Linear Optimal Runoff Aggregate (LORA): a global gridded synthesis runoff product

Abstract

Abstract. No synthesized global gridded runoff product, derived from multiple sources, is available, despite such a product being useful for meeting the needs of many global water initiatives. We apply an optimal weighting approach to merge runoff estimates from hydrological models constrained with observational streamflow records. The weighting method is based on the ability of the models to match observed streamflow data while accounting for error covariance between the participating products. To address the lack of observed streamflow for many regions, a dissimilarity method was applied to transfer the weights of the participating products to the ungauged basins from the closest gauged basins using dissimilarity between basins in physiographic and climatic characteristics as a proxy for distance. We perform out-of-sample tests to examine the success of the dissimilarity approach, and we confirm that the weighted product performs better than its 11 constituent products in a range of metrics. Our resulting synthesized global gridded runoff product is available at monthly timescales, and includes time-variant uncertainty, for the period 1980–2012 on a 0.5∘ grid. The synthesized global gridded runoff product broadly agrees with published runoff estimates at many river basins, and represents the seasonal runoff cycle for most of the globe well. The new product, called Linear Optimal Runoff Aggregate (LORA), is a valuable synthesis of existing runoff products and will be freely available for download on https://geonetwork.nci.org.au/geonetwork/srv/eng/catalog.search#/metadata/f9617_9854_8096_5291 (last access: 31 January 2019).

Evaluation of CMIP6 Global Climate Models for Simulating Land Surface Energy and Water Fluxes During 1979–2014

“…These improvements include new soil and snow hydrology (Balsamo et al, 2009;Dutra et al, 2010), revised soil thermal conductivity (Peters-Lidard et al, 1998), vegetation seasonality (Boussetta et al, 2013), and bare soil evaporation (Albergel et al, 2012). LandFlux-EVAL (Mueller et al, 2013), derived optimal linear combination ET (DOLCE, version 2.1; Hobeichi et al, 2018), and linear optimal RF aggregate (LORA; Hobeichi et al, 2019) are the global gridded synthesis products of ET, LH, and RF, respectively. They were generated by merging an ensemble of individual data sets of different categories, such as diagnostic data sets, LSMs, and reanalysis products.…”

A continental-scale evaluation of the calibration-free complementary relationship with physical, machine-learning, and land-surface models

“…To achieve the grid-scale ETwb, some syntheses (e.g., spatial and temporal gap filling and/or conceptual modelling) are inevitable due to the non-uniformity and unavailability of in-situ precipitation, streamflow, and terrestrial water storage (TWS) observations. We collected the global precipitation (P) product v.2018 of the Global Precipitation Climate Centre (GPCC) together with the grid runoff (Q) products by Ghiggi et al (2019) and Hobeichi et al (2019) and the TWS anomalies reconstructed by Humphrey and Gudmundsson (2019). The GPCC monthly P data are readily available for 1891present from https://psl.noaa.gov/data/gridded/data.gpcc.html (last access on Jun-01/2020).…”

Comment on essd-2021-84

“…Figure 1 shows the schematic of the merging procedure to create the seven SM products. The unweighted averaging and OLC (Hobeichi et al, 2018(Hobeichi et al, , 2019 methods were applied over the observational or observation-forced datasets (i.e., offline LSMs, reanalysis, satellite [ORS]). The EC method (Mystakidis et al, 2016) was applied over both the ORS datasets and the CMIP5/CMIP6 simulations (Eyring et al, 2016;Taylor et al, 2012).…”

“…The OLC is an ensemble weighting and rescaling algorithm that is optimal in the sense that the weighted average minimizes the mean squared difference with respect to the site-level observations (Bishop and Abramowitz, 2013). The OLC method was previously found to lead to improved performance in the merged product relative to the source datasets in terms of the global evapotranspiration and runoff (Hobeichi et al, 2018(Hobeichi et al, , 2019. The EC method is common for reducing uncertainty in future ESM simulations (Mystakidis et al, 2016;Padrón et al, 2019).…”