Site Scale Research Articles

Historical variability of cotton yield and response to climate and agronomic management in Xinjiang, China

Cotton is the primary fibre crop in the world with high economic value, and its yield can be affected by climate and agronomic management. Xinjiang, the largest cotton-producing province in China, contributes to approximately 90 % of the national and over 20 % of global production. Earlier studies focused on cotton yield variability and/or drivers on the site scale, for only one to several counties or cities within Xinjiang, in several years before 2000, or provincial production for a short period. It remains poorly understood how and why cotton yields change in Xinjiang. This study analyzes the spatiotemporal variability of cotton yields at the provincial and county (73 counties) levels from 1989 to 2017 using yield statistics, and identifies the dominant climate and agronomic management factors as well as their optimal ranges, historical states, and interaction effects using the geographical detector method (Geodetector). The results show that the Xinjiang cotton yield has increased markedly over the past decades, with the long-term trend outweighing the interannual variability. High yields are mostly found in southern and northwestern Xinjiang. Yield has increased significantly in over 95.6 % of cotton-planting areas, primarily in the west. Nitrogen fertilization is the leading driver of cotton yield changes, mainly impacting long-term trends. The combination of nitrogen fertilization and agricultural mechanization enhances the explanatory power in a bivariate way and is the strongest in the interaction effect between factors. Temperature variability has the greatest influence on detrended yield variability, and the explanatory power is enhanced and the highest when combined with precipitation. In addition, historical states of these factors are generally lower than their optimal ranges, indicating potential for cotton yield enhancement through improved agronomic management practices and in the context of global warming. This study could enhance understanding of cotton Xinjiang yield variability and drivers, and provides scientific guidance for local cotton cultivation.

Associations of Lower-Limb Phase Angle with Locomotion and Motor Function in Japanese Community-Dwelling Older Adults.

Whole-body phase angle (PhA) is associated with motor function and geriatric diseases. However, it was unclear which parts of the upper- or lower-limb PhA were involved. This study investigated the differences in the PhA of the upper and lower extremities and their relationships with frailty, locomotive syndrome indices, and motor function in community-dwelling older adult participants. This study was a cross-sectional observational study. In 69 community-dwelling older adults, the PhA at each measurement site (whole body, upper limbs, and lower limbs), motor function, Kihon Checklist (KCL), and 25-Geriatric Locomotive Function Scale (GLFS-25) were measured. This study examined differences in each PhA and its relationship with motor function and geriatric diseases. Multiple regression analysis was performed using GLFS-25 as the dependent variable and sex and lower-limb PhA as independent variables. In this cohort (mean age: 72.3 ± 5.7 years; 18 males and 51 females), lower-limb PhA was significantly lower than upper-limb PhA. Unlike other PhAs, lower-limb PhA was related to grip strength, TUG, F/w, and GLFS-25. Multiple regression analysis showed that the lower-limb PhA was independently related to GLFS-25. Cellular health of the lower extremity is associated with gait, standing function, and indicators of locomotive syndrome.

Aquaponics System Design and Construction

This text provides a comprehensive guide for planning, designing, and constructing an aquaponics system, a sustainable agricultural system that integrates fish farming and soilless plant cultivation. The text covers the key aspects of aquaponics system design, such as site selection, system goals, system size and scale, layout design, fish tank setup, grow bed design, plumbing and water circulation, equipment and tools selection, and construction techniques. The text also discusses the benefits and challenges of aquaponics systems, as well as the best practices and guidelines for optimizing system functionality, productivity, and long-term success. The text aims to provide practical and useful information for individuals who are interested in implementing aquaponics systems in their own settings.

