Water Requirements Research Articles

Water use and yield response of rainfed safflower (Carthamus tinctorius L.) in Vertisols with varying soil depths

AbstractSafflower (Carthamus tinctorius L.) is an edible oilseed crop mainly cultivated in marginal lands. This study evaluates safflower crop water requirements to understand its feasibility to cultivate under rainfed ecosystem through a field experiment undertaken at the International Crops Research Institute for the Semiarid Tropics research farm, India. Eight improved and stress‐tolerant safflower cultivars (five spiny and three non‐spiny) were evaluated in Vertisols at three soil depths, that is, shallow: <0.60 m, medium: 0.60–1.20 m, and deep:1.20–1.80 m, over 3 years (2009–2012). Wet, normal, and deficit rainfall years were experienced during 2009/2010, 2010/2011, and 2011/2012, respectively. Soil moisture, crop yield, and growth parameters were measured, and field‐scale hydrology was captured through a calibrated one‐dimensional water balance model. Safflower responded to available residual soil moisture which varied with soil depth and rainfall received in different years. Total crop water use was 300–320 mm during the postrainy season, of which about 70% was extracted in deep Vertisols and 55% in medium Vertisols through residual soil moisture. In addition, 30% of water requirement was met through postrainy season rainfall. Safflower grown in shallow Vertisols could only meet 40% of crop water requirement. Spiny cultivar NARI‐H‐15 grown in deep soil recorded a maximum yield of 1890 kg ha−1 in the wet year. Seed yield from spiny cultivars grown in deep and medium soils was nearly similar (1500–1600 kg ha−1) during wet and normal years; a significant reduction in yield (>50%) occurred in shallow soils and also during a rainfall deficit year. Spiny cultivars produced 10%–50% higher seed yield compared to non‐spiny cultivars. Growing safflower in medium and deep Vertisols provides opportunities for crop intensification.

Operational workflow to simulate biophysical variables, based on the coupled WRF/SEBAL models

Three subsystems including an atmospheric regional forecast and analysis system, satellite data retrieval and processing, and a surface energy balance model have been employed in a 24/7 operational system to measure and simulate some primary agricultural variables, including evapotranspiration, biomass production, and other associated productivity metrics such as NDVI and Albedo. The final products are generated in a daily routine, covering Iran as the domain of simulation. The WRF atmospheric model, VIIRS, and SENTINEL2 satellite data, as well as the SEBAL model (implemented in pySEBAL) are the main components of the operational system, with products in two different spatial resolutions of 375 m and 100 m. The output products can be used by farmers and stakeholders to manage agricultural water requirements and monitor biophysical parameters.

Ecosystem services of irrigated and controlled drainage agricultural systems: A contemporary global perspective

AbstractIrrigated agriculture provides 40% of the world's crop‐based foods but often with a negative impact on the environment. It is important to recognize that in addition to providing food and fibre, irrigation and controlled drainage systems can be optimized to provide additional ecosystem services and mitigate climate change by using resources in a more efficient way. Contemporary case studies were identified from around the world, including flood control by paddy fields in Japan, water quality enhancement and wastewater reuse in South Africa and Taiwan, micro‐/meso‐climate regulation in Ethiopia and Japan, controlled drainage and sub‐irrigation to maximize carbon sequestration and minimize leaching in Finland, and groundwater table management to reduce irrigation water and pumping requirements in Turkey. Irrigation infrastructure, such as rice paddy terraced landscapes (Japan) and large dams and canals (Australia), have also achieved notable additional ecotourism job creation. Case studies were analysed in terms of funding opportunities and compared using the Common International Classification of Ecosystem Services system. It is recommended that planning frameworks be developed that seek to optimize ecosystem services such as the ones discussed above. Policy should be updated to recognize these services and provide incentives to irrigators and water management entities accordingly.

