COHERENCE AND EVENT DETECTION METHODOLOGY OF RIVER DISCHARGE AND SNOW WATER EQUIVALENT IN NEW MEXICO, USA. University of New Mexico Albuquerque, NM

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1 JOURNAL OF ENVIRONMENTAL HYDROLOGY The Electronc Journal of the Internatonal Assocaton for Envronmental Hydrology On the World Wde Web at VOLUME COHERENCE AND EVENT DETECTION METHODOLOGY OF RIVER DISCHARGE AND SNOW WATER EQUIVALENT IN NEW MEXICO, USA J.L. Tchy Unversty of New Mexco Albuquerque, NM To fnd events n both rver dscharge data and snow water equvalent data for the Ro Grande, New Mexco, movng averages (short term average over long term average, STA/LTA) are used to create an event detector. Addtonally, a cross-correlaton s performed on the data from the Unted States Geologcal Survey and the Natonal Resource Conservaton Servce ( ) to see the relatonshp between the rver dscharge data and the snow water equvalent data. Usng lag tmes calculated from the cross correlaton, the dfference n peak tmes from dscharge and snowmelt ndcates a shft n snowmelt to earler n the sprng. Because of the hgh correlaton between snow water equvalent and rver dscharge, plus the results of the cross-correlaton, t s found that the peak n snowmelt s occurrng earler n the year and that the tme lag between peak snowpack and peak rver dscharge s decreasng, meanng that the snowpack s generally meltng faster. One mplcaton resultng from hydrologc changes such as these s the adaptaton of aquatc and terrestral speces dependng on the system for survval. Journal of Envronmental Hydrology 1

2 INTRODUCTION New Mexco, lke many regons that rely on snowpack for water supply, s partcularly vulnerable to clmate change and the possblty of peak snowmelt occurrng earler n the year (Serreze et al. 1999). In the western Unted States, for example, approxmately 50-70% of the precptaton falls as snow, and sprng/early summer snowmelt runoff accounts for approxmately 50-80% of the total annual runoff for snowmelt-domnated basns (Serreze et al. 1999), thus creatng a need to study the nteracton of sprng snowmelt runoff and rver dscharge (Adams and Comre 1997, Pelleter and Turcotte 1997, and Peterson et al. 2000). Aprl 1st s typcally used as the date of maxmum snow accumulaton (Maurer et al and Graf 2006) but models show that a 1º - 2ºC temperature ncrease could yeld a day peak shft of dscharge (IPCC 2007). A 3ºC temperature ncrease (under md to hgh green house gas emsson scenaros) could also shft peak streamflow by 30 days (IPCC 2007). Addtonally, snow has a hgh albedo, so there wll be a postve feedback of polar amplfcaton of global warmng f there s a decrease n yearly snowpack (Mote 2003). Warmer wnters yeld less snowpack due to precptaton fallng as ran rather than snow, and addtonally move the peak snowmelt runoff to an earler date. If the peak of sprng snowmelt shfts to be earler n the year, water managers wll be faced wth an annual challenge of storng wnter precptaton for use later n the year (Stewart 2009). Prevous studes have found hydroclmatologcal changes n the last 50 years n the western Unted States. The changes are evdent n the tmng of sprng runoff (Stewart 2009), n the fracton of ran versus snow (Knowles et al. 2006), n the amount of water contaned n the snow (Mote 2003), n the fracton of annual streamflow throughout the year (e.g. Hdalgo et al found that the March fracton of annual streamflow has ncreased whle the Aprl July fracton of annual streamflow has decreased), and n clmate-senstve bologcal varables (Cayan et al. 2001). It s thought that these changes are related manly to temperature ncreases as they affect snowmeltdomnated basns n ways predcted n response to warmng (Mote 2003), and t s suspected that the warmng trends causng the changes are n part due to anthropogenc effects. Recent studes have shown that snowpack volumes and snowmelt runoff have vared wth clmate on many temporal and spatal levels (Zhang et al. 2007), but recent global surface temperature ncreases due to anthropogenc greenhouse gas emssons are now well recognzed along wth ther potental mplcatons on the hydrologc system (IPCC 2007). In ths study, two methods