scholarly journals Comparing seasonal streamflow forecast systems for management of a fresh water reservoir in the Netherlands

2022 ◽  
Author(s):  
Ruud T. W. L. Hurkmans ◽  
Bart van den Hurk ◽  
Maurice J. Schmeits ◽  
Fredrik Wetterhall ◽  
Ilias G. Pechlivanidis

Abstract. For efficient management of the Dutch surface water reservoir Lake IJssel, (sub)seasonal forecasts of the water volumes going in and out of the reservoir are potentially of great interest. Here, streamflow forecasts were analyzed for the river Rhine at Lobith, which is partly routed through the river IJssel, the main influx into the reservoir. We analyzed multiple seasonal forecast data sets derived from EFAS, E-HYPE and HTESSEL, which differ in their underlying hydrological formulation, but are all forced with similar input from the ECMWF SEAS5 meteorological forecasts. We post-processed the streamflow forecasts using quantile matching (QM) and analyzed several forecast quality metrics. Forecast performance was assessed based on the available reforecast period, as well as on individual summer seasons. QM increased forecast skill for nearly all metrics evaluated. Particularly HTESSEL, a land surface scheme that is not optimized for hydrology, needed the largest correction. Averaged over the reforecast period, forecasts were skillful for the longest lead times in spring and early summer. For this period, E-HYPE showed the highest skill; Later in summer, however, skill deteriorated after 1–2 months. When investigating specific years with either low or high flow conditions, forecast skill increased with the extremity of the event. Although raw forecasts for both E-HYPE and EFAS were more skilful than HTESSEL, bias correction based on QM can significantly reduce the difference. In operational mode, the three forecast systems show comparable skill. In general, dry conditions can be forecasted with high success rates up to three months ahead, which is very promising for successful use of Rhine streamflow forecasts in downstream reservoir management.

2014 ◽  
Vol 27 (24) ◽  
pp. 9253-9271 ◽  
Author(s):  
Stefano Materia ◽  
Andrea Borrelli ◽  
Alessio Bellucci ◽  
Andrea Alessandri ◽  
Pierluigi Di Pietro ◽  
...  

Abstract The impact of land surface and atmosphere initialization on the forecast skill of a seasonal prediction system is investigated, and an effort to disentangle the role played by the individual components to the global predictability is done, via a hierarchy of seasonal forecast experiments performed under different initialization strategies. A realistic atmospheric initial state allows an improved equilibrium between the ocean and overlying atmosphere, increasing the model predictive skill in the ocean. In fact, in regions characterized by strong air–sea coupling, the atmosphere initial condition affects forecast skill for several months. In particular, the ENSO region, eastern tropical Atlantic, and North Pacific benefit significantly from the atmosphere initialization. On the mainland, the effect of atmospheric initial conditions is detected in the early phase of the forecast, while the quality of land surface initialization affects forecast skill in the following seasons. Winter forecasts in the high-latitude plains benefit from the snow initialization, while the impact of soil moisture initial state is particularly effective in the Mediterranean region and central Asia. However, the initialization strategy based on the full value technique may not be the best choice for land surface, since soil moisture is a strongly model-dependent variable: in fact, initialization through land surface reanalysis does not systematically guarantee a skill improvement. Nonetheless, using a different initialization strategy for land, as opposed to atmosphere and ocean, may generate inconsistencies. Overall, the introduction of a realistic initialization for land and atmosphere substantially increases skill and accuracy. However, further developments in the procedure for land surface initialization are required for more accurate seasonal forecasts.


2021 ◽  
Author(s):  
Mark Thyer ◽  
David McInerney ◽  
Dmitri Kavetski ◽  
Richard Laugesen ◽  
Narendra Tuteja ◽  
...  

