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Article

Atmospheric Rivers, Floods and the Water Resources of California

by
Michael D. Dettinger
1,*,
Fred Martin Ralph
2,
Tapash Das
3,
Paul J. Neiman
2 and
Daniel R. Cayan
1
1
U.S. Geological Survey, Scripps Institution of Oceanography, La Jolla, CA 92093, USA
2
NOAA Earth Systems Research Laboratory, Boulder, CO 80305, USA
3
Scripps Institution of Oceanography, La Jolla, CA 92093, USA
*
Author to whom correspondence should be addressed.
Water 2011, 3(2), 445-478; https://doi.org/10.3390/w3020445
Submission received: 12 February 2011 / Revised: 3 March 2011 / Accepted: 21 March 2011 / Published: 24 March 2011
(This article belongs to the Special Issue Managing Water Resources and Development in a Changing Climate)
Browse Figures
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Abstract

:
California’s highly variable climate and growing water demands combine to pose both water-supply and flood-hazard challenges to resource managers. Recently important efforts to more fully integrate the management of floods and water resources have begun, with the aim of benefitting both sectors. California is shown here to experience unusually large variations in annual precipitation and streamflow totals relative to the rest of the US, variations which mostly reflect the unusually small average number of wet days per year needed to accumulate most of its annual precipitation totals (ranging from 5 to 15 days in California). Thus whether just a few large storms arrive or fail to arrive in California can be the difference between a banner year and a drought. Furthermore California receives some of the largest 3-day storm totals in the country, rivaling in this regard the hurricane belt of the southeastern US. California’s largest storms are generally fueled by landfalling atmospheric rivers (ARs). The fractions of precipitation and streamflow totals at stations across the US that are associated with ARs are documented here and, in California, contribute 20–50% of the state’s precipitation and streamflow. Prospects for long-lead forecasts of these fractions are presented. From a meteorological perspective, California’s water resources and floods are shown to derive from the same storms to an extent that makes integrated flood and water resources management all the more important.

Graphical Abstract

1. Introduction

California’s water resources are finite and increasingly demands and diversions are approaching their limits. The State has a Mediterranean climate with wet winters and long, warm, dry summers; so that seasonal and longer term water scarcities have been a problem throughout the State’s development. On those longer times scales, California’s climate, and in particular its precipitation and streamflow, are notably variable from year to year. Thus it is common wisdom that California’s lot is either flood or drought, with seemingly few occasions when average conditions prevail.
Meanwhile, the State’s population and economy are growing rapidly, with a current population of over 30 million people expected to grow to over 50 million by midcentury [1]. Attending this population growth will likely be significant increases in demands for water. These demands are aggravated by the fact that, while 2/3 of the precipitation and runoff in California occurs in the northern third of California, roughly 3/4 of the population and water demands are in the southern two-thirds of the State. In order to meet these demands, the State and Federal governments have constructed massive storage and conveyance systems that span the State.
In this challenging context, local, State and Federal agencies are innovating to balance supplies, water rights, environmental-flow requirements, and conveyance capacities to meet the growing demands. In its feast or famine way, California has also regularly been battered by major winter storms and floods that damage property on the order of billions of dollars and threaten lives; consequently the State has an even older (than most of its water supply systems) and equally extensive flood control infrastructure [2]. Increasingly in the past decade, the State has recognized that one potentially important response to growing water demands is greater coordination of flood management with water-resources management in many settings [1]. Improved floodplain management provides benefits to both flood management and the kinds of ecosystems restoration and sustainability that are increasingly required to maintain water supplies and quality. Water released from reservoirs to moderate or mitigate flood damages is water lost from many of the State’s water supply systems, so that coordinating flood and supply management may increase water supplies with relatively little impact on flood-control benefits. Risks of flood damages can also be reduced by land-development and land-use practices that accommodated and avoided flood-prone areas in ways that could result in future reductions in required flood-control releases from reservoirs.
Although meteorological understanding of the midlatitude cyclones that make landfall on the west coast has a long history (e.g., [3]), and the pineapple express (PE) concept is well known by meteorologists and the public [4,5], scientific and technological advances have only recently documented just how strong the storms can be, and how they become so strong. The enlightenment has come with the recognition of the significance of a key feature within these storms that has only recently become observable with satellite technology, a feature termed the “atmospheric river” (AR; e.g., [6,7,8,9,10]). AR storms that impact California are a result of low-level jets along the pre-cold frontal edge of the warm sectors of major winter cyclones over the eastern North Pacific. Figure 1 illustrates several depictions of a classic example of one of these storms, from New Years 1997 when a major PE storm drawing water vapor and warm air through an AR extending from the tropics near Hawaii to central California resulted in heavy precipitation and major flooding in California [11]. The low-level jet (often >20 m/s [9]) in ARs typically carries substantial water vapor, such that vertically integrated water-vapor contents total >3 cm on average, as in Figure 1a, but occasionally range up to 5.5 cm at the extreme [8]. In combination, the winds and vapor contents yield large vapor transport rates (e.g., >500 kg of water vapor per second across each meter of horizontal width of the AR as in Figure 1c. This vapor transport is concentrated into narrow and intense corridors 2,000 and more kilometers long, a few hundreds of kilometers wide, in the lowest ~2.5 km of the atmosphere [7,9]. The atmospheric profiles within these corridors tend to be moist-statically neutral throughout the first three kilometers above the surface, so that once they encounter the slopes of California’s Coastal Range and Sierra Nevada, orographic uplift is strong and orographic precipitation is copious (e.g., [10]).
Water 03 00445 g001 1024
Figure 1. Visualizations of atmospheric-river conditions impacting California on 1–2 January 1997: (a) SSM/I composite satellite image of integrated water vapor (cm) on 2 January modified from figure 6 in [11]; grey diamonds represent areas not overflown by SSM/I instrument that day, (b) infrared weather-satellite imagery of the Pacific Ocean basin (GOES-West) (light colors are cloud bands, coasts indicated in green), and (c) daily-average vertically integrated water-vapor transport directions and relative rates based on NCEP-NCAR Reanalysis fields [12] describing 6-hourly historical atmospheric conditions; arrow at bottom indicates length of a 1,000 kg/m/s vapor-transport vector.
Figure 1. Visualizations of atmospheric-river conditions impacting California on 1–2 January 1997: (a) SSM/I composite satellite image of integrated water vapor (cm) on 2 January modified from figure 6 in [11]; grey diamonds represent areas not overflown by SSM/I instrument that day, (b) infrared weather-satellite imagery of the Pacific Ocean basin (GOES-West) (light colors are cloud bands, coasts indicated in green), and (c) daily-average vertically integrated water-vapor transport directions and relative rates based on NCEP-NCAR Reanalysis fields [12] describing 6-hourly historical atmospheric conditions; arrow at bottom indicates length of a 1,000 kg/m/s vapor-transport vector.
Water 03 00445 g001
This article investigates some meteorological aspects of the connection between floods and water resources in California, with a particular focus on the dual role that AR storms play in bringing California both (1) its most dangerous storms and floods and (2) the basis for much of the State’s water resources. While a series of recent papers has explored the relation between ARs and extreme rainfall and flooding in a few locations or events, as referenced above, this paper expands on these by assessing linkages to runoff and water supply across the entire western US, aided by long-term streamflow observations and a state-of-the-art hydrologic model. The paper also addresses a common question in the region: How are the AR and PE concepts related? By analyzing historical precipitation and streamflow records and simulations, along with two chronologies of atmospheric rivers making landfalls on the West Coast of the US, we will weigh and compare these two roles that ARs play in California meteorology.

