scholarly journals Contribution of Potential Evaporation Forecasts to 10-day streamflow forecast skill for the Rhine river

2018 ◽  
Author(s):  
Bart van Osnabrugge ◽  
Remko Uijlenhoet ◽  
Albrecht Weerts
Keyword(s):  
Radiative Forcing ◽  
Potential Evapotranspiration ◽  
Global Radiation ◽  
Forecast Skill ◽  
Meteorological Variables ◽  
Rhine River ◽  
Potential Evaporation ◽  
Streamflow Forecasting ◽  
Streamflow Forecast ◽  
Rhine Basin

Abstract. Medium term hydrologic forecast uncertainty is strongly dependent on the forecast quality of meteorological variables. Of these variables, the influence of precipitation has been studied most widely, while temperature, radiative forcing and their derived product potential evapotranspiration (PET) have received little attention from the perspective of hydrological forecasting. This study aims to fill this gap by assessing the usability of potential evaporation forecasts for 10-day-ahead streamflow forecasting in the Rhine basin, Europe. In addition, the forecasts of the meteorological variables are compared with observations. Streamflow reforecasts were performed with the daily wflow_hbv model used in previous studies of the Rhine using the ECMWF 20-year meteorological reforecast dataset. Meteorological forecasts were compared with observed rainfall, temperature, global radiation and potential evaporation for 148 subbasins. Secondly, the effect of using PET climatology versus using observation-based estimates of PET was assessed for hydrological state and for streamflow forecast skill. We find that: (1) there is considerable skill in the ECMWF reforecasts to predict PET for all seasons, (2) using dynamical PET forcing based on observed temperature and satellite global radiation estimates results in lower evaporation and wetter initial states, but (3) the effect on forecasted 10-day streamflow is limited. Implications of this finding are that it is reasonable to use meteorological forecasts to forecast potential evaporation and use this is in medium-range streamflow forecasts. However, it can be concluded that an approach using PET climatology is also sufficient, most probably not only for the application shown here, but for most models similar to the HBV concept and for moderate climate zones. As a by-product, this research resulted in gridded datasets for temperature, radiation and potential evaporation based on the Makkink equation for the Rhine basin. The datasets have a spatial resolution of 1.2 × 1.2 km and an hourly timestep for the period from July 1996 through 2015. This dataset complements an earlier precipitation dataset for the same area, period and resolution.

scholarly journals Contribution of potential evaporation forecasts to 10-day streamflow forecast skill for the Rhine River

2019 ◽  
Vol 23 (3) ◽  
pp. 1453-1467 ◽  
Author(s):  
Bart van Osnabrugge ◽  
Remko Uijlenhoet ◽  
Albrecht Weerts
Keyword(s):  
Radiative Forcing ◽  
Potential Evapotranspiration ◽  
Global Radiation ◽  
Forecast Skill ◽  
Meteorological Variables ◽  
Potential Evaporation ◽  
Streamflow Forecasting ◽  
Time Step ◽  
Streamflow Forecast ◽  
Rhine Basin

Abstract. Medium-term hydrologic forecast uncertainty is strongly dependent on the forecast quality of meteorological variables. Of these variables, the influence of precipitation has been studied most widely, while temperature, radiative forcing and their derived product potential evapotranspiration (PET) have received little attention from the perspective of hydrological forecasting. This study aims to fill this gap by assessing the usability of potential evaporation forecasts for 10-day-ahead streamflow forecasting in the Rhine basin, Europe. In addition, the forecasts of the meteorological variables are compared with observations. Streamflow reforecasts were performed with the daily wflow_hbv model used in previous studies of the Rhine using the ECMWF 20-year meteorological reforecast dataset. Meteorological forecasts were compared with observed rainfall, temperature, global radiation and potential evaporation for 148 subbasins. Secondly, the effect of using PET climatology versus using observation-based estimates of PET was assessed for hydrological state and for streamflow forecast skill. We find that (1) there is considerable skill in the ECMWF reforecasts to predict PET for all seasons, and (2) using dynamical PET forcing based on observed temperature and satellite global radiation estimates results in lower evaporation and wetter initial states, but (3) the effect on forecasted 10-day streamflow is limited. Implications of this finding are that it is reasonable to use meteorological forecasts to forecast potential evaporation and use this is in medium-range streamflow forecasts. However, it can be concluded that an approach using PET climatology is also sufficient, most probably not only for the application shown here, but also for most models similar to the HBV concept and for moderate climate zones. As a by-product, this research resulted in gridded datasets for temperature, radiation and potential evaporation based on the Makkink equation for the Rhine basin. The datasets have a spatial resolution of 1.2×1.2 km and an hourly time step for the period from July 1996 through 2015. This dataset complements an earlier precipitation dataset for the same area, period and resolution.

