River Flow Forecasting Research Articles

Daily river flow simulation using ensemble disjoint aggregating M5-Prime model

Accurate prediction of daily river flow (Qt) remains a challenging yet essential task in hydrological modeling, particularly crucial for flood mitigation and water resource management. This study introduces an advanced M5 Prime (M5P) predictive model designed to estimate Qt as well as one- and two-day-ahead river flow forecasts (i.e. Qt+1 and Qt+2). The predictive performance of M5P ensembles incorporating Bootstrap Aggregation (BA), Disjoint Aggregating (DA), Additive Regression (AR), Vote (V), Iterative classifier optimizer (ICO), Random Subspace (RS), and Rotation Forest (ROF) were comprehensively evaluated. The proposed models were applied to a case study data in Tuolumne County, US, using a dataset comprising measured precipitation (Pt), evaporation (Et), and Qt. A wide range of input scenarios were explored for predicting Qt, Qt+1, and Qt+2. Results indicate that Pt and Qt significantly influence prediction accuracy. Notably, relying solely on the most correlated variable (e.g., Qt-1) does not guarantee robust prediction of Qt. However, extending the forecast horizon mitigates the influence of low-correlation input variables on model accuracy. Performance metrics indicate that the DA-M5P model achieves superior results, with Nash-Sutcliff Efficiency of 0.916 and root mean square error of 23 m3/s, followed by ROF-M5P, BA-M5P, AR-M5P, AR-M5P, RS-M5P, V-M5P, ICO-M5P, and the standalone M5P model. The ensemble M5P modeling framework enhanced the predictive capability of the stand-alone M5P algorithm by 1.2 %–22.6 %, underscoring its efficacy and potential for advancing hydrological forecasting.

Ensemble empirical mode decomposition based deep learning models for forecasting river flow time series

River flow forecasting is important for flood prediction and effective utilization of water resources. This study proposed a comprehensive methodology that simultaneously enables the creation of multiple datasets and a comparison of multiple learning models for forecasting river flows. First, the data characteristics of the river flow time series were assessed based on its nature and pattern characteristics. Second, data decomposition was made for simplifying the complex data into decomposed modes. Ensemble Empirical Mode Decomposition (EEMD) was used for this purpose that provides intrinsic mode functions (IMFs). In addition, as benchmark, traditional time series decomposition was also applied over the complex data. Third, based on their data characteristics, the IMFs were combined into different components. These components along with the traditional time series decomposition create multiple datasets. Then, the components from the different datasets were forecasted using deep learning models, mainly BPNN (Back-Propagation Neural Network), CNN (Convoluted Neural Network) and LSTM (Long-Short Term Memory). Then, ensemble prediction was used to get the final output. For verification and benchmark purpose, we have also used statistical models like SARIMA over the datasets. A comprehensive evaluation and model comparisons were made using Diebold-Mariano (DM) test, k-fold cross validation and with the results of Prophet. In addition, the temporal convolution network (TCN) and the gated recurrent unit (GRU) were also modelled. Finally, all the DL modelling was performed for two data situations: with missing data deleted and missing data imputed. An empirical case study was done considering the average of daily time series data of Neeleswaram Hydrological Observation (HO) station over the Periyar river of South India for the period 2017–2021. The empirical assessment was made using several tests for all the different datasets and different learning models for further insights. Our results indicate that the deep learning-based models offer better predictions for complex time series in comparison to traditional statistical models. The DL models applied on the original time series data provided robust predictions with good performance such as low RMSE and high correlation values. However, the DL models’ performances are not enhanced through integration with time series decomposition and EEMD. Furthermore, the empirical results and the k-fold cross validation-based test indicate that all the DL models show equivalent performances. Interestingly, the DL models show superior performance over Prophet.

