Search Results (129)

Leaf photosynthesis plays an important role in maize growth and yield components due to its involvement in dry matter partitioning and organ formation. Nevertheless, how varying planting patterns affect maize leaf photosynthesis, chlorophyll fluorescence and subsequently maize yield remains poorly understood, particularly at various nitrogen rates. A two-season field experiment was performed on rainfed maize in 2021 and 2022 to explore the responses of photosynthetic physiological characteristics, leaf N and chlorophyll contents, chlorophyll fluorescence parameters, grain yield and water productivity to various planting patterns and N rates. The experiment included six planting patterns, i.e., flat planting without mulching (CK), flat planting with straw mulching (SM), ridge mulched with transparent film and furrow without mulching (RP1), flat planting with full transparent film mulching (FM1), ridge mulched with black film and furrow without mulching (RP2), and flat planting with full black film mulching (FM2). Additionally, there were two nitrogen rates, i.e., 0 kg N ha⁻¹ (N0) and 180 kg N ha⁻¹. The results showed that nitrogen application significantly improved leaf physiological characteristics. Under various planting patterns, leaf photosynthetic pigments, leaf area duration, leaf nitrogen content, QYmax and ΦPSII ranked as RP2 > RP1(FM2) > FM1 > SM(CK) in 2021, and RP2(RP1) > FM1(FM2) > SM(CK) in 2022. No significant variations were observed in water productivity (WP) among different film colors, with overall performance of RP2(FM2) > RP1(FM1) > SM > CK. WP significantly improved by 36.14% and 25.15% under N1 compared to N0 in 2021 and 2022, respectively. This pattern paralleled the fluctuation in water consumption intensity. Compared to CK, RP significantly increased leaf nitrogen content (29.3%), total Chl content (16.0%), QYmax (6.39%), ΦPSII (32.01%), and net photosynthesis rate (14.2%), thereby significantly improving grain yield (46.35%) and WP (27.69%), while reducing evapotranspiration (6.84%). Yield performance ranked as RP2 > (RP1 and FM2) > FM1 > SM > CK in 2021 and RP2 > RP1 > (FM1 and FM2) > SM > CK in 2022. Overall, RP2N1 obtained the highest principal component scores in both years, suggesting great potential to improve leaf photosynthetic physiological characteristics, thereby increasing grain production and ensuring food security in rainfed maize cultivation areas. Full article

Identifying the dominant mechanisms of water and soil salinization in arid and semi-arid endorheic basins is fundamental for our understanding of basin-scale water–salt balance and supports water resources management. In many inland basins, mineral dissolution, evaporation, and transpiration govern salinization, but disentangling these processes remains difficult. Using the Barkol Basin in northwestern China as a representative endorheic system, we sampled waters and soils along a transect from the mountain front through alluvial fan springs and rivers to the terminal lake. We integrated δ¹⁸O–δ²H with hydrochemical analyses, employing deuterium excess (d-excess) to partition salinity sources and quantify contributions. The results showed that mineral dissolution predominated, contributing 65.8–81.8% of groundwater salinity in alluvial fan settings and ~99.7% in the terminal lake, whereas direct evapoconcentration was minor (springs and rivers ≤ 4%; lake ≤ 0.2%). Water chemistry types evolved from Ca-HCO₃ in mountainous runoff, to Ca·Na-HCO₃·SO₄ in groundwater and groundwater-fed rivers, and finally to Na-SO₄·Cl in the terminal lake. The soil profiles showed that groundwater flow and vadose-zone water–salt transport control spatial patterns: surface salinity rises from basin margins (<1 mg/g) to the lakeshore and is extremely high near the lake (23.85–244.77 mg/g). In spring discharge belts and downstream wetlands, the sustained evapotranspiration of groundwater-supported soil moisture drives surface salt accumulation, making lakeshores and wetlands into terminal sinks. The d-excess-based method can robustly separate the salinization processes despite its initial isotopic variability. Full article

