Net Irrigation Water Research Articles

Research and application of the method of measuring the effective utilization coefficient of irrigation water on farmland

Abstract Under the guidance of the new water management idea of “water conservation priority, spatial balance, system management, and two-handed effort,” the National Water Conservation Action Plan released in 2019 puts forward the key task requirements of “double control of total intensity,” in which the effective utilization coefficient of irrigation water in farmland is one of the indicators of water intensity control. The effective utilization coefficient of irrigation water is one of the water intensity control indicators. At present, there are practical difficulties in measuring the effective utilization coefficient of irrigation water in farmland, such as the low measurement rate of water consumption in the current irrigation area, the high input equipment and manpower requirements, and the difficulty in accurately measuring the net irrigation water absorbed by crops. Therefore, this paper proposed the method of “field monitoring - remote sensing inversion - a comprehensive analysis of data.” The method of “field monitoring - remote sensing inversion - econometric model construction - comprehensive analysis” was proposed in this paper, which can obtain gross irrigation water utilizing field monitoring, overcome the difficulty of accurate monitoring of net irrigation water using remote sensing, and carried out a comprehensive analysis, to realize the scientific, effective and rapid calculation of effective utilization coefficient of irrigation water. This method was applied to the Meixian District, Guangdong Province, and the research results show that it not only generates reliable measurement data but also alleviates workload. Furthermore, it offers scientific measurement techniques for water use efficiency indicators, facilitating the achievement of “dual control over total volume and intensity.” This paper provides an important technical basis for the rational allocation of water consumption and scientific approval of water use plans and supports the assessment of the effectiveness of water-saving irrigation development and the reasonable evaluation of the water-saving potential of farmland irrigation, thus providing a basis for local government departments at all levels to make planning, scientific decision-making, and macro-management.

Establishing the water resources implications for closing the land and water productivity gaps using remote sensing – A case study of sugarcane

ContextTwo of the key limitations for sustainably increasing agricultural production are the scarcity of land and fresh water resources. Establishing land and water productivity gaps is, therefore, essential for measuring how efficiently these resources are being utilised and assessing the scope for increasing feed and food production. Monitoring the productivity gaps at large scales or over time using field data is challenging and expensive. Remote sensing offers an alternative data source to reveal spatial and temporal variations in productivity. ObjectiveThis paper presents a framework that integrates remote sensing derived data and field data to assess (1) land and water productivity gaps, (2) bright spots – fields exhibiting land- and water productivity equal to or higher than the target, and (3) net irrigation water demand for increasing production. MethodsThe framework is developed and applied to the Xinavane sugarcane estate in Mozambique, demonstrating its practical application through systematic evaluation on a 6637 ha section of the estate divided by different irrigation application methods. ResultsThe results reveal that the productivity gap is the highest on fields irrigated by furrow (13.1 tonnes (ton) per ha), followed by sprinkler (12.6 ton/ha) and centre pivot (9.4 ton/ha). Bridging the productivity gap on the same cropland results in an increased sugarcane production of 12.5 % requiring 8.5 % additional irrigation water, whereas achieving the same production increase through irrigation expansion requires more blue water. ConclusionsThe analyses show that remote sensing provides a viable source of information to diagnose the productivity constraints and how bright spots can provide insights into the best field management practices to overcome them. The framework demonstrates its usefulness for policy makers and stakeholders to make informed decisions on the scarce blue water allocation for enhancing agricultural production.

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.

