Evaluating the Performance of Irrigation Using Remote Sensing Data and the Budyko Hypothesis: A Case Study in Northwest China

Remote Sensing, Год журнала: 2025, Номер 17(6), С. 1085 - 1085

Опубликована: Март 19, 2025

Evaluating the performance of irrigation water use is essential for efficient and sustainable resource management. However, existing approaches often lack systematic quantification consumption fail to differentiate between precipitation anthropogenic appropriation flows. Building on green–blue concept, consumptive use, assumed equal actual evapotranspiration (ETa), was partitioned into green ET (GET) blue (BET) using remote sensing data Budyko hypothesis. A novel BET metric developed applied irrigated lands in northwest China evaluate from 2001 2021. The results showed that terms total available resources (precipitation + gross (GIW)) compared demand, estimated as reference (ET0), Ningxia has sufficient supply meet while Hexi Corridor faces increasing risks unsustainable use. Hetao scheme shifted a fragile supply–demand balance situation where demand far exceeds availability. In Xinjiang, tight. Furthermore, when considering (GIW) relative net (ET0-GET), significant deficits, Xinjiang are close meeting local demands by relying current availability practices. It noteworthy remains lower than GIW (excluding recent years). ratio an estimate efficiency, which 0.54 all schemes taken together. addition, water, evaluated detail, it found last 10 years efficiency improved Ningxia, scheme, Xinjiang. continues face severe suggesting likelihood groundwater sustain agriculture. innovatively separates (green water) (blue water), critical advancement beyond conventional approaches’ estimates merge these distinct hydrological components help quantifying efficiency.

Язык: Английский

Harnessing Digital Twins for Sustainable Agricultural Water Management: A Systematic Review

Applied Sciences, Год журнала: 2025, Номер 15(8), С. 4228 - 4228

Опубликована: Апрель 11, 2025

This systematic review explores the use of digital twins (DT) for sustainable agricultural water management. DTs simulate real-time environments, enabling precise resource allocation, predictive maintenance, and scenario planning. AI enhances DT performance through machine learning (ML) data-driven insights, optimizing usage. In this study, from an initial pool 48 papers retrieved well-known databases such as Scopus Web Science, etc., a rigorous eligibility criterion was applied, narrowing focus to 11 pertinent studies. highlights major disciplines where technology is being applied: hydroponics, aquaponics, vertical farming, irrigation. Additionally, literature identifies two key sub-applications within these disciplines: simulation prediction quality soil water. also types maturity levels concepts applications. Based on their current implementation, in agriculture can be categorized into functional types: monitoring DTs, which emphasize response environmental control, enable proactive irrigation management forecasting. techniques used framework were identified based These findings underscore transformative role that play enhancing efficiency sustainability Despite technological advancements, challenges remain, including data integration, scalability, cost barriers. Further studies should conducted explore issues practical farming environments.

Язык: Английский

Mid-Term achievements in the Dragon 5 Cooperation between MOST and ESA

Geo-spatial Information Science, Год журнала: 2024, Номер 27(3), С. 523 - 525

Опубликована: Май 3, 2024

Язык: Английский