Explore the vast range of titles available.

Streamflow forecasting and predicting are significant concern for several applications of water resources and management including flood management, determination of river water potentials, environmental flow analysis, and agriculture and hydro-power generation. Forecasting and predicting of monthly streamflows are investigated by using three heuristic regression techniques, least square support vector regression (LSSVR), multivariate adaptive regression splines (MARS) and M5 Model Tree (M5-Tree). Data from four different stations, Besiri and Malabadi located in Turkey, Hit and Baghdad located in Iraq, are used in the analysis. Cross validation method is employed in the applications. In the first stage of the study, the heuristic regression models are compared with each other and multiple linear regression (MLR) in forecasting one month ahead streamflow of each station, individually. In the second stage, the models are evaluated and compared in predicting streamflow of one station using data of nearby station. The research investigated also the influence of the periodicity component (month number of the year) as an external sub-set in modeling long-term streamflow. In both stages, the comparison results indicate that the LSSVR model generally performs superior to the MARS, M5-Tree and MLR models. In addition, it is seen that adding periodicity as input to the models significantly increase their accuracy in forecasting and predicting monthly streamflows in both stages of the study.

High-accuracy streamflow forecasting with long lead times can help promote the efficient utilization of water resources. However, the construction of cascade reservoirs has allowed the evolution of natural continuous rivers into multi-block rivers. The existing streamflow forecasting methods fail to consider the impact of reservoir operation. Thus, a novel short-term streamflow forecasting method for multi-block watersheds was proposed by integrating machine learning and hydrological models. Firstly, based on IMERG precipitation, the forecast precipitation product’s error is corrected by the long short-term memory neural network (LSTM). Secondly, coupling convolutional LSTM (ConvLSTM) and LSTM, operation rules for cascade reservoirs are extracted. Thirdly, a short-term deterministic streamflow forecasting model was built for multi-block watersheds. Finally, according to the sources of forecasting errors, probabilistic streamflow forecasting models based on the Gaussian mixture model (GMM) were proposed, and their performances were compared. Taking the Yalong River as an example, the main results are as follows: (1) Deep learning models (ConvLSTM and LSTM) show good performance in forecast precipitation correction and reservoir operation rule extraction, contributing to streamflow forecasting accuracy. (2) The proposed streamflow deterministic forecasting method has good forecasting performance with NSE above 0.83 for the following 1–5 days. (3) The GMM model, using upstream evolutionary forecasted streamflow, interval forecasted streamflow, and downstream forecasted streamflow as the input–output combination, has good probabilistic forecasting performance and can adequately characterize the “non-normality” and “heteroskedasticity” of forecasting uncertainty.

Long-term streamflow forecasting is of great significance to the optimal management of water resources. However, the forecast lead time of long-term streamflow forecasting is relatively long and the forecasted precipitation within the forecast lead time has inherent uncertainty, so long-term streamflow forecasting has major challenges. In this paper, a hybrid forecasting model is developed to improve accuracy of long-term streamflow forecasting by combining random forests (RF) and the Soil and Water Assessment Tool (SWAT). The RF model is used to forecast monthly precipitation which is further downscaled to a daily dataset according to the hydrological similarity principle for use in the SWAT model of the Danjiangkou Reservoir basin, China. Performance of this hybrid model is compared to that of seasonal autoregressive (SAR (P)) model. Results show the RF precipitation generator yields accurate predictions at the monthly scale and the hybrid model produces acceptable streamflow series in long-term forecasting cases. In addition, the comparison shows that in the Danjiangkou Reservoir basin, the hybrid model performs better than the SAR (P) model, with average Nash–Sutcliffe efficiency (NSE) values of 0.94 and 0.51, which is better when it is closer to 1. This study provides a method of improving accuracy of long-term streamflow forecasting.

