Seasonal Meteorological Forecasts Research Articles

Seasonal forecasting of local-scale soil moisture droughts with Global BROOK90: a case study of the European drought of 2018

Abstract. Prolonged deficit of soil moisture can result in significant ecosystem and economical losses. General slowdown of vegetation growth and development, withering of foliage cover, reduction of carbon, nutrients and water cycling, increase of fire and insect outbreaks are just a few examples of soil moisture drought impacts. Thus, an early and timely warning via monitoring and forecast could help to prepare for a drought and manage its consequences. In this study, a new version of Global BROOK90, an automated framework to simulate water balance at any location, is presented. The new framework integrates seasonal meteorological forecasts (SEAS5 forecasting system) from European Centre for Medium-Range Weather Forecasts (ECMWF). Here we studied how well the framework can predict the soil moisture drought on a local scale. Twelve small European catchments (from 7 to 115 km2) characterized by various geographical conditions were chosen to reconstruct the 2018–2019 period, when a large-scale prolonged drought was observed in Europe. Setting the ERA5-forced soil moisture simulations as a reference, we analysed how the lead time of the SEAS5 hindcasts influences the quality of the soil moisture predictions under drought and non-drought conditions. It was found that the hindcasted soil moisture fits well with the reference model runs only within the first (in some cases until the second and third) month of lead time. Afterwards, significant deviations up to 50 % of soil water volume were found. Furthermore, within the drought period the SEAS5 hindcast forcings resulted in overestimation of the soil moisture for most of the catchment, indicating an earlier end of a drought period. Finally, it was shown that application of the probabilistic forecast using the ensembles' quantiles to account for the uncertainty of the meteorological input is reasonable only for a lead time of up to 3 months.

Seasonal forecasting of pest population dynamics based on downscaled SEAS5 forecasts

Among the varied environmental factors that influence insect life-history, temperature has a relatively profound effect that can be mathematically estimated with non-linear equations. Thus, many models that aim to predict insect-pest population dynamics use meteorological data as input to descriptive functions that predict the development rate, survival and reproduction of pest populations. In a previous study, we developed a temperature-dependent population dynamics model for the global insect-pest Bemisia tabaci, and verified its accuracy under field conditions. In the current work, which focused on Northern Israel, seasonal meteorological forecasts from the ECMWF SEAS5 coupled model were spatially and temporally stochastically downscaled by a weather generator tool using records from ERA5-Land reanalysis and meteorological stations. The local, hourly temperature time series served as input data to a population dynamics model, creating an ensemble of seasonal population forecasts from which probabilistic predictions could be made already at the beginning of the season (which lasts from March to November). Post-hoc evaluation of the seasonal forecast was done using the observed station temperatures as model input. Comparisons to predictions made using climatologic temperatures found the weather generator-based ones much more accurate in predicting the timing of each insect generation, although there was no difference between the two approaches in predicting the population size. Moreover, the weather generator-based predictions highly matched field observations made by pest inspectors during the growing season of 2021. Taken together, our findings indicate that the developed forecasting tool is capable of providing decision makers with the supporting data required for smart seasonal planning and economical- and environmental-driven optimal management of agricultural systems.

Seasonal local rainfall and hydrological forecasting for Limpopo communities – A pragmatic approach

This paper describes the development and testing of a simple local seasonal forecast system of rainfall and hydrological conditions. The primary target group is agricultural extension officers who communicate forecasts to small-scale farmers at local level. Two pilot areas within the Limpopo river basin in South Africa were used, one in the Luvuvhu river basin in Vhembe district and the other in the Letaba river basin in Mopani district. Local rainfall and hydrological forecasts of runoff, soil moisture and evapotranspiration were produced, built on readily available deterministic seasonal meteorological forecasts for large-scale rainfall from CSIR (Council for Scientific and Industrial Research, South Africa), produced from an ensemble of seasonal forecasts using the CCAM (Conformal-Cubic Atmospheric Model) global forecast model. Hydrological forecasts were produced through a “proxy” approach, whereby outputs from the ACRU (Agricultural Catchment Research Unit) agrohydrological model provided expected hydrological responses from observed years that are representative of the rainfall anomalies predicted by the global seasonal forecast. Locally monitored soil moisture augmented the hydrological forecasts. The local seasonal forecast system does not require sophisticated calculations or a complex operational environment and complements coarser scale forecasts disseminated by the provincial departments of agriculture. Results of three rainfall seasons from 2013 to 2016 in the pilot areas showed the proxy approach to have relatively good matches between forecasts and available observations, showing better predictability for below normal rainfall seasons with exception for an extreme monthly rainfall event. The forecasts matched observed conditions best during the strong El Niño phase of ENSO (El Niño Southern Oscillation) for 2015/2016.

