Runoff Time Series Research Articles

The impact of hydroclimate-driven periodic runoff on hydropower production and management

This study evaluates the impact of hydroclimate-driven periodic runoff on hydropower operations and production, with a focus on how the forecasted biennial periodicity of runoff time series could affect the efficiency of hydropower generation. Hydrologic stochastic processes are utilized to forecast long-term runoff, and seven hydroclimate scenarios are developed to be input into a production management model, allowing for an analysis of how periodic hydroclimate variations influence hydropower management and output. The results reveal that the biennial alternation between wet and dry years is a key factor affecting hydropower operations in the Dalälven River Basin. Notable differences between wet- and dry-year scenarios were observed in terms of power efficiency, production output, and forecasting accuracy. Operating hydropower systems based on dry-year runoff forecasts in wet years results in a 1.63% decrease in production efficiency and a reduction of 9,104 MWh in power generation. Conversely, applying wet-year forecasts in dry years slightly boosts production efficiency by 0.31% and increases power generation by 7,832 MWh. Scenarios that adhere to biennial periodicity offer the highest forecasting accuracy, particularly when applying dry-year forecasts in dry years in winter and spring, which produce the most precise predictions. In contrast, using dry-year forecasts in wet years results in the lowest forecasting accuracy.

Климатические изменения годового стока рек Северного Приохотоморья

The aim of this study was to assess the climatic changes in the annual river runoff of the Northern Okhotsk Sea region within the Magadan region. In the present work the analyzed series end with data for 2019. Observation series of river runoff at eight hydrological stations were used in this work. By the method of hydrological analogy, these series were brought to a multiyear period by restoring year-on-year values. Thus, the series of annual runoff for 1958–2019 were analyzed. The research method was the analysis of runoff time series for the presence of homogeneity in the mean (Student’s criterion), as well as spectral analysis. Comparison of mean long-term values of runoff layers for two periods (1958–2000 and 2001–2019) showed that the “norm” increased by 5–37%. It should be noted that these changes are not random according to the Student’s criterion with significance level of 5%. The presence of a trend in long-term water availability fluctuations is also evidenced by the time course of the dynamic mean annual runoff. Analysis of the runoff series, mean annual air temperature and total annual precipitation made it possible to conclude that annual runoff increase is caused by climatic changes, mainly by an increase in precipitation. It is possible that runoff increase is also due to the thawing of ice in the permafrost strata. But you should pay attention to the fact that with climate warming, moisture losses for evapotranspiration increase, primarily due to the growth of trees and shrubs. Moreover, this effect is more noticeable in large catchments. A 6-year cyclicity was revealed in the long-term fluctuations of annual runoff. These fluctuations are most likely a non-stationary complex Markov process. The results of this work will be useful for hydrological calculations, long-term and ultra-long-term runoff forecasts, and studying the river – sea ecosystems. Further hydrological studies should be carried out with the obligatory study of evapotranspiration in various landscape conditions.

Exploring the future hydropower production of a run-of-river type plant in the source region of the Tigris Basin (Türkiye) under CMIP6 scenarios