Site and landscape scale drivers of bird and insect diversity in Native and novel forest ecosystems of Central Portugal

The natural forests of southern Europe have been altered over the last decades. One of these changes is the proliferation of invasive woody alien species, resulting in highly altered ecosystems, the so-called “novel ecosystems”. These changes affect biodiversity at both local and landscape scales. Using piecewise structural equation models, we evaluate the effects of local and landscape variables on the diversity of birds and insects across three common forest types in Central Portugal, namely: Native Forests (dominated by Quercus sp.) and two novel forest ecosystems, Acacia Forests and Eucalyptus Forests (forests formed by natural regeneration of Acacia dealbata Link and Eucalyptus globulus Labill). Local variables were the most influential on bird and insect diversity, and Native Forests was the forest type that most favored diversity in both groups. The structure of this forest type may explain this difference, as Native Forests have a greater local complexity compared to novel forest ecosystems, providing foraging and breeding resources and increasing the microhabitats. Contrarily to what was expected, no variable, including structural landscape complexity, has significantly influenced biodiversity at the landscape level. Fire seems to promote Eucalyptus Forest, resulting in negative impacts upon biodiversity in fire-prone areas. Our results show the extended impacts of deep ecosystem alterations on diversity patterns, with relevant insight into management and conservation practices.

Tidal Flood Hazard Assessment in Pekalongan City, Central Java

Climate change has had a significant impact on regional climate in many countries, including Indonesia. The coastal sector is one of the most vulnerable to climate change. Pekalongan City is one of the most vulnerable coastal areas in Indonesia to the effects of climate change. Tidal flooding is a disaster that occurs in coastal areas due to rising sea levels, which average 6-10 mm per year on Java’s northern coast. Tidal flood hazard analysis was performed using indicators of sea-level rise, distance to the sea, distance to the river, slope, topography, land cover, and land subsidence. The results of the field validation were consistent with those of the tidal flood hazard analysis. Areas covered by the tidal flood hazard modeling include Pasirkratonkramat, Bandengan, Kandang Panjang, Panjang Baru, Panjang Wetan, Krapyak, and Degayu. Pekalongan City experienced an increase in hazard area of 6.18% of the total area of Pekalongan City based on the results of the CSIRO modeling RCP 4.5 scenario. In comparison, the other three models show a decrease of up to 3.13% in the total area of Pekalongan City. However, when viewed at the site scale (per urban village), Podosugih Village experienced the highest increase in hazard area, up to 20.5% of the total area for the CSIRO-modeled RCP 4.5. The highest decrease in the area occurred in Klego Village, at 8.94% of the area for the RCP 4.5 scenario of the MIROC model. Future changes in rainfall, sea level, and land subsidence will cause these changes.

Surface flux equilibrium estimates of evaporative fraction and evapotranspiration at global scale: Accuracy evaluation and performance comparison

Accurate estimation of terrestrial evapotranspiration (ET) is crucial to understand the water cycle and partitioning of turbulent energy fluxes at the land surface. However, large-scale ET estimation is always difficult to achieve due to the complexity of controlling factors and the heterogeneity of landscapes. A recent model, referred to as the surface flux equilibrium (SFE) model, proposes that evaporative fraction (EF, the ratio of ET to available energy at the surface) can be estimated accurately from near-surface specific humidity and air temperature, which makes it applicable to common weather stations and climate reanalysis datasets. However, previous validation only focused on the evaluation of this model at site and basin scale for ET estimation, making the real performance of the SFE model for EF estimation heretofore unknown. There also exists a gap in the comparison between the SFE model and other existing products in the representation of global ET patterns and variation trend. Against this background, a comprehensive validation was performed in this study to evaluate the accuracy of the SFE model for both EF and ET estimation. The validation against 136 flux towers worldwide shows that the ground-based SFE model achieved EF estimation with a root-mean-square-error (RMSE) of 0.22 and 0.16 at daily and monthly scale, respectively. The corresponding RMSE of ET estimation was 0.94 mm/day and 16.37 mm/month. The application of the SFE model on a global scale was achieved by using two reanalysis products (ERA5-Land and GLDAS-CLSM) as inputs respectively. The results indicate that the SFE model did hold potential to reduce the RMSE of these two products for ET estimation, but the cost was the decrease in correlation coefficient (r). The comparison with five existing global ET products (BESS, GLEAM, AVHRR, MOD16, and SSEBop) shows that the SFE model performed on the basis of ERA5-Land product has achieved moderate accuracy among these products. Judged from r, mean absolute error, RMSE, and bias, its accuracy was ranked as fourth, fourth, second, and second, respectively. However, a further evaluation over different land cover types suggests that the ERA-based SFE model has significant superiority over cropland, relative superiority over forest and savannas, and significant inferiority over shrubland and grassland. This analysis is the first global validation of the SFE model for EF estimation as well as its comparison with existing global ET products. The validation analysis proves that this simple model has generally achieved comparable accuracy to existing complex models in the representation of global ET patterns and variation trend.

