Search Results (3)

Efficient water management strategies are of utmost importance in drought-prone regions, given the fundamental role irrigation plays in avoiding yield losses and food shortages. Traditional methodologies for estimating irrigation amounts face limitations in terms of overall precision and operational scalability. This study proposes to estimate irrigation amounts from soil moisture (SM) data by adapting the PrISM (Precipitation Inferred from Soil Moisture) methodology. The PrISM assimilates SM into a simple Antecedent Precipitation Index (API) model using a particle filter approach, which allows the creation and estimation of irrigation events. The methodology is applied in a semi-arid region in the Ebro basin, located in the north-east of Spain (Catalonia), from 2016 to 2023. Multi-year drought, which started in 2020, particularly affected the region starting from the spring of 2023, which led to significant reductions in irrigation district water allocations in some of the areas of the region. This study demonstrates that the PrISM approach can correctly identify areas where water restrictions were adopted in 2023, and monitor the water usage with good performances and reliable results. When compared with in situ data for 8 consecutive years, PrISM showed a significant person’s correlation between $0.58$ and $0.76$ and a cumulative weekly root mean squared error (rmse) between 7 and 11 mm. Additionally, PrISM was applied to three irrigation districts with different levels of modernization, due to the different predominant irrigation systems: flood, sprinkler, and drip. This analysis underlined the strengths and limitations of PrISM depending on the irrigation techniques monitored. PrISM has good performances in areas irrigated by sprinkler and flood systems, while difficulties are present over drip irrigated areas, where the very localized and limited irrigation amounts could not be detected from $SM$ observations. Full article

Several recent studies have shown that knowledge of the spatiotemporal dynamics of soil moisture intrinsically contains information on precipitation. In this study, we show how SMOS measurements can be used to generate a near-real-time precipitation product with a spatial resolution of 0.1° and a temporal resolution of 3 h. The principle consists of assimilating the SMOS data into a model that simulates the evolution of soil moisture, which is forced by a satellite precipitation product. The assimilation of SMOS soil moisture leads to an adjustment of the satellite precipitation rates. Using data from more than 200 rain gauges set up in Africa between 2010 and 2021, we show that the PrISM algorithm (for Precipitation Inferred from Soil Moisture) almost systematically improves the initial precipitation product. One of the original features of this study is that we used the IMERG-Early satellite precipitation product, which has a finer spatial resolution (0.1°) than SMOS (~0.25°). Despite this, the methodology reduces both the RMSE and bias of IMERG-Early. The RMSE is reduced from 8.0 to 6.3 mm/day, and the absolute bias is reduced from 0.81 to 0.63 mm/day on average over the 200 rain gauges. PrISM performs even slightly better on average than IMERG-Final in terms of RMSE (6.8 mm/day for IMERG-Final) but better scores are obtained by IMERG-Final in terms of absolute bias (0.35 mm/day), which utilizes a network of field measurements to correct the biases of the IMERG-Early product with a 2.5-month delay. Therefore, the use of SMOS soil moisture measurements for Africa can be an advantageous alternative to the use of gauge measurements for debiasing rainfall satellite products in real time. Full article

Information security management (ISM) has emerged as a major challenge to sustainable management of companies that highly rely on information technology (IT). To achieve organizational sustainability for managing assets, it is critical for all members of the organization to be assimilated into ISM. An important consideration of ISM assimilation is sustainable IT capabilities. However, so far, there are limited empirical studies on ISM assimilation, particularly those focusing on importance of the organization’s sustainable IT capabilities. Therefore, this study proposes three sustainable IT capabilities (viz., IT infrastructure, IT business spanning capability, and IT proactive stance) with their antecedents based on the existing research, and attempts to empirically prove the impact of these sustainable IT capabilities on ISM assimilation for sustainable management of assets. Additionally, this study proposes policy-to-technology balance as a moderator on the relationships between the three sustainable IT capabilities and ISM assimilation to examine the impact of the non-technical aspect. Responses from 232 upper-management-level employees at various firms currently implementing ISM were collected. Structural equation analysis was run using AMOS 22.0. The results show that the three sustainable IT capabilities were found to have a positive effect on ISM. Furthermore, policy-to-technology balance was found to strengthen the relationship between two IT capabilities (IT infrastructure and IT business spanning capability) and ISM assimilation. However, it emerges that the policy-to-technology balance does not impact IT proactive stance and assimilation. The findings provide meaningful information for future research on sustainable IT capabilities and ISM along with key guidance for the organization to establish a complementary strategy for sustainable assets. Full article