Evaluating skill and robustness of seasonal meteorological and hydrological drought forecasts at the catchment scale – Case Catalonia (Spain)

Hateren, Theresa C. van; Sutanto, Samuel Jonson; Lanen, H.A.J. van

doi:10.1016/j.envint.2019.105206

Cited by 24 publications

(21 citation statements)

References 72 publications

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“…The hydrological skill of the LIS-FLOOD model expressed by the Kling-Gupta Efficiency (KGE) shows that 42% of all calibration stations score a KGE higher that 0.75, 33% of all stations score a KGE between 0.5 and 0.75, and 25% of all stations score a KGE below 0.5 (Arnal et al, 2019). Although the model was originally developed for operational flood forecasts in the EU under European Flood Awareness System (EFAS) platform (Thielen et al, 2009;Pappenberger et al, 2011;Cloke et al, 2013), the LISFLOOD model has been tested for drought identification, forecasting and projections (Feyen and Dankers , 2009;Trambauer et al, 2013;Forzieri et al, 2014;Sutanto et al, 2019Sutanto et al, , 2020avan Hateren et al, 2019). It appears that the model also performs rather well for drought studies.…”

Section: Datamentioning

confidence: 99%

“…The Ebro River has VT drought that starts in early spring and autumn. This likely is caused by the lack of heavy precipitation associated with convective weather events that normally occur in spring and autumn (Barrera-Escoda and Llasat , 2015;van Hateren et al, 2019). Moreover, the VT drought that occurred in autumn (November) can also be triggered by sustained low flows that started in summer and continued in autumn (from August to October).…”

Section: Summary Of Drought Occurrences and Timing In Selected Riversmentioning

confidence: 99%

“…Usually, a percentile of the flow duration curve is taken using all flow data to identify the fixed threshold. In contrary, Fundel et al (2013), Sutanto et al (2020a), andvan Hateren et al (2019) have used the variable threshold approach to identify drought events with their hydrological drought forecasting system. In this approach the threshold varies over the year and accounts for seasonality, which means that drought can occur in every season.…”

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confidence: 99%

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Streamflow drought: implication of drought definitions and its application for drought forecasting

Sutanto¹,

Lanen²

2020

Preprint

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Abstract. Streamflow drought forecasting is a key element of contemporary Drought Early Warning Systems (DEWS). The term streamflow drought forecasting, rather than streamflow forecasting, however, has created confusion within the scientific hydro-meteorological community, as well as in operational weather and water management services. The way, how streamflow drought is defined, is the main reason for this misperception. The purpose of this study, therefore, is to provide a comprehensive overview of the differences within streamflow droughts using different identification approaches for European rivers, including an analysis of both historical drought and implications of forecasting of these extreme events. Streamflow data were obtained from a LISFLOOD hydrological model forced with gridded meteorological observed (known as LISFLOOD-Simulation Forced with Observed, SFO). The same model fed with seasonal meteorological forecasts of the European Centre for Medium-range Weather Forecasts system 5 (ECMWF SEAS 5) was used to obtain the forecasted streamflow. Streamflow droughts were analyzed using the Variable Threshold (VT), Fixed Threshold (FT), and the Standardized Streamflow Index (SSI). Our results clearly show that streamflow droughts derived from different approaches deviate from each other both in occurrence and timing, associated with different climate regions across Europe. The occurrence of FT drought is higher than droughts based upon VT and SSI, which highlights the importance of seasonality. FT drought happens earlier in the year than droughts obtained from VT and SSI. The use of aggregating daily streamflow data into monthly time windows for forecasting drought, such as the application of 30-day Moving Average (30DMA), is recommended to identify the VT and FT droughts. This approach will eliminate the undesired minor drought events, which are identified when using non-aggregated daily flow data. There is no unique hydrological drought definition that fits all purposes, hence developers of DEWS and end-users should clearly agree among themselves upon a sharp definition on which type of streamflow drought is required to be forecasted for a specific application.

