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Irrigation cools near surface air temperature by increasing evapotranspiration from wetter soil. However, elevated evapotranspiration can also increase atmospheric albedo and enhance the local greenhouse effect via increased atmospheric water vapor. Their net effects on daily air temperature remains controversial. Here we show that in several considered regions, Northwest India and Central Valley of California, irrigation could result in warmer air temperature if night‐time warming is stronger than daytime cooling by irrigation. During the daytime, air temperature reduces through evaporative cooling and reduced solar radiation from increased atmospheric albedo outweighing the local greenhouse effect. At night‐time, the increased atmospheric water vapor by irrigation tends to make a stronger local greenhouse effect that increases night‐time temperature. Our results highlight the possible increase of air temperature by irrigation and the importance of considering sub‐daily processes when assessing the impact of irrigation on daily air temperature and temperature related socioeconomic phenomena. Plain Language Summary: Irrigation can influence air temperature in two contrasting ways. Evaporative cooling from wetter soil can reduce air temperature. However, evaporated water is a greenhouse gas, which can increase air temperature. The impact of these two influences differs for daytime and night‐time air temperature. Here, we examine the impact of irrigation on daytime and night‐time air temperature under two different assumptions. Our simulations show that the warming influence of irrigation on night‐time temperature could be greater than the cooling influence on daytime temperature in several considered regions, Northwest India and Central Valley of California. Our results underline that the impact of irrigation is a complex interplay of evaporative cooling and greenhouse warming and still needs to be further investigated. Key Points: Irrigation may result in warmer night‐time temperature by enhancing the local greenhouse effectThe temperature response of local irrigation can be influenced by irrigation practices over nearby areasHighlight the importance of water vapor as a greenhouse gas in climate change [ABSTRACT FROM AUTHOR]

From the 1980s, Indian summer monsoon rainfall (ISMR) shows a decreasing trend over north and northwest India, and there was a significant observed reduction in July over central and south India in 1982–2003. The key drivers of the changed ISMR, however, remain unclear. It was hypothesized that the large-scale irrigation development that started in the 1950s has resulted in land surface cooling, which slowed large-scale atmospheric circulation, exerting significant influences on ISMR. To test this hypothesis, a fully coupled model, the CESM v1.0.3, was used with a global irrigation dataset. In this study, spatially varying irrigation-induced feedback mechanisms are investigated in detail at different stages of the monsoon. Results show that soil moisture and evapotranspiration increase significantly over India throughout the summertime because of the irrigation. However, 2-m air temperature shows a significant reduction only in a limited region because the temperature change is influenced simultaneously by surface incoming shortwave radiation and evaporative cooling resulting from the irrigation, especially over the heavily irrigated region. Irrigation also induces a 925-hPa northeasterly wind from 30°N toward the equator. This is opposite to the prevailing direction of the Indian summer monsoon (ISM) wind that brings moist air to India. The modeled rainfall in the irrigated case significantly decreases up to 1.5 mm day−1 over central and north India from July to September. This paper reveals that the irrigation can contribute to both increasing and decreasing the surface temperature via multiple feedback mechanisms. The net effect is to weaken the ISM with the high spatial and temporal heterogeneity. [ABSTRACT FROM AUTHOR]

Mapping irrigated areas using remotely sensed imagery has been widely applied to support agricultural water management; however, accuracy is often compromised by the in-field heterogeneity of and interannual variability in crop conditions. This paper addresses these key issues. Two classification methods were employed to map irrigated fields using normalized difference vegetation index (NDVI) values derived from Landsat 7 and Landsat 8: a dynamic thresholding method (method one) and a random forest method (method two). To improve the representativeness of field-level NDVI aggregates, which are the key inputs in our methods, a Gaussian mixture model (GMM)-based filtering approach was adopted to remove noncrop pixels (e.g., trees and bare soils) and mixed pixels along the field boundary. To improve the temporal transferability of method one we dynamically determined the threshold value to account for the impact of interannual weather variability based on the dynamic range of NDVI values. In method two an innovative training sample pool was designed for the random forest modeling to enable automatic calibration for each season, which contributes to consistent performance across years. The irrigated field mapping was applied to a major irrigation district in Australia from 2011 to 2018, for summer and winter cropping seasons separately. The results showed that using GMM-based filtering can markedly improve field-level data quality and avoid up to 1/3 of omission errors for irrigated fields. Method two showed superior performance, exhibiting consistent and good accuracy (kappa > 0.9) for both seasons. The classified maps in wet winter seasons should be used with caution, because rainfall alone can largely meet plant water requirements, leaving the contribution of irrigation to the surface spectral signature weak. The approaches introduced are transferable to other areas, can support multiyear irrigated area mapping with high accuracy, and significantly reduced model development effort. [ABSTRACT FROM AUTHOR]

Farm-level seasonal irrigation water usage is often highly variable across time and space in an irrigation district. Identifying the driving factors of this variation can help researchers and managers understand the underlying efficiency of water usage, identify the sources of water waste and develop best irrigation practices to facilitate the development of more efficient and sustainable irrigated cropping systems. This study explored driving factors for the seasonal irrigation water usage across an irrigation district in south-eastern Australia, using extensive climate, soil, cropping and water use data from 312 farms (a total of 1099 annual crop water use records). It focused on three irrigated crops (corn/maize, cotton and rice) for 2011–2019. Factors considered included climate, soils, irrigation practices and water allocation, from which key driving factors and their effects on the seasonal irrigation water usage were identified. A Bayesian hierarchical model averaging approach was developed to determine key predictors and estimate seasonal irrigation water usage. The Bayesian hierarchical modelling framework allowed seasonal irrigation water usage to be estimated at all crop-farm-year observation points simultaneously while also enabling different key predictors to be selected for different crop types. Results showed that the seasonal irrigation water usage was mainly driven by irrigation practices and soil, but the water use of different crop types was driven by different subsets of irrigation and soil predictors. Modelling was undertaken in natural log space and the model showed reasonable accuracy in estimating the seasonal irrigation water usage (R2 = 0.62). Our model is considered useful in 1) identifying drivers of variation of the seasonal irrigation water usage across time and space, and 2) suggesting potential adjustments to irrigation practices and quantifying their benefits to reduce irrigation water usage. • Factors driving seasonal irrigation usage were explored in an irrigation district. • A Bayesian hierarchical model (BHM) was developed for 1099 crop water use records. • Seasonal irrigation usage was mainly influenced by soil and irrigation practices. • The BHM showed good accuracy in estimating seasonal irrigation usage (R2 = 0.62). [ABSTRACT FROM AUTHOR]