Seasonality of monthly runoff over the continental United States: Causality and relations to mean annual and mean monthly distributions of moisture and energy

Petersen, Thomas; Devineni, Naresh; Sankarasubramanian, A.

doi:10.1016/j.jhydrol.2012.08.028

Cited by 57 publications

(41 citation statements)

References 22 publications

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“…Thornthwaite, 1931Thornthwaite, , 1948Köppen, 1936;Holdridge, 1967;Trewartha, 1968;Budyko, 1974;Alley, 1984;Kottek et al, 2006;Peel et al, 2007). The mean intra-annual pattern of energy availability and moisture availability has a distinct imprint on a diverse range of factors, such as vegetation type (Köppen, 1936;Holdridge, 1967;Trewartha, 1968;Stephenson, 1990;Gonzalez et al, 2010), ecosystem productivity (Harris et al, 2000;Parton et al, 2012;Robinson et al, 2013), agricultural production (Kurukulasuriya and Rosenthal, 2003;Deryng et al, 2011), carbon storage and release (Heimann and Reichstein, 2008), dissolved nutrient retention dynamics (Ye et al, 2012), evaporation rates (Wolock and McCabe, 1999;Berghuijs et al, 2014a), soil moisture storage (Milly, 1994;Seneviratne et al, 2010), snowpack and glacial dynamics (Woods, 2009;Bartholomew et al, 2010), droughts (Reynolds et al, 1999;Mishra and Singh, 2010;Van Loon et al, 2014), river flow (Thornthwaite, 1931;Budyko, 1974;Petersen et al, 2012;Berghuijs et al, 2014b), aquatic communities (Poff et al, 1997;Kattwinkel et al, 2011), animal activity (Richardson, 1990;…”

Section: Introductionmentioning

confidence: 99%

A simple framework to quantitatively describe monthly precipitation and temperature climatology

Berghuijs

Woods

2015

Intl Journal of Climatology

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Climate descriptors and classifications are vital for ordering past, current and future climatic conditions. Yet, these parsimonious descriptors of climatic conditions only capture specific aspects of this climate signal, and lose all other information available in the observations. As a result, climate descriptions are often not physically insightful when they are applied in other studies. In this study, we show that a sinusoidal function with an annual period can adequately describe the vast majority of monthly precipitation and temperature climates around the world. This finding allows us to synthesize intra-annual monthly precipitation and temperature climatology using five indices that are easy to interpret. The indices describe (1) the mean precipitation rate (P), (2) the mean temperature (T), (3) the seasonal precipitation amplitude ( P ), (4) the seasonal temperature amplitude (ΔT) and (5) the phase difference between the precipitation and temperature regimes (s d ). The combination of the five indices describes the relative time series of precipitation and temperature climatology, in contrast to earlier proposed similarity indices that only capture specific aspects of these time series. We demonstrate how the framework can reproduce many earlier proposed indices and classifications, and provide an example how the framework can be used to classify regions. We argue that the framework provides comprehensive insight into global climatology and can function as a quantitative conceptual basis for climate descriptions among different sciences.

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Section: Introductionmentioning

confidence: 99%

A simple framework to quantitatively describe monthly precipitation and temperature climatology

Berghuijs

Woods

2015

Intl Journal of Climatology

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“…It is a signature frequently used in hydrometeorological studies (e.g., Holmgren et al, 2013;Lehmann et al, 2014;Staal et al, 2016). Even though it was proposed for analyzing precipitation (Markham, 1970), it can also be used for other variables such as streamflow (e.g., Barco et al, 2013;Petersen et al, 2012). Markham Seasonality Index is a circular statistic, where its values range from 0 to 1.…”

