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Francina Dominguez
and
Praveen Kumar

Abstract

This study investigates the principal modes of seasonal moisture flux transport over North America, analyzing their possible dependence on large-scale atmospheric circulation patterns. It uses 23 yr (1979–2001) of 6-hourly data from the NCEP–NCAR reanalysis I project. Complex empirical orthogonal function (complex-EOF) analysis is implemented on the vertically integrated and seasonally averaged moisture flux, to identify the dominant modes. For every season, the characteristic spatial pattern of the two most dominant modes is compared to the geopotential height anomaly field and precipitation anomaly field using correlation analysis.

The two dominant winter modes capture the variability in the moisture flux field associated with extreme precipitation events over the western coast of the United States. The first winter mode captures 52% of the variability of the season and is related to the strong ENSO events of 1982/83 and 1997/98 (El Niño) and 1989 (La Niña). The second winter mode captures anomalous high moisture flux over the southwest related to the east Pacific teleconnection pattern.

The intense moisture transport associated with high-precipitation events in the central United States (including the 1993 flood) is captured by summer mode 1, while the second mode of the summer season captures the moisture flux variability related to the 1983 and 1988 droughts. The results show that these summer flood and drought events are characterized by very different moisture flux anomalies and are not the positive and negative phases of a given mode.

The use of complex-EOF analysis captures extreme hydrologic events as characteristic modes of interannual variability and allows a better understanding of the atmospheric circulation patterns associated with these events.

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Ji Chen
and
Praveen Kumar

Abstract

The influence of the El Niño–Southern Oscillation (ENSO) on the terrestrial energy profile over North America is studied using a 15-yr model simulation. A large-area basin scale (LABs) land surface model is driven using the European Centre for Medium-Range Weather Forecasts 15-yr Re-Analyses (1979–93) dataset. It is found that the fluctuations of the soil temperature anomalies at different soil depths, in certain geographic regions, are correlated with the ENSO signal. In other words, the temperature anomaly can penetrate into the deeper soil layers due to the long wavelength associated with the ENSO signal. Using a simplified theoretical method, it is shown that the propagation of the ENSO-related long-wavelength temperature anomaly from the land surface to deep soil needs several months. In addition, it is found that the variation of the anomaly of the terrestrial enthalpy, consisting of the soil water enthalpy and soil particle enthalpy, in the shallow soil zone is dominated by the variation of the soil water storage, while that in the deep soil zone is determined by the variation of the soil temperature.

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Ji Chen
and
Praveen Kumar

Abstract

Relationships among the terrestrial hydrologic processes over the North American continent associated with the El Niño–Southern Oscillation (ENSO) are investigated using a large-area basin-scale land surface model driven by the European Centre for Medium-Range Weather Forecasts Re-Analyses 15-yr (1979–93) dataset. The modeling approach allows for the study of the relationships of ENSO with several hydrologic variables simultaneously, such as soil water storage, basin runoff, snow-water equivalent, and precipitation. The cross-correlation coefficients between terrestrial variables and the ENSO index are computed. The runoff from the northern part of North America was found to be most often negatively correlated with ENSO, and there are four distinct coherent regions over the continent where the runoff anomalies are positively correlated. The terrestrial systems have a delayed response to the ENSO signal, as compared to the precipitation, and the delay may range from a month to a season or longer. The shorter and longer delays are typically associated with rainfall runoff, and snow accumulation and melt processes, respectively. The soil moisture storage plays a very vital role in delaying the effects of the climate variability on the terrestrial hydrologic processes and in extending the influences of the El Niño or La Niña events on the terrestrial climate.

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Ji Chen
and
Praveen Kumar

Abstract

A large area basin-scale (LABs) hydrologic model is developed for regional, continental, and global hydrologic studies. The heterogeneity in the soil-moisture distribution within a basin is parameterized through the statistical moments of the probability distribution function of the topographic (wetness) index. The statistical moments are derived using GTOPO30 (30 arc sec; 1-km resolution) digital elevation model data for North America. River basins and drainage network extracted using this dataset are overlaid on computed topographic indices for the continent and statistics are extracted for each basin. A total of 5020 basins with an average size of 3255 square kilometers, obtained from the United States Geological Survey HYDRO1K data, is used over the continent.