are used to quantfy sprng snowpack and snowmelt. The frst method s an averagng method that uses the STA/LTA algorthm, whch s a short tme runnng average dvded by a long tme runnng average. Ths method can be used to pck events out of tme seres data based on a threshold level (Wong et al. 2010). The use of ths movng tme-averagng method s necessary for a quanttatve analyss of rver dscharge and SWE data because t mathematcally calculates ratos. Addtonally, wth an event detector such as ths, we can make proper decsons regardng water qualty lnked to events of nterest and other management concerns (Cayan et al. 2001). The second method s a cross-correlaton to analyze the smlartes and dfferences between snowpack and sprng snowmelt runoff. Lag tmes between peaks are also calculated n order to determne f the tmng of snowmelt runoff has shfted n the last 30 years, how senstve the Ro Grande Catchment s to mountan snowpack runoff, and f the lag tme between peak snowpack and peak rver dscharge has also shfted n the last 30 years. Journal of Envronmental Hydrology 2

3 BACKGROUND Each year, the Natonal Weather Servce and the Sol Conservaton Servce ssue monthly forecasts of the streamflow that can be expected durng the man runoff perod, Aprl through July, for much of the western Unted States (Redmond and Koch 1991). These forecasts are mostly based on the exstng snowpack but also on expected future precptaton. They are used extensvely throughout the western Unted States to develop reservor operaton plans for flood control and water supply plans; n turn, decsons regardng the management of many agrcultural operatons are also based on expected water avalablty (Graf 2006 and Walton and Hunter 2009). The mportance of these predctons ndcates the economc mplcatons of mproved estmates of snowpack and sprng runoff. Redmond and Koch (1991) suggest that the confdence nterval on a water supply forecast made very early n the water year (e.g. October or November) mght be ncreased by as much as 15% due to clmate change. It s for ths reason that t s better to make predctons later n the water year. These late predctons may not gve water managers suffcent tme to make crtcal decsons regardng water use. Thus, t s mportant to accurately quantfy the hgh correlaton between snowpack and sprng runoff (Krshna 2005). Others have used varous methods to quantfy event data from hydrologc tme seres (e.g. Guralnk and Srvastava 1999, Smth et al. 1998, and Hamed 1998), but Norbato et al. (2007), for nstance, use the ndex varable method to do regonal frequency analyses, allowng them to use data from nearby or smlar stes to estmate quantles of the underlyng varable at ther gven stes. They fnd that attrbutng a sngle return perod to a storm event s not realstc and that usng a tradtonal ran gauge network can be too sparse to provde adequate samplng (radar data can gve an advantage over actual ran gauge data (Delreu et al and Velasco-Forero et al. 2009)). Despte these fndngs, standard gauges can be used for the analyss presented here snce we are studyng events rather than entre volumes of precptaton for a gven watershed. Guralnk and Srvastava (1999) use a modelng approach to detect a change pont by detectng the change of a model (or the parameters of the model) that descrbe the underlyng data, and Smth et al. (1998) use wavelets to dentfy transent features to quantfy the temporal varablty of streamflow. Ther study estmates precse locatons of both stochastc and perodc events n tme that revealed subtle structures not seen n tme seres data. Hamed (1998) then went on to use the Mann-Kendall test to detect trends n hydrologcal data. Snce the test s not overly senstve to outlers, t s accepted as a decent statstcal method for hydrology. Gven the prevous work, further analyses and correlatons wll be useful nformaton for water management technques. The magntude of a gven peak, plus ts tmng, are the two most mportant features of a hydrograph (Jan and Indurthy 2003), so those are the parameters that are analyzed here. METHODS In ths analyss, we analyze Natonal Resource Conservaton Servce (NRCS) SNOTEL (for SNOwpack TELemetry) data from the NRCS webste ( at the Quemazon ste n New Mexco. The ste number s 708 and t s located n Los Alamos County, the lattude s 35 deg 55 mn N, the longtude s 106 deg 24 mn W, and the elevaton s 9500 feet (Fgure 1). The daly data, ncludng snow water equvalent (SWE), precptaton, varous ar temperature quanttes, and snow depth were downloaded from January 1981 through May The parameter of nterest for ths study s the SWE, measured n nches. To measure ths Journal of Envronmental Hydrology 3

