A quantitative evaluation of the issue of drought definition: a source of disagreement in future drought assessments

Droughts are anticipated to intensify in many parts of the world due to climate change. However, the issue of drought definition, namely the diversity of drought indices, makes it difficult to compare drought assessments. This issue is widely known, but its relative importance has never been quantitatively evaluated in comparison to other sources of uncertainty. Here, encompassing three drought categories (meteorological, agricultural, and hydrological droughts) with four temporal scales of interest, we evaluated changes in the drought frequency using multi-model and multi-scenario simulations to identify areas where the definition issue could result in pronounced uncertainties and to what extent. We investigated the disagreement in the signs of changes between drought definitions and decomposed the variance into four main factors: drought definitions, greenhouse gas concentration scenarios, global climate models, and global water models, as well as their interactions. The results show that models were the primary sources of variance over 82% of the global land area. On the other hand, the drought definition was the dominant source of variance in the remaining 17%, especially in parts of northern high-latitudes. Our results highlight specific regions where differences in drought definitions result in a large spread among projections, including areas showing opposite signs of significant changes. At a global scale, 7% of the variance resulted independently from the definition issue, and that value increased to 44% when 1st and 2nd order interactions were considered. The quantitative results suggest that by clarifying hydrological processes or sectors of interest, one could avoid these uncertainties in drought assessments to obtain a clearer picture of future drought change

The timing of unprecedented hydrological drought under climate change

Droughts that exceed the magnitudes of historical variation ranges could occur increasingly frequently under future climate conditions. However, the time of the emergence of unprecedented drought conditions under climate change has rarely been examined. Here, using multimodel hydrological simulations, we investigate the changes in the frequency of hydrological drought (defined as abnormally low river discharge) under high and low greenhouse gas concentration scenarios and existing water resource management measures and estimate the time of the first emergence of unprecedented regional drought conditions centered on the low-flow season. The times are detected for several subcontinental-scale regions, and three regions, namely, Southwestern South America, Mediterranean Europe, and Northern Africa, exhibit particularly robust results under the high-emission scenario. These three regions are expected to confront unprecedented conditions within the next 30 years with a high likelihood regardless of the emission scenarios. In addition, the results obtained herein demonstrate the benefits of the lower-emission pathway in reducing the likelihood of emergence. The Paris Agreement goals are shown to be effective in reducing the likelihood to the unlikely level in most regions. However, appropriate and prior adaptation measures are considered indispensable when facing unprecedented drought conditions. The results of this study underscore the importance of improving drought preparedness within the considered time horizons

Similarities and differences among fifteen global water models in simulating the vertical water balance

Hydrological models have been developed in response to the need to understand the complex water cycle of the Earth and to assess its interaction with historical and future climate scenarios. In the global water sector of the Inter-Sectoral Impact Model Intercomparison Project phase 2b (ISIMIP2b), six land surface models (LSMs), eight hydrological models (GHMs), and one dynamic vegetation model (DGVM) are contributing with transient simulations spanning from 1660 to 2300. The model simulations follow a common protocol and are driven by common bias adjusted climate model outputs combined with plausible socio-economic scenarios and representative concentration pathways. The main goal of this study is to highlight similarities and differences among these models in simulating the vertical water balance. The main similarity of these models consists in the water cycle simulation, even if the models have been developed for different purposes such as energy cycle (LSMs), water cycle (GHMs), or vegetation cycle (DGVM) simulation. In particular, we address the following research question: 1) what equations are used to compute water storages and water fluxes; 2) how different are the equations among the models; 3) how the equations were adjusted; 4) how many parameters are used by the models; 5) how often the parameters are used; 6) how similar or different are the parameters among the models. To this end, we apply a standard writing style of the water storages and water fluxes included in the models, to easily identify the similarities and differences among them. Most of the models include in their structure the canopy, soil, and snow storages, and almost half of them include the groundwater storage. Furthermore, we find that: 1) a model needs a very good documentation, this would help to easily identify and understand the equations in the code; 2) some modelers teams use common approaches resulting in similar equations of water storages or water fluxes, but different models structures still lead to different models results; 3) collaboration and communication among the modelers are necessary, on the one hand, for the realization of the models standard writing style, and on the other hand, for a better understanding of the models themselves, especially their strengths, limitations and results. Overall, our results (i) help to better explain the different models results and to attribute these to the differences in simulating specific processes; (ii) contribute to the remarkable efforts in creating a common protocol and a common input datasets for well-defined simulations; (iii) foster a better understanding of how the models work and finding new ways of improvement and development