Seasonal forecasts offer economic benefit for hydrological decision making in semi-arid regions

Portele, Tanja; Lorenz, Christof; Dibrani, Berhon; Laux, Patrick; Bliefernicht, Jan; Kunstmann, Harald

doi:10.1038/s41598-021-89564-y

Cited by 10 publications

(5 citation statements)

References 48 publications

(50 reference statements)

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“…While meteorological to hydrological drought is forecasted at a monthly to the seasonal time scale in practice 91 , timely issuance of targeted drought early warning systems (DEWS) 92 and a dynamical low flow forecast at a higher temporal resolution involving primary drought attributes, such as growth, persistence and recovery pattern, could be effective in mitigating impacts. Further, for climatologically heterogeneous regions of India, developing an improved probabilistic S2S low flow forecast, integrating the static and dynamic controls could be of great interest in aiding economic resilience to droughts 93 .…”

Section: Discussionmentioning

confidence: 99%

Climate-catchment-soil control on hydrological droughts in peninsular India

Ganguli

Singh

Reddy

et al. 2022

Sci Rep

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Most land surface system models and observational assessments ignore detailed soil characteristics while describing the drought attributes such as growth, duration, recovery, and the termination rate of the event. With the national-scale digital soil maps available for India, we assessed the climate-catchment-soil nexus using daily observed streamflow records from 98 sites in tropical rain-dominated catchments of peninsular India (8–25° N, 72–86° E). Results indicated that climate-catchment-soil properties may control hydrological drought attributes to the tune of 14–70%. While terrain features are dominant drivers for drought growth, contributing around 50% variability, soil attributes contribute ~ 71.5% variability in drought duration. Finally, soil and climatic factors together control the resilience and termination rate. The most relevant climate characteristics are potential evapotranspiration, soil moisture, rainfall, and temperature; temperature and soil moisture are dominant controls for streamflow drought resilience. Among different soil properties, soil organic carbon (SOC) stock could resist drought propagation, despite low-carbon soils across the Indian subcontinent. The findings highlight the need for accounting feedback among climate, soil, and topographical properties in catchment-scale drought propagations.

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Section: Discussionmentioning

confidence: 99%

Climate-catchment-soil control on hydrological droughts in peninsular India

Ganguli

Singh

Reddy

et al. 2022

Sci Rep

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“…Previous studies have addressed this issue, showing that better skill does not always result in positive value (Boucher et al, 2012;Chiew et al, 2003). Although, in terms of 'skill', earlier findings argue that SFFs are generally less skilful than ESP (e.g., Lucatero et al, 2018;Yossef et al, 2013), in terms of 'value', a recent study has demonstrated that the use of seasonal forecasts in semi-arid regions offers significant economic benefits by mitigating hydro-energy losses in a dry year (Portele et al, 2021). Therefore, our future research efforts should concentrate on a quantitative evaluation of the value of SFFs for reservoir operations to informing decision-making in water resources management.…”

Section: The Skill Of Seasonal Flow Forecastsmentioning

confidence: 97%

Skill of seasonal flow forecasts at catchment-scale: an assessment across South Korea

