Irrigation Water Resources Research Articles

Assessing multi-decadal climatic variability and its impact on cardamom cultivation in the Indian Cardamom Hills.

This study examines the multi-decadal variability and trends of surface air temperature and precipitation in the Indian Cardamom Hills (ICH), a degraded tropical rainforest area unique for cardamom cultivation. Utilizing observed long-term climatic data (1958-2017), statistical methods such as the Mann-Kendall test (MKT), Sen's Slope Estimator (SSE), and Incremental Trend Analysis (ITA) were applied to assess the impact of surface air temperature, rainfall, and the number of rainy days on cardamom yield. The analysis revealed a significant decline in annual rainfall by approximately 13.62mm per year, with pronounced seasonal declines 0.87mm for winter, 12.33mm for pre-monsoon, 24.93mm for southwest monsoon, and 18.10mm for post-monsoon. Simultaneously, the number of rainy days dropped by nearly 19.75days over the 40-year period. A noticeable increase in decadal minimum and average temperatures was observed, highlighting potential adverse effects on cardamom yield and irrigation water resources. The findings suggest that excessive rainfall during the southwest monsoon negatively correlates with cardamom yield, while slightly warmer temperatures show a weak positive correlation. The study also emphasizes the need for adaptive agricultural practices and climate-resilient policies to mitigate the effects of changing climatic conditions on cardamom production. This research contributes valuable insights for farmers and other stakeholders as well as policymakers aiming to ensure sustainable cardamom cultivation amidst climate change.

Reclamation and Improvement of Saline Soils Using Organo–Mineral–Natural Resources, Treated Saline Water, and Drip Irrigation Technology

Reclamation and management of saline soil in arid regions fundamentally require more consideration to attain sustainable agriculture. Experiments were conducted at Abo-Kalam Farm, South Sinai, Egypt. Split-split-plot design experiments were carried out to study the effect of treatments on saline soil hydrophysical properties, sorghum, and cv. ‘Dorado’ plants during the summer season. Pea cv. ‘Entsar 3’ plants were cultivated during the winter season for the residual effect of treatments. Organo–mineral amendment (rice straw compost + mineral sulfur at different rates) was assigned as the main factor, natural rock or artificial fertilizers were assigned as subfactors, and humic acid at different rates was the sub-subfactor. Results showed that organo–mineral amendments improved the hydrophysical properties of the soil, plant nutrient uptake, crop yield, and crop water productivity; however, it diminished by 10 tons/fed (4200 m2) of compost plus 700 kg/fed of mineral sulfur. Therefore, it is recommended that economically using the combination of applying organic–mineral amendments of 4 tons/fed of compost plus 400 kg/fed of mineral sulfur and 5 kg/fed of humic acid plus natural rock fertilizer is the best safe management for reclamation and improvement of saline soils using partially treated saline irrigation water and natural resources.

Uncertain multi‐objective programming approach for planning supplementary irrigation areas in rainfed agricultural regions

AbstractRainfed agriculture is crucial for ensuring global food and water security, and supplementary irrigation is an effective means of improving the economic benefits in rainfed agricultural regions. This study proposes a novel uncertain optimization approach to optimize supplementary irrigation areas in rainfed agricultural regions. The approach incorporates multi‐objective linear programming, interval linear programming, fuzzy goal programming and stochastic expected value programming. In the proposed interval multi‐fuzzy goal stochastic expected‐value programming, uncertainties are expressed in the form of discrete intervals, probability distributions and fuzzy goals. This approach, which considers the randomness of precipitation during the optimization process and allocates limited irrigation water resources to different subareas and crops, was applied to a case study of crop irrigation area planning in Guyuan City, Ningxia Hui Autonomous Region, northwestern China. The maximum economic benefits and the minimum sum of the Gini coefficients among the different subareas and crops were regarded as the planning objectives, and a series of optimal irrigation areas with different crops and subareas under different water levels were obtained. The optimization results revealed that vegetables and fruits consumed large amounts of irrigation water to increase their economic benefits. In addition, allocating a large amount of irrigation water to wheat is essential to decrease the Gini coefficient and meet food security constraints, particularly at extremely low water levels. Compared with current management, the optimized irrigation area decreased by 30%, the crop water deficit index increased by 9%, the economic benefits increased by 3% and the total Gini coefficient of crops decreased by 17%, indicating that the optimization approach could fairly and reasonably allocate irrigation water resources. Our research provides a mathematical approach for decision‐makers to plan supplementary irrigation areas in rainfed agricultural regions.

