Irrigated Agricultural Areas Research Articles

A quantitative analysis framework for analyzing impacts of climate change on water-food-energy-ecosystem nexus in irrigation areas based on WEAP-MODFLOW

Agricultural irrigation areas are vital for ensuring food security under climate change conditions with extensive water and energy consumption, which will inevitably exert pressures on ecosystem especially in arid and semi-arid areas normally with higher ecosystem degradation risk. The key to realize sustainable development lies in a quantitative analysis framework for analyzing impacts of climate change on water-food-energy-ecosystem nexus. Climate change exacerbates these challenges by impacting food production, water resources utilization, energy consumption for water supply and ecological conditions in irrigation areas. In this work, a quantitative analysis framework to analyze the trade-offs and synergies among various scenarios within the water-food-energy-ecosystem nexus in irrigation area was proposed. Two climate models (BCC and MIR1) were selected from the Coupled Model Intercomparison Project Phase 6 (CMIP6), and three Shared Socioeconomic Pathway (SSP) scenarios (SSP245, SSP370, and SSP585) were employed to quantitatively evaluate different sustainable development strategies among water, food, energy and ecosystem sectors from five key aspects: irrigation demand, food production, groundwater supply for irrigation, ecological base flow and irrigation water shortage. The results show that the sustainability index (SI) of combined management scenarios CC1-IM, CC5-IM, CC1-WSI and CC5-WSI could be 12% to 25% higher than that of the corresponding single management scenario. Among the four climate change and management scenarios, the SI of the integrated measures scenario (CC1-IM, CC5-IM) was the highest, with values of 1.39 and 1.12 respectively. This work highlights the pomising results of the integrated measures scenario in alleviating food production challenges, protecting ecological base flows, reducing groundwater extraction, alleviating irrigation water shortages and improving water supply efficiency when dealing with climate change challenges.

Spatiotemporal variability of pesticides concentration in honeybees (Apis mellifera) and their honey from western Mexico. Risk assessment for honey consumption

The study conducted in the state of Colima, western Mexico, aimed to assess the 1) occurrence, 2) temporal variability, 3) spatial variability, and 4) potential risk for honeybees and human consumption of pesticide-contaminated honey. For that purpose, 48 pesticides were determined in bees and their honey during both dry and wet seasons. The research considered two variables: land use categorization (irrigated agriculture, rainfed agriculture, grassland, and forest area) and location (coastal, valley, and mountain). Bee and honey samples were collected, pre-treated using solid-phase extraction (SPE), and analyzed using LC-MS/MS and GC–MS techniques. Occurrence: of the total number of pesticides, 17 were detected in the bee samples and 12 in the honey samples. The pesticides with the highest concentrations in the bee samples were glufosinate ammonium, picloram, and permethrin, while in the honey samples, picloram, permethrin, and atrazine were the most prevalent. Temporal variability: analyses revealed significant differences between dry and wet seasons for glufosinate ammonium and DEET in bee samples and only for glufosinate ammonium in honey samples. Spatial variability: analyses showed a trend in the number of detected pesticides, with irrigated agriculture areas having the highest detection and grassland areas having the least. The human potential risk assessment of contaminated honey consumption indicated no risk. The bee's potential risk for consumption of pesticides contaminated honey revealed chronic effects due to permethrin in a general scenario, and carbofuran, diazinon and permethrin in the worst scenario, and potential risk of acute effects by permethrin. The findings of this study contribute to understanding the contamination levels of pesticides in bees and their honey, emphasizing the importance of monitoring and mitigating the adverse effects of pesticide exposure on bee populations and environmental health.