4 parameter, a measurng devce called a snow pllow s postoned so that t can determne the watercontent of the snow coverng. The workng prncple of the sensor s based on the detecton of the hydrostatc pressure caused by the layer of snow (Peterson et al. 2000). Ths SNOTEL ste s located northwest of the USGS stream gauge montorng rver dscharge for ths project (Fgure 1). Addtonally, we also analyze Unted States Geologcal Survey (USGS) stream gauge data from the USGS webste ( for the Ro Grande at Otow Brdge ste n New Mexco. The ste number s USGS , the county s Santa Fe, the lattude s 35 deg 52 mn 28.2 sec, the longtude s 106 deg 8 mn 32.8 sec, and the elevaton s 5488 feet (Fgure 1). The daly data, ncludng rver dscharge, were downloaded from January 1981 through May Hstorcally, the Otow gauge used several dfferent types of nstruments, but the technology today s the followng: The stream channel cross secton s dvded nto numerous vertcal subsectons and n each subsecton the area s obtaned by measurng the wdth and depth of the subsecton, and the water velocty s determned usng a current meter. The dscharge n each subsecton s computed by multplyng the subsecton area by the measured velocty. The total dscharge s then computed by summng the dscharge of each subsecton. Ths ste s located southeast of the NRCS SNOTEL ste used for ths project (Fgure 1). Fgure 1. New Mexco map depctng the NRCS SNOTEL ste at Quemazon and the USGS stream gauge ste at the Otow Brdge. Journal of Envronmental Hydrology 4

5 STA/LTA The frst step of the movng average analyss s to calculate short term averages (STA) and long term averages (LTA). Let x be the tme seres representng ether rver dscharge or SWE data. Let the number of ponts n a short-term wndow be n s and the number of ponts n a long-term wndow be n l wth n l >n s. The average values n the short term and the long-term wndows precedng the ndex are as follows: 1 STA = ns 1 LTA = nl 2 x j j= ns 2 x j j = nl (1) (2) If j 0, set x j =(x 1 +x 2 )/2 (Wong et al. 2010). We can then defne the STA/LTA rato: STA r = (3) LTA For both the rver dscharge and the SWE calculatons, n s =100 and n l =400. These values are determned by optmzng the length of the data set wth the length of the yearly dscharge and SWE event peaks, respectvely. The n s and n l values for both data sets are the same: both data sets are daly and of the same number of years. To calculate the STA/LTA, the flter functon s utlzed n Matlab. Ths functon flters the orgnal tme seres data by the n s and n l values, gvng a smoothed, averaged value. The STA/LTA s then plotted for each orgnal data pont of the tme seres data. A threshold value s also set n order to make a quanttatve cut-off pont of sgnfcant events (Wong et al. 2010). Based on the movng average, the threshold value s set at r =1 for dscharge and r =1.5 for SWE. Ths s determned through careful exploraton of average values among the years (.e. percentages of ponts fallng above and below the cut-off). Cross-Correlaton To perform the cross-correlaton, the followng procedure s employed for both the dscharge data and the SWE data. To fnd an average year, each annual peak s shfted to an arbtrary tme scale. Ths allows us to normalze the data for lag tme. For example, f we smply averaged each block of 365 days, there would be some years wth an earler peak and some years wth a later peak. These off-years would not be ncluded n the average and thus would not be fully represented. The goal s to see the peak of data wthout ntally lookng at the tmng of the event. Once each year s maxmum peak event s overlad on the other peaks from the 30-year data set, one average year can be determned. Ths nformaton s plotted as both a 2D and a 3D plot. The Matlab functon xcorr s used to shft each year s peak of data through the followng sequence: r( d ) = [ x( ) mx ( y( d) my] ( x( ) mx) 2 ( y( d) my) 2 (4) Journal of Envronmental Hydrology 5