In this study, we evaluate elastic net regression (ENR), support vector regression (SVR), random forest (RF) and eXtreme Gradient Boosting (XGB) models and propose a modified multi-model integration method named a modified stacking ensemble strategy (MSES) for monthly streamflow forecasting. We apply the above methods to the Three Gorges Reservoir in the Yangtze River Basin, and the results show the following: (1) RF and XGB present better and more stable forecast performance than ENR and SVR. It can be concluded that the machine learning-based models have the potential for monthly streamflow forecasting. (2) The MSES can effectively reconstruct the original training data in the first layer and optimize the XGB model in the second layer, improving the forecast performance. We believe that the MSES is a computing framework worthy of development, with simple mathematical structure and low computational cost. (3) The forecast performance mainly depends on the size and distribution characteristics of the monthly streamflow sequence, which is still difficult to predict using only climate indices.

Middle-term and long-term streamflow forecasting is of great significance for water resources planning and management, cascade reservoirs optimal operation, agriculture and hydro-power generation. In this work, a framework was proposed which integrates least absolute shrinkage and selection operator (lasso), DBN and bootstrap to improve the performance and the stability of streamflow forecasting with the lead-time of one month. Lasso helps to screen the appropriate predictors for the DBN model, and the DBN model simulates the complex relationship between the selection predictors and streamflow, and then bootstrap with the DBN model contributes to evaluate the uncertainty. The Three-River Headwaters Region (TRHR) was taken as a case study. The results indicated that lasso-DBN-bootstrap model produced significantly more accurate forecasting results than the other three models and provides reliable information on the forecasting uncertainty, which will be valuable for water resources management and planning.

This work aims at improving the accuracy of ensemble streamflow forecasts at short-to-medium ranges with the conditional bias-penalized regression (CBPR)-aided Meteorological Ensemble Forecast Processor (MEFP) and streamflow data assimilation (DA). To assess the potential impact of the CBPR-aided MEFP and streamflow DA, or CBPR-DA, 20-yr hindcast experiments were carried out using the Global Ensemble Forecast System version 12 reforecast dataset for 46 locations in the service areas of 11 River Forecast Centers of the US NWS. The results show that, relative to the current practice of using the MEFP and no DA, or MEFP-NoDA, CBPR-DA improves the accuracy of ensemble forecasts of 3-day flow over lead times of 0 to 3 days by over 40% for 4 RFCs and by over 20% for 9 of the 11 RFCs. The margin of improvement is larger where the predictability of precipitation is larger and the hydrologic memory is stronger. As the lead time increases, the margin of improvement decreases but still exceeds 10% for the prediction of 14-day flow over lead times of 0 to 14 days for all but 3 RFCs.

Machine learning (ML) models have been applied to monthly streamflow forecasting in recent decades. In this study, forecasting skills of eight ML models are evaluated based on the Model Parameter Estimation Experiment (MOPEX) dataset. We consider two skill scores, i.e., the Nash–Sutcliffe efficiency (NSE) and the adjusted NSE (ANSE), and the latter is the skill score based on the interannual mean monthly value (MMV) as the reference (benchmark) model. Furthermore, NSE of the MMV model (NSEmmv) is used as a measure of the seasonality of monthly streamflow, as it is the ratio of variance explained by the MMV process. An important result is that forecasting skills of ML models for monthly streamflow are largely controlled by NSEmmv. Moreover, based on comparisons of different ML models, we have found that the selection of models is not a dominating factor impacting the final skill. Three key factors influencing NSE, i.e., NSEmmv, the base flow index (BFI) and the aridity index (AI), are explored in this paper. Specifically, NSEmmv impacts NSE directly and is the predominant factor; BFI influences the memory of the monthly streamflow and therefore influences NSE. The relationship between AI and NSE is much complex and indirect. Firstly, basins with higher AI tend to have lower NSEmmv, and this will lead to lower NSE; secondly, basins with higher AI tend to have lower BFI, which will also lead to lower NSE; thirdly, for a given BFI level, basins with higher AI tend to have higher memory and higher NSE. For ANSE, basins with AI between 1 and 2 show higher ANSE, which corresponds to higher autocorrelation coefficients.

