regional and local hydroclimate [e.g., Ozdogan et al., 2010]. Often, all crops in the Noah LSM, especially in the coupled MM5/Noah LSM, are categorized as one type of land use, and the related vegetation parameters are kept the same. In a recent study by Sorooshian et al. [2011, hereinafter S2011], using the Noah LSM in offline mode, the SWm at each irrigation grid or site (point) was set to values recommended by Hanson et al. . We note that Hanson et al.  recommended that SWm values, which are based on crop types, be prescribed for optimal (i.e., minimum) use of water without affecting maximum crop yield. It should be recognized, however, that, in the real world, it is difficult to control and monitor two factors. The first factor is the practicality of maintaining SWm at the exact recommended values. The second factor is the potential inexactness of information about crop types as they change from season to season and annually. The combination of these two factors introduces some error in the model simulations because of prescribed irrigation assumptions that may not reflect the exact conditions. In coupling runs performed by S2011, the SWm values used for the two major irrigation areas of the Sacramento and San Joaquin valleys were the average from all crops within the respective irrigation areas based on monthly data provided by Hanson et al. . The results from S2011 indicate that the integrated model using the averaged SWm can reproduce observed meteorological fields as compared to ground observation as well as remote sensing data at the intraseasonal scale.
 In this paper, we extend the study of S2011, which only focused on interseasonal scale, to interannual and interdecadal scales with the primary focus on ET variations. Remote sensing estimates of ET obtained from the MODIS (called MODIS-ET here) instruments, onboard Terra and Aqua Earth Observing Satellites [Tang et al., 2009], were used as reference for comparison purposes.