2. Data, Models and Storm Chronologies

2.1. Data

Station precipitation totals analyzed here are based on daily accumulated precipitation measurements reported in the Summary of the Day (SOD; [13] and updates thereto) observations from cooperative weather stations across the United States, obtained from the National Climatic Data Center. Daily data from 1950–2008, and subsets thereof, were considered from 5,877 stations across the contiguous US, or an average of one cooperative station per 1,375 km2. Missing data were excluded, as were accumulations from multiple days reported as a multi-day totals. It should be noted that the periods of record and observation times of day differ among sites. The average length of record for the sites used is 53 years. Sites with less than 8 years of precipitation data from water years (October–September) 1998–2008 were excluded from the composites based on the AR chronology (described below); sites with less than 30 years of data from water years 1951–2008 were likewise excluded from composites based on the longer PE chronology. For the present analysis, data from other networks (e.g., snow data) were not included in our analysis to avoid network-to-network inconsistencies.
The streamflow records analyzed here are from the U.S. Geological Survey Hydro- Climatic Data Network (HCDN; [14] and updates thereto). The HCDN dataset contains daily streamflow observations from streamflow gauges that are considered to be relatively unaffected by anthropogenic influences, land-use changes, measurement changes, and measurement errors. Gauges with less than 8 years of discharge data from water years 1998–2008 were excluded from the composites based on the AR chronology (described below); gauges with less than 30 years of data from water years 1951–2008 were likewise excluded from composites based on the longer PE chronology.

2.2. Models

To provide some more geographically complete depictions of the contributions that ARs make to water resources of the western US, gridded daily precipitation fields used as historical inputs for a large-scale hydrologic model, and simulated daily runoff outputs from that model, will also be evaluated here. The hydrologic model is the Variable Infiltration Capacity (VIC) model [15], a macroscale land-surface hydrologic model that has been used in many process, sensitivity, forecast, and climate-change studies in recent years. VIC simulates (among other variables) daily runoff plus baseflow at grid cells on a 1/8-degree resolution over the conterminous US, although we focus here on basins in the western US. VIC is driven by daily maximum and minimum temperatures, precipitation, and wind speeds on the same grid. Temperatures and precipitation values for historical simulations were obtained from interpolated fields of daily observations [16] that derive much of their geographic structure from monthly historical PRISM climate fields [17]. These meteorological-input fields for VIC reflect daily observations at cooperative weather stations augmented by higher quality Global Historical Climatology Network stations [18], with interpolations to grid points based on local weather-elevation relations (e.g., lapse rates) derived separately for each historical month in the PRISM procedures. The primary gridded meteorological dataset [16] used is available for the period 1915 through 2003. To extend that dataset through 2008, daily gridded meteorological fields for the period 2004 through 2008—from another dataset produced by the same group (the Surface Water Modeling Group at the University of Washington) based on a reduced network of stations—were appended to the primary set. This reduced-network dataset is available with near-real-time updates, and is produced and used operationally as part of a West-wide seasonal hydrologic forecast system [19]. Wind speeds, used in the hydrologic model, were interpolated from National Centers for Environmental Prediction (NCEP)/National Center for Atmospheric Research (NCAR) Reanalysis [12] data [16].
The VIC hydrologic model uses a tiled representation of the land surface within each model grid cell to allow for sub-grid variability in topography, infiltration, and land surface vegetation classes [20]. VIC forms and then melts snowpacks, and accounts for water storage and release from several soil layers, evapotranspiration, and interception losses. Surface runoff is simulated with an infiltration formulation based on the Xinanjiang model [21], while baseflow follows the ARNO model [15]. In this article, the gridded historical precipitation inputs and the simulated historical runoff-plus-baseflow, on and following days with landfalling ARs, are analyzed.

