In [1]:

!pip install -q rasterstats
!pip install -q odc-stac -U

In [2]:

import pandas as pd
import geopandas as gpd
import matplotlib.pyplot as plt
import numpy as np
import rasterstats
import rioxarray as rxr
import rasterio
import adlfs
import contextily as ctx
from tqdm import tqdm
from pathlib import Path

In [3]:

#from dask.distributed import Client, LocalCluster
#cluster = LocalCluster(processes=False, n_workers=4, local_directory='/tmp')
#client = Client(cluster)
#client

In [4]:

import dask_gateway
cluster = dask_gateway.GatewayCluster()
client = cluster.get_client()
cluster.adapt(minimum=10, maximum=100)
print(client.dashboard_link)

https://pccompute.westeurope.cloudapp.azure.com/compute/services/dask-gateway/clusters/prod.76584750d1ae4a98add3ee9dfe8e0711/status

In [5]:

gf = gpd.read_file('https://raw.githubusercontent.com/scottyhq/snotel/967b0071f3716118bfcae866c740cffb51fbb5f5/snotel-sites.geojson')

In [6]:

gf

Out[6]:

	code	name	elevation_m	latitude	longitude	county	state	beginDate	endDate	isActive	mgrs	timeseries	geometry
0	301_CA_SNTL	Adin Mtn	1886.712036	41.235828	-120.791924	Modoc	California	1983-10-01 00:00:00	2100-01-01	True	10TFL	True	POINT (-120.79192 41.23583)
1	907_UT_SNTL	Agua Canyon	2712.719971	37.522171	-112.271179	Kane	Utah	1994-10-01 00:00:00	2100-01-01	True	12SUG	True	POINT (-112.27118 37.52217)
2	916_MT_SNTL	Albro Lake	2529.840088	45.597229	-111.959023	Madison	Montana	1996-09-01 00:00:00	2100-01-01	True	12TVR	True	POINT (-111.95902 45.59723)
3	1267_AK_SNTL	Alexander Lake	48.768002	61.749668	-150.889664	Matanuska-Susitna	Alaska	2014-08-28 00:00:00	2100-01-01	True	05VPJ	True	POINT (-150.88966 61.74967)
4	908_WA_SNTL	Alpine Meadows	1066.800049	47.779572	-121.698471	King	Washington	1994-09-01 00:00:00	2100-01-01	True	10TET	True	POINT (-121.69847 47.77957)
...	...	...	...	...	...	...	...	...	...	...	...	...	...
925	877_AZ_SNTL	Workman Creek	2103.120117	33.812420	-110.917732	Gila	Arizona	1938-12-01 00:00:00	2100-01-01	True	12SWC	True	POINT (-110.91773 33.81242)
926	1228_UT_SNTL	Wrigley Creek	2842.869629	39.132332	-111.356850	Sanpete	Utah	2012-10-01 00:00:00	2100-01-01	True	12SVJ	True	POINT (-111.35685 39.13233)
927	1197_UT_SNTL	Yankee Reservoir	2649.321533	37.747971	-112.774948	Iron	Utah	2012-10-01 00:00:00	2100-01-01	True	12SUG	True	POINT (-112.77495 37.74797)
928	878_WY_SNTL	Younts Peak	2545.080078	43.932251	-109.817749	Park	Wyoming	1979-10-01 00:00:00	2100-01-01	True	12TWP	True	POINT (-109.81775 43.93225)
929	1033_CO_SNTL	Zirkel	2846.832031	40.794880	-106.595352	Jackson	Colorado	2002-08-14 06:00:00	2100-01-01	True	13TCF	True	POINT (-106.59535 40.79488)

930 rows × 13 columns

In [7]:

snotel_gf = gf[gf.isActive==True]
snotel_gf = snotel_gf[(snotel_gf.beginDate<'2015-01-01') & (snotel_gf.endDate>'2022-01-01')]
snotel_gf = snotel_gf[snotel_gf.state!='Alaska']

