Ensemble Streamflow Forecasts Research Articles

Application of weather post-processing methods for operational ensemble hydrological forecasting on multiple catchments in Canada

Hydrological forecasts contain biases that need to be addressed for their effective use in operational decision-making in water resources management. Performing post-processing allows reducing the overall systematic bias while improving the distribution and accuracy of hydrological forecasts. In this study, a Quantile Mapping (QM) post-processing method was applied on weather forecasts following three temporal configurations (monthly, seasonal, and annual) of the quantile mapping scheme. The evaluation encompasses 20 catchments in southern Canada, employing a leave-one-out approach with the QM method on ECMWF ensemble weather forecasts spanning 2015–2020 inclusively. These processed forecasts are subsequently utilized as forcings for eight hydrological models, generating ensemble streamflow forecasts over a 6-year period with a lead time of 10 days and a sub-daily timestep of 6 h. The performance of the QM method is mainly assessed using the Continuous Ranked Probability Score (CRPS) metric, in complement with a forecast reliability score (ABDU) and a forecast sharpness metric (NMIQR). Significant improvements are discerned in precipitation forecasts upon the application of QM. Notably, these improvements are translated into enhanced hydrological forecasts for over half of the catchments studied (55 %). Surprisingly, no discernible differences in performance are observed among the three QM configurations in most catchments. Interestingly, there are watersheds where the implementation of QM exhibit either poorer or no change in performance and sharpness compared to raw forecasts.

Simplified Approach to Mixed-Integer Chance-Constrained Optimization with Ensemble Streamflow Forecasts for Risk-Based Dam Operation

Simplified Approach to Mixed-Integer Chance-Constrained Optimization with Ensemble Streamflow Forecasts for Risk-Based Dam Operation

Uncertainty in three dimensions: the challenges of communicating probabilistic flood forecast maps

Abstract. Real-time operational flood forecasting most often concentrates on issuing streamflow predictions at specific points along the rivers of a watershed. However, we are now witnessing an increasing number of studies aimed at also including flood mapping as part of the forecasting system. While this additional new information (flood extent, depth, velocity, etc.) can potentially be useful for decision-makers, it could also be overwhelming. This is especially true for probabilistic and ensemble forecasting systems. While ensemble streamflow forecasts for a given point in space can be visualized relatively easily, the visualization and communication of probabilistic forecasts for water depth and extent pose additional challenges. Confusion typically arises from too much information, counterintuitive interpretation, or simply too much complexity in the representation of the forecast. The communication and visualization of probabilistic streamflow forecasts has been studied in the past, but this is not the case for the probabilistic flood forecast map, which is still an emerging product. In this paper, we synthesize the results of a large-scale survey (28 government representatives, 52 municipalities, 9 organizations, and 38 citizens and farmers, for a total of 140 people) regarding the users' preferences in terms of visualizing probabilistic flood forecasts over an entire river reach. The survey was performed through interviews, during which the interviewees were asked about their needs in terms of hydrological forecasting. We also presented the interviewees with four prototypes representing alternative visualizations of the same probabilistic forecast in order to understand their preferences in terms of colour maps, wording, and the representation of uncertainty. Our results highlight several issues related to the understanding of probabilities in the specific context of visualizing forecasted flood maps. We propose several suggestions for visualizing probabilistic flood maps and also describe potential adaptations for different categories of end users. This study is the first to investigate the visualization of probabilistic flood maps, which are gaining popularity. Given that the interview questions were not tied to a specific geographical location, our findings are applicable outside of the study area and, therefore, to other operational centres interested in providing probabilistic flood forecast maps to decision-making organizations and citizens.

