Bias correction of climate model outputs influences watershed model nutrient load predictions

Enhancing phosphorus source apportionment in watersheds through species-specific analysis

Phosphorus (P) is a pivotal element responsible for triggering watershed eutrophication, and accurate source apportionment is a prerequisite for achieving the targeted prevention and control of P pollution. Current research predominantly emphasizes the allocation of total phosphorus (TP) loads from watershed pollution sources, with limited integration of source apportionment considering P species and their specific implications for eutrophication. This article conducts a retrospective analysis of the current state of research on watershed P source apportionment models, providing a comprehensive evaluation of three source apportionment methods, inventory analysis, diffusion models, and receptor models. Furthermore, a quantitative analysis of the impact of P species on watersheds is carried out, followed by the relationship between P species and the P source apportionment being critically clarified within watersheds. The study reveals that the impact of P on watershed eutrophication is highly dependent on P species, rather than absolute concentration of TP. Current research overlooking P species composition of pollution sources may render the acquired results of source apportionment incapable of assessing the impact of P sources on eutrophication accurately. In order to enhance the accuracy of watershed P pollution source apportionment, the following prospectives are recommended: (1) quantifying the P species composition of typical pollution sources; (2) revealing the mechanisms governing the migration and transformation of P species in watersheds; (3) expanding the application of traditional models and introducing novel methods to achieve quantitative source apportionment specifically for P species. Conducting source apportionment of specific species within a watershed contributes to a deeper understanding of P migration and transformation, enhancing the precise of management of P pollution sources and facilitating the targeted recovery of P resources.

Multi-scenario simulation of runoff and nutrient loads in a rapidly urbanizing watershed during China's Dual Carbon periods

Apprehending the hydrological and nutrient variations in rapidly urbanizing watersheds under changing environments is crucial for pollution control and water resource management. However, existing studies have primarily focused on hydrological processes, neglecting water quality aspects, and comprehensive assessment of future runoff and nutrient loads in these watersheds during China's Dual Carbon periods is limited. This study firstly bridges these gaps by constructing multi-scenario with different levels of "Urban Development - Ecological Conservation" and utilizing latest bias-corrected General Circulation Models or Global Climate Models (GCMs) projections to evaluate future runoff and nutrient loads in the Shenzhen River. The calibrated and validated models display satisfactory performance in simulating runoff, nutrient loads, and land use types. The bias-corrected GCMs projections exhibit enhanced accuracy for temperature variables, particularly during the wet season. Implementing effective ecological protection measures is paramount in mitigating water quantity fluctuations and controlling total nitrogen pollution, which is closely associated with urban development and human activities. Conversely, total phosphorus loads demonstrate greater simulation uncertainty, particularly during the dry season of the Carbon Neutrality period, requiring further exploration. Compared to the baseline period, runoff changes minimally, with notable seasonal variations. The findings highlight the escalating uncertainty in load predictions as time progresses. Additionally, addressing uncertainties in precipitation projections driven by GCMs is imperative, given their substantial influence on runoff and nutrient load simulations, particularly during challenging dry seasons. While further research is needed to reduce simulation uncertainty, our study provides valuable insights into nitrogen-phosphorus pollution control and sustainable water resource management in rapidly urbanizing watersheds, especially during the near-term period.

A Large Ensemble Global Dataset for Climate Impact Assessments

We present a self-consistent, large ensemble, high-resolution global dataset of long-term future climate, which accounts for the uncertainty in climate system response to anthropogenic emissions of greenhouse gases and in geographical patterns of climate change. The dataset is developed by applying an integrated spatial disaggregation (SD) - bias-correction (BC) method to climate projections from the MIT Integrated Global System Model (IGSM). Four emission scenarios are considered that represent energy and environmental policies and commitments of potential future pathways, namely, Reference, Paris Forever, Paris 2 °C and Paris 1.5 °C. The dataset contains nine key meteorological variables on a monthly scale from 2021 to 2100 at a spatial resolution of 0.5°x 0.5°, including precipitation, air temperature (mean, minimum and maximum), near-surface wind speed, shortwave and longwave radiation, specific humidity, and relative humidity. We demonstrate the dataset's ability to represent climate-change responses across various regions of the globe. This dataset can be used to support regional-scale climate-related impact assessments of risk across different applications that include hydropower, water resources, ecosystem, agriculture, and sustainable development.