scholarly journals Benchmarking Real-Time Streamflow Forecast Skill in the Himalayan Region

Forecasting ◽  
2020 ◽  
Vol 2 (3) ◽  
pp. 230-247
Author(s):  
Ganesh R. Ghimire ◽  
Sanjib Sharma ◽  
Jeeban Panthi ◽  
Rocky Talchabhadel ◽  
Binod Parajuli ◽  
...  
Keyword(s):  
Real Time ◽  
Water Policy ◽  
Low Complexity ◽  
Critical Issue ◽  
Forecast Skill ◽  
Streamflow Forecasting ◽  
Streamflow Forecast ◽  
Time Flow ◽  
Wide Range ◽  
Streamflow Forecasts

Improving decision-making in various areas of water policy and management (e.g., flood and drought preparedness, reservoir operation and hydropower generation) requires skillful streamflow forecasts. Despite the recent advances in hydrometeorological prediction, real-time streamflow forecasting over the Himalayas remains a critical issue and challenge, especially with complex basin physiography, shifting weather patterns and sparse and biased in-situ hydrometeorological monitoring data. In this study, we demonstrate the utility of low-complexity data-driven persistence-based approaches for skillful streamflow forecasting in the Himalayan country Nepal. The selected approaches are: (1) simple persistence, (2) streamflow climatology and (3) anomaly persistence. We generated the streamflow forecasts for 65 stream gauge stations across Nepal for short-to-medium range forecast lead times (1 to 12 days). The selected gauge stations were monitored by the Department of Hydrology and Meteorology (DHM) Nepal, and they represent a wide range of basin size, from ~17 to ~54,100 km2. We find that the performance of persistence-based forecasting approaches depends highly upon the lead time, flow threshold, basin size and flow regime. Overall, the persistence-based forecast results demonstrate higher forecast skill in snow-fed rivers over intermittent ones, moderate flows over extreme ones and larger basins over smaller ones. The streamflow forecast skill obtained in this study can serve as a benchmark (reference) for the evaluation of many operational forecasting systems over the Himalayas.

scholarly journals Monthly streamflow forecasting at varying spatial scales in the Rhine basin

2018 ◽  
Vol 22 (2) ◽  
pp. 929-942 ◽  
Author(s):  
Simon Schick ◽  
Ole Rössler ◽  
Rolf Weingartner
Keyword(s):  
Initial Conditions ◽  
Spatial Scales ◽  
Surface Air Temperature ◽  
Absolute Error ◽  
Environmental Variable ◽  
Rhine River ◽  
Streamflow Forecasting ◽  
Rhine Basin ◽  
Monthly Streamflow ◽  
Model Output Statistics

Abstract. Model output statistics (MOS) methods can be used to empirically relate an environmental variable of interest to predictions from earth system models (ESMs). This variable often belongs to a spatial scale not resolved by the ESM. Here, using the linear model fitted by least squares, we regress monthly mean streamflow of the Rhine River at Lobith and Basel against seasonal predictions of precipitation, surface air temperature, and runoff from the European Centre for Medium-Range Weather Forecasts. To address potential effects of a scale mismatch between the ESM's horizontal grid resolution and the hydrological application, the MOS method is further tested with an experiment conducted at the subcatchment scale. This experiment applies the MOS method to 133 additional gauging stations located within the Rhine basin and combines the forecasts from the subcatchments to predict streamflow at Lobith and Basel. In doing so, the MOS method is tested for catchments areas covering 4 orders of magnitude. Using data from the period 1981–2011, the results show that skill, with respect to climatology, is restricted on average to the first month ahead. This result holds for both the predictor combination that mimics the initial conditions and the predictor combinations that additionally include the dynamical seasonal predictions. The latter, however, reduce the mean absolute error of the former in the range of 5 to 12 %, which is consistently reproduced at the subcatchment scale. An additional experiment conducted for 5-day mean streamflow indicates that the dynamical predictions help to reduce uncertainties up to about 20 days ahead, but it also reveals some shortcomings of the present MOS method.

