Accurate estimation of global solar radiation (Rs) is essential to the design and assessment of solar energy utilization systems. Existing empirical and machine learning models for estimating Rs from ...sunshine duration were comprehensively reviewed. The performances of 12 empirical model forms and 12 machine learning algorithms for estimating daily Rs were further evaluated in different climatic zones of China as a case study, i.e. the temperate continental zone (TCZ), temperate monsoon zone (TMZ), mountain plateau zone (MPZ) and (sub)tropical monsoon zone (SMZ). The best-performing model at each station and the overall best model for each climatic zone were selected based on six statistical indictors, a global performance index (GPI) and computational costs (computational time and memory usage). The results revealed that the machine learning models (RMSE: 2.055–2.751 MJ m−2 d−1; NRMSE: 12.8–21.3%; R2: 0.839–0.936) generally outperformed the empirical models (RMSE: 2.118–3.540 MJ m−2 d−1; NRMSE: 12.1–27.5%; R2: 0.834–0.935) in terms of prediction accuracy. The cubic model (M3), modified linear-logarithmic model (M5) and power model (M10) attained generally better ranks among empirical models based on GPI. M3 was the top-ranked model in TMZ and MPZ, while general best performance was obtained by M5 and M2 in SMZ and TCZ, respectively. ANFIS, ELM, LSSVM and MARS obtained generally better performance among machine learning models, with the overall best ranking by ANFIS in TCZ and SMZ and by ELM in MPZ and SMZ. XGBoost (8.1 s and 74.2 MB), M5Tree (11.3 s and 29.7 MB), GRNN (12.3 s and 295.3 MB), MARS (14.4 s and 42.6 MB), MLP (22.4 s and 41.3 MB) and ANFIS (29.8 s and 23.1 MB) showed relatively small computational time and memory usage. Comprehensively considering both the prediction accuracy and computational costs, ANFIS is highly recommended, while MARS and XGBoost are also promising models for daily Rs estimation.
•Sunshine-based empirical and machine learning models for predicting Rs were comprehensively reviewed.•Performances of 12 empirical and 12 machine learning models were evaluated across China.•Accuracy and ranking of models were evaluated using six statistical indictors and Global Performance Index.•Computational costs of 12 types of sunshine-based machine learning models were compared.•The best-performing model at each station and appropriate model in each climatic zone were recommended.
•Potential of tree-based ensemble models for daily ET0 estimation with limited climatic data is explored.•Proposed ensemble models are compared with their corresponding SVM and ELM models.•ELM and ...SVM models offered the best combination of prediction accuracy and stability.•XGBoost and GBDT models have comparable accuracy and stability to those of ELM and SVM models.•XGBoost and GBDT models have less computational costs than the other models.
Accurate estimation of reference evapotranspiration (ET0) is of great importance for the regional water resources planning and irrigation scheduling design. The FAO-56 Penman-Monteith model is recommended as the reference model to predict ET0, but its application is commonly restricted by lack of complete meteorological data at many worldwide locations. This study evaluated the potential of machine learning models, particularly four relatively simple tree-based assemble algorithms (i.e. random forest (RF), M5 model tree (M5Tree), gradient boosting decision tree (GBDT) and extreme gradient boosting (XGBoost)), for estimating daily ET0 with limited meteorological data using a K-fold cross-validation method. For assessment of the tree-based models in terms of prediction accuracy, stability and computational costs, these models were further compared with their corresponding support vector machine (SVM) and extreme learning machine (ELM) models. Four input combinations of daily maximum and maximum temperature (Tmax and Tmin), relative humidity (Hr), wind speed (U2), global and extra-terrestrial solar radiation (Rs and Ra) with Tmax, Tmin and Ra as the base dataset were considered using meteorological data during 1961–2010 from eight representative weather stations in different climates of China. The results showed that, when lack of complete meteorological data, the machine learning models using Tmax, Tmin, Hr, U2 and Ra obtained satisfactory ET0 estimates in the temperate continental, mountain plateau and temperate monsoon zones of China (RMSE < 0.5 mm d−1). However, models with three input parameters of Tmax, Tmin and Rs were superior for daily ET0 prediction in the tropical and subtropical zones. The ELM and SVM models offered the best combination of prediction accuracy and stability. The simple tree-based XGBoost and GBDT models showed comparable accuracy and stability to the SVM and ELM models, but exhibited much less computational costs. Considering the complexity level, prediction accuracy, stability and computational costs of the studied models, the XGBoost and GBDT models have been recommended for daily ET0 estimation in different climatic zones of China and maybe elsewhere with similar climates around the world.