2.3. Storm Chronologies

For many of the analyses here, historical occurrences of landfalling ARs were identified from twice-daily Special Sensor Microwave Imager (SSM/I) imagery of vertically integrated water vapor in the atmosphere. An AR is identified here following the strategy and thresholds initially developed in [7] for one year, and extended in [22], such that narrow plumes of SSM/I vapor with values >2 cm that were >2,000 km long and <1,000 km wide were defined as atmospheric rivers. Following this procedure, Neiman et al. ([22] and Table 1) constructed a chronology of all dates in the past dozen years when ARs have made landfall along the west coast of the conterminous US. This chronology provides a context for judging whether daily precipitation totals are associated with AR storms or non-AR storms. The chronology is based primarily on water-vapor imagery from SSM/I [23] carried on each of four Defense Meteorological Satellite Program polar orbiters that circled the globe every 102 minutes since late 1997. The SSM/I measurements are reliable [24], although water-vapor measurements can be degraded in heavy rain. However, because the SSM/I observations are used here solely for detecting long, narrow water-vapor plumes associated with atmospheric rivers (i.e., for pattern recognition) rather than for quantitative analysis, the degradation of vapor imagery due to heavy rainfall and small inter-satellite differences does not prove detrimental. Prior to the start of WY1998, fewer satellites were available and yielded comparatively poorer composite daily spatial coverage for assessing water-vapor plume characteristics. Consequently, those earlier years were not included in the SSM/I data analysis; however, since publication of [22], an additional 2 more recent years of ARs have been added to the chronology, with the updated chronology presented here as Table 1).
PE storms form a particularly challenging subset of ARs. PE storms are so called because they draw heat and vapor from the subtropics or tropics near Hawaii and in at least some cases (e.g., 1997, Figure 1) transport that heat and vapor towards the West Coast. Three ways of visualizing one such storm, the notorious New Years 1997 storm, were shown in Figure 1. Figure 1c shows the vertically integrated water-vapor transports on that day, illustrating—on the coarse Reanalysis [12] grid—the strong transport of vapor from over Hawaii to the West Coast, crossing the coast near 40° N. By searching for days with transport patterns like this, with strong (>500 kg/m/s) average southwesterly vapor transports extending in an unbroken path back from the West Coast (anywhere between 32.5° N and 55° N) into the subtropics between 170° E and 120° W, a catalog of days with large-scale PE circulations has been developed [5] for the period from 1948 through 2008, with the new chronology presented here as Table 2. The PE chronology used in the present analysis was modified from that in [5] only in that the starting locations for backtracking the pineapple-express transports are along the west coast of North America (herein) rather than along a single longitude circle (in [5]), in order to more closely parallel the approach used in the AR chronology [22]. The PE chronology is longer than the AR chronology of [22], but less exhaustive because many of the SSM/I-identified ARs do not follow the simple path sought by this PE algorithm. The overlap period between the AR and PE chronologies covers 11 water years (1998–2008), during which 73 dates with PE conditions were detected. Comparison with the AR chronology, using the criterion that a date is considered an AR date if the pattern in the SSM/I imagery qualified in either the AM or PM satellite overpasses anywhere along the West Coast, shows that 71 of the 73 PE dates were also AR dates. The other 2 PE dates might also have involved ARs but may have been missed due to differences between the data and resolutions used for the PE and AR chronologies.
On average along the west coast of the conterminous U.S., the AR chronology used hereafter records 16 AR episodes per November-April, while the PE chronology records 6.4 PE episodes per season. Thus analyses using the PE chronology do not consider as many events per year as the AR-based analyses and instead focus on a particularly strong and narrow subset of ARs with a specific geographic orientation and position, but makes those analyses over a much longer historical period.
Table
Table 1a. Dates of SSM/I observations of landfalling atmospheric rivers (ARs; long, narrow plumes of integrated water vapor with core values >~2 cm) during the water years 1998–2008 that impacted the California coast (32.5° N–41.0° N) on both the morning ascending SSM/I passes and the afternoon descending passes. The AR events in the November-April window considered in this paper are boldfaced.
Table 1a. Dates of SSM/I observations of landfalling atmospheric rivers (ARs; long, narrow plumes of integrated water vapor with core values >~2 cm) during the water years 1998–2008 that impacted the California coast (32.5° N–41.0° N) on both the morning ascending SSM/I passes and the afternoon descending passes. The AR events in the November-April window considered in this paper are boldfaced.
#WY1998WY1999WY2000WY2001WY2002WY2003WY2004WY2005WY2006WY2007WY2008
11997100219981108199910142001032820011011200211072003100520041009200510262006110320071019
21997100919981121199910262001051520011030200211082003110920041018200511072006111320071028
31997102919981123199910282001051620011116200211092003112920041026200511082006111720071029
41997103119990115199911102001060520011122200212132003113020041227200511252006112020071111
51997110619990207199911112001062520011129200212142003120520050109200511292006121220071204
61997111919990324199911152001062620011202200212162003120620050110200512012007020920080105
71997112519990325200001162001062720011229200212272003120720050322200512022007021020080110
81997120519990511200001172001073020020102200301122003122420050504200512222007060520080603
91997121419990802200001182001092520020106200301132004021620050508200512282007061020080622
101998011419990830200001242001092620020414200302122004021720050515200512302007062820080703
1119980115 20000125 20020529200303102004052720050516200512312007071820080730
1219980126 20000305 2002061820030315200405282005051920060227
1319980202 20000417 20030323200409112005052220060306
1419980203 20000507 20030326200409172005060820060325
1519980205 20000508 20030908 2005060920060328
1619980322 20000523 2005061720060403
1719980323 20000612 2005070920060404
1819980525 2005071020060412
1919980625 20060508
20 20060519
21 20060521
22 20060531
23 20060601
24 20060602
25 20060604
Table
Table 1b. Dates of SSM/I observations of landfalling atmospheric rivers (ARs; long, narrow plumes of integrated water vapor with core values >~2 cm) during the water years 1998–2008 that impacted the Oregon/Washington/British Columbia coast (41.0° N–52.5° N) on both the morning ascending SSM/I passes and the afternoon descending passes. The AR events in the November-April window considered in this paper are boldfaced.
Table 1b. Dates of SSM/I observations of landfalling atmospheric rivers (ARs; long, narrow plumes of integrated water vapor with core values >~2 cm) during the water years 1998–2008 that impacted the Oregon/Washington/British Columbia coast (41.0° N–52.5° N) on both the morning ascending SSM/I passes and the afternoon descending passes. The AR events in the November-April window considered in this paper are boldfaced.
#WY1998WY1999WY2000WY2001WY2002WY2003WY2004WY2005WY2006WY2007WY2008
11997100119981005199910082000100120011010200210032003100620041006200510102006100820071007
21997101519981006199910132000100820011104200210272003101620041008200510132006101520071009
31997101619981012199910172000101720011109200211062003101720041011200510142006101920071019
41997102619981017199910302000102220011114200211122003101820041012200510212006102620071022
51997102919981113199911032000102320011115200211192003101920041015200510222006110620071028
61997103019981114199911062000112320011119200212122003102020041016200510252006110720071107
71997103119981121199911112001043020020106200301022003102120041022200510312006122520071116
81997110219981125199911122001060620020107200301262003102220041102200511012006122620071117
91997110319981228199911132001062020020221200301312003102720041105200511092007010220071118
101997110519981229199911142001070320020413200303132003112920041106200511102007012220071203
111997121619990110199911252001070720020604200305232004012220041107200511132007031120071223
121997122819990114199912152001070920020625200305242004052620041108200512242007031220080102
131998012319990224199912162001080220020626200305272004070620041115200512302007060920080513
141998032219990227200005212001080320020627200306262004071220041124200601172007061320080518
151998060919990511200005222001080520020706200307092004071720041125200605312007061420080627
161998061419990531200005232001082120020710200307112004071820041208200606162007070220080705
171998061519990604200005272001082220020715200307122004072920041209200607082007070320080708
181998062419990623200006072001082520020716200307142004080320041210200607092007071420080720
191998070619990628200006122001082820020717200307202004080420041211200607142007071520080721
201998071219990629200006142001083020020718200308182004081720041217200607202007071720080726
211998071319990720200006172001090120020723200308212004082020050117200607262007072020080729
221998071419990726200006302001090320020725200308252004082120050118200608052007072120080801
