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Monitoring irrigation is crucial for sustainable water management in freshwater-limited regions. Even though soil moisture (SM)-based inversion algorithms have been widely used to estimate irrigation, scarcity of irrigation records has prevented a thorough understanding of their uncertainties, especially in humid regions. This study assesses the suitability of the SM2RAIN algorithm for estimating irrigation at field scale using high-temporal-resolution data from four corn growing experiments conducted in north-central Florida. Daily irrigation estimates were compared with observations, revealing root mean squared differences of 1.26 to 3.84 mm/day and Nash–Sutcliffe Efficiencies of 0.33 to 0.89. The estimates were more sensitive to uncertainties in static inputs of porosity, saturation moisture and soil thickness than they were to noise in time series inputs. Defining the saturation moisture as porosity made the algorithm insensitive to both parameters, while increasing soil thickness from 40 to 200 mm improved detection accuracies by 34–46%. In addition, the impact of SM on the estimations was investigated based on satellite overpass times. The analysis showed that morning passes produced more accurate estimates for the study site, while evening passes doubled the uncertainty. This study enhances the understanding of the SM2RAIN algorithm for irrigation estimation in subtropical humid conditions, guiding future high-resolution applications. [ABSTRACT FROM AUTHOR]

This study investigates the relationship between soil moisture and the growth of Pinus halepensis, P. nigra, P. sylvestris and P. uncinata, which are some of the main pine species of the Iberian Peninsula, and the response of these species to soil drought. The role played by climatic and geographic factors in the resilience of these species to drought events is also evaluated. A total of 110 locations of the four species studied were selected, with data ranging from 1950 to 2007. The results show that the species that are less dependent on soil moisture best withstood droughts, while those more dependent on it showed better adaptability. Additionally, climatic and geographic factors had a stronger influence on the species' resilience to soil drought at higher altitudes. The results of this study can help us to better understand forest ecosystem dynamics and their reaction to droughts in Mediterranean areas, where this phenomenon will be much more severe in the future due to climate change. [ABSTRACT FROM AUTHOR]

Rainfed cereal yields show high variability depending on the varying conditions of concurrent factors during the crop year. Among them, hydrometeorological factors such as maximum temperature, rainfall, and notably, soil moisture, strongly affect crop production, but the greatest source of uncertainty on yield estimates stems from their interaction. This is of special interest in water-limited regions where climate change is expected to affect more intensely, but also in others where water is increasingly limited. Despite the highly non-linear nature of the interactions, simple statistic models such as multilinear regression accurately explore a notable proportion of the variability of cereal yields. To describe the impacts behind interactions, we perform stepwise multilinear regression of meteorological factors derived from E-OBSv23 database and soil moisture from ERA5-Land against annual wheat and barley yields for the period 1981–2019 in the main cereal regions of Spain. The multivariate approach characterizes the temporal shifts of factors' influence. Beyond the temporal shifts on the synchrony of the factors, some of them tend to co-dominate the impact during the critical period of crop development, with soil moisture exceeding all others in relevance. Multivariate analysis fosters discussion about the impact of the choice of variables on the model fit, as well as on the pertinence of monthly and annual scales for explorative and predictive purposes. Monthly models perform particularly well during the critical period of growth and reproduction of crops and consistently better than univariate estimates. The annual model built using the data of the months of maximum impact of key variables outperforms the model at a monthly scale, which underlines the decisive role of the critical period. Similarly, results highlight the worth of parsimony in modelling. Soil moisture stands out as the principal concurrent variable to improve yield estimates from environmental data, which governs yields of rainfed water-limited croplands. • Soil moisture stands out from environmental factors on influence on cereal yields. • Stepwise multilinear regression of less colinear factors largely explains yield variability. • Using key variables in the critical period of crop sensitivity improves model fit. • Soil moisture notably improves model estimates both at monthly and yearly scales. [ABSTRACT FROM AUTHOR]