PREMHYCE: An operational tool for low-flow forecasting

Abstract. In many countries, rivers are the primary supply of water. A number of uses are concerned (drinking water, irrigation, hydropower, etc.) and they can be strongly affected by water shortages. Therefore, there is a need for the early anticipation of low-flow periods to improve water management. This is strengthened by the perspective of having more severe summer low flows in the context of climate change. Several French institutions (Inrae, BRGM, Météo-France, EDF and Lorraine University) have been collaborating over the last years to develop an operational tool for low-flow forecasting, called PREMHYCE. It was tested in real time on 70 catchments in continental France in 2017, and on 48 additional catchments in 2018. PREMHYCE includes five hydrological models: one uncalibrated physically-based model and four storage-type models of various complexity, which are calibrated on gauged catchments. The models assimilate flow observations or implement post-processing techniques. Low-flow forecasts can be issued up to 90 d ahead, based on ensemble streamflow prediction (ESP) using historical climatic data as ensembles of future input scenarios. These climatic data (precipitation, potential evapotranspiration and temperature) are provided by Météo-France with the daily gridded SAFRAN reanalysis over the 1958–2017 period, which includes a wide range of conditions. The tool provides numerical and graphical outputs, including the forecasted ranges of low flows, and the probability to be under low-flow warning thresholds provided by the users. Outputs from the different hydrological models can be combined through a simple multi-model approach to improve the robustness of forecasts. Results are illustrated for the Ill River at Didenheim (northeastern France) where the 2017 low-flow period was particularly severe and for which PREMHYCE provided useful forecasts.

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PREMHYCE: An operational tool for low-flow forecasting

10.5194/egusphere-egu2020-19335 ◽

2020 ◽

Author(s):

Pierre Nicolle ◽

François Besson ◽

François Bourgin ◽

Didier François ◽

Matthieu Le Lay ◽

...

Keyword(s):

Graphical Representation ◽

Evaluation Framework ◽

Low Flow ◽

Climatic Data ◽

Hydrological Models ◽

Streamflow Prediction ◽

Water Shortages ◽

Low Flows ◽

Wide Range ◽

Operational Tool

In many countries, rivers are the primary supply of water. A number of uses are concerned (drinking water, irrigation, hydropower&#8230;) and they can be strongly affected by water shortages. Therefore, there is a need of early anticipation of low-flow periods to improve water management. This is strengthened by the perspective of having more severe summer low-flows in the context of climate change. Several French institutes (Irstea, BRGM, M&#233;t&#233;o-France, EDF and Lorraine University) have been collaborating to develop an operational tool for low-flow forecasting, called PREMHYCE. It is tested in real time since 2017, and implemented on 259 catchments in metropolitan France, in cooperation with operational services which provide streamflow observations and use low-flow forecasts from the tool. PREMHYCE includes five hydrological models which can be calibrated on gauged catchments and which assimilate flow observations. Low-flow forecasts can be issued up to 90 days ahead, based on several inputs scenarios: ECMWF 10-days ensemble forecasts, ensemble streamflow prediction (ESP) using historical climatic data as ensembles of future input scenarios, and a no precipitation scenario. Climatic data (precipitation, evapotranspiration and temperature) are provided by M&#233;t&#233;o-France with the daily gridded SAFRAN reanalysis on the 1959-2019 period, which includes a wide range of conditions. The tool provides text files and graphical representation of forecasted low-flows, and probability to be under low-flow thresholds provided by users. Outputs from the different hydrological models can be combined within a multi-model approach to improve robustness of the forecastsThe presentation will show the main characteristics of this operational tool, the probabilistic evaluation framework, results on the recent low-flow periods, and how feedbacks from end-users can help improving the tool.

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Low-flow forecasting in France: update on the latest developments of the PREMHYCE operational forecast platform

10.5194/egusphere-egu21-2856 ◽

2021 ◽

Author(s):

François Bourgin ◽

François Tilmant ◽

Anne-Lise Véron ◽

François Besson ◽

Didier François ◽

...