Conditional seasonal markov-switching autoregressive model to simulate extreme events: Application to river flow

Extreme events have the potential to significantly impact transportation infrastructure performance. For example, in the case of bridges, climate change impacts the river discharge, hence scouring patterns, which in turn, affects the bridge foundation stability. Therefore, extreme events (river flow) forecasting is mandatory in bridge reliability analysis. This paper approaches this river flow forecasting problem by developing a Markov-Switching Autoregressive model coupled with a conditional hidden seasonal Markov component. In addition, the proposed model is also combined with the deep machine learning neural networks method to forecast river flow from a dataset or from simulations. The proposed method is illustrated by using realistic data: historic river flow values of the Thames River. The results indicate that the proposed model well represented the extreme events within the dataset. In terms of river flow forecasting, the results indicate that the forecasts improve when the training period changes from 20 years to 40 years.

ПРОГНОЗЫ ГОДОВОГО СТОКА Р. ЖАЙЫК (УРАЛ) С УЧЕТОМ АВТОКОРРЕЛЯЦИОННЫХ МОДЕЛЕЙ ЕГО МНОГОЛЕТНИХ КОЛЕБАНИЙ ЗА ОТДЕЛЬНЫЕ МЕСЯЦЫ

The study is devoted to the development and application of autocorrelation and general regression models for long-term forecasting of the Ural (Zhaiyk) River flow based on the analysis of multi-year fluctuations. The Ural River is an important water resource of the Russian Federation and the Republic of Kazakhstan, demonstrating significant variability in annual runoff, which affects various sectors of economic activity. In the course of the study, annual and monthly series of the river flow for the period from 1943 to 2010 were estimated using the autocorrelation method of Y.M. Alekhin. Based on these data, forecasts were made for the period from 2011 to 2015. The results show that autocorrelation models provide more accurate forecasts compared to models based on average values of series. The general regression model integrating monthly and annual data showed the best results, confirming the effectiveness of the combined approach in predicting hydrological characteristics. The scientific significance of the work is to improve the accuracy and reliability of the Ural River flow forecasts, which contributes to more effective water resources management in this region.

Comparison of Met Office regional model soil moisture with COSMOS‐UK field‐scale in situ observations

AbstractThe UK Met Office state‐of‐the‐art, deterministic, convection‐permitting, coupled land‐atmosphere, regional weather forecasting system, known as the UKV or UK Variable resolution model (Tang et al. Meteorological Applications, 2013; 20:417–426), has been operational since 2015. Science updates are regularly made to the UKV land surface data assimilation scheme when those updates improve predictions of screen temperature and humidity, since these quantities have a direct impact on atmospheric states and weather forecasts. Less attention has been paid to whether UKV soil moisture analyses are close to independent, in‐situ soil moisture observations, partly because it is difficult to make meaningful comparisons between 1.5 km2 gridded model outputs and traditional point sensor measurements. Soil moisture is recognized to be important when hydrological forecasts for runoff and rivers are required. This is because soil moisture controls the extent to which rainfall can infiltrate the soil, and the amount of surface runoff affects the timing of peak river flows (Ward & Robinson, Principles of Hydrology. McGraw‐Hill Publishing Company; 2000; Singh et al. Water Resources Research, 2021, 57, e2020WR028827). Gómez et al. (Remote Sensing, 2020; 12:3691) report benefits to river flow forecasts when using soil moisture data assimilation in the UKV system instead of a daily downscaled product from the Met Office global model. The Met Office measures soil temperature and soil moisture at Cardington (Osborne & Weedon, Journal of Hydrometeorology, 2021, 22:279–295); there is no other UK Met Office site at which soil moisture is measured. In this study, we use field‐scale (~200 m radius) soil moisture measurements from the UK Centre for Ecology and Hydrology's (UKCEH's) COSMOS‐UK network to provide independent verification and analysis of UKV soil moisture during summer 2018, an unusually dry period in the United Kingdom. We find that the match to COSMOS‐UK soil moisture observations is generally good, and that changes made to the land data assimilation approach during a recent operational upgrade had a generally beneficial impact on UKV soil moisture analyses under very dry conditions.