Sustainable agriculture in semi-arid regions like the Awash Basin is critically dependent on water availability, which is increasingly threatened by rapid land use and land cover (LULC) change. This study assesses the impact of multi-decadal LULC changes on water resources essential for agriculture. Using satellite-derived LULC scenarios (2001, 2010, 2020) to drive the WRF-Hydro/Noah-MP modeling framework, we provide a holistic assessment of water dynamics in Ethiopia’s Awash Basin. The model was calibrated and validated with observed streamflow (R² = 0.80–0.89). Markov analysis revealed rapid cropland expansion and urbanization (2001–2010), followed by notable woodland recovery (2010–2020) linked to national initiatives. Simulations show that early-period changes increased surface runoff, potentially enhancing reservoir storage for large-scale irrigation. In contrast, later changes promoted subsurface flow, indicating a shift towards enhanced groundwater recharge, which is critical for small-scale and well-based irrigation. Evapotranspiration (ET) trends, validated against GLEAM (monthly R² = 0.88–0.96), reflected these shifts, with urbanization suppressing water fluxes and woodland recovery fostering their resurgence. This research demonstrates that land use trajectories directly alter the partitioning of agricultural water sources. The findings provide critical evidence for designing sustainable land and water management strategies that balance crop production with forest conservation to secure irrigation water and support initiatives like Ethiopia’s Green Legacy Initiative. Full article

Accurate estimation of evapotranspiration (ET) is critical for precision irrigation in hyper-arid perennial systems. This study quantified ET in an 8 ha olive orchard in La Yarada–Los Palos (Tacna, Peru) by integrating the METRIC satellite-based energy-balance model (Landsat 8/9, Google Earth Engine) with the process-based AquaCrop model, using ET_FAO-56 as an empirical benchmark. Sixteen cloud-free Landsat scenes from two contrasting seasons—2021–2022 (high-yield) and 2023–2024 (water-limited)—were processed to derive daily ET maps and model simulations aligned with satellite overpasses. Results revealed marked intra-parcel heterogeneity and clear seasonal dynamics. METRIC detected local ET peaks of ~6–7 mm d⁻¹ in densely vegetated central blocks and orchard-mean values up to 4.25 ± 1.76 mm d⁻¹. During the high-yield season, ET_METRIC and ET_AQUACROP showed excellent agreement (R² = 0.94; RMSE = 0.21 mm d⁻¹; bias μ = 0.11 mm d⁻¹), whereas FAO-56 consistently underestimated ET (R² = 0.88; RMSE = 0.82 mm d⁻¹). Under water-limited conditions, model correspondence remained strong but attenuated (ET_METRIC–ET_AQUACROP: R² = 0.75; RMSE = 0.64 mm d⁻¹; ET_METRIC–ET_FAO-56: R² = 0.95; RMSE = 0.59 mm d⁻¹), with METRIC exhibiting a persistent positive bias (μ = 0.43–0.56 mm d⁻¹) attributable to localized soil evaporation and micro-advection. Overall, METRIC provided high-resolution spatial diagnostics of canopy stress, while AquaCrop offered daily continuity and explicit evaporation/transpiration (E/Tr) partitioning, enabling a coherent multiscale assessment of ET. The integrated framework enhances operational monitoring of water use and supports deficit-irrigation optimization in hyper-arid olive systems. Full article

Efficient water management is critical for sustainable crop production in arid and semi-arid regions. This study investigated the effects of two irrigation regimes—25% and 50% Management Allowable Depletion (MAD) and two planting patterns (single-row and double-row) on evapotranspiration (ET) partitioning, water use efficiency (WUE), and water footprint (WF) in drip-irrigated faba bean (Vicia faba L.). Field data were combined with a leaf area index (LAI)-based model to estimate the relative contributions of transpiration (T) and evaporation (E) to total ET. The highest grain yield (6171 kg ha⁻¹) and the lowest blue (570 m³ ton⁻¹) and green (68 m³ ton⁻¹) water footprints were recorded under the 25% MAD with double-row planting. This treatment also achieved the highest proportion of transpiration in ET (70%), indicating a shift toward productive water use. In contrast, the lowest-performing treatment (50% MAD, single-row) had the highest total water footprint (792 m³ ton⁻¹) and the lowest transpiration share (44%). Although high-density planting slightly reduced WUE based on transpiration, it improved overall water efficiency when total input (ETc) was considered (1.57 kg m⁻³ for total input WUE, 4.17 kg/m⁻³ for T-based WUE). These findings highlight the importance of integrating irrigation scheduling and planting pattern to improve both physiological and agronomic water productivity. The approach offers a practical strategy for sustainable faba bean production in water-scarce environments and supports climate-resilient irrigation planning aligned with Iraq’s National Water Strategy. Full article