Highly reliable forecasting of streamflow is essential in many water resources planning and management activities. Recently, least squares support vector machine (LSSVM) method has gained much attention in streamflow forecasting due to its ability to model complex non-linear relationships. However, LSSVM method belongs to black-box models, that is, this method is primarily based on measured data. In this paper, we attempt to improve the performance of LSSVM method from the aspect of data preprocessing by singular spectrum analysis (SSA) and discrete wavelet analysis (DWA). Kharjeguil and Ponel stations from Northern Iran are investigated with monthly streamflow data. The root mean square error (RMSE), mean absolute error (MAE), correlation coefficient (R) and coefficient of efficiency (CE) statistics are used as comparing criteria. The results indicate that both SSA and DWA can significantly improve the performance of forecasting model. However, DWA seems to be superior to SSA and able to estimate peak streamflow values more accurately. Thus, it can be recommended that LSSVM method coupled with DWA is more promising.

Short-term hydrological forecasting is crucial for suitable multipurpose water resource management involving water uses, hydrological security, and renewable production. In the Alpine Regions such as South Tyrol, characterized by several small watersheds, quick information is essential to feed the decision processes in critical cases such as flood events. Predicting water availability ahead is equally crucial for optimizing resource utilization, such as irrigation or snow-making. The increasing data availability and computational power led to data-driven models becoming a serious alternative to physically based hydrological models, especially in complex conditions such as the Alpine Region and for short predictive horizons. This paper proposes a data-driven pipeline to use the local ground station data to infer information in a Support Vector Regression model, which can forecast streamflow in the main closure points of the area at hourly resolution with 48 h of lead time. The main steps of the pipeline are analysed and discussed, with promising results that depend on available information, watershed complexity, and human interactions in the catchment. The presented pipeline, as it stands, offers an accessible tool for integrating these models into decision-making processes to guarantee real-time streamflow information at several points of the hydrological network. Discussion enhances the potentialities, open challenges, and prospects of short-term streamflow forecasting to accommodate broader studies.HighlightsData-driven approach offers viable alternatives to traditional hydrological models for short-term predictions.Support Vector Regression model results suitable for hydrological modelling also in complex Alpine Region.Data-driven pipeline can effectively bridge the gap between research and operational aspects of water management.

The combination of Hydrological Models and high-resolution Satellite Precipitation Products (SPPs) or regional Climatological Models (RCMs), has provided the means to establish baselines for the quantification, propagation, and reduction in hydrological uncertainty when generating streamflow forecasts. This study aimed to improve operational real-time streamflow forecasts for the Upper Zambezi River Basin (UZRB), in Africa, utilizing the novel Variational Ensemble Forecasting (VEF) approach. In this regard, we describe and discuss the main steps required to implement, calibrate, and validate an operational hydrologic forecasting system (HFS) using VEF and Hydrologic Processing Strategies (HPS). The operational HFS was constructed to monitor daily streamflow and forecast them up to eight days in the future. The forecasting process called short- to medium-range (SR2MR) streamflow forecasting was implemented using real-time rainfall data from three Satellite Precipitation Products or SPPs (The real-time TRMM Multisatellite Precipitation Analysis TMPA-RT, the NOAA CPC Morphing Technique CMORPH, and the Precipitation Estimation from Remotely Sensed data using Artificial Neural Networks, PERSIANN) and rainfall forecasts from the Global Forecasting System (GFS). The hydrologic preprocessing (HPR) strategy considered using all raw and bias corrected rainfall estimates to calibrate three distributed hydrological models (HYMOD_DS, HBV_DS, and VIC 4.2.b). The hydrologic processing (HP) strategy considered using all optimal parameter sets estimated during the calibration process to increase the number of ensembles available for operational forecasting. Finally, inference-based approaches were evaluated during the application of a hydrological postprocessing (HPP) strategy. The final evaluation and reduction in uncertainty from multiple sources, i.e., multiple precipitation products, hydrologic models, and optimal parameter sets, was significantly achieved through a fully operational implementation of VEF combined with several HPS. Finally, the main challenges and opportunities associated with operational SR2MR streamflow forecasting using VEF are evaluated and discussed.