Hybrid wavelet transform – MLR and ANN models for river flow prediction: Case study of Brahmaputra river (Pancharatna station)

In this research, discrete wavelet transform (DWT) is combined with MLR and ANN to develop WMLR and WANN hybrid models, respectively, for the Brahmaputra river (Pancharatna station) flow forecasting. Daily flow data for the period of 10 year were decomposed (up to fifth level) into detailed and approximation coefficients (using Daubechies wavelets db1, db2, db3, db8 and db10) which were fed as input to MLR and ANN to get the predicted discharge values two days, four days, seven days and 14 days ahead. For all lead times, the WMLR-db10 model was found to be superior as compared to WANN-db1, WANN-db2, WANN-db3, WANN-db8, WMLR-db1, WMLR-db2, WMLR-db3, WMLR-db8 and single MLR and ANN models. During testing period, the values of determination coefficient (R2) and RMSE for WMLR-db10 model for two-, four-, seven- and 14-day lead time were found to be, respectively, 0.996 (751.87 m3·s–1), 0.991 (1,174.80 m3·s–1), 0.984 (1,585.02 m3·s–1), and 0.968 (2,196.46 m3·s–1). Also, it was observed that for lower order wavelets (db1, db2, db3) WANN’s performance was better, and for higher order wavelets (db8, db10) WMLR’s performance was better. Correspondingly, it was observed that all hybrid models’ efficiency increased with increase in the decomposition level.

СИСТЕМЫ ПОДГОТОВКИ И ВЫПУСКА ГИДРОЛОГИЧЕСКИХ ПРОГНОЗОВ ГИДРОМЕТЦЕНТРА РОССИИ

Nowadays operational forecasts of river flow are implemented in the form of automated systems for issuing forecasts, as well as delivering forecast products to users. Only within the framework of these systems, using modern software, the maximum efficiency is achieved from the use of hydrological models by automating all processes, combining them with operational database management systems, as well as with modern GIS and web tools for generating forecast products and delivering them to users. Over the past decade, the Hydrometeorological Research Center of the Russian Federation (Hydrometcenter of Russia) has been actively developed automated systems for issuing hydrological forecasts and communicating them to users. The systems are based on modern hydrological models, all available hydrometeorological information, including numerical weather forecasts, as well as modern software tools, including GIS and web technologies. Such systems have been implemented for large river basins, including the basin of the Kuban and the rivers of the Black Sea coast of Russia, the Amur and Volga basins. In recent years, the system has been developed for the entire territory of Russia. These systems are actively used by the Roshydromet operational forecasting divisions, as well as by specialists from the EMERCOM of Russia.

Rainfall-Runoff Modelling Using HEC-HMS Model, Remote Sensing and GIS in Middle Gujarat, India

Hydrological modeling is a widely used approach for estimating the hydrological response of a basin to precipitation. Floods are among the most catastrophic natural disasters in small urban watersheds, inflicting loss of life, massive property destruction, and a severe danger to the economy. As a result, appropriate modeling can be a useful tool in preventing and mitigating such flood hazards. Despite this, flash flood prediction remains one of the challenges of hydrological modeling in ungauged basins due to a lack of runoff observations. This study aims to calibrate and validate the rainfall-runoff transformation model for Hathmati river sub watershed in the Sabarmati River basin using HEC-HMS (Hydrologic Engineering Centre Hydrology Modeling System). For the loss rate, SCS Curve Number method was selected while Clark Unit Hydrograph and SCS unit hydrograph was used for the transform method. The model is calibrated and verified using two rainfall-runoff events from 2006 and 2007 year The model calibration and validation efficiency were verified for both methods using the Nash-Sutcliffe efficiency (NSE), The coefficient of determination (R2), and the Percent Bias (PBIAS) As a result, the model calibration and validation were found to be satisfactory with the acceptable value of NSE between 0.869 to 0.914, with R2 0.901 to 0.947 and PBIAS from 9.76 to 14.8. it is observed that the model shows a very good correlation between simulated flow and observed flow. As a result, the model can be used to forecast river flow and aid in flood mitigation efforts to lessen their effects and associated costs. Additionally, the findings of this study can serve as guidelines for future assessments of the flood risk in the study area.