Agricultural Water Management for Rice Production: A Comprehensive Analysis

The identified problem revolves around establishing the water requirements for rice seeding, growth, and maturity in hot climatic conditions. This study reviews the net water requirement for rice growth or production under various climatic conditions in the Cross River Basin. It was observed that the water requirement from seeding to harvest varies from location to location, maintaining a FAO range, as postulated by Brouwer et al., 1986, ultimately falling between 450mm to 700mm for hot weather.The methodology used for investigating the Net Irrigation Water Requirements for rice growth to maturity involves the application of New_Clim Location and CropWater software. These tools were utilized to compute Reference Evapotranspiration (ETo) and Crop Water Requirements (CWR) for the net irrigation water requirements of rice, respectively. The data were obtained from meteorological stations within the Cross River Basin. In rainfed conditions, no water stress is practically observed throughout the entire crop cycle, affirming that no irrigation system is needed in such a specific climate. This study uses data generated through New_LocClim as input into CropWat, which, among other factors, determines the net water requirement for rice growth and production. Consequently, the results obtained from the use of the CropWat model in determining rice water requirements for Obudu, Nkari, Ikom, and Ijegu-Yala were 633.7 mm, 397.9 mm, 303.0 mm, and 548.4 mm, respectively. The water requirement of only two locations, Obudu and Ijegu-Yala, fell within the FAO-suggested range of 450 mm to 700 mm (Brouwer et al., 1986) for hot climates, while Nkari and Ikom LGAs did not comply with Brower et al., 1986 postulation due to their location in a relatively less hot climate.

Irrigation Water Management in a Water-Scarce Environment in the Context of Climate Change

Climate change has a considerable impact on irrigated agriculture, which is vital for food and fiber production. In this study, in the context of climate change, simulation model CROPWAT 8 was employed to compute the reference evapotranspiration, and net irrigation water requirement for wheat, barley, maize, sugar beet, potato, tomato, and date palm. In addition, the WaterGEMS model was utilized to design a new sprinkler irrigation system to run long-term simulations of hydraulic behavior within pressurized pipe networks to irrigate 43 acres for two arid sites (Siwa Oasis and West Elminya fields) inside the 1.5-million-acre groundwater irrigation project in the Egyptian western desert. Five scenarios for climate change were employed in the current (1991–2023), representative concentration path (RCP) 8.5 greenhouse gas emission scenarios for the 2040s, 2060s, 2080s, and 2100s. Mean ETo values for the current scenario show 4.56 and 5.7 mm for the Siwa Oasis and West Elminya fields, respectively. The climate changes cause an increase of the reference evapotranspiration by 4.6, 5.9, 9.4, and 12.7% for RCP: 8.5 greenhouse gas emissions for the 2040s, 2060s, 2080s, and 2100s scenarios, respectively, for the Siwa Oasis field. On the other hand, an increased ratio for the reference evapotranspiration by 4.2, 5.4, 8.6, and 11.6% was observed for the scenarios in the West Elminya field, respectively. The designed sprinkler system indicated a capacity of 111.4 m3 h−1 and 167 m3 h−1 for Siwa and West Elminya fields, respectively. The study suggests using crop patterns for wheat, barley, potato, and sugar beet to save irrigation water.Graphical abstract

An Analytical Study on the Measurement of Agricultural Net Irrigation Water Requirements

The net irrigation water requirement (NIWR) in the irrigated area is the “terminal” of water-use efficiency and the basis for implementing quota management and water-saving incentives. Also, it is significant for farmland water management and water conservation in irrigated areas. In light of the average water consumption per mu and the crop area, which are two key indicators of the NIWR, this paper adopts the field selection measurement method and the statistical method based on the image patches achieved by China’s third national land survey to acquire the data of water consumptions and crop areas. In this case, four principles of field selection are proposed, including “basic requirements”, “moderate size”, “controllable water inflow”, and “measurement practicality”, in an attempt to increase the number of fields selected for analysis. In addition, based on the image patches, the “complete investigation” and the “typical investigation” are suggested for irrigated areas at different scales to determine the crop areas, which are proven practicable and available for evaluating the water-use efficiency of irrigated areas at different scales.

IWRAM: A hybrid model for irrigation water demand forecasting to quantify the impacts of climate change

