Runoff conditions in the upper Danube basin under an ensemble of climate change scenarios

Kling, Harald; Fuchs, M; Paulin, Maria

doi:10.1016/j.jhydrol.2012.01.011

Cited by 1,059 publications

(744 citation statements)

References 36 publications

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“…Pagano et al, 2010 (Coron et al, 2016(Coron et al, , 2017 with the inbuilt calibration optimisation algorithm based on a steepest descent local search procedure and default parameter ranges. The modified Kling-Gupta efficiency (KGEmod, Gupta et al, 2009, Kling et al, 2012 applied to root squared transformed flows KGEmod[sqrt] was used as the objective function for automatic fitting, thus placing weight on mid-range flows, rather than high or low flows. This was decided given ESP forecasts are made across the year during both dry and wet conditions.…”

Section: Hydrological Modellingmentioning

confidence: 99%

Benchmarking Ensemble Streamflow Prediction skill in the UK

Harrigan¹,

Smith²,

Parry³

et al. 2017

Preprint

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Abstract. Skilful hydrological forecasts at sub-seasonal to seasonal lead times would be extremely beneficial for decisionmaking in water resources management, hydropower operations, and agriculture, especially during drought conditions. Ensemble Streamflow Prediction (ESP) is a well-established method for generating an ensemble of streamflow forecasts in 10 the absence of skilful future meteorological predictions, instead using Initial Hydrological Conditions (IHCs), such as soil moisture, groundwater, and snow, as the source of skill. We benchmark when and where the ESP method is skilful across a diverse sample of 314 catchments in the UK and explore the relationship between catchment storage and ESP skill. The GR4J hydrological model was forced with historic climate sequences to produce 51-member ensemble of streamflow hindcasts. We evaluated forecast skill seamlessly from lead times of 1-day to 12-months initialised at the first of each month over a 50-year 15 hindcast period from 1965-2015. Results show ESP was skilful against a climatology benchmark forecast in the majority of catchments across all lead times up to a year ahead, but the degree of skill was strongly conditional on lead time, forecast initialisation month, and individual catchment location and storage properties. UK-wide mean ESP skill decays exponentially as a function of lead time with mean squared error skill scores across the year of 0.839, 0.303, and 0.179 for 1-day, 1-month, and 3-month lead times, respectively. However, skill was not uniform across all initialisation months. For lead times up to 1-20 month, ESP skill was higher than average when initialised in summer and lower in winter, whereas for longer seasonal and annual lead times skill was highest when initialised in autumn and winter months and lowest in April. ESP is most skilful in the south and east of the UK, where slower responding catchments with higher soil moisture and groundwater storage are mainly located; correlation between catchment Baseflow Index (BFI) and ESP skill was very strong ( = 0.896 at 1-month lead time). This is in contrast to the more highly responsive catchments in the north and west which are generally not skilful 25 at seasonal lead times. Overall, this work provides a scientifically defensible justification for when and where use of such a relatively simple forecasting approach is appropriate in the UK and creates a low cost benchmark against which potential skill improvements from more sophisticated hydro-meteorological ensemble prediction systems can be judged.Hydrol. Earth Syst. Sci. Discuss., https://doi

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Section: Hydrological Modellingmentioning

confidence: 99%

Benchmarking Ensemble Streamflow Prediction skill in the UK

Harrigan¹,

Smith²,

Parry³

et al. 2017

Preprint

View full text Add to dashboard Cite

show abstract

“…The 500 best parameter sets were subsequently used, to produce simulation ranges as an indication of posterior parameter variability in the absence of a formal uncertainty analysis. The KGE criterion is a three dimensional decomposition of the Nash-Sutcliffe efficiency (NSE) measure and evaluates the dynamics (r), bias (beta) and variability (alpha) on a scale fromInf to 1, with 1 being a perfect simulation (Gupta et al 2009;Kling et al 2012):…”