Spatiotemporal variation in rice water requirements and area in the cold rice cultivation region of China: Past and Future

Studying rice water requirements (ETc) and area is crucial for developing irrigation schedules and adjusting crop structures. The cold rice cultivation region is China’s main high-quality rice production base and has the lowest temperature, highest latitude, and greatest sensitivity to climate. However, little research has focused on its past rice distributions and ETc, and its future variations are not yet clear. Past ETc was calculated by the Penman–Monteith and crop-corrected coefficient method, and past rice distribution patterns were analyzed based on the annual paddy rice planting area datasets. Future reference crop evapotranspiration (ET0) was projected by the statistical downscaling model (SDSM) based on the CMIP6 ensemble of daily predictor variables. The food and agriculture projections to 2050 datasets were used to project future rice areas, and the patch-generating land use simulation (PLUS) model simulated rice distribution. Finally, we projected future rice water supply demand. The results showed that ETc showed a decreasing trend, and the total rice area and production increased by 4.52 and 5.49 times from 1990 to 2020. Both in the past and future, rice was densely distributed in and expanded into the Sanjiang and Songnen Plains, and the spatial characteristic of ETc was as follows: the west was larger than the east, the north was larger than the south, and the central was in between. In the four future periods, the total ETc demonstrated an upward trend, increasing by 7.44–21.04, 15.64–34.74, 24.74–45.84, and 16.14–41.14 mm under the SSP126, SSP245, SSP370, and SSP585 scenarios, respectively, compared to the historical period. And it is generally ranked as SSP370 > SSP245 > SSP126. Finally, we projected future rice water supply meet the demand caused by rice area expansion.

Clear hands, Clear conscience: Fog disinfection, dry hand washing

Proposing a water-saving hand washing solution, we introduce a system designed to reduce water usage by over 95%. Highlighting the importance of hand hygiene in combating pandemics, we challenge the notion of excessive water requirement for effective disinfection. Our system, controlled by an Arduino microcontroller, incorporates ultrasonic sensors, mist maker, UV lamp, and timer buttons. It activates mist and UV components based on user-selected time, ensuring efficient disinfection. Alerts are issued if hands are prematurely removed, enhancing hygiene without water wastage

Assessing the Water Footprints (WFPs) of Agricultural Products across Arid Regions: Insights and Implications for Sustainable Farming

Water resource management has emerged as a pivotal concern within arid regions in recent times. The water footprint (WFP) index stands out as a principal gauge for facilitating comprehensive watershed management. This study endeavors to compute the WFP of diverse agricultural products encompassing major crops, orchards, cucurbits, and medicinal plants across arid regions. This research focuses on three distinct climate scenarios: the Shazand Plain with a semidry climate, the Khomein Plain characterized by a dry climate, and the Saveh Plain exhibiting a very dry climate. This study also seeks to ascertain the climate most conducive to cultivating crops from a WFP (green, blue, and gray) perspective. To achieve these objectives, this study employed the CropWat family software to determine crop water requirements, as well as considering crop yield and relevant parameters for calculations. The findings of the investigation unveiled that the cultivated areas in the respective climates amounted to 19,479 ha (semidry), 18,166 ha (dry), and 41,682 ha (very dry). These areas were allocated as follows: 88%, 85%, and 55% for crops; 11%, 13%, and 40% for orchards; and 1%, 2%, and 5% for cucurbit crops. Importantly, the very dry climate was predisposed to allocating more land for low-water-demand orchards. Among the major crops, wheat occupied 44%, 39%, and 43% of the total areas in the semidry, dry, and very dry climates, respectively. Analyzing the overall agricultural output in these climates, it was revealed that over 79%, 69%, and 66% of production correlated with crops; 17%, 19%, and 22% with orchards; and 4%, 12%, and 12% with cucurbits, respectively. In terms of water consumption, maize and apples emerged as the highest performers, with varying consumption patterns across different crops. Interestingly, canola exhibited a substantially higher WFP, surpassing wheat and barley by 56.48% and 58.85%, respectively, in dry climates. Cucurbit crops, on the other hand, displayed a lower WFP in dry climates, which could potentially encourage their cultivation. The influence of climate warming on canola’s WFPgray introduced complexity, challenging the conventional correlation between WFP and yields. Medicinal plants consistently demonstrated lower WFP values, underscoring the need for deliberate and considerate cultivation decisions in this regard.