Investigating Economic and Climate Signals in Groundwater Data

Hydrologic, climatic, regulatory and economic factors interact in complex ways to influence groundwater conditions. These relationships can be difficult to measure and model but are important for water management and for considering viable futures for regional economies. This paper proposes a strategy for an improved understanding of how groundwater levels reflect signals of regulatory, economic and climate factors. Most existing econometric studies of groundwater use focus on large-scale irrigated agriculture and urbanized areas in first world nations, where groundwater extraction data is available. In much of the world, groundwater extraction data are not available for individual farms and other water users. The goal of this study is to explore the responsiveness of groundwater levels (a more widely available measure of groundwater conditions) to regulatory, economic and climate signals in rural areas, using a growing body of remotely sensed land cover data. Pooled Ordinary Least Squares econometric models examine the effects of regulatory, climatic and economic factors on groundwater levels in rural Arizona. Groundwater regulations, recharge projects, housing units and irrigated acreage have statistically significant relationships to groundwater levels in the study areas. This approach can be valuable for understanding factors that influence groundwater conditions and which may be useful in managing groundwater use in widespread areas where groundwater extraction data is unavailable.

The Evaluation of Water Loss in The Western North Tarum Irrigation Channel

The Western North Tarum Irrigation channel plays a crucial role in supplying water to irrigated agricultural areas. However, it encounters challenges in ensuring adequate water delivery to all channel segments. This research assesses water loss during the distribution process in the Western North Tarum Irrigation channel, focusing on the evaluation of channel capacity and the impact of suboptimal water management on water loss. The study employs the HEC-RAS model, a hydraulic system analysis tool, to evaluate the channel’s capacity and simulates its behavior under various discharge conditions. The evaluation reveals that two channel segments, B.TUB 13 and B.TUB 25, have exceeded their capacity limits, resulting in overflow. Sedimentation downstream, particularly in these segments, exacerbates the issue by altering the channel slope and impeding water flow. This research identifies poor water distribution management as a significant factor contributing to water loss in the irrigation channel. Inadequate scheduling and the absence of proper water measurement tools result in instances of overwatering or underwatering in some areas. The lack of monitoring and control in the irrigation system hampers the detection of uncontrolled flow in the channels, leading to substantial water loss and inefficient water use. This research underscores the importance of evaluating and maintaining irrigation channel capacity to prevent overflow and water loss. Furthermore, it emphasizes the significance of effective water management to achieve more efficient water distribution and irrigation. Addressing these challenges is crucial for ensuring the long-term sustainability of irrigated agriculture in the Western North Tarum Irrigation area.Keywords: HEC-RAS, Irrigation Channel, Water Distribution, Water Loss, Western North Tarum

Impacts of land use/land cover and climate change on landscape sensitivity in Tunca River sub-basin: Use in spatial planning and sectoral decision processes

Managing landscape change is increasingly challenging due to rapid anthropogenic shifts. A delicate balance must be struck between the environment and change to ensure landscapes can withstand these impacts. This study conducted in the Tunca River sub-basin of Edirne province, aims to assess landscape sensitivity by examining the influence of land use/land cover (LULC) and climate change on landscape function processes. For this purpose, a methodology was developed based on ecosystem services to determine landscape sensitivity. The results revealed a LULC transformation that could lead to a 60% reduction in forest areas and a 5% and 20% increase in urban and irrigated agricultural areas, respectively. Water and erosion emerged as the most affected landscape function processes. Future scenarios from 2050 to 2070 indicate noteworthy changes in landscape sensitivity, showing an increase in sensitivity in the upper regions of the basin. The study identified high sensitivity in forested areas, moderate sensitivity in agricultural zones, and low sensitivity in micro-basins near residential areas. Protection and improvement strategies are recommended for areas with high and moderate sensitivity, while use-oriented strategies are suggested for those with low sensitivity. This study also establishes a scientific foundation for guiding the protection and management of ecologically sensitive basin areas, offering insights into the effects of landscape change processes at the micro-basin level in connection with climate change models.