Simulating the exploration of the optimal irrigation of spring wheat in drought areas based on SWAP model

AbstractWater scarcity and low irrigation efficiency are the main factors causing yield losses in spring wheat in arid areas. To propose optimised irrigation scheduling for spring wheat in the arid areas of Ningxia, China, we first calibrated the SWAP (soil–water–atmosphere–plant) model and then utilised the well‐tested SWAP model during three typical rainfall years to evaluate the 542 irrigation schemes. The results showed that the model performed well in simulating the plant height, leaf area index (LAI) and biomass of spring wheat under different water stress conditions. The R2 values of the above three crop growth indicators were all greater than 0.89, and the normalised root mean square error (NRMSE) was less than 18% in the validation period. Additionally, the model achieved a high degree of accuracy in simulating the soil water content, evapotranspiration and yield, with RMSEs of 2%, 31 mm, and 326 kg/ha, respectively. The total irrigation amounts of 290, 320 and 350 mm with five, six and six irrigation times in the wet, average, and dry years, respectively, could achieve relatively high yields as well as improved water use efficiency (WUE) and irrigation water use efficiency (IWUE). The results can provide useful information for the efficient utilisation of irrigation water resources in arid areas.

Development of a water resources utilization model and optimization of the patterns of rice genotypes with system dynamics approach

The system dynamics approach was conducted to optimize genotype patterns in various climatic and water availability scenarios, to address the water productivity improvement of the Sefidrood irrigation network. The studied cropping pattern scenarios were SD1 (the continuation of current trends), SD2 (the reduction in the cultivated area), SD3 (replacing modified crops on an annual basis), and SD4 (increasing the annual cultivated area of Hashemi type rice). Moreover, the impacts of irrigation efficiency improvements and water resources management strategies on the physical and economic water productivity indices were studied. The results indicated that the SD3 scenario has the most positive impact on water productivity with 0.45 kg/m3 increases. However, a questionnaire survey revealed that this scenario that belongs to the increase in the modified genotype area cannot be recommended due to cooking quality and low prices. The Hashemi genotype was the favorite rice among farmers and experts. Also, analyzing the irrigation efficiency improvement scenario showed that this strategy has a limited impact on the physical and economic water productivity. Increasing the capacity of local rainwater storage pools, Ab-bandans, and increasing the drainage water reuse had the highest water productivity in the condition of river water reduction and uncertain water availability.

GIS‐based surface irrigation potential assessment of Gololcha River Watershed, Awash Basin, Ethiopia

AbstractThe Gololcha River Watershed is lacking comprehensive assessment of land suitability for surface irrigation, available water potential, and prioritized irrigable areas. This study aims to evaluate the potential of land and water resources for surface irrigation in the Gololcha River Watershed. The study employed a multi‐criteria analysis approach using the analytical hierarchy process (AHP) and ArcGIS to assess land suitability, considering factors like slope, land use/cover, soil characteristics, and proximity to water sources. The observed mean monthly water flow potential for each sub‐watershed was statistically evaluated, and the area ratio approach was used to transfer discharges from gauged sites to the site of interest. Irrigation water requirements for three major crops in the study area were calculated using the CROPWAT8.0 model. The findings revealed an average water availability for irrigation of 2.70 m3/s/month, 1.10 m3/s/month, and 1.03 m3/s/month for the Kurkura, Midagdu, and Wefi sub‐watersheds, respectively, with a total annual river flow of 161 m3/s. Based on the suitability analysis, 2508 ha (7.27%) of the watershed area was classified as highly suitable for surface irrigation, 16,420 ha (47.565%) as moderately suitable, 1502 ha (4.35%) as marginally suitable, and 14,029 ha (40.71%) as unsuitable. The overall surface irrigation potential, aligned with available water, was estimated at 4872.58 ha. However, due to water limitations, only 14.15% of the total suitable land can be irrigated using existing flows. The study highlights the significant potential for expanding surface irrigation in the Gololcha River Watershed, particularly during the rainy seasons when substantial water flows are available. The findings provide valuable insights for developing sustainable irrigation strategies, enhancing agricultural productivity, and contributing to food security in the region.