In [8]:

f,ax=plt.subplots()
snotel_gf.plot(ax=ax)
ctx.add_basemap(ax=ax, crs=snotel_gf.crs, source=ctx.providers.Stamen.Terrain, attribution=False)

In [9]:

years = [2015,2016,2017,2018,2019,2020,2021,2022]
#years = [2016,2017,2018,2019,2020,2021,2022]
#years = [2017,2018,2019,2020,2021,2022]
max_swe_years = [f'max_swe_{year}' for year in years]

In [10]:

#for i,row in tqdm(snotel_gf.iterrows()):
#    try:
#        snotel_data = pd.read_parquet(f'https://github.com/scottyhq/snotel/raw/main/parquet/{row.code}.parquet').loc[slice(f'{years[0]}-01-01',f'{years[-1]}-12-31')]
#        snotel_data = snotel_data[(snotel_data.index.month>1)&(snotel_data.index.month<10)]
#        dates = snotel_data.groupby(snotel_data.index.year).idxmax()['WTEQ']
    #print(dates)
#        if len(dates)==len(years):
#            snotel_gf.loc[i,max_swe_years] = dates.values
#    except:
#        pass
    #date_index = []
    #for date in dates.index.values:
    #    date_index.append(f'max_swe_{date}')
    #snotel_gf = snotel_gf.loc[i,iter(date_index)] = dates.loc[2015:2022]

In [11]:

%%time
my_file = Path("snotel_gf_comparison.geojson")
if my_file.is_file():
    snotel_gf = gpd.read_file('snotel_gf_comparison.geojson')
else:
    for i,row in tqdm(snotel_gf.iterrows()):
        try:
            snotel_data = pd.read_parquet(f'https://github.com/scottyhq/snotel/raw/main/parquet/{row.code}.parquet').loc[slice(f'{years[0]}-01-01',f'{years[-1]}-12-31')]
            snotel_data = snotel_data[(snotel_data.index.month>1)&(snotel_data.index.month<8)]

            dates = snotel_data.groupby(snotel_data.index.year).idxmax()['WTEQ']
            dates = dates[dates.notna()]
        #print(dates)
            #if len(dates)==len(years):
            #    snotel_gf.loc[i,max_swe_years] = dates.values
            for year in [date for date in dates.index]:
                snotel_gf.loc[i,f'max_swe_{year}'] = int(dates.dt.dayofyear[year])
        except:
            pass
        
        #for year in years:
        #    snotel_gf[f'max_swe_{year}'] = pd.to_datetime(snotel_gf[f'max_swe_{year}']).dt.dayofyear
            
    snotel_gf.to_file("snotel_gf_comparison.geojson", driver='GeoJSON')

CPU times: user 109 ms, sys: 0 ns, total: 109 ms
Wall time: 115 ms

In [12]:

snotel_gf

Out[12]:

	code	name	elevation_m	latitude	longitude	county	state	beginDate	endDate	isActive	...	timeseries	max_swe_2015	max_swe_2016	max_swe_2017	max_swe_2018	max_swe_2019	max_swe_2020	max_swe_2021	max_swe_2022	geometry
0	301_CA_SNTL	Adin Mtn	1886.712036	41.235828	-120.791924	Modoc	California	1983-10-01 00:00:00	2100-01-01	True	...	True	32.0	51.0	68.0	86.0	75.0	32.0	74.0	76.0	POINT (-120.79192 41.23583)
1	907_UT_SNTL	Agua Canyon	2712.719971	37.522171	-112.271179	Kane	Utah	1994-10-01 00:00:00	2100-01-01	True	...	True	64.0	33.0	65.0	78.0	81.0	88.0	76.0	71.0	POINT (-112.27118 37.52217)
2	916_MT_SNTL	Albro Lake	2529.840088	45.597229	-111.959023	Madison	Montana	1996-09-01 00:00:00	2100-01-01	True	...	True	107.0	97.0	122.0	107.0	121.0	114.0	117.0	154.0	POINT (-111.95902 45.59723)
3	908_WA_SNTL	Alpine Meadows	1066.800049	47.779572	-121.698471	King	Washington	1994-09-01 00:00:00	2100-01-01	True	...	True	105.0	96.0	109.0	108.0	83.0	102.0	107.0	116.0	POINT (-121.69847 47.77957)
4	302_OR_SNTL	Aneroid Lake #2	2255.520020	45.213280	-117.192581	Wallowa	Oregon	1980-10-01 00:00:00	2100-01-01	True	...	True	99.0	122.0	123.0	112.0	112.0	117.0	106.0	135.0	POINT (-117.19258 45.21328)
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
791	877_AZ_SNTL	Workman Creek	2103.120117	33.812420	-110.917732	Gila	Arizona	1938-12-01 00:00:00	2100-01-01	True	...	True	32.0	40.0	32.0	51.0	54.0	80.0	32.0	55.0	POINT (-110.91773 33.81242)
792	1228_UT_SNTL	Wrigley Creek	2842.869629	39.132332	-111.356850	Sanpete	Utah	2012-10-01 00:00:00	2100-01-01	True	...	True	63.0	89.0	66.0	82.0	89.0	90.0	83.0	76.0	POINT (-111.35685 39.13233)
793	1197_UT_SNTL	Yankee Reservoir	2649.321533	37.747971	-112.774948	Iron	Utah	2012-10-01 00:00:00	2100-01-01	True	...	True	63.0	91.0	94.0	64.0	81.0	88.0	86.0	80.0	POINT (-112.77495 37.74797)
794	878_WY_SNTL	Younts Peak	2545.080078	43.932251	-109.817749	Park	Wyoming	1979-10-01 00:00:00	2100-01-01	True	...	True	NaN	89.0	76.0	105.0	106.0	108.0	92.0	119.0	POINT (-109.81775 43.93225)
795	1033_CO_SNTL	Zirkel	2846.832031	40.794880	-106.595352	Jackson	Colorado	2002-08-14 06:00:00	2100-01-01	True	...	True	66.0	97.0	70.0	100.0	96.0	92.0	86.0	109.0	POINT (-106.59535 40.79488)

796 rows × 21 columns

In [13]:

snotel_gf = snotel_gf.to_crs('EPSG:32611')

In [14]:

buffer = 500 # at 1000m with 2015 excluded, medians of medians at 38 days, with 500m 34 days, with 200m 36.25 days, with 2000m 37 days
# 27 days new method 500m method, 22.0 with 1000m new new method, 23.5 with 500m new new method, 22.0 with 2000m
snotel_gf_buff = gpd.GeoDataFrame(snotel_gf.copy(),geometry=snotel_gf.buffer(buffer))

In [15]:

snotel_gf

Out[15]:

	code	name	elevation_m	latitude	longitude	county	state	beginDate	endDate	isActive	...	timeseries	max_swe_2015	max_swe_2016	max_swe_2017	max_swe_2018	max_swe_2019	max_swe_2020	max_swe_2021	max_swe_2022	geometry
0	301_CA_SNTL	Adin Mtn	1886.712036	41.235828	-120.791924	Modoc	California	1983-10-01 00:00:00	2100-01-01	True	...	True	32.0	51.0	68.0	86.0	75.0	32.0	74.0	76.0	POINT (182202.056 4571874.067)
1	907_UT_SNTL	Agua Canyon	2712.719971	37.522171	-112.271179	Kane	Utah	1994-10-01 00:00:00	2100-01-01	True	...	True	64.0	33.0	65.0	78.0	81.0	88.0	76.0	71.0	POINT (917980.554 4163320.291)
2	916_MT_SNTL	Albro Lake	2529.840088	45.597229	-111.959023	Madison	Montana	1996-09-01 00:00:00	2100-01-01	True	...	True	107.0	97.0	122.0	107.0	121.0	114.0	117.0	154.0	POINT (893146.663 5061670.452)
3	908_WA_SNTL	Alpine Meadows	1066.800049	47.779572	-121.698471	King	Washington	1994-09-01 00:00:00	2100-01-01	True	...	True	105.0	96.0	109.0	108.0	83.0	102.0	107.0	116.0	POINT (148062.869 5302498.875)
4	302_OR_SNTL	Aneroid Lake #2	2255.520020	45.213280	-117.192581	Wallowa	Oregon	1980-10-01 00:00:00	2100-01-01	True	...	True	99.0	122.0	123.0	112.0	112.0	117.0	106.0	135.0	POINT (484878.074 5006661.556)
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
791	877_AZ_SNTL	Workman Creek	2103.120117	33.812420	-110.917732	Gila	Arizona	1938-12-01 00:00:00	2100-01-01	True	...	True	32.0	40.0	32.0	51.0	54.0	80.0	32.0	55.0	POINT (1063320.876 3758033.963)
792	1228_UT_SNTL	Wrigley Creek	2842.869629	39.132332	-111.356850	Sanpete	Utah	2012-10-01 00:00:00	2100-01-01	True	...	True	63.0	89.0	66.0	82.0	89.0	90.0	83.0	76.0	POINT (987900.153 4346652.811)
793	1197_UT_SNTL	Yankee Reservoir	2649.321533	37.747971	-112.774948	Iron	Utah	2012-10-01 00:00:00	2100-01-01	True	...	True	63.0	91.0	94.0	64.0	81.0	88.0	86.0	80.0	POINT (872299.352 4186264.528)
794	878_WY_SNTL	Younts Peak	2545.080078	43.932251	-109.817749	Park	Wyoming	1979-10-01 00:00:00	2100-01-01	True	...	True	NaN	89.0	76.0	105.0	106.0	108.0	92.0	119.0	POINT (1076540.895 4889486.939)
795	1033_CO_SNTL	Zirkel	2846.832031	40.794880	-106.595352	Jackson	Colorado	2002-08-14 06:00:00	2100-01-01	True	...	True	66.0	97.0	70.0	100.0	96.0	92.0	86.0	109.0	POINT (1378449.187 4568410.906)

796 rows × 21 columns

In [16]:

sas_token = 'se=2023-03-13T02%3A45Z&sp=racwdl&sv=2018-11-09&sr=c&skoid=b4d39ccb-ca9c-4e9d-a183-5e0f3ba3e609&sktid=f6b6dd5b-f02f-441a-99a0-162ac5060bd2&skt=2023-03-06T20%3A38%3A54Z&ske=2023-03-13T02%3A45%3A00Z&sks=b&skv=2018-11-09&sig=Nl9p59uFIaJRJgtbHhnVBgYgwh1MnZ/fcJrttie9gAM%3D'
tile_names = [tile[-5:] for tile in adlfs.AzureBlobFileSystem(account_name="snowmelt", credential=sas_token, anon=False).ls('snowmelt/eric/MGRS/')]

In [17]:

stats=['count', 'min', 'max', 'mean', 'median', 'std']

In [18]:

resolution = 80

In [19]:

#raster = rasterio.open(f"https://snowmelt.blob.core.windows.net/snowmelt/eric/MGRS/{'13UDP'}/runoff_onset_{'13UDP'}_{2016}_median_{resolution}m.tif")
#raster.read(1)

In [ ]:

for i,row in tqdm(snotel_gf.iterrows()):
    if row.mgrs in tile_names:
        #print(f'{row.code} in MGRS square {row.mgrs}')
        for year in years:
            try:
                raster = rasterio.open(f"https://snowmelt.blob.core.windows.net/snowmelt/eric/MGRS/{row.mgrs}/runoff_onset_{row.mgrs}_{year}_median_{resolution}m.tif")
                snotel_row_proj = snotel_gf_buff[snotel_gf_buff.index==i].to_crs(raster.crs)
                statistics = rasterstats.zonal_stats(snotel_row_proj, raster.read(1), affine=raster.transform, stats=stats, nodata=-32768)
                snotel_gf.loc[i,f'sar_runoff_{year}'] = statistics[0]['median']
                snotel_gf.loc[i,f'sar_runoff_{year}_std'] = statistics[0]['std']
                if year == 2022:
                    snotel_gf.loc[i,f'sar_runoff_pixel_count'] = statistics[0]['count']
            except:
                #print(f'Error with tile {row.mgrs} for year {year}')
                pass