The phenomenon of climate change exerts a substantial influence on the water consumption patterns of agricultural crops, thereby affecting the overall demand for irrigation water and regional water security. The quantitative assessment of future changes in irrigation water requirements has great importance for long-term water resource planning and the water-energy-food-ecosystem nexus. This work employed a comprehensive approach (IWRAM) by integrating many models and techniques, including a global climate model (CanESM2), a statistical downscaling model (SDSM), a bias correction technique (QTM), a temperature-based crop phenology method (GDD), and deep learning technology (LSTM), in order to assess the potential impact of climate change on irrigation water demand. Therein, the QTM is utilized for the purpose of bias correction for the CanESM2-SDSMed daily precipitation data. Additionally, the LSTM is implemented to forecast ETo using the CanESM2-SDSMed daily temperature data. The findings from the case study conducted in the Jinghuiqu irrigation area (JIA) indicate that the application of bias correction techniques resulted in notable enhancements in the frequency distribution of predicted precipitation data. Consequently, this led to a more coherent relationship between historical observed data and anticipated future data, aligning them more closely with natural patterns. The deep learning technique exhibited a strong degree of concordance, suggesting its exceptional capacity for modeling daily reference evapotranspiration. By using the IWRAM model, relatively accurate predictions were made for future precipitation, temperature, crop evapotranspiration, and effective precipitation. Consequently, an estimation was made regarding the anticipated need for irrigation water in JIA. These results highlighted that JIA total irrigation water demand (or net irrigation water demand) will increase by 121 mm (net irrigation water: 189 mm) and 119 mm (net irrigation water: 187 mm) in the year 2050 under RCP 2.6 and 8.5 scenarios, respectively. Climate change could potentially affect the seasonal distribution of precipitation. Therefore, it is crucial for the authorities in JIA to not only take into account the overall irrigation water requirement but also give priority to the water requirements of each stage of crop growth.

Simulation of Irrigation Strategy Based on Stochastic Rainfall and Evapotranspiration

The North China Plain plays a pivotal role in China’s crop production, contributing to 30% of the maize yield. Nevertheless, summer maize in this region faces challenges due to climatic constraints characterized by concurrent high temperatures and rainfall during the growing season, resulting in a relatively high evapotranspiration rate. In this study, we explored eight soil moisture-based threshold irrigation strategies, consisting of two upper limits and four lower limits, along with a rainfed mode (E). The upper and lower irrigation limits are expressed as a percentage of the field’s water-holding capacity (sfc). For the four full irrigation modes (A1, A2, A3, A4), the lower limits were set at 0.6 sfc, 0.6 sfc, 0.5 sfc, and 0.5 sfc, respectively. The upper limits were defined at two levels: 0.8 sfc for A1 and A2 and sfc for A3 and A4. Similarly, for the four deficit irrigation modes (B1, B2, B3, B4), the lower limits were established at 0.4 sfc, 0.4 sfc, 0.3 sfc, and 0.3 sfc, respectively, with the upper limits set at two levels: 0.8 sfc for B1 and B2 and the full sfc for B3 and B4. To investigate the impact of rainfall and potential evapotranspiration on these irrigation modes under long-term fluctuations, we employed a stochastic framework that probabilistically linked rainfall events and irrigation applications. The Monte Carlo method was employed to simulate a long-term series (4000a) of rainfall parameters and evapotranspiration using 62 years of meteorological data from the Xinxiang region, situated in the southern part of the North China Plain. Results showed that the relative yield and net irrigation water requirement of summer maize decreased with decreasing irrigation lower limits. Additionally, the interannual variation of rainfall parameters and evapotranspiration during the growing season were remarkable, which led to the lowest relative yield of the rainfed mode (E) aligned with a larger interannual difference. According to the simulation results, mode A4 (irrigation lower limit equals 0.5 sfc, irrigation upper limit equals 0.8 sfc) could be adopted for adequate water resources. Conversely, mode B2 is more suitable for a lack of water resources.

CROP WATER REQUIREMENT IN A SUB-WATERSHED OF MAHABUBNAGAR DISTRICT OF TELANGANA FOR KHARIF RICE CULTIVATION USING GEOSPATIAL TECHNIQUES

In a sub-watershed of the Mahabubnagar district of Telangana state, India, the paper focuses on estimating the irrigation water requirements of paddy crops for the kharif season from 2019 to 2021. The Random Forest Classifier in Google Earth Engine has used Sentinel-2 to determine the area that is being used for paddy cultivation. Using the Hargreaves model, potential evapotranspiration (ETο) has been calculated. The crop coefficient (Kc) for paddy and potential evapotranspiration (ETο) were used to calculate the crop's water requirements (Etc). Utilizing gridded rainfall data from the CHRS (Center for Hydrometeorology and Remote Sensing) - PERSIANN-Cloud Classification System (PERSIANN-CCS 4km x 4km), effective rainfall was calculated. To calculate the amount of irrigation water needed, effective rainfall and crop water needs were taken into consideration. Net irrigation water need was calculated by assuming 30% losses. The amount of water needed for paddy is calculated by multiplying the paddy cropped area by the net irrigation water requirement.