6 where x() and y() arethe two data sets beng compared, mx and my are the respectve means, and d s the delay. If we assume mx and my to be zero, we have the followng: r( d) = [ x( ) ( y( d)] ( x( )) 2 ( y( d)) 2 (5) To fgure the lag tmes assocated wth the correlatons, a seres of delays (d) are run through the above equaton and the maxmum value s determned. Ths s equal to the lag tme. Ths functon computes raw correlatons wthout normalzaton, so a separate process s then employed to normalze the calculatons. To do ths, a cross correlaton s performed between the average year and each separate year, the average year wth tself, and each separate year wth tself. The aforementoned 3 cross-correlatons are then compared. Ths allows us to determne the normalzed correlaton between each year and the average year prevously determned. These calculatons also allow us to determne the lag tme between each year s peak and the average year s peak. The correlatons are then plotted as tme seres to see the correlaton of each year s peak to the average year s peak. Addtonally, the lag tmes are plotted as a tme seres to see the tme dfference between each year s peak and the average year s peak (wth the mean removed to normalze the data). To then see the correlaton and the lag tmes between each SWE peak and the correspondng rver dscharge peak, a cross correlaton s performed between each seasonal peak (.e. each SWE peak versus ts correspondng dscharge peak). Ths allows us to see how, exactly, each year s SWE data relates to the followng sprng s rver dscharge. We then plot a tme seres graph for both the correlatons and the lag tmes. Fnally, to see how the lag tmes and the correlatons compare among both the SWE peaks and the rver dscharge peaks, a plot s created to compare the correlatons of each SWE year to the average SWE year versus the correlatons of each rver dscharge year to the average rver dscharge year. Addtonally, another plot s created to compare the lag tmes of each SWE year from the average SWE year versus the lag tmes of each rver dscharge year from the average rver dscharge year. RESULTS STA/LTA values are plotted for both the Ro Grande dscharge data and the SWE data. These are presented n Fgure 2. The rato s on the vertcal axs and tme s on the horzontal axs. The threshold values are also plotted on each graph. The threshold for a sgnfcant peak on the dscharge STA/LTA s 1 whle the threshold value for the sgnfcant peak on the SWE STA/LTA s 1.5. To vew the average dscharge year wth each ndvdual year peak, a plot s created wth the shfted yearly peaks, as mentoned n the methods secton. Fgure 3 shows the average dscharge peak wth each of the other years dscharge peaks shfted to a generc tme scale. The mportance of the plot s the dscharge peak value, not the tme that the peak occurs. Fgure 3 also shows, as a surface plot, each year s peak dscharge shfted to the generc tme scale. Dscharge values are shown through a color bar as well as on a thrd axs. Trends over the 30-year tme perod are apparent. Journal of Envronmental Hydrology 6

7 To vew the average SWE year wth each ndvdual year peak, a plot s created wth the shfted yearly peaks, as mentoned n the methods secton. Fgure 4 shows the average SWE peak wth each of the other years SWE peaks shfted to a generc tme scale. Agan, the mportance of the plot s the SWE peak value, not the tme that the peak occurs. Fgure 4 also shows, as a surface plot, each year s peak SWE shfted to the generc tme scale. SWE values are shown through a color bar as well as on a thrd axs. Trends over the 30-year tme perod can be seen. Several other relatonshps are plotted n Fgures 5 and 6: the correlaton of each year s dscharge peak wth the average dscharge peak, the tme dfference of each year s dscharge peak wth the average dscharge peak, the correlaton of each year s SWE peak wth the average SWE peak, and the tme dfference of each year s SWE peak wth the average SWE peak. The correlatons are