Impacts of Irrigated Water Use on Household’s Income in South Wollo Zone, Amhara Regional State, Ethiopia

Agriculture is the backbone of the Sub-Saharan African economy. Ethiopia's GDP is also shared by 45 from the agriculture sectors. However, it is confronted and challenged by variable rainfall and climate changes. Particularly, in South Wollo zone, agriculture is not exceptional in facing such daunting challenges and effects of variable rainfall and climate change despite there are abundant alternative irrigated water resources. River and groundwater sources have not yet been used as alternative uses for agricultural production. As a result, rural households' income couldn’t meet their minimum food and nonfood requirements. This study, thus, attempted to evaluate the impact of irrigated water use on rural household's income in South Wollo Zone, Amhara regional state, Ethiopia. In pursuit of this, it employed a descriptive type of research design and triangulated research approach that consists of both quantitative and qualitative techniques. Primary and secondary data were collected from small scale irrigated households and secondary sources. Binary logistic regression was used to determine major significant factors that affect households' irrigated water use for agricultural production. A propensity core matching model was employed to evaluate irrigated water use and agriculture on household’s income. This study result (binary logistic regression result) showed that the household’s family size, land size and willingness were significant factors that determined irrigated water use for agricultural production at 95 percent level of significance. The propensity score matching model result also shows that irrigated water use significantly created significant impact on household’s income. It is, thus, keenly important to change and improve the smallholder farmer’s land size, extension services, rural credit, technology, education level, awareness about family size, and willingness to use irrigated water sources, such as, ground and river water for agriculture agricultural production that increase their level of income.

Impacts of illegal mining activities on water quality for irrigation and implications for public health: A case study of the Oda River in the Ashanti Region of Ghana

ABSTRACT This study evaluated the biological and physicochemical parameters of the Oda River, including the detection of heavy metals. To achieve this purpose, a quantitative experimental research design was employed. Twenty-four water samples were taken along the Oda River from Obeng ne Obeng, Abuakwaa, and Odaso communities. The samples were analysed in a laboratory, and the results were compared to the irrigation water quality standards from the Food and Agricultural Organization (FAO). The study found that the concentration of turbidity and total suspended solids exceeded the recommended standards of FAO, while the river's pH, electrical conductivity, and total dissolved solids concentrations were within permissible boundaries. Lead, cadmium, mercury, arsenic, and fluoride were present at concentrations lower than the recommended guidelines, whereas copper and cyanide were not discovered. However, iron concentrations exceeded the FAO guidelines. Escherichia coli concentrations in the Obeng ne Obeng were lower than the FAO irrigation standards but were higher in the Abuakwaa and Odaso. The Pearson correlation coefficient highlighted significant correlations between the physicochemical parameters. This paper concludes that unregulated mining activities may endanger vital water resources for irrigation, public health, food safety, ecosystems, and livelihoods.

