Water Requirements Research Articles

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.

IoT Based Smart Irrigation System using Artificial Intelligence

This proposed of the project introduces an innovative IoT-based AI-driven solution engineered to revolutionize water management in agricultural contexts, catering to both large-scale piped irrigation networks and micro-irrigation systems. Our proposed system leverages cutting-edge AI algorithms to forecast dynamic crop water requirements, seamlessly integrating real-time soil moisture data to optimize water utilization and amplify crop yields. By amalgamating AI-driven predictive analytics with instantaneous sensor feedback, our system facilitates proactive water resource management, curbing wastage and mitigating environmental repercussions. Moreover, the integration of Deep Learning-based pest detection augments the system's capabilities by safeguarding crops against potential threats, thus fostering sustainable agricultural practices. Additionally, our solution pioneers the incorporation of Deep Learning-based crop production for the detection of wild animals, thereby enabling timely alerts through buzzer sound notifications, thereby ensuring comprehensive crop protection. This amalgamation of IoT, AI, and Deep Learning technologies promises to significantly enhance agricultural sustainability, optimize resource utilization, and mitigate ecological impact, thus paving the way for a more resilient and productive agricultural ecosystem.

Precision Estimation of Crop Coefficient for Maize Cultivation Using High-Resolution Satellite Imagery to Enhance Evapotranspiration Assessment in Agriculture.

The estimation of crop evapotranspiration (ETc) is crucial for irrigation water management, especially in arid regions. This can be particularly relevant in the Po Valley (Italy), where arable lands suffer from drought damages on an annual basis, causing drastic crop yield losses. This study presents a novel approach for vegetation-based estimation of crop evapotranspiration (ETc) for maize. Three years of high-resolution multispectral satellite (Sentinel-2)-based Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Normalized Difference Red Edge Index (NDRE), and Leaf Area Index (LAI) time series data were used to derive crop coefficients of maize in nine plots at the Acqua Campus experimental farm of Irrigation Consortium for the Emilia Romagna Canal (CER), Italy. Since certain vegetation indices (VIs) (such as NDVI) have an exponential nature compared to the other indices, both linear and power regression models were evaluated to estimate the crop coefficient (Kc). In the context of linear regression, the correlations between Food and Agriculture Organization (FAO)-based Kc and NDWI, NDRE, NDVI, and LAI-based Kc were 0.833, 0.870, 0.886, and 0.771, respectively. Strong correlation values in the case of power regression (NDWI: 0.876, NDRE: 0.872, NDVI: 0.888, LAI: 0.746) indicated an alternative approach to provide crop coefficients for the vegetation period. The VI-based ETc values were calculated using reference evapotranspiration (ET0) and VI-based Kc. The weather station data of CER were used to calculate ET0 based on Penman-Monteith estimation. Out of the Vis, NDWI and NDVI-based ETc performed the best both in the cases of linear (NDWI RMSE: 0.43 ± 0.12; NDVI RMSE: 0.43 ± 0.095) and power (NDWI RMSE: 0.44 ± 0.116; NDVI RMSE: 0.44 ± 0.103) approaches. The findings affirm the efficacy of the developed methodology in accurately assessing the evapotranspiration rate. Consequently, it offers a more refined temporal estimation of water requirements for maize cultivation in the region.

Simultaneous Synthesis of Single- and Multiple-Contaminant Water Networks Using LINGO and Excel Software

Controlling the distribution of water and wastewater between industrial processes is vital to rationalize water usage and preserve the environment. In this paper, a mathematical technique is proposed to optimize water–wastewater networks, and a nonlinear program is introduced to minimize the consumption of freshwater and, consequently, the flowrate of wastewater discharge. A general mathematical model, able to handle industrial plants containing up to eight sources and eight sinks, is developed using LINGO optimization software to facilitate dealing with complex case studies. The introduced model can handle single-contaminant networks as well as multiple-contaminant ones. The optimal water network is synthesized through two steps; the first step involves the introduction of the case study data into the developed mathematical model. The second step considers using the optimal solution produced after running the developed LINGO model as feed data for a pre-designed Excel sheet able to deal with these results and simultaneously draw the optimal water–wastewater network. The proposed mathematical model is applied to two case studies. The first case study includes actual data from four fertilizer plants located in Egypt; the water resources and requirements are simultaneously integrated to obtain a sensible cutting in both freshwater consumption (lowered by 52.2%) and wastewater discharge (zero wastewater discharge). The second case study regards a Brazilian petrochemical plant; the obtained results show noticeable reductions in freshwater consumption by 12.3%, while the reduction percentage of wastewater discharge is 4.5%.