231998071519990728200007182001091020020728200308312004082820050119200608082007072220080812
241998071619990729200007192001091220020729200309032004082920050122200608282007072320080823
251998071719990802200007202001092120020730200309042004083020050123200609082007072820080824
261998072319990817200007272001092220020808200309052004083120050327200609112007081220080828
271998072419990818200007282001092320020809200309062004091020050416200609172007081320080829
28199808091999081920000729 20020822200309112004091120050514200609182007082520080905
29199808121999082120000730 20020823200309142004091520050515 20070828
30199808131999082220000731 20020828200309182004092220050629 20070829
31199808271999082320000817 20020829200309222004092520050704 20070903
32199808301999082420000818 2002090120030923 20050705 20070930
33199809011999082520000823 2002090220030925 20050708
34199809021999082620000824 20020910 20050716
35199809031999082720000825 20020911 20050727
36199809071999082820000829 20020912 20050728
37199809081999082920000907 20020916 20050730
38199809111999090420000910 20050731
39199809131999090520000917 20050801
40199809241999092320000918 20050805
41 1999092920000920 20050817
42 20000929 20050820
43 20000930 20050821
44 20050822
45 20050826
46 20050827
47 20050908
48 20050929
49 20050930
Table
Table 2. Dates and latitudes of landfalling pineapple-express (PE) transports of water vapor (>500 kg m−1 sec−1) during the water years 1948–2008 that impacted the California coast (32.5° N–52.5° N) in NCAR/NCEP Reanalysis atmospheric fields [5,12].
Table 2. Dates and latitudes of landfalling pineapple-express (PE) transports of water vapor (>500 kg m−1 sec−1) during the water years 1948–2008 that impacted the California coast (32.5° N–52.5° N) in NCAR/NCEP Reanalysis atmospheric fields [5,12].
WY1948WY1949WY1950WY1951WY1952WY1953
DateLandfall LatitudeDateLandfall LatitudeDateLandfall LatitudeDateLandfall LatitudeDateLandfall LatitudeDateLandfall Latitude
1948012052.5° N1949011652.5° N1949102952.5° N1950110140.0° N1951122835.0° N1952121250.0° N
1949112352.5° N1950111735.0° N1952012350.0° N1953010140.0° N
1950112037.5° N1952032352.5° N1953010842.5° N
1950120242.5° N 1953010940.0° N
1950120337.5° N 1953011640.0° N
1950120552.5° N 1953012242.5° N
1950120642.5° N 1953013045.0° N
1950121050.0° N
1951020537.5° N
1951020850.0° N
WY1954WY1955WY1956WY1957WY1958WY1959
DateLandfall LatitudeDateLandfall LatitudeDateLandfall LatitudeDateLandfall LatitudeDateLandfall LatitudeDateLandfall Latitude
1953121940.0° N1954110452.5° N1955110752.5° N1956110252.5° N1958012152.5° N1958120145.0° N
1954030735.0° N1954111350.0° N1955112642.5° N1956120947.5° N1958012252.5° N1959011545.0° N
1954030837.5° N1954112052.5° N1955121937.5° N1956121550.0° N1958021750.0° N1959011645.0° N
1955122045.0° N1956122552.5° N1958021835.0° N1959021440.0° N
1955122145.0° N1956122652.5° N1958022150.0° N1959021542.5° N
1956011342.5° N1957011232.5° N1958032040.0° N1959021630.0° N
1956011437.5° N1957022242.5° N1958032130.0° N
1956011852.5° N1957022440.0° N
1956011940.0° N1957022542.5° N
1956012052.5° N1957022640.0° N
1957061052.5° N
WY1960WY1961WY1962WY1963WY1964WY1965
DateLandfall LatitudeDateLandfall LatitudeDateLandfall LatitudeDateLandfall LatitudeDateLandfall LatitudeDateLandfall Latitude
1959112245.0° N1960121247.5° N1961101352.5° N1962102752.5° N1963112452.5° N1964122142.5° N
1960012842.5° N1961011047.5° N1961112532.5° N1963013037.5° N1963112545.0° N1964122235.0° N
1960012945.0° N1961011550.0° N1962010452.5° N1963013137.5° N1963122252.5° N1965011252.5° N
1960020550.0° N1961020147.5° N1962010752.5° N1963020135.0° N1963122447.5° N
1960020645.0° N1961020450.0° N1962020935.0° N1963020242.5° N
1961020547.5° N 1963020347.5° N
1963020450.0° N
1963020650.0° N
1963032735.0° N
WY1966WY1967WY1968WY1969WY1970WY1971
DateLandfall LatitudeDateLandfall LatitudeDateLandfall LatitudeDateLandfall LatitudeDateLandfall LatitudeDateLandfall Latitude
1965102052.5° N1966120432.5° N1968011350.0° N1968110745.0° N1969122042.5° N1970112242.5° N
1965111242.5° N1967012640.0° N1968011440.0° N1968110845.0° N1970010837.5° N1970112342.5° N
1965111532.5° N1967020247.5° N1968011750.0° N1969010352.5° N1970011345.0° N1970112442.5° N
1965112230.0° N1967020852.5° N1968011847.5° N1969010445.0° N1970011435.0° N1971011542.5° N
1965112332.5° N1963031635.0)N1968011947.5° N1969011135.0° N1970011540.0° N1971011647.5° N
1966032952.5° N 1968012050.0° N1969011235.0° N1970011737.5° N1971011745.0° N
1968012150.0° N1969011332.5° N1970011847.5° N1971011842.5° N
1968012252.5° N1969011835.0° N1970012145.0° N1971012952.5° N
1968021742.5° N1969012435.0° N1970012245.0° N1971013050.0° N
1968021840.0° N1969012532.5° N1970021542.5° N1971032240.0° N
1968022040.0° N1969060152.5° N
1968022142.5° N
WY1972WY1973WY1974WY1975WY1976WY1977
DateLandfall LatitudeDateLandfall LatitudeDateLandfall LatitudeDateLandfall LatitudeDateLandfall LatitudeDateLandfall Latitude
1972030950.0° N1972103150.0° N1973110840.0° N1975011747.5° N1975122345.0° N1977011652.5° N
1972031452.5° N1972121642.5° N1973111137.5° N 1975122550.0° N1977011752.5° N
1972121940.0° N1973121545.0° N 1975122752.5° N1977021047.5° N
1972122042.5° N1974011445.0° N 1976012652.5° N
1973021550.0° N1974011542.5° N 1976012750.0° N
1974011637.5° N
1974011735.0° N
1974031440.0° N
WY1978WY1979WY1980WY1981WY1982WY1983
DateLandfall LatitudeDateLandfall LatitudeDateLandfall LatitudeDateLandfall LatitudeDateLandfall LatitudeDateLandfall Latitude
1977112342.5° N1978112752.5° N1979121645.0° N1980110350.0° N1981111540.0° N1982102850.0° N
1977112442.5° N1979010940.0° N1979121745.0° N1980121052.5° N1982010750.0° N1983010242.5° N
1977112547.5° N1979011040.0° N1980011132.5° N1980122445.0° N1982010852.5° N1983010640.0° N
1977112745.0° N1979021240.0° N1980020145.0° N1980122545.0° N1982012442.5° N1983012542.5° N
1977122632.5° N1979021335.0° N1980021430.0° N1981011850.0° N1982021247.5° N1983012640.0° N
1977122732.5° N1979030550.0° N1980021535.0° N1981011950.0° N1982021345.0° N1983021050.0° N
1978010750.0° N1979042535.0° N 1981012140.0° N1982021750.0° N1983021152.5° N
1981021245.0° N1982021847.5° N1983021745.0° N
1981021340.0° N1982021940.0° N1982022835.0° N
1981021442.5° N1982040935.0° N1983030135.0° N
1981021547.5° N1982041035.0° N1983030835.0° N
1981021742.5° N1982041132.5° N
1981021842.5° N
WY1984WY1985WY1986WY1987WY1988WY1989
DateLandfall LatitudeDateLandfall LatitudeDateLandfall LatitudeDateLandfall LatitudeDateLandfall LatitudeDateLandfall Latitude
1983122337.5° N 1985120137.5° N1987020142.5° N1988010337.5° N1988121052.5° N
1983122440.0° N 1986011745.0° N1987030345.0° N1988011445.0° N1989030540.0° N
1983122845.0° N 1986011847.5° N1987030450.0° N
1983122945.0° N 1986012940.0° N1987030535.0° N
1983123150.0° N 1986021337.5° N
1984010152.5° N 1986021440.0° N
1984010252.5° N 1986021635.0° N
1984010352.5° N 1986021737.5° N
1984010447.5° N 1986021835.0° N
1984012652.5° N 1986022245.0° N
1984012750.0° N 1986022345.0° N
1984032452.5° N
WY1990WY1991WY1992WY1993WY1994WY1995
DateLandfall LatitudeDateLandfall LatitudeDateLandfall LatitudeDateLandfall LatitudeDateLandfall LatitudeDateLandfall Latitude
1989120447.5° N1990110945.0° N1991101852.5° N1993010532.5° N1994011152.5° N1995010942.5° N
1990111150.0° N1992012350.0° N1993010730.0° N 1995011340.0° N
1990111247.5° N1992012947.5° N1993022652.5° N 1995012737.5° N
1991020152.5° N1992013045.0° N1993031640.0° N 1995012842.5° N
1991020345.0° N 1995012942.5° N
1991021245.0° N 1995013052.5° N
1991021850.0° N 1995013145.0° N
1991022730.0° N 1995021747.5° N
WY1990WY1991WY1992WY1993WY1994WY1995
DateLandfall LatitudeDateLandfall LatitudeDateLandfall LatitudeDateLandfall LatitudeDateLandfall LatitudeDateLandfall Latitude
1991030340.0° N 1995021850.0° N
1991030437.5° N 1995021947.5° N
1995030935.0° N
1995031035.0° N
1995043035.0° N
WY1996WY1997WY1998WY1999WY2000WY2001
DateLandfall LatitudeDateLandfall LatitudeDateLandfall LatitudeDateLandfall LatitudeDateLandfall LatitudeDateLandfall Latitude
1995120947.5° N1996111052.5° N1997121352.5° N1998111245.0° N1999111142.5° N2000110252.5° N
1995122940.0° N1996111840.0° N1997122850.0° N1998122842.5° N1999111245.0° N
1996010647.5° N1996111940.0° N1998011740.0° N 1999121445.0° N
1996013035.0° N1996112652.5° N1998012240.0° N 2000011835.0° N
1996020235.0° N1996120935.0° N1998020235.0° N 2000012337.5° N
1996020340.0° N1996122445.0° N1998020537.5° N 2000012435.0° N
1996020547.5° N1996122842.5° N1998021140.0° N 2000092945.0° N
1996020645.0° N1996122940.0° N1998032240.0° N
1996040552.5° N1996123035.0° N
1996123140.0° N
1997010140.0° N
1997011745.0° N
1997012847.5° N
1997012952.5° N
1997013050.0° N
1997031942.5° N
WY2002WY2003WY2004WY2005WY2006WY2007WY2008
DateLandfall LatitudeDateLandfall LatitudeDateLandfall LatitudeDateLandfall LatitudeDateLandfall LatitudeDateLandfall LatitudeDateLandfall Latitude
2001111447.5° N2003010150.0° N2003101747.5° N2004101150.0° N2005121942.5° N2006110547.5° N2007120247.5° N
2001122930.0° N2003010245.0° N2003102045.0° N2004110647.5° N2005122050.0° N2006110650.0° N2007120345.0° N
2001123140.0° N2003012242.5° N2003120440.0° N2004111352.5° N2005122347.5° N2006111547.5° N2007120440.0° N
2002010140.0° N2003012447.5° N2004010545.0° N2004111447.5° N2006022647.5° N2006122542.5° N
2002010642.5° N2003012550.0° N2004021542.5° N2004120940.0° N2006022740.0° N2006123150.0° N
2002010742.5° N2003012645.0° N2004021637.5° N2004121047.5° N 2007010150.0° N
2002040937.5° N2003013042.5° N 2005011650.0° N 2007012252.5° N
2002041242.5° N 2005011750.0° N 2007021452.5° N
2005011847.5° N 2007021545.0° N
2005011947.5° N 2007031045.0° N
2005012252.5° N 2007031147.5° N
2005012352.5° N 2007050552.5° N
2005030550.0° N
2005032647.5° N

