Nutrient recycling has been practiced for thousands of years in China to maintain food production with- out environmental pollution. In the past three decades, however, the traditional nutrient ...recycling systems have been replaced with waste treatment systems, which have resulted in rapid and severe environmental pollution. By analyzing the primary driving forces of the changing nutrient flows （technol- ogy, labor costs, food supplies, fertilizer demands, environmental quality, human health, and public awareness）, this paper argues that technology fundamentally motivated the nutrient-recycling strategy to address the malnutrition problem in traditional societies but has constrained the reconstruction of nutrient recycling systems in modern cities. With the availability of synthetic fertilizers in modern soci- ety, the lack of interdisciplinary views in policy making for nutrient management is the root cause of today＇s environmental situation. Ongoing fast urbanization has concentrated more nutrients in urban areas, creating the need for a national nutrient management plan to coordinate multiple ministries and fix the uncoupled nutrient cycling between urban and rural systems, Rebuilding the traditional nutrient-recycling systems is an environmentally and economically effective solution, There are three fundamental technological barriers to reconstructing the nutrient recycling systems, as follows： userfriendly toilets, the separation of sewage pipelines, and easy-to-use organic fertilizers made from human manure or other organic waste. Overcoming these barriers requires building institutional mechanisms, developing the necessary infrastructure, creating research funding, and providing open experimental platforms for technological development.
•Using Bayesian model averaging (BMA) to integrate multiple crop models, including WOFOST, AquaCrop, and DNDC.•A broad-scale application for maize is conducted in Liaoning Province, China.•The BMA ...approach results in more accurate predictions than using any individual models or simple averaging approaches.•The BMA weight values to some extent are associated with the local limiting factors for crop growth in Liaoning Province.
Process-based crop models are popular tools to evaluate the impact of climate change and agricultural management on crop growth. Accurate simulation of crop production over large geographic regions using an individual crop model remains challenging due to different sources of uncertainty. We present a Bayesian model averaging (BMA) method for a multiple crop-growth model ensemble to provide more reliable predictions of maize yields in Liaoning Province, northeastern China, which covers an area of 148,000km2 and has 2200,000ha of maize. We apply the photosynthesis-oriented WOFOST (WOrld FOod STudy) model, the water-oriented AquaCrop model and the nitrogen-oriented DNDC (DeNitrification and DeComposition) model to independently generate original predictions of county-level maize yields. The integrated prediction is achieved using a linear combination of the three ensemble members using BMA weights. This integrated approach results in more accurate and precise predictions than any individual model over the entire province. This is because the BMA framework effectively compensates for the uncertainty of individual model simulation and takes advantage of each competing model for reliable prediction. Furthermore, the interpretation of the BMA weight values is also strengthened by comparison with regional precipitation, fertilization and radiation data. We find these values adequately fit the regional limiting factors, e.g., the AquaCrop model generally has a high weight value in counties with frequent droughts, while WOFOST is the dominant member in areas with radiation deficit. Compared with the simple average method and median estimate, the results show that the BMA framework is powerful in computing the ensemble weights and interpreting the mechanism beyond the observed data.
The nitrogen cycle has been radically changed by human activities
. China consumes nearly one third of the world's nitrogen fertilizers. The excessive application of fertilizers
and increased ...nitrogen discharge from livestock, domestic and industrial sources have resulted in pervasive water pollution. Quantifying a nitrogen 'boundary'
in heterogeneous environments is important for the effective management of local water quality. Here we use a combination of water-quality observations and simulated nitrogen discharge from agricultural and other sources to estimate spatial patterns of nitrogen discharge into water bodies across China from 1955 to 2014. We find that the critical surface-water quality standard (1.0 milligrams of nitrogen per litre) was being exceeded in most provinces by the mid-1980s, and that current rates of anthropogenic nitrogen discharge (14.5 ± 3.1 megatonnes of nitrogen per year) to fresh water are about 2.7 times the estimated 'safe' nitrogen discharge threshold (5.2 ± 0.7 megatonnes of nitrogen per year). Current efforts to reduce pollution through wastewater treatment and by improving cropland nitrogen management can partially remedy this situation. Domestic wastewater treatment has helped to reduce net discharge by 0.7 ± 0.1 megatonnes in 2014, but at high monetary and energy costs. Improved cropland nitrogen management could remove another 2.3 ± 0.3 megatonnes of nitrogen per year-about 25 per cent of the excess discharge to fresh water. Successfully restoring a clean water environment in China will further require transformational changes to boost the national nutrient recycling rate from its current average of 36 per cent to about 87 per cent, which is a level typical of traditional Chinese agriculture. Although ambitious, such a high level of nitrogen recycling is technologically achievable at an estimated capital cost of approximately 100 billion US dollars and operating costs of 18-29 billion US dollars per year, and could provide co-benefits such as recycled wastewater for crop irrigation and improved environmental quality and ecosystem services.