Accurately partitioning actual evapotranspiration ET_{c act} into soil evaporation E_s and plant transpiration T_{c act} is crucial for improving water use efficiency and devising precise irrigation schedules. In water-saving irrigated rice fields, ET_{c act}, E_s and T_{c act} were estimated using a dual crop coefficient method based on three approaches: FAO56 adjusted, locally calibrated and leaf area index LAI-based coefficients. Continuous measurements of hourly and daily ET_{c act}, E_s and T_{c act} with weighing lysimeters were used to validate these coefficients. Results showed that hourly ET_{c act}, E_s and T_{c act} exhibited a distinct inverted “U” shape single-peak trend. Daily ET_{c act} and T_{c act}, along with the corresponding crop coefficients K_{c act} and basal crop coefficients K_{cb act}, initially increased and then decreased throughout the rice growth stages, while daily E_s and soil evaporation coefficient K_{e act} were high during the initial stage and gradually decreased as the development stage progressed. FAO56 adjusted coefficients consistently underestimated both hourly and daily ET_{c act}, E_s and T_{c act}. Locally calibrated basal crop coefficients K_{cb Cal} were determined as 0.28, 1.17 and 1.09 for the initial, mid-season and end-season stages, respectively, and locally calibrated turbulent transport coefficient of water vapor K_{cp Cal} (recommended as 1.2 by FAO) was determined to be 1.59. Based on these calibrated coefficients, estimates of hourly and daily evapotranspiration ET_{c Cal}, soil evaporation E_{s Cal} and plant transpiration T_{c Cal} performed poorly during the initial stage but showed improved accuracy during subsequent growth stages. Hourly and daily evapotranspiration and its components based on LAI-based coefficients exhibited similar performance in estimating measurements, albeit slightly inferior to FAO56 calibrated coefficients. Overall, both the FAO56 calibrated coefficients and LAI-based coefficients are recommended for estimating evapotranspiration and its components at daily and hourly scales. These research findings provide valuable insights for optimizing irrigation regimes and improving water use efficiency in rice cultivation. Full article

In arid regions, water scarcity and soil potassium destruction are major constraints on the sustainable development of the jujube industry. In this regard, the use of crop models can compensate for time-consuming and costly field trials to screen for better irrigation regimes, but their predictive accuracy is often compromised by parameter uncertainty. To address this issue, we utilized data from a three-year (2022–2024) field trial (with irrigation at 50%, 75%, and 100% of evapotranspiration and potassium applications of 120, 180, and 240 kg/ha) to simulate the growth process of jujube trees in arid regions using the WOFOST model. In this study, parameter sensitivity analyses were conducted to determine that photosynthetic capacity maximization (A_max), the potassium nutrition index (K_status), the water stress factor (SWF), the water–potassium photosynthetic coefficient of synergy (α), and potassium partitioning weight coefficients (β_i) were the important parameters affecting the simulated growth process of the crop. Path analysis using segmented structural equations also showed that water stress factor (SWF) and potassium nutrition index (K_status) indirectly controlled yield by significantly affecting photosynthesis (path coefficients: 0.72 and 0.75, respectively). The ability of the crop model to simulate the growth process and yield of jujube trees was improved by the introduction of water and potassium parameters (R² = 0.94–0.96, NRMSE = 4.1–12.2%). The subsequent multi-objective optimization of yield and crop water productivity of dates under different combinations of water and potassium treatments under a bi-objective optimization model based on the NSGA-II algorithm showed that the optimal strategy was irrigation at 80% ET_c combined with 300 kg/ha of potassium application. This management model ensures yield and maximizes crop water use efficiency (CWP), thus providing a scientific and efficient irrigation and fertilization regime for jujube trees in arid zones. Full article