The model predicts runoff generation due to both saturation and infiltration excess mechanisms along with the baseflow and snowmelt. Simulation studies are performed for 1987 and 1988 using the International Satellite Land Surface Climatology Project data. Improvement in the terrestrial water balance and streamflow is observed due to improvements in the surface runoff and baseflow components achieved by incorporating the topographic influences. It is found that subsurface redistribution of soil moisture, and anisotropy in hydraulic conductivities in the vertical and horizontal directions play an important role in determining the streamflow and its seasonal variability. These enhancements also impact the surface energy balance. It is shown that the dynamics of several hydrologic parameters such as basin mean water table depth and saturated fraction play an important role in determining the total streamflow response and show realistic seasonal and interannual variations. Observed streamflow of the Mississippi River and its subbasins (Ohio, Arkansas, Missouri, and Upper Mississippi) are used for validation. It is observed that model baseflow has a significant contribution to the streamflow and is important in realistically capturing the seasonal and annual cycles.

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Francina Dominguez
and
Praveen Kumar

Abstract

Precipitation recycling is one of the key mechanisms linking the land surface and atmospheric dynamics. This work explores the physical mechanisms that modulate precipitation recycling variability at the daily-to-intraseasonal time scales in the central U.S. plains ecoregion using a set of land–atmosphere variables derived from the North American Regional Reanalysis dataset. Recycling estimates are performed using the Dynamic Recycling Model, which allows for analysis at shorter time scales than the previous bulk recycling models.

In the central U.S. plains ecoregion local evapotranspiration only becomes an important contributor to precipitation when moisture of advective origin, the largest contributor to precipitation, diminishes. Consequently, the recycling ratio is negatively correlated to precipitation. The dominant mechanism is a negative feedback, which ensures that, even when precipitation is low, evapotranspiration continues to feed moisture into the overlying atmosphere and contribute to rainfall. Consequently, in the central U.S. plains, precipitation recycling acts as a mechanism for ecoclimatological stability through local negative feedbacks. Additionally, the zonal and meridional winds and moisture fluxes were also found to be important drivers of recycling variability. As winds decrease, the air has more time to traverse the region and capture moisture of evaporative origin. Evapotranspiration variability is not an important driver for recycling ratio variability in the central U.S. plains. Only during the extremely dry 1988 summer drought, when soil moisture storage was depleted, did the recycling ratio variability closely follow evapotranspiration.

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Renu Joseph
,
Mingfang Ting
, and
Praveen Kumar

Abstract

The spatio–temporal variability of precipitation over the United States using a 30-yr, gridded hourly precipitation dataset is studied. Orthogonal wavelet transform is applied to the time series at each grid box to capture the temporal scales of fluctuation at 17 different timescales ranging from 2 h to 15 yr. Rotated principal component analysis is then applied to the transformed series to identify spatial coherence of the temporal scales of fluctuations. The results indicate that the energy of the fluctuations shows an approximate power-law relationship with respect to scale in most regions. The spatial organization of the temporal variability shows coherence at distinct scales identified as the subdiurnal (2–16 h), synoptic (16 h–22 days), seasonal (42 days–1 yr), and climatic mode (15 yr). The synoptic scale explains the largest spatial variance of the fluctuations in precipitation and is spatially coherent; the subdiurnal mode is spatially less coherent. The seasonal mode is dominant over the Pacific Northwest, whereas the climatic mode has large amplitude only over California. When examining the winter and summer seasons separately, it is found that the winter precipitation fluctuation is more associated with synoptic scale; the summer fluctuation is associated with shorter timescales or the subdiurnal scale. Studies of extreme summer drought and flood events over the Midwest indicate that anomalously wet or dry years are manifestations of persistent anomalous wet or dry conditions across all temporal scales, with the maximum contribution for the wet events being affected by the synoptic-scale activities.