Integrating seasonal forecasts into real-time drought management: Júcar River Basin case study

In future years, and due to climate change, the frequency and intensity of extreme droughts will increase in some areas of the planet with water scarcity problems, affecting the reliability and vulnerability of water resource systems (WRS). Therefore, several approaches for real-time drought management were proposed in this study to improve the predictive capacity of currently used methodologies.This study was conducted in the Júcar River Basin, a highly regulated Mediterranean WRS whose experience in drought management is currently based on the combination of a stochastic model for future inflow series generation (using previous historical inflows) and a risk assessment model. Here, the possibility of improving and updating this approach was analysed by proposing three different models that integrate seasonal meteorological forecasts into the series generation process: i) an auto-regressive moving-average model with exogenous variables (ARMAX); ii) a hydrological model (HBV); and iii) an Artificial Neural Network (ANN) model. These models were also combined (individually) with a risk assessment model to assist in the decision-making process through a very intuitive drought risk indicator for several months in advance.The main results confirmed the potential for improving the predictive capacity of the current method using seasonal forecasts, especially with the ARMAX and ANN models under drought scenarios. Their results were more robust, with lower variabilities and uncertainty even after seven months, which represents a good opportunity to improve the decision-making process of this basin in a changing near future.

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

Abstract. Streamflow drought forecasting is a key element of contemporary drought early warning systems (DEWS). The term streamflow drought forecasting (not streamflow forecasting), however, has created confusion within the scientific hydrometeorological community as well as in operational weather and water management services. Streamflow drought forecasting requires an additional step, which is the application of a drought identification method to the forecasted streamflow time series. The way streamflow drought is identified is the main reason for this misperception. The purpose of this study, therefore, is to provide a comprehensive overview of the differences between different drought identification approaches to identify droughts in European rivers, including an analysis of both historical drought and implications for forecasting. Streamflow data were obtained from the LISFLOOD hydrological model forced with gridded meteorological observations (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 daily and monthly variable threshold methods (VTD and VTM, respectively), the daily and monthly fixed threshold methods (FTD and FTM, respectively), and the Standardized Streamflow Index (SSI). Our results clearly show that streamflow droughts derived from different approaches deviate from each other in their characteristics, which also vary in different climate regions across Europe. The daily threshold methods (FTD and VTD) identify 25 %–50 % more drought events than the monthly threshold methods (FTM and VTM), and accordingly the average drought duration is longer for the monthly than for the daily threshold methods. The FTD and FTM, in general, identify drought occurrences earlier in the year than the VTD and VTM. In addition, the droughts obtained with the VTM and FTM approaches also have higher drought deficit volumes (about 25 %–30 %) than the VTD and FTD approaches. Overall, the characteristics of SSI-1 drought are close to what is being identified by the VTM. The different outcome obtained with the drought identification methods illustrated with the historical analysis is also found in drought forecasting, as documented for the 2003 drought across Europe and for the Rhine River specifically. In the end, there is no unique hydrological drought definition (identification method) that fits all purposes, and hence developers of DEWS and end-users should clearly agree in the co-design phase upon a sharp definition of which type of streamflow drought is required to be forecasted for a specific application.