791it [08:04,  1.52it/s]

In [ ]:

for year in years:
        snotel_gf[f'offset_{year}'] = snotel_gf[f'max_swe_{year}'] - snotel_gf[f'sar_runoff_{year}']

In [ ]:

snotel_gf['median_offset'] = snotel_gf[snotel_gf.columns[pd.Series(snotel_gf.columns).str.startswith('offset')]].abs().median(axis=1)
snotel_gf['mean_offset'] = snotel_gf[snotel_gf.columns[pd.Series(snotel_gf.columns).str.startswith('offset')]].abs().mean(axis=1)
snotel_gf['mean_std'] = snotel_gf[snotel_gf.columns[pd.Series(snotel_gf.columns).str.endswith('std')]].mean(axis=1)

In [ ]:

snotel_gf.sort_values('mean_std').dropna()

In [ ]:

snotel_gf = snotel_gf[(snotel_gf['mean_std']>0) & (snotel_gf['mean_std']<25)]

In [ ]:

#snotel_gf.sort_values('mean_offset').dropna().tail(50)
snotel_gf.sort_values('mean_offset').head(20)

In [ ]:

snotel_gf.sort_values('sar_runoff_pixel_count').dropna().tail(20)

In [27]:

for year in years:
        f,ax=plt.subplots(1,2,figsize=(10,4))
        pairwise = snotel_gf[snotel_gf[[f'sar_runoff_{year}', f'max_swe_{year}']].notnull().all(1)]
        ax[0].hist(pairwise[f'sar_runoff_{year}'],bins=30,alpha=0.5,label='SAR runoff dates')
        ax[0].hist(pairwise[f'max_swe_{year}'],bins=30,alpha=0.5,label='SNOTEL Max SWE')
        ax[0].legend()
        ax[0].set_xlim([0,365])
        ax[0].set_title(f'{year} Runoff Date / Max SWE Agreement')
        
        ax[1].hist(snotel_gf[f'offset_{year}'],bins=50,label='pairwise offsets')
        ax[1].legend()
        mean = snotel_gf[f'offset_{year}'].mean()
        median = snotel_gf[f'offset_{year}'].median()
        ax[1].set_title(f'{year} Mean offset = {mean:.2f} Median offset = {median:.2f}')
        ax[1].set_xlim([-100,100])

In [28]:

f,ax=plt.subplots(2,1,figsize=(10,10))
snotel_gf['median_offset'].hist(bins=50,ax=ax[0])
snotel_gf['mean_offset'].hist(bins=50,ax=ax[1])
ax[0].set_title('(Peak SNOTEL SWE - SAR Derivied Runoff Onset) 2017-2022 Median Offset for Each Station')
ax[1].set_title('(Peak SNOTEL SWE - SAR Derivied Runoff Onset) 2017-2022 Mean Offset for Each Station')
ax[0].set_xlabel('Offset [Days]')
ax[1].set_xlabel('Offset [Days]')

Out[28]:

Text(0.5, 0, 'Offset [Days]')

In [29]:

snotel_gf['median_offset'].median()
# 25 all years
# 22.5 2017 on

Out[29]:

17.5

In [30]:

f,ax=plt.subplots(figsize=(10,10))
#snotel_gf_projected = gpd.GeoDataFrame(snotel_gf,geometry=gf.geometry).to_crs('EPSG:32611')
snotel_gf.plot(column='median_offset',ax=ax,legend=True,cmap='cool',vmin=0,vmax=60)
ax.axis('equal')
ax.set_title('2015-2022 Median Absolute Difference between \nPeak SNOTEL SWE and SAR Derivied Runoff Onset [Days]')
ctx.add_basemap(ax=ax, crs=snotel_gf.crs, source=ctx.providers.Stamen.Terrain, attribution=False)