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Section: Datamentioning

confidence: 99%

Section: Summary Of Drought Occurrences and Timing In Selected Riversmentioning

confidence: 99%

mentioning

confidence: 99%

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Streamflow drought: implication of drought definitions and its application for drought forecasting

Sutanto¹,

Lanen²

2020

Preprint

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“…For BSclim, a threshold of -0.5 was chosen to give a good balance between capturing either too many minor droughts or too few drought events (Trambauer et al, 2015). The standardized index of -0.5 stems from a normal distribution, and therefore the value for p is 0.3085 for every month (van Hateren et al, 2019). The BSS measures the improvement of the probabilistic forecasts relative to climatology.…”

Section: Forecasting Skill Scorementioning

confidence: 99%

Skill of large-scale seasonal drought impact forecasts

Sutanto¹,

Weert²,

Blauhut³

et al. 2020

Preprint

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Abstract. Forecasting drought impacts is still missing in drought early warning systems that presently do not go beyond hazard forecasting. Therefore, we developed drought impact functions using machine learning approaches (Logistic Regression and Random Forest) to predict drought impacts with a lead-time of 7 months ahead. The skill of the drought impact functions to forecast drought impacts was evaluated using the Brier Skill Score and Relative Operating Characteristic metrics for 5 Cases representing different spatial aggregation and lumping of impacted sectors. For German regions, impact functions developed using Random Forest show a higher discriminative ability to forecast drought impacts than Logistic Regression. Moreover, skill is higher for Cases with higher spatial resolution and less-lumped impacted sectors (Cases 4 and 5), with considerable skill up to 3–4 months ahead.

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“…Four meteorological factors were selected to analyze the major cause of the exceptional drought event: annual maximum temperature, total annual precipitation, annual maximum evaporation, and soil moisture in the upper 5 cm of soil. Taking into account that the time series of 30 years was generally selected as the time scale for meteorology analysis [47], therefore, the period of 1989-2018 was determined as the reference period for analyzing Australian meteorological anomalies in 2018 in this study. The annual maximum temperature, total annual precipitation, and annual maximum evaporation during the period 1989-2018 were derived from the data production ERA5 monthly averaged data on single levels from 1979 to the present and the fifth generation European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis data for global climatic circumstance and meteorological conditions over the past four to seven decades.…”

Section: Remote Sensing Datamentioning

confidence: 99%

Exceptional Drought across Southeastern Australia Caused by Extreme Lack of Precipitation and Its Impacts on NDVI and SIF in 2018

Feng

Liu

et al. 2019

Remote Sensing

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Increased drought frequency in Australia is a pressing concern for scholars. In 2018, a severe drought in eastern Australia was recorded by the Emergency Events Database (EM-DAT). To investigate the main causes and impacts of this drought across southeastern Australia, this work presents an overview of the drought mechanism and depicts its evolutionary process. The Standardized Precipitation Evapotranspiration Index (SPEI) from the Global Drought Monitor was used to identify the drought event and characterize its spatiotemporal distribution. The Normalized Difference Vegetation Index (NDVI) and the sun-induced chlorophyll fluorescence (SIF) were used to investigate the drought impacts on vegetation growth. In addition, the effects of drought response measures on Sustainable Development Goals (SDGs) were analyzed. Our results showed that the exceptional drought occurred across southeastern Australia from April to December, and it was most severe in July, owing to an extreme lack of precipitation and increase in temperature. Moreover, we identified profound and long-lasting impacts of the drought on NDVI and SIF levels, especially for cropland. Furthermore, we also found that SIF was superior to NDVI in detecting drought impacts. This study advised on how to formulate timely and effective drought-response measures and supports sustainable socioeconomic development in Australia.

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Evaluating skill and robustness of seasonal meteorological and hydrological drought forecasts at the catchment scale – Case Catalonia (Spain)

Cited by 24 publications

References 72 publications

Streamflow drought: implication of drought definitions and its application for drought forecasting

Streamflow drought: implication of drought definitions and its application for drought forecasting

Skill of large-scale seasonal drought impact forecasts

Exceptional Drought across Southeastern Australia Caused by Extreme Lack of Precipitation and Its Impacts on NDVI and SIF in 2018

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