Regionalization of Root Zone Moisture Estimations from Downscaled Surface Moisture and Environmental Data with the Soil Moisture Analytical Relationship Model

Root-zone soil moisture (RZSM) plays a key role in the hydrologic cycle and regulates water–heat exchange. Although site observations can provide soil profile moisture measurements, they have a restricted representation. Satellites can determine soil moisture on a large scale, yet the depth of detection is limited. RZSM can be estimated on a large scale using the soil moisture analytical relationship (SMAR) and surface soil moisture (SSM). However, the applicability of the SMAR to different deep-root zones and covariate sources is unclear. This paper investigates the applicability of the SMAR in the Shandian River Basin, upstream of the Luan River in China, by combining site and regional soil moisture, soil properties, and meteorological data. In particular, we first compared the estimation results of the SMAR at different depths (10–20 cm; 10–50 cm) and using covariates from different sources (dataset, SMAR-P1; literature, SMAR-P2) at the site in order to generate SMAR calibration parameters. The parameters were then regionalized based on multiple linear regression by combining the SMAR-P1, SMAR-P2, and SMAR calibration parameters in the 10–50 cm root zone. Finally, the Shandian River RZSM was estimated using regional surface soil moisture and the aforementioned regionalized parameters. At the site scale, diffusion coefficient b obtained in the 10–20 cm root zone at the same depth as the surface layer exceeded the upper limit of the SMAR by one. This is not fit an environment within the site context, and thus the SMAR is not applicable at this particular depth. The opposite is observed for the 10–50 cm root zone. In addition, SMAR-P1 (RMSE = 0.02) outperformed SMAR-P2 (RMSE = 0.04) in the estimation of the RZSM at 10–50 cm. Parameter regionalization analysis revealed the failure of SMAR-P2 to pass the significance test (p > 0.05) for building a multivariate linear model, while SMAR-P1 successfully passed the significance test (p < 0.05) and finished the parameter regionalization process. The median RMSE and median R2adj of the regional RZSM results were determined as 0.12 and 0.3, respectively. The regional RZSM agrees with the spatial trend of the Shandian River. This study examines the suitability of the SMAR model in varying deep-root zones and with diverse covariate sources. The results provide a crucial basis for future utilization of the SMAR.

Selection of summer feeding sites and food resources by female migratory caribou (Rangifer tarandus) determined using camera collars.

Migratory caribou (Rangifer tarandus) is a socioeconomically and culturally key species for northern communities in the Arctic, and most of its populations are experiencing a sharp decline. Female migratory caribou depend on the availability of summer habitat resources to meet the needs associated with lactation and the accumulation of fat reserves to survive when resources are less abundant. Because of the large scales at which habitat and resource data are usually available, information on how female migratory caribou select habitat and resources at fine scales in the wild is lacking. To document selection of summer feeding sites, we equipped 60 female caribou with camera collars from 2016 to 2018. We collected a total of 65,150 10-sec videos between June 1st and September 1st for three years with contrasted spring phenology. We determined the selection at the feeding site scale (3rd scale of Johnson) and food item scale (4th scale of Johnson) using resource selection probability functions. Wetlands were highly selected as feeding sites in June and July while they were avoided in August. Shrublands were mostly selected in July and August. At the resources scale, lichen, birch, willow, and mushrooms were the most strongly selected resources. Our results provide precise and novel information on habitat selection at feeding sites and food resources selected by female caribou in the wild. This information will help understand foraging patterns and habitat selection behavior of female migratory caribou and will contribute to the management and conservation of its declining populations.