A Benchmarking Study of Irrigation Advisory Platforms

In the contemporary agricultural landscape, agriculture faces four pressing demands: competitiveness, ensuring food security for a growing population, environmental sustainability, and providing farmers with acceptable living conditions. To meet this global challenge, digital technologies represent a major avenue for innovation and development towards modernized digital agriculture. In this context, irrigation advisory platforms have proven to be transformational tools for both farmers and policymakers, offering insights into the appropriate crop water requirements. This article presents a benchmarking analysis of around 20 professional irrigation advisory platforms. The methodology involves selecting 20 platforms based on accessibility ensuring geographical characteristic diversity. Our findings highlight key criteria shaping the ecosystem of such platforms, including the services offered and their objectives, the types of covered crops, the target users, the form, as well as the source, the availability of the platform, and the variety of data utilized. Lastly, we discuss the main conclusions drawn from our analysis and provide insights into the challenges and future perspectives of irrigation advisory platforms in enhancing agricultural practices and sustainability.

Does climate change affect wheat productivity and water demand in arid regions? Case study of Egypt

The study investigates the effects of climate change on wheat productivity (Yw) and crop water requirements (CWR) across different regions in Egypt, highlighting the potential threat to the country's wheat self-sufficiency goals. Data spanning from 1987 to 2019 from lower, middle, and upper Egypt, as well as areas outside the Nile valley of Egypt, underwent both parametric and non-parametric statistical analysis to discern trends in climate parameters and their impact on wheat growth stages. Findings reveal a significant increase (P ≤ 0.05) in Yw across the study areas, attributed to effective self-sufficiency strategies, with increases of 0.61, 0.60, 0.59, and 0.56 Mg ha−1 per decade in middle Egypt, outside the Nile valley, lower, and upper Egypt study zones, respectively. Although the observed significant increase in Yw, results revealed a significant negative relationship between Yw, maximum air temperature (Tmax), minimum air temperature (Tmin), and wheat growing degree days (GDD) depending on the study zone and the wheat growth stage. The study notes a more significant rise in Tmin than Tmax, adversely affecting Yw, particularly in upper Egypt, where Tmin rose by 0.43 °C per decade. CWR rose by ∼12 % in lower Egypt, ∼15 % in middle and upper Egypt, and ∼18 % outside the Nile valley between 2009 and 2019 compared to 1987–1997 under the best farming conditions. Wheat in middle Egypt showed resilience to climatic change, whereas in the Nile valley zone, the node development stage was significantly sensitive to Tmax fluctuations. In lower Egypt, Tmin and wheat GDD changes during anthesis and physiological stages significantly negatively impacted Yw. The study suggests that wheat self-sufficiency strategies in Egypt and similar climates should start to incorporate air temperature and GDD considerations. Recommendations include adopting heat, drought, and disease-resistant wheat varieties and focusing on expanding wheat cultivation in other zones rather than upper Egypt to cope with climate change.

