scholarly journals GloFAS – global ensemble streamflow forecasting and flood early warning

2012 ◽  
Vol 9 (11) ◽  
pp. 12293-12332 ◽  
Author(s):  
L. Alfieri ◽  
P. Burek ◽  
E. Dutra ◽  
B. Krzeminski ◽  
D. Muraro ◽  
...  
Keyword(s):  
Early Warning ◽  
Large Scale ◽  
River Basins ◽  
Developed Countries ◽  
Large River ◽  
Streamflow Forecasting ◽  
Forecast Horizon ◽  
Hydrological Simulation ◽  
Streamflow Forecasts ◽  
Awareness System

Abstract. Anticipation and preparedness for large-scale flood events have a key role in mitigating their impact and optimizing the strategic planning of water resources. Although several developed countries have well-established systems for river monitoring and flood early warning, figures of population affected every year by floods in developing countries are unsettling. This paper presents the Global Flood Awareness System, which has been set up to provide an overview on upcoming floods in large world river basins. The Global Flood Awareness System is based on distributed hydrological simulation of numerical ensemble weather predictions with global coverage. Streamflow forecasts are compared statistically to climatological simulations to detect probabilistic exceedance of warning thresholds. In this article, the system setup is described, together with an evaluation of its performance over a two-year test period and a qualitative analysis of a case study for the Pakistan flood, in summer 2010. It is shown that hazardous events in large river basins can be skilfully detected with a forecast horizon of up to 1 month. In addition, results suggest that an accurate simulation of initial model conditions and an improved parameterization of the hydrological model are key components to reproduce accurately the streamflow variability in the many different runoff regimes of the Earth.

scholarly journals GloFAS – global ensemble streamflow forecasting and flood early warning

2013 ◽  
Vol 17 (3) ◽  
pp. 1161-1175 ◽  
Author(s):  
L. Alfieri ◽  
P. Burek ◽  
E. Dutra ◽  
B. Krzeminski ◽  
D. Muraro ◽  
...  
Keyword(s):  
Early Warning ◽  
Large Scale ◽  
River Basins ◽  
Developed Countries ◽  
Large River ◽  
Streamflow Forecasting ◽  
Forecast Horizon ◽  
Hydrological Simulation ◽  
Streamflow Forecasts ◽  
Set Up

Abstract. Anticipation and preparedness for large-scale flood events have a key role in mitigating their impact and optimizing the strategic planning of water resources. Although several developed countries have well-established systems for river monitoring and flood early warning, figures of populations affected every year by floods in developing countries are unsettling. This paper presents the Global Flood Awareness System (GloFAS), which has been set up to provide an overview on upcoming floods in large world river basins. GloFAS is based on distributed hydrological simulation of numerical ensemble weather predictions with global coverage. Streamflow forecasts are compared statistically to climatological simulations to detect probabilistic exceedance of warning thresholds. In this article, the system setup is described, together with an evaluation of its performance over a two-year test period and a qualitative analysis of a case study for the Pakistan flood, in summer 2010. It is shown that hazardous events in large river basins can be skilfully detected with a forecast horizon of up to 1 month. In addition, results suggest that an accurate simulation of initial model conditions and an improved parameterization of the hydrological model are key components to reproduce accurately the streamflow variability in the many different runoff regimes of the earth.

Using large-scale climatic patterns for improving long lead time streamflow forecasts for Gunnison and San Juan River Basins

2012 ◽  
Vol 27 (11) ◽  
pp. 1543-1559 ◽  
Author(s):  
Ajay Kalra ◽  
William P. Miller ◽  
Kenneth W. Lamb ◽  
Sajjad Ahmad ◽  
Thomas Piechota
Keyword(s):  
Lead Time ◽  
Large Scale ◽  
River Basins ◽  
San Juan ◽  
Streamflow Forecasts ◽  
San Juan River

scholarly journals Design and implementation of an operational multimodel multiproduct real-time probabilistic streamflow forecasting platform

2017 ◽  
Vol 19 (6) ◽  
pp. 911-919 ◽  
Author(s):  
Tirthankar Roy ◽  
Aleix Serrat-Capdevila ◽  
Juan Valdes ◽  
Matej Durcik ◽  
Hoshin Gupta
Keyword(s):  
Real Time ◽  
Modular Design ◽  
Model Averaging ◽  
River Basins ◽  
Streamflow Forecasting ◽  
Hydrologic Models ◽  
Design And Implementation ◽  
Probabilistic Forecasts ◽  
Streamflow Forecasts ◽  
Monitoring And Forecasting