Keyword(s):

Graphical Representation ◽

Low Flow ◽

Climatic Data ◽

Hydrological Models ◽

Streamflow Prediction ◽

National Scale ◽

Ensemble Forecasts ◽

Operational Forecast ◽

Set Up ◽

Model Approach

Low-flow forecasting can help to improve water management at places where a number of uses can be affected by diminishing water supply from rivers. Several French institutes (INRAE, BRGM, EDF, Lorraine University and M&#233;t&#233;o-France) have been collaborating to set up an operational platform, called PREMHYCE, for low-flow forecasting at the national scale, in cooperation with operational services. PREMHYCE includes five hydrological models and low-flow forecasts can be issued up to 90 days ahead for more than 800 basins. Several input scenarios are considered: ECMWF 14-days ensemble forecasts, ensemble streamflow prediction (ESP) using historical climatic data, and a no precipitation scenario. Outputs from the different hydrological models are combined into a multi-model approach to improve robustness of the forecasts. The tool provides text files and graphical representation of forecasted low-flows, as well as key low-flow indicators, such as the probabilities of being under low-flow thresholds provided by operational services. The presentation will show the main characteristics of this operational forecast platform, its latest developments and the results on the recent low-flow periods.

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The skill of seasonal ensemble low flow forecasts for four different hydrological models

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-11-5377-2014 ◽

2014 ◽

Vol 11 (5) ◽

pp. 5377-5420 ◽

Cited By ~ 1

Author(s):

M. C. Demirel ◽

M. J. Booij ◽

A. Y. Hoekstra

Keyword(s):

Conceptual Models ◽

Potential Evapotranspiration ◽

The Other ◽

Data Driven ◽

Low Flow ◽

Lead Times ◽

Hydrological Models ◽

Meteorological Forcing ◽

Low Flows ◽

Better Than

Abstract. This paper investigates the skill of 90 day low flow forecasts using two conceptual hydrological models and two data-driven models based on Artificial Neural Networks (ANNs) for the Moselle River. One data-driven model, ANN-Indicator (ANN-I), requires historical inputs on precipitation (P), potential evapotranspiration (PET), groundwater (G) and observed discharge (Q), whereas the other data-driven model, ANN-Ensemble (ANN-E), and the two conceptual models, HBV and GR4J, use forecasted meteorological inputs (P and PET), whereby we employ ensemble seasonal meteorological forecasts. We compared low flow forecasts without any meteorological forecasts as input (ANN-I) and five different cases of seasonal meteorological forcing: (1) ensemble P and PET forecasts; (2) ensemble P forecasts and observed climate mean PET; (3) observed climate mean P and ensemble PET forecasts; (4) observed climate mean P and PET and (5) zero P and ensemble PET forecasts as input for the other three models (GR4J, HBV and ANN-E). The ensemble P and PET forecasts, each consisting of 40 members, reveal the forecast ranges due to the model inputs. The five cases are compared for a lead time of 90 days based on model output ranges, whereas the four models are compared based on their skill of low flow forecasts for varying lead times up to 90 days. Before forecasting, the hydrological models are calibrated and validated for a period of 30 and 20 years respectively. The smallest difference between calibration and validation performance is found for HBV, whereas the largest difference is found for ANN-E. From the results, it appears that all models are prone to over-predict low flows using ensemble seasonal meteorological forcing. The largest range for 90 day low flow forecasts is found for the GR4J model when using ensemble seasonal meteorological forecasts as input. GR4J, HBV and ANN-E under-predicted 90 day ahead low flows in the very dry year 2003 without precipitation data, whereas ANN-I predicted the magnitude of the low flows better than the other three models. The results of the comparison of forecast skills with varying lead times show that GR4J is less skilful than ANN-E and HBV. Furthermore, the hit rate of ANN-E is higher than the two conceptual models for most lead times. However, ANN-I is not successful in distinguishing between low flow events and non-low flow events. Overall, the uncertainty from ensemble P forecasts has a larger effect on seasonal low flow forecasts than the uncertainty from ensemble PET forecasts and initial model conditions.