Improving soil moisture prediction with deep learning and machine learning models

Reliable soil moisture (SM) data is critical for effective water resources management, yet its accurate measurement and prediction remain challenging. This study was conducted to develop a deep learning regression network for sub-hourly SM prediction and compare its performance with traditional machine learning models, including the eXtreme gradient boosting (XGB), light gradient-boosting (LGB), cat boosting (CB), random forest (RF), k-nearest neighbors (kNN), and long short-term memory (LSTM) models. Sub-hourly SM, electrical conductivity (EC), soil temperature (ST), and weather parameters were collected during research experiments conducted for two years (2020–2021 and 2021–2022) at the Tropical Research and Education Center (TREC), University of Florida. A network of SM sensors and a weather station were installed at the experimental site with 24 plots of green beans and sweet corn under full and three deficit irrigation treatments with three replications. Model performance metrics such as coefficient of determination (r2) and global performance indicator (GPI) were used to evaluate the performance of the models. Results showed that all MLs and DL models performed more than satisfactorily in simulating SM of green beans and sweet corn plots. The testing average r2 and GPI of MLs were 0.83 and 0.02 (green beans) and 0.85 and 0.02 (sweet corn). However, XGB and LGB models outperformed the remaining ML and DL models. The testing r2 and GPI of XGB were 0.86 and 0.014 for green beans, whereas 0.88 and 0.015 for sweet corn. The r2 and GPI values for the LGB were 0.85 and 0.014 for green beans, while 0.88 and 0.015 for sweet corn. Even though DL model took longer and resources to be trained, its performance was not as accurate as that of XGB and LGB models. However, the performance of DL was better than the LSTM model. The r2 and RMSE of the LSTM model were 0.68 and 0.02cm 3 cm-3 for green beans and 0.75 and 0.02cm 3 cm-3 for sweet corn, respectively. Whereas the r2 and RMSE of DL were 0.84 and 0.015cm 3 cm-3 (green beans) and 0.85 and 0.02 cm 3 cm-3 (sweet corn). The ML and DL models performed better in simulating SM of sweet corn plots than green beans. Overall, these results confirmed that the ML and DL models could be alternative tools for SM prediction for agricultural fields, with potential applications for irrigation scheduling and water resources management.

Machine learning-based estimation of evapotranspiration under adaptation conditions: a case study in Heilongjiang Province, China.

The prediction of evapotranspiration (ET0) is crucial for agricultural ecosystems, irrigation management, and environmental climate regulation. Traditional methods for predicting ET0 require a variety of meteorological parameters. However, obtaining data for these multiple parameters can be challenging, leading to inaccuracies or inability to predict ET0 using traditional methods. This affects decision-making in critical applications such as agricultural irrigation scheduling and water management, consequently impacting the development of agricultural ecosystems. This issue is particularly pronounced in economically underdeveloped regions. Therefore, this paper proposes a machine learning-based evapotranspiration estimation method adapted to evapotranspiration conditions. Compared to traditional methods, our approach relies less on the variety of meteorological parameters and yields higher prediction accuracy. Additionally, we introduce a 'region of evapotranspiration adaptability' division method, which takes into account geographical differences in ET0 prediction. This effectively mitigates the negative impact of anomalies or missing data from individual meteorological stations, making our method more suitable for practical agricultural irrigation and ecosystem water resource management. We validated our approach using meteorological data from 25 stations in Heilongjiang, China. Our results indicate that non-adjacent geographical areas, despite different climatic conditions, can have similar impacts on ET0 prediction. In summary, our method facilitates accurate ET0 prediction, offering new insights for the development of agricultural irrigation and ecosystems, and further contributes to agricultural food supply.

Pre - Extension Demonstration of Alternate Furrow Irrigation for Better Water Management Technologies at Dugda District