Long-term validation and error analysis of DB and MAIAC aerosol products over bright surface of China

Two distinct algorithms, namely Deep Blue (DB) and Multiangle Implementation of Atmospheric Correction (MAIAC), based on the Moderate Resolution Imaging Spectroradiometer (MODIS), can provide aerosol optical depth (AOD) products over bright surfaces. Recently, the newest MAIAC Collection 6.1 (C6.1) product was available, which corrected many known deficiencies of C6. Due to the significant impact of high-reflectivity surfaces on the accuracy of aerosol retrieval, long-term validation and comprehensive assessment of AOD over bright surfaces are required at a regional scale. Firstly, this study conducted a systematic evaluation of three products against ground-based Sunphotometer data obtained from 21 sites of the China Aerosol Remote Sensing Network (CARSNET) and 2 sites of the Aerosol Robotic Network (AERONET) from the year 2002 to 2014. The validation results showed a significant underestimation in DB and MAIAC AOD retrievals as indicated by the large percent of retrievals below the expected error (EE) envelope, and significant negative biases. In comparison, both versions of MAIAC products performed better than DB product, which was also revealed by the site scale validation. Besides, the performance of the C6.1 MAIAC was slightly improved over the study region compared to the C6 MAIAC. Finally, this study made an in-depth investigation of the underestimation of AOD products affected by various factors, i.e., aerosol loading, land type, NDVI, surface albedo, and scattering angle. The results indicated that the large negative bias of AOD retrievals for the three products tended to occur in the case of large aerosol loadings and high bright surfaces, which could be attributed to the inaccurate assumption of aerosol models and the insensitivity of top of atmosphere (TOA) reflectance to AOD variations caused by the high reflectivity of the underlying surface. The findings of this study are expected to provide some reference for the improvement of aerosol retrieval algorithms over bright surfaces.

The spatiotemporal variation of land surface heat fluxes in Tibetan Plateau during 2001–2022

The surface energy budget is important for understanding of energy and water cycle processes in the Tibetan Plateau (TP). In this study, the daily sensible (SH) and latent (LE) heat fluxes at the horizontal resolution of 1° are first estimated using the maximum entropy production (MEP) model (hereinafter SHMEP and LEMEP) in the entire TP during 2001–2022. The MEP model is built on physical and statistical principles to simulate surface heat fluxes. The surface net radiation, soil moisture (SM), and land surface temperature (LST) are the main driving variables for MEP model. To select the relatively accurate MEP input data, the merged surface net radiation (Rn–merged) under all-sky conditions are generated from CERES, ISCCP-FH, and ERA5 using the Bayesian Model Averaging scheme. Besides, the TP SM and LST from various data sources are evaluated using the in-situ observations at site scale. Based on the daily Rn–merged, ERA5 SM, CERES LST, and the MEP model, the daily SH and LE are estimated in the entire TP. The results show the daily SHMEP and LEMEP perform well at the validation sites, with the regional mean correlation coefficient (R) above 0.7, root-mean-square error (RMSE) of <19 W m−2, absolute value of bias and mean absolute error (MAE) below 11 W m−2. The monthly SHMEP has the regional mean R of 0.96 and RMSE of 6.30 W m−2 at all the measurement stations. For LEMEP, the regional mean R and RMSE values are 0.93 and 10.01 W m−2, respectively. The MEP simulation results are superior to the SH and LE in ERA5, ERA-Interim, MERRA-2, and JRA-55 reanalysis datasets and previous studies, especially for LE. Based on this new dataset, the spatial and temporal varying characteristics of SH and LE in the TP are analyzed. The annual mean SHMEP value are large in the western TP, the Qaidam Basin in the northern TP, and the Himalaya ranges, and small in the southeastern TP. The annual mean LEMEP has the maximum value in southeastern TP, and minimum value in western TP and the Qaidam Basin. The annual mean SHMEP and LEMEP over the entire TP are 34.79 W m−2 and 20.16 W m−2, with the significant declining trends of −0.17 W m−2 year−1 and − 0.052 W m−2 year−1 during the study period, respectively. The spatial distributions of the MEP surface heat fluxes and their trends are mainly influenced by the model inputs of Rn and SM in the TP.