Section: Water Resources Researchmentioning

confidence: 99%

The Role of Land Cover in the Propagation of Rainfall Into Streamflow Trends

Chagas

Chaffe

2018

Water Resources Research

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The replacement of natural forests with agriculture is generally associated with modifications in the hydrological behavior of a basin. This is particularly notable in the tropics and subtropics. Southern Brazil is a region with extensive agricultural production, forest conservation, and a vast unexplored streamflow data despite substantial rainfall trends observed in recent decades. In this work, we explore trends in the streamflow regime in the majority of the monitored basins in Southern Brazil. Additionally, we evaluate if pristine forested basins and agricultural nonforested basins have significantly different streamflow responses to changes in rainfall. We analyzed annual averages, maxima, minima, and seasonality of a 36‐year data set (1975–2010) of 675 rainfall and 140 streamflow gauges. Results reveal that large trends are widespread in Southern Brazil, especially in basins with areas smaller than 10,000 km2. Changes in the rainfall regime did not directly translate into changes in the streamflow regime. Changes in the annual maximum flow of forested basins were not statistically significant even when the annual average and maximum rainfall increased significantly. Correlations between changes in rainfall and streamflow for two indices, namely, duration of low‐magnitude events and seasonality, were statistically significant (p < 0.05) only for agricultural basins. The results indicate a higher propagation of hydrological changes through anthropogenically modified systems, providing evidence that agricultural basins are more sensitive to changes in the rainfall regime.

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“…Taking a more regional focus, they found an increase in precipitation and streamflow in the eastern US with an increase in dryness in the west. Petersen et al (2012) determined that the spatial variability in runoff seasonality in the eastern US depends on covariation between moisture and energy cycles, whereas the west shows a negative correlation leading to dependence on basin aridity and the seasonality of precipitation. Focusing on the western US, detection and attribution (D&A) studies attribute declining snowpack and streamflow timing changes to human effects, especially the human-induced elevation of CO 2 concentration (Barnett et al 2008, Pierce et al 2008, Hidalgo et al 2009.…”

Section: Introductionmentioning

confidence: 99%

“…Focusing on the western US, detection and attribution (D&A) studies attribute declining snowpack and streamflow timing changes to human effects, especially the human-induced elevation of CO 2 concentration (Barnett et al 2008, Pierce et al 2008, Hidalgo et al 2009. The limitation of Groisman et al (2004), Petersen et al (2012), and Alkama et al (2013), however, was that causality of changes in runoff could not be addressed due to solely using observational data or focusing on the detection issue. The work presented in this paper takes a step forward by addressing the causality using a gridded observational dataset and an ensemble of offline land surface models (LSMs) driven by the same observed environmental conditions in order to perform more robust D&A analysis.…”

Section: Introductionmentioning

confidence: 99%

Contribution of environmental forcings to US runoff changes for the period 1950–2010

Forbes

Mao

Jin

et al. 2018

Environ. Res. Lett.

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Runoff in the United States is changing, and this study finds that the measured change is dependent on the geographic region and varies seasonally. Specifically, observed annual total runoff had an insignificant increasing trend in the US between 1950 and 2010, but this insignificance was due to regional heterogeneity with both significant and insignificant increases in the eastern, northern, and southern US, and a greater significant decrease in the western US. Trends for seasonal mean runoff also differed across regions. By region, the season with the largest observed trend was autumn for the east (positive), spring for the north (positive), winter for the south (positive), winter for the west (negative), and autumn for the US as a whole (positive). Based on the detection and attribution analysis using gridded WaterWatch runoff observations along with semi-factorial land surface model simulations from the Multi-scale Synthesis and Terrestrial Model Intercomparison Project (MsTMIP), we found that while the roles of CO 2 concentration, nitrogen deposition, and land use and land cover were inconsistent regionally and seasonally, the effect of climatic variations was detected for all regions and seasons, and the change in runoff could be attributed to climate change in summer © 2018 The Author(s). Published by IOP Publishing Ltd Environ. Res. Lett. 13 (2018) 054023 and autumn in the south and in autumn in the west. We also found that the climate-only and historical transient simulations consistently underestimated the runoff trends, possibly due to precipitation bias in the MsTMIP driver or within the models themselves.

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Seasonality of monthly runoff over the continental United States: Causality and relations to mean annual and mean monthly distributions of moisture and energy

Cited by 57 publications

References 22 publications

A simple framework to quantitatively describe monthly precipitation and temperature climatology

A simple framework to quantitatively describe monthly precipitation and temperature climatology

The Role of Land Cover in the Propagation of Rainfall Into Streamflow Trends

Contribution of environmental forcings to US runoff changes for the period 1950–2010

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