The agricultural sector is the main consumer of fresh irrigation water so any effort to improve WUE in this sector gets a lot of attention so, raising WUE through improved of modern irrigation techniques and increasing the capacity of soil output complementary to make better employ of irrigation water and saving water for other uses. The aim of this study was to demonstrate the most effective water-saving techniques and improve the water use efficiency of irrigated onions under limited agricultural water availability for better water management technologies. The treatment consists of conventional furrow irrigation and alternate furrow irrigation method with full crop water requirement in three replications on two farmers. Crop water requirement was estimated using actual daily climatic data. The result showed that conventional furrow irrigation method was the highest bulb yield and yield components when compared to alternate furrow irrigation method. Alternate furrow irrigation method produced total yield of 18.73 t/ha which was not significantly different with that obtained under every furrow irrigation (220.6 Qt/ha). It was also found that yield reduction may be low compared to the benefits gained by diverting the saved water to irrigate extra cultivated land. The result showed that decreasing WP with increased irrigation water from 50% crop water requirement (ETc) from alternate furrow irrigation (5.68 kg m-3) to conventional furrow irrigation 100% Etc (3.6 kg m-3). Increasing water deficit from 100 to 50% ETc led to an increase of onion WP up to 75%. For increasing marketable bulb yield of onion under no water stress scenario, irrigation of onion with conventional furrow irrigation methods could be used. However, under limiting irrigation water resource condition, irrigation of onion could be done with alternate furrow irrigation method to maximize water use efficiency of onion for similar agro-ecology and soil type.

Impact of irrigation systems on water saving and yield of greenhouse and open field cucumber production in Saudi Arabia

Water resources worldwide are limited, especially in desert areas like Saudi Arabia. Therefore, the rational and sustainable management of irrigation water supply is an imperative requirement. This research aimed to assess how different irrigation systems, such as partial root-zone drying (PRD) and regulated deficit irrigation (RDI), would affect the growth of cucumber crops. The study specifically focused on comparing the impact of these irrigation techniques when implemented with surface (S) and subsurface (SB) drip irrigation methods. The research was conducted in both controlled environments and open-field settings in central Saudi Arabia. Additionally, the study aimed to promote the adoption of RDI and PRD irrigation systems among Saudi farmers as a means to conserve essential irrigation water resources. The treatments consisted of two groups: the drip irrigation group with a single line (RDI), which includes full irrigation and deficit irrigation, and the group with the (PRD) for the root zone with two lines with the same irrigation ratios as the first group. The productivity of the RDI-S regular drip irrigation treatment was considered 100 % as the control on which the results of all treatments are measured. The results showed that in terms of irrigation technique, the PRD-SB technique had the highest productivity during the winter season in a greenhouse, with an average of 13.8 kg m−2 for all irrigation levels, while the RDI-SB technique had the highest productivity during the summer season, with an average of 16.1 kg m−2. Regarding irrigation level results, the study showed that an irrigation level of 100 % is the highest yield in all irrigation techniques, with a general average of 14.9 kg m−2 for all irrigation techniques during the winter season and 16.4 kg m−2 during the summer. The study showed that irrigation with the PRD-SB system was the most productive in the open field at all irrigation levels, with an average of 7.3 kg m−2. It could be concluded that using PRD and RDI systems for indoor and outdoor cucumber production can save irrigation water in arid regions.

Study on the Appropriate Degree of Water-Saving Measures in Arid Irrigated Areas Considering Groundwater Level

Irrigated areas are major vectors of agricultural development and components of ecosystems. The groundwater level maintains the irrigated areas’ ecology safety and sustainable development. Under the influence of irrational irrigation practices—such as flood irrigation or extreme water saving without consideration of ecological impact—different areas within an irrigation district may experience anomalies in groundwater levels (either too deep or too shallow). It is of great significance to carry out research on water resource allocation and future water-saving strategies, taking into consideration groundwater depths. In this study, a method for the optimal allocation of irrigation water resources that considered groundwater level was used to regulate irrational irrigation practices and to reveal the future direction of water saving. Helan County in Ningxia province, an ecologically fragile and arid irrigated area, was selected as a case study. Multiple scenarios of different water use and different degrees of water-saving were analyzed. The results showed that non-engineering water-saving measures (such as adjusting the planting structure and controlling the amount of irrigation for rice) had better benefits compared to engineering measures (such as efficient water-saving irrigation and channel lining). When implementing only one water-saving measure, the strategy of replacing 75% of the rice area with corn yielded the best results. This approach can reduce the irrigation water shortage rate to 11% and increase by 4.58% the acreage where the groundwater level is reasonable. When multiple water-saving measures are implemented together, the most effective strategy for future water-saving efforts involves the joint implementation of several measures: replacing 75% of the rice area with corn, limiting irrigation for rice to no more than 11.85 thousand m3/ha, adopting high-efficiency water-saving irrigation in 90% of the pump-diverted water irrigation region and 40% of the channel-diverted water irrigation region, and maintaining the channel’s water utilization coefficient at 0.62. This strategy can keep the irrigation water shortage below 3.66% and increase the acreage where the groundwater level is reasonable, by 4.58% per year. The conclusions and research approaches can provide references for the formulation of water-saving measures for irrigated areas’ sustainable development.

