Taking full advantage of 'low-quality' datasets in watershed modeling and management: From a perspective of parameter calibration.

The quality of calibration datasets is critical for establishing well-calibrated models for reliable decision-making support. However, the analysis of the influence of calibration dataset quality and the discussion on how to use flawed and/or incomplete datasets are still far from sufficient. An evaluation framework for the impact of model calibration data on parameter identifiability, sensitivity, and uncertainty (ISU) was established. Three quantitative and normalized indicators were designed to describe the magnitude of ISU. With the case study of the upper Daqing River watershed, China and the model SWAT (Soil and Water Assessment Tool), one ideal dataset without quality flaws and 79 datasets with different types of flaws including observation error, low monitoring frequency, short data duration and low data resolution were evaluated. The result showed that 4 of 13 parameters that control canopy, groundwater and channel processes have higher ISU values, indicating the high identifiability, high sensitivity, and low uncertainty. The largest gap of parameter ISU between dataset with quality flaw and ideal dataset was 0.61 due to short data duration, while the smallest gap was -0.28 due to low monitoring data frequency. Although some defective datasets caused unacceptable calibration results and model output, some defective datasets can still be valuable for model calibration which depends on the hydrological processes of interest when applying the model. Equivalent calibration results were yielded by the datasets with similar statistical properties. When using datasets with traditional defective issues for calibration, a new step checking the consistency among decision goal, representative system process, determinative parameters and calibration datasets is suggested. Practices including process-related data selection, dataset regrouping and risk self-reporting when using low-quality datasets are encouraged to increase the reliability of model-based watershed management.

Comprehensive effects of climate, land use/cover and management practices on runoff and nutrient variations in a rapidly urbanizing watershed.

As non-point source pollution has emerged as a significant global and regional concern, climate change (CC), land use/cover transformation (LUCT), and management practices (MP) play vital roles in addressing nutrient pollution. However, current studies lack comprehensive quantification and consistent conclusions on the response to these factors, especially for management practices. To quantify and elucidate the impact of representative environmental factors on rapidly urbanizing regions, this study focused on the Shenzhen River, which serves as the most typical urbanizing watershed. Using a process-based distributed hydrological model with a factor-controlled simulation method, we identified significant differences in nutrient concentrations and the impacts of climate variability, land use/cover changes, and anthropogenic interventions from 2003 to 2020. Moreover, effective measures greatly improved water quality in the Shenzhen River during study period, as evident from trend and cluster analysis. However, ecological water supplements implemented since 2016 have led to a slight reduction in simulated runoff performance, and CC may amplify the synergistic effects of precipitation and temperature on the river system. While the implemented practices have been effective in reducing total nitrogen (TN) and total phosphorus (TP) loads, strong TN pollution control is still needed in rapidly urbanizing areas due to the results of land use/cover type changes. Our findings emphasize the intricate interplay among CC, LUCT, and MP in shaping water quality and hydrological processes in rapidly urbanizing watersheds, and clarify the independent effects of these factors on nutrients. This study contributes to a better understanding of the complex interactions between multiple factors in watersheds and provides guidance for sustainable watershed management.

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.

Learning and inferring the diurnal variability of cyanobacterial blooms from high-frequency time-series satellite-based observations.

Observational evidences have suggested that the surface scums of cyanobacterial harmful blooms (CyanoHABs) are highly patchy, and their spatial patterns can vary significantly within hours. This stresses the need for the capacity to monitor and predict their occurrence with better spatiotemporal continuity, in order to understand and mitigate their causes and impacts. Although polar-orbiting satellites have long been used to monitor CyanoHABs, these sensors cannot be used to capture the diurnal variability of the bloom patchiness due to their long revisit periods. In this study, we use the Himawari-8 geostationary satellite to generate high-frequency time-series observations of CyanoHABs on a sub-daily basis not possible from previous satellites. On top of that, we introduce a spatiotemporal deep learning method (ConvLSTM) to predict the dynamics of bloom patchiness at a lead time of 10 min. Our results show that the bloom scums were highly patchy and dynamic, and the diurnal variability was assumed to be largely associated with the migratory behavior of cyanobacteria. We also show that, ConvLSTM displayed fairly satisfactory performance with promising predictive capability, with Root Mean Square Error (RMSE) and determination coefficient (R2) varying between 0.66∼1.84 µg/L and 0.71∼0.94, respectively. This suggests that, by adequately capturing spatiotemporal features, the diurnal variability of CyanoHABs can be well learned and inferred by ConvLSTM. These results may have important practical implications, because they suggest that spatiotemporal deep learning integrated with high-frequency satellite observations could provide a new methodological paradigm in nowcasting of CyanoHABs.

