Irrigation Zone Research Articles

Shallow groundwater salinization risks under climate change: FEFLOW model application in Cebala Borj-Touil irrigation zone, northern Tunisia

ABSTRACT The Cebala Borj-Touil irrigation perimeter in the low valley of the Medjerda River is marked by a shallow saline groundwater rise. The seasonal water salinity shows a high spatial and temporal variation. This study introduces a novel methodological approach dealing with the data scarcity problem in a complex deltaic area, using only available data and numerical models. The numerical model FEFLOW was used to simulate the shallow groundwater flow and salt transfer. Several simulations were run to explore future trends of groundwater salinity under different climate change (CC) conditions. The results show a significant increase in water salinity under all the climatic scenarios. A 15% decrease in precipitation leads to an average increase in salinity of 10–15 g/l. The significant rise in sea level also affects the salinization process. The intrusion of seawater results in concentrations of salinity between 16 and 20 g/l. In addition, the quality of irrigation water has a marked impact, contributing to a significant increase in salinity, reaching a maximum of 15 g/l. These concerning results are to be expected, as the coastal study area is characterized by a semi-arid climate and is increasingly influenced by anthropogenic factors, including irrigation practices and drainage deficiency.

Delimitación de zonas de gestión del riego en el cultivo del banano mediante imágenes de satélite y atributos físicos-químicos del suelo

In banana plantations, irrigation is managed in a homogeneous way, which is inadequate due to the variability of the soil in different areas, leading to significant losses in productivity. To address this issue, the delimitation of agricultural management zones (AMZ) was proposed, based on the spatial variability of the physical and chemical soil attributes, along with information obtained from spectral indices derived from satellite imagery. Additionally, the soil-climate-plant relationship was considered to improve the accuracy and reliability of the information. For this purpose, Sentinel-2 satellite images were processed, and various spectral indices were calculated using the Sen2R package. These indices allowed the generation of AMZ in QGIS using the Smart Map plugin. The satellite images facilitated the delimitation of homogeneous zones based on spectral information. Through a correlation matrix between the mean values of physical and chemical soil variables and the spectral indices per hectare, a correlation was identified between the water stress index and factors such as sand content, electrical conductivity, soil texture class, and available water. The geospatial analysis allowed for the accurate delimitation of irrigation zones, compared to those defined solely by the physical and chemical properties of the soil. The vegetation’s response to soil characteristics, such as water retention capacity, cation exchange, and base assimilation in the soil, demonstrated the effectiveness of this delimitation.

Initial Experimental Investigation of Hydraulic Characteristics at Right-Angle Diversion in a Combined Canal and Pipe Water Conveyance System

To enhance the efficiency of irrigation water utilisation, China is progressively converting irrigation ditches into pipelines. The water distribution outlets in irrigation zones are predominantly right-angled, and there are typically occurrences of erosion, sedimentation, and structural deterioration in the surrounding areas. This article employs a synthesis of indoor physical model experiments and theoretical analysis to examine the distribution of channel flow velocity and variations in water surface profile, pipeline flow rate, diversion ratio, circulation intensity, and turbulence energy across different relative water depths. The experimental results indicate that the water surface adjacent to the main canal wall demonstrates a pattern of initial decline, followed by an increase and subsequently another decline; furthermore, as the water level in the main channel rises, the magnitude of this fluctuation progressively diminishes. In some sections of the canal, the water surface elevation progressively increases, albeit with minimal amplitude. With a constant relative water depth, an increase in main channel flow results in a corresponding increase in pipeline flow; however, the diversion ratio is inversely related to the main channel flow. Conversely, when the main channel flow rate is constant, the diversion ratio increases as relative water depth rises. The vertical flow velocity near the water diversion outlet has a negative value, signifying the existence of a backflow zone, while the horizontal flow velocity varies considerably, facilitating the formation of circulation and resulting in localised deposition and erosion. The water flow near the pipe inlet downstream of the lower lip of 0.5 times the pipe diameter is impacted by the return zone, which has a higher turbulence energy and circulation strength and is more susceptible to siltation. The turbulence energy of the water flow is higher in the range of 0.5 times the pipe diameter upstream and downstream of the pipe inlet. This research is highly significant in facilitating the conversion of irrigation channels into pipelines.