3. Precipitation Variability in California

At the core of many of California’s water issues is its generally high degree of year-to-year and within-year precipitation variability. Addressing year-to-year variability, Figure 2a–b show coefficients of variation (standard deviation/mean) of water-year (October–September) precipitation (2a) and streamflow (2b) totals at sites across the conterminous US. Clearly precipitation and, somewhat less so, streamflow in California are proportionally more variable from year to year than are flows in other parts of the West Coast, and broadly more variable than most parts of the western and eastern states. Only some rivers contributing to the Mississippi from the west and in eastern Texas are similarly variable. The larger variations in California necessitate heroic levels of management of the State’s water resources to accommodate wider swings of wet and dry years than in any other state.
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Figure 2. Coefficients of variation of water-year (a) precipitation and (b) streamflow totals at long-term monitoring stations across the conterminous US, from water year 1951–2008, along with (c) tallies of the minimum number of wet days per year, on average, that provide half of the year’s precipitation in the western States.
Figure 2. Coefficients of variation of water-year (a) precipitation and (b) streamflow totals at long-term monitoring stations across the conterminous US, from water year 1951–2008, along with (c) tallies of the minimum number of wet days per year, on average, that provide half of the year’s precipitation in the western States.
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Much of the large variability of California’s precipitation and water resources in turn derives from the fact that its annual precipitation totals are dependent on precipitation from a relatively few storms. If just a couple of storms do not arrive in California, or yield significantly less precipitation than needed, in a given year, that year’s precipitation total and water resources suffer disproportionately, compared to other regions. Alternatively a relatively few large or “extra” storms may result in a particularly wet year. Figure 2c shows the average number of wet days (days with a nonzero, nontrace precipitation amount) per year required to accumulate half of the water year’s precipitation total. In much of California, a third to a half of all the precipitation that falls, on average, falls in only 5 to 10 wet days per year. Furthermore, even allowing for intervening dry days, a far shorter wet season is available in California to accumulate 2/3 of the total precipitation than in any other State (<125 days per year in California versus >150 to 250 in other states), as shown in Figure 3.
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Figure 3. Number of days (L67) required to accumulate 67% of the annual climatological total precipitation, calculated from long-term daily mean precipitation over the period of record available at each station prior to 2000. The beginning of the L67 “season” is the day for which L67 length is at its minimum [25].
Figure 3. Number of days (L67) required to accumulate 67% of the annual climatological total precipitation, calculated from long-term daily mean precipitation over the period of record available at each station prior to 2000. The beginning of the L67 “season” is the day for which L67 length is at its minimum [25].
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4. AR Precipitation and Floods

To properly understand the intensity of the (usually few) large storms that contribute so much to California’s precipitation totals but that also go beyond resource to yield some of the State’s greatest hazards, it is worthwhile to consider the largest recorded storm precipitation totals, nationwide [26]. Colored dots in Figure 4 show all recorded occasions since 1950 when 3-day precipitation totals have exceeded 40 cm at cooperative weather stations in the conterminous US. These largest storms have concentrated in two regions, along the hurricane and tropical storm belts of Texas through the Carolinas in the southeastern US and in California. The large storms in California have occurred primarily in winter whereas the large southeastern storms have occurred in summer [26]. The frequency of these largest storms are comparable from the southeast to California, with 0.48 reports/year/1,000 stations east of 105° W and 0.53 reports/year/1,000 stations west of 105° W. Notably, at least within the past dozen years (i.e., the period with suitable SSM/I imagery), a comparison of the large storms counted in Figure 4 to the chronology of ARs making land fall in California (Table 1) indicates that all of the large California storms in Figure 4 have been associated with ARs; in the longer Reanalysis period since 1948, 69% of the large storms in California in Figure 4 are associated with PEs. Thus extreme precipitation events, associated with landfalling ARs in California, have been historically comparable, in magnitudes and frequencies (for 3-day precipitation totals), to those mostly associated with landfalling hurricanes and tropical storms in the southeastern US and are among the largest historical storms in the conterminous US.
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Figure 4. Numbers of reported occasions of (nonoverlapping) 3-day precipitation totals at cooperative weather stations that exceeded 40 cm, from 1950–2008.
Figure 4. Numbers of reported occasions of (nonoverlapping) 3-day precipitation totals at cooperative weather stations that exceeded 40 cm, from 1950–2008.
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As a result of these large AR precipitation totals in California, as well as the typically warm AR-storm conditions that favor high snowlines and widespread rainfall [22], historical ARs and PEs in California (and elsewhere along the West Coast) have frequently resulted in flooding. Indeed, ARs are the primary (and, in some settings, essentially only) cause of flooding of California rivers. For example, all declared floods of the Russian River near Guerneville north of San Francisco in the period of SSM/I–based AR chronologies (water years 1998–2010) have been associated directly with ARs making landfall on the central California coast [10]. Comparison of the longer, but more restrictive, chronology of landfalling PE storms on the West Coast since 1948 to streamflows [5] has shown that streamflow increments associated with PE storms in the Merced and American Rivers of the western Sierra Nevada of Central California are an order of magnitude larger, at each recurrence level, than corresponding increments associated with non-PE storms (e.g., for the American River, Figure 5). As a final example, all cool-season floods since 1948 in the Carson River basin in the eastern, rain-shadowed part of the Sierra Nevada have been associated with PEs [27]. Farther north, in Washington and Oregon, AR and PE storms produce roughly twice as much precipitation as the overall average storms, and have been associated with several recent (at least) extreme flooding events [8,22].
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Figure 5. Exceedence probabilities for day-to-day changes in river discharge of the North Fork American River above North Fork Dam, above Sacramento, CA, under various December-February storm and precipitation conditions.
Figure 5. Exceedence probabilities for day-to-day changes in river discharge of the North Fork American River above North Fork Dam, above Sacramento, CA, under various December-February storm and precipitation conditions.
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5. Contributions of ARs to Overall Precipitation and Streamflow