<p>Sub-seasonal streamflow forecasts (with lead times of 1-30 days) provide valuable information for many consequential water resource management decisions, including reservoir operation to meet environmental flow and irrigation demands, issuance of early flood warnings, and others. A key aim is to produce “seamless” forecasts, with high quality performance across the full range of lead times and time scales.  </p><p>This presentation introduces the <em><strong>Multi-Temporal Hydrological Residual Error model (MuTHRE)</strong> </em>to address the challenge of obtaining “seamless” sub-seasonal forecasts, i.e., daily forecasts with consistent high-quality performance over multiple lead times (1-30 days) and aggregation scales (daily to monthly).</p><p>The model is designed to overcome common errors in streamflow forecasts:</p><ul><li>Seasonality</li> <li>Dynamic biases due to hydrological non-stationarity</li> <li>Extreme errors poorly represented by the common Gaussian distribution.</li> </ul><p>The model is evaluated comprehensively over 11 catchments in the Murray-Darling Basin, Australia, using multiple performance metrics to scrutinize forecast reliability, sharpness and bias, across a range of lead times, months and years, at daily and monthly time scales.</p><p>The MuTHRE model provides ”high” improvements, in terms of reliability for</p><ul><li>Short lead times (up to 10 days), due to representing non-Gaussian errors</li> <li>Stratified by month, due to representing seasonality in hydrological errors</li> <li>Dry years, due to representing dynamic biases in hydrological errors.</li> </ul><p>Forecast performance also improved in terms of sharpness, volumetric bias and CRPS skill score; Importantly, improvements are consistent across multiple time scales (daily and monthly).</p><p><em><strong>This study highlights the benefits of modelling multiple temporal characteristics of hydrological errors, and demonstrates the power of the MuTHRE model for producing seamless sub-seasonal streamflow forecasts that can be utilized for a wide range of applications.</strong></em></p><p> </p><p> </p><p> </p>


2020 ◽  
Author(s):  
Bastian Klein ◽  
Ilias Pechlivanidis ◽  
Louise Arnal ◽  
Louise Crochemore ◽  
Dennis Meissner ◽  
...  

<p>Many sectors, such as hydropower, agriculture, water supply and waterway transport, need information about the possible evolution of meteorological and hydrological conditions in the next weeks and months to optimize their decision processes on a long term. With increasing availability of meteorological seasonal forecasts, hydrological seasonal forecasting systems have been developed all over the world in the last years. Many of them are running in operational mode. On European scale the European Flood Awareness System EFAS and SMHI are operationally providing seasonal streamflow forecasts. In the context of the EU-Horizon2020 project IMPREX additionally a national scale forecasting system for German waterways operated by BfG was available for the analysis of seasonal forecasts from multiple hydrological models.</p><p>Statistical post processing tools could be used to estimate the predictive uncertainty of the forecasted variable from deterministic / ensemble forecasts of a single / multi-model forecasting system. Raw forecasts shouldn’t be used directly by users without statistical post-processing because of various biases. To assess the added potential benefit of the application of a hydrological multi-model ensemble, the forecasting systems from EFAS, SMHI and BfG were forced by re-forecasts of the ECMWF’s Seasonal Forecast System 4 and the resulting seasonal streamflow forecasts have been verified for 24 gauges across Central Europe. Additionally two statistical forecasting methods - Ensemble Model Output Statistics EMOS and Bayesian Model Averaging BMA - have been applied to post-process the forecasts.</p><p>Overall, seasonal flow forecast skill is limited in Central Europe before and after post-processing with a current predictability of 1-2 months. The results of the multi-model analysis indicate that post-processing of raw forecasts is necessary when observations are used as reference. Post-processing improves forecast skill significantly for all gauges, lead times and seasons. The multi-model combination of all models showed the highest skill compared to the skill of the raw forecasts and the skill of the post-processed results of the individual models, i.e. the application of several hydrological models for the same region improves skill, due to the different model strengths.</p>


2020 ◽  
Author(s):  
Bart van den Hurk ◽  
Ruud Hurkmans ◽  
Fredrik Wetterhal ◽  
Ilias Pechlivanidis ◽  
Albrecht Weerts