Water scarcity assessment index from the realistic perspective of human basic water requirements

Significant population growth with rapid economic development have led to water scarcity problems around the world. Currently, most of the simple water scarcity assessment methods only refer to per capita water use, failing to take into account actual water availability and actual human domestic water use. Therefore, we have developed a simple and practical method to assess water scarcity, incorporating total water resources, water use efficiency, and the proportion of domestic water. In this approach, a new water scarcity index is employed to describe different levels of water scarcity using various thresholds based on the basic daily human water use per person. This may help to visualize the extent of water scarcity in a region while focusing on human interests. To illustrate this approach, we take China–the world's largest developing country–as an example, analysing its nationwide water scarcity in 2021 while validating it with the results of other scholars on China's water scarcity. The results show that our water scarcity assessment index is very accurate, revealing that in 2021, the disparity in water resources between the northern and southern regions of China remains substantial, with severe water scarcity still widely concentrated in northern China.

Irrigation Schedule Optimization for Wheat and Sunflower Intercropping under Water Supply Restrictions in Inner Mongolia, China

Precise water management is essential for the efficient development of irrigated agricultural crops in the Hetao Irrigation Area of Inner Mongolia. Given the severe water scarcity in the region and the significant use of intercropping as a cropping method, the development of rational irrigation scheduling is crucial. The objective of this work was to combine the ISAREG model with wheat–sunflower intercropping crops in order to enhance the effectiveness of irrigation scheduling in intercropping systems. This was achieved by changing and verifying crucial parameters for simulating irrigation patterns in intercropping. We conducted an assessment of nine irrigation schedules for a wheat–sunflower intercropping system in order to provide a range of irrigation scenarios that effectively fulfill the water requirements of the system. In light of this, we suggested implementing restrictions on the dates and volumes of irrigation based on the demand for agricultural irrigation. This approach aimed to establish irrigation schedules that are highly efficient and tailored to the specific crops in the area. As a result, we achieved a water use efficiency rate of 100%, saved 28.78% of water resources, optimized crop irrigation schedules, and enhanced crop economics by 6.7%. This study presents a novel and efficient method to optimize agricultural irrigation schedules, boost agricultural water use efficiency, and maximize crop yields in order to promote sustainable agricultural development.

Recovery and utilisation of waste heat for sponge iron process through combined preheating and power generation

This paper deals with an option accounted for total waste gas heat recovered through combined preheating as well as power generation of feed material, kiln air, as well as slinger coal when it reaches 250°C from 994°C. The heat recovery is obtained through GCC Profile is plotted using HINT software. The power produced is 2.757 MW with an optimal condition having a temperature of 280°C saturated, 600°C superheated, as well as superheated pressure of 350 bar. The heat required around the superheater, boiler and preheater is 5,066.9 kW, 12,669.93 kW and 6,873.6 kW. The air requirement for this option is 52.588 t/h after iteration. Also, the iterative waste gas flow rate is 22.31 kg/s. Three new heat exchangers are required to achieve possible heat recovery by designing HEN through Pinch Analysis. The water requirement around EC is 14.88 t/h and 7.8 t/h as in the case of actual and modified HEN. The calculated pressure drops of different ducts having lengths 25 m and pressure 0.034 atm, 80 and 105 m are obtained pressure as 0.034, 0.12 and 0.16 atm. 3 FD fans are required for maintenance of required pressures in the duct. The TAC is INR 8068.8/y; payback period is 2.62 y.