Impact of climatic changes on future irrigation water requirement in the Middle East and North Africa's region: a case study of upper Egypt

Freshwater shortages in the Middle East and North Africa's region (MENA) cause serious issues, while climate change causes even more issues. The current study examines how climate change may impact future irrigation requirements in Egypt's Upper Region using the medium greenhouse emission RCP 4.5 and high emission RCP 8.5 scenarios. Based on the Intergovernmental Panel on Climate Change (IPCC) and the Fifth Assessment Report, the climatic projections for RCPs 4.5 and 8.5 for the years 2023–2080 and 2081–2100 were used to determine the patterns of temperature and rainfall in the upper Egypt territory. The CROPWAT model was employed to estimate reference evapotranspiration (ETo) and net irrigation water requirement (NIWR) for the upper Egypt territory key crops sugar cane, potatoes, berseem clover (alfalfa), dry bean, wheat, cotton, maize, and tomato based on the predicted meteorological conditions. Five different scenarios for the key crops' future irrigation needs were calculated. The results reveal that under RCP 4.5 greenhouse emission for the periods 2023–2080 and 2081–2100, respectively, the overall NIWR for all examined crops increases by 5.1 and 5.9% in comparison to the current (2022) total NIWR for all crops. The total NIWR for all crops analyzed increases by 7.7 and 9.7% under the RCP 8.5 greenhouse emission scenario for the periods 2023–2080 and 2081–2100, respectively, in contrast to the current total NIWR for all crops. It is important to consider changes in water resource management in the MENA region, such as applying modern irrigation systems, using crop pattern rotation strategies with minimal water demands, and changing crop calendars or the times and locations of cropping activities.

Creation of Geospatial Database and Estimation of Irrigation Water Requirement of a Canal Command in Gayathri Irrigation Project, Kerala

The water requirement of crops in the canal command of distributaries of Gayathri Irrigation Project in Palakkad district, Kerala was estimated using GIS and CROPWAT 8 model. The geospatial data base for canal network, land use, soil and climate were prepared using Geographic Information System (GIS) tools. This data base was used as input to CROPWAT model to estimate irrigation water requirement of different distributaries of the canal command. The gross irrigation requirement was estimated for the historical climate data (2004-2018) as well as the predicted climate data for the years 2026 and 2031. The net irrigation water requirement of different crops in the distributary command was found to vary spatially depending on soil, crop and climate. The net irrigation water requirement of paddy (mundakan) in distributaries located at head to tail reaches varied from 1004.5 to 1573.6 mm. The high value of net irrigation requirement in tail reach was due to the influence of soil series with high infiltration rate compared to head reach soil. The net irrigation requirement also varied for the same crop in same soil series in different distributaries depending upon the climatic conditions. The net irrigation water requirement of paddy (mundakan) in distributaries located at head to middle reaches for the same soil series varied from 1056.5 to 1004.5 mm due to the influence of variations in rainfall. The gross estimated irrigation water requirement showed that distributaries at the middle and tail reaches required more water than the distributaries at the head end. The maximum gross irrigation water requirement of 25369.7 m3/ha was observed for Peringhotukavu distributary in tail reach and lowest of 4390.9 m3/ha for Parakkalchalla distributary in head reach. The estimation of irrigation requirement for projected climate data also showed the same trend as that of historical data.