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The effects of climatic anomalies on low flows in Switzerland

10.5194/hess-2019-448 ◽

2019 ◽

Cited By ~ 2

Author(s):

Marius G. Floriancic ◽

Wouter R. Berghuijs ◽

James W. Kirchner ◽

Peter Molnar

Keyword(s):

River Flow ◽

Potential Evapotranspiration ◽

Weather Conditions ◽

Climatic Conditions ◽

Low Flow ◽

Weather Forecasts ◽

Environmental Consequences ◽

Low Flows ◽

Insight Into ◽

Seasonal Weather

Abstract. Large parts of Europe have faced extreme low river flows in recent summers (2003, 2011, 2015, 2018) with major economic and environmental consequences. Understanding the origins of extremes like these is important for water resources management. To reveal how weather drives low flows, we explore how deviations from mean seasonal climatic conditions (i.e. climatic anomalies) of precipitation and potential evapotranspiration shaped the occurrence and magnitude of the annual 7-day lowest flows (Qmin) across 380 Swiss catchments from 2000 through 2018. Most annual low flows followed periods of below average precipitation and above average potential evapotranspiration, and the most extreme low flows resulted from both of these drivers acting together. Extremely dry years saw simultaneous drought conditions across large parts of Europe, but low flow timing during these years was still spatially variable across Switzerland. Longer climatic anomalies led to lower low flows. Most low flows were typically preceded by climatic anomalies lasting up to two months, whereas low flows in the extreme years (2003, 2011, 2015, 2018) were associated with much longer-lasting climatic anomalies. Weather conditions on even longer time scales have been reported to sometimes affect river flow. However, across Switzerland, we found that precipitation totals in winter only slightly influenced the magnitude and timing of summer and autumn low flows. Our results provide insight into how precipitation and potential evapotranspiration jointly shape summer and winter low flows across Switzerland, and could potentially aid in assessing low-flow risks in similar mountain regions using seasonal weather forecasts.

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Does the Complexity of Evapotranspiration and Hydrological Models Enhance Robustness?

Sustainability ◽

10.3390/su10082837 ◽

2018 ◽

Vol 10 (8) ◽

pp. 2837 ◽

Cited By ~ 5

Author(s):

Dereje Birhanu ◽

Hyeonjun Kim ◽

Cheolhee Jang ◽

Sanghyun Park

Keyword(s):

Potential Evapotranspiration ◽

Estimation Method ◽

Model Performance ◽

Low Flow ◽

Estimation Methods ◽

Model Parameters ◽

Hydrological Models ◽

Shuffled Complex Evolution ◽

University Of Arizona ◽

Optimizing Model

In this study, five hydrological models of increasing complexity and 12 Potential Evapotranspiration (PET) estimation methods of different data requirements were applied in order to assess their effect on model performance, optimized parameters, and robustness. The models were applied over a set of 10 catchments that are located in South Korea. The Shuffled Complex Evolution-University of Arizona (SCE-UA) algorithm was implemented to calibrate the hydrological models for each PET input while considering similar objective functions. The hydrological models’ performance was satisfactory for each PET input in the calibration and validation periods for all of the tested catchments. The five hydrological models’ performance were found to be insensitive to the 12 PET inputs because of the SCE-UA algorithm’s efficiency in optimizing model parameters. However, the five hydrological models’ parameters in charge of transforming the PET to actual evapotranspiration were sensitive and significantly affected by the PET complexity. The values of the three statistical indicators also agreed with the computed model evaluation index values. Similarly, identical behavioral similarities and Dimensionless Bias were observed in all of the tested catchments. For the five hydrological models, lack of robustness and higher Dimensionless Bias were seen for high and low flow as well as for the Hamon PET input. The results indicated that the complexity of the hydrological models’ structure and the PET estimation methods did not necessarily enhance model performance and robustness. The model performance and robustness were found to be mainly dependent on extreme hydrological conditions, including high and low flow, rather than complexity; the simplest hydrological model and PET estimation method could perform better if reliable hydro-meteorological datasets are applied.

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Frequency Analysis of Low Flows: Hypothetical Distribution Methods and a Physically Based Approach

Hydrology Research ◽

10.2166/nh.1986.0009 ◽

1986 ◽

Vol 17 (3) ◽

pp. 129-150 ◽

Cited By ~ 11

Author(s):

G. V. Loganathan ◽

P. Mattejat ◽

C. Y. Kuo ◽

M. H. Diskin

Keyword(s):