Abstract The task of real-time streamflow monitoring and forecasting is particularly challenging for ungauged or sparsely gauged river basins, and largely relies upon satellite-based estimates of precipitation. We present the design and implementation of a state-of-the-art real-time streamflow monitoring and forecasting platform that integrates information provided by cutting-edge satellite precipitation products (SPPs), numerical precipitation forecasts, and multiple hydrologic models, to generate probabilistic streamflow forecasts that have an effective lead time of 9 days. The modular design of the platform enables adding/removing any model/product as may be appropriate. The SPPs are bias-corrected in real-time, and the model-generated streamflow forecasts are further bias-corrected and merged, to produce probabilistic forecasts that are computed via several model averaging techniques. The platform is currently operational in multiple river basins in Africa, and can also be adapted to any new basin by incorporating some basin-specific changes and recalibration of the hydrologic models.

scholarly journals Remote Sensing of Large River Basins

2020 ◽  
Author(s):  
Soner Uereyen ◽  
Felix Bachofer ◽  
Juliane Huth ◽  
Igor Klein ◽  
Claudia Kuenzer
Keyword(s):  
Surface Water ◽  
Spatial Scale ◽  
Land Surface ◽  
Large Scale ◽  
River Basins ◽  
Large River ◽  
Research Articles ◽  
Human Society ◽  
Large River Basins

<p>Irrespective of administrative boundaries, river basins are natural spatial units covering the entire land area. They provide many resources, including freshwater, which is essential for the environment and human society, as well as irrigation water and hydropower. At the same time, river basins are highly pressured i.e. by human induced environmental changes, such as deforestation, urban expansion, dam construction, as well as climate change induced sea level rise at estuarine regions and extreme events such as droughts and flooding. Therefore, monitoring of river basins is of high importance to understand their current and future state, in particular for researchers, stake holders and decision makers. However, land surface and surface water variables of many large river basins remain mostly unmonitored at basin scale. Currently, only a few inventories characterizing large scale river basins exist. Here, spatially and temporally consistent databases describing the evolution and status of large river basins are lacking. In this context, Earth observation (EO) is a potential source of spatial information providing large scale data at global scale. In this study, we provide a comprehensive overview of research articles focusing on EO-based characterization of large river basins and corresponding land surface and surface water parameters, we summarize the spatial distribution and spatial scale of investigated study areas, we analyze used sensor types and their temporal resolution, and we identify how EO can further contribute to characterization of large river basins. The results reveal that most of the reviewed research articles focus on mapping of vegetation, surface water, as well as land cover and land use properties. In addition, we found that research articles related to EO applications hardly investigate study areas at the spatial scale of large river basins. Overall, the findings of our review contribute to a better understanding of the potentials and limitations of EO-based analyses of large river basins.</p>

scholarly journals Fuzzy Postprocessing to Advance the Quality of Continental Seasonal Hydrological Forecasts for River Basin Management

2020 ◽  
Vol 21 (10) ◽  
pp. 2375-2389
Author(s):  
Hector Macian-Sorribes ◽  
Ilias Pechlivanidis ◽  
Louise Crochemore ◽  
Manuel Pulido-Velazquez
Keyword(s):  
River Basin ◽  
Large Scale ◽  
Hydrological Model ◽  
Hydrological Models ◽  
Streamflow Forecasting ◽  
Seasonal Forecasts ◽  
Quantile Mapping ◽  
River Basin Scale ◽  
Basin Scale ◽  
Streamflow Forecasts

