Large increase in global storm runoff extremes driven by climate and anthropogenic changes

Yin, Jiabo; Gentine, Pierre; Zhou, Sha; Sullivan, Sylvia; Wang, Ren; Zhang, Yao; Guo, Shenglian

doi:10.1038/s41467-018-06765-2

Cited by 292 publications

(197 citation statements)

References 70 publications

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“…Large precipitation events can lead to an increased partitioning of abiotic‐driven water losses from the system through runoff, deep soil water percolation or evaporation (Knapp et al, ; Yin et al, ). Such repackaging of rainfall into larger events necessarily results in longer intervening dry periods between rainfall events if total amounts do not change.…”

Section: Discussionmentioning

confidence: 99%

Precipitation amount and event size interact to reduce ecosystem functioning during dry years in a mesic grassland

Felton

Slette

Smith

et al. 2019

Global Change Biology

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Ongoing intensification of the hydrological cycle is altering rainfall regimes by increasing the frequency of extreme wet and dry years and the size of individual rainfall events. Despite long‐standing recognition of the importance of precipitation amount and variability for most terrestrial ecosystem processes, we lack understanding of their interactive effects on ecosystem functioning. We quantified this interaction in native grassland by experimentally eliminating temporal variability in growing season rainfall over a wide range of precipitation amounts, from extreme wet to dry conditions. We contrasted the rain use efficiency (RUE) of above‐ground net primary productivity (ANPP) under conditions of experimentally reduced versus naturally high rainfall variability using a 32‐year precipitation–ANPP dataset from the same site as our experiment. We found that increased growing season rainfall variability can reduce RUE and thus ecosystem functioning by as much as 42% during dry years, but that such impacts weaken as years become wetter. During low precipitation years, RUE is lowest when rainfall event sizes are relatively large, and when a larger proportion of total rainfall is derived from large events. Thus, a shift towards precipitation regimes dominated by fewer but larger rainfall events, already documented over much of the globe, can be expected to reduce the functioning of mesic ecosystems primarily during drought, when ecosystem processes are already compromised by low water availability.

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Section: Discussionmentioning

confidence: 99%

Precipitation amount and event size interact to reduce ecosystem functioning during dry years in a mesic grassland

Felton

Slette

Smith

et al. 2019

Global Change Biology

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“…Flooding is one of the costliest natural disasters in the world [1], which has been projected to occur more frequently under global warming conditions [2,3]. Reasonable and reliable projection of maximum discharge is critical for flood risk assessment and climate change adaptation.…”

Section: Introductionmentioning

confidence: 99%

Evaluation and machine learning improvement of global hydrological model-based flood simulations

Tao

Sun

Gentine

et al. 2019

Environ. Res. Lett.

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100

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A warmer climate is expected to accelerate global hydrological cycle, causing more intense precipitation and floods. Despite recent progress in global flood risk assessment, the accuracy and improvement of global hydrological models (GHMs)-based flood simulation is insufficient for most applications. Here we compared flood simulations from five GHMs under the Inter-Sectoral Impact Model Intercomparison Project 2a (ISIMIP2a) protocol, against those calculated from 1032 gauging stations in the Global Streamflow Indices and Metadata Archive for the historical period 1971-2010. A machine learning approach, namely the long short-term memory units (LSTM) was adopted to improve the GHMs-based flood simulations within a hybrid physics-machine learning approach (using basin-averaged daily mean air temperature, precipitation, wind speed and the simulated daily discharge from GHMs-CaMa-Flood model chain as the inputs of LSTM, and observed daily discharge as the output value). We found that the GHMs perform reasonably well in terms of amplitude of peak discharge but are relatively poor in terms of their timing. The performance indicated great discrepancy under different climate zones. The large difference in performance between GHMs and observations reflected that those simulations require improvements. The LSTM used in combination with those GHMs was then shown to drastically improve the performance of global flood simulations (especially in terms of amplitude of peak discharge), suggesting that the combination of classical flood simulation and machine learning techniques might be a way forward for more robust and confident flood risk assessment.

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“…As water requirements and weather extremes continue to increase, water issues rely on detailed models for effective planning and management ( B Jia et al, ; Yin et al, ; Zhu et al, ). River flow simulation is a critical step for risk and uncertainty analyses of water resource systems (Loucks et al, ; Zhu et al, ).…”

Section: Introductionmentioning

confidence: 99%

Monthly Streamflow Simulation for the Headwater Catchment of the Yellow River Basin With a Hybrid Statistical‐Dynamical Model

Wang

Dong

Lall

et al. 2019

Water Resources Research

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Streamflow simulation of the headwater catchment of the Yellow River basin (HCYRB) in China is important for water resources management of the Yellow River basin. A statistical‐dynamical model, combining regular vine copulas with an optimization method for structure estimation, is presented with an application for simulating the monthly streamflow with local climate drivers at HCYRB. Local climate drivers for streamflow in every month are analyzed using rank‐based correlation. Precipitation, evaporation, and temperature generally show strong associations with streamflow. Winter streamflows relate to total precipitation of the wet season and total evaporation of October and November, while unfrozen‐month streamflows are correlated with evaporation and precipitation of current month and previous 1 month in the wet season. Both canonical vine and D‐vine copulas are applied to develop different conditional quantile functions for streamflows in different months with their dynamical covariates. The covariates are selected from historical streamflows and climate drivers with appropriate lags using partial correlations. The optimal vine trees are selected using the sequential maximum spanning tree algorithm with the weight based on both dependence and goodness of fit. The model demonstrates higher skill than existing vine‐based models and the seasonal autoregressive integrated moving average model. The enhanced skill of the hybrid statistical‐dynamical model comes from an improved capability of capturing nonlinear correlation and tail dependence of streamflow and climate drivers with the optimization of vine structure selection. The model provides an effective advance to enhance water resources planning and management for HCYRB and the whole basin.

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Large increase in global storm runoff extremes driven by climate and anthropogenic changes

Cited by 292 publications

References 70 publications

Precipitation amount and event size interact to reduce ecosystem functioning during dry years in a mesic grassland

Precipitation amount and event size interact to reduce ecosystem functioning during dry years in a mesic grassland

Evaluation and machine learning improvement of global hydrological model-based flood simulations

Monthly Streamflow Simulation for the Headwater Catchment of the Yellow River Basin With a Hybrid Statistical‐Dynamical Model

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