Irrigation automation: overcoming challenges and promoting opportunities in family farming

Family farming in Brazil faces significant challenges regarding access to modern technologies, such as mechanisation and irrigation, which are more accessible to agribusiness. This disparity directly impacts the productivity and sustainability of family farming. The article aims to explore the economic and environmental benefits of irrigation automation for family farming, highlighting the importance of adapting this technology to local conditions and techniques already used by small producers. A multidisciplinary approach was used, combining literature review, data analysis, and the inclusion of a proposed irrigation automation board. Data were collected through interviews with agricultural producers and content analysis to identify patterns and research gaps. The proposed irrigation automation board proved efficient and accessible for small producers, allowing control of up to four distinct irrigation zones. The technology provided more precise and efficient irrigation, reducing costs and increasing productivity. Additionally, automation allowed farmers more time for other activities, improving their quality of life. Irrigation automation offers significant benefits for family farming, such as greater water efficiency, cost reduction, and increased productivity. However, it is essential to adapt this technology to local conditions and techniques already used by small producers. Public policies that encourage and value local production are essential to promote the sustainable development of family farming and reduce social inequalities

Potato growth, nitrogen balance, quality, and productivity response to water-nitrogen regulation in a cold and arid environment.

The pervasively imprudent practices of irrigation and nitrogen (N) application within Oasis Cool Irrigation zones have led to significant soil nitrogen loss and a marked decrease in water and nitrogen use efficiency. To address this concern, a comprehensive field experiment was conducted from April to September in 2023 to investigate the impact of varying degrees of water and fertilization regulation strategies on pivotal parameters including potato yield, quality, nitrogen balance, and water-nitrogen use efficiency. The experimental design incorporated two water deficit degrees at potato seedling (W1, 55%-65% of Field Capacity (FC); W2, 45%-55% of FC), and four distinct nitrogen application gradients (N0, 0 kg ha-1 of N; N1, 130 kg ha-1 of N; N2, 185 kg ha-1 of N; N3, 240 kg ha-1 of N). A control was also included, comprising N0 nitrogen application and full irrigation (W0, 65%-75% of FC), totally eight treatments and one check. The results indicated that the tuber yield, plant dry matter accumulation, plant height, plant stem, and leaf area index increased with higher nitrogen fertilizer application and irrigation volume. However, tuber starch content, vitamin C, and protein content initially increased and then decreased, while reducing sugar content consistently decreased. Except for W1N2 treatment, the irrigation water use efficiency increased as the N application rate rose, while the nitrogen partial factor productivity, crop nitrogen use efficiency and soil nitrogen use efficiency decreased with an increase in N fertilizer application. The W1N2 treatment resulted in a higher yield (43.16 t ha-1), highest crop nitrogen use efficiency (0.95) and systematic nitrogen use efficiency (0.72),while maintaining moderate levels of soil nitrate and ammonium nitrogen. Therefore, through the construction of an integrated evaluation index (IEI), the W1N2 treatment of mild water deficit (55%-65% of FC) at potato seedling combined with the medium nitrogen application (185 kg ha-1 of N) has the highest IEI (0.978), it was recommended as the optimal water-nitrogen regulation and management strategies to facilitate high-yield, high-efficiency, and environmentally sustainable potato production in the cold and arid oasis areas of northwest China.

Application of knowledge graph in smart irrigation district management decision making

Regarding the traditional irrigation and drainage management methods in China's irrigation districts, there are problems such as complex management processes, dispersed information systems, difficulty in resource sharing, diverse data sources, and insufficient intelligent auxiliary decision-making. In order to further realize refined intelligent management decision-making on issues related to smart irrigation districts, this article uses the basic information of 28 Yellow River diversion irrigation districts in Henan Province, as well as the overall plan reports and related issue management manuals of 7 management offices of the first and second phase projects of Zhaokou Irrigation District as data sources. Collect and organize data from irrigation areas, and use these to build a knowledge graph for management of issues in irrigation areas diverted from the Yellow River. At the same time, the BERT + BiLSTM + CRF model is used to intelligently identify entities such as irrigation projects and problem events in the Zhaokou Irrigation District inspection log text, and entity alignment technology is used to match the inspection text with entities in the knowledge graph. Finally, combined with the graph retrieval function, the intelligent generation of smart irrigation district management decision-making solutions is realized. The validation of specific irrigation district examples and analysis of the evaluation indexes of relevant models demonstrate the reliability of the problem management decision-making scheme proposed in this paper. This application effectively facilitates the integration of information data in the Yellow River diversion irrigation area and enables visual management of intelligent irrigation zones, presenting a novel concept for the informationization construction of China's irrigation areas.