High-resolution groundwater storage is essential for effective regional water resource management. While Gravity Recovery and Climate Experiment (GRACE) satellite data offer global coverage, the coarse spatial resolution (0.25–0.5°) limits the data’s applicability at regional scales. Traditional downscaling methods often fail to effectively capture spatial heterogeneity within regions, leading to reduced model performance. To overcome this limitation, a zoned downscaling strategy based on time series clustering is proposed. A K-means clustering algorithm with dynamic time warping (DTW) distance, combined with a random forest (RF) model, was employed to partition the Hexi Corridor region into relatively homogeneous subregions for downscaling. Results demonstrated that this clustering strategy significantly enhanced downscaling model performance. Correlation coefficients rose from 0.10 without clustering to above 0.84 with K-means clustering and the RF model, while correlation with the groundwater monitoring well data improved from a mean of 0.47 to 0.54 in the first subregion (a) and from 0.40 to 0.45 in the second subregion (b). The driving factor analysis revealed notable differences in dominant factors between subregions. In the first subregion (a), potential evapotranspiration (PET) was found to be the primary driving factor, accounting for 33.70% of the variation. In the second subregion (b), the normalized difference vegetation index (NDVI) was the dominant factor, contributing 29.73% to the observed changes. These findings highlight the effectiveness of spatial clustering downscaling methods based on DTW distance, which can mitigate the effects of spatial heterogeneity and provide high-precision groundwater monitoring data at a 1 km spatial resolution, ultimately improving water resource management in arid regions. Full article

The accurate estimation of evapotranspiration ($ET$) and its components are vital for water resource management and irrigation planning. This study models tree transpiration ($T$) and $ET$ for grapefruit, litchi, and mango orchards using light gradient-boosting machine (LightGBM) optimized using the Bayesian hyperparameter optimization. Grounds $T$ and $ET$ for these crops were measured using the heat ratio method of monitoring sap flow and the eddy covariance technique for quantifying $ET$. The Sentinel 2 satellite was used to compute field leaf area index ($LAI$). The modelled data were used to partition the orchard $ET$ into beneficial ($T$) and non-beneficial water uses (orchard floor evaporation—$E_s$). We adopted the 10-fold cross-validation to test the model robustness and an independent validation to test performance on unseen data. The 10-fold cross-validation and independent validation on $ET$ and $T$ models produced high accuracy with coefficient of determination ($R^2$) $\ge$ 0.88, Kling–Gupta efficiency ($KGE$) $\ge$ 0.91, root mean square error ($RMSE$) $\le$ 0.04 mm/h, and mean absolute error ($MAE$) $\le$ 0.03 mm/h for all the crops. The study demonstrates that LightGBM can accurately model the transpiration and evapotranspiration for subtropical tree crops using Sentinel 2 data. The study found that $E_s$ which combined soil evaporation and understorey vegetation transpiration contributed 35, 32, and 31% to the grapefruit, litchi and mango orchard evapotranspiration, respectively. We conclude that improvements on orchard floor management practices can be utilized to minimize non-beneficial water losses while promoting the productive water use ($T$). Full article

Evapotranspiration (ET) is an important part of agricultural water consumption, yet little is known about nocturnal evapotranspiration (ETN) patterns. An eddy covariance system was used to observe ET over five consecutive years (2020–2024) during the growing season in a dry farming area of the Loess Plateau. Daytime and nocturnal evapotranspiration were partitioned using the photosynthetically active radiation threshold to reveal the changing characteristics of ETN at multiple time scales and its control variables. The results showed the following: (1) In contrast to the non-significant trend in ETN on the diurnal and daily scales, monthly ETN dynamics exhibited two peak fluctuations during the growing season. (2) The contribution of ETN to ET exhibited seasonal characteristics, being relatively low in summer, with interannual variations ranging from 10.9% to 14.3% and an annual average of 12.8%. (3) The half-hourly ETN, determined by machine learning methods, was driven by a combination of factors. The main driving factors were the difference between surface temperature and air temperature (Ts-Ta) and net radiation (Rn), which have almost equivalent contributions. Regression analysis results suggested that Ta was the main factor influencing ETN/ET at the monthly scale. This study focuses on the nighttime water loss process in dry farming fields in Northwest China, and the results provide a basis for rational allocation and efficient utilization of agricultural water resources in arid regions. Full article