then calculated and plotted for each SWE peak and ts correspondng dscharge peak (Fgure 7) where the normalzed correlatons are plotted on the vertcal axs and the years are plotted on the horzontal axs. The lag tmes are then calculated and plotted for each SWE peak and ts correspondng dscharge peak (Fgure 7) where the lag tmes are plotted on the vertcal axs and the years are plotted on the horzontal axs. To see the correlatons between each year and the average year of both the dscharge data and the SWE data, a plot s created wth the normalzed SWE correlatons on the vertcal axs and the normalzed dscharge correlatons on the horzontal axs (Fgure 8). To see how the lag tmes between each year and the average year of both the dscharge data and the SWE data, another plot s created wth the normalzed SWE lag tmes on the vertcal axs and the normalzed dscharge lag tmes on the horzontal axs (Fgure 8). DISCUSSION In ths study, two methods are used to quantfy sprng snowpack and snowmelt. The frst method s an averagng method that uses the STA/LTA algorthm. Ths method s used to pck events out of tme seres data based on a threshold level (Wong et al. 2010). Interannual varablty s clearly vsble n the tme seres data of both rver dscharge and SWE so the STA/LTA method allowed us to smooth the data nto detectng events of sgnfcance. Fgure 2 shows that there was a sgnfcant amount of dscharge n most years, but there were a few dry years wthn the 30-year data set (e.g and 1996). The drought n 1988 was part of a drought that covered 36% of the Unted States and had a large effect on crop producton (Rebsame et al. 2001), whle the drought n 1996 covered much of Texas, New Mexco, Calforna, Nevada, Utah, Colorado, Oklahoma, and Kansas. The depleton of ground water durng both of these years affected the ecosystem for many years (Reuters 1996). Both Fgures 3 and 4 show the shfted yearly peaks wth the average peaks on a generc tme scale, for both rver dscharge and snow water equvalent, respectvely. The mportance of the plots s the peak values n the respectve unts, not the tme that the peaks occurred. Due to the nterannual varablty of rver dscharge and SWE, the peaks appear dverse, whch allows a further analyss of each peak s correlaton and tme lag n relaton to the average peak as seen n the fgure. Fgures 3 and 4 also show each year s peak shfted to the generc tme scale, whch gves a nce vsual representaton of the varablty over tme. For nstance, there are several large peaks earler n the data sets that are not n the later part of the data sets. These results ndcate a general decrease n both peak dscharge and peak SWE over tme. Journal of Envronmental Hydrology 7

8 Fgure 2. LEFT: Short term average over long term average of Ro Grande dscharge data at the Otow Gauge, The threshold for a sgnfcant peak on the dscharge STA/LTA s 1. RIGHT: Short term average over long term average of snow water equvalent data at the Quemazon Gauge, The threshold for a sgnfcant peak on the SWE STA/LTA s 1.5. In order to see the correlaton of each year s dscharge peak wth the average dscharge peak, a tme seres plot s created (Fgure 5), whch shows that the correlatons may have a subharmonc throughout tme. There s a weak oscllaton among the 30 years that would have to be further studed before any conclusons can be drawn. Each year s dscharge peak has a correlaton of at least 0.7 wth the average dscharge peak, meanng that most years are smlar to each other and thus the average year. Fgure 5 also shows the dfference n tme between each ndvdual year of dscharge data wth the average dscharge curve. The lag tmes seem to fluctuate around the zero lne, wth a bg, dstnct drop n the late 80s. In the years that have a postve lag tme, the peak n dscharge s actually later than average, and n the years that have a negatve lag tme (e.g., 1987, Fgure 3. LEFT: Average dscharge peak and each ndvdual dscharge peak, shfted to a generc tme scale for the Ro Grande dscharge data at the Otow Brdge, , measured n cubc feet per second. RIGHT: Each ndvdual dscharge peak, shfted to a generc tme scale for the Ro Grande dscharge data at the Otow Brdge, , measured n cubc feet per second. Journal of Envronmental Hydrology 8