Soil Moisture Memory: State‐Of‐The‐Art and the Way Forward

AbstractSoil moisture is an essential climate variable of the Earth system. Understanding its spatiotemporal dynamics is essential for predicting weather patterns and climate variability, monitoring and mitigating the effects and occurrence of droughts and floods, improving irrigation in agricultural areas, and sustainably managing water resources. Here we review in depth how soils can remember information on soil moisture anomalies over time, as embedded in the concept of soil moisture memory (SMM). We explain the mechanisms underlying SMM and explore its external and internal drivers; we also discuss the impacts of SMM on different land surface processes, focusing on soil‐plant‐atmosphere coupling. We explore the spatiotemporal variability, seasonality, locality, and depth‐dependence of SMM and provide insights into both improving its characterization in land surface models and using satellite observations to quantify it. Finally, we offer guidance for further research on SMM.

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.

Observed interannual variability and projected scenarios of drought in the Chorotega region, Costa Rica

The observation-based analysis of drought development in the Chorotega region showed that, despite the area being relatively small, agricultural drought exhibits high spatial variability across the region. However, the lack of net radiation data hinders the capacity to provide reliable estimates of evapotranspiration (ET), affecting the assessment of drought occurrence, since its propagation across the hydrological system is very sensitive to the ET estimation method. The coarse resolution of satellite-derived Normalized Difference Vegetation Index (NDVI) products and the lack of information on irrigation in agricultural areas limits the ability to properly establish a relationship between drought and vegetation response. Based on the observations, the most prominent precipitation deficits occur between September and October (–100 mm on average), showing that changes in the large-scale circulation are responsible for the impact of severe drought in the region. In agreement with previous studies, El Niño-Southern Oscillation (ENSO) is the main modulator of the drought severity, with the warm ENSO phase favoring an enhanced drought development and its influence being more significant between August and October, displaying correlations greater than –0.6. The climate change projections under RCP4.5 and RCP8.5 scenarios suggest the intensification of drought events in the Chorotega region at mid-century, with the Tempisque-Bebedero basin being the most affected area in terms of precipitation decrease and warming. The projected scenarios correspond to an increase of 1 oC for mean temperature and more of 2 oC for minimum and maximum temperature in the 2050 horizon, as well as a decrease of 400 to 800 mm for annual precipitation under both RCPs.

Study on the Response of Vegetation Water Use Efficiency to Drought in the Manas River Basin, Xinjiang, China

Ecosystem water use efficiency (WUE) is an important measure of the degree of water–hydrogen coupling and an important indicator for assessing ecosystem responses to climate change. Drought adversely affects ecosystem security, particularly in irrigated agricultural areas; therefore, understanding the relationship between WUE and drought is important. This study revealed the spatial and temporal characteristics of drought in the Manas River Basin, Xinjiang, China, from 2001 to 2020 through multi-source data using standardised anomaly indices and mutation detection. It also quantitatively analysed the hysteresis effect and resilience characteristics of drought for different vegetation types in the study area. The results showed that droughts at a severe level occurred less frequently in most of the study area on average from 2001 to 2020, and that droughts in the vegetation growing season occurred more frequently, particularly in grasslands; the frequency of droughts in woodlands was low. Furthermore, the lag in WUE to drought occurred on a 3-month scale and accounted for 64.0% of the total watershed area. Finally, 38.16% of the regional vegetation ecosystems in the Manas River Basin exhibited drought resistance. In conclusion, our results provide novel insights into the water-use strategies of plants in the study area and will help facilitate WUE optimisation.