Using Ensemble Streamflow Forecasts to Inform Seasonal Outlooks for Water Allocations in the Murray Darling Basin

Using Ensemble Streamflow Forecasts to Inform Seasonal Outlooks for Water Allocations in the Murray Darling Basin

Hydropower System Operation and the Quality of Short-Term Hydrologic Ensemble Forecasts

Improving the operational effectiveness of hydropower systems is becoming more relevant considering the shift to renewable energy sources and the rising social, financial, and environmental costs associated with the construction of new hydropower facilities. This challenge is interdisciplinary as it involves technical, managerial, and institutional aspects. This paper focuses on a technical aspect, more specifically on the relationship between the quality of short-term ensemble streamflow forecasts and the energy produced by a hydropower system. A secondary objective is to measure the contribution of both the meteorological and structural uncertainties on the energy output. To achieve this, a numerical experiment comprising multiple sets of hydrologic ensemble forecasts of different quality and a suite of reservoir optimization models is developed for a case study in Canada (the hydropower system of the Gatineau River basin). These ensemble forecasts are processed by the short-term reservoir operation model in rolling-horizon mode over a planning period of 6 years. Each day, the short-term optimization model seeks to maximize the energy output over the 14-day forecast lead time considering the expected future value of the system derived from a midterm optimization model. The relationship between hydropower generation and common statistical scores characterizing the ensemble forecasts indicates that although there is a link between the quality of the forecasts and the energy production, it is not a one-to-one causal relationship. Our results also show that the diversity of hydrological models is beneficial to the production of energy, indicating that the diversity of model structures compensates the deficiencies of individuals models and adds value to the forecast.

Pre- and postprocessing flood forecasts using Bayesian model averaging

Abstract In this study, pre- and postprocessing of hydrological ensemble forecasts are evaluated with a special focus on floods for 119 Norwegian catchments. Two years of ECMWF ensemble forecasts of temperature and precipitation with a lead time of up to 9 days were used to force the operational hydrological HBV model to establish streamflow forecasts. A Bayesian model averaging processing approach was applied to preprocess temperature and precipitation forecasts and for postprocessing streamflow forecasts. Ensemble streamflow forecasts were generated for eight schemes based on combinations of raw, preprocessed, and postprocessed forecasts. Two datasets were used to evaluate the forecasts: (i) all streamflow forecasts and (ii) forecasts for flood events with streamflow above mean annual flood. Evaluations based on all streamflow data showed that postprocessing improved the forecasts only up to a lead time of 2–3 days, whereas preprocessing temperature and precipitation improved the forecasts for 50–90% of the catchments beyond 3 days' lead time. We found large differences in the ability to issue warnings between spring and autumn floods. Spring floods had predictability for up to 9 days for many events and catchments, whereas the ability to predict autumn floods beyond 3 days was marginal.

Assessing the Potential of Combined SMAP and In-Situ Soil Moisture for Improving Streamflow Forecast

Soil moisture is an essential hydrological variable for a suite of hydrological applications. Its spatio-temporal variability can be estimated using satellite remote sensing (e.g., SMOS and SMAP) and in-situ measurements. However, both have their own strengths and limitations. For example, remote sensing has the strength of maintaining the spatial variability of near-surface soil moisture, while in-situ measurements are accurate and preserve the dynamics range of soil moisture at both surface and larger depths. Hence, this study is aimed at (1) merging the strength of SMAP with in-situ measurements and (2) exploring the effectiveness of merged SMAP/in-situ soil moisture in improving ensemble streamflow forecasts. The conditional merging technique was adopted to merge the SMAP-enhanced soil moisture (9 km) and its downscaled version (1 km) separately with the in-situ soil moisture collected over the au Saumon watershed, a 1025 km2 watershed located in Eastern Canada. The random forest machine learning technique was used for downscaling of the near-surface SMAP-enhanced soil moisture to 1 km resolution, whereas the exponential filter was used for vertical extrapolation of the SMAP near-surface soil moisture. A simple data assimilation technique known as direct insertion was used to update the topsoil layer of a physically-based distributed hydrological model with four soil moisture products: (1) the merged SMAP/in-situ soil moisture at 9 and 1 km resolutions; (2) the original SMAP-enhanced (9 km), (3) downscaled SMAP-enhanced (1 km), and (4) interpolated in-situ surface soil moisture. In addition, the vertically extrapolated merged SMAP/in-situ soil moisture and subsurface (rootzone) in-situ soil moisture were used to update the intermediate layer of the model. Results indicate that downscaling of the SMAP-enhanced soil moisture to 1 km resolution improved the spatial variability of soil moisture while maintaining the spatial pattern of its original counterpart. Similarly, merging of the SMAP with in- situ soil moisture preserved the dynamic range of in-situ soil moisture and maintained the spatial heterogeneity of SMAP soil moisture. Updating of the top layer of the model with the 1 km merged SMAP/in-situ soil moisture improved the ensemble streamflow forecast compared to the model updated with either the SMAP-enhanced or in-situ soil moisture alone. On the other hand, updating the top and intermediate layers of the model with surface and vertically extrapolated SMAP/in-situ soil moisture, respectively, did not further improve the accuracy of the ensemble streamflow forecast. Overall, this study demonstrated the potential of merging the SMAP and in-situ soil moisture for streamflow forecast.

