Runoff modeling is one of the key challenges in the field of hydrology. Various approaches exist, ranging from physically based over conceptual to fully data driven models. In this paper, we propose a data driven approach using the state-of-the-art Long-Short-Term-Memory (LSTM) network. The proposed model was applied in the Poyang Lake Basin (PYLB) and its performance was compared with an Artificial Neural Network (ANN) and the Soil & Water Assessment Tool (SWAT). We first tested the impacts of the number of previous time step (window size) in simulation accuracy. Results showed that a window in improper large size will dramatically deteriorate the model performance. In terms of PYLB, a window size of 15 days might be appropriate for both accuracy and computational efficiency. We then trained the model with 2 different input datasets, namely, dataset with precipitation only and dataset with all available meteorological variables. Results demonstrate that although LSTM with precipitation data as the only input can achieve desirable results (where the NSE ranged from 0.60 to 0.92 for the test period), the performance can be improved simply by feeding the model with more meteorological variables (where NSE ranged from 0.74 to 0.94 for the test period). Moreover, the comparison results with the ANN and the SWAT showed that the ANN can get comparable performance with the SWAT in most cases whereas the performance of LSTM is much better. The results of this study underline the potential of the LSTM for runoff modeling especially for areas where detailed topographical data are not available.

Purpose: The Yellow River delta boasts rich land resources but lacks fresh water and exhibits poor natural conditions. To rationally develop and utilize the land resources therein, it is necessary to evaluate the soil quality. Methods: Adopting specific screening conditions, principal component analysis (PCA) was used to construct a minimum data set (MDS) from 10 soil indicators. Then, a complete soil quality evaluation index system of the Yellow River delta was developed. The soil quality comprehensive index (SQI) method was used to assess the soil quality in the Kenli District, and the soil quality grades and spatial distribution were analyzed. Results: (1) The average SQI of the Kenli District is 0.523, and the best soil quality is concentrated near the Yellow River, especially in Huanghekou town. (2) The normalized difference vegetation index was positively correlate with SQI, whereas Dr (nearest distance between the sampling site and Yellow River) and Ds (nearest distance between the sampling site and Bohai Sea) were negatively correlated with SQI. Elev (sampling site elevation) was not correlated with SQI. (3) The SQI of agricultural planting is greater than that of the natural land type and significantly greater than that of nudation. The main factors limiting farmland soil quality are SK (water-soluble potassium) and pH, whereas the factor limiting the natural land type are the soil nutrient indicators. Conclusions: To improve soil quality and develop and utilize land resources, the towns should adopt systematic land development/utilization methods based on local conditions. These results have important guiding significance and practical value for the more objective and accurate evaluation of soil quality in coastal areas and the development and utilization of land resources.

Total phosphorus (TP) concentration is a crucial parameter to assess eutrophication in lakes. As one of the most concentrated regions for freshwater lakes, the Yangtze-Huaihe region plays a significant role in monitoring TP concentrations for the sustainable utilisation of China’s water resources. In this study, a TP concentration estimation model suitable for large-sized lake groups was developed using a combination of measured and remote sensing data powered by advanced machine learning algorithms. Compared to traditional empirical models, the model developed in this study demonstrates significant accuracy in fitting (R² = 0.53, RMSE = 0.08 mg/L, MAPE = 34.20%). Moreover, the application of this model to lakes in the Yangtze-Huaihe region from 2017 to 2022 has been conducted. The multi-year average TP concentration was 0.18 mg/L. Spatial distribution analyses showed that total phosphorus concentrations were higher in small lakes. In terms of temporal changes, the interannual decreases in total phosphorus concentrations were 0.02 mg/L, 0.01 mg/L, and 0.01 mg/L for small, medium, and large lakes, respectively. We also found that large lakes typically exhibited a “high in spring and summer, low in autumn and winter” pattern until 2020, but transitioned to a “high in summer and autumn, low in spring and winter” pattern after 2020 due to the removal of closed fish nets, which were having a significant impact on the lake ecosystem. Other lakes in the area consistently showed a pattern of “high in spring and summer, low in autumn and winter” during the six-year period. These findings may provide useful references and suggestions for the environmental protection and management of lakes in China.

Littoral wetlands are globally important for sustainable development; however, they have recently been identified as critical hotspots of nitrous oxide (N₂O) emissions. N₂O flux from subtropical littoral wetlands remains unclear, especially under the current global warming environment. In the littoral zone of Lake Poyang, a simulated warming experiment was conducted to investigate N₂O flux. Open-top chambers were used to raise temperature, and the static chamber-gas chromatograph method was used to measure N₂O flux. Results showed that the littoral zone of Lake Poyang was an N₂O source, with an average flux rate of 8.9 μg N₂O m⁻² h⁻¹. Warming significantly increased N₂O emission (13.8 μg N₂O m⁻² h⁻¹ under warming treatment) by 54% compared to the control treatment. N₂O flux in the spring growing season was also significantly higher than that of the autumn growing season. In addition, temperature was not significantly related to N₂O flux, while soil moisture only explained about 7% of N₂O variation. These results imply that N₂O emission experiences positive feedback effect on the ongoing warming of the climate, and abiotic factors (e.g., soil temperature and soil moisture) were not main controls on N₂O variation in this littoral wetland.

Understanding the spatiotemporal regime of summer precipitation at local scales plays a key role in regional prevention and mitigation of floods disasters and water resources management. Previous works focused on spatiotemporal characteristics of a region as a whole but left the influence of associated physical factors on sub-regions unexplored. Based on the precipitation data of 77 meteorological stations in the Poyang Lake basin (PYLB) from 1959 to 2013, we have investigated regional characteristics of summer precipitation in the PYLB by integrating the rotated empirical orthogonal function (REOF) analysis with hierarchical clustering algorithm (HCA). Then the long-term variability of summer precipitation in sub-regions of the PYLB and possible links with large-scale circulations was investigated using multiple trend analyses, wavelet analysis and correlation analysis. The results indicate that summer precipitation variations in the PYLB were of very striking regional characteristics. The PYLB was divided into three independent sub-regions based on two leading REOF modes and silhouette coefficient (SC). These sub-regions were located in northern PYLB (sub-region I), central PYLB (sub-region II), and southern PYLB (sub-region III). The summer precipitation in different sub-regions exhibited distinct variation trends and periodicities, which was associated with different factors. All sub-regions show no trends over the whole period 1959–2013, rather they show trends in different periods. Trends per decade in annual summer precipitation in sub-region I and sub-region II were consistent for all periods with different start and end years. The oscillations periods with 2–3 years were found in summer precipitation of all the three sub-regions. Summer precipitation in sub-region I was significantly positively correlated with the previous Indian Ocean Dipole (IOD) event, but negatively correlated with East Asian Summer Monsoon (EASM). While summer precipitation in sub-region II and sub-region III showed weak teleconnections with climate indices. All of the results of this study are conducive to further understand both the regional climate variations in the PYLB and response to circulation patterns variations.