Lee,

Pianosi,

Peñuela

et al. 2023

Preprint

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Abstract. Recent advancements in numerical weather predictions have improved their forecasting performance at longer lead times for several months. As a result, seasonal weather forecasts, providing predictions of weather variables for the next several months, have gained significant attention from researchers due to their potential benefits for water resources management. Many efforts have been made to link seasonal weather forecasts with Seasonal Flow Forecasts (SFFs) using diverse hydrological models. However, generating SFFs with good skill at finer scales such as catchment remain challenging, hindering their application in practice and adoption by water managers. Consequently, water management decisions, not only in South Korea but also in many other countries, continue to rely on worst-case scenarios and the conventional Ensemble Streamflow Prediction (ESP) method. This study examines the potential of SFFs in South Korea at a catchment-scale. The analysis was conducted across 12 operational reservoir catchments of various size (from 59 to 6648 km2) over a last decade (2011–2020). Seasonal weather forecasts data (precipitation, temperature and evapotranspiration) from the ECMWF (European Centre for Medium-Range Weather Forecasts, system5) is used to drive a Tank model (conceptual hydrological model) to generate the flow ensemble forecasts. The actual skill of the forecasts is quantitatively evaluated using the Continuous Ranked Probability Skill Score (CRPSS), and it is probabilistically compared with ESP, which is the most popular forecasting system. Our results highlight that precipitation is the most important variable in determining the skill of SFFs, while temperature also plays a key role during the dry season in snow-affected catchments. Given the coarse resolution of seasonal weather forecasts, a linear scaling method to adjust the forecasts is applied, and it is found that bias correction is highly effective in enhancing the skill of SFFs. Furthermore, bias corrected SFFs showed higher skill than ESP up to 3 months ahead, and it was particularly evident during abnormally dry years. To facilitate future applications to other regions, freely available Python packages for analysing seasonal weather and flow forecasts have been made accessible.

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“…In the agricultural sector, for example, a recent study shows that flowering time can be reliably predicted from seasonal meteorological forecasts in central and eastern Europe, enabling early variety selection and planning of farm management (Ceglar and Toreti, 2021). Seasonal meteorological forecasts were also shown to provide useful information for the wind energy sector (Lledó et al, 2019) and to avoid significant economic losses from hydropower generation during droughts (Portele et al, 2021). Nevertheless, the use of seasonal meteorological forecasts for water temperature in lakes and reservoirs has been limited so far, where the focus has been on water quantity (Arnal et al, 2018;Giuliani et al, 2020;Greuell et al, 2019;Pechlivanidis et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Sources of skill in lake temperature, discharge and ice-off seasonal forecasting tools

Clayer

Jackson-Blake

Mercado‐Bettín

et al. 2023

Hydrol. Earth Syst. Sci.

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Abstract. Despite high potential benefits, the development of seasonal forecasting tools in the water sector has been slower than in other sectors. Here we assess the skill of seasonal forecasting tools for lakes and reservoirs set up at four sites in Australia and Europe. These tools consist of coupled hydrological catchment and lake models forced with seasonal meteorological forecast ensembles to provide probabilistic predictions of seasonal anomalies in water discharge, temperature and ice-off. Successful implementation requires a rigorous assessment of the tools' predictive skill and an apportionment of the predictability between legacy effects and input forcing data. To this end, models were forced with two meteorological datasets from the European Centre for Medium-Range Weather Forecasts (ECMWF), the seasonal forecasting system, SEAS5, with 3-month lead times and the ERA5 reanalysis. Historical skill was assessed by comparing both model outputs, i.e. seasonal lake hindcasts (forced with SEAS5), and pseudo-observations (forced with ERA5). The skill of the seasonal lake hindcasts was generally low although higher than the reference hindcasts, i.e. pseudo-observations, at some sites for certain combinations of season and variable. The SEAS5 meteorological predictions showed less skill than the lake hindcasts. In fact, skilful lake hindcasts identified for selected seasons and variables were not always synchronous with skilful SEAS5 meteorological hindcasts, raising questions on the source of the predictability. A set of sensitivity analyses showed that most of the forecasting skill originates from legacy effects, although during winter and spring in Norway some skill was coming from SEAS5 over the 3-month target season. When SEAS5 hindcasts were skilful, additional predictive skill originates from the interaction between legacy and SEAS5 skill. We conclude that lake forecasts forced with an ensemble of boundary conditions resampled from historical meteorology are currently likely to yield higher-quality forecasts in most cases.

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Seasonal forecasts offer economic benefit for hydrological decision making in semi-arid regions

Cited by 10 publications

References 48 publications

Climate-catchment-soil control on hydrological droughts in peninsular India

Climate-catchment-soil control on hydrological droughts in peninsular India

Skill of seasonal flow forecasts at catchment-scale: an assessment across South Korea

Sources of skill in lake temperature, discharge and ice-off seasonal forecasting tools

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