Quantifying streamflow predictability across North America on sub-seasonal to seasonal timescales

2021 ◽  
Author(s):  
Louise Arnal ◽  
Martyn Clark ◽  
Vincent Vionnet ◽  
Vincent Fortin ◽  
Alain Pietroniro ◽  
...  
Keyword(s):  
North America ◽  
Climate Prediction ◽  
Forecast Skill ◽  
Streamflow Forecasting ◽  
Water Sector ◽  
Forecast System ◽  
Streamflow Forecast ◽  
Streamflow Forecasts ◽  
Forecasting System ◽  
Seasonal Streamflow

<div> <p><span><span>Sub-seasonal to seasonal streamflow forecasts represent critical operational inputs for many water sector applications of societal relevance, such as spring flood early warning, water supply, hydropower generation, and irrigation scheduling. However, the skill of such forecasts has not risen greatly in recent decades despite recognizable advances in many relevant capabilities, including hydrologic modeling and S2S climate prediction. In order to build a continental-scale forecasting system that has value at the local scale, the sources and nature of predictability in the forecasts should be quantified and communicated. This can additionally help to target science investments for tangible improvements in the sub-seasonal to seasonal streamflow forecasting skill.</span></span></p> </div><div> <p><span><span>As part of the Canada-based Global Water Futures (GWF) program, we are advancing capabilities for probabilistic sub-seasonal to seasonal streamflow forecasts over North America. The overall aim is to improve sub-seasonal to seasonal streamflow forecasts for a range of water sector applications. We are implementing an array of forecasting methods that integrate state-of-the-art mechanistic models and statistical methods. These include, for instance, a </span></span><span>probabilistic sub-seasonal to seasonal streamflow forecasting system based on quantile regression of snow water equivalent observations, and a system based on the ESP approach (Day, 1985). </span></p> <p><span><span>To guide forecast system developments over North America, we are currently quantifying streamflow predictability for different hydroclimatic regimes, forecast initialization times, and lead times, against both streamflow simulations and observations to quantify the effect of model errors. Building on the work from Wood et al. (2016) and Arnal et al. (2017), we are disentangling the dominant predictability sources (i.e., initial hydrological conditions and atmospheric forcings) of sub-seasonal to seasonal streamflow across North American watersheds. The results provide insights into the elasticity of predictability, i.e., the increase in streamflow forecast skill possible by improving a specific component of the forecast system, and will inform the forecasting system development.</span></span></p> </div><div> <p><span><span>Arnal Louise, Wood Andrew W., Stephens Elisabeth, Cloke Hannah L., Pappenberger Florian, 2017: An Efficient Approach for Estimating Streamflow Forecast Skill Elasticity. Journal of Hydrometeorology, doi: 10.1175/JHM-D-16-0259.1</span></span></p> </div><div> <p>Day, Gerald N., 1985: Extended streamflow forecasting using NWSRFS. Journal of Water Resources Planning and Management, doi:10.1061/(ASCE)0733-9496(1985)111:2(157)</p> </div><p>Wood, Andrew W., Tom Hopson, Andy Newman, Levi Brekke, Jeff Arnold, and Martyn Clark, 2016: Quantifying streamflow forecast skill elasticity to initial condition and climate prediction skill. Journal of Hydrometeorology, doi: 10.1175/JHM-D-14-0213.1</p>

scholarly journals Monthly streamflow forecasting at varying spatial scales in the Rhine basin

2017 ◽  
Author(s):  
Simon Schick ◽  
Ole Rössler ◽  
Rolf Weingartner
Keyword(s):  
Land Surface ◽  
General Circulation ◽  
Initial Conditions ◽  
Spatial Scales ◽  
General Circulation Models ◽  
Surface Model ◽  
Rhine River ◽  
Streamflow Forecasting ◽  
Rhine Basin ◽  
Monthly Streamflow

Abstract. Model output statistics (MOS) methods empirically relate an environmental variable of interest to predictions from general circulation models (GCMs). This variable often belongs to a spatial scale not resolved by the GCM. Here, using the linear model fitted by least squares, we regress monthly mean streamflow of the Rhine River at Lobith and Basel against seasonal predictions of precipitation, surface air temperature, and runoff from the European Centre for Medium-Range Weather Forecasts. To address potential effects of a scale mismatch between the GCM's horizontal grid resolution and the hydrological application, the MOS method is further tested with an experiment conducted at the subcatchment scale. This experiment applies the MOS method to 133 additional gauging stations located within the Rhine basin and combines the forecasts from the subcatchments to predict streamflow at Lobith and Basel. In so doing, the MOS method is tested for catchments areas covering four orders of magnitude. Using data from the period 1981–2011, the results show that skill, with respect to climatology, is restricted to the first month ahead. This result holds for both the predictor combination that mimics the initial conditions and the predictor combinations that additionally include the dynamical seasonal predictions. The latter, however, reduces the mean absolute error of the former in the range of 5 to 11 percent, which is consistently reproduced at the subcatchment scale. The results further indicate that bias corrected runoff from the H-TESSEL land surface model is an interesting option when it comes to seasonal streamflow forecasting in large river basins.