Probability Distribution ◽

Exponential Decay ◽

Low Flow ◽

Independent Random Variables ◽

Intermittent Streams ◽

Steady State Probability ◽

Partial Duration Series ◽

Low Flows ◽

Pearson Type ◽

Physically Based

A mixed log Pearson type III distribution, a double bounded probability density function, partial duration series and a physically based approach are analyzed for frequency estimates of low flows. The mixed log Pearson III involves a point probability mass at zero for intermittent streams. The double bounded probability distribution has lower and upper bounds with a point mass at the lower bound. Two approaches are used in partial duration series i) truncation, and ii) censoring which represent curtailing of the population and the sample respectively. The parameters are estimated by maximum likelihood procedure. Considering low flows as part of the recession limb of stream flow hydrographs a physically based approach is formulated. By using the exponential decay of stream recessions and considering the initial recession flows, recession durations, and recharge due to incoming storms as statistically independent random variables, a first order random coefficient Markov model for initial recession flows is formed. The resulting steady state probability distribution for initial recession flows is combined with the probability distribution of the exponential decay to obtain the probabilities of low flow events. The methods are applied to both perennial and intermittent streams.

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Climatic water balance in Hamelmalo, Eritrea

Acta Agraria Debreceniensis ◽

10.34101/actaagrar/1/8307 ◽

2021 ◽

pp. 69-76

Author(s):

Mehari Gebreyesus ◽

Arzu Rivera Garcia ◽

Géza Tuba ◽

Györgyi Kovács ◽

Lúcia Sinka ◽

...

Keyword(s):

Climate Change ◽

Water Balance ◽

Potential Evapotranspiration ◽

Climatic Data ◽

Total Production ◽

Relative Air Humidity ◽

Wide Range ◽

Long Time ◽

Time Changes ◽

Evaporation Pan

Agricultural production is an important sector for peoples to live, but it is highly affected by climate change. To have a good production we need to understand the climatic parameters which adversely affect production. Hamelmalo, which is located in the semi-arid area of Eritrea, is vulnerable to climate change and this is realised in the total production loss. Nevertheless, there is no concrete reference about the climate of the region due to lack of data for a long time. Changes in precipitation (P), evapotranspiration (ET) and, implicitly, in the climatic water balance (CWB), are imminent effects of climate change. However, changes in the CWB, as a response to changes in P and ET, have not yet been analysed thoroughly enough in many parts of the world, including Eritrea. This study also explores the changes of the CWB in the Hamelmalo region, based on a wide range of climatic data (P, relative air humidity and evaporation pan necessary for computing potential evapotranspiration (PET) with the pan evaporation method) recorded at Hamelmalo from 2015-2019. This analysis shows that the annual cumulative CWB for Hamelmalo is negative in 67% of the years. The dry season without precipitation leads to negative CWB and the change in CWB only starts from the raining or crop season. Based on this recent study, 2015 had the highest PET and lowest P, and this resulted in the lowest CWB in the investigated period. Opposite to this, 2019 had lower PET and highest P, which led to the highest CWB. However, the monthly values of CWB did not correlate with the annual P or ET. On the base of our study, it can be concluded that PET and P were very variable in the investigated years and P was the most influential elements of CWB.

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Comparison of hydrological model structures based on recession and low flow simulations

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-8-6833-2011 ◽

2011 ◽

Vol 8 (4) ◽

pp. 6833-6866 ◽

Cited By ~ 2

Author(s):

M. Staudinger ◽

K. Stahl ◽

J. Seibert ◽

M. P. Clark ◽

L. M. Tallaksen

Keyword(s):

Hydrological Model ◽

Lower Layer ◽

Model Performance ◽

Low Flow ◽

Model Structure ◽

Hydrological Models ◽

Flow Simulations ◽

Low Flows ◽

The Impact ◽

Model Structures

Abstract. Low flows are often poorly reproduced by commonly used hydrological models, which are traditionally designed to meet peak flow situations. Hence, there is a need to improve hydrological models for low flow prediction. This study assessed the impact of model structure on low flow simulations and recession behaviour using the Framework for Understanding Structural Errors (FUSE). FUSE identifies the set of subjective decisions made when building a hydrological model, and provides multiple options for each modeling decision. Altogether 79 models were created and applied to simulate stream flows in the snow dominated headwater catchment Narsjø in Norway (119 km2). All models were calibrated using an automatic optimisation method. The results showed that simulations of summer low flows were poorer than simulations of winter low flows, reflecting the importance of different hydrological processes. The model structure influencing winter low flow simulations is the lower layer architecture, whereas various model structures were identified to influence model performance during summer.