Annual 30 m winter wheat yield mapping in the Huang-Huai-Hai plain using crop growth model and long-term satellite images

There are great challenges for the accurate prediction of wheat yield at large-scale region due to the spatial heterogeneity of soil, management and meteorological conditions. The Huang-Huai-Hai plain (HHHP) is the most important wheat producing area in China. Timely and effective prediction of wheat yield in the HHHP is of great significance for ensuring food security. However, there is still a lack of studies on long-term wheat yield prediction with high spatial resolution in the HHHP. In this study, the simulation results (including phenological information, LAI and yield) of WheatGrow model and the meteorological variables were used to train regression model, which was applied in the corresponding satellite images and gridded meteorological variables to develop wheat yield prediction model (SCYMvp). Then time-series yield maps of wheat in the HHHP (2001-2020) were obtained and the digitization footprint was estimated. We found that SCYMvp model developed by combining machine learning algorithm (ML) with two state variables (phenological information and GCVI) had the best performance (r = 0.7, NRMSE = 15 %) for wheat yield prediction in the HHHP. Compared with other yield prediction models, SCYMvp model reduced the error of yield prediction to some extent. Meanwhile, SCYMvp model showed good accuracy in wheat yield prediction at the irrigated fields and rainfed fields. In addition, extreme climate events had a weak influence on the SCYMvp model in wheat yield prediction. The yield prediction with high spatial resolution generated by SCYMvp model in the HHHP during past 20 years had good consistency and low error with the statistical yield in the spatio-temporal scale. Compared with the GlobalWheatYield4km product, the SCYMvp model had a better ability to capture spatial heterogeneity of wheat yield at site scale. In addition, required data size per hectare for predicting wheat yield showed a significant increasing trend. The results indicated that the SCYMvp model developed in this study can effectively map wheat yield with high spatial resolution in the study area, and has good potential in smallholder farming economy with large spatial heterogeneity of management conditions.

Assessing the Reliability of Global Carbon Flux Dataset Compared to Existing Datasets and Their Spatiotemporal Characteristics

Land carbon fluxes play a critical role in ecosystems, and acquiring a comprehensive global database of carbon fluxes is essential for understanding the Earth’s carbon cycle. The primary methods of obtaining the spatial distribution of land carbon fluxes include utilizing machine learning models based on in situ measurements, estimating through satellite remote sensing, and simulating ecosystem models. Recently, an innovative machine learning product known as the Global Carbon Flux Dataset (GCFD) has been released. In this study, we assessed the reliability of the GCFD by comparing it with existing data products, including two machine learning products (FLUXCOM and NIES (National Institute for Environmental Studies)), two ecosystem model products (TRENDY and EC-LUE (eddy covariance–light use efficiency model)), and one remote sensing product (Global Land Surface Satellite), on both site and global scales. Our findings indicate that, in terms of average absolute difference, the spatial distribution of the GCFD is most similar to the NIES product, albeit with slightly larger discrepancies compared to the other two types of products. When using site observations as the benchmark, gross primary production (GPP), respiration of ecosystem (RECO), and net ecosystem exchange of machine learning products exhibit higher R2 (ranging from 0.57 to 0.85, 0.53–0.79, and 0.31–0.70, respectively) compared to model products and remote sensing products. Furthermore, we analyzed the spatial and temporal distribution characteristics of carbon fluxes in various regions. The results demonstrate an upward trend in both GPP and RECO over the past two decades, while NEE exhibits an opposite trend. This trend is particularly pronounced in tropical regions, where higher GPP is observed in tropical, subtropical, and oceanic climate zones. Additionally, two remote sensing variables that influence changes in carbon fluxes, i.e., fraction absorbed photosynthetically active radiation and leaf area index, exhibit relatively consistent spatial and temporal characteristics. Overall, our study can provide valuable insights into different types of carbon flux products and contribute to understanding the general features of global carbon fluxes.