This assessment presents a framework for exploring the changing climate impacts on the energy production capacity of a run-of-river type plant, using the Basoren Weir and Hydropower Plant (HPP) as a case study. The Basoren Project is planned considering historical streamflow records in the source region of the Euphrates-Tigris River Basin (ETRB), which is a prominent hotspot warming at nearly double the global average rate. The quantification is built on precipitation and maximum/minimum temperature datasets from 24 Global Climate Models (GCMs) belonging to the sixth phase of the Coupled Model Intercomparison Project (CMIP6) under the moderate- and high-end Shared Socioeconomic Pathway (SSP) scenarios of SSP2-4.5 and SSP5-8.5, as well as the CMIP6 historical experiment (HEXP) scenario. The distribution mapping method is employed to adjust the raw GCM datasets for systematic biases. The Soil and Water Assessment Tool (SWAT) is preferred in producing daily runoff time series for the bias-adjusted simulations of each GCM over the historical (1988-2009) and three future (2025-2049, 2050-2074, and 2075-2099) periods. The ramifications of the changing climate on the Basoren HPP's energy production capacity are assessed based on the medians of the operational results reached for each GCM under the future societal development scenarios of SSP2-4.5 and SSP5-8.5, considering the medians achieved under the HEXP scenario as the reference case. The results indicate potential reductions in the mean yearly energy production of the Basoren HPP by 7.9%, 5.5%, and 5.3% under the SSP2-4.5 scenario, and by 5.8%, 8.0%, and 17.3% under the SSP5-8.5 scenario for the periods 2025-2049, 2050-2074, and 2075-2099, respectively. While declining spillway releases are expected to partly offset the impact of decreasing streamflow rates on energy production, the shift from a snow-dominated to a rain-dominated hydrologic regime necessitates re-optimizing the power capacities of the ETRB plants to maintain effective use of hydropower potential.

An extreme forecast index-driven runoff prediction approach using stacking ensemble learning

Runoff prediction plays a crucial role in hydropower generation and flood prevention, enhancing prediction accuracy in hydrology. This study proposes an extreme forecast index (EFI)-driven runoff prediction approach using stacking ensemble learning to improve prediction performance. EFI is introduced as an input into four machine learning models (Support Vector Regression, Multi-layer Perceptron, Gradient Boosting Decision Tree, and Ridge Regression) for runoff prediction with lead times of 24 h, 48 h, and 72 h. The stacking ensemble learning framework comprises four base-models and a meta-model, and model hyperparameters are re-optimized using the particle swarm optimization algorithm. The approach focuses on predicting the inflow processes of the Geheyan Reservoir in the Qing River using EFI and runoff time series. Results demonstrate that the EFI-runoff simulation can improve runoff prediction capability due to EFI’s higher sensitivity to observed runoff, and the proposed stacking ensemble learning model outperforms the individual model in predicting runoff with all lead times. The relative flood peak error, mean relative error, root mean square error, and Nash-Sutcliffe efficiency coefficient of the model’s one-day-ahead prediction are 7.987%, 22.421%, 632.871 m3/s, and 0.771, respectively. Therefore, this approach can be effectively utilized to improve accuracy in short-term runoff prediction applications.

A hydrologic signature approach to analysing wildfire impacts on overland flow

AbstractPost‐fire flooding and debris flows are often triggered by increased overland flow resulting from wildfire impacts on soil infiltration capacity and surface roughness. Increasing wildfire activity and intensification of precipitation with climate change make improving understanding of post‐fire overland flow a particularly pertinent task. Hydrologic signatures, which are metrics that summarize the hydrologic regime of watersheds using rainfall and runoff time series, can be calculated for large samples of watersheds relatively easily to understand post‐fire hydrologic processes. We demonstrate that signatures designed specifically for overland flow reflect changes to overland flow processes with wildfire that align with previous case studies on burned watersheds. For example, signatures suggest increases in infiltration‐excess overland flow and decrease in saturation‐excess overland flow in the first and second years after wildfire in the majority of watersheds examined. We show that climate, watershed and wildfire attributes can predict either post‐fire signatures of overland flow or changes in signature values with wildfire using machine learning. Normalized difference vegetation index (NDVI), air temperature, amount of developed/undeveloped land, soil thickness and clay content were the most used predictors by well‐performing machine learning models. Signatures of overland flow provide a streamlined approach for characterizing and understanding post‐fire overland flow, which is beneficial for watershed managers who must rapidly assess and mitigate the risk of post‐fire hydrologic hazards after wildfire occurs.