scholarly journals Black carbon vertical profiles strongly affect its radiative forcing uncertainty

2013 ◽  
Vol 13 (5) ◽  
pp. 2423-2434 ◽  
Author(s):  
B. H. Samset ◽  
G. Myhre ◽  
M. Schulz ◽  
Y. Balkanski ◽  
S. Bauer ◽  
...  
Keyword(s):  
Black Carbon ◽  
Vertical Profile ◽  
Radiative Forcing ◽  
Global Radiation ◽  
Radiation Balance ◽  
Upper Troposphere ◽  
Aerosol Model ◽  
Intercomparison Project ◽  
To Come ◽  
The Impact

Abstract. The impact of black carbon (BC) aerosols on the global radiation balance is not well constrained. Here twelve global aerosol models are used to show that at least 20% of the present uncertainty in modeled BC direct radiative forcing (RF) is due to diversity in the simulated vertical profile of BC mass. Results are from phases 1 and 2 of the global aerosol model intercomparison project (AeroCom). Additionally, a significant fraction of the variability is shown to come from high altitudes, as, globally, more than 40% of the total BC RF is exerted above 5 km. BC emission regions and areas with transported BC are found to have differing characteristics. These insights into the importance of the vertical profile of BC lead us to suggest that observational studies are needed to better characterize the global distribution of BC, including in the upper troposphere.

scholarly journals SEASONAL AND INTERANNUAL PATTERN OF METEOROLOGICAL VARIABLES IN CUIABÁ, MATO GROSSO STATE, BRAZIL

2015 ◽  
Vol 33 (3) ◽  
pp. 477 ◽  
Author(s):  
Nadja Gomes Machado ◽  
Marcelo Sacardi Biudes ◽  
Carlos Alexandre Santos Querino ◽  
Victor Hugo De Morais Danelichen ◽  
Maísa Caldas Souza Velasque
Keyword(s):  
Wind Speed ◽  
Relative Humidity ◽  
Meteorological Data ◽  
Seasonal Pattern ◽  
Global Radiation ◽  
Meteorological Variables ◽  
Maximum Temperature ◽  
Wet Season ◽  
Agricultural Producers ◽  
Mato Grosso

ABSTRACT. Cuiab´a is located on the border of the Pantanal and Cerrado, in Mato Grosso State, which is recognized as one of the biggest agricultural producers of Brazil. The use of natural resources in a sustainable manner requires knowledge of the regional meteorological variables. Thus, the objective of this study was to characterize the seasonal and interannual pattern of meteorological variables in Cuiab´a. The meteorological data from 1961 to 2011 were provided by the Instituto Nacional de Meteorologia (INMET – National Institute of Meteorology). The results have shown interannual and seasonal variations of precipitation, solar radiation, air temperature and relative humidity, and wind speed and direction, establishing two main distinct seasons (rainy and dry). On average, 89% of the rainfall occurred in the wet season. The annual average values of daily global radiation, mean, minimum and maximum temperature and relative humidity were 15.6 MJ m–2 y–1, 27.9◦C, 23.0◦C, 30.0◦C and 71.6%, respectively. Themaximum temperature and the wind speed had no seasonal pattern. The wind speed average decreased in the NWdirectionand increased in the S direction.Keywords: meteorological variables, climatology, ENSO. RESUMO. Cuiabá está localizado na fronteira do Pantanal com o Cerrado, no Mato Grosso, que é reconhecido como um dos maiores produtores agrícolas do Brasil. A utilização dos recursos naturais de forma sustentável requer o conhecimento das variáveis meteorológicas em escala regional. Assim, o objetivo deste estudo foi caracterizar o padrão sazonal e interanual das variáveis meteorológicas em Cuiabá. Os dados meteorológicos de 1961 a 2011 foram fornecidos pelo Instituto Nacional de Meteorologia (INMET). Os resultados mostraram variações interanuais e sazonais de precipitação, radiação solar, temperatura e umidade relativa do ar e velocidade e direção do vento, estabelecendo duas principais estações distintas (chuvosa e seca). Em média, 89% da precipitação ocorreu na estação chuvosa. Os valores médios anuais de radiação diária global, temperatura do ar média, mínima e máxima e umidade relativa do ar foram 15,6 MJ m–2 y–1, 27,9◦C, 23,0◦C, 30,0◦C e 71,6%, respectivamente. A temperatura máxima e a velocidade do vento não tiveram padrão sazonal. A velocidade média do vento diminuiu na direção NW e aumentou na direção S.Palavras-chave: variáveis meteorológicas, climatologia, ENOS.