Aerial dispersal of Venturia inaequalis ascospores with under-canopy sprinkler irrigation for apple scab management

AbstractSprinkler irrigation systems can release ascospores of Venturia inaequalis, the cause of apple scab, from infected leaves on the ground under conditions unsuitable for infection, and thus reducing the primary inoculum. Under-canopy irrigation was carried out for two hours in the middle of the day over overwintered apple leaves heavily infected with scab, either in a wind-protected enclosure or in a wind-exposed orchard. Ascospores were captured with rotating-arm spore traps at heights ranging from 0.3 m to 3.0 m above the ground. Ascospores dispersed above the irrigated layer and were detected at all heights above the sprinklers. Wind played a critical role in spore transport, evident from the set-up where wind interference was minimised by a wind fence, resulting in higher airborne spore numbers across all measured heights compared with the orchard exposed to unrestricted wind conditions. Furthermore, vertical temperature gradients significantly correlated with spore distributions, particularly where negative gradients at heights between 0.3 m and 0.05 m and positive gradients at heights between 1.0 m and 0.3 m led to spore retention within the irrigated zone. The findings highlight that ascospores, dispersed above the irrigated layers, could settle on susceptible tissues. It thus becomes imperative to ensure a rain-free period of at least 24 h post-irrigation and, if a rainfall shortly occurs after irrigation, the application of curative fungicides becomes essential following unexpected rain. Reliable weather forecasts are therefore crucial in determining the effectiveness of under-canopy irrigation to reduce apple scab incidence.

Partial root zone irrigation and K application improves summer maize production and salt resistance in saline soil

Irrigation patterns and K fertilization are the key measures used to improve the salt tolerance of crops in saline land. A pot experiment was carried out to assess the effects of partial root-zone irrigation and K fertilization on maize in saline land. For the study parameters, we used a split plot design with three irrigation treatments (conventional irrigation (CI), fixed root-zone irrigation (FRI) and alternate root-zone irrigation (ARI)) and two K application rates (0 (K0) and 120 (K1) kg K2O ha−1). Compared with the CI treatment, the FRI and ARI treatments reduced the transpiration rate (Tr) and MDA content but increased the root length density (RLD), root surface density (RSD), root volume density (RVD), root dry weight density (RDWD), proline content, plant K/Na ratio, photosynthetic rate (Pn), yield, water use efficiency (WUE) and K use efficiency (KUE). Compared with the FRI treatment, the ARI treatment increased RLD, RSD, RVD, RDWD, the Tr, the proline content, the plant K/Na ratio and the Pn but decreased the MDA content; these factors increased yield, WUE and KUE. K application decreased the MDA content; improved root growth; and increased the stomatal conductance, Tr, proline content, Pn, yield and WUE. Of all the treatments, the ARI-K1 treatment maximized RLD, RSD, RVD, RDWD, the Pn and yield while minimizing the MDA content. Therefore, ARI combined with K application can maximize resistance to salt stress and osmotic stress, delay senescence, and improve photosynthesis, yield and WUE.

Soybean plants enhance growth through metabolic regulation under heterogeneous drought stress