•The N-zone model of Boxall and Guymer is extended.•Estimate the longitudinal dispersion coefficient in partially vegetated rivers.•The accuracy of the proposed model is tested using experimental ...datasets.
The longitudinal dispersion coefficient is an important parameter for describing the transport processes in rivers. Riparian vegetation significantly influences the velocity profile and transport processes. This paper examines the longitudinal dispersion coefficient under the condition that rigid emergent vegetation grows symmetrically along the river bank. We build a three-zone model by extending the N-zone models of Chickwendu and Boxall & Guymer. We also analyze the velocity profiles that are significantly affected by vegetation to estimate the parameters in our model. Our tests using the experimental data from a series of experiments validate the acceptable accuracy of our three-zone model.
China is continually seeking to improve river water quality. Implemented in 1996, the total pollutant load control system (TPLCS) is a regulatory strategy to reduce total pollutant loads, under which ...a Pollutant Discharge Permit (PDP) program tracks and regulates nutrient inputs from point source polluters. While this has been promising, the input-response relationship between discharge permits and water quality targets is largely unclear – especially in China's large and complex river basins. In response, this study involved a quantitative analysis method to combine the water quality targets of the 12th Five-Year Plan (2011–2015) with allocated PDPs in the Nenjiang River Basin, China. We demonstrated our approach by applying the Soil and Water Assessment Tool (SWAT) to the Nenjiang River Basin for hydrological and water quality simulation. Ammonia nitrogen (NH3-N) was used as the primary water quality indicator. Modelling indicated that only one control section in the wider river basin did not achieve the water quality target, suggesting that the TPLCS is largely effective. The framework should be applied in other basins to study the effectiveness of PDP policies, advise further updates to the TPLCS, and ultimately aim to achieve freshwater quality targets nationally.
•Pollutant Discharge Permits Program and water quality targets were coupled.•The effectiveness of the Pollutant Discharge Permit Program was evaluated.•R2 values showed good performance of SWAT model in the Nenjiang River Basin.•Only one out of nine control sections did not achieve water quality targets.•Results suggest that the total pollutant load control system is largely effective.
Agroecosystem modelling has increasingly focused on the integration of soil biogeochemical processes and crop growth. However, few models are available that offer high computing efficiencies for ...region-scale simulations, integrated decision support tools, and a structure that allows for easy extension. This paper introduces a new modelling tool to fill this gap: the GDNDC (Gridded DNDC) system for gridded agro-biogeochemical simulations. Based on the established DeNitrification and DeComposition (DNDC) model version-95, its main advancements include (i) implementation of parallel computation to significantly reduce computation time across multiple scales; (ii) a built-in parameter optimization algorithm to improve the predictive accuracy, and (iii) several decision support tools. We demonstrate each of these for county-level maize growth simulations in Liaoning Province (China) and reveal the potential of this new modelling tool to guide both long-term policy decisions regarding optimal fertilizer application and near-term crop yield forecasting for reactive decisions required in times of drought.
•Parallel computing mode realized in GDNDC to improve the computing efficiency for regional-scale simulation.•Multiple modules are coupled into GDNDC to enable complete end-to-end simulation for decision support.•Coupler is developed in GDNDC to integrate different modules by coordinating the information exchange.•GDNDC presents a new structure of agricultural modelling system for easy maintenance and extension.
•MODIS data products are incorporated into data assimilations of grass modelling.•Multiple scenarios of data availability are assumed for data-limited areas.•Different methods of data assimilation ...are compared based on data usage.
Process-based grass models (PBGMs) are widely used for predicting grass growth under potential climate change and different management practices. However, accurate predictions using PBGMs heavily rely on field observations for data assimilation. In data-limited areas, performing robust and reliable estimates of grass growth remains a challenge. In this paper, we incorporated satellite-based MODIS data products, including leaf area index, gross primary production and evapotranspiration, as an additional supplement to field observations. Popular data assimilation methods, including Bayesian calibration and the updating method ensemble Kalman filter, were applied to assimilate satellite derived information into the BASic GRAssland model (BASGRA). A range of different combinations of data assimilating methods and data availability were tested across four grassland sites in Norway, Finland and Canada to assess the corresponding accuracy and make recommendations regarding suitable approaches to incorporate MODIS data. The results demonstrated that optimizing the model parameters that are specific for grass species and cultivar should be targeted prior to updating model state variables. The MODIS derived data products were capable of constraining model’s simulations on phenological development and biomass accumulation by parameter optimization with its performance exceeding model outputs driven by default parameters. By integrating even a small number of field measurements into the parameter calibration, the model’s predictive accuracy was further improved - especially at sites with obvious biases in the input MODIS data. Overall, this comparative study has provided flexible solutions with the potential to strengthen the capacity of PBGMs for grass growth estimation in practical applications.