scholarly journals Competing Influences of Anthropogenic Warming, ENSO, and Plant Physiology on Future Terrestrial Aridity

2017 ◽  
Vol 30 (17) ◽  
pp. 6883-6904 ◽  
Author(s):  
Céline Bonfils ◽  
Gemma Anderson ◽  
Benjamin D. Santer ◽  
Thomas J. Phillips ◽  
Karl E. Taylor ◽  
...  
Keyword(s):  
Global Warming ◽  
Radiative Forcing ◽  
Potential Evapotranspiration ◽  
Southern Oscillation ◽  
Stomatal Resistance ◽  
Ocean Warming ◽  
Evaporative Demand ◽  
Enso Variability ◽  
Soil Desiccation ◽  
Plant Water Use

The 2011–16 California drought illustrates that drought-prone areas do not always experience relief once a favorable phase of El Niño–Southern Oscillation (ENSO) returns. In the twenty-first century, such an expectation is unrealistic in regions where global warming induces an increase in terrestrial aridity larger than the changes in aridity driven by ENSO variability. This premise is also flawed in areas where precipitation supply cannot offset the global warming–induced increase in evaporative demand. Here, atmosphere-only experiments are analyzed to identify land regions where aridity is currently sensitive to ENSO and where projected future changes in mean aridity exceed the range caused by ENSO variability. Insights into the drivers of these changes in aridity are obtained using simulations with the incremental addition of three different factors to the current climate: ocean warming, vegetation response to elevated CO2levels, and intensified CO2radiative forcing. The effect of ocean warming overwhelms the range of ENSO-driven temperature variability worldwide, increasing potential evapotranspiration (PET) in most ENSO-sensitive regions. Additionally, about 39% of the regions currently sensitive to ENSO will likely receive less precipitation in the future, independent of the ENSO phase. Consequently aridity increases in 67%–72% of the ENSO-sensitive area. When both radiative and physiological effects are considered, the area affected by arid conditions rises to 75%–79% when using PET-derived measures of aridity, but declines to 41% when an aridity indicator for total soil moisture is employed. This reduction mainly occurs because plant stomatal resistance increases under enhanced CO2concentrations, resulting in improved plant water-use efficiency, and hence reduced evapotranspiration and soil desiccation. Imposing CO2-invariant stomatal resistance may overestimate future drying in PET-derived indices.

scholarly journals Evaluating Predictor Strategies for Regression-Based Downscaling with a Focus on Glacierized Mountain Environments

2017 ◽  
Vol 56 (6) ◽  
pp. 1707-1729 ◽  
Author(s):  
Marlis Hofer ◽  
Johanna Nemec ◽  
Nicolas J. Cullen ◽  
Markus Weber
Keyword(s):  
Large Scale ◽  
Global Radiation ◽  
Local Scale ◽  
Forecast Skill ◽  
Model Complexity ◽  
High Mountain ◽  
European Alps ◽  
Limited Information ◽  
Mountain Environments ◽  
Measuring Site

AbstractThis study explores the potential of different predictor strategies for improving the performance of regression-based downscaling approaches. The investigated local-scale target variables are precipitation, air temperature, wind speed, relative humidity, and global radiation, all at a daily time scale. Observations of these target variables are assessed from three sites in close proximity to mountain glaciers: 1) the Vernagtbach station in the European Alps, 2) the Artesonraju measuring site in the tropical South American Andes, and 3) the Mount Brewster measuring site in the Southern Alps of New Zealand. The large-scale dataset being evaluated is the ERA-Interim dataset. In the downscaling procedure, particular emphasis is put on developing efficient yet not overfit models from the limited information in the temporally short (typically a few years) observational records of the high mountain sites. For direct (univariate) predictors, optimum scale analysis turns out to be a powerful means to improve the forecast skill without the need to increase the downscaling model complexity. Yet the traditional (multivariate) predictor sets show generally higher skill than the direct predictors for all variables, sites, and days of the year. Only in the case of large sampling uncertainty (identified here to particularly affect observed precipitation) is the use of univariate predictor options justified. Overall, the authors find a range in forecast skill among the different predictor options applied in the literature up to 0.5 (where 0 indicates no skill, and 1 represents perfect skill). This highlights that a sophisticated predictor selection (as presented in this study) is essential in the development of realistic, local-scale scenarios by means of downscaling.

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