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

Abstract

Streamflow 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, post-processing of forecasts is a crucial step to ensure usefulness at the river basin scale. In this paper we present a methodology to post-process 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 post-processed forecasts are compared to post-processed forecasts derived from a quantile mapping approach as a benchmark. Fuzzy post-processing 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 post-processed forecasts.

Benchmarking an operational hydrological model for providing seasonal forecasts in Sweden

“…the baseflow index) as the main driver of hydrological predictability, we have found that, for Sweden, it is instead the result of the overall hydrological behaviour, even if some specific streamflow signatures may have a greater impact than others. The exact magnitude of the impact of the different signatures is however difficult to quantify since, even if no consensus exists on an representative set of signatures (McMillan et al, 2017), one can argue the hydrological system is generally characterized by a wider set of streamflow signatures than that considered here. In this context, we hypothesized that the selected signatures are robust enough to describe the hydrological regimes and further guide the analysis towards the identification of hydrologic similarities.…”

“…More specifically, we selected a set of 15 hydrologic signatures (statistics describing the hydrological behaviour; see Table 1) to provide diagnostics of the hydrological regime. Since no consensus exists on an adequate set of hydrological signatures (McMillan et al, 2017), the set we used in this study draws on previous literature on hydrological classification (Euser et al, 2013;Viglione et al, 2013), process understanding (Kuentz et al, 2017;Pechlivanidis and Arheimer, 2015), and forecasting skill attribution and is based on the assumption that these signatures are not prone to large uncertainties and can thus provide information towards the identification of hydrologically similar river systems (Knoben et al, 2018;Westerberg et al, 2016). We used the non-parametric Spearman rank test to assess the correlation between forecast skill and each of the hydrologic signatures.…”

Evaluation of Earth Observations and In Situ Data Assimilation for Seasonal Hydrological Forecasting

“…Yuan et al, 2019). Various techniques are used to improve forecasts: data assimilation, multi-modeling, and pre-and post-processing, that is, bias-adjustment of meteorological and/ or hydrological forecasts (see e.g., Macian-Sorribes et al (2020)). For streamflow forecasts, it is important to understand the spatiotemporal patterns of predictability and the drivers behind them (Girons Lopez et al, 2021) because the patterns can be mapped onto physical catchment characteristics.…”

“…Current ensembles are commonly obtained by applying different perturbations to the control (unperturbed) forecast, and should preferably be based on multiple models (Sahai et al., 2021). Ensemble forecasting is used to predict flooding (Alfieri et al., 2014; Ramos et al., 2007), droughts (Colossi & Tucci, 2020); and in hydro‐climate services, for example, for water resources management applications (Macian‐Sorribes et al., 2020; Pechlivanidis et al., 2020), agriculture (Villani et al., 2021) and hydropower (Contreras et al., 2020).…”

Benchmarking an operational hydrological model for providing seasonal forecasts in Sweden

“…ESP forecasts have been used by the scientific community to assess forecast skill sensitivity and uncertainties and to benchmark seasonal forecast improvements (Arnal et al, 2018;Harrigan et al, 2018), as well as for operational flood forecasting in many different settings and scales (Candogan Yossef et al, 2017). Over the years, different techniques have been developed to improve the performance of forecasting systems, such as data assimilation for improving the initial conditions of forecasts (DeChant and Moradkhani, 2011), multi-model approaches (Muhammad et al, 2018), or pre-and post-processing techniques such as using artificial neural networks for reducing the effects of model errors (Jeong and Kim, 2005;Macian-Sorribes et al, 2020), historical scenario selection and weighting (Crochemore et al, 2017;Trambauer et al, 2015), and calibration techniques (Wood and Schaake, 2008).…”