Robust in-plane ferroelectricity, high hole mobility, and low thermal conductivity in GeO monolayer: A first-principles study

Motivated by the experimental advancements in 2D crystalline germanium oxides, we investigated the electronic and transport properties of GeO and GeO2 monolayers (MLs) using first-principles calculations. GeO ML exhibits an in-plane ferroelectricity until the melting point of 1100 K. Compressive strain facilitates polarization reversion by lowering the ferroelectric transition barrier. The band edges meet the requirements for photocatalytic water splitting across a wide variety of strains. Meanwhile, GeO ML has a high hole mobility of 4854 cm2/V⋅s along the y-axis, owing to its low deformation potential constant. The large difference in hole and electron mobility promotes electron–hole separation. In addition, GeO ML has a low thermal conductivity of κx = 3.37 W/mK and κy = 12.53 W/mK at 300 K, due to the strong anharmonicity caused by lone-pair electrons. In contrast, GeO2 ML has an isotropic electron mobility of 382 cm2/V⋅s and an κ of 22.60 W/mK at 300 K. At last, we discussed the probable reaction to grow 2D GeO crystal and calculated the Raman intensity to distinguish it in future experiments. Our results show that 2D GeO has potential applications in ferroelectrics, thermoelectrics, and water splitting.

Hydrologic applicability of satellite-based precipitation estimates for irrigation water management in the data-scarce region

Reliable precipitation estimates are crucial for planning and managing water resources, monitoring hydrologic extremes, and fulfilling irrigation water requirements. Accurate precipitation estimates are particularly challenging in complex mountain terrains, where monitoring gauges are often sparsely distributed due to their remote locations, and high installation and long-term operation costs. Recent advances in satellite-based precipitation estimates offer promising opportunities to improve our understanding of hydrologic processes and their applications for irrigation water management. Several datasets are available varying considerably in terms of their data sources, quality control methods, estimation procedure, and spatiotemporal resolutions. Choosing the most suitable dataset for a particular application is a complex task. In this study, we (1) evaluate the performance of six satellite-based precipitation estimates (SPEs): i) CHIRPS V2.0, ii) CMORPH V1.0, iii) ERA5, iv) IMERG V6.0, v) MSWEP V2.8, and vi) PERSIANN-CDR against the gauge precipitation using continuous statistical and categorical indices, (2) integrate SPEs with a calibrated semi-distributed hydrologic model to predict streamflow, and (3) demonstrate practical implications of improved streamflow prediction for irrigation water management in the central Himalayan region, Nepal. Our results illustrate that satellite-based precipitation estimates have competitive performance in capturing a wide range of rainfall characteristics, with demonstrated variability across river basins and time scales. There are no significant discrepancies observed in satellite-based precipitation estimates for estimating irrigation water requirements for the three major crops (maize, wheat, and paddy) during the cropping period across the selected river basins, showing a greater promise for irrigation water management planning and decision making.

Adaptable heat pump-assisted dividing-wall column design for intensified downstream processing of bio-propionic acid

Propionic acid is a valuable platform chemical that is usually produced via fossil routes. As these are energy-intensive and eco-unfriendly processes, fermentative production of propionic acid is becoming more attractive. However, the complex downstream processing (due to low achievable product concentrations, high water content, presence of by-products and thermodynamic constraints), presents a potential barrier to scale-up this technology. This original research proposes a novel intensified large-scale (production capacity of ∼ 20 ktonne/y) process for the final recovery of propionic acid after the initial biomass and counterion removal. The vacuum distillation steps ensure recovery of high-purity succinic acid product (>99.9 wt%) and a water stream that may be recycled to the fermentation to reduce the fresh water requirements. The main unit that follows is a highly integrated heat pump-assisted dividing-wall column (DWC), which allows the effective separation of propionic (>99.9 wt%) and acetic acid (99.4 wt%) products. Alternatively, it can serve as a reactive DWC that performs the esterification and separation of methyl propionate (99.8 wt%), methyl acetate (91.0 wt%) as higher added value products, and water by-product. If needed, extractive distillation can be implemented additionally to recover methyl acetate at higher purity (99.9 wt%). Overall, both options are proven to be economically interesting (recovery costs of 0.399 – 0.469 $/kg considering the product price of 1.349 – 1.802 $/kg) and environmentally attractive (3.206 – 3.678 kWthh/kg).