Heterogeneous drought stress (HED) is prevalent in drought-resistant practices such as deficit irrigation, root zone irrigation and strip intercropping. The mechanisms and improvement of crop drought resistance under HED are not fully understood. This study used double-root grafted Nandou 12 (ND12) soybean seedlings to simulate HED treatment under controlled conditions. Seedlings were transplanted into root cups with waterproof partitions to establish different soil moisture treatments: sufficient irrigation (SI) with 80 % soil moisture on both sides, HED with 80 % on one side and 40 % on the other, and homogeneous drought stress (HOD) with 60 % on both sides. The results indicated that soybean plants treated with HED exhibited healthier growth compared to those treated with HOD. Photosynthesis rate (Pn), stomatal conductance (cond.), transpiration rate (Tr), and relative water content (RWC) decreased by 10.24 %, 43.90 %, 152.66 %, and 8.69 % in HED-treated plants, respectively, but dry biomass increased by 3.38 %. Conversely, HOD conditions led to a significant decline in these parameters. Metabolomic and transcriptomic analyses revealed significant changes in the biosynthesis and signaling pathways of key phytohormones and metabolites, including abscisic acid (ABA), gibberellin (GA), jasmonic acid (JA), isoflavones, starch, and sugars. In HED-treated plants, GmNCED downregulation resulted in 54.22 % less ABA than HOD. GA levels increased under HED with upregulation of GmGA3OX1 and GmGA3OX2. JA content in HED-treated roots was 90.90 % higher than in HOD-treated roots. Isoflavones concentration including genistein (73.01 %), genistin (63.63 %), malonylgenistin (20.58 %), malonylgenistin (65 %), diadzin (38.15 %), and malonyldiadzin (47.61 %) levels, were significantly higher in HED-treated plants. Antioxidant enzyme activities indicated a 20 % increase in peroxidase (POD) activity under HED, while malondialdehyde (MDA) content was 27 % higher in HOD-treated plants, indicating greater oxidative stress. Chlorophyll content remained stable, and starch concentration increased by 33.33 % in HED-treated plants compared to HOD-treated plants. HED enhances phytohormonal responses and metabolic adjustments in soybean plants, boosting photosynthetic efficiency, antioxidant capabilities, growth, and drought resilience. This regulatory mechanism balances growth promotion and drought resistance, highlighting HED potential in improving crop resilience.

Soil health influenced more by conservation tillage and cropping practice than irrigation level in a sandy semiarid cotton system

AbstractCropping systems in semiarid regions have frequently relied on continuous tillage and irrigation, but declining groundwater resources have prompted a greater focus on conservation practices to improve soil health and water storage. We compared soil health responses from cotton production systems in semiarid, coarse‐textured soils with different crop management strategies under high or low irrigation levels. Management systems included continuous cotton with conventional tillage (CCCT) compared to no‐till cotton with a rye cover crop (NTCR) and no‐till cotton with a wheat‐fallow rotation (NTCW), including high or low irrigation zones within each system. Samples were collected annually from two bulk soil depths (0–10 cm and 10–20 cm) and root‐associated soils 7 years after systems were established and continued for 2 years. We found that cropping system, but not irrigation level, altered soil microbial communities and other soil health indicators. Despite variation between study years and sampling zones, the conservation systems had greater soil microbial community size via ester‐linked fatty acid methyl ester (EL‐FAME or FAME) analysis, extracellular enzyme activities, and soil organic matter than the CCCT system. The NTCW system also had greater arbuscular mycorrhizal fungi FAME abundance. Our study suggests that no‐till and conservation strategies such as cover cropping and rotation can improve biological soil health indicators in these sandy semiarid soils even with limited irrigation.

Modeling Reliability Analysis for the Branch-Based Irrigation Water Demands Due to Uncertainties in the Measured Surface Runoff

This study aims to model the uncertainty and reliability quantification of estimating the planning irrigation water demands in the multi-canal irrigation zone, named the RA_IWD_Canal model. The proposed RA_IWD_Canal could estimate the zone-based and branch-based water demands and quantify their uncertainties and reliabilities via the weighted frequency quantile curves. The historical planning irrigation water demands and measured surface runoff from 2019 to 2024 in the Zhudong irrigation zone are utilized in the model development and application. Using the proposed RA_IWD_Canal model, the estimated branch-based irrigation water demands exhibit a significant variation (on average, from 0.02 m3/s to 1.7 m3/s) in time and space attributed to uncertainties in the historical gauged surface runoff. Also, the Zhudong Canal zone is demonstrated to be sufficiently supplied irrigation water subject to existing introduced water demands with a high reliability of 0.85; instead, the associated branches have considerable difficulty achieving the expected irrigation efficiency based on the desired water requirements with low reliability (nearly 0.25). To keep all branches in the irrigation zone consistent in irrigation efficiency, the probabilistic-based water demands could be introduced via the proposed RA_IWD_Canal model with the desired reliability.