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Praveen Kumar
and
Efi Foufoula-Georgiou

Abstract

In this paper the authors study how zero intermittency in spatial rainfall, as described by the fraction of area covered by rainfall, changes with spatial scale of rainfall measurement or representation. A statistical measure of intermittency that describes the size distribution of “voids” (nonrainy areas imbedded inside rainy areas) as a function of scale is also introduced. Morphological algorithms are proposed for reconstructing rainfall intermittency at fine scales given the intermittency at coarser scales. These algorithms are envisioned to be useful in hydroclimatological studies where the rainfall spatial variability at the subgrid scale needs to be reconstructed from the results of synoptic- or mesoscale meteorological numerical models. The developed methodologies are demonstrated and tested using data from a severe springtime midlatitude squall line and a mild midlatitude winter storm monitored by a meteorological radar in Norman, Oklahoma.

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Praveen Kumar
and
Efi Foufoula-Georgiou

Abstract

It has been observed that the finite-dimensional distribution functions of rainfall cannot obey simple scaling laws due to rainfall intermittency (mixed distribution with an atom at zero) and the probability of rainfall being an increasing function of area. Although rainfall fluctuations do not suffer these limitations, it is interesting to note that very few attempts have been made to study them in terms of their self-similarity characteristics. This is due to the lack of unambiguous definition of fluctuations in multidimensions. This paper shows that wavelet transforms offer a convenient and consistent method for the decomposition of inhomogeneous and anisotropic rainfall fields in two dimensions and that the components of this decomposition can be looked at as fluctuations of the rainfall field. It is also shown that under some mild assumptions, the component fields can be treated as homogeneous and thus are amenable to second-order analysis, which can provide useful insight into the nature of the process. The fact that wavelet transforms are a space-scale method also provides a convenient tool to study scaling characteristics of the process. Orthogonal wavelets are used, and these properties are investigated for a squall-line storm to study the presence of self-similarity.

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Amey Pathak
,
Subimal Ghosh
, and
Praveen Kumar

Abstract

The Indian summer monsoon rainfall is dominated by oceanic sources of moisture. However, land surface processes also have a significant role in the generation of precipitation within the Indian subcontinent. Evapotranspiration over a region supplies moisture to the atmosphere, which may lead to precipitation in the same region. This is known as recycled precipitation. The role of evapotranspiration as an additional source of moisture to precipitation has been investigated in earlier studies at continental scales; however, the amount of monsoon precipitation generated from evapotranspiration has not been quantified at the daily scale for the Indian subcontinent. To examine the role of land surface hydrology in regional precipitation and to quantify recycled precipitation, the dynamic recycling model at a daily scale with NCEP Climate Forecast System Reanalysis (CFSR) data for the period of 1980–2010 is used. A high precipitation recycling ratio, that is, the ratio of recycled precipitation to total precipitation, is found at the end of the monsoon (September). As the monsoon progresses in India, enhanced soil moisture and vegetation cover lead to increased evapotranspiration and recycled precipitation. The recycling ratio is highest (around 25%) in northeastern India, which has high vegetation cover leading to high evapotranspiration. Recycled precipitation over central and northeastern India in September is responsible for delaying the withdrawal of the summer monsoon over these regions. A trend analysis of recycled precipitation shows a statistically significant decreasing trend in northeastern India.

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Allison E. Goodwell
and
Praveen Kumar

Abstract

The sequencing, or persistence, of daily precipitation influences variability in streamflow, soil moisture, and vegetation states. As these factors influence water availability and ecosystem health, it is important to identify spatial and temporal trends in precipitation persistence and predictability. We take an information theoretic perspective to address regional and temporal trends in daily patterns, based on the Climate Prediction Center (CPC) gridded gauge-based dataset of daily precipitation over the continental United States from 1948 to 2018. We apply information measures to binary sequences of precipitation occurrence to quantify uncertainty, predictability in the form of lagged mutual information between the current state and two time-lagged histories, and associated dominant time scales. We find that this information-based predictability is highest in the western United States, but the relative influence of longer lagged histories in comparison to a 1-day history is highest in the east. Information characteristics and time scales vary seasonally and regionally and constitute an information climatology that can be compared with traditional indices of precipitation and climate. Trend analyses over the 70-yr time period also show varying regional characteristics that differ between seasons. In addition to increasing precipitation frequency over most of the country, we detect increasing and decreasing predictability in western and eastern regions, respectively, with average trend magnitudes corresponding to shifts in predictability ranging from −50% to 110%. This new perspective on precipitation persistence has broad potential to link shifts in climate and weather to patterns and predictability of related environmental factors.

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