Fertilizer management for global ammonia emission reduction.

Crop production is a large source of atmospheric ammonia (NH3), which poses risks to air quality, human health and ecosystems1-5. However, estimating global NH3 emissions from croplands is subject to uncertainties because of data limitations, thereby limiting the accurate identification of mitigation options and efficacy4,5. Here we develop a machine learning model for generating crop-specific and spatially explicit NH3 emission factors globally (5-arcmin resolution) based on a compiled dataset of field observations. We show that global NH3 emissions from rice, wheat and maize fields in 2018 were 4.3 ± 1.0 Tg N yr-1, lower than previous estimates that did not fully consider fertilizer management practices6-9. Furthermore, spatially optimizing fertilizer management, as guided by the machine learning model, has the potential to reduce the NH3 emissions by about 38% (1.6 ± 0.4 Tg N yr-1) without altering total fertilizer nitrogen inputs. Specifically, we estimate potential NH3 emissions reductions of 47% (44-56%) for rice, 27% (24-28%) for maize and 26% (20-28%) for wheat cultivation, respectively. Under future climate change scenarios, we estimate that NH3 emissions could increase by 4.0 ± 2.7% under SSP1-2.6 and 5.5 ± 5.7% under SSP5-8.5 by 2030-2060. However, targeted fertilizer management has the potential to mitigate these increases.

Spatiotemporal variation evaluation of water quality in middle and lower Han River, China.

As the water source for the middle route of the South-to-North Water Transfer Project, the Han River in China plays a role of the world's largest inter-basin water transfer project. However, this human-interfered area has suffered from over-standard pollution emission and water blooms in recent years, which necessitates urgent awareness at both national and provincial scales. To perform a comprehensive analysis of the water quality condition of this study area, we apply both the water quality index (WQI) and minimal WQI (WQImin) methods to investigate the spatiotemporal variation characteristics of water quality. The results show that 8 parameters consisting of permanganate index (PI), chemical oxygen demand (COD), total phosphorus (TP), fluoride (F-), arsenic (As), plumbum (Pb), copper (Cu), and zinc (Zn) have significant discrepancy in spatial scales, and the study basin also has a seasonal variation pattern with the lowest WQI values in summer and autumn. Moreover, compared to the traditional WQI, the WQImin model, with the assistance of stepwise linear regression analysis, could exhibit more accurate explanation with the coefficient of determination (R2) and percentage error (PE) values being 0.895 and 5.515%, respectively. The proposed framework is of great importance to improve the spatiotemporal recognition of water quality patterns and further helps develop efficient water management strategies at a reduced cost.

Adaptive optimal allocation of water resources response to future water availability and water demand in the Han River basin, China.

Global warming and anthropogenic changes can result in the heterogeneity of water availability in the spatiotemporal scale, which will further affect the allocation of water resources. A lot of researches have been devoted to examining the responses of water availability to global warming while neglected future anthropogenic changes. What's more, only a few studies have investigated the response of optimal allocation of water resources to the projected climate and anthropogenic changes. In this study, a cascade model chain is developed to evaluate the impacts of projected climate change and human activities on optimal allocation of water resources. Firstly, a large set of global climate models (GCMs) associated with the Daily Bias Correction (DBC) method are employed to project future climate scenarios, while the Cellular Automaton-Markov (CA-Markov) model is used to project future Land Use/Cover Change (LUCC) scenarios. Then the runoff simulation is based on the Soil and Water Assessment Tool (SWAT) hydrological model with necessary inputs under the future conditions. Finally, the optimal water resources allocation model is established based on the evaluation of water supply and water demand. The Han River basin in China was selected as a case study. The results show that: (1) the annual runoff indicates an increasing trend in the future in contrast with the base period, while the ascending rate of the basin under RCP 4.5 is 4.47%; (2) a nonlinear relationship has been identified between the optimal allocation of water resources and water availability, while a linear association exists between the former and water demand; (3) increased water supply are needed in the water donor area, the middle and lower reaches should be supplemented with 4.495 billion m3 water in 2030. This study provides an example of a management template for guiding the allocation of water resources, and improves understandings of the assessments of water availability and demand at a regional or national scale.

Dataset for reservoir impoundment operation coupling parallel dynamic programming with importance sampling and successive approximation.

The dataset contains reservoir characteristic parameters, streamflow series of reservoirs in the upper Yangtze River, the standard operating rules (SORs) and the seasonal top of buffer pools (seasonal TBPs) for these reservoirs, which were provided by the Yangtze River Commission. Moreover, annual hydropower of these reservoirs is tested to evaluate operation performance. These research materials are related to the research article in Advances in Water Resources, entitled 'Optimal impoundment operation for cascade reservoirs coupling parallel dynamic programming with importance sampling and successive approximation' (He et al., 2019). The dataset could be used to derive optimal operating rules to explore the potential benefits of water resources via our proposed algorithm (importance sampling - parallel dynamic programming, IS-PDP) in different runoff scenarios. It can also be further applied for water resources management and other potential users.