Comparative analysis of ANFIS models in Prediction of Streamflow: the case of Seyhan Basin

In order to sustain human life without problems, a rational planning is required for the conservation and use of existing water resources. The potential of future water sources should be determined as the first step in such planning. Therefore, river flow forecasting is necessary to provide basic information about a variety of problems related to the operation of river systems. In this study, the long-term daily flow values of the Zamantı River-Değirmenocağı, Zamantı River-Ergenuşağı, and Eğlence River-Eğribük stations in the Seyhan Basin in Turkey were examined. In order to predict the forward flow rate from past flow measurement values, the Adaptative Neuro-Fuzzy Inference System (ANFIS) model was trained using Backpropagation (BP), Hybrid Learning (HB), and Simulated Annealing (SA) algorithms, and the results were compared. The performance of ANFIS models created with different input parameters using Grid Partitioning (GP) and Fuzzy C-Means Clustering (FCM) methods was also examined. The evaluation criteria used for comparison were Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Determination Coefficient (R2), and Mean Absolute Percentage Error (MAPE). The best results for R2 values of 0.6854, 0.9242, and 0.9373 were obtained for FMSs using the BP model. As a result of the analysis, it was concluded that the BP algorithm could be used more successfully and effectively than other algorithms for training ANFIS parameters in nonlinear problems.

Evolutionary Design of a PSO-Tuned Multigene Symbolic Regression Genetic Programming Model for River Flow Forecasting

The earth’s population is growing at a rapid rate, while the availability of water resources remains limited. Water is required for various purposes, including drinking, agriculture, industry, recreation, and development. Accurate forecasting of river flows can have a significant economic impact, particularly in agricultural water management and planning during water resource scarcity. Developing precise river flow forecasting models can greatly improve the management of water resources in many countries. In this study, we propose a two-phase model for predicting the flow of the Blackwater river located in the South Central United States. In the first phase, we use Multigene Symbolic Regression Genetic Programming (MG-GP) to develop a mathematical model. In the second phase, Particle Swarm Optimization (PSO) is employed to fine-tune the model parameters. Fine-tuning the MG-GP parameters improves the prediction accuracy of the model. The newly fine-tuned model exhibits 96% and 94% accuracy in training and testing cases, respectively.

A comparison of three water discharge forecasting models for monsoon climate region: A case study in cimanuk-jatigede watershed Indonesia

Forecasting the discharge of the water resources model of a river or reservoir is crucial in making decisions on water resource management. Although numerous popular discharge forecasting models have been developed, real-time forecasts remain challenging. This study evaluates discharge forecasts using model of water balance model (FJ Mock and NRECA), autoregressive stochastic (Chain Markov). This study compared the accuracy of the discharge forecast results produced by the F.J. Mock, NRECA, and Markov Chain by five statistical indicators. Based on the simulation results, these models all have an accuracy level of probability in discharge ranging from above 70% which indicates a significant relationship between each model and are suitable alternatives for forecasting discharge and indicating an increase or decrease in discharge of up to 70% is predictable using this model. In comparison to other models, the highest correlation (r) is the Markov Chain model (0.84) with KGE (0.81) and the next sequence is the NRECA, and F.J. Mock. Therefore, the most accurate, precise, and representative water source model alternative for forecasts is the Markov Chain model. The FJ Mock model and the NRECA model are physical-based rainfall-runoff models, while the Markov model is time series generation model. In addition, this model is to be selected as the basis for modeling in forecasting river flow or optimal management of a reservoir, as well as determining the future discharge, especially in monsoon climate regions.