Conceptual approach for a holistic low‐flow risk analysis

AbstractLow‐flow events, characterized by a significant water deficiency in river systems, have profound impacts on various water users and river ecology. Recent low‐flow events in Europe have had severe economic and ecological consequences such as disruptions to hydropower production, irrigation bans, constraints on navigation and complete river drying. These events highlight the urgent need for effective low‐flow risk management and demand a holistic risk analysis as a basis. The existing approaches to low‐flow analysis often focus on hydrological aspects, utilizing indices such as the Standardized Runoff Index (SRI) or Low‐flow Index. However, these indices lack information regarding consequences and impacts. Other approaches consider parts of a risk approach but often focus on special aspects, such as the economy; in general, no holistic assessment is made. This study introduces a conceptual approach to a holistic low‐flow risk analysis. The approach provides a continuous long‐term simulation to capture the special long‐term behaviour of low‐flow events and therefore avoids the complex definition of scenarios. In this conceptual approach, the low‐flow risk is analysed using a combination of various analyses that cover all aspects from occurrence to consequences. Meteorological analysis is used to generate synthetic long‐term weather data time series, which are transformed into runoff time series in hydrological analysis. Based on these results, hydrodynamic analysis quantifies the water levels, water temperatures, and flow velocities along the river. The consequences are analysed in terms of socio‐economic and ecological consequences. The results represent a long‐term series of damage values. Finally, the damage values are summed in the risk analysis and divided by the number of years considered in the analysis. For testing and demonstration purposes, the presented conceptual risk approach is partly applied to a proof‐of‐concept at the Selke catchment, a small river catchment in Germany. Finally, the results are presented, evaluated, and discussed.

Monthly Runoff Prediction for Xijiang River via Gated Recurrent Unit, Discrete Wavelet Transform, and Variational Modal Decomposition

Neural networks have become widely employed in streamflow forecasting due to their ability to capture complex hydrological processes and provide accurate predictions. In this study, we propose a framework for monthly runoff prediction using antecedent monthly runoff, water level, and precipitation. This framework integrates the discrete wavelet transform (DWT) for denoising, variational modal decomposition (VMD) for sub-sequence extraction, and gated recurrent unit (GRU) networks for modeling individual sub-sequences. Our findings demonstrate that the DWT–VMD–GRU model, utilizing runoff and rainfall time series as inputs, outperforms other models such as GRU, long short-term memory (LSTM), DWT–GRU, and DWT–LSTM, consistently exhibiting superior performance across various evaluation metrics. During the testing phase, the DWT–VMD–GRU model yielded RMSE, MAE, MAPE, NSE, and KGE values of 245.5 m3/s, 200.5 m3/s, 0.033, 0.997, and 0.978, respectively. Additionally, optimal sliding window durations for different input combinations typically range from 1 to 3 months, with the DWT–VMD–GRU model (using runoff and rainfall) achieving optimal performance with a one-month sliding window. The model’s superior accuracy enhances water resource management, flood control, and reservoir operation, supporting better-informed decisions and efficient resource allocation.

Technical note: Testing the connection between hillslope-scale runoff fluctuations and streamflow hydrographs at the outlet of large river basins

Abstract. A series of numerical experiments were conducted to test the connection between streamflow hydrographs at the outlet of large watersheds and the time series of hillslope-scale runoff yield. We used a distributed hydrological routing model that discretizes a large watershed (∼ 17 000 km2) into small hillslope units (∼ 0.1 km2) and applied distinct surface runoff time series to each unit that deliver the same volume of water into the river network. The numerical simulations show that distinct runoff delivery time series at the hillslope scale result in indistinguishable streamflow hydrographs at large scales. This limitation is imposed by space-time averaging of input flows into the river network that are draining the landscape. The results of the simulations presented in this paper show that, under very general conditions of streamflow routing (i.e., nonlinear variable velocities in space and time), the streamflow hydrographs at the outlet of basins with Horton–Strahler (H–S) order 5 or above (larger than 100 km2 in our setup) contain very little information about the temporal variability of runoff production at the hillslope scale and therefore the processes from which they originate. In addition, our results indicate that the rate of convergence to a common hydrograph shape at larger scales (above H–S order 5) is directly proportional to how different the input signals are to each other at the hillslope scale. We conclude that the ability of a hydrological model to replicate outlet hydrographs does not imply that a correct and meaningful description of small-scale rainfall–runoff processes has been provided. Furthermore, our results provide context for other studies that demonstrate how the physics of runoff generation cannot be inferred from output signals in commonly used hydrological models.