9 Fgure 4. LEFT: Average snow water equvalent peak and each ndvdual snow water equvalent peak, shfted to a generc tme scale for the SWE data at the Quemazon gauge, , measured n nches. RIGHT: Each ndvdual snow water equvalent peak, shfted to a generc tme scale for the SWE data at the Quemazon gauge, , measured n nches. 1988, and 1989), the peak n dscharge s actually earler than the average. Gven the drought of 1988 (Rebsame et al. 1991), the dscharge that year s smaller and earler than other years n the data set. Fgure 6 presents the correlaton of each year s SWE peak wth the average SWE peak, and shows that the correlatons may have subharmonc throughout tme (perhaps every 8 years). There s a weak oscllaton among the 30 years that would have to be further studed to make a concluson. Each year s SWE peak has a correlaton of at least 0.9 wth the average SWE peak, meanng that the yearly SWE s qute consstent. Fgure 6 also shows that durng the begnnng of the data set, there are postve lag tmes between the yearly SWE peaks and the average SWE peak, but over tme, ths value swtches to be prmarly negatve. Havng a negatve lag tme means that the SWE peaks Fgure 5. LEFT: Normalzed correlaton between each year s dscharge peak and the average dscharge peak. RIGHT: Normalzed lag tme between each year s dscharge peak and the average dscharge peak. All data are from the Ro Grande Otow gauge. Journal of Envronmental Hydrology 9

10 Fgure 6. LEFT: Normalzed correlaton between each year s SWE peak and the average SWE peak. RIGHT: Normalzed lag tme between each year s SWE peak and the average SWE peak. All data are from the Quemazon gauge. are becomng earler n the year. SWE peaks are beng seen earler n the year, showng evdence for the hypothess that water wll have to be stored for longer perods of tme gven ths shft. If ths trend contnues, the bologcal organsms around the Ro Grande wll be forced to change ther reproductve cycles to match the rver system, potentally havng adverse affects whle the speces adapt. Fgure 7 shows the correlaton between each wnter s SWE peak and each sprng s dscharge peak, n an attempt to see the close relatonshp between the two. The correlatons range from 0.3 to 0.9, meanng that there s a hgh correlaton n some years but not n all. Ths leads us to the concluson that there are multple other factors affectng rver dscharge. The STA/LTA analyss also hnts at ths on/off relatonshp, but t s apparent here. The groundwater and surface water Fgure 7. LEFT: Normalzed correlaton between each year s SWE peak and the correspondng dscharge peak. RIGHT: Lag tmes between each year s SWE peak and the correspondng dscharge peak. All data are from the Ro Grande at the Otow Brdge gauge (rver dscharge) and the Quemazon gauge (snow water equvalent). Journal of Envronmental Hydrology 10

11 relatonshp most lkely has a hgh effect on ths system (Wang et al. 2008), n addton to other anthropogenc forcngs (Burkholder 1997). In the same manner that the correlatons between each wnter s SWE peak and the correspondng sprng s dscharge peak are not unformly strong, nether are the lag tmes between each wnter s SWE peak and the correspondng sprng s dscharge peak. The tme between the 2 peaks for each year ranges from 30 to 130 days (Fgure 7). In 1990, there s an unusually hgh lag tme between the wnter s peak n SWE and the sprng s rver dscharge (128 days), whch could ndcate a colderthan-normal sprng and n 2001 there s an unusually low lag tme between the wnter s peak n SWE and the sprng s rver dscharge (33 days), whch could ndcate a warmer-than-normal sprng. Addtonally, to see how the correlatons compare among both the SWE peaks and the rver dscharge peaks, Fgure 8 compares the correlatons of each SWE year to the average SWE year versus the correlatons of each rver dscharge year to the average rver dscharge year. Ths fgure shows that, n general, when there s a hgh correlaton between the SWE peak and the average SWE peak, there s also a hgh correlaton between the yearly dscharge peak and the average dscharge peak. Although there are several years where there s a hgh SWE correlaton wth a low dscharge