Journal of hydrology 

About: Journal of hydrology is an academic journal published by Elsevier BV. The journal publishes majorly in the area(s): Environmental science & Geology. It has an ISSN identifier of 2589-9155. It is also open access. Over the lifetime, 2096 publications have been published receiving 5246 citations.

Research on Particle Swarm Optimization in LSTM Neural Networks for Rainfall-Runoff Simulation

Abstract: Flood forecasting is an essential non-engineering measure for flood prevention and disaster reduction. Many models have been developed to study the complex and highly random rainfall-runoff process. In recent years, artificial intelligence methods, such as the artificial neural network (ANN), have attempted to construct rainfall-runoff models. The more advanced deep learning methods of long short-term memory (LSTM) network have been proved to better predict hydrological time series. However, the selection of LSTM hyperparameters in the past mostly relied on the experience of the staff, which often led to failure to achieve the best performance. The aim of this study is to develop a method to improve flood forecast accuracy and lead time. A deep learning neural network model based on LSTM networks and particle swarm optimization (PSO) is proposed in this paper. The PSO algorithm was used to optimize the LSTM hyperparameter to improve the ability to learn data sequence features. The model focuses on the Jingle Watershed in the Fenhe River and the Lushi Watershed in the Luohe River and was used to predict flood processes using rainfall and runoff observation data from stations in the watersheds. We evaluated the performance of the model with the Nash Sutcliffe efficiency coefficient, root mean square error, and bias. The results show that the PSO-LSTM model outperforms the M-EIES, ANN, PSO-ANN, and LSTM at all stations in the watersheds. The PSO-LSTM model improves the flood forecasting accuracy at different lead times, especially for those exceeding 6 h, and has higher prediction accuracy and stability. The PSO-LSTM model could be used to improve accuracy in short-term flood forecast applications.

Do ERA5 and ERA5-land precipitation estimates outperform satellite-based precipitation products? A comprehensive comparison between state-of-the-art model-based and satellite-based precipitation products over mainland China

Abstract: • Hourly ERA5-Land precipitation products are firstly evaluated over mainland China. • ERA5 and ERA5-Land share similar performance and have their own advantages. • Model-based products outperform satellite-based estimates in some circumstances. • Product recommendations are proposed for different application scenarios. Accurate precipitation retrievals with fine spatio-temporal resolutions are critical in global and regional analyses. As an important alternative of satellite-based precipitation products, model-based precipitation estimates have undergone rapid development over the past few decades. With the recent public release of the fifth generation of atmospheric reanalysis by the European Centre for Medium Range Weather Forecasts (ERA5) and ERA5-Land, it is necessary to verify whether these two latest model-based precipitation products outperform satellite-based precipitation products. This study comprehensively evaluates the performances of state-of-the-art satellite-based and model-based precipitation products, including the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG), Global Satellite Mapping of Precipitation (GSMaP), ERA5, and ERA5-Land, over mainland China from 2016 to 2019. The main findings are as follows: (1) Satellite-based products generally outperform model-based products, but the latter significantly perform better than the former over high-latitude regions and in winter. (2) ERA5 and ERA5-Land share similar spatio-temporal patterns and have their own advantages in terms of different types of metrics. (3) Satellite-based products perform best over subregions of subtropical and tropical monsoon climate (ST), whereas model-based products show highest performance over subregions of temperate monsoon climate (TM) and temperate continental climate (TC); both types of products show the poorest performance over subregions of plateau mountain climate (PM). (4) IMERG-Final performs best in terms of precipitation events, while GSMaP-Gauge tends to overestimate the duration of precipitation events, and model-based products tend to underestimate the mean precipitation rate of the events. These findings provide valuable insights into the error characteristics of state-of-the-art model-based and satellite-based precipitation products in the recent years, and will also serve as useful reference for the potential improvement of precipitation retrieval algorithms in the next generation.