In [31]:

tooltip_list = ['code','name','elevation_m']
for year in years:
    tooltip_list.append(f'offset_{year}')  
    tooltip_list.append(f'sar_runoff_{year}_std') 
tooltip_list.extend(['median_offset','mean_offset','sar_runoff_pixel_count'])

In [32]:

snotel_gf = snotel_gf.drop(['beginDate','endDate','isActive'],axis=1)

In [33]:

snotel_gf.explore(column='median_offset',cmap='cool',tooltip=tooltip_list,vmin=0,vmax=60)

Out[33]:

Make this Notebook Trusted to load map: File -> Trust Notebook

In [34]:

f,ax=plt.subplots()
ax.scatter(snotel_gf['sar_runoff_pixel_count'],snotel_gf['median_offset'])
#ax.set_xlim([0,10])

Out[34]:

<matplotlib.collections.PathCollection at 0x7f86a32d39d0>

In [35]:

f,ax=plt.subplots()
ax.scatter(snotel_gf['elevation_m'],snotel_gf['median_offset'])

Out[35]:

<matplotlib.collections.PathCollection at 0x7f86a3bfe1d0>

In [36]:

f,ax=plt.subplots()
ax.scatter(snotel_gf['mean_std'],snotel_gf['median_offset'])

Out[36]:

<matplotlib.collections.PathCollection at 0x7f86a3e051e0>

In [37]:

corr = snotel_gf.corr()
corr.style.background_gradient(cmap='coolwarm')

Out[37]:

	elevation_m	latitude	longitude	timeseries	max_swe_2015	max_swe_2016	max_swe_2017	max_swe_2018	max_swe_2019	max_swe_2020	max_swe_2021	max_swe_2022	sar_runoff_2015	sar_runoff_2015_std	sar_runoff_2016	sar_runoff_2016_std	sar_runoff_2017	sar_runoff_2017_std	sar_runoff_2018	sar_runoff_2018_std	sar_runoff_2019	sar_runoff_2019_std	sar_runoff_2020	sar_runoff_2020_std	sar_runoff_2021	sar_runoff_2021_std	sar_runoff_2022	sar_runoff_2022_std	sar_runoff_pixel_count	offset_2015	offset_2016	offset_2017	offset_2018	offset_2019	offset_2020	offset_2021	offset_2022	median_offset	mean_offset	mean_std
elevation_m	1.000000	-0.745938	0.751235	0.061055	0.424969	0.427471	0.179848	0.027293	0.429122	0.304668	0.212845	0.107060	-0.031014	-0.063173	0.321091	0.088123	0.157681	0.233307	-0.075801	0.268945	0.282690	0.001767	0.133856	0.131848	0.073790	0.280766	0.001313	0.172179	0.178913	0.360923	0.069264	0.027953	0.043037	0.035349	0.106157	0.060244	0.074893	-0.080771	-0.144865	0.267050
latitude	-0.745938	1.000000	-0.454117	-0.027512	-0.038416	0.033001	0.229788	0.441604	0.036327	0.161184	0.231667	0.368764	0.041429	-0.023857	-0.213953	-0.148823	-0.033563	-0.162994	0.274470	-0.337485	-0.075981	0.012653	0.056169	-0.128019	0.158653	-0.195474	0.147524	-0.131339	-0.169680	-0.062874	0.200931	0.245605	0.084405	0.092604	0.093301	0.029970	0.204911	-0.112779	-0.078920	-0.276511
longitude	0.751235	-0.454117	1.000000	0.019693	0.351122	0.339809	-0.028849	0.051972	0.256816	0.240480	0.210851	0.123419	-0.095757	-0.146375	0.240539	0.033734	0.004457	0.219698	-0.066826	0.188648	0.147355	0.078995	0.127311	0.114719	0.050287	0.249394	-0.042745	0.142293	-0.016107	0.345837	0.087881	-0.035576	0.067613	0.045803	0.067996	0.093660	0.130860	-0.042383	-0.092861	0.198294
timeseries	0.061055	-0.027512	0.019693	1.000000	nan	nan	nan	nan	nan	nan	nan	nan	0.070484	-0.034852	-0.033695	0.057211	0.039850	0.020435	-0.070594	0.018229	0.029860	0.012997	0.036316	0.039293	-0.009371	0.027959	0.021052	-0.022815	-0.066203	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	0.026022
max_swe_2015	0.424969	-0.038416	0.351122	nan	1.000000	0.556783	0.560949	0.468257	0.600395	0.502876	0.602325	0.528269	0.042034	-0.119797	0.207991	-0.109964	0.183141	0.104022	0.124706	-0.083282	0.157430	0.016371	0.250430	-0.016160	0.233265	0.066103	0.145503	0.020884	-0.071635	0.766510	0.269839	0.377800	0.231653	0.269034	0.187790	0.198184	0.322872	-0.220959	-0.278872	-0.035862
max_swe_2016	0.427471	0.033001	0.339809	nan	0.556783	1.000000	0.571545	0.578455	0.635594	0.680069	0.589576	0.593654	0.070847	-0.065689	0.148744	-0.119767	0.186910	0.098107	0.090105	-0.105163	0.250717	0.005919	0.232445	-0.032017	0.234939	0.098900	0.112220	0.030982	-0.092550	0.398776	0.661322	0.383056	0.327613	0.206777	0.349687	0.187726	0.408688	-0.372035	-0.425454	-0.025247
max_swe_2017	0.179848	0.229788	-0.028849	nan	0.560949	0.571545	1.000000	0.613594	0.641417	0.573035	0.617811	0.665355	0.127755	-0.103778	0.151909	-0.151714	0.260054	0.005084	0.267109	-0.172699	0.220047	-0.030209	0.315227	-0.098325	0.334156	-0.022215	0.245424	-0.070054	0.008730	0.367036	0.323163	0.718455	0.196931	0.239490	0.206431	0.121436	0.357099	-0.314467	-0.337072	-0.155467
max_swe_2018	0.027293	0.441604	0.051972	nan	0.468257	0.578455	0.613594	1.000000	0.555713	0.631416	0.630206	0.711382	0.129032	-0.097802	0.035804	-0.216536	0.084707	-0.060842	0.263360	-0.246220	0.190263	-0.036022	0.259225	-0.118116	0.325665	-0.055931	0.200480	-0.017123	-0.064297	0.288167	0.423750	0.494348	0.474341	0.206352	0.287924	0.133114	0.445661	-0.354716	-0.358080	-0.205486