From skill to value: isolating the influence of end user behavior on seasonal forecast assessment

Abstract. Recent improvements in initialization procedures and representation of large-scale hydrometeorological processes have contributed to advancing the accuracy of hydroclimatic forecasts, which are progressively more skillful over seasonal and longer timescales. These forecasts are potentially valuable for informing strategic multisector decisions, including irrigated agriculture, for which they can improve crop choices and irrigation scheduling. In this operational context, the accuracy associated with the forecast system setup does not necessarily yield proportional marginal benefit, as this is also affected by how forecasts are employed by end users. This paper aims at quantifying the value of hydroclimatic forecasts in terms of potential economic benefit to the end users, which allows for the inference of a relation between gains in forecast skill and gains in end user profit. We also explore the sensitivity of this benefit to both forecast system setup and end user behavioral factors. These analyses are supported by an evaluation framework demonstrated on the Lake Como system (Italy), a regulated lake operated for flood protection and irrigation supply. Our framework relies on an integrated modeling chain composed of three building blocks: bias-adjusted seasonal meteorological forecasts are used as input to the continentally calibrated E-HYPE hydrological model; predicted lake inflows are used for conditioning the daily lake operations; and the resulting lake releases feed an agricultural model to estimate the net profit of the farmers in a downstream irrigation district. Results suggest that despite the gain in average conditions being negligible, informing the operations of Lake Como based on seasonal hydrological forecasts during intense drought episodes allows about 15 % of the farmers' profit to be gained with respect to a baseline solution not informed by any forecast. Moreover, our analysis suggests that behavioral factors capturing different perceptions of risk and uncertainty significantly impact the quantification of the benefit to the end users, whereby the estimated forecast value is potentially undermined by different levels of end user risk aversion. Lastly, our results show an intricate skill-to-value relation modulated by the underlying hydrologic conditions, which is well aligned over an exponential function in dry years, while the gains in profit are almost insensitive to the improvements in forecast skill in wet years.

Fuzzy Postprocessing to Advance the Quality of Continental Seasonal Hydrological Forecasts for River Basin Management

AbstractStreamflow forecasting services driven by seasonal meteorological forecasts from dynamic prediction systems deliver valuable information for decision-making in the water sector. Moving beyond the traditional river basin boundaries, large-scale hydrological models enable a coordinated, efficient, and harmonized anticipation and management of water-related risks (droughts, floods). However, the use of forecasts from such models at the river basin scale remains a challenge, depending on how the model reproduces the hydrological features of each particular river basin. Consequently, postprocessing of forecasts is a crucial step to ensure usefulness at the river basin scale. In this paper we present a methodology to postprocess seasonal streamflow forecasts from large-scale hydrological models and advance their quality for local applications. It consists of fuzzy logic systems that bias-adjust seasonal forecasts from a large-scale hydrological model by comparing its modeled streamflows with local observations. The methodology is demonstrated using forecasts from the pan-European hydrological model E-HYPE at the Jucar River basin (Spain). Fuzzy postprocessed forecasts are compared to postprocessed forecasts derived from a quantile mapping approach as a benchmark. Fuzzy postprocessing was able to provide skillful streamflow forecasts for the Jucar River basin, keeping most of the skill of raw E-HYPE forecasts and also outperforming quantile-mapping-based forecasts. The proposed methodology offers an efficient one-to-one mapping between large-scale modeled streamflows and basin-scale observations preserving its temporal dependence structure and can adapt its input set to increase the skill of postprocessed forecasts.

On the skill of raw and post-processed ensemble seasonal meteorological forecasts in Denmark

Abstract. This study analyzes the quality of the raw and post-processed seasonal forecasts of the European Centre for Medium-Range Weather Forecasts (ECMWF) System 4. The focus is given to Denmark, located in a region where seasonal forecasting is of special difficulty. The extent to which there are improvements after post-processing is investigated. We make use of two techniques, namely linear scaling or delta change (LS) and quantile mapping (QM), to daily bias correct seasonal ensemble predictions of hydrologically relevant variables such as precipitation, temperature and reference evapotranspiration (ET0). Qualities of importance in this study are the reduction of bias and the improvement in accuracy and sharpness over ensemble climatology. Statistical consistency and its improvement is also examined. Raw forecasts exhibit biases in the mean that have a spatiotemporal variability more pronounced for precipitation and temperature. This variability is more stable for ET0 with a consistent positive bias. Accuracy is higher than ensemble climatology for some months at the first month lead time only and, in general, ECMWF System 4 forecasts tend to be sharper. ET0 also exhibits an underdispersion issue, i.e., forecasts are narrower than their true uncertainty level. After correction, reductions in the mean are seen. This, however, is not enough to ensure an overall higher level of skill in terms of accuracy, although modest improvements are seen for temperature and ET0, mainly at the first month lead time. QM is better suited to improve statistical consistency of forecasts that exhibit dispersion issues, i.e., when forecasts are consistently overconfident. Furthermore, it also enhances the accuracy of the monthly number of dry days to a higher extent than LS. Caution is advised when applying a multiplicative factor to bias correct variables such as precipitation. It may overestimate the ability that LS has in improving sharpness when a positive bias in the mean exists.