<p><span>During dry spells, a large part of the Netherlands depends on water from the IJssel lake, a large surface water reservoir. Water is extracted for a number of purposes, such as irrigation, water quality, shipping and drinking water. Besides local precipitation, the main source of water flowing into the lake is the river IJssel; a distributary of the Rhine. To keep water available for extraction by the surrounding regions, lake levels cannot be allowed to fall more than about 20 cm under the regular summer maintenance level. Prior to the onset of a drought, therefore, it might be desirable to raise lake levels to maintain sufficient water availability during the dry spell. For adequate management of the reservoir, therefore, long-range forecasting of precipitation and river discharge would be extremely helpful. However, meteorological forecast skill is known to be nearly absent for lead times longer than about a month in northwestern Europe. The land surface contains a number of components that may increase forecast skill for Rhine river discharge; examples are the amount of snow in the Alps, groundwater, and soil moisture. We investigate to what extent this is the case and whether the forecast skill of Rhine river discharge forecasts increases with increasing detail in the land surface parameterization of the initial conditions. We collected streamflow reforecasts from various sources: ECMWF SEAS5, EFAS, SMHI-HYPE and a high-resolution distributed hydrological model (WFLOW), forced by ECMWF SEAS5 meteorological forecasts. </span></p>


2014 ◽  
Vol 15 (1) ◽  
pp. 69-88 ◽  
Author(s):  
Randal D. Koster ◽  
Gregory K. Walker ◽  
Sarith P. P. Mahanama ◽  
Rolf H. Reichle

Abstract Offline simulations over the conterminous United States (CONUS) with a land surface model are used to address two issues relevant to the forecasting of large-scale seasonal streamflow: (i) the extent to which errors in soil moisture initialization degrade streamflow forecasts, and (ii) the extent to which a realistic increase in the spatial resolution of forecasted precipitation would improve streamflow forecasts. The addition of error to a soil moisture initialization field is found to lead to a nearly proportional reduction in large-scale seasonal streamflow forecast skill. The linearity of the response allows the determination of a lower bound for the increase in streamflow forecast skill achievable through improved soil moisture estimation, for example, through the assimilation of satellite-based soil moisture measurements. An increase in the resolution of precipitation is found to have an impact on large-scale seasonal streamflow forecasts only when evaporation variance is significant relative to precipitation variance. This condition is met only in the western half of the CONUS domain. Taken together, the two studies demonstrate the utility of a continental-scale land surface–modeling system as a tool for addressing the science of hydrological prediction.


2008 ◽  
Vol 21 (15) ◽  
pp. 3617-3641 ◽  
Author(s):  
Andrew M. Carleton ◽  
David J. Travis ◽  
Jimmy O. Adegoke ◽  
David L. Arnold ◽  
Steve Curran