Drip irrigation technology, known for its advantages in high water use efficiency and yield increase, has been a focal point of research regarding its combined effects with the plastic film-mulching technique on field water consumption and crop growth. To accurately quantify the water-saving effect of plastic film-mulching techniques and investigate the mechanisms of mulching on evaporation (E) and transpiration (T), this study was conducted on soybean using the Bowen ratio–energy balance system and micro-lysimeters as the observation means and the MSW model as the data partitioning tool, during 2019–2021 in arid northwest China. We compared evapotranspiration (ET) under the film-mulched drip irrigation (FM) and non-mulched drip irrigation (NM) treatments. The results show that ET, E, and T under FM were reduced by 32.6 mm, 76.1 mm, and −43.5 mm, respectively. Moreover, mulching increased the leaf area index (LAI) by 20.7%, soybean yield from 2727.0 kg ha⁻¹ to 3250.5 kg ha⁻¹, and WUE from 0.64 kg m⁻³ to 0.83 kg m⁻³ on average, which means mulching reduced water consumption in the field by decreasing soil evaporation and improved water use efficiency by promoting crop growth. Further analysis indicated that mulching has strengthened the connection between soil temperature and humidity and weakened the effect of soil temperature on soybean leaf growth. Soil water content (SWC) and LAI had a direct negative effect on E, with LAI causing a stronger effect on E under the FM treatment. Mulching has weakened the direct effect of SWC on T, so that only LAI and soil temperature had a significant direct positive effect on T. Following the rapid growth of soybean LAI, the isolating effect of the mulch was gradually replaced by the dense leaf canopy. The results provide a reference for further exploring the water-saving and yield-increasing benefits of plastic film-mulching techniques, and to facilitate wider promotion of the plastic film-mulching techniques and the water–fertilizer integration technology in arid regions. Full article

The generalized proportionality hypothesis (GPH) highlights the competitive relationships among hydrological components as precipitation (P) transforms into runoff (Q) and evapotranspiration (E), providing a novel perspective on E partitioning that differs from the traditional physical source-based approach. To achieve sequential partitioning of E into initial (E_i) and continuing (E_c) evapotranspiration under the GPH, a P-Q relationship-based E_i estimation method was proposed for the Model Parameter Estimation Experiment (MOPEX) catchments. On this basis, we analyzed the relationship between the GPH-based E components and the physical source-based ones separated by the Penman-Monteith-Mu algorithm. Additionally, we explored the differences between the calculated and inverse Budyko-WT model parameter (E_i/E) and discussed the implications for the Budyko framework. The results showed the following: (1) A significant linear P-Q relationship (p < 0.05) prevailed in the MOPEX catchments, providing a robust data foundation for E_i estimation. Across the MOPEX catchments, E_i and E_c contributed 73% and 27% of total E, respectively. (2) The combined proportion of evaporation from canopy interception and wet soil averaged about 25%, and it was much lower than that of E_i, indicating that it was difficult to establish a connection between E_i and the physical source-based E components. (3) The potential evapotranspiration (E_P) satisfying the Budyko-WT model was strictly constrained by the GPH, while the inappropriate E_P estimation method largely explained the discrepancy between the calculated and inverse E_i/E. This study deepens the knowledge of the sequential partitioning of E components, uncovers the discrepancies between different E partitioning frameworks, and provides new insights into the characterization of key variables in Budyko models. Full article

Modeling the soil–plant–atmosphere continuum (SPAC) system requires multiple subprocesses and numerous parameters. Sensitivity analysis is effective to identify important model components and improve the modeling efficiency. However, most sensitivity analyses for SPAC models focus on parameter-level assessment, providing limited insights into process-level importance. To address this gap, this study proposes a process sensitivity analysis method that integrates the Bayesian network with variance-based sensitivity measures. Four subprocesses are demarcated based on the physical relationships between model components revealed by the network. Applied to a winter wheat SPAC system under different water conditions, the method effectively and reliably identifies critical processes. The results indicate that, under minimal water stress, the subprocesses of photosynthesis and dry matter partitioning primarily determine agricultural outputs. As the water supply decreases, the subprocesses of soil water movement and evapotranspiration gain increasing importance, becoming predominant under sever water stress. Throughout the crop season, the subprocess importance and its response to water stress are modulated by the crop phenology. Compared to conventional parameter sensitivity analysis, our method excels in synthesizing divergent parameter importance changes and identifying influential subprocesses, even without high-sensitivity parameters. This study provides new insights into adaptive SPAC modeling by dynamically simplifying unimportant subprocesses in response to environmental changes. Full article