correlaton and several years where there s a hgh dscharge correlaton wth a low SWE correlaton, n general, most correlatons are hgh for both SWE and dscharge. The years wth a hgh SWE correlaton and a low dscharge correlaton (upper left hand corner of Fgure 8) and the years wth a hgh dscharge correlaton and a low SWE correlaton (lower rght hand corner of Fgure 8) gve us ndcaton that other factors are at play n rver dscharge values. Ranfall and temperatures could have played a role wth these outlers. It s nterestng to see that none of the years had both a low SWE correlaton and a low dscharge correlaton, ndcatng that the overarchng factors of SWE and dscharge are strongly related. A comparson of the lag tmes of each SWE year from the average SWE year versus the lag tmes of each rver dscharge year from the average rver dscharge year (Fgure 8) shows that some of the years n ths 30 year data set have both a postve SWE lag tme and a postve dscharge lag tme, Fgure 8. LEFT: Snow water equvalent correlatons between each SWE year and the average SWE year versus dscharge correlatons between each dscharge year and the average dscharge year. RIGHT: Snow water equvalent lag tmes between each SWE year and the average SWE year versus dscharge lag tmes between each dscharge year and the average dscharge year. Journal of Envronmental Hydrology 11

12 meanng that these years have peaks later than the average year for both SWE and dscharge (Quadrant I). Just a few years have a postve SWE lag tme and a negatve dscharge lag tme, meanng that these years have a later-than-normal SWE peak but an earler-than-normal dscharge peak (Quadrant II). Stll other years have negatve lag tmes for both the SWE peak and the dscharge peak, meanng that these years have an earler-than-normal SWE peak and an earlerthan-normal dscharge peak (Quadrant III). The fnal groupng of data has a postve dscharge lag tme and a negatve SWE lag tme, meanng that these years have an earler-than-normal SWE peak and a later than normal dscharge peak (Quadrant IV). Quadrants I and III show the same-sded correlaton between SWE and rver dscharge, meanng that the lag tmes are ether both postve or both negatve. If both lag tmes are postve, the peaks occur after the average peak, but f both lags are negatve, the peaks occur before the average peak. Wth the SWE peak analyss, t s shown that the peak occurs after the average peak n the early part of the data set, but n recent years the peak occurs before the average peak. Quadrants II and IV show us that there are other factors at play n the system, such as groundwater and other anthropogenc forcngs. The analyss presented here allows us to answer several questons regardng snowpack and snowmelt but also rases others. It s found that the peak n snowmelt s occurrng earler n the year and that there s generally a hgh correlaton between SWE and dscharge. Future studes wll need to analyze yearly data wth an STA/LTA event detecton. Ths wll allow us to see events smaller than the annual cycle, pullng further nformaton out of the data beng collected. From ths and other studes of the Ro Grande (e.g. Vvon et al. 2009), proper management decsons can be made. Ideally, management wll focus on restorng rver processes, allowng the bologcal communtes to naturally adjust to the system (Hanson et al. 2004). ACKNOWLEDGMENTS Carolyn Donnelly (U.S. Department of the Interor Bureau of Reclamaton), Chrstan Gunnng (Unversty of New Mexco), Hed Hopkns (Unversty of New Mexco), Kelly Isaacson (Danel B. Stephens & Assocates), Etsuko Nonaka (Umeä Unversty), Bob Snsabaugh (Unversty of New Mexco), and Helen Wearng (Unversty of New Mexco) provded helpful feedback and are greatly apprecated for ther comments on ths text. REFERENCES Burkholder, J. L., Report of the Chef Engneer: Submttng a plan for flood control, dranage, and rrgaton of the Mddle Ro Grande Conservancy Project. Techncal report, State of New Mexco Mddle Ro Grande Conservancy Dstrct. Cayan, D., S. Kammerdener, M. Dettnger, J. Capro, and D. Peterson Changes n the onset of sprng n the Western Unted States. Bulletn of the Amercan Meteorologcal Socety 82: Delreu, G., S. Caoudal, and J. Creutn