Global catchment modelling using World-Wide HYPE (WWH), open data and stepwise parameter estimation

Abstract

Abstract. Recent advancements in catchment hydrology (such as understanding hydrological processes, accessing new data sources, and refining methods for parameter constraints) make it possible to apply catchment models for ungauged basins over large domains. Here we present a cutting-edge case study applying catchment-modelling techniques at the global scale for the first time. The modelling procedure was challenging but doable and even the first model version show better performance than traditional gridded global models of river flow. We used the open-source code of the HYPE model and applied it for > 130 000 catchments (with an average resolution of 1000 km2), delineated to cover the Earths landmass (except Antarctica). The catchments were characterized using 20 open databases on physiographical variables, to account for spatial and temporal variability of the global freshwater resources, based on exchange with the atmosphere (e.g. precipitation and evapotranspiration) and related budgets in all compartments of the land (e.g. soil, rivers, lakes, glaciers, and floodplains), including water stocks, residence times, interfacial fluxes, and the pathways between various compartments. Global parameter values were estimated using a step-wise approach for groups of parameters regulating specific processes and catchment characteristics in representative gauged catchments. Daily time-series (> 10 years) from 5338 gauges of river flow across the globe were used for model evaluation (half for calibration and half for independent validation), resulting in an average monthly KGE of 0.4. However, the world-wide HYPE (WWH) model shows large variation in model performance, both between geographical domains and between various flow signatures. The model performs best in Eastern USA, Europe, South-East Asia, and Japan, as well as in parts of Russia, Canada, and South America. The model shows overall good potential to capture flow signatures of monthly high flows, spatial variability of high flows, duration of low flows and constancy of daily flow. Nevertheless, there remains large potential for model improvements and we suggest both redoing the calibration and reconsidering parts of the model structure for the next WWH version. The calibration cycle should be repeated a couple of times to find robust values under new fixed parameter conditions. For the next iteration, special focus will be given to precipitation, evapotranspiration, soil storage, and dynamics from hydrological features, such as lakes, reservoirs, glaciers, and floodplains. This first model version clearly indicates challenges in large scale modelling, usefulness of open data and current gaps in processes understanding. Parts of the WWH can be shared with other modellers working at the regional scale to appreciate local knowledge, establish a critical mass of experts and improve the model in a collaborative manner. Setting up a global catchment model has to be a long-term commitment of continuous model refinements to achieve successful and truly useful results.

Climate-dependent propagation of precipitation uncertainty into the water cycle

“…The climate-dependent (propagation of) precipitation uncertainties illustrates that there is no best overall product but instead a careful regional, climate-based selection can support hydrological applications. Overall, these findings highlight the usefulness of streamflow measurements capturing truly large-scale hydrological dynamics which can even be used to make inference on the accuracy of precipitation datasets (Behrangi et al, 2011;Thiemig et al, 2013;Beck et al, 2017aBeck et al, , 2019aArheimer et al, 2019;Bhuiyan et al, 2019;Mazzoleni et al, 2019).…”

Lessons learnt from checking the quality of openly accessible river flow data worldwide

“…In hydrological modelling, datasets used throughout model delineation, parametrization, calibration, and validation, are essential and integral parts of the modelling, which will later influence model performance and may limit model applications (e.g. Arheimer et al 2012, 2019, Crochemore et al 2019.…”

“…Lehner and Döll 2004, Portmann et al 2010, Yamazaki et al 2014) has further enhanced opportunities for large-scale and large-sample hydrological studies (e.g. Pechlivanidis and Arheimer 2015, Siqueira et al 2018, Arheimer et al 2019. In hydrology, river flow is one of the most crucial variables for water resources projects, such as energy production, irrigation planning, water quality improvements or waterway transport.…”

“…Many recent studies have made use of datasets of river flow at the global scale (e.g. Arheimer et al 2019, Beck et al 2013, Gudmundsson et al 2018a, 2018b. These use different ways of ensuring a minimum level of trust in the river flow observations, such as removing anthropogenic influences based on land-use information (e.g.…”

“…These were examined for 21 586 unique time series across the globe. The incentive for this work was the setup of the HYPE model at global scale (Worldwide-HYPE; Arheimer et al 2019), which required the location and time series for a large number of river flow stations to define catchments, calibrate model parameters, and assess model performance. To the authors' knowledge, no database of quality indicators covering simultaneously all 13 datasets compiled in this study is currently available.…”