Evaluating the Performance of Several Data Preprocessing Methods Based on GRU in Forecasting Monthly Runoff Time Series

Evaluating the Performance of Several Data Preprocessing Methods Based on GRU in Forecasting Monthly Runoff Time Series

Multivariate adaptive regression splines-assisted approximate Bayesian computation for calibration of complex hydrological models

Abstract Approximate Bayesian computation (ABC) relaxes the need to derive explicit likelihood functions required by formal Bayesian analysis. However, the high computational cost of evaluating models limits the application of Bayesian inference in hydrological modeling. In this paper, multivariate adaptive regression splines (MARS) are used to expedite the ABC calibration process. The MARS model is trained using 6,561 runoff simulations generated by the soil and water assessment tool (SWAT) model and subsequently replaces the SWAT model to calculate the objective functions in ABC and multi-objective evolutionary algorithm (MOEA). In experiments, MARS can successfully reproduce the runoff time series simulations of the SWAT model at a low time cost, with a runoff variance determination coefficient of 0.90 as compared to the Monte Carlo method. MARS-assisted ABC can quickly and accurately estimate the parameter distributions of the SWAT model. The comparison of ABC with non-Bayesian MOEAs helps in the selection of an appropriate calibration approach.

Improving the forecasting accuracy of monthly runoff time series of the Brahmani River in India using a hybrid deep learning model

Abstract Accurate prediction of monthly runoff is critical for effective water resource management and flood forecasting in river basins. In this study, we developed a hybrid deep learning (DL) model, Fourier transform long short-term memory (FT-LSTM), to improve the prediction accuracy of monthly discharge time series in the Brahmani river basin at Jenapur station. We compare the performance of FT-LSTM with three popular DL models: LSTM, recurrent neutral network, and gated recurrent unit, considering different lag periods (1, 3, 6, and 12). The lag period, representing the interval between the observed data points and the predicted data points, is crucial for capturing the temporal relationships and identifying patterns within the hydrological data. The results of this study show that the FT-LSTM model consistently outperforms other models across all lag periods in terms of error metrics. Furthermore, the FT-LSTM model demonstrates higher Nash–Sutcliffe efficiency and R2 values, indicating a better fit between predicted and actual runoff values. This work contributes to the growing field of hybrid DL models for hydrological forecasting. The FT-LSTM model proves effective in improving the accuracy of monthly runoff forecasts and offers a promising solution for water resource management and river basin decision-making processes.

Geospatial patterns in runoff projections using random forest based forecasting of time-series data for the mid-Atlantic region of the United States

This research explores the geospatial patterns of historical runoff for the period 1958–2021 in the Mid-Atlantic region and uses these time-series data plus nine external climatic and hydrologic variables to predict future runoff for the period 2022–2031. Gridded, average monthly climatic water balance data were obtained from the TerraClimate dataset. A cluster analysis of the long term (1958–2021) historical runoff found 13 significant temporal trends, which tend to form large contiguous regions associated with climate gradients and topographic patterns. The runoff time-series clusters, and the associated time-series of 9 TerraClimate variables, were used to generate random forest based forecast models to predict future (2022–2031) runoff. The random forest-based forecast with the greatest accuracy included inputs of actual evapotranspiration, climate water deficit, minimum, average, and maximum temperature, and vapor pressure deficit. The final model predicted significantly increasing runoff in nine of the 13 clusters.