AbstractStreamflow forecasting services driven by seasonal meteorological forecasts from dynamic prediction systems deliver valuable information for decision-making in the water sector. Moving beyond the traditional river basin boundaries, large-scale hydrological models enable a coordinated, efficient, and harmonized anticipation and management of water-related risks (droughts, floods). However, the use of forecasts from such models at the river basin scale remains a challenge, depending on how the model reproduces the hydrological features of each particular river basin. Consequently, postprocessing of forecasts is a crucial step to ensure usefulness at the river basin scale. In this paper we present a methodology to postprocess seasonal streamflow forecasts from large-scale hydrological models and advance their quality for local applications. It consists of fuzzy logic systems that bias-adjust seasonal forecasts from a large-scale hydrological model by comparing its modeled streamflows with local observations. The methodology is demonstrated using forecasts from the pan-European hydrological model E-HYPE at the Jucar River basin (Spain). Fuzzy postprocessed forecasts are compared to postprocessed forecasts derived from a quantile mapping approach as a benchmark. Fuzzy postprocessing was able to provide skillful streamflow forecasts for the Jucar River basin, keeping most of the skill of raw E-HYPE forecasts and also outperforming quantile-mapping-based forecasts. The proposed methodology offers an efficient one-to-one mapping between large-scale modeled streamflows and basin-scale observations preserving its temporal dependence structure and can adapt its input set to increase the skill of postprocessed forecasts.

Climate change decisive for Asia’s snow meltwater supply

2020 ◽  
Author(s):  
Philip Kraaijenbrink ◽  
Emmy Stigter ◽  
Tandong Yao ◽  
Walter Immerzeel
Keyword(s):  
Climate Change ◽  
Water Supply ◽  
Large Scale ◽  
Snow Water Equivalent ◽  
River Basins ◽  
Large River ◽  
Future Climate Change ◽  
Seasonal Snow ◽  
Temperature And Precipitation ◽  
Snow Model

<p>Meltwater from seasonal snow provides a substantial amount of runoff to many of the rivers that originate in the high mountains of Asia, yet the importance of snow in the region as streamflow component, its changes over the past decades, and its sensitivity to future climatic changes are relatively unknown. To understand future changes in the water supply to the millions of people living downstream, a better understanding of snow dynamics at large scale is key. Using a novel snow model, forced by ERA5 climate reanalysis and calibrated by MODIS remote sensing observations, we generate daily snow water equivalent output at 0.05° resolution covering all major river basins in Asia. We show that between 1979 and 2018 significant and spatially variable changes have occurred in snow meltwater availability and its timing, with melt peaks attenuating and/or advancing in time, and snowmelt seasons shortening. Additionally, our results reveal that snowmelt is a much more important contributor to streamflow than glacier melt in many of Asia's large river basins. In a bottom-up elasticity analysis we project strong changes in snowmelt in the future under changing temperature and precipitation. Sensitivity of snowmelt to climate change varies among basins, however, and actual losses are strongly dependent on the degree of future climate change. Limiting climate change in the current century is therefore crucial in order to sustain the role of seasonal snow packs in Asia’s water supply.</p>

scholarly journals Assessment of an ensemble seasonal streamflow forecasting system for Australia

2017 ◽  
Author(s):  
James C. Bennett ◽  
Quan J. Wang ◽  
David E. Robertson ◽  
Andrew Schepen ◽  
Ming Li ◽  
...  
Keyword(s):  
Lead Times ◽  
Streamflow Forecasting ◽  
Forecast Horizon ◽  
Australian Continent ◽  
Wide Range ◽  
Streamflow Forecasts ◽  
Ephemeral Rivers ◽  
Forecasting System ◽  
Rainfall Runoff Model ◽  
Seasonal Streamflow

Abstract. Despite an increasing availability of skillful long-range streamflow forecasts, many water agencies still rely on simple resampled historical inflow sequences (stochastic scenarios) to plan operations over the coming year. We assess a recently developed forecasting system called forecast guided stochastic scenarios (FoGSS) as a skillful alternative to standard stochastic scenarios for the Australian continent. FoGSS uses climate forecasts from a coupled ocean-land-atmosphere prediction system, post-processed with the method of calibration, bridging and merging. Ensemble rainfall forecasts force a monthly rainfall-runoff model, while a staged hydrological error model quantifies and propagates hydrological forecast uncertainty through forecast lead times. FoGSS is able to generate ensemble streamflow forecasts in the form of monthly time series to a 12-month forecast horizon. FoGSS is tested on 63 Australian catchments that cover a wide range of climates, including 21 ephemeral rivers. In all perennial and many ephemeral catchments, FoGSS provides an effective alternative to resampled historical inflow sequences. FoGSS generally produces skillful forecasts at shorter lead times (

scholarly journals Error Evolution in Multi-Step Ahead Streamflow Forecasting for the Operation of Hydropower Reservoirs