Machine learning for postprocessing ensemble streamflow forecasts

AbstractSkillful streamflow forecasts can inform decisions in various areas of water policy and management. We integrate numerical weather prediction ensembles, distributed hydrological model, and machine learning to generate ensemble streamflow forecasts at medium-range lead times (1–7 days). We demonstrate the application of machine learning as postprocessor for improving the quality of ensemble streamflow forecasts. Our results show that the machine learning postprocessor can improve streamflow forecasts relative to low-complexity forecasts (e.g., climatological and temporal persistence) as well as standalone hydrometeorological modeling and neural network. The relative gain in forecast skill from postprocessor is generally higher at medium-range timescales compared to shorter lead times; high flows compared to low–moderate flows, and the warm season compared to the cool ones. Overall, our results highlight the benefits of machine learning in many aspects for improving both the skill and reliability of streamflow forecasts.

Ensemble streamflow prediction considering the influence of reservoirs in Narmada River Basin, India

Abstract. Developing an ensemble hydrological prediction system is essential for reservoir operations and flood early warning. However, efforts to build hydrological ensemble prediction systems considering the influence of reservoirs have been lacking in India. We examine the potential of the Extended Range Forecast System (ERFS, 16 ensemble members) and Global Ensemble Forecast System (GEFS, 21 ensemble members) forecast for streamflow prediction in India using the Narmada River Basin as a test bed. We use the variable infiltration capacity (VIC) with reservoir operations (VIC-Res) scheme to simulate the daily river flow at four locations in the Narmada Basin. Streamflow prediction skills of the ERFS forecast were examined for the period 2003–2018 at 1–32 d lead. We compared the streamflow forecast skills of raw meteorological forecasts from ERFS and GEFS at a 1–10 d lead for the summer monsoon (June–September) 2019–2020. The ERFS forecast underestimates extreme precipitation against the observations compared to the GEFS forecast during the summer monsoon of 2019–2020. However, both forecast products show better skills for minimum and maximum temperatures than precipitation. Ensemble streamflow forecast from the GEFS performs better than the ERFS during 2019–2020. The performance of GEFS-based ensemble streamflow forecast declines after 5 days lead. Overall, the GEFS ensemble streamflow forecast can provide reliable skills at a 1–5 d lead, which can be utilized in streamflow prediction. Our findings provide directions for developing a flood early warning system based on ensemble streamflow prediction considering the influence of reservoirs in India.

A Bayesian Hierarchical Model Combination Framework for Real‐Time Daily Ensemble Streamflow Forecasting Across a Rainfed River Basin

