An ensemble approach to assess hydrological models’ contribution to uncertainties in the analysis of climate change impact on water resources

Over the recent years, several research efforts investigated the impact of climate change on water resources for different regions of the world. The projection of future river flows is affected by different sources of uncertainty in the hydro-climatic modelling chain. One of the aims of the QBic3 5 project (Que´bec-Bavarian International Collaboration on Climate Change) is to assess the contribution to uncertainty of hydrological models by using an ensemble of hydrological models presenting a diversity of structural complexity (i.e. lumped, semi distributed and distributed models). The study investigates two humid, mid-latitude catchments with natural flow conditions; one located in 10 Southern Que´bec (Canada) and one in Southern Bavaria (Germany). Daily flow is simulated with four different hydrological models, forced by outputs from regional climate models driven by a given number of GCMs’ members over a reference (1971–2000) and a future (2041–2070) periods. The results show that the choice of the hydrological model does strongly affect the climate change response of selected hydrological indicators, especially those related to low flows. Indicators related to high flows seem less sensitive on the choice of the hydrological model. Therefore, the computationally less demanding models (usually simple, lumped and conceptual) give a significant level of trust for high and overall mean flows

On the need for bias correction in regional climate scenarios to assess climate change impacts on river runoff

In climate change impact research, the assessment of future river runoff as well as the catchment scale water balance is impeded by different sources of modeling uncertainty. Some research has already been done in order to quantify the uncertainty of climate 5 projections originating from the climate models and the downscaling techniques as well as from the internal variability evaluated from climate model member ensembles. Yet, the use of hydrological models adds another layer of incertitude. Within the QBic3 project (Qu´ebec-Bavaria International Collaboration on Climate Change) the relative contributions to the overall uncertainty from the whole model chain (from global climate 10 models to water management models) are investigated using an ensemble of multiple climate and hydrological models. Although there are many options to downscale global climate projections to the regional scale, recent impact studies tend to use Regional Climate Models (RCMs). One reason for that is that the physical coherence between atmospheric and land-surface 15 variables is preserved. The coherence between temperature and precipitation is of particular interest in hydrology. However, the regional climate model outputs often are biased compared to the observed climatology of a given region. Therefore, biases in those outputs are often corrected to reproduce historic runoff conditions from hydrological models using them, even if those corrections alter the relationship between temperature and precipitation. So, as bias correction may affect the consistency between RCM output variables, the use of correction techniques and even the use of (biased) climate model data itself is sometimes disputed among scientists. For those reasons, the effect of bias correction on simulated runoff regimes and the relative change in selected runoff indicators is explored. If it affects the conclusion of climate change analysis in 25 hydrology, we should consider it as a source of uncertainty. If not, the application of bias correction methods is either unnecessary in hydro-climatic projections, or safe to use as it does not alter the change signal of river runoff. The results of the present paper highlight the analysis of daily runoff simulated with four different hydrological models in two natural-flow catchments, driven by different regional climate models for a reference and a future period. As expected, bias correction of climate model outputs is important for the reproduction of the runoff regime of the 5 past regardless of the hydrological model used. Then again, its impact on the relative change of flow indicators between reference and future period is weak for most indicators with the exception of the timing of the spring flood peak. Still, our results indicate that the impact of bias correction on runoff indicators increases with bias in the climate simulations

An ensemble approach to assess hydrological models' contribution to uncertainties in the analysis of climate change impact on water resources

Over the recent years, several research efforts investigated the impact of climate change on water resources for different regions of the world. The projection of future river flows is affected by different sources of uncertainty in the hydro-climatic modelling chain. One of the aims of the QBic<sup>3</sup> project (Québec-Bavarian International Collaboration on Climate Change) is to assess the contribution to uncertainty of hydrological models by using an ensemble of hydrological models presenting a diversity of structural complexity (i.e., lumped, semi distributed and distributed models). The study investigates two humid, mid-latitude catchments with natural flow conditions; one located in Southern Québec (Canada) and one in Southern Bavaria (Germany). Daily flow is simulated with four different hydrological models, forced by outputs from regional climate models driven by global climate models over a reference (1971–2000) and a future (2041–2070) period. The results show that, for our hydrological model ensemble, the choice of model strongly affects the climate change response of selected hydrological indicators, especially those related to low flows. Indicators related to high flows seem less sensitive on the choice of the hydrological model

The ExtremeEarth software architecture for Copernicus earth observation data

Current deep learning architectures for remote sensing are trained on small datasets typically using 1 GPU without taking advantage of new innovative approaches such as distributed scale-out deep learning. In this paper, we present the ExtremeEarth software architecture for Copernicus Earth Observation data. We show how we go beyond the state-of-the-art by scaling to the petabytes of data using Hopsworks and demonstrate our big data technologies in two Thematic Exploitation Platforms (TEPs): Food Security and Polar. Furthermore, we present the integration of Hopsworks with the Polar and Food Security use cases and the flow of events for the products offered through the TEPs

ExtremeEarth meets satellite data from space

Bringing together a number of cutting-edge technologies that range from storing extremely large volumes of data all the way to developing scalable machine learning and deep learning algorithms in a distributed manner and having them operate over the same infrastructure poses unprecedented challenges. One of these challenges is the integration of European Space Agency (ESA)'s Thematic Exploitation Platforms (TEPs) and data information access service platforms with a data platform, namely Hopsworks, which enables scalable data processing, machine learning, and deep learning on Copernicus data, and development of very large training datasets for deep learning architectures targeting the classification of Sentinel images. In this article, we present the software architecture of ExtremeEarth that aims at the development of scalable deep learning and geospatial analytics techniques for processing and analyzing petabytes of Copernicus data. The ExtremeEarth software infrastructure seamlessly integrates existing and novel software platforms and tools for storing, accessing, processing, analyzing, and visualizing large amounts of Copernicus data. New techniques in the areas of remote sensing and artificial intelligence with an emphasis on deep learning are developed. These techniques and corresponding software presented in this article are to be integrated with and used in two ESA TEPs, namely Polar and Food Security TEPs. Furthermore, we present the integration of Hopsworks with the Polar and Food Security use cases and the flow of events for the products offered through the TEPs. © 2008-2012 IEEE

A comparison of changes in river runoff from multiple global and catchment-scale hydrological models under global warming scenarios of 1°C, 2°C and 3°C

We present one of the first climate change impact assessments on river runoff that utilises an ensemble of global hydrological models (Glob-HMs) and an ensemble of catchment-scale hydrological models (Cat-HMs), across multiple catchments: the upper Amazon, Darling, Ganges, Lena, upper Mississippi, upper Niger, Rhine and Tagus. Relative changes in simulated mean annual runoff (MAR) and four indicators of high and low extreme flows are compared between the two ensembles. The ensemble median values of changes in runoff with three different scenarios of global-mean warming (1°C, 2°C and 3°C above pre-industrial levels) are generally similar between the two ensembles, although the ensemble spread is often larger for the Glob-HM ensemble. In addition the ensemble spread is normally larger than the difference between the two ensemble medians. Whilst we find compelling evidence for projected runoff changes for the Rhine (decrease), Tagus (decrease) and Lena (increase) with global warming, the sign and magnitude of change for the other catchments is unclear. Our model results highlight that for these three catchments in particular, global climate change mitigation, which limits global-mean temperature rise to below 2°C above pre-industrial levels, could avoid some of the hydrological hazards that could be seen with higher magnitudes of global warming