Ecological Security of Desert–Oasis Areas in the Yellow River Basin, China

The desert–oasis interaction zone plays a crucial role in safeguarding oasis ecological security and maintaining stability within oases. This paper proposed a framework of EN-DSS, based on long-term remote sensing data and fundamental data, adopted morphological spatial pattern analysis (MSPA) and Linkage Mapper among other methods, and it took Lingwu City in the Ningxia Hui Autonomous Region, which is located in the desert–oasis interaction zone in the upper reaches of the Yellow River, as a case study. The results indicate the following: since 1995, this desert–oasis system has exhibited the characteristics whereby the oasis is expanding eastward and the desert is significantly receding. The vegetation coverage has improved overall, forming an ecological security pattern characterized predominantly by shrub forests, which is referred to as the “one core, two corridors, three zones, and multiple clusters” pattern. This pattern has significantly reduced the risk of wind and sand erosion in the agricultural irrigation areas along the Yellow River. However, the construction of this ecological security pattern still faces challenges, including high construction and maintenance costs and the need to enhance the network’s quality. In the future, it will be necessary to strengthen the integrated ecological network construction of ecological areas, agricultural areas, and urban areas to enhance the stability of this regional ecological network system.

Semiautomatic Mapping of Center Pivot Irrigated Areas Using Sentinel-2 Images and GEOBIA Approach

Image analysis and feature extraction of remoted sensing data are significant for mapping irrigated agriculture areas as a source of information to improve water management and agricultural planning. This paper presents an image segmented base approach GEOBIA (Geographic Object-Based Image Analysis) to extract irrigated areas by Center Pivot Irrigation System (CPIS). This study suggests a semi-automated recognition of circular patterns for the mapping of irrigated regions by center pivots, using Sentinel -2 MSI images, 10 meters spatial resolution. A set of images from different seasons, humid and dry are used to maximize de CPIS’s occurrence. A multiresolution segmentation method was applied, and a large number of segment-based shape features was extracted and used as input to a feature selection procedure (shape descriptors: Area; Compactness; Circularity Factor; Length/Width; Radius of smallest enclosing ellipse; and Roundness). In addition, another shape descriptor “Circularity Factor” was developed in this research and played an important role during preliminaries classification processes. The accuracy assessment of preliminaries classifications has validated used the Circularity Factor together with the other chosen shape descriptors to reach better results to CPIS’s detection. Furthermore, 86.23% of the CPIS mapped in the classification process is in accordance with the ground truth map. This methodology can be used to map large areas in a relatively short time and provides a tool for monitoring irrigated areas.

Timely monitoring of soil water-salt dynamics within cropland by hybrid spectral unmixing and machine learning models

Soil salinization and water scarcity are main restrictive factors for irrigated agriculture development in arid regions. Knowing dynamics of soil water and salt content is an important antecedent in remediating salinized soils and optimizing irrigation management. Previous studies mostly used remote sensing technologies to individually monitor water or salt content dynamics in agricultural areas. Their ability to asses different levels of crop water and salt management has been less explored. Therefore, how to extract effective diagnostic features from remote sensing images derived spectral information is crucial for accurately estimating soil water and salt content. In this study, Linear spectral unmixing method (LSU) was used to obtain the contribution of soil water and salt to each band spectrum (abundance), and endmember spectra from Sentinel-2 images. Calculating spectral indices and selecting optimal spectal combination were individually based on soil water and salt endmember spectra. The estimation models were constructed using six machine learning algorithms: BP Neural Network (BPNN), Support Vector Regression (SVR), Partial Least Squares Regression (PLSR), Random Forest Regression (RFR), Gradient Boost Regression Tree (GBRT), and eXtreme Gradient Boosting tree (XGBoost). The results showed that the spectral indices calculated from endmember spectra were able to effectively characterize the response of crop spectral properties to soil water and salt, which circumvent spectral ambiguity induced by water-salt mixing. NDRE spectral index was a reliable indicator for estimating water and salt content, with determination coefficients (R2) being 0.55 and 0.57, respectively. Compared to other models, LSU-XGBoost model achieved the best performance. This model properly reflected the process of soil water-salt dynamics in farmland during crop growth period. This study provided new methods and ideas for soil water-salt estimation in dry irrigated agricultural areas, and provided decision support for governance of salinized land and optimal management of irrigation.