AbstractThe frequent occurrence of floods during the rainy season is one of the threats in rainfed river basins, especially in river basins of India. This study implemented a Bayesian hierarchical model combination (BHMC) framework to generate skillful and reliable real‐time daily ensemble streamflow forecast and peak flow and demonstrates its utility in the Narmada River basin in Central India for the peak monsoon season (July–August). The framework incorporates information from multiple sources (e.g., deterministic hydrological forecast, meteorological forecast, and observed data) as predictors. The forecasts were validated with a leave‐1‐year‐out cross‐validation using accuracy metrics such as BIAS and Pearson correlation coefficient (R) and probabilistic metrics such as continuous ranked probability skill score, probability integral transform (PIT) plots, and the average width of the 95% confidence intervals (AWCI) plots. The results show that the BHMC framework can increase the forecast skill by 40% and reduce absolute bias by at least 28% compared to the raw deterministic forecast from a physical model, the Variable Infiltration Capacity model. In addition, PIT and AWCI show that the framework can provide sharp and reliable streamflow forecast ensembles for short lead times (1–3‐day lead time) and provide useful skills beyond up to 5‐day lead time. These will be of immense help in emergency and disaster preparedness.

Advancing global hydrologic modeling with the GEOGloWS ECMWF streamflow service

AbstractMost people face some level of water insecurity. Wise water management practices to address water security issues typically require data derived from a combination of observation and model data. This data has historically proven difficult to sustainably supply in many areas of the world. We present the design and development of a global, modeled streamflow data source for the Group on Earth Observation (GEO) Global Water Sustainability (GEOGloWS) implemented at the European Centre for Medium‐Range Weather Forecasts (ECMWF). This GEOGloWS ECMWF Streamflow Service (GEOGloWS Service) is a solution and prototype to sustainably address this need for data. The GEOGloWS Service centralizes computing and human resources to build a global hydrologic model and exposes data and mapping web services that allow users to consume the resulting data to meet their specific needs. The global hydrologic model produces global 15‐day ensemble streamflow forecasts and a historical simulation since January 1979. We present case studies in several countries and research environments which demonstrate the utility of the approach taken by the GEOGloWS Service. The case studies show how the global modeled data are being applied to make informed decisions and advance projects in ways that otherwise would not have been possible.

CREST-VEC: a framework towards more accurate and realistic flood simulation across scales

Abstract. Large-scale (i.e., continental and global) hydrologic simulation is an appealing yet challenging topic for the hydrologic community. First and foremost, model efficiency and scalability (flexibility in resolution and discretization) have to be prioritized. Then, sufficient model accuracy and precision are required to provide useful information for water resource applications. Towards this goal, we craft two objectives for improving US current operational hydrological models: (1) vectorized routing and (2) improved hydrological processes. This study presents a hydrologic modeling framework, CREST-VEC, that combines a gridded water balance model and a newly developed vector-based routing scheme. First, in contrast to a conventional fully gridded model, this framework can significantly reduce the computational cost of river routing by at least 10 times, based on experiments at regional (0.07 vs. 0.002 s per step) and continental scales (0.35 vs. 7.2 s per step). This provides adequate time efficiency for generating operational ensemble streamflow forecasts and even probabilistic estimates across scales. Second, the performance using the new vector-based routing is improved, with the median-aggregated NSE (Nash–Sutcliffe efficiency) score increasing from −0.06 to 0.18 over the CONUS (contiguous US). Third, with the lake module incorporated, the NSE score is further improved by 56.2 % and the systematic bias is reduced by 17 %. Lastly, over 20 % of the false alarms on 2-year floods in the US can be mitigated with the lake module enabled, at the expense of only missing 2.3 % more events. This study demonstrated the advantages of the proposed hydrological modeling framework, which could provide a solid basis for continental- and global-scale water modeling at fine resolution. Furthermore, the use of ensemble forecasts can be incorporated into this framework; and thus, optimized streamflow prediction with quantified uncertainty information can be achieved in an operational fashion for stakeholders and decision-makers.