5.1. Long-Term Contributions

Based on results like those presented above, it is increasingly understood that California’s most severe storms and much of its flood risks derive from land-falling ARs. What is less well recognized is the large extent to which California’s overall water resources derive from this same category of storms. Figure 6 and Figure 7 show the precipitation contributions, on a station-by-station basis, from the 1998–2008 SSM/I-derived AR events (Figure 6) and from the 1951–2008 period of the Reanalysis-derived PE events (Figure 7). During the recent SSM/I period (Figure 6), AR storms have contributed anywhere from about 31 to 36% of overall precipitation at most cooperative weather stations in Central and Northern California, rising to as much as 46% (Table 3). On average, precipitation during these events amounted to about two to three times the long-term average rates on the same days of year (disregarding AR occurrences). Many ARs making landfall north of California are included in this analysis, despite the fact that such storms are less likely to result in major precipitation contributions in California. Nonetheless the landfall of all ARs anywhere on the West Coast are found to have contributed between 30 and 45% of all precipitation in central and northern California. Fractions of total precipitation in Oregon and Washington deriving from landfalling ARs are similar at many, but not all, stations to these California fractions. When only AR episodes that made landfall in California are considered (not shown here), the fractions contributed remain roughly 30 to 45% of all precipitation in California because ARs making landfall farther north contribute little precipitation in California so that results are almost the same with or without them.
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Figure 6. Contributions of precipitation during wet-season (November–April) days on which atmospheric rivers made landfall on the West Coast (based on Table 1) to overall precipitation from water year 1998 through 2008 at cooperative weather stations in the western US. Inset map shows the ratio of average precipitation on the AR days (including concurrent day and following day) to climatological means for the same combination of days. Concurrent and one following day are composited here to allow for a 1-day uncertainty between the GMT-based AR chronology and local-time cooperative precipitation observations.
Figure 6. Contributions of precipitation during wet-season (November–April) days on which atmospheric rivers made landfall on the West Coast (based on Table 1) to overall precipitation from water year 1998 through 2008 at cooperative weather stations in the western US. Inset map shows the ratio of average precipitation on the AR days (including concurrent day and following day) to climatological means for the same combination of days. Concurrent and one following day are composited here to allow for a 1-day uncertainty between the GMT-based AR chronology and local-time cooperative precipitation observations.
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Table
Table 3. Ratios of precipitation and streamflow or runoff during AR and PE events, and subsequent days (as indicated), to overall precipitation, streamflow or runoff, among cooperative precipitation sites and VIC-gridded precipitation data, and streamflow or runoff, among HCDN streamflow gauging stations and gridded VIC simulations, respectively, in central and northern California (east of 120° W and south of 42° N). See section 2 for descriptions of data sources.
Table 3. Ratios of precipitation and streamflow or runoff during AR and PE events, and subsequent days (as indicated), to overall precipitation, streamflow or runoff, among cooperative precipitation sites and VIC-gridded precipitation data, and streamflow or runoff, among HCDN streamflow gauging stations and gridded VIC simulations, respectively, in central and northern California (east of 120° W and south of 42° N). See section 2 for descriptions of data sources.
Maximum ratio among central and northern California sites and grid cells shown in Figure 6, Figure 7 and Figure 875th-percentile ratio among central and northern California sites and grid cells shown in Figure 9 and Figure 1025th-percentile ratio among central and northern California sites and grid cells shown in Figure 9 and Figure 10
AR eventsPE eventsAR eventsPE eventsAR eventsPE events
Precipitation (days 0 and +1)
Cooperative precipitation observations46%22%36%17%31%13%
VIC-gridded precipitation45%21%40%16%33%12%
Streamflow or Runoff (days 0 to +3 days)
HCDN streamflow71%55%52%38%34%25%
VIC-simulated runoff61%40%43%24%26%12%
During the longer term Reanalysis period (Figure 7), PE storm contributions have amounted to 13 to 17% of overall precipitation at most stations in central and northern California (Table 3) with the fractions rising to as much at 22% at the most heavily influenced site, about half the fractions shown in Figure 6. Recall, however, that there are 2.5 times as many ARs per season as PEs, in the chronologies used here, so that the contributions from ARs and PEs are roughly comparable on a storm-by-storm basis. Precipitation from the PE storms average about two to four times the long-term average precipitation rates. To verify the patterns shown, and to fill in spatially (based on the interpolation schemes of [16]), Figure 8 and Table 3 show the corresponding contributions from ARs, water years 1998–2008, and PEs, 1948–2008, as indicated by the gridded VIC-input fields of precipitation. Overall, Figure 6, Figure 7 and Figure 8 suggest that, whether ARs are considered for a short recent period or PEs for a longer historical period, similar contributions to overall precipitation, on a per-storm basis, are indicated. Thus the large contributions to overall precipitation associated with ARs in the recent (SSM/I) period are unlikely to be somehow unusual over the longer historical period.
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Figure 7. Same as Figure 6, except for PE days from water year 1951–2008, based on Table 2.
Figure 7. Same as Figure 6, except for PE days from water year 1951–2008, based on Table 2.
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Figure 8. Same as main map in Figure 6, except based on 1/8-degree gridded VIC-input precipitation fields, for (a) AR events and (b) PE events.
Figure 8. Same as main map in Figure 6, except based on 1/8-degree gridded VIC-input precipitation fields, for (a) AR events and (b) PE events.
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Corresponding analyses of streamflow observations and simulations of runoff-plus-baseflow are presented in Figure 9 and Figure 10. The wet season along the West Coast is also the cool season so that much precipitation falls as snow, especially in the Sierra Nevada mountains along the eastern edge of Central California. This tendency towards snowfall rather than rain is important because the Sierra Nevada receives many of the highest precipitation totals in the State and generates many of the largest streamflow contributions. As a consequence, much of the precipitation associated with ARs and PEs is stored in seasonal snowpacks until much later in the year and thus it is not possible to catalog precisely as runoff what is from AR or non-AR events using the method here. As an alternative, Guan et al. [28] documented the contribution of AR days to snow water equivalent at snow pillow sites in the Sierra Nevada during WY 2004–2010. Their analysis concluded that on average 6–7 AR events occurred per year, and that they provided 40% of the total snow water equivalents over the 7 years studied, in broad agreement with the contributions to precipitation documented above. However, low- to mid-altitude parts of the Sierra Nevada and other mountain ranges of California often receive rain from cool-season ARs and from PEs so that significant runoff and streamflow signatures are nonetheless closely associated with AR and PE episodes in river basins across much of the State. When a few days of delay, following the AR or PE episodes, are allowed in calculations of streamflow contributed by these episodes, significant AR and PE contributions to overall streamflow are indicated, with 25–75% of the sites falling in the range of 34–52% of streamflow (Table 3).
In the SSM/I period, ARs (and a three-day recovery period) are found to yield 50% and more of overall streamflow in coastal basins from California to the Canadian border (Table 3; Figure 9a). Farther inland, in the Central Sierra Nevada and in the Peninsular Range of southern California, some rivers yield 30 to 50% of overall streamflow, with most rivers in central and northern California yielding between from about 34 to 52% of overall streamflow during the AR-influenced periods. These AR streamflows average about two times the long-term mean streamflow rates from similar days of year (not shown). VIC-simulated runoff rates associated with AR episodes amount to broadly similar percentages of overall runoff in coastal basins and in the mountains of California (Figure 10a). Significant fractional contributions to simulated runoff actually extend considerably father inland than in the observed streamflows. However, much of the area with the largest fractional contributions to overall runoff are areas that do not contribute much overall runoff at all, in absolute terms; AR runoff from such areas does not add to overall streamflows much and thus do not show up in the measured river discharges. Therefore, contributions mapped in Figure 10 were masked to exclude all areas where average AR runoff generation was less than 0.5 mm/day. With this masking, comparisons between observed streamflow contributions from ARs and simulated runoff contributions are in good geographic agreement, although simulated fractional runoff contributions are generally smaller than their nearby streamflow counterparts.
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Figure 9. Contributions of streamflow during wet-season (November-April) episodes when atmospheric rivers (as ARs and PEs) made landfall on the West Coast to overall streamflow at HCDN streamflow gauging stations [14] in the western US, for (a) AR episodes, water years 1998–2008, based on Table 1, and (b) PE episodes, water years 1949–2008, based on Table 2 (including streamflow in each case from PE-concurrent day and three following days). Inset map shows the ratio of average streamflow on the PE days (including concurrent day and 3 following days) to climatological means for the same combination of days. Concurrent and three following days are considered to allow for streamflow response times from the basins. Nonetheless, because higher-altitude mountains, such as the Sierra, capture large amounts of snow during AR or PE events that does not runoff until spring or summer, runoff percentages from these regions are expected to be underestimates overall.
Figure 9. Contributions of streamflow during wet-season (November-April) episodes when atmospheric rivers (as ARs and PEs) made landfall on the West Coast to overall streamflow at HCDN streamflow gauging stations [14] in the western US, for (a) AR episodes, water years 1998–2008, based on Table 1, and (b) PE episodes, water years 1949–2008, based on Table 2 (including streamflow in each case from PE-concurrent day and three following days). Inset map shows the ratio of average streamflow on the PE days (including concurrent day and 3 following days) to climatological means for the same combination of days. Concurrent and three following days are considered to allow for streamflow response times from the basins. Nonetheless, because higher-altitude mountains, such as the Sierra, capture large amounts of snow during AR or PE events that does not runoff until spring or summer, runoff percentages from these regions are expected to be underestimates overall.
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Figure 10. Same as main map in Figure 8, except based on VIC-simulated runoff-plus-baseflow, and for (a) AR episodes and (b) PE episodes; both maps are masked to show AR- (or PE-) episode contributions to runoff-plus-baseflow in areas where average simulated runoff-plus-baseflow during episodes is >0.5 mm/day.
Figure 10. Same as main map in Figure 8, except based on VIC-simulated runoff-plus-baseflow, and for (a) AR episodes and (b) PE episodes; both maps are masked to show AR- (or PE-) episode contributions to runoff-plus-baseflow in areas where average simulated runoff-plus-baseflow during episodes is >0.5 mm/day.
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On the longer term, streamflow yields associated with the PE episodes from water year 1949–2009 (Figure 9b and Table 3) are somewhat less than the AR contributions (Figure 9a) in California, because there are about 2.5 times as many AR episodes per year as PE episodes. The streamflow contributions from PEs in the rivers of the Pacific Northwest are, similarly, about half as large as the (percentage) contributions from the AR episodes. Simulated runoff associated with PEs (Figure 10b) approximates but is smaller than the AR contributions (Figure 10a) in mountainous areas of northern California, and are much smaller in the Pacific Northwest.
Overall then, significant fractions of all precipitation and streamflow in the West Coast states derive from AR storms. This observation, together with the fact that a notably few storms (most often, AR storms) contribute most of California’s precipitation each year, on average, and that California’s year-to-year precipitation (and streamflow) variability is quite large, makes increased understanding of the details of how, where and when ARs arrive to support the State’s water resources crucial.