•Using DPSIR to analyze interaction between socio-economic system and water system.•Regional water resource system sustainability displayed a degradation trend.•Though WUE was improved, social and ...economic growth causes Jevons paradox.•Sustainable water use can be achieved by changing short and medium term factors.
Fresh water is a scarce and critical resource in both natural and socioeconomic systems. Increasing populations combined with an increasing demand for water resources have led to water shortages worldwide. Current water management strategies may not be sustainable, and comprehensive action should be taken to minimize the water budget deficit. Sustainable water resources management is essential because it ensures the integration of social, economic, and environmental issues into all stages of water resources management. This paper establishes the indicators to evaluate the sustainability of water utilization based on the Drive-Pressure-Status-Impact-Response (DPSIR) model. Based on the analytic hierarchy process (AHP) method, a comprehensive assessment of changes to the sustainability of the water resource system in the city of Bayannur was conducted using these indicators. The results indicate that there is an increase in the driving force of local water consumption due to changes in society, economic development, and the consumption structure of residents. The pressure on the water system increased, whereas the status of the water resources continued to decrease over the study period due to the increasing drive indicators. The local government adopted a series of response measures to relieve the decreasing water resources and alleviate the negative effects of the increasing driver in demand. The response measures improved the efficiency of water usage to a large extent, but the large-scale expansion in demands brought a rebounding effect, known as “Jevons paradox” At the same time, the increasing emissions of industrial and agriculture pollutants brought huge pressures to the regional water resources environment, which caused a decrease in the sustainability of regional water resources. Changing medium and short-term factors, such as regional economic pattern, technological levels, and water utilization practices, can contribute to the sustainable utilization of regional water resources.
Deep sewage discharge leads to inestimable damage to the ambient water (lakes, oceans and reservoirs), which has caused widespread social concern. In the current paper, a three‐dimensional (3D) ...buoyancy plume model for deep sewage discharge was developed. It simulates sediment‐laden flow with the effects of temperature and salinity differences. Taking the turbulent diffusion coefficient of salinity (αsal) as the calibration parameter, a comparison between the RNG k‐ε and the standard k‐ε models was performed. The proposed model was verified well and agreement with experiment, which proved that the RNG model with the αsal value of 0.4 was the optimal calibration. Then the model was applied to quantify the behavior of the buoyant plume in the ambient fluid (salt water) with respect to different contours of turbulent kinetic energy (k), temperature, salinity and sediment. The dimensionless centerline trajectory positions and boundary positions of them were determined. The results indicated that the discharge of low‐salinity sediment‐bearing water influenced the deep ambient water spatially, both near the free surface and in the vertical plane. Different diffusion and spreading shapes (butterfly, Ginkgo biloba, bean) can be observed on the surface. Accurately evaluating the impact of deep discharge on ambient water has great significance for maintaining healthy and sustainable environments in offshore areas, deep lakes and reservoirs.
•Several possible optimal irrigation practices for spring wheat were evaluated.•CERES-Wheat was used to test irrigation related management like delay of irrigation dates.•Simulation indicated water ...stress in grain filling and milky ripe stages can heavily reduce yield.•Supplementary irrigation at the end of boot stage can be particularly beneficial.•The schedule with 4 irrigation events was the best choice for spring wheat in the Minqin Oasis.