Section: Modelling Approachmentioning

confidence: 99%

Modelling landscape controls on dissolved organic carbon sources and fluxes to streams

et al. 2014

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Catchment dissolved organic carbon (DOC) fluxes are governed by complex interactions, which control biogeochemical processes generating DOC and hydrological connectivity, facilitating transport through the landscape to streams. This paper presents the development of a coupled hydrologicalbiogeochemical model for a northern watershed with organic-rich soils, to simulate daily DOC concentrations. The parsimonious model design allows the relative importance of DOC fluxes from the major landscape units (e.g. hillslopes, groundwater and riparian saturation area) to be determined. The dynamic extent of the saturated riparian zone, which at maximum wetness comprised 40 % of the drainage area, contributed 84 % of DOC to the stream, of which 16 % was derived from the hillslope soils. This shows the disproportional riparian influence on stream water chemistry and the importance of the non-linearity in hydrological connectivity. The temporal connectivity of each of the landscape units was dependent on antecedent moisture conditions, with highly transient connections between the hillslope and valley bottom saturated area, which were entirely disconnected during the driest periods. The groundwater contribution remained constant, but its relative importance increased during the driest periods. The study emphasises the importance of conceptualising hydrological connectivity and its relation to hydroclimatic factors, as well soil biogeochemical processes, when modelling stream water DOC.

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“…-Kling-Gupta efficiency (KGE) which is a combination of r, β and VR (Gupta et al, 2009;Kling et al, 2012) and is calculated as:…”

Section: National-scale Performance Metrics For Final Pet Grids Qualimentioning

confidence: 99%

Historical gridded reconstruction of potential evapotranspiration for the UK

Tanguy¹,

Prudhomme²,

Smith³

et al. 2018

Preprint

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Abstract. Potential Evapotranspiration (PET) is a necessary input data for most hydrological models and is usually needed at a daily or shorter time-step. An accurate estimation of PET requires many input climate variables which are in most cases not 10 available prior to the 1960s for the UK, nor indeed most parts of the world. Therefore, when applying hydrological models for reconstructing earlier periods, modellers have to rely on PET estimation derived from simplified methods. Given that only monthly observed temperature data is readily available for the late 19th and early 20th century at a national scale for the UK, the objective of this work was to derive the best possible UK-wide gridded PET dataset with the limited data available.To that end, firstly, a combination of (i) seven temperature-based PET equations, (ii) four different calibration approaches and 15 (iii) seven input temperature data were evaluated against a gridded daily PET product based on the physically-based PenmanMonteith equation (the CHESS PET dataset), the rationale being that this provides a reliable 'ground-truth' PET dataset for evaluation purposes, given that no directly observed, distributed PET datasets exist. The performance of the models was also compared to the simplest possible estimation of PET ('naïve method') in the absence of any available climate data, the CHESS PET daily long term average (the period from 1961 to 1990 was chosen for this study), or CHESS-PET daily climatology. The 20 analysis revealed that the type of calibration and the input temperature dataset had only a minor effect in the accuracy of the PET estimations at catchment scale. From the seven equations tested, only the calibrated version of the McGuinness-Bordne equation was able to outperform the 'naïve method' and was therefore used to derive the gridded, reconstructed dataset. The equation was calibrated using 43 catchments across Great Britain.The dataset produced is a 5-km gridded PET dataset for the period 1891 to 2015, using as input data for the PET equation the 25 Met Office 5-km monthly gridded temperature data available for that time period, which was disaggregated to daily temperature using pchip (piecewise cubic hermite interpolating polynomial) method. The dataset includes daily and monthly PET grids and is complemented with a suite of mapped performance metrics to help users assess the quality of the data spatially.The data can be accessed here: https://doi.org/10.5285/17b9c4f7-1c30-4b6f-b2fe-f7780159939c.Earth Syst. Sci. Data Discuss., https://doi

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Runoff conditions in the upper Danube basin under an ensemble of climate change scenarios

Cited by 1,059 publications

References 36 publications

Benchmarking Ensemble Streamflow Prediction skill in the UK

Benchmarking Ensemble Streamflow Prediction skill in the UK

Modelling landscape controls on dissolved organic carbon sources and fluxes to streams

Historical gridded reconstruction of potential evapotranspiration for the UK

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