5.2. Year-To-Year Contributions

Contributions of PE events to water year precipitation (and streamflow) vary from year to year. Average PE contributions to water-year precipitation at cooperative weather stations in central and northern California are shown in Figure 11, with contributions ranging from zero in several years to as much as 54% in 1986. To what extent do these variations (and variations elsewhere along the west coast) reflect large-scale climate modes either as concurrent or predictive associations?
Long-term, reproducible influences of large-scale Pacific climate influences on the occurrence of PEs, in general, remain difficult to discern or predict at present [5], although on an event-by-event basis, strong ties to the tropical Pacific have been documented [11,29]. Nonetheless, year-to-year phases [30] of the interannual El Nino-Southern Oscillation (ENSO) climate mode in the tropical Pacific [31] and of the multidecadal Pacific Decadal Oscillation (PDO, [32]) mode in the North Pacific climate are significantly associated with increases in the PE fractions of water-year precipitation (Figure 12). The association indicated is such that, in El Nino and positive PDO (El Nino-like) years, PE fractions of water-year precipitation at stations in southern and south-central California, and, more spottily, in southern California and western Washington, respectively, are significantly larger than average. Comparison of Figure 12 with Figure 7 suggests that the correlations in Figure 12 have their largest practical impacts in south central California and, perhaps, Washington, where both (a) the correlations with ENSO and PDO are significant and (b) the fractions of precipitation associated with PEs are also large. Notably, significant connections between ENSO or PDO and PE-precipitation contributions are not found in central and northern California from which so most of the State’s water resources are generated. Although such relations may eventually support improvements in long-lead forecasts of the role that PEs and floods will play in the overall water resources of southern California and western Washington, the simple (concurrent) relations in Figure 12 could not be extended backwards in time into significant predictive associations between the status of ENSO or PDO in summer with PE-precipitation contributions at western weather stations.
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Figure 11. Averages of contributions to water-year total precipitation on PE days (plus day +1) at 202 cooperative weather stations in central and northern California (same area as defined in caption of Table 3), 1951–2008.
Figure 11. Averages of contributions to water-year total precipitation on PE days (plus day +1) at 202 cooperative weather stations in central and northern California (same area as defined in caption of Table 3), 1951–2008.
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Figure 12. Correlations between PE contributions to water-year precipitation totals and concurrent water-year averages of (a) the Nino3.4 [31] sea-surface temperature (SST) index of the tropical ENSO climate mode, averaging SSTs in the region 5° N–5° S, 120° W–170° W, and (b) the Pacific Decadal Oscillation SST-based index [32]; colored dots indicate cooperative weather stations where the correlations are significantly different from zero at 95% confidence level and small gray dots are stations where the relations are not significant at this level.
Figure 12. Correlations between PE contributions to water-year precipitation totals and concurrent water-year averages of (a) the Nino3.4 [31] sea-surface temperature (SST) index of the tropical ENSO climate mode, averaging SSTs in the region 5° N–5° S, 120° W–170° W, and (b) the Pacific Decadal Oscillation SST-based index [32]; colored dots indicate cooperative weather stations where the correlations are significantly different from zero at 95% confidence level and small gray dots are stations where the relations are not significant at this level.
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As noted in Figure 12, no reliable associations exist between ENSO or PDO status and PE-contributions to water-year precipitation in central and northern California, but this part of California lies along transition zones of many ENSO and PDO hydroclimatic influences [33,34], so that reliable teleconnections of those modes to northern California hydroclimatic conditions do not generally occur. Thus, in order to identify concurrent or predictive climate associations with PE-precipitation contributions in central and northern California, a wider range of possibilities must be investigated. In Figure 13, rank correlations between the time series in Figure 12 and concurrent (a) global sea-surface temperatures and (b) Pacific-North American 700-mbar heights are mapped. Above the planetary boundary layer, the large-scale atmospheric flow is nearly in geostrophic balance, and thus lines of equal height to the 700 mbar pressure surface approximate closely the stream lines followed by winds at the same level. Correlations with 700-mbar heights also indicate areas where the passage of low- and high-pressure weather systems are associated with the PE contributions.
Correlations are only mapped in Figure 13 where they are significantly different from zero correlations with a simple student-t test. Thus the most significant correlations between winter sea-surface temperatures and PE-precipitation contributions in central and northern California (Figure 13a) are found in the westernmost Pacific, with positive correlations (red) with sea-surface temperatures northeast of Japan and negative correlations (blue) in the tropical Pacific near the Philippines and Indonesia. A dipole of correlations is also found in the tropical and subtropical Atlantic basin north of the equator. The pattern of positive sea-surface temperature correlations in the western Pacific is located just north of the Kuroshio Extension Current and just north of the strongest part of the PDO sea-surface temperature pattern ([32], and outlined in green in Figure 13a). Thus the Pacific sea-surface temperature correlations with PE-precipitation contributions in central and northern California straddle (but do not parallel) the canonical PDO pattern and thus may reflect connections between large-than-average PE-precipitation contributions and concurrent southward displacements of the canonical positive-PDO sea-surface temperature pattern. Similarly, correlations between the PE-precipitation contributions and 700-mb heights (Figure 13b) appear to indicate increased PE contributions in winters with lower-than-average 700-mbar heights or southward displacements of the climatological Aleutian Low pressure pattern (beneath the 700-mb center of action for PDO is indicated in green in Figure 13b) [33], which in turn reflects enhanced passage of low-pressure systems and storms across the North Pacific basin. Thus the 700-mb height correlations in Figure 13b suggest a tendency for large PE-precipitation contributions in central and northern California to be associated with southern displacements of the storm tracks over the midlatitude Pacific and enhancement of the subtropical jet over the North Pacific basin (not shown). In particular, the structure of the low 700 mb height anomalies indicated by the negative corrections positioned north of Hawaii is consistent with atmospheric circulations that would favor northward advection of water vapor from the tropics near Hawaii—a classic characteristic of PE-type AR events [5,29].
Notably, no significant sea-surface temperature correlations are indicated in the equatorial central or eastern Pacific areas associated with tropical ENSO variability, and the only significant ties to equatorial sea-surface temperatures appear in the far westernmost parts of the Tropical Pacific “warm pool” around the Philippines. This warm pool is location of some of the warmest sea-surface temperatures on the planet, is a source of some of the warm waters associated with El Niños, and perhaps most importantly is a region that has seen important multi-decadal warming trends since the 1970s [35]. The negative (blue) correlations indicated in this area would, on long-term average, suggest that warming of the Pacific warm pool since the 1970s may have contributed to the broad declines in PE contributions from the 1980s to 2008 in Figure 11 (in agreement with the North American precipitation teleconnections found in [35]), and that continued warming might be associated with decreasing contributions of PEs to California precipitation, if the historical associations continue.
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Figure 13. Rank correlations between average PE contributions to water-year precipitation totals at 202 cooperative weather stations in central and northern California (same area defined in Table 3) and concurrent (a) November-April sea-surface temperatures (SSTs), and (b) November-April 700 mbar height anomalies [12]; green hatched shapes in North Pacific surround primary centers of action (largest correlations) with respect to Pacific Decadal Oscillation [31].
Figure 13. Rank correlations between average PE contributions to water-year precipitation totals at 202 cooperative weather stations in central and northern California (same area defined in Table 3) and concurrent (a) November-April sea-surface temperatures (SSTs), and (b) November-April 700 mbar height anomalies [12]; green hatched shapes in North Pacific surround primary centers of action (largest correlations) with respect to Pacific Decadal Oscillation [31].
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Finally, historical relations between climatic conditions at the beginning of a water year and the PE-precipitation contributions in central and northern California during that water year are explored in Figure 14. The correlations between sea-surface temperatures at the start of a water year and PE contributions (Figure 14a) indicate that the connections between Pacific (and, in this case, Indian Ocean) warm pool and California PE-precipitation contributions may have some predictive elements. Cooler-than-normal warm pool sea-surface temperatures have historically tended to be associated with larger-than-normal PE contributions. As with the concurrent correlations in Figure 13a, no other equatorial connection is apparent. Intriguingly, an impressive, if difficult to understand, historical connection appears, in the three panels of Figure 14, that associates unusually wet conditions in the Pacific Northwest (14c), unusually low offshore pressures (14b) and underlying slightly cool sea-surface temperatures (14a) at the beginning of a water year (September–October) with higher than normal PE-precipitation contributions for central and northern California during the rest of the water year.
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Figure 14. Rank correlations between average PE contribution to water-year precipitation totals at 202 cooperative weather stations in central and northern California (same area defined in Table 3) and (a) preceding September-October sea-surface temperatures (SSTs), (b) preceding September–October 700 mbar height anomalies, and (c) preceding September–October precipitation [12].
Figure 14. Rank correlations between average PE contribution to water-year precipitation totals at 202 cooperative weather stations in central and northern California (same area defined in Table 3) and (a) preceding September-October sea-surface temperatures (SSTs), (b) preceding September–October 700 mbar height anomalies, and (c) preceding September–October precipitation [12].
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In principle, such relations might be used to condition the expectations of decision makers or to explicitly predict water years in which California will receive above-normal fractions of its overall precipitation and surface water supplies from PE storms. In such years, tradeoffs between managing reservoirs during major storms to focus on the hazardous aspects of these storms rather than their contributions to overall water resources might be particularly difficult, as the PE contributions are large and their forfeiture to flood-management practices particularly painful to resource managers. In years when PE storms will provide smaller fractions of the overall resources, more aggressive flood-management strategies during PE episodes might be implemented with greater confidence that the overall water resource will still fare well.

