Historical Trends in Mean and Extreme Runoff and Streamflow Based on Observations and Climate Models

Asadieh, Behzad; Krakauer, Nir Y.; Fekete, B M

doi:10.3390/w8050189

Cited by 29 publications

(15 citation statements)

References 59 publications

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“…In another study, Tao et al [17] investigated the trends in daily flows for a 50-year period in the rivers within the Tarim basin and analyzed the effects on climatic changes on the hydrological regime. A recent study by Asadieh et al [18], conducted in the USA in the multi-year period of 1971-2001, pointed out very similar relative changes in extreme runoff and mean runoff, in terms of global and continental averages. However, the extreme runoff showed relatively faster rate of change compared to the mean runoff (and streamflow).…”

Section: Introductionmentioning

confidence: 87%

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Analysis of the Course and Frequency of High Water Stages in Selected Catchments of the Upper Vistula Basin in the South of Poland

Wałęga

Młyński

Bogdał

et al. 2016

Water

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Abstract:The paper presents an analysis of the course and frequency of high water stages in selected catchments of the upper Vistula basin in the south of Poland. The following rivers were investigated: the Dunajec-Nowy Targ-Kowaniec cross-section, the Rudawa-Balice cross-section, the Kamienica-Nowy Sącz cross-section, the Wisłok-Tryńcza cross-section and the San-Przemyśl cross-section. Daily flows from the years 1983-2014 were used to determine maximum annual flows and maximum flows per summer and winter half-year. Selected floods were analyzed with reference to the following metrics: POTX (mean size of the flow determined based on high water stages exceeding the assumed threshold value), POT3F (number of high water stages exceeding the threshold value for each hydrological year), WPOT3F (number of high water stages exceeding the threshold value for the winter half-year and), LOPT3F (number of high water stages exceeding the threshold value for the summer half-year). The determined metrics were analyzed for trend (Mann-Kendall test), homogeneity (Kruskal-Wallis test), and heteroscedasticity (Levene test). Additionally, periodograms were used to determine periodicity of time series for maximum annual flows. The resulting computations indicated upward trends in the analyzed flood metrics but they were not significant in any case. Therefore, in the years 1983-2014 no factors were observed that would significantly affect the size and frequency of high water runoff from the investigated catchments.

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Section: Introductionmentioning

confidence: 87%

“…The minimum size of sample for the Mann-Kendall test could be below ten observations. The Mann-Kendall S statistic for a time series is determined from the equation [18,[28][29][30]:…”

Section: Metricmentioning

confidence: 99%

Analysis of the Course and Frequency of High Water Stages in Selected Catchments of the Upper Vistula Basin in the South of Poland

Wałęga

Młyński

Bogdał

et al. 2016

Water

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“…There have been many studies of flood magnitude and frequency characteristics at global (Asadieh et al, 2016;Dankers et al, 2014;Do et al, 2017Do et al, , 2019Hodgkins et al, 2017;Wasko & Sharma, 2017;Woldemeskel & Sharma, 2016), continental (Alfieri et al, 2015;Gudmundsson et al, 2012;Hall et al, 2014;Ishak et al, 2013;Mallakpour & Villarini, 2015;Mediero et al, 2015;Parajka et al, 2010), and national scales (Beurton & Thieken, 2009;Burn & Whitfield, 2016;Merz et al, 2018;Slater & Villarini, 2016;Stevens et al, 2016). However, there have been fewer and more recent studies of flood timing (Berghuijs et al, 2019(Berghuijs et al, , 2016Blöschl et al, 2017;Burn & Whitfield, 2016;Cunderlik & Ouarda, 2009;Dettinger & Diaz, 2000;Hall & Blöschl, 2018;Villarini, 2016;Ye et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Global‐Scale Prediction of Flood Timing Using Atmospheric Reanalysis