2017 ◽  
Author(s):  
Georgia Papacharalampous ◽  
Hristos Tyralis ◽  
Demetris Koutsoyiannis
Keyword(s):  
Large Scale ◽  
Practical Interest ◽  
Streamflow Forecasting ◽  
Forecast Horizon ◽  
Time Step ◽  
Forecasting Methods ◽  
Forecasting Method ◽  
Multiple Case ◽  
Hydropower Reservoirs

Multi-step ahead streamflow forecasting is of practical interest for the operation of hydropower reservoirs. We provide generalized results on the error evolution in multi-step ahead forecasting by conducting several large-scale experiments based on simulations. We also present a multiple-case study using monthly time series of streamflow. Our findings suggest that some forecasting methods are more useful than others. However, the errors computed at each time step of a forecast horizon within a specific case study strongly depend on the case examined and can be either small or large, regardless of the forecasting method used and the time step of interest.

To the comparative analysis of the digitalization process of state administration in Russia and other countries

2020 ◽  
pp. 75-117
Author(s):  
A.N. Shvetsov
Keyword(s):  
Public Administration ◽  
Information And Communication Technologies ◽  
Large Scale ◽  
Communication Technologies ◽  
Developed Countries ◽  
Electronic Government ◽  
Digital Government ◽  
Information And Communication ◽  
The Developed Countries ◽  
Comprehensive Programs

The article compares the processes of dissemination of modern information and communication technologies in government bodies in Russia and abroad. It is stated that Russia began the transition to «electronic government» later than the developed countries, in which this process was launched within the framework of large-scale and comprehensive programs for reforming public administration in the 1980s and 1990s. However, to date, there is an alignment in the pace and content of digitalization tasks. At a new stage in this process, the concept of «electronic government» under the influence of such newest phenomena of the emerging information society as methods of analysis of «big data», «artificial intelligence», «Internet of things», «blockchain» is being transformed into the category of «digital government». Achievements and prospects of public administration digitalization are considered on the example of countries with the highest ratings — Denmark, Australia, Republic of Korea, Great Britain, USA and Russia.

scholarly journals Seasonal streamflow forecasting by conditioning climatology with precipitation indices

2017 ◽  
Vol 21 (3) ◽  
pp. 1573-1591 ◽  
Author(s):  
Louise Crochemore ◽  
Maria-Helena Ramos ◽  
Florian Pappenberger ◽  
Charles Perrin
Keyword(s):  
Long Range ◽  
Precipitation Forecast ◽  
Low Flow ◽  
Drought Risk ◽  
Streamflow Forecasting ◽  
Streamflow Forecasts ◽  
Precipitation Indices ◽  
Seasonal Streamflow ◽  
Prediction Approach ◽  
The Impact

Abstract. Many fields, such as drought-risk assessment or reservoir management, can benefit from long-range streamflow forecasts. Climatology has long been used in long-range streamflow forecasting. Conditioning methods have been proposed to select or weight relevant historical time series from climatology. They are often based on general circulation model (GCM) outputs that are specific to the forecast date due to the initialisation of GCMs on current conditions. This study investigates the impact of conditioning methods on the performance of seasonal streamflow forecasts. Four conditioning statistics based on seasonal forecasts of cumulative precipitation and the standardised precipitation index were used to select relevant traces within historical streamflows and precipitation respectively. This resulted in eight conditioned streamflow forecast scenarios. These scenarios were compared to the climatology of historical streamflows, the ensemble streamflow prediction approach and the streamflow forecasts obtained from ECMWF System 4 precipitation forecasts. The impact of conditioning was assessed in terms of forecast sharpness (spread), reliability, overall performance and low-flow event detection. Results showed that conditioning past observations on seasonal precipitation indices generally improves forecast sharpness, but may reduce reliability, with respect to climatology. Conversely, conditioned ensembles were more reliable but less sharp than streamflow forecasts derived from System 4 precipitation. Forecast attributes from conditioned and unconditioned ensembles are illustrated for a case of drought-risk forecasting: the 2003 drought in France. In the case of low-flow forecasting, conditioning results in ensembles that can better assess weekly deficit volumes and durations over a wider range of lead times.

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