An Optimisation–Evaluation Framework for the Sustainable Management of the Water–Energy–Food Nexus for an Irrigation District under Uncertainty

The synergistic regulation of the water–energy–food nexus in irrigation districts is important for promoting the sustainable management of agricultural resources in irrigation districts. In this paper, a new integrated optimization–evaluation modelling framework for the water–energy–food nexus in agricultural irrigation areas is developed. It can measure the synergistic effects of economic, social and environmental multidimensional objectives on the sustainable management of agricultural resources in irrigation areas. The model couples an optimisation module and an evaluation module, combines a multiobjective nonlinear planning model with an opportunity-constrained planning model and uses an entropy-weighted TOPSIS assessment approach to sustainably assess the multidimensional indicators of the water–energy–food nexus in irrigation districts, with full consideration given to the effects of uncertainty in agricultural water and soil resources and social systems. The feasibility of the constructed model is verified through a study of the Jinxi irrigation district. The results show that compared to the actual area, the optimised surface water and groundwater availability increased by 23.5% and 22.7%; the optimised total area increased by 4%, whereas corn decreased by 40%, rice increased by 34.6% and soybean decreased by 33.8%; the energy consumption decreased by 17.6% and the total recycled resources amounted to 8.97 × 109 kg, with a combined net economic benefit of CNY 1.25 × 109 more than the actual current amount. The synergistic development of the water–energy–food nexus (WEFN) in the district is relatively harmonious, suggesting that the district should focus on developing agricultural mechanisation and balancing economic benefits with environmental and ecological protection; furthermore, the model constructed should provide decision-making support for the efficient use of agricultural resources in the irrigation district.

Characterization and outlook of climatic hazards in an agricultural area of Pakistan

Many dimensions of human life and the environment are vulnerable to anthropogenic climate change and the hazards associated with it. There are several indices and metrics to quantify climate hazards that can inform preparedness and planning at different levels e.g., global, regional, national, and local. This study uses biased corrected climate projections of temperature and precipitation to compute characteristics of potential climate hazards that are pronounced in the Gomal Zam Dam Command Area (GZDCA)— an irrigated agricultural area in Khyber Pakhtunkhwa province of Pakistan. The results answer the question of what the future holds in the GZDCA regarding climate hazards of heatwaves, heavy precipitation, and agricultural drought. The results of heatwaves and agricultural drought present an alarming future and call for immediate actions for preparedness and adaptation. The magnitude of drought indices for the future is correlated with the crop yield response based on AquaCrop model simulations with observed climate data being used as input. This correlation provides insight into the suitability of various drought indices for agricultural drought characterization. The results elaborate on how the yield of wheat crop grown in a typical setting common in the South Asian region respond to the magnitude of drought indices. The findings of this study inform the planning process for changing climate and expected climate hazards in the GZDCA. Analyzing climate hazards for the future at the local level (administrative districts or contiguous agricultural areas) might be a more efficient approach for climate resilience due to its specificity and enhanced focus on the context.

Multi-layer multi-objective cooperative regulation of agricultural water resources in large agricultural irrigation areas based on runoff prediction

Reasonable and optimal allocation of irrigation water is of great significance to improve agricultural water use efficiency for large-area agricultural irrigation systems. To solve the problems of large agricultural production scale, unreasonable water distribution structure, many rivers in the region and large changes in runoff volume, this paper takes the Sanjiang Plain in Heilongjiang Province, China, as the research object to develop an efficient and accurate water optimal allocation method. First, the runoff volume of many rivers in the region was obtained through the ARIMA-SVM-BP combined runoff prediction model, based on which a ‘regional-irrigation district-farmland’ multilayer multiobjective water resource cooperative deployment system model was established to achieve the synergistic enhancement of farmers' interests and the economic-social and regional economic-engineering volume of the irrigation district. The joint optimization method, fuzzy mathematical planning, and large system decomposition-coordination coupling were used to solve the multilayer multiobjective regulation system. The results show that the optimal agricultural water allocation volumes for the current year (2020) and the forecast year (2025) of 37 irrigation districts in the Sanjiang Plain are 3.61 × 109 m3 and 3.89 × 109 m3, saving 17% of water and resulting in a 24% decrease in the agricultural water use ratio compared with the actual situation, respectively. In the process of repeated coordination of the large system, the water distribution error is controlled within 1%. The fairness of water distribution in the multilayer system was improved by 34.7% while obtaining high economic benefits. The method proposed in this study helps the coordinated regulation and planning of multiple water sources in large agricultural irrigation areas, improves the ability of water distribution in irrigation areas to cope with different levels of incoming water, and promotes the sustainable development of regional irrigated agriculture.