Three-dimensional model and environmental fragility in the Guarani Aquifer system, SE-Brazil

The Guarani Aquifer System (GAS) is a vast transboundary continental sedimentary basin in southern South America, encompassing sedimentary rocks and basalt flows across Uruguay, Paraguay, Argentina, and Brazil. It serves as a crucial water resource for urban supply and irrigation. This study aims to propose geophysical, geometric, and fragility models of the GAS in a critical exploitation area, utilizing geological data, geophysical logs, geomorphometry, and information from tubular wells. The models define distinct geophysical ranges for each formation and identify contacts between sedimentary and basaltic rocks. Integration of horizontal and vertical spatial distributions of geophysical and geological data forms a 3D model, revealing basaltic flows and intrusions distribution in fragile areas between formations, and potentially connecting aquifer layers. Natural fragility zones, characterized by high fracture densities, are observed in the central-east region, while outcrop and recharge areas of the main aquifer hydrofacies are found in the north. The 3D model highlights similarities between the topography of the Botucatu Formation (KJb – eolian sandstones) and the potentiometry of GAS at local scales, and overexploitation zones in Ribeirão Preto's city center. The assessment prioritizes areas at risk and conservation strategies for sedimentary aquifers, emphasizing interactions between surface and groundwater and addressing issues of overexploitation, identifying high-impact risks on GAS hydrodynamics and water quality.

Ranking willingness to reuse water in cotton irrigation with hybrid MCDM methods: Soke plain case study

Soke Plain, located within the Büyük Menderes River Basin is one of the highest producers of cotton in Türkiye. The overall irrigation water supply is based on scarce conventional water resources that are being depleted at an increasing pace due to climate change impacts in B. Menderes. The inclusive objective of this research is to pave the way for a "water efficiency action plan" incorporating non-conventional (alternative) water resources for irrigation in Soke Plain to address adaptive management. Integrated Water Resources Management (IWRM) principles help decision makers (DMs) to identify and apply the most adequate alternatives among other possible ones in resource planning processes. Therefore, the preference ranking of DMs among possible water resource alternatives for irrigation is vital for implementation. This paper marks the first instance of using a multi-criteria decision-making (MCDM) method to evaluate both conventional and non-conventional water resource alternatives for cotton irrigation. The evaluation and ranking of water resource alternatives is processed using the hybrid MCDM method, integration of “Hesitant Fuzzy-Analytic Hierarchy Process” (HF-AHP) and “Hesitant Fuzzy Evaluation based on Distance from Average Solution” (HF-EDAS), namely HF-AHP-EDAS. This procedure implies several possibly contradictory qualitative and quantitative criteria, incorporates ambiguity, vagueness, and hesitancy in decision-makers’ decisions, and achieves a consistent, dependable ranking of alternatives. Eight different water resources for irrigation are evaluated by 5 experts, for 15 assessment criteria, in Soke Plain. Conventional water resources blended with drainage water is concluded to be the best irrigation water resource alternative, with HF-AHP-EDAS and also with HF-AHP-PROMETHEE II (Preference Ranking Organization Method for Enriching Evaluations II), that is used for comparison analysis. This choice aligns well with the outlined arguments, culminating in an overall result deemed compliant with the field survey.