Abstract In Part I of this observational study inquiring into the relative influences of “top down” synoptic atmospheric conditions and “bottom up” land surface mesoscale conditions in deep convection for the humid lowlands of the Midwest U.S. Central Corn Belt (CCB), the composite atmospheric environments for afternoon and evening periods of convection (CV) versus no convection (NC) were determined for two recent summers (1999 and 2000) having contrasting precipitation patterns and amounts. A close spatial correspondence was noted between composite synoptic features representing baroclinity and upward vertical motion with the observed precipitation on CV days when the “background” (i.e., free atmosphere) wind speed exceeded approximately 10 m s−1 at 500 hPa (i.e., “stronger flow”). However, on CV days when wind speeds were <∼10 m s−1 (i.e., “weaker flow”), areas of increased precipitation can be associated with synoptic composites that are not so different from those for corresponding NC days. From these observations, the presence of a land surface mesoscale influence on deep convection and precipitation is inferred that is better expressed on weaker flow days. Climatically, a likely candidate for enhancing low-level moisture convergence to promote deep convection are the quasi-permanent vegetation boundaries (QPVBs) between the two major land use and land cover (LULC) types of crop and forest that characterize much of the CCB. Accordingly, in this paper the role of these boundaries on summer precipitation variations for the CCB is extracted in two complementary ways: 1) for contrasting flow day types in the summers 1999 and 2000, by determining the spatially and temporally aggregated land surface influence on deep convection from composites of thermodynamic variables [e.g., surface lifted index (SLI), level of free convection (LFC), and lifted condensation level (LCL)] that are obtained from mapped data of the 6-h NCEP–NCAR reanalyses (NNR), and 0000 UTC rawinsonde ascents; and 2) for summer seasons 1995–2001, from the statistical associations of satellite-retrieved LULC boundary attributes (i.e., length and width) and precipitation at high spatial resolutions. For the 1999 and 2000 summers (item 1 above), thermodynamic composites determined for V(500) categories having minimal differences in synoptic meteorological fields on CV minus NC (CV − NC) days (i.e., weaker flow), show statistically significant increases in atmospheric moisture (e.g., greater precipitable water; lower LCL and LFC) and static instability [e.g., positive convective available potential energy (CAPE)] compared to NC days. Moreover, CV days for both weaker and stronger background flow have associated subregional-scale thermodynamic patterns indicating free convection at the earth’s surface, supported by a synoptic pattern of at least weakly upward motion of air in the midtroposphere in contrast to NC days. The possibility that aerodynamic contrasts along QPVBs readily permit air to be lofted above the LFC when the lower atmosphere is moist, thereby assisting or enhancing deep convection on CV days, is supported by the multiyear analysis (item 2 above). In early summer when LULC boundaries are most evident, precipitation on weaker flow days is significantly greater within 20 km of boundaries than farther away, but there is no statistical difference on stronger flow days. Statistical relationships between boundary mean attributes and mean precipitation change sign between early summer (positive) and late summer (negative), in accord with shifts in the satellite-retrieved maximum radiances from forest to crop areas. These phenological changes appear related, primarily, to contrasting soil moisture and implied evapotranspiration differences. Incorporating LULC boundary locations and phenological status into reliable forecast fields of lower-to-midtropospheric humidity and wind speed should lead to improved short-term predictions of convective precipitation in the Corn Belt and also, potentially, better climate seasonal forecasts.


2019 ◽  
Author(s):  
Jean-Pierre Vergnes ◽  
Nicolas Roux ◽  
Florence Habets ◽  
Philippe Ackerer ◽  
Nadia Amraoui ◽  
...  

Abstract. The new AquiFR hydometeorological modelling platform was developed to provide short to long-term forecasts for groundwater resource management in France. The present study aims to describe and assess this new tool over a long-term period of 60 years. This platform gathers in a single numerical tool different hydrogeological models covering much of the French metropolitan area. Eleven aquifer systems are simulated through spatially distributed models using either the MARTHE groundwater modelling software or the EauDyssée hydrogeological platform. Twenty-three karstic systems are simulated by lumped models using the EROS software. AquiFR computes the groundwater level, the groundwater surface water exchanges, and the river flows at multiple river gauging stations. A simulation covering a 60 year period from 1958 to 2018 is achieved in order to evaluate the performance of this platform. The 8 km resolution SAFRAN meteorological reanalysis provides the atmospheric variables needed by the SURFEX land surface model in order tocompute surface runoff that are used by all the hydrogeological models. The assessment is based on a wide range of selected piezometers as well as gauging stations corresponding to simulated rivers and outlets of karstic systems. For the simulated piezometric heads, 40 % and 60 % of the absolute biases are lower than 2 m and 4 m respectively. The Standardized Piezometric Level Index (SPLI) was computed to assess the ability of AquiFR to identify extreme events such as groundwater flooding or droughts in long-term simulations over a set of piezometers used for groundwater resource management. 55 % of the Nash-Sutcliff scores calculated between the observed and simulated SPLI time series are greater than 0.5. Further work will focus on the use of this platform for short-term to seasonal forecasts in an operational mode and for climate change impact assessment.


2021 ◽  
Author(s):  
Nicola Cortesi ◽  
Verónica Torralba ◽  
Llorenó Lledó ◽  
Andrea Manrique-Suñén ◽  
Nube Gonzalez-Reviriego ◽  
...  