Perancangan dan Penempatan Alat Pemadam Kebakaran Jenis Sprinkler

The fire suppression ability of this sprinkler installation compared to other fire extinguishing equipment has been proven to be more effective. Because the sprinkler system does not require an operator when operating, fires can be prevented earlier before causing greater losses. For this reason, the implementation of sprinkler systems in buildings is crucial. According to KEPMEN PU NO. 10/KPTS/2000, a building must have elements that can maintain structural stability to a certain level during a fire. This research was conducted directly in the field, and the necessary data was obtained independently through interviews with relevant parties. The author conducted research observations in the archive warehouse to obtain both primary and secondary data. Primary data included field documentation and calculations. Planning calculations were done directly by calculating the number of sprinklers, water requirements, and reservoirs to determine the precise needs. Based on the survey, it was found that the size of the archive warehouse is 48 x 48 m, totaling 2304 m2. The maximum distance between sprinklers used was 4.6 m. Therefore, the effective distance between sprinkler heads is 193 units. The result of the calculation for the water supply for the sprinkler system obtained 34,139.77 liters, with the total overall cost for the equipment and materials used being Rp. 63,121,925.74. Based on the data and calculation results regarding the planning and design of fire protection equipment in open active spaces adhering to the NFPA-13 Standard for the Year 2019, it is expected to provide comfort and safety against the threat of fire incidents.

Evaluation of water management effects on potato yield and water productivity in northeast Iran using the SWAP model

ABSTRACT The agro-hydrological SWAP model was employed for simulation of evapotranspiration, yield, and water productivity of potato under six irrigation scenarios (100, 90, 80, 70, 60, and 50% potato water requirement (WR)) in Fariman, Ghoochan, and Golmakan in the northeast Iran. The results showed that the SWAP model well-simulated potato yield and water productivity. The model slightly overestimates the potato yield and underestimates the water productivity. The results revealed that irrigation scheduling is an important factor effecting on evapotranspiration, yield, and water productivity of potatoes. By decreasing irrigation water to 50% WR, potato evapotranspiration and yield decreased in all three study areas. However, potato water productivity increased in Fariman and Golmakan and decreased in Ghoochan, as irrigation volume decreased to 50% WR. In Fariman and Golmakan, irrigation at the rate of 80% WR led to the best irrigation management to have maximum water productivity (2.96 and 2.48 kg m−3, respectively) and acceptable potato yield (21,376.2 and 10,998.7 kg ha−1). In Ghoochan, by adopting the irrigation scenario at the level of 90% WR, the potato yield decreased by approximately 7.6% compared to the full irrigation conditions. However, the highest amount of water productivity (2.27 kg m−3) was achieved.

GIS-BASED SPATIAL ASSESSMENT OF RESORTS IN CALAMBA CITY, LAGUNA, PHILIPPINES

Abstract. Mount Makiling Forest Reserve (MMFR) supports the water requirements of many towns in its periphery. One of the towns that benefit from the forest reserve is Calamba City, known for its resort-based tourism. The number of resorts in Calamba has significantly increased from 193 in 1998 to 466 in 2014 which has an implication on water consumption on which one of the initial steps in the monitoring aspect is the determination of its spatial distribution. Therefore, this study aims to determine and analyze the spatial distribution of resorts in Calamba City using Geographic Information System (GIS) to generate a distribution map of all the operating resorts showing the hotspots and clustering of resorts. The study recorded 852 resorts in Calamba City. The dense resort areas and the most significant hot spot area are located at Miramonte Village (Purok 6) and Purok 5 of Barangay Pansol. The result indicates a concentration of resorts in certain regions which raised concerns and should further be studied in terms of sustainable water use and environmental impact. The baseline data generated by this study will supplement the existing data of Calamba City on its resorts and help the local government in formulating effective land use and water extraction policies for the resort-based industry for a more sustainable management and conservation.

Impact of different water regimes on maize grown at two distinctive pedo-climatic locations in Bosnia and Herzegovina

Aim of study: A two-year experiment (2021-2022) was conducted to assess the response of a local maize hybrid BL-43 to different water regimes (full irrigation, deficit irrigation and rainfed) at two distinguished pedo-climatic locations (Aleksandrovac and Butmir) in Bosnia and Herzegovina (BiH).
 Area of study: The field experiment was located in Aleksandrovac (near Banja Luka) and Butmir (near Sarajevo) in BiH.
 Material and methods: A randomized block design was adopted at both experimental locations with three replicates. An Excel-based irrigation tool was used to manage crop water requirements and irrigation scheduling.
 Main results: Crop response to water was affected by site-specific agronomic management, the duration of phenological stages and their interconnection with precipitation events. At both locations, the effect of the water inputs on grain yield was statistically significant confirming the beneficial impact of irrigation. The effect of water stress on yield was particularly pronounced at Aleksandrovac, which was under water and temperature stresses during flowering time. During both seasons and for all water regimes, the total average grain yield was greater at Butmir than at Aleksandrovac for 38% and 27%, respectively.
 Research highlights: This is the first experimental study conducted in BiH on the effect of irrigation on maize grain production under different pedoclimatic conditions. The study emphasizes the need for knowledge regarding the impacts that climate change is having on the productivity of one of the region's most important crops.