Optimal allocation of agricultural water resources in Yanghe watershed considering blue water to green water ratio.

Yanghe Watershed has low annual rainfall, uneven spatial and temporal distribution, extreme shortage of water resources in some areas. The contradiction between supply and demand of water for agricultural production is prominent and the expected production value cannot be achieved. Therefore, it is necessary to investigate the supply and demand of agricultural water resources and the impact of green water on agricultural crops in Yanghe Watershed. This article proposes a new crop economic model for increasing the green-water footprint to blue-water footprint ratio (GWF:BWF) in accordance with the regional characteristics, alleviating agricultural water shortage in irrigation areas, optimizing water resource allocation, and achieving sustainable agricultural development. The proposition is based on a study of five crops in eight districts and counties in the Yanghe River watershed. By combining the economic model F with a crop water production function, we achieved 89.3%, 88.9%, 97.1%, 81.5%, and 87.0% of the optimal water demands of the five crops, respectively, and effectively improved the underground irrigation of crops and the water resource utilization efficiency. The GWF:BWF threshold interval was subsequently selected based on the temporal changes in the BWF and GWF in the study area. This enabled significant reduction of the planting area of blue-water crops and increase in the proportion of green-water crops, while also improving the agricultural economy of the Yanghe Watershed. The proposed model promises to afford enhanced management of agricultural irrigation areas that experience rainfall shortage. © 2023 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Assessment of phytosanitary practices on the environment: case study potato of Loukkos (northwest Morocco).

Loukkos perimeter is among the most important irrigated agricultural areas in Morocco. It covers horticulture and market garden production, including potato. This crop is characterized by the intensive use of pesticides that could lead to health and ecological risks, via the food chain and contamination of natural resources, including groundwater. This study is aimed at assessing the use of pesticides in potato cultivation and their impacts on the environment and human health. Here, pesticide use was characterized by the number of treatments (NT), quantity of active substances indicator (QASI), and the treatment frequency indicator (TFI), through field surveys carried out on 50 Loukkos potato producers. The results showed that farmers use heavy pesticide treatments, mainly against late blight. We determined NT = 19 treatments, total TFI = 28.10, and QASI = 14.86kg/ha. These values reflect a massive use of pesticides on this crop, which could therefore constitute a challenge and a major constraint for the development of sustainable agriculture in this zone, due to their negative environmental and health effects. It is, therefore, necessary to react quickly to make changes in phytosanitary practices with the aim to monitoring pesticide use via the agro-environmental indicators to reduce health and environmental impact of intensive pesticide use.

Microplastic levels on sandy beaches: Are the effects of tourism and coastal recreation really important?