Forecasting Monthly River Flows in Ukraine under Different Climatic Conditions

River-flow forecasts are important for the management and planning of water resources and their rational use. The present study, based on direct multistep-ahead forecasting with multiple time series specific to the XGBoost algorithm, estimates the long-term changes and forecast monthly flows of selected rivers in Ukraine. In a new, applied approach, a single multioutput model was proposed that forecasts over both short- and long-term horizons using grouped or hierarchical data series. Three forecast stages were considered: using train and test subsets, using a model with train-test data, and training with all data. The historical period included the measurements of the monthly flows, precipitation, and air temperature in the period 1961–2020. The forecast horizons of 12, 60, and 120 months into the future were selected for this dataset, i.e., December 2021, December 2025, and December 2030. The research was conducted for diverse hydrological systems: the Prut, a mountain river; the Styr, an upland river; and the Sula, a lowland river in relation to the variability and forecasts of precipitation and air temperature. The results of the analyses showed a varying degree of sensitivity among rivers to changes in precipitation and air temperature and different projections for future time horizons of 12, 60, and 120 months. For all studied rivers, variable dynamics of flow was observed in the years 1961–2020, yet with a clearly marked decrease in monthly flows during in the final, 2010–2020 decade. The last decade of low flows on the Prut and Styr rivers was preceded by their noticeable increase in the earlier decade (2000–2010). In the case of the Sula River, a continuous decrease in monthly flows has been observed since the end of the 1990s, with a global minimum in the decade 2010–2020. Two patterns were obtained in the forecasts: a decrease in flow for the rivers Prut (6%) and the Styr (12–14%), accompanied by a decrease in precipitation and an increase in air temperature until 2030, and for the Sula River, an increase in flow (16–23%), with a slight increase in precipitation and an increase in air temperature. The predicted changes in the flows of the Prut, the Styr, and the Sula rivers correspond to forecasts in other regions of Ukraine and Europe. The performance of the models over a variety of available datasets over time was assessed and hyperparameters, which minimize the forecast error over the relevant forecast horizons, were selected. The obtained RMSE parameter values indicate high variability in hydrological and meteorological data in the catchment areas and not very good fit of retrospective data regardless of the selected horizon length. The advantages of this model, which was used in the work for forecasting monthly river flows in Ukraine, include modelling multiple time series simultaneously with a single model, the simplicity of the modelling, potentially more-robust results because of pooling data across time series, and solving the “cold start” problem when few data points were available for a given time series. The model, because of its universality, can be used in forecasting hydrological and meteorological parameters in other catchments, irrespective of their geographic location.

A hybrid forecasting model based on the group method of data handling and wavelet decomposition for monthly rivers streamflow data sets

Abstract The natural streamflow of the River is encouraged to forecast through multiple methods. The impartiality of this study is the comparison of the forecast accuracy rates of the time-series (TS) hybrid model with the conventional model. The behavior of the natural monthly statistical chaotic streamflow to use in the forecasting models has been compiled by projecting two distinguished rivers, the Indus and Chenab of Pakistan. Therefore, this article is based on the monthly streamflow forecast analysis that has been reported using the group method of data handling with wavelet decomposition (WGMDH) as a new forecasting attribute. Discrete wavelets decompose the perceived data into sub-series and forecast hydrological variables; these fittingly have been endorsed as inputs in the hybrid model. The forecast efficiency and estimations of the hybrid model are measured by the appropriate statistical techniques such as mean absolute error (RME), root mean square error (RMSE), and correlation coefficients (R) and compared to the group method of data handling (GMDH), least-square support vector machine and artificial neural network conventional models. The comparative analysis shows that the hybrid WGMDH model is more stable and more potent for forecasting river flow than other predictive models and significantly proved that the hybrid model is a robust alternate forecasting tool for TS data sets.

Assessment of the river flow regimes over the Chitral and Gilgit Basins, Pakistan, under IPCC climate change scenarios using the HyMoLAP-SM model

Abstract Modelling the river flow process during uncertain climatic conditions is a challenging task. This paper attempts to apply the hydrological model based on the least action principle (HyMoLAP) at Chitral and Gilgit stations with few modifications. The topographic wetness index, the concept of degree-day factor and snowmelt (SM) using the snow cover area (SCA) have been incorporated into the improved HyMoLAP-SM structure. It is found that the HyMoLAP-SM model significantly enhanced the accuracy of the river flow estimation and forecast. Furthermore, the model seems highly sensitive to the choice of nonlinearity parameter and moderately sensitive to SM coefficients. Moreover, the response of river flow to climate change scenarios has been projected by utilizing modelled outcomes under temperature and precipitation variations. Overall, the results suggest that average river flow may get increased (reduced) by about 60% (40%) by the increase (decrease) in temperature. On the other hand, an increase (decrease) in precipitation at Chitral (Gilgit) may increase (decrease) the average flow by about 27% (200%) [19% (8%)] at the respective station. These results may be utilized for future flood/agricultural planning in Pakistan. Additionally, results obtained in this study may not be applicable to other geographical regions or interchangeable with other modelling purpose.