Evaluating the impact of post-processing medium-range ensemble streamflow forecasts from the European Flood Awareness System

Abstract. Streamflow forecasts provide vital information to aid emergency response preparedness and disaster risk reduction. Medium-range forecasts are created by forcing a hydrological model with output from numerical weather prediction systems. Uncertainties are unavoidably introduced throughout the system and can reduce the skill of the streamflow forecasts. Post-processing is a method used to quantify and reduce the overall uncertainties in order to improve the usefulness of the forecasts. The post-processing method that is used within the operational European Flood Awareness System is based on the model conditional processor and the ensemble model output statistics method. Using 2 years of reforecasts with daily timesteps, this method is evaluated for 522 stations across Europe. Post-processing was found to increase the skill of the forecasts at the majority of stations in terms of both the accuracy of the forecast median and the reliability of the forecast probability distribution. This improvement is seen at all lead times (up to 15 d) but is largest at short lead times. The greatest improvement was seen in low-lying, large catchments with long response times, whereas for catchments at high elevation and with very short response times the forecasts often failed to capture the magnitude of peak flows. Additionally, the quality and length of the observational time series used in the offline calibration of the method were found to be important. This evaluation of the post-processing method, and specifically the new information provided on characteristics that affect the performance of the method, will aid end users in making more informed decisions. It also highlights the potential issues that may be encountered when developing new post-processing methods.

Exploring hydrologic post-processing of ensemble streamflow forecasts based on affine kernel dressing and non-dominated sorting genetic algorithm II

Abstract. Forecast uncertainties are unfortunately inevitable when conducting a deterministic analysis of a dynamical system. The cascade of uncertainty originates from different components of the forecasting chain, such as the chaotic nature of the atmosphere, various initial conditions and boundaries, inappropriate conceptual hydrologic modeling, and the inconsistent stationarity assumption in a changing environment. Ensemble forecasting proves to be a powerful tool to represent error growth in the dynamical system and to capture the uncertainties associated with different sources. In practice, the proper interpretation of the predictive uncertainties and model outputs will also have a crucial impact on risk-based decisions. In this study, the performance of evolutionary multi-objective optimization (i.e., non-dominated sorting genetic algorithm II – NSGA-II) as a hydrological ensemble post-processor was tested and compared with a conventional state-of-the-art post-processor, the affine kernel dressing (AKD). Those two methods are theoretically/technically distinct, yet share the same feature in that both of them relax the parametric assumption of the underlying distribution of the data (the streamflow ensemble forecast). Both NSGA-II and AKD post-processors showed efficiency and effectiveness in eliminating forecast biases and maintaining a proper dispersion with increasing forecasting horizons. In addition, the NSGA-II method demonstrated superiority in communicating trade-offs with end-users on which performance aspects to improve.

Postprocessing of hydrometeorological ensemble forecasts based on multisource precipitation in Ganjiang River basin, China

Flood forecasting is a challenging task with major concerns that include forecast uncertainty and lead time. This paper aims to comprehensively address the data correction issue for hydrological models by developing an integrated postprocessing framework for hydrometeorological ensemble forecasts based on three types of ensemble precipitation forecasts (ECMWF, GEFS, and CFSv2). This framework mainly comprises the Ensemble Preprocessor (EPP) and the Bayesian model averaging (BMA) scheme: (1) the EPP eliminates various deviations from the ensemble precipitation forecasts, with the canonical event model extracting useful predictive information and extending the lead time by linking the forecasts seamlessly, and (2) the BMA scheme develops more skillful and reliable probabilistic hydrological forecasts from ensemble streamflow forecasts generated by three hydrological models (XAJ, VIC and DTVGM) driven by the postprocessed ensemble precipitation forecasts. The Ganjiang River Basin was selected as a case study to examine the capacity and efficiency of our developed framework. We found that the performance of the integrated postprocessing of hydrometeorological ensemble forecasts benefitted from a reasonable design of canonical events, especially when the observed precipitation was factored into the canonical events. Streamflow forecasts with increased lead times and accuracy were obtained by employing postprocessed precipitation forecasts instead of raw forecasts as the input for the hydrological models. We also found that the BMA scheme further improved the forecast effect by generating more skillful and reliable forecasts than a single hydrological model, which was verified by a series of statisticalindices and uncertainty analysis. Our results underscore the importance of considering both input and output for hydrological models in hydrological forecasting.