Calibrating a remote sensing evapotranspiration model using the Budyko framework

Remote sensing (RS) evapotranspiration (ET) models are commonly used to estimate ET at a large scale. These models often include spatially varying parameters that need to be determined. Typically, these parameters are either held as constants or obtained from a pre-specified lookup table, potentially introducing bias into ET estimates. This study aimed to evaluate the feasibility of calibrating a RS ET model using Budyko-derived mean annual ET. We applied this approach to calibrate the single parameter of the Penman-Monteith-Leuning (PML) model. Instead of using the default Budyko-Fu parameter ω (ω=2.6), we developed a data-driven model that relates the basin-calibrated ω and its primary controls. Subsequently, we mapped the spatial pattern of ω across China. The calibrated PML model was evaluated using flux observations from 12 ChinaFlux sites and water balance-based ET estimates from 80 basins in China. Results indicate that the Budyko-based calibration significantly improved the performance of the PML model at both site and basin scales compared to the uncalibrated model. This study highlights the potential of leveraging the Budyko framework to constrain the parameter estimates of RS ET models, thereby enhancing the accuracy of large-scale ET estimates.

A prognostic vegetation phenology model to predict seasonal maximum and time series of global leaf area index using climate variables

Vegetation phenology models provide us with tools for understanding and predicting vegetation responses to a changing climate. Vegetation phenology can be characterized by periodic changes in leaf area index (LAI), which quantifies leaf areas per unit ground surface areas. As an essential variable in land surface studies, LAI is generally used to simulate a range of fundamental vegetation processes and is essential to the simulations of land surface processes and land-atmosphere fluxes. Yet few studies directly provide one key variable required in simulating LAI, the annual seasonal maximum leaf area index (LAISM). There is a need to develop a method for predicting LAISM and allow for simulating daily LAI time series (LAITS). In this study, we developed a Prognostic Vegetation Phenology Model (PVPM) that predicts both LAISM and daily LAITS across biomes at the site and global scales, using climate variables. The method predicts LAISM based on an improved Budyko-like function and then predicts LAITS by integrating a previously developed semi-prognostic model. Experimental results show that PVPM is suitable for simulating vegetation dynamics and provides two parameterization schemes across different biomes at both site and global levels. For different biomes, the model could explain a large variance in satellite-derived LAISM and daily LAITS at the site scale, and well capture the spatial patterns and temporal trends of satellite-derived LAISM and LAITS across the globe. Compared to results obtained from other two process-based climate models (i.e., the GFDL-CM4 and CESM2), PVPM could make predictions on LAITS that match global satellite observations well (r = 0.9 and RMSE < 0.82). This study provides a prognostic vegetation phenology model that can be used in the land surface models to forecast global LAI time series for modeling vegetation processes.

Reimagining large river management using the Resist–Accept–Direct (RAD) framework in the Upper Mississippi River

BackgroundLarge-river decision-makers are charged with maintaining diverse ecosystem services through unprecedented social-ecological transformations as climate change and other global stressors intensify. The interconnected, dendritic habitats of rivers, which often demarcate jurisdictional boundaries, generate complex management challenges. Here, we explore how the Resist–Accept–Direct (RAD) framework may enhance large-river management by promoting coordinated and deliberate responses to social-ecological trajectories of change. The RAD framework identifies the full decision space of potential management approaches, wherein managers may resist change to maintain historical conditions, accept change toward different conditions, or direct change to a specified future with novel conditions. In the Upper Mississippi River System, managers are facing social-ecological transformations from more frequent and extreme high-water events. We illustrate how RAD-informed basin-, reach-, and site-scale decisions could: (1) provide cross-spatial scale framing; (2) open the entire decision space of potential management approaches; and (3) enhance coordinated inter-jurisdictional management in response to the trajectory of the Upper Mississippi River hydrograph.ResultsThe RAD framework helps identify plausible long-term trajectories in different reaches (or subbasins) of the river and how the associated social-ecological transformations could be managed by altering site-scale conditions. Strategic reach-scale objectives may reprioritize how, where, and when site conditions could be altered to contribute to the basin goal, given the basin’s plausible trajectories of change (e.g., by coordinating action across sites to alter habitat connectivity, diversity, and redundancy in the river mosaic).ConclusionsWhen faced with long-term systemic transformations (e.g., > 50 years), the RAD framework helps explicitly consider whether or when the basin vision or goals may no longer be achievable, and direct options may open yet unconsidered potential for the basin. Embedding the RAD framework in hierarchical decision-making clarifies that the selection of actions in space and time should be derived from basin-wide goals and reach-scale objectives to ensure that site-scale actions contribute effectively to the larger river habitat mosaic. Embedding the RAD framework in large-river decisions can provide the necessary conduit to link flexibility and innovation at the site scale with stability at larger scales for adaptive governance of changing social-ecological systems.