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Constraining a lumped rainfall-runoff model with piezometry to improve low-flow simulation

10.5194/egusphere-egu21-4402 ◽

2021 ◽

Author(s):

Antoine Pelletier ◽

Vakzen Andréassian

Keyword(s):

Flow Simulation ◽

Daily Rainfall ◽

Climatic Conditions ◽

Low Flow ◽

Hydrological Models ◽

Rainfall Runoff ◽

Low Flows ◽

Sample Test ◽

Rainfall Runoff Model ◽

Runoff Model

Most lumped hydrological models are focused on the rainfall-runoff relationship, since climatic conditions are the driving force of the hydrological behaviour of a catchment. Many hydrological models, like the ones used by the French national PREMHYCE platform, only take climatic variables as inputs &#8211; daily rainfall and potential evaporation &#8211; to simulate and forecast low-flows. Yet, a hydrological drought is generally a medium- to long-term phenomenon, which is the consequence of long records of dry climatic conditions. Daily lumped hydrological models often struggle to integrate these records to reproduce catchment memory.In many French catchments, it was observed that this memory of past hydroclimatic conditions is well represented in piezometric signals that are broadly available over the national territory. Indeed, aquifers, especially the large ones, do store water on the long, feeding rivers during droughts: aquifers are not only water carriers &#8211; the etymology for the word aquifer &#8211; they are also memory carriers. A dataset of 108 catchments, each of them being associated with one or several piezometers, was used to investigate whether the GR6J daily lumped rainfall-runoff model could be constrained by piezometric time series to improve low-flow simulations. We found that a particular state of the model, the exponential store, is particularly well correlated with piezometry in most studied catchments.In order to get a univocal relationship between the exponential store and piezometry, a multi-objective calibration approach was implemented, optimising both (i) flow simulation with a criterion focused on low-flows and (ii) affine correspondence between the exponential store level and piezometry. For that purpose, a new parameter was added to the model. The modified calibration was then evaluated through a split-sample test and the performance in simulating particular drought events. The calibrated store-piezometry relationship can now be used for data assimilation to improve low-flow forecasting.

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Future changes in snow and its influence on seasonal runoff and low flows in Czechia

10.5194/egusphere-egu21-80 ◽

2021 ◽

Author(s):

Michal Jenicek ◽

Jan Hnilica ◽

Ondrej Nedelcev ◽

Vaclav Sipek

Keyword(s):

Air Temperature ◽

Future Climate ◽

Low Flow ◽

Large Set ◽

Large Variability ◽

Low Flows ◽

Mitigation And Adaptation ◽

Wide Range ◽

The Future ◽

Seasonal Runoff

Mountains are often called as &#8220;water towers&#8221; because they substantially affect hydrology of downstream areas. However, snow storages are decreasing and snow melts earlier mainly due to air temperature increase. These changes largely affect seasonal runoff distribution, including summer low flows and thus influence the water availability. Therefore, it is important to investigate the future change in relation between snow and summer low flows, specifically to assess a wide range of hydrological responses to different climate predictions. Therefore, the main objectives of this study were 1) to simulate the future changes in snow storages for a large set of mountain catchments representing different elevations and to 2) analyse how the changes in snow storages will affect streamflow seasonality and low flows in the future reflecting a wide range of climate predictions. The predictions of the future climate from EURO-CORDEX experiment for 59 mountain catchments in Czechia were considered. These data were further used to drive a bucket-type catchment model, HBV-light, to simulate individual components of the rainfall-runoff process for the reference period and three future periods.Future simulations showed a dramatic decrease in snow-related variables for all catchments at all elevations. For example, annual maximum SWE decreased by 30%-70% until the end of the 21st century compared to the current climate. Additionally, the snow will melt on average by 3-4 weeks earlier in the future. The results also showed the large variability between individual climate chains and indicated that the increase in air temperature causing the decrease in snowfall might be partly compensated by the increase in winter precipitation. Expected changes in snowpack will cause by a month earlier period with highest streamflow during melting season in addition to lower spring runoff volume due to lower snowmelt inputs. The future climate scenarios leading to overall dry conditions in summer are associated with both lowest summer precipitation and seasonal snowpack. The expected lower snow storages might therefore contribute to more extreme low flow periods. The results also showed considerably smaller changes for the RCP 2.6 scenario compared to the RCP 4.5 and RCP 8.5 both in terms snow storages and seasonal runoff. The results are therefore important for mitigation and adaptation strategy related to climate change impacts in mountain regions.

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