Feasblty of usng mountan return for the correcton of groundbased X-band weather radar data. Journal of Atmospherc and Oceanc Technology 14: Graf, W Downstream hydrologc and geomorphc effects of large dams on Amercan rvers. Geomorphology 79: Guralnk, V., and J. Srvastava Event detecton from tme seres data. Journal of the Assocaton for Computng Machnery pages Hamed, K Trend detecton n hydrologc data: The Mann-Kendall trend test under the scalng hypothess. Journal of Hydrology 349: Hanson, R., M. Newhouse, and M. Dettnger A methodology to assess relatons between clmatc varablty and varatons n hydrologc tme seres n the southwestern Unted States. Journal of Hydrology 287: Journal of Envronmental Hydrology 12

13 Hdalgo, H., T. Das, M. Dettnger, D. Cayan, D. Perce, T. Barnett, G. Bala, A. Mrn, A. Wood, C. Bonfls, B. Santer, and T. Nozawa Detecton and attrbuton of stream flow tmng changes to clmate change n the Western Unted States. Journal of Clmate 22: IPCC, Clmate Change 2007: The Physcal Scence Bass Summary for Polcy Makers. Jan, A., and S. P. Indurthy Comparatve analyss of event-based ranfall-runoff modelng technques - determnstc, statstcal, and artfcal neural networks. Journal of Hydrologc Engneerng pages Knowles, N., M. Dettnger, and D. Cayan Trends n snowfall versus ranfall for the Western Unted States. Journal of Clmate 19: Krshna, A Snow and glacer cover assessment n the hgh mountans of Skkm Hmalaya. Hydrologcal Processes 19: Maurer, E., I. Stewart, C. Bonfls, P. Duffy, and D. Cayan Detecton, attrbuton, and senstvty of trends toward earler stream flow n the Serra Nevada. Journal of Geophyscal Research 112:D Mote, P Trends n snow water equvalent n the Pacfc Northwest and ther clmatc causes. Geophyscal Research Letters 30: Norbato, D., M. Borga, M. Sangat, and F. Zanon Regonal frequency analyss of extreme precptaton n the eastern Italan Alps and the August 29, 2003 flash flood. Journal of Hydrology 345: Pelleter, J., and D. Turcotte Long-range persstence n clmatologcal and hydrologcal tme seres: analyss, modelng and applcaton to drought hazard assessment. Journal of Hydrology 203: Peterson, D., R. Smth, M. Dettnger, D. Cayan, and L. Rddle An organzed sgnal n snowmelt runoff over the western Unted States. Journal of the Amercan Water Resources Assocaton 36: Redmond, K., and R. Koch Surface clmate and streamflow varablty n the Western Unted States and ther relatonshp to large-scale crculaton ndces. Water Resources Research 27: Reuters, Southwest drought threatens broad economc damage. News. Rebsame, W., S. Changnon, and T. Karl Drought and natural resources management n the Unted States. Westvew Press pages Serreze, M., M. Clark, and R. Armstrong Characterstcs of the Western Unted States snowpack from snow telemetry SNOTEL data. Water Resources Research 35. Smth, L., D. Turcotte, and B. Isacks Stream flow characterzaton and feature detecton usng a dscrete wavelet transform. Hydrologcal Processes 12: Stewart, I Changes n snowpack and snowmelt runoff for key mountan regons. Hydrologcal Processes 23: Velasco-Forero, C., D. Sempere-Torres, E. Cassraga, and J. Gomez-Hernandez A non-parametrc automatc blendng methodology to estmate ranfall felds from ran gauge and radar data. Advances n Water Resources pages Vvon, E., C. Aragon, L. Malczynsk, and V. Tdwell Semard watershed response n central New Mexco and ts senstvty to clmate varablty and change. Hydrology and Earth System Scences 13: Walton, R., and H. Hunter Isolatng the water qualty responses of multple land uses from stream montorng data through model calbraton. Journal of Hydrology 378: Wang, G., T. Jang, R. Blender, and K. Fraedrch Yangtze 1/f dscharge varablty and the nteractng rverlake system. Journal of Hydrology 351: Wong, J., L. Han, J. Bancroft, and R. Stewart, Automatc tme-pckng of frst arrvals on nosy mcrosesmc data. CREWES. Zhang, X., F. Zwers, G. Hegerl, F. Lambert, N. Gllett, S. Solomon, P. Stott, and T. Nozawa Detecton of human nfluence on twenteth-century precptaton trends. Nature 448: ADDRESS FOR CORRESPONDENCE Jennfer L. Tchy Unversty of New Mexco Jcama Way SE Albuquerque, NM jltchy@gmal.com Journal of Envronmental Hydrology 13