Investigating the sub-daily dynamics of cyanobacterial blooms by coupling high-frequency time-series remote sensing with hydro-ecological modelling.

Cyanobacterial Harmful Algal Blooms (CyanoHABs) are a health-threatening and increasingly prevalent environmental issue at both regional and global levels. An improved understanding of the short-term dynamics of CyanoHABs is required to better capture their spatial pattern and temporal evolution. However, the heterogeneous and dynamic nature of CyanoHABs, and the interacting factors and processes that drive them, make interpreting and predicting the blooms a very challenging task. In this paper, we used an integrative approach that combines high-frequency time-series remote sensing with hydro-ecological modelling, to reproduce and investigate the sub-daily dynamics of CyanoHABs in Taihu Lake. Results show that the distribution of CyanoHABs is highly patchy and dynamic without intensive wind-induced circulation and turbulence, which suggests that the dynamic pattern may be largely caused by the migratory behavior of cyanobacteria. The hydro-ecological model well reproduced the observed pattern and trend, and the average of Root Mean Square Error (RMSE) and coefficient of determination (R2) were 9.82 µg/L and 0.52, respectively. Results from sensitivity analysis suggest that photosynthesis rate and respiration rate are two most influential model parameters. Conclusively, there is a lack of adequate representation of physiological processes in currently used modelling framework, thereby suggesting the need for microscale modelling for future modelling exercises of CyanoHABs.

[Characteristic Analysis of SWAT Model Parameter Values Based on Assessment of Model Research Quality].

The soil and water assessment tool (SWAT) model is currently one of the most widely used watershed models in China. Since the model has been developed with distributed parameters and is customized to satisfy the environmental characteristics of the U.S.A., determining appropriate parameter values that reflect local features for model application in China is crucial. Some studies have proposed parameter values for the SWAT model by summarizing reported values in the literature; however, these studies neither differentiate the literature with respect to its quality nor consider non-uniformity in parameter values and the impact of extreme values. To address this, an indicator system for assessing the quality of SWAT model research was established, taking into account the process of model development, parameter calibration, and model validation as well as model performance. This screening approach was applied to a total of 428 journal articles on SWAT model research published between 2015 and 2017 were retrieved from the China National Knowledge Infrastructure database. The reported values of 15 model parameters involved in hydrology and sediment and nutrient simulation were extracted from highly credible articles and analysed in terms of statistical distributions, differences among geographic regions, and discrepancies between calibrated and default values. Results showed that the 129 highly credible journal articles screened generally followed good modelling practice and consisted of case studies from different regions across China. The statistical distributions of the 15 model parameters derived from the SWAT model studies exhibited a range of features including positive and negative skewness, and those of 4 parameters showed significant differences among regions where the watersheds are located. Furthermore, the calibrated values of 12 out of 15 parameters were significantly different from their default values. Considering the statistical characteristics of these model parameters, recommended parameter values for SWAT model application in China are proposed in the form of confidence intervals, and specific suggestions are also provided based on data availability.

Multicriteria Decision Analysis Applied to Sponge City Construction in China: A Case Study.

To address stormwater management, China initiated the construction of sponge cities (SPCs) in 2014. Sponge city construction in China has previously been evaluated using subjective expert methods. Based on China's SPC evaluation regulations, the evaluation criteria system was divided into 6 categories and 22 indicators. This study presents a multicriteria approach to evaluating SPC construction through multicriteria attributes value theory (MAVT). Coupled with the entropy method, this approach can reduce errors caused by individual methods. This work improved the evaluation method from a subjective to an objective method. Additionally, the sensitivity of an alternative to these criteria was conducted. The analysis reveals the inner relationship between the SPC alternative and the criteria. The city of Hebi, an actual SPC project, was selected as a case study. The findings indicate that the proposed approach is efficient and simple. It can help decision makers better understand SPC performance, and it is a pilot SPC evaluation method using relatively objective means. Integr Environ Assess Manag 2019;15:703-713. © 2019 SETAC.