Effect of zinc fortified, arid and irrigated wheat varieties flour upon life history parameters of red flour beetle, Tribolium castaneum (Herbest) (Coleoptera: Tenebrionidae)

Red flour beetle, Tribolium castaneum (Herbest) (Coleoptera: Tenebrionidae) is a stored grain pest. Various fortified wheat varieties have been produced but data about the impact of different wheat varieties flour on life history parameters of T. castaneum are poorly understood. This study evaluated the effect of fortified (Zincol-16 T1), arid zone [(BARS-09 T2), and (Fatehjang-2016 T3)] and irrigated zone [(Faisalabad-2008 T4), (Gold-2016 T5), (Manthar-03 T6), (Fareed-06 T7), (Mairaj-08 T8)] wheat varieties flour upon some life history parameters of T. castaneum in the laboratory using age-stage two sex life table. The egg incubation period, adult male and female longevities, and total male and female longevities of T. castaneum were shortest in T5 and T6 as compared to all other treatments. The T5 presented shorter larval and pre-adult duration. Egg incubation, larval, and pupal durations and total male and female longevities were higher in T1. Total longevity (egg to adult) of T. castaneum was lowest in T1 as compared to T3 and T4 but similar to other treatments. Pre-adult duration was longer in T1 as compared to all other treatments. Pre-adult survival was significantly lower in T1 (53%) than all the treatments in which it remained at par. Lowest proportion of female adult (Nf/N) was found in T1 as compared to T3, T4, and T7. Age stage specific survival rate (sxj) was lower on T1, whereas life expectancy (exj) was higher than other varieties. These results suggest that T. castaneum develop poorly upon zinc fortified wheat variety, Zincol as compared to other varieties thus it can be a good integrated pest management tool against this pest.

New strategy based on Hammerstein–Wiener and supervised machine learning for identification of treated wastewater salinization in Al-Hassa region, Saudi Arabia

The agricultural sector faces challenges in managing water resources efficiently, particularly in arid regions dealing with water scarcity. To overcome water stress, treated wastewater (TWW) is increasingly utilized for irrigation purpose to conserve available freshwater resources. There are several critical aspects affecting the suitability of TWW for irrigation including salinity which can have detrimental effects on crop yield and soil health. Therefore, this study aimed to develop a novel approach for TWW salinity prediction using artificial intelligent (AI) ensembled machine learning approach. In this regard, several water quality parameters of the TWW samples were collected through field investigation from the irrigation zones in Al-Hassa, Saudi Arabia, which were later assessed in the lab. The assessment involved measuring Temperature (T), pH, Oxidation Reduction Potential (ORP), Electrical Conductivity (EC), Total Dissolved Solids (TDS), and Salinity, through an Internet of Things (IoT) based system integrated with a real-time monitoring and a multiprobe device. Based on the descriptive statistics of the data and correlation obtained through the Pearson matrix, the models were formed for predicting salinity by using the Hammerstein-Wiener Model (HWM) and Support Vector Regression (SVR). The models’ performance was evaluated using several statistical indices including correlation coefficient (R), coefficient of determination (R2), mean square error (MSE), and root mean square error (RMSE). The results revealed that the HWM-M3 model with its superior predictive capabilities achieved the best performance, with R2 values of 82% and 77% in both training and testing stages. This study demonstrates the effectiveness of AI-ensembled machine learning approach for accurate TWW salinity prediction, promoting the safe and efficient utilization of TWW for irrigation in water-stressed regions. The findings contribute to a growing body of research exploring AI applications for sustainable water management.

Physicochemical characterization of agricultural soils under a traditional system in the Mezquital Valley, Hidalgo

The study examines alkaline and sodic soils, characterized by a pH exceeding 7.5 and elevated exchangeable sodium, indicative of deficiencies in nitrogen, manganese, iron, zinc, and copper in crops. Its objective is to characterize agricultural soils under traditional and conventional systems using physical and chemical parameters to advocate for regenerative agriculture. Sampling and analysis adhere to Nom-021-RECNAT-2000 standards. Predominantly, the soils exhibit clay loam and sandy loam textures across Tula de Allende, Tezontepec de Aldama, Francisco I. Madero, and San Salvador municipalities. Organic matter percentages vary, classified as very high (Tula de Allende and San Salvador), high (Tezontepec de Aldama, Mixquiahuala de Juárez, and Francisco I. Madero), and low (Santiago de Anaya). The pH ranges from moderately alcaline to highly alkaline. Cation exchange capacity in irrigation zones varies from very high to high, and medium for Tezontepec and Santiago de Anaya, which rely on rainfed irrigation. Elevated nitrate (NO3-), phosphorus (H2PO4-), and potassium (K+) concentrations are noted across different municipalities. These findings prompt reconsideration of traditional tillage practices that exacerbate sodicity in agricultural soil.