Improving streamflow prediction in the WRF-Hydro model with LSTM networks

Abstract: Researchers have attempted to use machine learning algorithms to replace physically based models for streamflow prediction. Although existing studies have contributed to improving machine learning methods, they still have weaknesses, such as large dataset requirements and overfitting. Therefore, we propose an approach that combines the Weather Research and Forecasting hydrological modeling system (WRF-Hydro) and the Long Short-Term Memory (LSTM) network, i.e., WRF-Hydro-LSTM, to improve streamflow simulations. In this approach, LSTM was employed to predict the residual errors of WRF-Hydro; in contrast, the conventional approach with LSTM predicts streamflow directly. Here, we performed numerical experiments to predict the inflow of Soyangho Lake in South Korea using WRF-Hydro-LSTM, WRF-Hydro-only, and LSTM-only. WRF-Hydro-LSTM and LSTM-only showed better results (NSE = 0.95 and R greater than 0.96) compared to WRF-Hydro-only (NSE = 0.72 and R = 0.88); however, in terms of the percent bias, WRF-Hydro-LSTM had a better value (1.75) than LSTM-only (17.36). While the LSTM-only follows objective functions and not physical principles, WRF-Hydro-LSTM simulates residual errors and efficiently decreases uncertainties that are inherent with conventional methods. Furthermore, a sensitivity test on the training dataset indicated that the correlation coefficient and NSE value were not overly sensitive, but the PBIAS value differed substantially depending on the training set. This study demonstrates that WRF-Hydro-LSTM is particularly useful for representing real-world physical constraints and thus can potentially improve streamflow prediction compared to using either of the two approaches exclusively.

Runoff changes in the major river basins of China and their responses to potential driving forces

Abstract: Identifying the effect of climate variability and human activities on runoff changes is scientifically essential for understanding hydrological processes and sustainable water resources management. This study selected 64 catchments located in the mainland of China to quantify the effects of different driving forces on runoff changes. Results showed that annual runoff in the Haihe river basin, Liaohe river basin, and Yellow river basin exhibited significantly decreasing trends from 1965 to 2018 (P < 0.05), whereas the Northwest river basin had positive trends in the annual runoff. Meanwhile, the Pettitt test method was applied to detect abrupt changes in annual runoff. Compared to the rivers in Southern China, the northern rivers had significant abrupt changes in annual runoff and mostly occurred in the 1990 s. The Choudhury-Yang equation based on the Budyko hypothesis was used to assess the sensitivity of runoff to precipitation (P), potential evapotranspiration (ET0), and the land surface (n) changes. The results showed that runoff was more sensitive to P and n, compared to ET0. Attribution analysis revealed that P was the dominant factor in the Northwest river basin, Southwest river basin, Yangtze river basin, Southeast river basin, and Pearl river basin, whereas the changes in n were responsible for runoff changes in the Liaohe river basin, Haihe river basin, Yellow river Basin, Songhuajiang river basin, and Huaihe river basin. The land surface changes (n) were resulted from vegetation restoration, urbanization expansion, construction of reservoirs/check dams, and surface water withdrawals, leading to significant changes in river runoff in recent years. The findings can provide good insight for water resources management across China.

Daily flow duration curve model for ungauged intermittent subbasins of gauged rivers

Abstract: In this study, we develop a daily flow duration curve model for ungauged intermittent subbasins of gauged rivers. The long-term mean streamflow of the river basin, one of the central parts in the model, is calculated with a regression model of annual precipitation and physical characteristics of the river basin; e.g., drainage area, basin relief, topographical slope, drainage density. The input data of the model are normally accesible, and this makes the model applicable to ungauged points within the river basin. Another central part of the model is the cease-to-flow point which makes the model significant for intermittent rivers. The daily streamflow discharge recorded in gauging stations over the river basin is nondimensionalized by dividing each with their own long-term mean, and their collection is transformed to fit the normal probability distribution; i.e., the normalized nondimensional daily streamflow data are used in the model. The streamflow data are inverted back to the original distribution for any given exceedance percentage by incorporating the cease-to-flow point, and are dimensionalized finally by using the empirically-derived long-term mean streamflow discharge. The model is applied on hundreds of thousands station-day daily streamflow data from three river basins in different geographical regions in Turkey. Results of the case studies are found promising to propose the model as a good foundation for the daily flow duration curve at an ungauged intermittent subbasin of gauged rivers: However, it is noticeable that the model might have low performance in some particular gauging stations where the hydrological behavior deviates from the general characteristics of the river basin. This can be overcome by developing empirical models better approaching the observed long-term mean streamflow, which is a key issue of the model.