Climate‐resilient water infrastructure in India

AbstractIndia receives an annual precipitation of about 3880 BCM and the average water availability is 1999 BCM. Out of this, utilizable water resources are 1126 BCM due to topographic constraints, distribution effects and so forth. In India, agriculture remains the principal source of livelihood for about 54.6% of the population. The overall water demand of the country in 2010 was estimated at 710 BCM, of which the water use in irrigation was about 557 BCM (78%). With limited water resources available for fulfilling the water requirement of all the sectors, it is projected that by 2050 our overall water demand (1180 BCM) would outgrow the total utilizable water resources, namely 1126 BCM. Moreover, the groundwater table in various regions of the country is seeing a decline at an alarming rate.Additionally, climate change poses significant and far‐reaching threats in all spheres of life and the economy. The erratic rainfall pattern makes a significant contribution to the frequent occurrence of floods and droughts in the country.This paper attempts to put in context the impact of climate change observed on various facets of water resources, the need to develop and invest in climate‐resilient water infrastructure, and to highlight several initiatives taken by the government of India in this direction.

Application of acoustic oscillations for accelerating the leaching process of saline soils

The escalating expansion of saline soils in arid and semi-arid regions presents a significant challenge to agricultural sustainability. In light of the widespread water scarcity, there has been a growing demand for innovative technologies aimed reducing water during the leaching of saline soils. This study explores the application of acoustic vibrations, particularly utilizing a seismic vibrator SV 10/100, to improve the efficiency of soil desalination. The mechanism of action of acoustic vibrations is explained by the formation of a vortex flow around soil particles oscillating within a defined amplitude and frequency range, thereby enhancing the dissolution of salts. This study specifically targets soda solonetz-solonchak soils with diverse mechanical compositions, situated in the Ararat plain of Armenia. Field experiments utilizing the lysimetric method were conducted in 2016. The primary aim was to determine optimal parameters of acoustic oscillations to expedite desalination process while assessing their impact on soil physical properties during leaching. Findings reveal that higher oscillation amplitudes (ranging from 4 to 7 mm) at frequencies of 10–70 Hz, within a 100-meter radius or 3.14-hectare area are watched. Specifically, oscillations at frequencies of 30–50 Hz for 1.0–1.5 h per leaching water portion (2500 m3/ha) effectively reduce the required leaching water volume and duration by 2 times when applied during soil leaching following acidification. It is important to note that the soil properties after leaching remain in the optimal ranges for the region (soil density 1.2–1.3 g/cm3, hydraulic conductivity 10–20 cm/day). Overall, this research underscores the efficacy of acoustic oscillations in expediting reclamation processes, reducing leaching water requirements, enhancing soil fertility, and facilitating integration into agricultural cycle. Therefore, the newly proposed method of utilizing acoustic vibrations shows potential for efficiently leaching saline soils.

Effect of Foliar Fertilizer and Irrigation Levels on Carrot Productivity

Effect of Foliar Fertilizer and Irrigation Levels on Carrot Productivity

Green and Blue Water Footprints of Major Crops in the Wami/Ruvu Basin, Tanzania: Implications for Water Scarcity and Sustainability

Accurate estimation of the blue and green water requirements of the various crops cultivated in a water basin is essential for planning specific plant/crop irrigation schedules. In this study, the crop water requirements of the major crops grown in the Wami/Ruvu basin were determined using the FAO-CROPWAT model based on meteorological parameters: monthly maximum and minimum temperature, wind speed, mean relative humidity, sunshine hours, rainfall data, and effective rainfall for 23 hydro-meteorological stations distributed in the study area. The studied crops are rice, maize, beans, tomatoes, and sugarcane. The crop reference evapotranspiration (ETo) and actual evapotranspiration (ETc) for each crop were determined using crop coefficients (Kc) of various growth stages of crops. The results indicated that the total annual reference evapotranspiration (ETo) was 1604 mm, whereas an average ETo per month was 134 mm. The highest total irrigation water requirements were recorded in sugarcane, followed by rice, while the lowest water requirements were observed in beans and tomatoes. The results presented in this study may facilitate plant-specific water irrigation implementation and maximize crop production while conserving the environment.