Soil quality evaluation for irrigated agroecological zones of Punjab, Pakistan: The Luenberger indicator approach

AbstractThis article describes the construction of the Luenberger soil quality indicator (SQI) using data on crop yield, non‐soil inputs, and soil profile from three irrigated agroecological zones of Punjab, Pakistan, namely, rice–wheat, maize–wheat–mix, and cotton–mix zones. Plot level data are used to construct a soil quality indicator by estimating directional distance functions within a data envelopment analysis (DEA) framework. We find that the SQI and crop yield relationships exhibit diminishing returns to improving soil quality levels. Using the constructed SQI values, we estimate linear regression models to generate weights that could be used directly to aggregate individual soil attributes into soil quality indicators without the necessity of fitting a frontier to the crop production data. For wheat and rice production, we find that SQI is most sensitive to changes in soil electrical conductivity (EC) and potassium (K). The SQI has direct relevance for site‐specific decision‐making problems where policymakers need to price land resources and conservation services to achieve agricultural and environmental goals.

Quality assessment using water quality indicators with geospatial analysis of groundwater quality El-Oued region, Northern Sahara, Algeria

ABSTRACT The present study was carried out to assess groundwater quality in two layers (Albian and complex terminal) intended for irrigation, to identify their original chemical composition and overall risk by different attack techniques in the province of El-Oued. Eleven physico-chemical parameters were measured at 16 locations. Emphasis was placed on the selection of irrigation water quality index (IWQI) inputs through the application of multivariate statistical analysis, the estimation of various classical irrigation indices, and the integration of geographic information systems. The results of the IWQI, sodium adsorption ratio, Kelly index, permeability index, residual sodium carbonate and magnesium hazard showed that the majority of groundwater samples were classified for irrigation purposes in the following categories: severely restricted (100%), low sodium and excellent (100%), good (93.75%), marginally safe (100%), good (100%) and suitable (81.25%), respectively. In addition, the majority of selected samples were classified as “good to eligible for irrigation” based on %Na as shown in the distribution map. Whereas the principal component analysis results show that the four factors together explain 72.74% of the total variance according to the Kaiser rule. Cluster analysis divides the parameters into three groups. In summary, the combination of the geographic system and irrigation quality indices is useful for identifying irrigation zones.

Prediction of groundwater level changes based on machine learning technique in highly groundwater irrigated alluvial aquifers of south-central Punjab, India

Groundwater serves as a vital resource for all living organisms. In regions extensively reliant on groundwater irrigation, hydro-climatic factors, groundwater extraction, and the flow of surface water exhibit an indirect interdependence. This study primarily aims to anticipate GWL in such highly irrigated zones using the Machine Learning (ML) approach. To achieve this, the widely employed Random Forest (RF), Bagging-Reduce Error Pruning Tree (Bagging-REPTree), and Bagging-Decision Stump Tree (Bagging-DSTree) models have been employed for the accurate forecasting of groundwater levels. The long-term pre-monsoon and post-monsoon (fourteen locations) data set of South-Central Punjab state has been applied for the model calibration/training and validation/testing. Seven statistical indices were used such as percent bias (PBIAS), root mean square error (RMSE), normalized root mean square error (nRMSE), RMSE-observation standard deviation ratio (RSR), mean absolute error (MAE), Nash Sutcliffe efficiency (NSE) and correlation coefficient (CC) for the model performance analysis. The results revealed that the RF model outperformed in pre-monsoon (testing phase) (RMSE = 0.682, NSE = 0.958) as well as the post-monsoon (testing phase) (RMSE = 0.150, NSE = 0.997) compared to the other two models in the station Ahmadapur and the similar trend is observed in all the stations. Overall, the RF model demonstrates superior performance in predicting groundwater levels during both pre-monsoon and post-monsoon seasons, particularly in highly groundwater-irrigated alluvial aquifers of the southern region.