The impacts of spatio-temporal variation of natural and agricultural influences on the environmental water quality in a fluvial-lacustrine watershed in China

Despite the significant impacts of natural factors such as rainfall, topography, soil type, and river network as well as agricultural activities on the environmental water quality, little is known about the influence of their temporal and spatial variations in a fluvial-lacustrine watershed. In this study, a whole process accounting method based the export coefficient model (WP-ECM) was first developed to quantify how natural factors and agricultural activities distribution influenced water quality. A case study was performed in a typical fluvial-lacustrine area - Dongting basin, China. The simulated results indicated that the natural factors can promote and inhibit the migration and transformation of agricultural pollutants generated from the watershed and the spatial distribution of the natural factors displayed high variability. It should be priority to monitor the areas with greater natural impact in the basin. Moreover, the cultivated land area and the number of pig-breeding were positively correlated with the pollutant discharge. From the perspective of the spatial distribution of comprehensive influence, the comprehensive high-impact areas are mainly distributed in the Dongting Lake district in 2005-2010 and in Xiang River watershed in 2010-2020. A key strategy for controlling or reducing the cultivated land area and the intensity of livestock breeding in these high-impacts areas is recommended to reduce the impact of the environmental water quality for the entire basin.

Precipitation projection over Daqing River Basin (North China) considering the evolution of dependence structures

Understanding dynamic future changes in precipitation can provide prior information for nonpoint source pollution simulations under global warming. However, the evolution of the dependence structure and the unevenness characteristics of precipitation are rarely considered. This study applied a two-stage bias correction to daily precipitation and max/min temperature data in the Daqing River Basin (DQRB) with the HadGEM3-RA climate model. Validated from 1981 to 2015, future scenarios under two emission paths covering 2031-2065 and 2066-2100 were projected to assess variations in both the amount and unevenness of precipitation. The results suggested that, overall, the two-stage bias correction could reproduce the marginal distributions of variables and the evolution process of the dependence structure. In the future, the amount of precipitation in the plains is expected to increase more than that in the mountains, while precipitation unevenness, as measured by relative entropy, shows a slight increase in the mountains and a decrease in the plains, with enhanced seasonality. Conditioned on rising temperatures, high-/low-intensity precipitation tends to intensify/weaken precipitation unevenness. Additionally, the potential application of the bias correction method used herein and the possible impacts of uneven precipitation on nonpoint source pollution are given for further analyses. This study can provide useful information for future nonpoint source pollution simulations in the DQRB.

Spatiotemporal land use change and environmental degradation surrounding CAFOs in Michigan and North Carolina

Concentrated Animal Feeding Operations (CAFOs) have arisen and expanded in the U.S. and globally to address efficiencies in livestock production. CAFOs tend to cluster in space for logistical purposes. Efficient distribution of concentrated manures produced by these operations is often not economically feasible, which may lead to accumulation on land near CAFOs, potentially resulting in local environmental changes. Moreover, as CAFOs are established or expand, they may need more lands to apply their manures, likely driving land use changes even after their establishment. Studies have yet to investigate these spatiotemporal impacts of CAFOs. We investigated whether the presence of regulated liquid waste CAFOs is associated with land use change over time and space as well as degraded environmental conditions surrounding those facilities. We used MODIS (Moderate Resolution Imaging Spectrometer) images from 2000 to 2018 to examine these questions in Michigan and North Carolina- states with varied CAFO establishment histories. We found that cropland extent increased while savanna and forest decreased near CAFOs. Similar observations did not occur outside of areas influenced by CAFOs. We also found evidence of environmental degradation, including decreased evapotranspiration and increased day and nighttime land surface temperatures in North Carolina. This study advances our understanding of environmental impacts surrounding CAFOs. Our findings can support policy changes and highlight the need to better understand these globally increasing entities.