Evaluating the performance of irrigation water use is essential for efficient and sustainable water resource management. However, existing approaches often lack systematic quantification of irrigation water consumption and fail to differentiate between the use of precipitation and anthropogenic appropriation of water flows. Building on the green–blue water concept, consumptive water use, assumed equal to actual evapotranspiration (ET_a), was partitioned into green ET (GET) and blue ET (BET) using remote sensing data and the Budyko hypothesis. A novel BET metric of consumptive irrigation water use was developed and applied to the irrigated lands in northwest China to evaluate the performance of irrigation from 2001 to 2021. The results showed that in terms of total available water resources (precipitation + gross irrigation water (GIW)) compared to irrigation water demand, estimated as reference evapotranspiration (ET₀), Ningxia has sufficient water supply to meet irrigation demand, while the Hexi Corridor faces increasing risks of unsustainable water use. The Hetao irrigation scheme has shifted from a fragile supply–demand balance to a situation where water demand far exceeds availability. In Xinjiang, the balance between water supply and demand is tight. Furthermore, when considering the available water (GIW) relative to the net irrigation water demand (ET₀-GET), the Hexi Corridor faces significant water deficits, and Ningxia and Xinjiang are close to meeting local irrigation water demands by relying on current water availability and efficient irrigation practices. It is noteworthy that the BET remains lower than the GIW in northwest China (excluding the Hexi Corridor in recent years). The ratio of the BET to GIW is an estimate of the scheme irrigation efficiency, which was equal to 0.54 for all irrigation schemes taken together. In addition, the irrigation water use efficiency, estimated as the ratio of BET to net irrigation water, was evaluated in detail, and it was found that in the last 10 years the irrigation water use efficiency improved in Ningxia, the Hetao irrigation scheme, and Xinjiang. However, the Hexi Corridor continues to face severe net irrigation water deficits, suggesting the likelihood of groundwater use to sustain irrigated agriculture. BET innovatively separates consumptive use of precipitation (green water) and consumptive use of irrigation (blue water), a critical advancement beyond conventional approaches’ estimates that merge these distinct hydrological components to help quantifying water use efficiency. Full article

The comprehension of seasonal patterns of evapotranspiration (ET), as well as the interactive response to environmental factors, holds paramount importance for illuminating the intricate interaction within the carbon–water cycle of desert riparian forest ecosystems. Nonetheless, the driving mechanism behind ET changes is complex, and different components show significant differences in response to the same factor. Moreover, water resources are scarce in the region, and sustainable water resources management in arid regions usually aims to maximize transpiration (T) and minimize evaporation (E); therefore, reasonable calculation of ET components is urgent to effectively assess water resources consumption and improve water use efficiency. This discussion assessed the suitability and reliability of different methods for partitioning ET within the desert oasis in Northwestern China, calculated water use efficiency (WUE), and explored the differences in the response patterns of ET, transpiration (T), and WUE to environmental elements of constructive Populus euphratica forests in this region during the growing season. Continuous measurements of meteorological, soil, and vegetation factors were collected from 2014 to 2021 to facilitate this investigation. This study demonstrated that the underlying water use efficiency (uWUE) method effectively partitions ET into vegetation T and soil evaporation (E). Seasonal variations in ET and T were predominantly driven by temperature (Ta), radiation (Rn), soil moisture, and leaf area index (LAI). In addition, the exchange of water and carbon across different scales was governed by distinct regulatory mechanisms, where canopy-level WUE (WUEc) primarily depended on climatic conditions, while ecosystem-level WUE (WUEe) was more strongly influenced by vegetation structural characteristics. This study provided valuable insights into the ET characteristics, influencing factors, and water–carbon consumption mechanisms of desert vegetation in arid regions, and the conclusions of the discussion may provide theoretical insights for policymakers and ecosystem managers interested in preserving the ecological balance of arid regions. Full article