Future Water Requirements and Crop Productivity at Al-Najaf Governorate Under Different Climate Change Scenarios (2020–2080)

This study aims to predict the effect of climate change on net irrigation water requirements (NIWR) and agricultural productivity from five common crops (wheat, barley, summer maize, and sorghum) in the Al-Najaf Governorate in Iraq. GFDL-ESM2M mode was used to predict the lower and upper temp and precipitation for two time periods (2020-2080) with 30 years for two periods (P1 and P2) under representative concentrations paths (RCP 2.5, RCP6, and RCP8.5). The CROPWAT model is used to determine NIWR, and the extreme learning model was used to estimate agricultural yields using previous crop yield production and weather data, supported vectors machine (SVM) is executed as a Machines Learns algorithm. Results showed NIWR increment to consider cropping owing to climate change. Barley is the crop most affected by climate change under the (RCP2.5, RCP6, and RCP8.5) scenarios, with increasing crop water requirements (NIWR) of (22%, 23%, and 24% ) for P1 and (23%, 24%, and 29%) for P2, respectively. Summer maize is the crop least affected by climate change under all climate change scenarios, with increasing crop water requirements of (1%, 2%, and 4%) for P1 and (1%, 2%, and 5% for P2. Climate change negatively affects the crop yield of all crops under the different climate change scenarios. The findings of this study could be used as a guide to developing adaptation strategies for dealing with potential changes in water availability and agricultural water productivity due to climate change.

Climate Change Impacts on Water Resources in Arid and Semi-Arid Regions: A Case Study in Saudi Arabia

In the coming years, climate change is predicted to impact irrigation water demand considerably, particularly in semi-arid regions. The aim of this research is to investigate the expected adverse impacts of climate change on water irrigation management in Saudi Arabia. We focus on the influence of climate change on irrigation water requirements in the Al Quassim (97,408 ha) region. Different climate models were used for the intermediate emission SSP2-4.5 and the high emission SSP5-8.5 Coupled Model Intercomparison Project Phase 6 (CMIP6) scenarios. The FAO-CROPWAT 8.0 model was used to calculate reference evapotranspiration (ETo) using weather data from 13 stations from 1991 to 2020 and for both the SSP2-4.5 and SSP5-8.5 scenarios for the 2040s, 2060s, 2080s, and 2100s. The findings indicated that, for the 2100s, the SSP2-4.5 and SSP5-8.5 scenarios forecast annual average ETo increases of 0.35 mm/d (6%) and 0.7 mm/d (12.0%), respectively. Net irrigation water requirement (NIWR) and growth of irrigation water requirement (GIWR) for the main crops in the Al Quassim region were assessed for the current, SSP2-4.5, and SSP5-8.5 scenarios. For SSP5-8.5, the GIWR for the 2040s, 2060s, 2080s, and 2100s are expected to increase by 2.7, 6.5, 8.5, and 12.4%, respectively, compared to the current scenario (1584.7 million m3). As a result, there will be higher deficits in 2100 under SSP5-8.5 for major crops, with deficits of 15.1%, 10.7%, 8.3%, 13.9%, and 10.7% in the crop areas of wheat, clover, maize, other vegetables, and dates, respectively. Optimal irrigation planning, crop pattern selection, and modern irrigation technologies, combined with the proposed NIWR values, can support water resources management. The findings can assist managers and policymakers in better identifying adaptation strategies for areas with similar climates.

An Artificial Intelligence Approach to Manage Crop Water Requirements in South Africa

Estimation of crop water requirements is of paramount importance towards the management of agricultural water resources, which is a major mitigating strategy against the effects of climate change on food security. South Africa water shortage poses a threat on agricultural efficiency. Since irrigation uses about 60% of the fresh water available, it therefore becomes important to optimise the use of irrigation water in order to maximize crop yield at the farm level in order to avoid wastage. In this study, combined application of an artificial neural network (ANN) and a crop – growth simulation model for the estimation of crop irrigation water requirements and the irrigation scheduling of potatoes at Winterton irrigation scheme, South Africa was investigated. The crop-water demand from planting to harvest date, when to irrigate, the optimum stage in the drying cycle when to apply water and the amount of irrigation water to be applied per time, were estimated in this study. Five feed –forward backward propagation artificial neural network predictive models were developed with varied number of neurons and hidden layers and evaluated. The optimal ANN model, which has 5 inputs, 5 neurons, 1 hidden layer and 1 output was used to predict monthly reference evapotranspiration (ETo) in the Winterton area. The optimal ANN model produced a root-mean-square error (RMSE) of 0.67, Pearson correlation coefficient (r) of 0.97 and coefficient of determination (R2) of 0.94. The validation of the model between the measured and predicted ETo shows a r value of 0.9048. The predicted ETo was one of the input variables into a crop growth simulation model, called CROPWAT. The results indicated that the total crop water requirement was 1259.2 mm/decade and net irrigation water requirement was 1276.9 mm/decade, spread over a 5-day irrigation time during the entire 140 days of cropping season for potatoes. A combination of the artificial neural networks and the crop growth simulation models have proved to be a robust technique for estimating crop irrigation water requirements in the face of limited or no daily meteorological datasets.