Flood Forecasting Using Artificial Neural Network

The process of assessing the timing, amount, and period of flood events based on observed features of a river basin is known as flood forecasting. Floods cause lots of damage to properties and create a risk to human life. Flood forecasting is critical for developing appropriate flood risk management strategies, reducing flood hazards, evacuating people from flood-prone areas. The main objective of this study is to apply artificial neural networks for forecasting of river flow in the Deo River, located in Gujarat. Rainfall and discharge are the parameters considered for model development. The developed model is validated to test the accuracy of the model. Trained and validated models are evaluated using performance indices. Six alternative flood prediction models have been developed using ANN. These models are developed based on various training algorithms. A single layer feed forward back-propagation neural network with six different training algorithms (Scaled conjugate gradient, Levenberg Marquardt, Resilient back-propagation, Conjugate gradient, and Cascade forward back propagation, Bayesian regularization) was developed, with 70% of the data used for training and 30% for validation. The created models’ performance is assessed using statistical performance parameters. The best performance was obtained with an ANN model developed using the Cascade forward back-propagation training algorithm, which had a coefficient of correlation (r) of 0.83, a coefficient of determination (R2) of 0.70, and a root mean squared error (RMSE) of 5.58 for training and a coefficient of correlation (r) of 0.89, a coefficient of determination (R2) of 0.70, and a root mean squared error (RMSE) of 7.27 for validation. The forecast inflow is very close to the observed values. This study shows that ANN can be used to successfully predict floods, and the model developed can be used by flood control departments across the country for flood forecasting.

Monthly Runoff Forecasting by Non-Generalizing Machine Learning Model and Feature Space Transformation (Vakhsh River Case Study)

Energy prices and сost of materials for solar and wind power plants have increased over the past year. Therefore, significance increases for the hydropower and long-term (1–10 years) planning generation for the existing hydropower plants, which requires forecasting the average monthly values of the river flow. This task is especially urgent for countries without their own oil-fields and opportunity to invest in the creation of solar or wind power plants. The aim of the research is to decrease the mean absolute forecasting error of the long-term prediction for the Vakhsh River flow (Tajikistan) based on the long-term observations. A study of existing methods for the river runoff forecasting in relation to the object under consideration was carried out, and a new transformation model for the space of the input features was developed. The most significant results are the decrease in the average forecast error in the Vakhsh river flow achieved by the use of the proposed space of polynomial logarithmic features in comparison with other methods, and the need to use at least the 20 year-old observational data for the long-term operation planning of the hydropower plants and cascades of the hydropower plants obtained from the results of computational experiments. The significance of the results lies in the fact that a new approach to the long-term forecasting of river flow has been proposed and verified using the long-term observations. This approach does not require the use of the long-term meteorological forecasts, which are not possible to obtain with high accuracy for all regions.

Hybrid CNN-LSTM models for river flow prediction

Abstract River flow prediction is a challenging problem due to highly nonlinear hydrological processes and high spatio-temporal variability. Here we present a hybrid network of convolutional neural network (CNN) and long short-term memory (LSTM) network for river flow prediction. The hybridization enables accurate identification of the spatial and temporal features in precipitation. A shortcut layer is used as an additional channel of passing input features through the deep network to increase feature diversity. The flows in Hun River Basin, China are predicted using the trained hybrid network and are compared with the results from the Soil and Water Assessment Tool (SWAT) model. The results demonstrate the learning efficiency of the hybrid network is greatly affected by its structure and parameters, including the number of convolutional layers and LSTM cell layers, the step size of pooling and training data size. Further, the shortcut layer can effectively solve the diversity reduction problem in a deep network. The hybrid network is shown to have a similar predictive performance to SWAT but is superior in wet seasons due to its nonlinear learning ability. This study shows that the hybrid network has great promise in learning nonlinear and high spatio-temporal variability in river flow forecasting.