Effect of establishment methods in rainy season (kharif) and tillage practices in winter season (rabi) on yield and economics of rice (Oryza sativa)–maize (Zea mays) cropping system under coastal saline ecosystem

A field experiment was carried out during 3 consecutive rainy seasons (kharif) June–November and winter (rabi) December–May seasons of 2013–16 at Canning Town, West Bengal to evaluate 3 rice (Oryza sativa L.) es tablishment methods, viz. dry direct seeding (DSR), unpuddled transplanting (UNPT) and puddled transplanting (PT), followed by 3 tillage practices for sowing rabi maize (Zea mays L.), viz. zero tillage (ZT), conventional tillage (CT) and raised bed sowing (RBS). The DSR was done in the last week of May when the soil salinity was higher (12–15 dS/m); however, it reduced to 4–5 dS/m in the month of June due to leaching of salts by monsoon rain. The transplanting in the UNPT and PT plots was done during the first week of July using 40 days old seedlings. Submergence caused by the heavy rain, affected the transplanted rice (PT and UNPT) but not the DSR, as the plants with DSR were sufficiently above the standing water due to higher plant height. The mean grain yields in 3 establishment methods of rice (5,117; 4,477 and 4,740 kg/ha in PT, UNPT and DSR respectively) were statistically at par. However, net returns ( 36,000/ha) and benefit: cost ratio (2.2) were the highest in DSR owing to 24% re duction in cost of cultivation than PT rice. The positive effects of DSR also observed on the succeeding rabi maize crop in terms of 7.4 ha-cm less irrigation water requirement and 27% higher irrigation water productivity. Maize gave the highest yield (5,600 kg/ha), net returns ( 37,910/ha) and benefit: cost ratio (BCR) 1.86 with RBS tillage practices. The system rice-equivalent yield, net returns and BCR were 11,659 kg/ha, 77,470/ha and 2.05, re spectively, for dry direct-seeded rice-raised bed sown maize with higher mean irrigation water productivity (79 kg/ ha-cm) and energy productivity (461 kg/GJ) of rabi maize.

Comparative Analysis of Earth Observation Methodologies for Irrigation Water Accounting in the Bekaa Valley of Lebanon

This study extensively examines the estimation of irrigation water requirements using different methodologies based on Earth Observation data. Specifically, two distinct methods inspired by recent remote sensing and satellite technology developments are examined and compared. The first methodology, as outlined by Maselli et al. (2020), focuses on using Sentinel-2 MSI data and a water stress scalar to estimate the levels of actual evapotranspiration and net irrigation water (NIW). The second methodology derives from the work of D’Urso et al. (2021), which includes the application of the Penman–Monteith equation in conjunction with Sentinel-2 data for estimating key parameters, such as crop evapotranspiration and NIW. In the context of the Bekaa Valley in Lebanon, this study explores the suitability of both methodologies for irrigated potato crops (nine potato fields for the early season and eight for the late season). The obtained NIW value was compared with measured field data, and the root mean square errors were calculated. The results of the comparison showed that the effectiveness of these methods varies depending on the growing season. Notably, the Maselli method exhibited better performance during the late season, while the D’Urso method proved more accurate during the early season. This comparative assessment provided valuable insights for effective agricultural water management in the Bekaa Valley when estimating NIW in potato cultivation.