Effects on Net Irrigation Water Requirement of Joint Distribution of Precipitation and Reference Evapotranspiration

To establish the uncertain influence that the joint distribution of precipitation and reference evapotranspiration has on net irrigation water requirement, a Copula function–Monte Carlo method (CFMC) was proposed to calculate the probability of irrigation water requirement. Taking the Jingdian Irrigation District in Northwest China as an example, the distribution laws of precipitation and reference evapotranspiration were studied. Furthermore, five typical years under different crop planting structure conditions were selected, and the variation characteristics of net irrigation water requirement in each typical year under the conditions of climate uncertainty were analyzed. The results revealed the optimal distribution functions of precipitation and reference evapotranspiration to be gamma distribution and lognormal distribution. The probability density map of the joint distribution of precipitation and reference evapotranspiration has a “saddle” shape; that is, irrigation water requirement and reference evapotranspiration are usually inversely related. As the probability of the irrigation water requirement increases, the net irrigation water requirement in the irrigation area also increases. The CFMC method can determine the design value of the net irrigation water requirement under a specific probability for typical years under different crop planting structure conditions, which can provide a reference for agricultural water resource allocation in irrigation areas.

Estimation of regional farmland irrigation water requirements and water balance in Northeast China.

Agricultural water use has long accounted for more than 70% of water consumption in Northeast China. Estimating farmland irrigation water requirements and water balance is essential to ensure safe agricultural water and promote rational development and utilization of regional water resources. In this study, based on the modified Penman-Monteith equation recommended by the Food and Agriculture Organization (FAO) and Geographic Information System (GIS) technology, the net crop irrigation water requirements for four main crops in Northeast China were calculated, and the spatiotemporal distribution characteristics were also analyzed. Additionally, regional farmland irrigation water requirements were estimated, water balance in a typical year was determined, and the dominant factors affecting farmland irrigation water requirements in different regions were analyzed. From 1986 to 2020, the net irrigation water requirements for four main crops all showed the temporal trend of no significant increase and the spatial distribution characteristic of being high in the west and low in the east. The farmland irrigation water requirement decreased, and the monthly average farmland irrigation water requirement peaked in July during 2010-2019. Compared with 2010, in 2019, the irrigation water requirement per cultivated land grid cell in 20 cities increased and that in 16 cities decreased. Most cities were facing varying degrees of water shortage. Precipitation had the greatest direct effect on the farmland irrigation water requirement in different regions. These results quantify the farmland irrigation water requirement and water balance in Northeast China, and provide a reference for water resources and related environmental governance.

Minimization of deep percolation by a parabolic simulation model in a blocked end furrow irrigation method

AbstractSurface irrigation methods enable a lower cost of investment and energy requirements than pressurized systems if they are designed and managed properly. The aim of this investigation was to devise a new simulation model that would enable the determination of the optimum water application time and furrow length in a blocked end furrow system. The infiltration properties of the soil, net irrigation water requirement of the crop and inflow to the furrow constitute the main variables of this process. Deep percolation and the cross‐sectional area of the wetting pattern where the net irrigation water requirement of the crop is met along the furrow are the main components that were simulated interactively in the model. They were described as a function of time. The proposed model devised in this investigation was run for two different sample applications. While values of 93 and 94% water application efficiencies were obtained by running the USDA SCS method for the first and second sample applications, respectively, values of 92 and 94% were obtained, respectively, from the proposed model. These results indicate that the proposed model can be used in the design of a blocked end furrow system.