Hydropower Operation Optimization Using Machine Learning: A Systematic Review

The optimal dispatch of hydropower plants consists of the challenge of taking advantage of both available head and river flows. Despite the objective of delivering the maximum power to the grid, some variables are uncertain, dynamic, non-linear, and non-parametric. Nevertheless, some models may help hydropower generating players with computer science evolution, thus maximizing the hydropower plants’ power production. Over the years, several studies have explored Machine Learning (ML) techniques to optimize hydropower plants’ dispatch, being applied in the pre-operation, real-time and post-operation phases. Hence, this work consists of a systematic review to analyze how ML models are being used to optimize energy production from hydropower plants. The analysis focused on criteria that interfere with energy generation forecasts, operating policies, and performance evaluation. Our discussions aimed at ML techniques, schedule forecasts, river systems, and ML applications for hydropower optimization. The results showed that ML techniques have been more applied for river flow forecast and reservoir operation optimization. The long-term scheduling horizon is the most common application in the analyzed studies. Therefore, supervised learning was more applied as ML technique segment. Despite being a widely explored theme, new areas present opportunities for disruptive research, such as real-time schedule forecast, run-of-river system optimization and low-head hydropower plant operation.

Modelling river flow in cold and ungauged regions: a review of the purposes, methods, and challenges

River flow forecasting models assist in the understanding, predicting, monitoring, and managing of issues related to surface-water resources, such as water quality deterioration and flooding, or developing adaptation strategies to cope with climate change and increasing water demand. This review presents an overview of the current research status and progress in river-flow forecasting, focusing on cold climates and ungauged locations. River-flow forecasting in cold regions represents a challenge because the natural processes that occur within catchments vary greatly both seasonally and annually. This variability, which highly depends on climatic and topo-geomorphological characteristics within a basin, translates into increased model uncertainty and a substantial limitation when attempting to forecast river flow in cold regions, which are often poorly gauged or ungauged. To address this limitation, the “Predictions in Ungauged Basins” initiative offers a variety of studies to improve forecasting performance by adopting regionalization, spatial calibration, interpolation, and regression approaches. Process-based models demonstrate significant improvement by including remote-sensing data to replicate and derive complex hydrological processes. Empirical models, which utilize observed data to formulate a graphical solution, unlike mathematical models that require formulating the relationships between the processes, are also implemented with the most recent developments in machine learning, showing exceptional forecasting accuracy. Although process-based models provide a wide understanding of a watershed hydrology, data are often unavailable, expensive, and time-consuming to collect. They also generate numerous calibration parameters, resulting in complex and computationally demanding methods to operate. River-flow forecasting using empirical models reduces the number of calibration parameters but could produce biased results when insufficient variables are available to explain the physical mechanisms of a watershed’s hydrology. Moreover, empirical models could be potentially sensitive to calibration and validation dataset selection. In this review, Canadian studies are primarily selected to highlight some of the efforts that may be necessary in other similar cold and ungauged regions, including: (i) coping with limited data availability through regionalization methods; (ii) providing user-friendly interfaces; (iii) advancing model structure; (iv) developing a universal method for transferring regionalization parameters; (v) standardizing calibration and validation dataset selection; (vi) integrating process-based and empirical models.

Russian Rivers Streamflow Forecasting Using Hydrograph Extrapolation Method

This paper presents a method of hydrograph extrapolation, intended for simple and efficient streamflow forecasting with up to 10 days lead time. The forecast of discharges or water levels is expressed by a linear formula depending on their values on the date of the forecast release and the five previous days. Such forecast techniques were developed for more than 2700 stream gauging stations across Russia. Forecast verification has shown that this method can be successfully applied to large rivers with a smooth shape of hydrographs, while for small mountain catchments, the accuracy of the method tends to be lower. The method has been implemented into real-time continuous operations in the Hydrometcentre of Russia. In the territory of Russia, 18 regions have been identified with a single dependency of the maximum lead time of good forecasts on the area and average slope of the catchment surface for different catchments of each region; the possibilities of forecasting river streamflow by the method of hydrograph extrapolation are approximately estimated. The proposed method can be considered as a first approximation while solving the problem of forecasting river flow in conditions of a lack of meteorological information or when it is necessary to quickly develop a forecasting system for a large number of catchments.