Integrated multi-objective chance-constrained fuzzy interval linear programming model with principal component analysis for optimizing agricultural water resource management under uncertainties

ABSTRACT This study addresses the pivotal challenge of water resource allocation in urban environments by introducing a novel approach – a multi-objective chance-constrained fuzzy interval linear programming model integrated with principal component analysis (PCA). This innovative model aims to alleviate subjectivity in urban water management processes, particularly in adjusting water demands across various sectors. The proposed model incorporates correlation analysis to identify dimensionality-reducing factors of multitarget components, determining the proportion of each target component relative to the total components. Fuzzy sets are applied to irrigation water resource allocation quantity, segmented into six levels of fuzzy membership to analyze the stochasticity of water supply. Results demonstrate the model's efficacy, revealing that variations in risk probabilities impact water supply, necessitating positive water management strategies to enhance agricultural efficiency and negative strategies to mitigate the risk of inadequate water supply. Key findings emphasize the significance of agricultural water availability and the structure of irrigation water use in optimal resource allocation. Importantly, the study showcases the enhanced precision achieved through the proposed multi-objective chance-constrained fuzzy interval linear programming with PCA, thereby refining the optimization outcomes for water management under multifaceted objectives.

Risk assessment of cigarette butts and microplastic pollution in a drinking and irrigation water basin of West Anatolia, Türkiye.

Cigarette butts (CBs) and Microplastics (MPs) have serious harmful effects on the environment and living organisms despite their small size. This research aims to investigate the abundance and pollution status of CBs and MPs in Tahtalı Dam Basin (West Anatolia, Türkiye) which is the most important drinking and irrigation water resources. Clean Environment Index (CEI) and Cigarette Butt Pollution Index (CBPI) were used to determine pollution degree of the basin. The total number of CBs were 1.478 items, the total number of MPs were 477 items/m2 in the basin. As a result of this study, MP particles weren't found in Balaban Stream. Highest number of MP particles observed in 100-250μm (45%) size class. The most abundant MP type and colour were, fragment (54%) and white (42%), respectively. Polyethylene terephthalate (50%) was the most abundant type of polymer according to the ATR- FTIR analysis. As a result of the CEI and CBPI, the upstream stations of the stream were classified as "clean" status, while downstream sampling points of the stream and Balaban Lake coasts were classified as "extremely dirty" status. The calculated volumes of MP particulates from mining facility, agricultural and recreational activities indicate that anthropogenic factors are the most important MP source in the Tahtalı Dam Basin. This study is the first study about MP and CB pollution of the freshwater ecosystems in the region.

The possibility of using groundwater and collector-drainage water to increase water availability in the Maktaaral district of the Turkestan region of Kazakhstan

The Turkestan region in the southern part of Kazakhstan is currently facing a water scarcity issue due to various factors. These factors include the declining transboundary flow of the Syr Darya River, the effects of climate change, the increasing population, and the economic growth of neighboring countries in Central Asia. The water availability of the operating irrigation systems in the region fluctuates between 75 % and 95 %, dropping to 50–60 % in dry years, leading to a significant shortage of water resources. In particular, the agro-industrial complex, the largest water consumer, is heavily affected, with over 80 % of available water resources needed for irrigation. Therefore, this research aimed to investigate the potential use of groundwater and collector-drainage water to enhance water availability in the Maktaaral district of this region. The research methodology consisted of analytical and experimental studies to examine the formation and contribution of groundwater to total water consumption based on its depth and to determine the qualitative composition of collector-drainage water for reuse in irrigation. The study results indicate substantial losses of irrigation water due to filtration in the region, leading to a significant rise in the groundwater table. Consequently, the area of irrigated lands with groundwater depths up to 1 m (hydromorphic regime) in the Maktaaral district increased from 105 ha in 1994–378 ha in 2002 and to 2562 ha in 2021, representing an 18-fold increase. Under these conditions, the contribution of groundwater to total water consumption was 74 %, with irrigation water accounting for 26 %. In areas with a semihydromorphic soil regime where the groundwater table varies within 2–3 m, the volume of groundwater consumption decreased, amounting to 51 % of the total water consumption. Moreover, under the automorphous soil regime, where the groundwater table is greater than 3 m, the total water consumption was fully supported by irrigation water. Research on the qualitative composition of collector-drainage water in the Maktaaral district revealed a predominance of toxic salts (74.3–76.6 %), indicating that their use for irrigation would lead to soil salinization and alkalinization. The reuse of these waters is feasible only through mixing them with irrigation water to reduce salinity and increase the volume of suitable water resources for irrigation. However, regular monitoring of the chemical composition of such waters is essential.