Display omitted
•An integrated framework for the quantification of land-sea carbon transfer is presented.•The riverine carbon in the study area is dominated by water volume of runoff rather than ...carbon concentration.•Human impacts can reduce a significant proportion of the land-sea riverine carbon transfer at both annual and seasonal scale.•Natural factors like seasonal drought can markedly enhance human impact by stricter water management strategies.
Land-sea riverine carbon transfer (LSRCT) is one of the key processes in the global carbon cycle. Although natural factors (e.g. climate, soil) influence LSRCT, human water management strategies have also been identified as a critical component. However, few systematic approaches quantifying the contribution of coupled natural and anthropogenic factors on LSRCT have been published. This study presents an integrated framework coupling hydrological modeling, field sampling and stable isotope analysis for the quantitative assessment of the impact of human water management practices (e.g. irrigation, dam construction) on LSRCT under different hydrological conditions. By applying this approach to the case study of the Nandu River, China, we find that carbon (C) concentrations originating from different land-uses (e.g. forest, cropland) are relatively stable and outlet C variations are mainly dominated by controlled runoff volumes rather than by input C concentrations. These results indicate that human water management practices are responsible for a reduction of ∼60% of riverine C at seasonal timescales, with an even greater reduction during drought conditions. Annual C discharges have been significantly reduced (e.g. 77 ± 5% in 2015 and 39 ± 11% in 2016) due to changes in human water extraction coupled with climate variation. In addition, isotope analysis also shows that C fluxes influenced by human activities (e.g. agriculture, aquaculture) could contribute the dominant particulate organic carbon under typical climatic conditions, as well as drought conditions. This research demonstrates the substantial effect that human water management practices have on the seasonal and annual fluxes of LSRCT, especially in such small basins. This work also shows the applicability of this integrated approach, using multiple tools to quantify the contribution of coupled anthropogenic and natural factors on LSRCT, and the general framework is believed to be feasible with limited modifications for larger basins in future research.
•Illustrating the effects of precipitation uncertainty from different sources on crop modelling in different scales.•Using BATEA method to correct the parameter estimation and reduce the predictive ...uncertainty.•Comparing the performance of BATEA with simple averaging of multiple datasets.
Precipitation is an important source of soil water, which is critical to crop growth, and is therefore an important input when modelling crop growth. Although advances are continually being made in predicting and recording precipitation, input uncertainty of precipitation data is likely to influence the robustness of parameter estimate and thus the predictive accuracy in soil water and crop modelling. In this study, we use the Bayesian total error analysis (BATEA) method for the water-oriented crop model AquaCrop to identify the input uncertainty from multiple precipitation products respectively, including gauge-corrected grid dataset CPC, remote sensing based TRMM and reanalysis based ERA-Interim. This methodology uses latent variables to correct the input data errors. Adopting a single-multiplier method for precipitation correction, we simulate maize growth in both field and regional levels in China for a range of different possible climatic scenarios. Meanwhile, we use the average of multiple products for model driving in comparison. The results show that the BATEA method can consistently reduce uncertainty for crop growth prediction among different precipitation products. In regional simulation, the improvements for the three products are 1%, 7.3% and 2.8% on average in drought scenarios. These results imply the BATEA approach can be of great assistance for crop modeling studies and agricultural assessments under future changing climates.
•Further acceleration of crop models and high-performance computing for large-scale crop modeling.•Combination of Bayesian inference and Bayesian model average to improve predictive ...accuracy.•Real-time simulation and prediction based on observational and scenario forces.•Risk analysis of yield losses in multiple scales and its spatial dependency.
Crop models are widely used to evaluate the response of crop growth to drought. However, over large geographic regions, the most advanced models are often restricted by available computing resource. This limits capacity to undertake uncertainty analysis and prohibits the use of models in real-time ensemble forecasting systems. This study addresses these concerns by presenting an integrated system for the dynamic prediction and assessment of agricultural yield using the top-ranked Sunway TaihuLight supercomputer platform. This system enables parallelization and acceleration for the existing AquaCrop, DNDC (DeNitrification and DeComposition) and SWAP (Soil Water Atmosphere Plant) models, thus facilitating multi-model ensemble and parameter optimization and subsequent drought risk analysis in multiple regions and at multiple scales. The high computing capability also opens up the possibility of real-time simulation during droughts, providing the basis for more effective drought management. Initial testing with varying core group numbers shows that computation time can be reduced by between 2.6 and 3.6 times. Based on the powerful computing capacity, a county-level model parameter optimization (2043 counties for 1996–2007) by Bayesian inference and multi-model ensemble using BMA (Bayesian Model Average) method were performed, demonstrating the enhancements in predictive accuracy that can be achieved. An application of this system is presented predicting the impacts of the drought of May–July 2017 on maize yield in North and Northeast China. The spatial variability in yield losses is presented demonstrating new capability to provide high resolution information with associated uncertainty estimates.
PDF