The Minqin County in Northwest China is known for its serious desertification and irrigation-dependent oasis farming that is mainly distributed along the Shiyang River. To answer some important hypothetical questions related to optimal irrigation scheduling for spring wheat (Triticum aestivum L.) production in the Minqin Oasis, the CERES-Wheat model in DSSAT V4.5 was used to simulate the spring wheat growth in irrigated farmland in this area. The results of model simulation indicate that if the soil water content is lower than 65% of field capacity (or about 190mm water) in the 1-m depth soil profile during the grain filling and milk ripe stages, the final grain yield can be remarkably reduced (e.g. more than 1000kgha−1 for some treatments in field experiment) even for a short period of water stress. The water stress in the early stages could be eliminated with luxurious water supply, but more nitrogen would be washed away from the soil profile and then the yield was impacted. The optimized irrigation dates played an important role in improving the water use efficiency of spring wheat. If there was only a single irrigation allowed by the limited water resources, the irrigation date should be at the end of booting or beginning of heading stage. If the second irrigation could be adjusted to the middle of heading stage, the 3-irrigation schedule (a total of three irrigations in the whole growth season, and so forth) could meet the water demand of spring wheat without losing too much yield (e.g. less than 20kgha−1 when compared with the 4-irrigation treatment). Furthermore, if the first irrigation of the 4-irrigation schedule could be delayed to the starting of jointing, the simulated yield could be increased from about 6500kgha−1 to the highest 7000kgha−1. Finally, the analysis of uncertainties of simulated dry yields across 58 years historical weather data showed that the schedules with fewer irrigation events cause larger uncertainties due to local weather variations. The 4-irrigation schedule seemed to be the best choice for spring wheat at the Minqin Oasis due to its relatively higher long-term average yields and lower uncertainties since it was under non-limited water conditions.
Water scarcity and poor irrigation practices limit crop productivity and increase greenhouse gas (GHG) emissions in arid Northwest China. Therefore, we investigated the effects of five growth ...stage-based deficit irrigation strategies on the yield, quality, and greenhouse gas emissions of winter wheat. Across treatments, CO2 emissions ranged from 3824.93 to 4659.05 kg ha−1 and N2O emissions from 3.96 to 4.79 kg ha−1. Compared with CK (irrigation in all growth stages), GHG emissions decreased significantly in T1, T2, T3, and T4 (p < 0.05). Water stress reduced the wheat yield, compared with CK, but the decrease depended on the stage without irrigation. Across treatments, the wheat yield was between 5610 and 6818 kg ha−1. The grain protein content decreased in the order T4 > T3 > T1 > T2 > CK. On the basis of a catastrophe progression method evaluation, we recommend T1 as the irrigation practice for winter wheat, because it maintained a high grain yield and quality and reduced GHG emissions. Thus, in practice, soil moisture should be sufficient before sowing, and adequate water should be supplied during the heading and filling stages of winter wheat. This study provides a theoretical basis for exploring the irrigation strategies of high-yield, good-quality, and emission reduction of winter wheat.
Agricultural adaptation is crucial for sustainable farming amid global climate change. By harnessing projected climate data and using crop modeling techniques, the future trends of food production ...can be predicted and better adaptation strategies can be assessed. The main objective of this study is to analyze the maize yield response to future climate projections in the Guanzhong Plain, China, by employing multiple crop models and determining the effects of irrigation and planting date adaptations. Five crop models (APSIM, AquaCrop, DSSAT, EPIC, and STICS) were used to simulate maize (
Zea mays
L.) yield under projected climate conditions during the 2030s, 2050s, and 2070s, based on the combination of 17 General Circulation Models (GCMs) and two Representative Concentration Pathways (RCPs 6.0 and 8.5). Simulated scenarios included elevated and constant CO
2
levels under current adaptation (no change from current irrigation amount, planting date, and fertilizer rate), irrigation adaptation, planting date adaptation, and irrigation-planting date adaptations. Results from both maize-producing districts showed that current adaptation practices led to a decrease in the average yield of 19%, 27%, and 33% (relative to baseline yield) during the 2030s, 2050s, and 2070s, respectively. The future yield was projected to increase by 1.1–23.2%, 1.0–22.3%, and 2–31% under irrigation, delayed planting date, and double adaptation strategies, respectively. Adaptation strategies were found effective for increasing the future average yield. We conclude that maize yield in the Guanzhong Plain can be improved under future climate change conditions if irrigation and planting adaptation strategies are used in conjunction.