Appraising water resources for irrigation and spatial analysis based on fuzzy logic model in the tribal-prone areas of Bangladesh.

The lack of quality water resources for irrigation is one of the main threats for sustainable farming. This pioneering study focused on finding the best area for farming by looking at irrigation water quality and analyzing its location using a fuzzy logic model on a Geographic Information System platform. In the tribal-prone areas of Khagrachhari Sadar Upazila, Bangladesh, 28 surface water and 39 groundwater samples were taken from shallow tube wells, rivers, canals, ponds, lakes, and waterfalls. The samples were then analyzed for irrigation water quality parameters like electrical conductivity (EC), total dissolved solids (TDS), sodium adsorption ratio (SAR), soluble sodium percentage (SSP), residual sodium bicarbonate (RSBC), magnesium hazard ratio (MHR), Kelley's ratio (KR), and permeability index (PI). Fuzzy Irrigation Water Quality Index (FIWQI) was employed to determine the irrigation suitability of water resources. Spatial maps for parameters like EC, KR, MH, Na%, PI, SAR, and RSBC were developed using fuzzy membership values for groundwater and surface water. The FIWQI results indicate that 100% of the groundwater and 75% of the surface water samples range in the categories of excellent to good for irrigation uses. A new irrigation suitability map constructed by overlaying all parameters showed that surface water (75%) and some groundwater (100%) in the northern and southwestern portions are fit for agriculture. The western and central parts are unfit for irrigation due to higher bicarbonate and magnesium contents. The Piper and Gibbs diagram also indicated that the water in the study area is magnesium-bicarbonate type and the primary mechanism of water chemistry is controlled by the weathering of rocks, respectively. This research pinpoints the irrigation spatial pattern for regional water resource practices, identifies novel suitable areas, and improves sustainable agricultural uses in tribal-prone areas.

Identification and Analysis of Long-Term Land Use and Planting Structure Dynamics in the Lower Yellow River Basin

Accurate identification of the spatio-temporal planting structure and analysis of its driving factors in an irrigation district are the important bases for scientific and reasonable utilization of irrigation water resources. In pursuit of this goal, the training sample migration method combined with the random forest algorithm were used to classify land use and planting structure over 2001–2022 in the lower Yellow River Basin. Moreover, an econometric regression model was applied to quantify the driving factors of the change in the crop-planted area. The results illustrated that the identification method has relatively high accuracy in identifying historical periods of land use and planting structures, with the average kappa coefficient equating to 0.953. From 2001 to 2022, the area of cultivated land was the largest, with the proportion of the total area increasing from 45.72% to 58.12%. The planted area of winter wheat–summer maize rotation increased from 74.84% to 88.11% of the cultivated land. While the planted area of cotton declined by 96.36%, about 50% of cotton planting was converted to the winter wheat–summer maize rotation planting. The government policies about grain purchase and storage were the dominant factors for the change in the crop-planted area. This resulted in an increase of 63.32 × 103 ha and 63.98 × 103 ha in the planted area of winter wheat and summer maize, respectively. The findings are of great significance to the allocation of water resources in irrigation districts of the lower Yellow River Basin.