Improved monthly runoff time series prediction using the CABES-LSTM mixture model based on CEEMDAN-VMD decomposition

Abstract Accurate runoff prediction is vital in efficiently managing water resources. In this paper, a hybrid prediction model combining complete ensemble empirical mode decomposition with adaptive noise, variational mode decomposition, CABES, and long short-term memory network (CEEMDAN-VMD-CABES-LSTM) is proposed. Firstly, CEEMDAN is used to decompose the original data, and the high-frequency component is decomposed using VMD. Then, each component is input into the LSTM optimized by CABES for prediction. Finally, the results of individual component predictions are combined and reconstructed to produce the monthly runoff predictions. The hybrid model is employed to predict the monthly runoff at the Xiajiang hydrological station and the Yingluoxia hydrological station. A comprehensive comparison is conducted with other models including back propagation (BP), LSTM, etc. The assessment of each model's prediction performance uses four evaluation indexes. Results reveal that the CEEMDAN-VMD-CABES-LSTM model showcased the highest forecast accuracy among all the models evaluated. Compared with the single LSTM, the root mean square error (RMSE) and mean absolute percentage error (MAPE) of the Xiajiang hydrological station decreased by 71.09 and 65.26%, respectively, and the RMSE and MAPE of the Yingluoxia hydrological station decreased by 65.13 and 40.42%, respectively. The R and NSEC of both sites are near 1.

Repeating patterns in runoff time series: A basis for exploring hydrologic similarity of precipitation and catchment wetness conditions

Runoff responses to precipitation at the catchment scale exhibit a high variability in space and time due to a complex interaction of numerous factors, e.g., topography, land use, soil properties, geology, and climatic conditions. To find similar patterns in the runoff response and examine the effects of these factors on runoff variability, previous studies have either compared and classified catchments in space or focused on grouping extreme events. Here, we analyzed runoff processes in three highly instrumented catchments in Germany and Austria individually and compared them to themselves in time. To this end, we used long-term time series of 10 to 13 years and classified runoff events as similar by performing a cluster analysis based on calculated goodness-of-fit criteria between each possible pair of runoff events. For each cluster, we examined the degree to which precipitation and catchment wetness conditions were similar to themselves at the respective times when similar runoff events occurred by calculating Spearman rank correlation coefficients (ρ) as well as their descriptive statistics. The similarities assessed varied among the three catchments, with the two catchments in western Germany with maritime climates showing a stronger correlation for soil moisture conditions (ρ = 0.76 and ρ = 0.74) for classified similar runoff events rather than precipitation (ρ = 0.26 and ρ = 0.36). The Austrian catchment with a predominantly continental climate showed an overall higher correlation for precipitation (ρ = 0.57) and a lower one for soil moisture (ρ = 0.53) for similar runoff events compared to the other two catchments. The proposed method assesses similarity of precipitation and wetness conditions under similar runoff responses, and gives an indication of possible influencing factors controlling runoff generation in the three catchments in relation to their respective wetness and precipitation patterns. The similarities investigated help identify similar catchment functioning and can be used, for instance, to develop enhanced catchment similarity indices.

An ensemble model for monthly runoff prediction using least squares support vector machine based on variational modal decomposition with dung beetle optimization algorithm and error correction strategy

In order to enhance the runoff prediction accuracy, an ensemble prediction model based on least squares support vector machine (LSSVM) is proposed by including variational mode decomposition (VMD), dung beetle optimization algorithm (DBO), and error correction (EC) strategy. First, the monthly runoff time series is decomposed using DBO-optimized VMD (DVMD), yielding a series of intrinsic mode functions (IMF) series and a residual (Res). Then, the LSSVM based on DBO optimization predicts each sub-series column and residual. The final forecast results are achieved after the preliminary forecast results have been stacked and corrected by the DBO-LSSVM prediction error. To verify the reliability of the proposed model, it is applied to the monthly runoff prediction of the Xiajiang hydrological station in the Ganjiang River Basin, the Hongshanhe hydrological station in the Heihe River Basin, and the Jiayugaun hydrological station in the Heihe River Basin. The proposed model is evaluated using four evaluation indicators: RMSE, MAPE, NSEC, and R, and is compared with SVM, LSSVM, PSO-LSSVM, DBO-LSSVM, EEMD-LSSVM, CEEMDAN-LSSVM, DVMD-LSSVM, EEMD-DBO-LSSVM, CEEMDAN-DBO-LSSVM, and DVMD-DBO-LSSVM. Results show that the DVMD-DBO-LSSVM-EC model has the highest accuracy. During the test period, the NSEC of Xiajiang hydrological station is 0.9829, R is 0.9921, the NSEC of Hongshanhe hydrological station is 0.9981, R is 0.9991, and the NSEC of Jiayugaun hydrological station is 0.9772, R is 0.9897. The prediction effect of the model on the extreme value of the three stations after adding the error correction strategy has increased by 45.14%, 62.22%, and 29.49%, respectively, compared with the previous, which is closer to the actual value. The developed combination model offers a new approach to forecasting monthly runoff and extreme values.