The 2013/14 Thames Basin Floods: Do Improved Meteorological Forecasts Lead to More Skillful Hydrological Forecasts at Seasonal Time Scales?

Abstract The Thames basin experienced 12 major Atlantic depressions in winter 2013/14, leading to extensive and prolonged fluvial and groundwater flooding. This exceptional weather coincided with highly anomalous meteorological conditions across the globe. Atmospheric relaxation experiments, whereby conditions within specified regions are relaxed toward a reanalysis, have been used to investigate teleconnection patterns. However, no studies have examined whether improvements to seasonal meteorological forecasts translate into more skillful seasonal hydrological forecasts. This study applied relaxation experiments to reforecast the 2013/14 floods for three Thames basin catchments with different hydrogeological characteristics. The tropics played an important role in the development of extreme conditions over the Thames basin. The greatest hydrological forecasting skill was associated with the tropical Atlantic and less with the tropical Pacific, although both captured seasonal meteorological flow anomalies. Relaxation applied over the northeastern Atlantic produced confident ensemble forecasts, but hydrological extremes were underpredicted; this was unexpected with relaxation applied so close to the United Kingdom. Streamflow was most skillfully forecast for the catchment representing a large drainage area with high peak flow. Permeable lithology and antecedent conditions were important for skillfully forecasting groundwater levels. Atmospheric relaxation experiments can improve our understanding of extratropical anomalies and the potential predictability of extreme events such as the Thames 2013/14 floods. Seasonal hydrological forecasts differed from what was expected from the meteorology alone, and thus knowledge is gained by considering both components. In the densely populated Thames basin, considering the local hydrogeological context can provide an effective early alert of potential high-impact events, allowing for better preparedness.

The skill of seasonal ensemble low-flow forecasts in the Moselle River for three different hydrological models

Abstract. This paper investigates the skill of 90-day low-flow forecasts using two conceptual hydrological models and one data-driven model based on Artificial Neural Networks (ANNs) for the Moselle River. The three models, i.e. HBV, GR4J and ANN-Ensemble (ANN-E), all use forecasted meteorological inputs (precipitation P and potential evapotranspiration PET), whereby we employ ensemble seasonal meteorological forecasts. We compared low-flow forecasts for five different cases of seasonal meteorological forcing: (1) ensemble P and PET forecasts; (2) ensemble P forecasts and observed climate mean PET; (3) observed climate mean P and ensemble PET forecasts; (4) observed climate mean P and PET and (5) zero P and ensemble PET forecasts as input for the models. The ensemble P and PET forecasts, each consisting of 40 members, reveal the forecast ranges due to the model inputs. The five cases are compared for a lead time of 90 days based on model output ranges, whereas the models are compared based on their skill of low-flow forecasts for varying lead times up to 90 days. Before forecasting, the hydrological models are calibrated and validated for a period of 30 and 20 years respectively. The smallest difference between calibration and validation performance is found for HBV, whereas the largest difference is found for ANN-E. From the results, it appears that all models are prone to over-predict runoff during low-flow periods using ensemble seasonal meteorological forcing. The largest range for 90-day low-flow forecasts is found for the GR4J model when using ensemble seasonal meteorological forecasts as input. GR4J, HBV and ANN-E under-predicted 90-day-ahead low flows in the very dry year 2003 without precipitation data. The results of the comparison of forecast skills with varying lead times show that GR4J is less skilful than ANN-E and HBV. Overall, the uncertainty from ensemble P forecasts has a larger effect on seasonal low-flow forecasts than the uncertainty from ensemble PET forecasts and initial model conditions.