Uncertainty propagation within a water level ensemble prediction system

Reliability of a water level ensemble prediction system (WL-EPS) is directly related to the quality of the available data and the processes integrated into each predictive layer. In this project, we evaluate a hydrometeorological ensemble prediction system (H-EPS) coupled to a hydraulic module (HM) developed on the Chaudière River watershed in Quebec, Canada. Ensemble streamflow forecasts account for the three main sources of uncertainty: meteorology (ensemble forecasts), initial conditions (through data assimilation), and model structure (multimodel). The HM consists of a 1D river model that is informed at the boundary conditions by the H-EPS outputs. In order to evaluate the forecasting system in an operational context, a sampling procedure is implemented on the hydrometeorological ensemble forecasts in order to limit the simulation time of the hydraulic component without altering the description of the uncertainty provided beforehand. Forecasts reliability and accuracy are evaluated, for events of various magnitude covering a wide range of flows, based on performance metrics such as the reliability diagram and the continuous rank probability score (CRPS). Overall, results show that the performances of the ensemble flow and water level forecasts provided by the proposed system are quite reliable over the range of forecast horizons evaluated. Water level forecasts are dependent on the quality of the streamflow forecast, which is coherent with the assumption that streamflow is the main source of uncertainty of the HM. However, at control point where flow and water level forecasts are available, a slight deterioration in the forecast reliability is noted from flow to water level; nonetheless, the reliability of the forecasts remains comparable, thus confirming the strong link between the H-EPS and HM uncertainties. Consideration of the uncertainty related to the roughness coefficient uncertainty turned out to have a negligible effect on the spread of the water level forecasts, confirming once more the domination of the hydrologic uncertainty.

Ensemble streamflow data assimilation using WRF-Hydro and DART: novel localization and inflation techniques applied to Hurricane Florence flooding

Abstract. Predicting major floods during extreme rainfall events remains an important challenge. Rapid changes in flows over short timescales, combined with multiple sources of model error, makes it difficult to accurately simulate intense floods. This study presents a general data assimilation framework that aims to improve flood predictions in channel routing models. Hurricane Florence, which caused catastrophic flooding and damages in the Carolinas in September 2018, is used as a case study. The National Water Model (NWM) configuration of the WRF-Hydro modeling framework is interfaced with the Data Assimilation Research Testbed (DART) to produce ensemble streamflow forecasts and analyses. Instantaneous streamflow observations from 107 United States Geological Survey (USGS) gauges are assimilated for a period of 1 month. The data assimilation (DA) system developed in this paper explores two novel contributions, namely (1) along-the-stream (ATS) covariance localization and (2) spatially and temporally varying adaptive covariance inflation. ATS localization aims to mitigate not only spurious correlations, due to limited ensemble size, but also physically incorrect correlations between unconnected and indirectly connected state variables in the river network. We demonstrate that ATS localization provides improved information propagation during the model update. Adaptive prior inflation is used to tackle errors in the prior, including large model biases which often occur in flooding situations. Analysis errors incurred during the update are addressed using posterior inflation. Results show that ATS localization is a crucial ingredient of our hydrologic DA system, providing at least 40 % more accurate (root mean square error) streamflow estimates than regular, Euclidean distance-based localization. An assessment of hydrographs indicates that adaptive inflation is extremely useful and perhaps indispensable for improving the forecast skill during flooding events with significant model errors. We argue that adaptive prior inflation is able to serve as a vigorous bias correction scheme which varies both spatially and temporally. Major improvements over the model's severely underestimated streamflow estimates are suggested along the Pee Dee River in South Carolina, and many other locations in the domain, where inflation is able to avoid filter divergence and, thereby, assimilate significantly more observations.