Runoff time series prediction based on hybrid models of two-stage signal decomposition methods and LSTM for the Pearl River in China

Abstract Hydrological runoff prediction is vital for water resource management. The non-linear and non-stationary runoff series and the complex hydrological features for large-scale basins make it difficult to predict. Long short-term memory (LSTM) is effective for runoff prediction but unstable for large-scale basins. This study develops three hybrid models combined with two-stage decomposition and LSTM, including wavelet transformation (WT) combined with complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), variational mode decomposition (VMD), and local mean decomposition (LMD), to predict the daily runoff of the Pearl River in China. The results indicate CEEMDAN's broader signal decomposition applicability for runoff series preprocessing, while VMD is simpler to extract high-runoff characteristics. VMD–WT–LSTM is appropriate for predicting high and median runoff, whereas CEEMDAN–WT–LSTM is better for low-runoff and high and median runoffs with low-violent fluctuations. These hybrid models provide satisfactory predictions for NSE and R2 indicators, and 97.2% of indicators fall within the acceptable range for high-runoff predictions. The hybrid models outperform traditional and standalone models in high-runoff but none of the decomposition methods in this research can identify low-runoff sub-sequence. This study provided runoff prediction methods requiring fewer data and processing time, and these methods are promising alternatives for daily runoff prediction in large-scale basins.

A conceptual metaheuristic-based framework for improving runoff time series simulation in glacierized catchments

Glacio-hydrological modeling is a key task for assessing the influence of snow and glaciers on water resources, essential for water resources management. The present study aims to enhance a conceptual hydrological model (namely Glacial Snow Melt (GSM)) by data-driven and swarm computing for enhancing the accuracy of rainfall runoff prediction. The proposed framework combines the conceptual hydrological model (i.e. GSM) with the time series predictor model (SVR) and optimization-driven parameter tuning of the firefly algorithm (SVR-FFA). This integration uniquely captures the complex interplay between meteorological variables, glacier processes, and hydrological responses. Applying the hybrid framework proved better results than the standalone GSM and ordinary SVR in simulating runoff time series. The performance of the proposed conceptual integrated metaheuristic-based framework (W-SG-SVR-FFA) demonstrated several enhancements over the standalone GSM model. During the calibration (validation) period, the evaluation metric coefficient of determination (R2) was 0.77 (0.77) for the standalone GSM model and 0.98 (0.91) for the W-SG-SVR-FFA model. The Kling-Gupta Efficiency (KGE) values were 0.81 (0.77) and 0.97 (0.87), respectively. Applying the method in glacierized catchments underscores its importance in areas undergoing swift climate change and glacial melting. This approach enables readers to witness the intricate equilibrium between the model's complexity and the accuracy of simulation outcomes.

A runoff prediction method based on hyperparameter optimisation of a kernel extreme learning machine with multi-step decomposition

To improve the accuracy of runoff forecasting, a combined forecasting model is established by using the kernel extreme learning machine (KELM) algorithm optimised by the butterfly optimisation algorithm (BOA), combined with the variational modal decomposition method (VMD) and the complementary ensemble empirical modal decomposition method (CEEMD), for the measured daily runoff sequences at Jiehetan and Huayuankou stations and Gaochun and Lijin stations. The results show that the combined model VMD-CEEMD-BOA-KELM predicts the best. The average absolute errors are 30.02, 23.72, 25.75, 29.37, and the root mean square errors are 20.53 m3/s, 18.79 m3/s, 18.66 m3/s, and 21.87 m3/s, the decision coefficients are all above 90 percent, respectively, and the Nash efficiency coefficients are all more than 90%, from the above it can be seen that the method has better results in runoff time series prediction.