The potential value of seasonal forecasts in a changing climate in southern Africa

Abstract. Subsistence farming in southern Africa is vulnerable to extreme weather conditions. The yield of rain-fed agriculture depends largely on rainfall-related factors such as total seasonal rainfall, anomalous onsets and lengths of the rainy season and the frequency of occurrence of dry spells. Livestock, in turn, may be seriously impacted by climatic stress with, for example, exceptionally hot days, affecting condition, reproduction, vulnerability to pests and pathogens and, ultimately, morbidity and mortality. Climate change may affect the frequency and severity of extreme weather conditions, impacting on the success of subsistence farming. A potentially interesting adaptation measure comprises the timely forecasting and warning of such extreme events, combined with mitigation measures that allow farmers to prepare for the event occurring. This paper investigates how the frequency of extreme events may change in the future due to climate change over southern Africa and, in more detail, the Limpopo Basin using a set of climate change projections from several regional climate model downscalings based on an extreme climate scenario. Furthermore, the paper assesses the predictability of these indicators by seasonal meteorological forecasts of the European Centre for Medium-Range Weather Forecasts (ECMWF) seasonal forecasting system. The focus is on the frequency of dry spells as well as the frequency of heat stress conditions expressed in the temperature heat index. In areas where their frequency of occurrence increases in the future and predictability is found, seasonal forecasts will gain importance in the future, as they can more often lead to informed decision-making to implement mitigation measures. The multi-model climate projections suggest that the frequency of dry spells is not likely to increase substantially, whereas there is a clear and coherent signal among the models of an increase in the frequency of heat stress conditions by the end of the century. The skill analysis of the seasonal forecast system demonstrates that there is a potential to adapt to this change by utilizing the weather forecasts, given that both indicators can be skilfully predicted for the December–February season, at least 2 months ahead of the wet season. This is particularly the case for predicting above-normal and below-normal conditions. The frequency of heat stress conditions shows better predictability than the frequency of dry spells. Although results are promising for end users on the ground, forecasts alone are insufficient to ensure appropriate response. Sufficient support for appropriate measures must be in place, and forecasts must be communicated in a context-specific, accessible and understandable format.

Using a Weather Generator to Simulate Daily Precipitation Scenarios from Seasonal Weather Forecasts

This work presents the conditioned upscaling procedure applied in the Po-FEWS system by Hydrology area of ARPA SIMC Emilia Romagna to select the rainfall daily scenarios which better fit seasonal meteorological forecasts. Seasonal meteorological forecast provides qualitatively useful information for management activities over a short-medium term horizon, but their temporal aggregation time is too coarse to be allow quantitatively considerations. For this reason, Hydrology area of ARPA SIMC Emilia Romagna developed a conditioned upscaling procedure to obtain daily scenarios coherent with the seasonal forecasts. The daily scenarios are the inputs to the hydrological/hydraulics chains available in the Po-FEWS system for short-medium term forecasts. The daily rainfall scenarios are generated through a Spatio-Temporal Neymann Scott Rectangular Pulse model calibrated using daily observations over the period 1987-2008. Seasonal forecasts provide also the expected anomalies of minimum and maximum temperatures. Minimum and maximum temperatures are simulated at daily scale using a multilinear regression model conditioned on rainfall scenarios.

Predictability of soil moisture and river flows over France for the spring season

Abstract. Sources of spring predictability of the hydrological system over France were studied on a seasonal time scale over the 1960–2005 period. Two random sampling experiments were set up in order to test the relative importance of the land surface initial state and the atmospheric forcing. The experiments were based on the SAFRAN-ISBA-MODCOU hydrometeorological suite which computed soil moisture and river flow forecasts over a 8-km grid and more than 880 river-gauging stations. Results showed that the predictability of hydrological variables primarily depended on the seasonal atmospheric forcing (mostly temperature and total precipitation) over most plains, whereas it mainly depended on snow cover over high mountains. However, the Seine catchment area was an exception as the skill mainly came from the initial state of its large and complex aquifers. Seasonal meteorological hindcasts with the Météo-France ARPEGE climate model were then used to force the ISBA-MODCOU hydrological model and obtain seasonal hydrological forecasts from 1960 to 2005 for the entire March-April-May period. Scores from this seasonal hydrological forecasting suite could thus be compared with the random atmospheric experiment. Soil moisture and river flow skill scores clearly showed the added value in seasonal meteorological forecasts in the north of France, contrary to the Mediterranean area where values worsened.