Generating Ensemble Streamflow Forecasts: A Review of Methods and Approaches Over the Past 40 Years

AbstractEnsemble forecasting applied to the field of hydrology is currently an established area of research embracing a broad spectrum of operational situations. This work catalogs the various pathways of ensemble streamflow forecasting based on an exhaustive review of more than 700 studies over the last 40 years. We focus on the advanced state of the art in the model‐based (dynamical) ensemble forecasting approaches. Ensemble streamflow prediction systems are categorized into three leading classes: statistics‐based streamflow prediction systems, climatology‐based ensemble streamflow prediction systems and numerical weather prediction‐based hydrological ensemble prediction systems. For each ensemble approach, technical information, as well as details about its strengths and weaknesses, are provided based on a critical review of the studies listed. Through this literature review, the performance and uncertainty associated with the ensemble forecasting systems are underlined from both operational and scientific viewpoints. Finally, the remaining key challenges and prospective future research directions are presented, notably through hybrid dynamical ‐ statistical learning approaches, which obviously present new challenges to be overcome in order to allow the successful employment of ensemble streamflow forecasting systems in the next decades. Targeting students, researchers and practitioners, this review provides a detailed perspective on the major features of an increasingly important area of hydrological forecasting.

Postprocessing continental-scale, medium-range ensemble streamflow forecasts in South America using Ensemble Model Output Statistics and Ensemble Copula Coupling

Probabilistic hydrological forecasting and ensemble techniques have leveraged streamflow prediction at regional to continental scales up to several weeks in advance. However, ensembles that only account for meteorological forecast uncertainty are typically biased and subject to dispersion errors, thus limiting their use for rational decision-making and optimization systems. Statistical postprocessing is therefore necessary to convert ensemble forecasts into calibrated and sharp predictive distributions, and it should also account for dependencies between lead times to enable realistic forecast trajectories. This work provides a continental-scale assessment of the use of statistical postprocessing on medium-range, ensemble streamflow forecasts over South America (SA). These forecasts were produced through a large-scale hydrologic–hydrodynamic model forced with a global precipitation dataset and ECMWF reforecast data. The Ensemble Model Output Statistics (EMOS) technique was used to generate conditional predictive distributions in 488 locations at each forecast lead time, while the Ensemble Copula Coupling method with the transformation scheme (ECC-T) was applied to derive ensemble traces from EMOS distributions. Postprocessed streamflow forecasts were cross-validated for the period from 1996 to 2014 using a range of verification metrics. Results showed that the skill and reliability of EMOS forecasts substantially improve over the raw ensembles, and that EMOS leads to skillful predictions relative to discharge climatology and persistence forecasts up to 15 days in advance in most locations. Furthermore, EMOS results in predictive distributions that are generally sharper than the climatology. Limitations in depicting autocorrelations of forecast trajectories were observed in rivers for which the raw ensemble spread is very low and EMOS has to largely increase dispersion, especially at short lead times. The study’s findings suggest that combining a continental-scale hydrological model with EMOS and ECC-T methods can lead to skillful predictions and realistic ensemble traces in several locations in SA, even if in situ hydrometeorological observations are not available in real time.

A multi-scenario ensemble streamflow forecast method for Amu Darya River Basin under considering climate and land-use changes

Land-use and climate changes have impacts on hydrological processes for river basin. In this study, a multi-scenario ensemble streamflow forecast (MESF) method is developed for analyzing the streamflow variation under considering climate and land-use changes, through incorporating CA-Markov model, global climate model (GCM) and Soil and Water Assessment Tool (SWAT) model within a general framework. The advantages of MESF are as follows: (i) it can simultaneously assess the impacts of land-use and climate changes on streamflow; (ii) it can obtain the possible trend and the range of future streamflows through ensemble forecast under multiple scenarios; (iii) based on analysis of streamflow processes under extreme scenarios, it can examine the effects of key factors on streamflow. The MESF method is applied to the upper reaches of the Amu Darya River Basin in Central Asia. Totally 72 scenarios, under different land-use patterns, GCMs and Representative Concentration Pathways (RCPs), are analyzed. Ensemble forecast results reveal that (i) during 2021–2050, the average annual precipitation and the average annual temperature would both increase, but the mean annual streamflow would decrease; (ii) compared to the impact of land-use change, climate change has more obvious effects on the streamflow (with contribution of 78.8%–98.7%); (iii) among all factors of land-use change, glacier melting triggered by climate warming is the most prominent factor; (iv) the peak flow in one year would have a tendency to shift from summer to spring due to the rising temperature and the speeding up snow melt.