EVALUATION OF NGABEAN SECONDARY CANAL IRRIGATION NETWORK, NGAWI DISTRICT PART OF SALURAN INDUK MADIUN IRRIGATION AREA

The SIM Irrigation Area has an area of 10,860 Ha and a primary canal length of 27,022 m, which passes through the Regencies of Madiun, Magetan, Ngawi, and Madiun City. One of the secondary canals in the D.I SIM is the Ngabean Secondary Canal, located in Ngawi Regency. This canal receives the main water supply from Jati Dam, 25% of the primary dam discharge and water withdrawal from the Ngabean Dam intake of 0.45 m3/ha. However, this value only meets the water needs of some of the irrigation areas. Due to the limited supply from the Jati Bendung, the government is working on rehabilitation work at the Jati Bendung. Apart from that, the Ngabean Secondary Canal also experienced a reduction in the irrigation area from 1840 ha to 1818 ha, to be precise in the NG3 Ki tertiary plot, which had an initial area of 120 ha to 98 ha. Therefore, an evaluation of the Ngabean Secondary Canal is needed to determine the value of water demand, water availability before and after rehabilitation and land reduction, and increasing agricultural profits.From the results of the analysis that has been carried out, it was concluded that the highest dependent flow at Ngabean Dam was 5.00 m3/s, and the lowest was 0.01 m3/s. Meanwhile, in conditions before rehabilitation, the highest dependent flow of Jati Dam was 1.01 m3/s, and the lowest was 0.00 m3/s with water withdrawal at the intake of 25%, able to fulfill the planting pattern of paddy (15%)-crops (75%)-crops (50%) generated a production profit of IDR 8.794.123.880,00. Meanwhile, after rehabilitation, the highest was 5.93 m3/s, and the lowest was 3.21 m3/s with an intake of 32%, able to fulfill the planting pattern of paddy (100%)-paddy (100%)-paddy (100%), resulting in a production profit of IDR 83, 564,115,480.00 with the highest irrigation water requirement value of 3.10 m3/sec. Apart from that, the existing canal dimensions still meet the requirements after land reduction, so no changes to the canal dimensions are required.

IoT Based Smart Irrigation System

In recent years, the integration of Internet of Things (IoT) technology into agriculture has shown great potential for enhancing irrigation practices, thereby improving crop yield and water efficiency. This paper presents the design, implementation, and performance evaluation of an IoT-based smart irrigation system tailored for agricultural applications. The proposed system employs various sensors to monitor soil moisture levels, weather conditions, and crop water requirements in real time. Data collected from these sensors are transmitted wirelessly to a central control unit, which utilizes an intelligent algorithm to optimize irrigation scheduling and water distribution. The system also incorporates remote monitoring and control capabilities, allowing farmers to access and manage irrigation operations from anywhere via a web or mobile interface. Experimental results demonstrate the effectiveness of the IoT-based smart irrigation system in conserving water, reducing energy consumption, and enhancing crop productivity. Moreover, the scalability, reliability, and cost-effectiveness of the proposed solution make it suitable for deployment in both small-scale and large- scale agricultural settings. Key Words: IoT, smart irrigation, agriculture, soil moisture sensor, wireless communication, optimization, remote monitoring

Exogenous Application of Gamma Aminobutyric Acid Improves the Morpho-Physiological and Biochemical Attributes in Lavandula dentata L. under Salinity Stress