Attribution analysis of non-stationary hydrological drought using the GAMLSS framework and an improved SWAT model

Climate change and human activities are two key factors that modify the hydrological cycle and drought characteristics of a watershed. Quantifying the effects of climate change and human activities on hydrological drought is of great significance for water resources management. The traditional method of hydrological drought assessment is based on the assumption of stationarity, which is no longer applicable. In this study, using the Hanjiang River basin (HRB) as a study area, the Pettitt test and heuristic segmentation algorithm are first used to identify the change points in the runoff time series. Then, a distributed hydrological model (SWAT) is used to reconstruct the natural runoff series during the impacted period. Finally, the impacts of climate change and human activities on hydrological drought are quantitatively assessed using the non-stationary Standardized Runoff Index (NSRI), developed based on the GAMLSS model. Results show that runoff processes in the basin are non-stationary, and the year 1991 is identified as the change point of the runoff series. Both climate change and human activities exacerbate the hydrological drought by increasing its duration, severity, and peak, with climate change playing a dominant role in the 1990s. Human activities, however, have gradually surpassed the impact of climate change on hydrological drought since the 21st century. In addition, reservoir regulation extends the drought duration and severity but mitigates the peak. These findings provide valuable information for drought management and water resource regulation under changing environments.

Estimating runoff from pan-Arctic drainage basins for 2002–2019 using an improved runoff-storage relationship

The Arctic is undergoing dramatic climate and environmental changes. The long-term alterations in river discharges from the boreal catchments, which serve as vital links between the ocean and land, are having a profound impact on various environmental factors, particularly ocean circulation and sea-ice content. However, comprehensive and continuous monitoring of Arctic river discharge at seasonal or higher temporal resolutions remains challenging. In this study, we propose a new approach to estimate runoff by generating monthly runoff time series at the basin scale. Our method is based on changes in water storage observed by the gravimetric satellites Gravity Recovery and Climate Experiment (GRACE) and its follow-on mission since 2002. The method utilizes an empirical runoff-storage (R-S) relationship, offering simplicity and low computational burden while maintaining good accuracy in estimating runoff. To validate our method, we utilize in-situ runoff measurements from the seven largest boreal drainage basins spanning the period from 2002 to 2019, encompassing a total of 18 years. We divide these 18 years of observations into two phases: a training phase (8 years) and a testing phase (10 years). The results indicate that the R-S method established during the training phase yields monthly Nash-Sutcliffe efficiency (NSE) values ranging from 0.65 to 0.92 when compared to in-situ runoff measurements. Moreover, the method demonstrates a consistent performance in estimating runoff during the testing phase (monthly NSE: 0.67–0.84). With the exception of the Ob and Mackenzie basins, which exhibit distinct climatic conditions and hydrological networks, the R-S models are interchangeable across basins. This makes it suitable for both temporal and spatial extrapolation to fill data gaps, provided that accurate water and snow storage data are available. All in all, our method enables the reconstruction of monthly surface runoff across the entire boreal basins between 2002 and 2019. The results indicate an average annual runoff of 3200 ± 160 Gt over the study area of 1.58 × 107 km2. To evaluate the accuracy of our estimates, we compare the total runoff estimates obtained using the R-S method with those from 12 model estimates. Our estimates exhibit the highest correlation with available in-situ runoff measurements and yield monthly NSE values >0.57 for five out of the twelve model estimates. This study presents a convenient method to address the urgent need for comprehensive, continuous, and monthly temporal resolution of runoff estimates throughout the entire boreal region.