This study assessed the effect of tourism and other recreational activities on microplastic (MP) levels and their characteristics in the sand and surf zone of the seawater. Six sites were chosen belonging to three sandy beaches with similar geomorphologic and morphodynamic characteristics but with different tourism activities. On average, a concentration of 1133.3 ± 811.3 items/kg dry weight (d.w.) and 12.7 ± 14.9 items/m3 were found in the sand and seawater samples, respectively. Fibers and films predominated and were less than 1 mm in length. In the sand, the films mainly matched the PE polymer spectra and the fibers matched PET polymer, cotton, and indigo blue dye; in the seawater samples, PP films and PET fibers prevailed. At the Pehuén-Co – Monte Hermoso Coastal Marine MPA where the flow of tourists is low, the MP levels were the lowest and the largest particles were found, mainly blue or black fibers, with less polymer diversity, cotton and PET being the most prevalent suggesting a recent input of textile fibers to this site. Moreover, the highest concentration of MPs was found on the southern site of a beach considered to be more pristine due to negligible human activity, including the smallest size pattern, mostly composed of white films or fibers with a greater diversity of polymers, predominantly PE > PET > PP. A great occurrence of PVC white films was also found in the surf zone at this site. Proximity to the mouth of a river, littoral drift, and other point sources were identified as the main sources, indicating that, apart from the local tourism and recreational activities, other sources might play a major role in the input of MPs to sandy beaches, such as extensive/intensive agricultural land use and irrigation areas.

Effects of irrigation on the fate of microplastics in typical agricultural soil and freshwater environments in the upper irrigation area of the Yellow River

Agricultural activities are among the most significant sources of microplastics (MPs) in water. However, few studies have explored the effect of irrigation on the fate of MPs in agricultural systems. This study investigated the distribution of MPs in agricultural soil, surface water, and sediment of adjacent rivers, as well as the “MP communities” in various environments before and after irrigation in a typical agricultural irrigation area of the Yellow River. MPs were detected in all of the examined sites. The number of MPs in surface water and sediment increased after irrigation, whereas those in the surface soil of croplands decreased. In the vertical direction, irrigation accelerated the migration of MPs (< 100 µm) deep into the soil. The vertical mobility of fibers in soil was faster than that of other types of MPs. Moreover, irrigation decreased the correlation between soil properties and MPs in soils. MP community analysis indicated that irrigation enhanced the differences between MP communities among adjacent environments. Collectively, our findings confirmed that river water irrigation caused secondary MP pollution in the soil environment and accelerated MP pollution in deep soil. Therefore, this study provides a theoretical basis for the development of strategies for MP pollution control in agricultural soil.

Characterizing spatial, diurnal, and seasonal patterns of agricultural irrigation expansion-induced cooling in Northwest China from 2000 to 2020

In the last two decades, agricultural irrigation areas expanded rapidly in arid Northwest China (NWC, including North and South Xinjiang and Hexi Corridor) and altered local and regional climates. However, the increasing irrigation's climatic effects with underlying biophysical mechanisms have rarely been investigated. Here we characterized the impact patterns of irrigation expansion on land surface temperature (LST) in NWC from spatial, diurnal, and seasonal dimensions from 2000 to 2020; we then decomposed the LST changes related to multiple biophysical components using the energy balance equation and quantified the contributions of albedo and evapotranspiration (ET) to LST changes based on partial correlation and dominance analyses. We found the space-and-time method can better represent the cooling effects of long-term irrigation expansion than the space-for-time method, despite that they derived similar cooling trends. The cooling effects were most evident in South Xinjiang, followed by North Xinjiang and Hexi Corridor, and varied in different irrigation expansion sources. Specifically, more intensive cooling occurred in new irrigated areas from unused lands (-0.69 ± 0.02 K) than that from grasslands (-0.47 ± 0.05 K) and forests (-0.28 ± 0.04 K). The cooling effects were dominated by marked daytime cooling compared to negligible nighttime warming. Seasonally, the cooling effects were concentrated in the growing season (May to September), especially in July and August. LST changes related to latent heat and sensible heat fluxes significantly surpassed other biophysical components. The changes in ET and albedo jointly modulated LST variations and increased ET dominated the daytime cooling effect (r = 0.52, p < 0.001) with a relative importance of over 55%, especially in North Xinjiang. But the ET-dominated cooling would be dampened by enhanced precipitation, implying the non-trivial impact of background climate. This study can provide important references for evaluating the climatic effects caused by irrigation expansion in arid regions and for improving the climate models.