Improving Phenology Representation of Deciduous Forests in the Community Land Model: Evaluation and Modification Using Long‐Term Observations in China

AbstractPhenology is an important factor indicating environmental changes and regulates the variations of carbon, water, and energy exchange. However, phenology models exhibit large uncertainties due to limited understanding of its mechanisms. In this study, we modified deciduous phenology scheme based on the evaluation of different phenological models using long‐term observations at Chinese Ecosystem Research Network with CLM4.5. The alternating leaf unfolding model and summer‐influenced autumn leaf falling model that we proposed, performed best in simulating leaf‐unfolding and leaf‐falling. Compared with the observed and remote‐sensed phenology, the modified model could better simulate the phenological dates at the site and regional scale. Moreover, the modified model improved the simulation of gross primary productivity (GPP) by decreasing the errors of modeled carbon uptake duration and amplitude. Furthermore, the advance in leaf‐unfolding slowed down from 0.20 days/year during 1981–2015 to 0.11 days/year during 2016–2100 under RCP4.5 because of the slowdown of climate warming, but the delay in leaf‐falling changed little. By the last decade of the twenty‐first century, the leaf‐unfolding would advance (8 days) and leaf‐falling would delay (16 days). The subtropical region had large interannual variation (IAV) in leaf‐unfolding because of the high sensitivity to temperature. The phenological dates IAV in the cold temperate region increased due to enhanced temperature IAV. We suggest that the deciduous phenology models, especially the leaf‐falling process, used in Community Land Model need to be improved to reduce the errors in predicting phenology and carbon flux in the future.

Scaling solar-induced chlorophyll fluorescence by using VPD0.5 improves the simulation of reference crop evapotranspiration in the arid and semiarid regions of northern China

Accurately estimating reference crop evapotranspiration (ET0) is crucial for decision-making regarding irrigation, improving farmland water use efficiency, planning water resources, and simulating the global water cycle. However, because traditional ET0 estimation models often require a large number of meteorological parameters as input variables, their application potential is limited, especially when simulating arid and semiarid regions where meteorological data are lacking. Therefore, it is still very challenging to estimate ET0 at large scales in arid and semiarid areas. In this study, we first proposed a semi-mechanistic model for estimating ET0 by deriving the relationship between canopy conductance to water vapor (gw) and solar-induced chlorophyll fluorescence (SIF) based on Fick's law and the light use efficiency (LUE) model, combining theories on optimal stomatal behaviour. Second, the relationship between SIF × VPD0.5 (VPD0.5: 0.5-power of the vapor pressure deficit) and ET0 in the model was verified at the site scale. On this basis, the semi-mechanistic model was applied to three machine learning models to simulate ET0. Four statistical indicators, including the coefficient of determination (R2), root mean square error (RMSE), normalized root mean square error (NRMSE) and mean absolute error (MAE), were used to verify the model performance and screen out the optimal model. Finally, the optimal model was compared with five traditional empirical models. The results showed that (1) the relationship between ET0 and the product of SIF and the 0.5-power vapor pressure deficit (SIF × VPD0.5) was closer than the relationship between ET0 and SIF at the site scale. (2) Compared to the case in which SIF was used as the only input variable, three machine learning models performed better when SIF was scaled using VPD0.5 as the input variable, and the random forest regression (RFR) model in which SIF × VPD0.5 was the input variable was the optimal model (ET0opt). (3) The total mean value of ET0 at 22 sites simulated by ET0opt is 15.46 mm/4 days. The total mean ET0 value for the 22 sites calculated by the five traditional empirical models vary from 10.70 mm/4 days to 20.96 mm/4 days, while our results were in this range and close to their mean value. Our study provides a valuable reference basis for large-scale ET0 estimations using remote sensing in arid and semiarid areas.