Soil water dynamics and deep percolation in an agricultural experimental area of the North China Plain over the past 50 years: Based on field monitoring and numerical modeling

The unregulated irrigation systems used in the late 20th century have led to increasingly severe deep percolation (DP) in the agricultural irrigation areas of the North China Plain. This has become an important factor limiting the efficient utilization of water resources and sustainable environmental development in these irrigation areas. However, the thick vadose zone is hydrodynamically exceptionally complex. The soil hydrological cycle is constantly changing under the influence of major climate change and human activity, thereby causing changes in DP that are difficult to quantify accurately. Here, the Luancheng Agricultural Irrigation District in North China was selected for a continuous 20-year in situ experiment. Soil-water dynamics were monitored using neutron probes and tensiometers, to determine the complete annual soil-water cycle and the hydrodynamic properties of the thick vadose zone irrigation district. For 1971–2021, DP was simulated using the HYDRUS-1D model and was verified by fitting observed values. Soil water content (SWC) exhibited similar trends in years that differed in terms of the amounts of irrigation and precipitation. The 0–100 cm soil layer was significantly affected by precipitation and other factors, and recharge >60 mm/d caused percolation. DP occurred mostly after irrigation or during the period of intensive precipitation in July–October. The maximum percolation rate was 16.9 mm/d under the present irrigation method. The main factors leading to DP were soil water storage capacity (R2 = 0.86) and precipitation (R2 = 0.54). Under the evolution of irrigation measures in the last 50 years, the average DP has gradually decreased from 574.2 mm (1971–1990) to 435.5 mm (2005–2021). However, a substantial amount of precipitation and irrigation water infiltrated the soil and percolated into the deep soil layer without being utilized by the crop. Therefore, there is an urgent need to consider measures to reduce DP to improve water-use efficiency in agriculture.

Detection and Attribution of Vegetation Dynamics in the Yellow River Basin Based on Long-Term Kernel NDVI Data

Detecting and attributing vegetation variations in the Yellow River Basin (YRB) is vital for adjusting ecological restoration strategies to address the possible threats posed by changing environments. On the basis of the kernel normalized difference vegetation index (kNDVI) and key climate drivers (precipitation (PRE), temperature (TEM), solar radiation (SR), and potential evapotranspiration (PET)) in the basin during the period from 1982 to 2022, we utilized the multivariate statistical approach to analyze the spatiotemporal patterns of vegetation dynamics, identified the key climate variables, and discerned the respective impacts of climate change (CC) and human activities (HA) on these variations. Our analysis revealed a widespread greening trend across 93.1% of the YRB, with 83.2% exhibiting significant increases in kNDVI (p < 0.05). Conversely, 6.9% of vegetated areas displayed a browning trend, particularly concentrated in the alpine and urban areas. With the Hurst index of kNDVI exceeding 0.5 in 97.5% of vegetated areas, the YRB tends to be extensively greened in the future. Climate variability emerges as a pivotal determinant shaping diverse spatial and temporal vegetation patterns, with PRE exerting dominance in 41.9% of vegetated areas, followed by TEM (35.4%), SR (13%), and PET (9.7%). Spatially, increased PRE significantly enhanced vegetation growth in arid zones, while TEM and SR controlled vegetation variations in alpine areas and non-water-limited areas such as irrigation zones. Vegetation dynamics in the YRB were driven by a combination of CC and HA, with relative contributions of 55.8% and 44.2%, respectively, suggesting that long-term CC is the dominant force. Specifically, climate change contributed to the vegetation greening seen in the alpine region and southeastern part of the basin, and human-induced factors benefited vegetation growth on the Loess Plateau (LP) while inhibiting growth in urban and alpine pastoral areas. These findings provide critical insights that inform the formulation and adaptation of ecological conservation strategies in the basin, thereby enhancing resilience to changing environmental conditions.

Coupled Calculation of Soil Moisture Content and PML Model Based on Data Assimilation in the Hetao Irrigation District

Most Penman-Monteith-Leuning (PML) evapotranspiration (ET) modeling studies are dominated by consideration of meteorological, energy, and land use information, etc., but the dynamic coupling of soil moisture content (SM), especially in terms of improving accuracy through assimilation, lacks sufficient attention. This paper proposes a research framework for the dynamic coupling simulation of PML model and SM based on data assimilation, i.e., the remote sensing monitored SM is combined with soil evaporation of PML to obtain high-precision time-continuous SM data through data assimilation; simultaneously, dynamical soil evaporation coefficients are generated based on the assimilated SM to improve the simulation accuracy of the PML model. The new scheme was validated at a typical irrigation zone in north China and showed obvious improvements in both SM and ET simulations. Moreover, the effect of the assimilation of SM on the simulation accuracy of ET for different crop growth periods is further analyzed. This research provides a new idea for the coupling simulation of the SM and PML models.