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David A. Lavers, Shaun Harrigan, and Christel Prudhomme

Abstract

Precipitation is a key component of the global water cycle and plays a crucial role in flooding, droughts, and water supply. One way to manage its socioeconomic effects is based on precipitation forecasts from numerical weather prediction (NWP) models, and an important step to improve precipitation forecasts is by diagnosing NWP biases. In this study, we investigate the biases in precipitation forecasts from the European Centre for Medium-Range Weather Forecasts Integrated Forecasting System (IFS). Using the IFS control forecast from 12 June 2019 to 11 June 2020 at 5219 stations globally, we show that in each of the boreal winter and summer half years, the IFS 1) has an average global wet bias and 2) displays similar bias patterns for forecasts starting at 0000 and 1200 UTC and across forecast days 1–5. These biases are dependent on observed (climatological) precipitation; stations with low observed precipitation have an IFS wet bias, while stations with high observed precipitation have an IFS dry bias. Southeast Asia has a wet bias of 1.61 mm day−1 (in boreal summer) and over the study period the precipitation is overestimated by 31.0% on forecast day 3. This is the hydrological signature of several hypothesized processes including issues specifying the IFS snowpack over the Tibetan Plateau, which may affect the mei-yu front. These biases have implications for IFS land–atmosphere feedbacks, river discharge, and for ocean circulation in the Southeast Asia region. Reducing these biases could lead to more accurate forecasts of the global water cycle.

Open access
Graham P. Weedon, Christel Prudhomme, Sue Crooks, Richard J. Ellis, Sonja S. Folwell, and Martin J. Best

Abstract

Nine distributed hydrological models, forced with common meteorological inputs, simulated naturalized daily discharge from the Thames basin for 1963–2001. While model-dependent evaporative losses are critical for modeling mean discharge, multiple physical processes at many time scales influence the variability and timing of discharge. Here the use of cross-spectral analysis is advocated to measure how the average amplitude—and independently, the average phase—of modeled discharge differ from observed discharge at daily to decadal time scales. Simulation of the spectral properties of the model discharge via numerical manipulation of precipitation confirms that modeled transformation involves runoff generation and routing that amplify the annual cycle, while subsurface storage and routing of runoff between grid boxes introduces most of the autocorrelation and delays. Too much or too little modeled evaporation affects discharge variability, as do the capacity and time constants of modeled stores. Additionally, the performance of specific models would improve if four issues were tackled: 1) nonsinusoidal annual variations in model discharge (prolonged low base flow and shortened high base flow; three models), 2) excessive attenuation of high-frequency variability (three models), 3) excessive short-term variability in winter half years but too little variability in summer half years (two models), and 4) introduction of phase delays at the annual scale only during runoff generation (three models) or only during routing (one model). Cross-spectral analysis reveals how reruns of one model using alternative methods of runoff generation—designed to improve performance at the weekly to monthly time scales—degraded performance at the annual scale. The cross-spectral approach facilitates hydrological model diagnoses and development.

Full access
Christel Prudhomme, Simon Parry, Jamie Hannaford, Douglas B. Clark, Stefan Hagemann, and Frank Voss

Abstract

This paper presents a new methodology for assessing the ability of gridded hydrological models to reproduce large-scale hydrological high and low flow events (as a proxy for hydrological extremes) as described by catalogues of historical droughts [using the regional deficiency index (RDI)] and high flows [regional flood index (RFI)] previously derived from river flow measurements across Europe. Using the same methods, total runoff simulated by three global hydrological models from the Water Model Intercomparison Project (WaterMIP) [Joint U.K. Land Environment Simulator (JULES), Water Global Assessment and Prognosis (WaterGAP), and Max Planck Institute Hydrological Model (MPI-HM)] run with the same meteorological input (watch forcing data) at the same spatial 0.5° grid was used to calculate simulated RDI and RFI for the period 1963–2001 in the same European regions, directly comparable with the observed catalogues. Observed and simulated RDI and RFI time series were compared using three performance measures: the relative mean error, the ratio between the standard deviation of simulated over observed series, and the Spearman correlation coefficient. Results show that all models can broadly reproduce the spatiotemporal evolution of hydrological extremes in Europe to varying degrees. JULES tends to produce prolonged, highly spatially coherent events for both high and low flows, with events developing more slowly and reaching and sustaining greater spatial coherence than observed—this could be due to runoff being dominated by slow-responding subsurface flow. In contrast, MPI-HM shows very high variability in the simulated RDI and RFI time series and a more rapid onset of extreme events than observed, in particular for regions with significant water storage capacity—this could be due to possible underrepresentation of infiltration and groundwater storage, with soil saturation reached too quickly. WaterGAP shares some of the issues of variability with MPI-HM—also attributed to insufficient soil storage capacity and surplus effective precipitation being generated as surface runoff—and some strong spatial coherence of simulated events with JULES, but neither of these are dominant. Of the three global models considered here, WaterGAP is arguably best suited to reproduce most regional characteristics of large-scale high and low flow events in Europe. Some systematic weaknesses emerge in all models, in particular for high flows, which could be a product of poor spatial resolution of the input climate data (e.g., where extreme precipitation is driven by local convective storms) or topography. Overall, this study has demonstrated that RDI and RFI are powerful tools that can be used to assess how well large-scale hydrological models reproduce large-scale hydrological extremes—an exercise rarely undertaken in model intercomparisons.