Estimation of the deep drainage for irrigated cropland based on satellite observations and deep neural networks

The deep drainage of irrigated cropland, which is one of the main causes of water losses in irrigation, plays an important role in water resource management. However, its magnitudes and variations remain unclear at regional scales. In this study, we established a remote sensing deep drainage model for irrigated cropland (ICDD_RS model) based on satellite observations and a deep neural network (DNN). First, deep drainage for natural vegetation was calculated by the water balance equation. Then, the ICDD_RS model was established by linking the calculated deep drainage and remote sensing data for natural vegetation based on a DNN. Finally, the model was used to predict the deep drainage of irrigated cropland through remote sensing data. The model was tested in the Heihe River Basin of China and the results showed the following: (1) The ICDD_RS model can well describe the relationship between deep drainage and selected influencing factors (0.83 < R2 < 0.86). At the site scale, it is highly consistent with in-situ observations with R2 values of 0.86, 0.74, and 0.71 for the Daman, Yingke, and Pingchuan stations, respectively. At the catchment scale, a low bias of 12% is obtained. (2) Deep drainage is large, accounting for 6–17% of the irrigation water. This model should be valuable in providing more detailed and effective information on agricultural water resource management.

Mobility of antimony in contrasting surface environments of a mine site: influence of redox conditions and microbial communities.

Microbial processes can influence the complex geochemical behaviour of the toxic metalloid antimony (Sb) in mining environments. The present study is aimed to evaluate the influence of microbial communities on the mobility of Sb from solid phases to water in different compartments and redox conditions of a mining site in southwest (SW) Spain. Samples of surface materials presenting high Sb concentrations, from two weathered mining waste dumps, and an aquatic sediment were incubated in slurries comparing oxic and anoxic conditions. The initial microbial communities of the three materials strongly differed. Incubations induced an increase of microbial biomass and an evolution of the microbial communities' structures and compositions, which diverged in different redox conditions. The presence of active bacteria always influenced the mobility of Sb, except in the neutral pH waste incubated in oxic conditions. The effect of active microbial activities in oxic conditions was dependent on the material: Sb oxic release was biologically amplified with the acidic waste, but attenuated with the sediment. Different bacterial genera involved in Sb, Fe and S oxidation or reduction were present and/or grew during incubation of each material. The results highlighted the wide diversity of microbial communities and metabolisms at the small geographic scale of a mining site and their strong implication in Sb mobility.

Machine learning modeling of water footprint in crop production distinguishing water supply and irrigation method scenarios

Crop water footprint (WF) calculation via physical process-based crop or hydrological models has basic requirements in terms of cost, time, and knowledge threshold. It is unsuitable for laypersons such as policymakers or cross-disciplinary researchers. Here, machine learning (ML) is an effective solution owing to low cost, high speed, and low processing difficulty. However, the feasibility and applicability of ML modeling for crop WFs at multiple spatial scales remain unverified. Here, we quantitatively evaluated the applicability, optimal model parameters and importance of the feature variables of four ML methods, including random forest (RF), light gradient boosting machine (LightGBM), generalized additive model (GAM), and artificial neural network (ANN), for constructing WF models for crop production at site and provincial scales. The models were tested through the case study for major grain crops (wheat, maize, soybean, and rice) in China over 2000–2019, distinguishing between irrigated and rainfed crops and between different irrigation methods. The findings show that the WF simulation accuracy for the irrigated scenario was higher than that for the rainfed scenario. The type of feasible ML model varied with crop types, variable combinations, water supply scenarios, and spatial scales. At the site scale, the RF model was generally suitable for simulating WF of maize, soybean, and rice, with R2 values ranging from 0.7 to 0.78 under different irrigation scenarios; the LightGBM was more suitable for simulating wheat with R2 values ranging from 0.76 to 0.82. The LightGBM was deemed to be the best model at the provincial scale, with some regions presenting a 50% lower root mean square error in the crop WF simulation than in RF. Variables related to spatio-temporal class feature were found to be the most important in both the RF and LightGBM simulations. This study provides a global methodological reference for different countries or regions to establish methods for rapid WF assessments in crop production with cross-disciplinary researchers or macro policymakers as the target users.