AbstractIt is often assumed that weather regimes adequately characterize atmospheric circulation variability. However, regime classifications spanning many months and with a low number of regimes may not satisfy this assumption. The first aim of this study is to test such hypothesis for the Euro-Atlantic region. The second one is to extend the assessment of sub-seasonal forecast skill in predicting the frequencies of occurrence of the regimes beyond the winter season. Two regime classifications of four regimes each were obtained from sea level pressure anomalies clustered from October to March and from April to September respectively. Their spatial patterns were compared with those representing the annual cycle. Results highlight that the two regime classifications are able to reproduce most part of the patterns of the annual cycle, except during the transition weeks between the two periods, when patterns of the annual cycle resembling Atlantic Low regime are not also observed in any of the two classifications. Forecast skill of Atlantic Low was found to be similar to that of NAO+, the regime replacing Atlantic Low in the two classifications. Thus, although clustering yearly circulation data in two periods of 6 months each introduces a few deviations from the annual cycle of the regime patterns, it does not negatively affect sub-seasonal forecast skill. Beyond the winter season and the first ten forecast days, sub-seasonal forecasts of ECMWF are still able to achieve weekly frequency correlations of r = 0.5 for some regimes and start dates, including summer ones. ECMWF forecasts beat climatological forecasts in case of long-lasting regime events, and when measured by the fair continuous ranked probability skill score, but not when measured by the Brier skill score. Thus, more efforts have to be done yet in order to achieve minimum skill necessary to develop forecast products based on weather regimes outside winter season.


Author(s):  
Philip E. Bett ◽  
Gill M. Martin ◽  
Nick Dunstone ◽  
Adam A. Scaife ◽  
Hazel E. Thornton ◽  
...  

AbstractSeasonal forecasts for Yangtze River basin rainfall in June, May–June–July (MJJ), and June–July–August (JJA) 2020 are presented, based on the Met Office GloSea5 system. The three-month forecasts are based on dynamical predictions of an East Asian Summer Monsoon (EASM) index, which is transformed into regional-mean rainfall through linear regression. The June rainfall forecasts for the middle/lower Yangtze River basin are based on linear regression of precipitation. The forecasts verify well in terms of giving strong, consistent predictions of above-average rainfall at lead times of at least three months. However, the Yangtze region was subject to exceptionally heavy rainfall throughout the summer period, leading to observed values that lie outside the 95% prediction intervals of the three-month forecasts. The forecasts presented here are consistent with other studies of the 2020 EASM rainfall, whereby the enhanced mei-yu front in early summer is skillfully forecast, but the impact of midlatitude drivers enhancing the rainfall in later summer is not captured. This case study demonstrates both the utility of probabilistic seasonal forecasts for the Yangtze region and the potential limitations in anticipating complex extreme events driven by a combination of coincident factors.


2021 ◽  
Author(s):  
Stefano Materia ◽  
Constantin Ardilouze ◽  
Chloé Prodhomme ◽  
Markus G. Donat ◽  
Marianna Benassi ◽  
...  

AbstractLand surface and atmosphere are interlocked by the hydrological and energy cycles and the effects of soil water-air coupling can modulate near-surface temperatures. In this work, three paired experiments were designed to evaluate impacts of different soil moisture initial and boundary conditions on summer temperatures in the Mediterranean transitional climate regime region. In this area, evapotranspiration is not limited by solar radiation, rather by soil moisture, which therefore controls the boundary layer variability. Extremely dry, extremely wet and averagely humid ground conditions are imposed to two global climate models at the beginning of the warm and dry season. Then, sensitivity experiments, where atmosphere is alternatively interactive with and forced by land surface, are launched. The initial soil state largely affects summer near-surface temperatures: dry soils contribute to warm the lower atmosphere and exacerbate heat extremes, while wet terrains suppress thermal peaks, and both effects last for several months. Land-atmosphere coupling proves to be a fundamental ingredient to modulate the boundary layer state, through the partition between latent and sensible heat fluxes. In the coupled runs, early season heat waves are sustained by interactive dry soils, which respond to hot weather conditions with increased evaporative demand, resulting in longer-lasting extreme temperatures. On the other hand, when wet conditions are prescribed across the season, the occurrence of hot days is suppressed. The land surface prescribed by climatological precipitation forcing causes a temperature drop throughout the months, due to sustained evaporation of surface soil water. Results have implications for seasonal forecasts on both rain-fed and irrigated continental regions in transitional climate zones.


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