Full access
Ervin Zsoter, Hannah Cloke, Elisabeth Stephens, Patricia de Rosnay, Joaquin Muñoz-Sabater, Christel Prudhomme, and Florian Pappenberger

Abstract

Land surface models (LSMs) have traditionally been designed to focus on providing lower-boundary conditions to the atmosphere with less focus on hydrological processes. State-of-the-art application of LSMs includes a land data assimilation system (LDAS), which incorporates available land surface observations to provide an improved realism of surface conditions. While improved representations of the surface variables (such as soil moisture and snow depth) make LDAS an essential component of any numerical weather prediction (NWP) system, the related increments remove or add water, potentially having a negative impact on the simulated hydrological cycle by opening the water budget. This paper focuses on evaluating how well global NWP configurations are able to support hydrological applications, in addition to the traditional weather forecasting. River discharge simulations from two climatological reanalyses are compared: one “online” set, which includes land–atmosphere coupling and LDAS with an open water budget, and an “offline” set with a closed water budget and no LDAS. It was found that while the online version of the model largely improves temperature and snow depth conditions, it causes poorer representation of peak river flow, particularly in snowmelt-dominated areas in the high latitudes. Without addressing such issues there will never be confidence in using LSMs for hydrological forecasting applications across the globe. This type of analysis should be used to diagnose where improvements need to be made; considering the whole Earth system in the data assimilation and coupling developments is critical for moving toward the goal of holistic Earth system approaches.

Open access
Luis Samaniego, Stephan Thober, Niko Wanders, Ming Pan, Oldrich Rakovec, Justin Sheffield, Eric F. Wood, Christel Prudhomme, Gwyn Rees, Helen Houghton-Carr, Matthew Fry, Katie Smith, Glenn Watts, Hege Hisdal, Teodoro Estrela, Carlo Buontempo, Andreas Marx, and Rohini Kumar

Abstract

Simulations of water fluxes at high spatial resolution that consistently cover historical observations, seasonal forecasts, and future climate projections are key to providing climate services aimed at supporting operational and strategic planning, and developing mitigation and adaptation policies. The End-to-end Demonstrator for improved decision-making in the water sector in Europe (EDgE) is a proof-of-concept project funded by the Copernicus Climate Change Service program that addresses these requirements by combining a multimodel ensemble of state-of-the-art climate model outputs and hydrological models to deliver sectoral climate impact indicators (SCIIs) codesigned with private and public water sector stakeholders from three contrasting European countries. The final product of EDgE is a water-oriented information system implemented through a web application. Here, we present the underlying structure of the EDgE modeling chain, which is composed of four phases: 1) climate data processing, 2) hydrological modeling, 3) stakeholder codesign and SCII estimation, and 4) uncertainty and skill assessments. Daily temperature and precipitation from observational datasets, four climate models for seasonal forecasts, and five climate models under two emission scenarios are consistently downscaled to 5-km spatial resolution to ensure locally relevant simulations based on four hydrological models. The consistency of the hydrological models is guaranteed by using identical input data for land surface parameterizations. The multimodel outputs are composed of 65 years of historical observations, a 19-yr ensemble of seasonal hindcasts, and a century-long ensemble of climate impact projections. These unique, high-resolution hydroclimatic simulations and SCIIs provide an unprecedented information system for decision-making over Europe and can serve as a template for water-related climate services in other regions.

Free access