6. Summary and Conclusions

Nationwide analyses of precipitation records from long-term cooperative weather stations, together with corresponding analyses of streamflow records from the HCDN network, reveals the remarkable interannual variability of California’s precipitation and streamflows. This variability derives in part from the relatively few storms that contribute the bulk of California’s precipitation each year, on average. When a few storms either don’t arrive or “underperform”, the precipitation totals and thus water resources in the State suffer. The capacity of a relatively few storms each year to support California’s water resources owes much to the extreme precipitation that some of its storms yield, and we find that, in recent decades, landfalling atmospheric rivers (ARs) have caused most of the largest of these storms. ARs generally, as well as in a particularly well known but not notably more or less intense subset of ARs called pineapple-express storms (PEs), bring intense and warm precipitation to the mountain catchments of California. The disproportionately large contributions made by ARs are a primary meteorological factor in flood generation on many California rivers, as well as a primary source of precipitation and water resources in the State. On long-term average, AR storms contribute from 20–50% of the state’s precipitation totals, with area-average contributions ranging from zero to 54% from year to year, historically. The storms result in comparable fractions of overall streamflow in much of the State. With respect to these year-to-year differences, correlations of the year-to-year differences in the fractions contributed in river basins along the west coast and large-scale ENSO, PDO, sea-surface temperatures, and atmospheric conditions suggest that it may be possible to forecast some of these fluctuations on interseasonal time scales.
Thus we find that close integration of water-resource and flood management actions and strategies may rightly be motivated by more than a need for efficiency or for eking a little more water supply out of a relatively dry landscape in the face of growing demands. Instead the need for such integration has a firm physical basis in the shared meteorology of water resources and floods in California. Both water resources and floods in California are strongly dependent on the character and frequency of landfalling AR storms arriving from over the North Pacific Ocean. Understanding of these storms has grown dramatically in recent years, but—in view of their crucial, dual role—continued and expanded research and monitoring are needed, focusing specifically on ARs and the contributions to water resources and to hazards that they yield. Of particular significance, the distributions of vapor transports, and thus precipitation, once the ARs are over land and interacting with orography need to be better understood and predicted. AR storms may also be key to determining and predicting changes of both California’s water resources and flood regimes under projected global climate changes. At present, the fate of ARs has only begun [36] to be explored in climate-change projections. More attention to the possible influences of greenhouse-gas-induced climate changes on AR frequencies and magnitudes, whether within modern climate models or from more basic physical principles, is needed.

Acknowledgments

The estimates of integrated water-vapor transports used for the most recent PE chronology here were kindly provided by Cathy Smith of the NOAA Earth Systems Research Laboratory. This work was conducted in the context of CalWater atmospheric-river research efforts [37] organized by the California Energy Commission, National Oceanic and Atmospheric Administration, U.S. Geological Survey, and Scripps Institution of Oceanography.

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Dettinger, M.D.; Ralph, F.M.; Das, T.; Neiman, P.J.; Cayan, D.R. Atmospheric Rivers, Floods and the Water Resources of California. Water 2011, 3, 445-478. https://doi.org/10.3390/w3020445

AMA Style

Dettinger MD, Ralph FM, Das T, Neiman PJ, Cayan DR. Atmospheric Rivers, Floods and the Water Resources of California. Water. 2011; 3(2):445-478. https://doi.org/10.3390/w3020445

Chicago/Turabian Style

Dettinger, Michael D., Fred Martin Ralph, Tapash Das, Paul J. Neiman, and Daniel R. Cayan. 2011. "Atmospheric Rivers, Floods and the Water Resources of California" Water 3, no. 2: 445-478. https://doi.org/10.3390/w3020445

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