Hong

Westra

Leonard

et al. 2020

Water Resources Research

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The annual timing of flood events is a useful indicator to study the interaction between atmospheric and catchment processes in generating floods. This paper presents an assessment of the seasonal timing of floods for 7,894 gauging locations across the globe over a common period from 1981 to 2010. The averaged ordinal date of annual maximum streamflow is then estimated for ungauged locations following a two-stage prediction scheme. The first stage identifies regions that share a common climatic predictor of flood timing by analyzing the similarity of flood timing with seven climate variables. These variables represent precipitation timing and snowmelt dynamics and are derived from a global climate reanalysis data set. Homogeneous regions in terms of the dominant predictor are generalized in the second stage through a rule-based classification. The classification partitions the world into 10 hydroclimate classes, where each class has flood timing predicted using the most relevant climate predictor. Using this relatively simple and interpretable model structure, flood timing could be predicted with a global mean absolute error of approximately 32 days while maintaining consistency across large regions. Potential applications of the developed map include better understanding of climatic drivers of flooding and benchmarking the performance of global hydrological models in simulating the processes relevant to flooding.Plain Language Summary Timing of annual maximum streamflow is a useful index to relate flood occurrence to appropriate flood generation processes. This study presents an assessment of flood timing across 7,894 gauging locations globally over the period from 1981 to 2010. The averaged date of annual maximum streamflow is compared to seven climate predictors, identifying regions that are likely to share a common flood generation process. These homogeneous regions are generalized across the globe using a gridded data set of daily precipitation and temperature. To derive a global map of flood timing, the date of annual maximum streamflow is predicted for both gauged and ungauged locations, using a linear function of the most important climate predictor in each region.

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“…At the same time, vagaries of climate, especially the frequency of extremes and changing seasons, and regional shortages of water and energy are inducing yield depressions and undermining the food security. Furthermore, climate extremes have been on the uptrend since the last century (Asadieh et al, ), and global climate change projections indicate that the frequency and severity of extremes may continue to increase, hence posing a challenge for the future (Parry, ). A burgeoning population, economic crises, political issues like sanctions, civil unrest, and social strife add to the uncertainties and make food security more complicated to address.…”

Section: Introductionmentioning

confidence: 99%

Understanding the Changes in Global Crop Yields Through Changes in Climate and Technology

et al. 2018

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During the last few decades, the global agricultural production has risen and technology enhancement is still contributing to yield growth. However, population growth, water crisis, deforestation, and climate change threaten the global food security. An understanding of the variables that caused past changes in crop yields can help improve future crop prediction models. In this article, we present a comprehensive global analysis of the changes in the crop yields and how they relate to different large‐scale and regional climate variables, climate change variables and technology in a unified framework. A new multilevel model for yield prediction at the country level is developed and demonstrated. The structural relationships between average yield and climate attributes as well as trends are estimated simultaneously. All countries are modeled in a single multilevel model with partial pooling to automatically group and reduce estimation uncertainties. El Niño‐southern oscillation (ENSO), Palmer drought severity index (PDSI), geopotential height anomalies (GPH), historical carbon dioxide (CO2) concentration and country‐based time series of GDP per capita as an approximation of technology measurement are used as predictors to estimate annual agricultural crop yields for each country from 1961 to 2013. Results indicate that these variables can explain the variability in historical crop yields for most of the countries and the model performs well under out‐of‐sample verifications. While some countries were not generally affected by climatic factors, PDSI and GPH acted both positively and negatively in different regions for crop yields in many countries.

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Historical Trends in Mean and Extreme Runoff and Streamflow Based on Observations and Climate Models

Cited by 29 publications

References 59 publications

Analysis of the Course and Frequency of High Water Stages in Selected Catchments of the Upper Vistula Basin in the South of Poland

Analysis of the Course and Frequency of High Water Stages in Selected Catchments of the Upper Vistula Basin in the South of Poland

Global‐Scale Prediction of Flood Timing Using Atmospheric Reanalysis

Understanding the Changes in Global Crop Yields Through Changes in Climate and Technology

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