Sensitivity of Irrigation Water Requirement to Climate Change in Arid and Semi-Arid Regions towards Sustainable Management of Water Resources

This study aimed to assess the spatiotemporal sensitivity of the net irrigation water requirement (NIWR) to changes in climate, for sixteen crops widely cultivated in four irrigation projects located in arid and semi-arid regions of Iraq. Using LARS-WG and five GCMs, the minimum and maximum temperature and precipitation were projected for three periods from 2021–2080 with 20-year steps (P1, P2, and P3) under representative concentration pathways (RCPs) 2.6, 4.5, and 8.5. Weather data available for a reference period from 1990–2019 in four representatives’ meteorological stations were used. The climate variables and other required data were inserted into the CROPWAT 8 NIWR tool. Findings revealed that the increase in the NIWR for the considered crops due to climate change falls in the range 0.1–42.4%, 1.8–44.5%, 1.2–25.1%, and 0.7–14.7% for the North Jazeera Irrigation Project (NJIP), Kirkuk Irrigation Project (KRIP), Upper Khalis Irrigation Project (UKIP), and Dalmaj Irri-gation Project (DLIP), respectively. Barley is more susceptible to changes in climate, whereas maize, potato, soybean, and millet are found to withstand changes in climate better than others. The novel outcomes of this study support optimal spatiotemporal allocation of irrigation water requirement and the sustainable management of water resources in a changing climate in arid and semi-arid regions.

Assessment and management of composite risk in irrigated agriculture under water-food-energy nexus and uncertainty

Growing demands for water, energy, and food put many systems at a composite risk of resource shortages. There was a lack of approaches capable of effectively assessing tridimensional composite risk and describing non-linear correlations among subsystem risks within a Water-Energy-Food (WEF) nexus system. In this study, an integrated approach was developed to assess the composite risk of WEF nexus systems and generate risk-based plans. Specifically, a composite risk assessment model that could capture the interdependence among the risk for water, energy, and food shortage was proposed. Furthermore, a generalized Copula-based chance-constrained programming model and its solving algorithm were developed. The proposed approach has been applied to an agricultural WEF nexus system in northern China, where the shortage of water, energy and / or land affected agricultural outputs. Results show that the composite risk of the entire system would be higher than the maximum value among subsystem risks, but less than their sum. Higher composite risks could bring higher benefits. Under a certain composite risk, the overall system benefit would vary with different combinations of subsystem risks; and it could be promoted through coordinating resources supplied by different subsystems. A risk - benefit frontier consisting of optimal solutions corresponding to different combinations of composite and subsystem risks was identified through Monte Carlo simulation. The scheme that could generate 12.76 billion Yuan under a moderate composite risk level of 0.22 was recommended for the study problem. Accordingly, net irrigation water, energy for agriculture, and effective irrigation area should be no less than 1.66 × 109 m3, 81.08 × 103 tce, and 509.12 × 103 hm2, respectively.

Climate-Induced Perspective Variations in Irrigation Schedules and Design Water Requirements for the Rice–Wheat System

Conceptualizing the implications of climate change for crop evapotranspiration (ETc) and subsequent net irrigation water requirement (NIWR) is critical to sustaining Pakistan’s agriculture and food security. In this article, future ETc, NIWR, and design water requirements (DWR) were projected for the rice–wheat system of Punjab, Pakistan. Consistently increasing temperatures signify an impending hotter transition in the future thermal regime, accompanied by a substantial increase in monsoon rainfall. Future climate warming accelerated ETc and NIWR, which were compensated by 2–5 and 1–2 additional irrigations during the rice and wheat seasons, respectively. Future rice and wheat required 13–18 and 2–5 irrigations per season, respectively. Effective rainfall increments did not compensate for the warming-driven higher ETc and NIWR because of uneven and erratic rainfall distribution. Rainfall occurrence and the duration of peak irrigation demand were mismatched, resulting in surplus rainwater availability during the future rice season. The results suggest that DWR for 5- and 10-year return period droughts during the baseline period (965 and 1000 mm, respectively) should be revised to accommodate the additional 100–200 mm of irrigation water per season; otherwise, the study area will face an acute water shortage in the future.