Saline water has been proposed as a solution to partially supply plants with their water requirements due to a lack of fresh water for cultivation in arid and semi-arid sites. Gamma-aminobutyric acid (GABA) is a non-protein amino acid participating in numerous metabolic processes to mitigate the undesirable effects of salinity. A pot experiment was carried out during 2021 and 2022 at Sakha Horticulture Research Station to investigate the effect of foliar application of GABA at 20 and 40 mM on vegetative growth and biochemical changes in French lavender under increasing levels of sea water salinity irrigation treatments (0, 1000, 2000, and 3000 ppm). Results indicated that increasing salinity concentration noticeably decreased plant height, number of branches, herb fresh and dry weight, root length, root fresh and dry weights, photosynthetic pigments, relative water content, and essential oil percentage. On the other hand, accumulation of proline and antioxidant enzymes was increased under increasing salinity concentrations. We conclude that foliar application of GABA acid at 40 mM can alleviate the adverse effects of salinity on the abovementioned French lavender plant characteristics by improving vegetative growth and root characteristics, as well as diminishing chlorophyll degradation, maintaining high leaf relative water content, increasing proline accumulation and antioxidant activity.

Assessment of satellite-based water requirements for a drip-irrigated apple orchard in Mediterranean agroclimatic conditions

Accurate assessment of evapotranspiration (ETa) and crop coefficient (Kc) is crucial for optimizing irrigation practices in water-scarce regions. While satellite-based surface energy balance models offer a promising solution, their application to sparse canopies like apple orchards requires specific validation. This study investigated the spatial and temporal dynamics of ETa and Kc in a drip-irrigated ‘Pink Lady’ apple orchard under Mediterranean conditions over three growing seasons (2012/13, 2013/14, 2014/15). The METRIC model, incorporating calibrated sub-models for leaf area index (LAI), surface roughness (Zom), and soil heat flux (G), was employed to estimate ETa and Kc. These estimates were validated against field-scale Eddy Covariance data. Results indicated that METRIC overpredicted Kc and ETa with errors less than 10 %. These findings highlight the potential of the calibrated METRIC model as a valuable decision-making tool for irrigation management in apple orchards.

Millets as Nutricereal Climate Resilient Smart Crop: A Review

Worldwide climate change and extreme weather variations are the most threatening challenge to agriculture and allied activities. The entire food production system has faced serious challenges due to increase in average temperature, intensity and frequency of drought and flood, aberration of rainfall patterns, and elevation in CO2 concentration. In today’s world climate change is a major concern. Review of different literature from different reports and studies for different nutritional benefit of millets and how it can thrive in climate change conditions are important. Different literature have been searched from different sources and complied. Millets are considered highly nutrition-rich and climate-resilient coarse grain cereals and it can enhance income as well as improve food and nutrition security. Millets have climate-resilient features and that’ s why they can thrive in adjust to a vast changing ecological conditions, less water requirements, low nutrient input conditions and also they have more resistance to environmental hassels. In comparison to cereals, millets have more dietary fibres, resistant starches and are nutritionally superior. Millet require less time to complete its life cycle than rice and wheat, so they have the capacity to escape the stresses. Millets also provide several health benefits as they have nutritious. The cost of cultivation is also less compare to other cereals. As population growth increases and climate is changing day by day so millets are the alternative choice for today’s world. In Karnataka most of the farmers already have started to cultivate drought resistant millets than more water required crop like rice, sugarcane, and maize to due to climate change. They commonly grown millets from domestic purposes are sorghum, pearl millet, finger - millet, barnyard, foxtail, kodo, proso and Little millet.