Irrigation practice is one of the main factors affecting soil carbon dioxide (CO
2
) emission from croplands and therefore on global warming. As a water-saving irrigation practice, the deficit ...irrigation has been widely used in summer maize fields and is expected to adapt to the shortage of water resources in Northwest China. In this study, we examined the impacts of deficit irrigation practices on soil CO
2
emissions through a plot experiment with different irrigation regimes in a summer maize field in Northwest China. The irrigation regimes consisted of three irrigation treatments: deficit irrigation treatments (T1: reduce the irrigation amount by 20%, T2: reduce the irrigation amount by 40%) and full irrigation (T0) treatments. The results showed that the soil CO
2
cumulative emissions with T1 and T2 were decreased by 9.8% (
p
< 0.05) and 14.3% (
p
< 0.05), respectively, compared with T0 treatment (1365.3 kg-C ha
−1
). However, there were no significant differences between T1 and T2 treatments (
p
> 0.05). Soil CO
2
fluxes with different irrigation treatments showed significant correlations with soil moisture (
p
< 0.001) and soil temperature (
p
< 0.05). It was also observed that summer maize yields with T1 and T2 treatments were reduced by 4.9% (
p
> 0.05) and 30.9% (
p
< 0.05), compared with T0 (34.3 t ha
−1
), respectively. The findings demonstrate that the deficit irrigation treatment (T1) resulted in a considerable decrease in soil CO
2
emissions without impacting the summer maize yields significantly. The results could be interpreted to develop better irrigation management practices aiming at reducing soil CO
2
emissions, saving water, and ensuring crop yield in the summer maize fields in Northwest China.
Accurate estimation and effective portioning of actual evapotranspiration (ETa) into soil evaporation (E) and plant transpiration (T) are important for increasing water use efficiency (WUE) and ...optimizing irrigation schedules in croplands. In this study, E/T partitioning was performed on ETa rates measured using the eddy covariance (EC) technique in three winter wheat growing seasons from October 2020 to June 2023. The variation in the crop coefficients (Kc, α, and KHc) were quantified by combining the ETa and reference evapotranspiration rates using the Penman–Monteith, Priestley–Taylor, and Hargreaves equations. In addition, the application of models based on the modified crop coefficient (Kc, α, and KHc) was proposed to estimate the ETa rates. According to the obtained results, the average cumulative ETa, T, and E rates in the three winter wheat growth seasons were 471.4, 265.2, and 206.3 mm, respectively. The average T/ETa ratio ranged from 0.16 to 0.72 at the different winter wheat growth stages. Vapor pressure deficit (VPD) affected the ETa rates at a threshold of 1.27 KPa. The average Kc, α, and KHc values in the middle stage were 1.34, 1.54, and 1.21, respectively. The measured ETa rates and ETa rates estimated using the adjusted Kc, α, and KHc showed regression slope coefficients of 0.96, 0.99, and 0.96, and coefficients of determination (R2) of 0.92, 0.93, and 0.90, respectively. Therefore, the Priestley–Taylor-equation-based adjusted crop coefficient is recommended. The adjusted crop-coefficient-based models can be used as valuable tools for local policymakers to effectively improve water use.
The Yellow River Basin in China
Agricultural drought (AD) poses a serious threat to national food security, however, responses to AD to meteorological drought (MD), factoring in influences of climate ...change and human activity, are not well understood. This study employed the Standardized Precipitation Evapotranspiration Index and the Standardized Soil Moisture Index to characterize the evolution of MD and AD. The drought propagation time (PT) was calculated with maximum correlation coefficients under full-crop-cycle, seasonal, and multi-threshold modes for spring and winter wheat in the Yellow River Basin (YRB; China).
Both spring and winter wheat faced increasing magnitude agricultural drought events (ADEM), with winter wheat being affected more severely from 1950–2021. Increasing temperature enhanced ADEM, while human activities had a more uncertain. The PT was generally prolonged for all propagation modes, with spring wheat having a longer PT except for in multi-threshold mode. The full-crop-cycle mode showed the entire period of crop growth propagation characteristics, the seasonal mode showed different propagation characteristics by month, and the multi-threshold mode showed the propagation characteristics at different degrees of AD. Sunspot Index had the highest influence on PT among all teleconnection factors, and human activities had increasing impact. The results of this study provide a basis for better understanding the agricultural drought propagation.
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•Dynamic drought characteristics and drought propagation are investigated under climate change and human activities.•Winter wheat had shorter propagation time than spring wheat in the Yellow River Basin.•Temperature increasing enhanced agricultural drought magnitude, while human activities showed more uncertain effect on it.•Propagation time had more significant variation for multi-threshold mode than seasonal and full-crop-cycle modes.
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