Evaluating the precise grapevine water stress detection using unmanned aerial vehicles and evapotranspiration-based metrics

AbstractPrecise irrigation management requires accurate knowledge of crop water demand to adequately optimize water use efficiency, especially relevant in arid and semi-arid regions. While unoccupied aerial vehicles (UAV) have shown great promise to improve the water management for crops such as vineyards, there still remains large uncertainties to accurately quantify vegetation water requirements, especially through physically-based methods. Notably, thermal remote sensing has been shown to be a promising tool to evaluate water stress at different scales, most commonly through the Crop Water Stress Index (CWSI). This work aimed to evaluate the potential of a UAV payload to estimate evapotranspiration (ET) and alternative ET-based crop water stress indices to better monitor and detect irrigation requirements in vineyards. As a case study, three irrigation treatments within a vineyard were implemented to impose weekly crop coefficient (Kc) of 0.2 (extreme deficit irrigation), 0.4 (typical deficit irrigation) and 0.8 (over-irrigated) of reference ET. Both the original Priestley-Taylor initialized two-source energy balance model (TSEB-PT) and the dual temperature TSEB (TSEB-2T), which takes advantage of high-resolution imagery to discriminate canopy and soil temperatures, were implemented to estimate ET. In a first step, both ET models were evaluated at the footprint level using an eddy covariance (EC) tower, with modelled fluxes comparing well against the EC measurements. Secondly, in-situ physiological measurements at vine level, such as stomatal conductance (gst), leaf (Ψleaf) and stem (Ψstem) water potential, were collected simultaneously to UAV overpasses as plant proxies of water stress. Different variants of the CWSI and alternative metrics that take advantage of the partitioned ET from TSEB, such as Crop Transpiration Stress Index (CTSI) and the Crop Stomatal Stress Index (CSSI), were also evaluated to test their statistical relationship against these in-situ physiological indicators using the Spearman correlation coefficient (ρ). Both TSEB-PT and TSEB-2T CWSI related similarly to in-situ measurements (Ψleaf: ρ ~ 0.4; Ψstem: ρ ~ 0.55). On the other hand, stress indicators using canopy fluxes (i.e. CTSI and CSSI) were much more effective when using TSEB-2 T (Ψleaf: ρ = 0.45; Ψstem: ρ = 0.62) compared to TSEB-PT (Ψleaf: ρ = 0.18; Ψstem: ρ = 0.49), revealing important differences in the ET partitioning between model variants. These results demonstrate the utility of physically-based models to estimate ET and partitioned canopy fluxes, which can enhance the detection of vine water stress and quantitatively assess vine water demand to better manage irrigation practices.

Study on Real-Time Water Demand Prediction of Winter Wheat–Summer Corn Based on Convolutional Neural Network–Informer Combined Modeling

The accurate prediction of crops’ water requirements is an important reference for real-time irrigation decisions on farmland. In order to achieve precise control of irrigation and improve irrigation water utilization, a real-time crop water requirement prediction model combining convolutional neural networks (CNNs) and the Informer model is presented in this paper, taking the real-time water demand of winter wheat–summer maize from 2017 to 2021 as the research object. The CNN model was used to extract the depth features of the day-by-day meteorological data of the crops, and the extracted feature values were inputted into the Informer model according to the time series for training and prediction to obtain the predicted water demand of winter wheat and summer maize. The results showed that the prediction accuracy of the constructed CNN–Informer combination model was higher compared to CNN, BP, and LSTM models, with an improvement of 1.2%, 25.1%, and 21.9% for winter wheat and 0.4%, 37.4%, and 20.3% for summer maize; based on the good performance of the model in capturing the long-term dependency relationship, the irrigation analysis using the model prediction data showed a significant water-saving effect compared with the traditional irrigation mode, with an average annual water saving of about 1004.3 m3/hm2, or 18.4%, which verified the validity of the model, and it can provide a basis for the prediction of crops’ water demand and sustainable agricultural development.