Single and basal crop coefficients for estimation of water requirements of subtropical and tropical orchards and plantations with consideration of fraction of ground cover, height, and training system

AbstractThis paper provides an overview of the research carried out over the last 25 years on the FAO56 single and basal crop coefficients of subtropical and tropical orchards and plantations of cactus pear, dragon fruit, fig, jujube, passion fruit, pomegranate, cape gooseberry, cherimoya, guava, longan, lychee, mango, papaya, acerola, carambola, cashew, cacao, coffee, jaboticaba, jatropha, macadamia, açai palm, coconut, date palm, guayule, oil palm, peach palm, ramie and rubber tree. The main objective of this review is to update standard single crop coefficients (Kc) and basal crop coefficients (Kcb) and complete the Kc and Kcb values tabulated in FAO56. Kc is the ratio between the non-stressed crop evapotranspiration (ETc) and the grass reference evapotranspiration (ETo), and Kcb is the ratio between the crop transpiration (Tc) and the ETo. When selecting and analysing the literature, only studies that used the FAO Penman–Monteith equation, or another equation well related to the former to compute ETo were considered, while ETc or Tc were obtained from accurate field measurements on crops under pristine (non-stress cropping conditions) or eustress (“good stress”) conditions. Articles meeting these conditions were selected to provide data for updating Kc and Kcb under standard conditions. The related description of orchards and plantations refers to crop cultivar and rootstock, irrigation systems and scheduling, planting spacing, fraction of ground cover (fc) by the crops, crop height (h), crop age and training systems, as Kc and Kcb values depend on these characteristics. To define the standard Kc and Kcb values of the selected crops, the values collected in the literature were compared with previously tabulated standard Kc and Kcb values. The updated tabulated values are transferable to other locations and climates and can be used to calculate and model crop water requirements, primarily for irrigation planning and scheduling, and thereby supporting of improved water use and savings, which is the overall aim of the current review.

The g‐ZnO/Hf2CO2 vdW heterostructure: A novel two‐dimensional material for sunlight‐driven photocatalytic water splitting

AbstractIn this work, by first‐principles calculations, we investigate the structural, photoelectric, and catalytic properties of g‐ZnO/Hf2CO2 vdW heterostructures to explore highly efficient photocatalysts of water splitting. Results show that the AA‐stacking g‐ZnO/Hf2CO2 vdW heterostructure (AA‐HS) with an indirect bandgap of 1.738 eV has a typical type‐II band alignment, which is benefit to effectively separate the photogenerated carriers into different layers and prevent their recombination. The conduction band minimum (CBM) and valence band maximum (VBM) of AA‐HS straddling the water redox potentials makes it satisfies the requirements of photocatalytic water splitting at pH = 7. In addition, to evaluate the photocatalytic performance of AA‐HS during oxidation evolution reaction (OER) process in water splitting, we calculated the change of Gibbs free energies of the intermediates on heterostructure under different conditions. Results show the Gibbs free energy decreases continuously at pH = 7 and U = 1.93 V. What's more, the AA‐HS has good sunlight‐absorption ability. External biaxial strain is not conducive to improving catalytic performance of AA‐HS.

The water footprint of hydrogen production

Hydrogen (H2) is the most promising energy carrier for reducing the carbon emissions of the energy sector, but the impact of its production on water resources remains unclear. Here, we quantify the water footprint (WF) of different H2 production pathways accounting for the WF of the primary energy used in the production process, as well as feedstock and infrastructure water requirements. Results suggest that green H2 obtained from water electrolysis powered by renewable energy has the lowest WF (65 ± 2 m3/TJ for wind and 204 ± 79 m3/TJ for solar) mostly due to the low WF of renewable energy. The WF of blue H2 derived from fossil fuels is significantly higher (369 ± 30 m3/TJ for natural gas and 564 ± 82 m3/TJ for coal) due to high WF of fossil fuels as well as the water required for carbon capture and storage (CCS). H2 produced from nuclear energy and biomass have extremely high WF (741 ± 277 m3/TJ for nuclear and > 50,000 m3/TJ for biomass). Considering global and country-based energy scenarios, where the main H2 colors (green and blue) individually account for 15 % of energy consumption, we find that the use of green H2 could reduce the water demand of the energy sector while blue H2 would generally increase it, except in countries already characterized by high water consumption due to reliance on water-intensive energy sources. At the global level, we find that for every 5 % of H2 energy adoption, the energy sector could have water savings between 1 and 4 % for green H2 and increase water consumption between 1 and 5 % for blue H2. These results highlight the potential and criticalities of H2 within the water-energy nexus.