max_swe_2019	0.429122	0.036327	0.256816	nan	0.600395	0.635594	0.641417	0.555713	1.000000	0.600510	0.639401	0.613646	0.040749	-0.057198	0.114232	-0.104792	0.229275	0.135886	0.194105	-0.028582	0.274109	-0.015733	0.236428	-0.026164	0.187072	0.079033	0.203588	0.117623	0.055059	0.451711	0.403834	0.415827	0.222809	0.437840	0.285606	0.264968	0.346131	-0.357196	-0.393670	0.023141
max_swe_2020	0.304668	0.161184	0.240480	nan	0.502876	0.680069	0.573035	0.631416	0.600510	1.000000	0.674695	0.652926	0.066533	-0.066819	0.089159	-0.124655	0.133256	-0.036869	0.173771	-0.170768	0.250882	-0.052219	0.257734	-0.053534	0.206737	0.047586	0.163384	0.079959	-0.023264	0.359015	0.460751	0.423942	0.289347	0.182900	0.593278	0.268611	0.421155	-0.492370	-0.489415	-0.089667
max_swe_2021	0.212845	0.231667	0.210851	nan	0.602325	0.589576	0.617811	0.630206	0.639401	0.674695	1.000000	0.741373	0.040793	-0.067951	0.045332	-0.158143	0.092282	0.058864	0.140170	-0.123457	0.103205	0.039217	0.179846	-0.046255	0.166825	0.015737	0.167984	0.001269	-0.021380	0.457412	0.414498	0.489331	0.307220	0.332138	0.379217	0.537871	0.490488	-0.253474	-0.260993	-0.071491
max_swe_2022	0.107060	0.368764	0.123419	nan	0.528269	0.593654	0.665355	0.711382	0.613646	0.652926	0.741373	1.000000	0.118743	-0.095009	0.103661	-0.176098	0.166978	-0.011855	0.235571	-0.175035	0.180047	0.037043	0.277347	-0.111351	0.308962	-0.039880	0.227179	-0.060705	-0.044697	0.346785	0.379072	0.479179	0.283608	0.246495	0.284840	0.240344	0.660579	-0.272268	-0.286512	-0.157486
sar_runoff_2015	-0.031014	0.041429	-0.095757	0.070484	0.042034	0.070847	0.127755	0.129032	0.040749	0.066533	0.040793	0.118743	1.000000	0.201489	0.151952	-0.043035	0.273055	-0.084682	0.078910	-0.179938	0.187109	-0.053683	0.244621	-0.083123	0.283914	0.040705	0.216351	-0.018293	0.066925	-0.609445	-0.083688	-0.092591	0.030706	-0.151451	-0.142301	-0.225396	-0.072220	0.151984	0.220646	-0.038520
sar_runoff_2015_std	-0.063173	-0.023857	-0.146375	-0.034852	-0.119797	-0.065689	-0.103778	-0.097802	-0.057198	-0.066819	-0.067951	-0.095009	0.201489	1.000000	-0.093996	0.174144	-0.054866	0.113317	0.045835	0.102235	-0.033246	-0.019209	-0.026265	0.177212	-0.049991	0.122883	0.025133	0.082399	0.144853	-0.235796	0.022132	-0.045288	-0.110514	-0.010357	-0.011732	0.002156	-0.109882	0.009323	0.041949	0.466035
sar_runoff_2016	0.321091	-0.213953	0.240539	-0.033695	0.207991	0.148744	0.151909	0.035804	0.114232	0.089159	0.045332	0.103661	0.151952	-0.093996	1.000000	0.026139	0.335671	0.105532	0.141226	0.155015	0.275121	-0.040557	0.304717	0.034096	0.339404	0.104580	0.296145	0.049647	0.124535	0.054473	-0.643390	-0.104256	-0.094837	-0.187367	-0.172050	-0.254102	-0.127787	0.245838	0.225145	0.075761
sar_runoff_2016_std	0.088123	-0.148823	0.033734	0.057211	-0.109964	-0.119767	-0.151714	-0.216536	-0.104792	-0.124655	-0.158143	-0.176098	-0.043035	0.174144	0.026139	1.000000	-0.032678	0.179000	-0.017214	0.217257	-0.038178	0.093680	-0.205832	0.247028	-0.117913	0.200640	-0.098543	0.085811	0.085795	-0.055825	-0.100068	-0.117727	-0.145353	-0.047504	0.062666	-0.004892	-0.099833	0.088829	0.100086	0.550566
sar_runoff_2017	0.157681	-0.033563	0.004457	0.039850	0.183141	0.186910	0.260054	0.084707	0.229275	0.133256	0.092282	0.166978	0.273055	-0.054866	0.335671	-0.032678	1.000000	0.081281	0.159751	-0.030338	0.289898	0.013248	0.347363	0.027287	0.288632	0.105241	0.290073	0.030798	0.089881	-0.043395	-0.107774	-0.484804	-0.070312	-0.129289	-0.153660	-0.173178	-0.066456	0.166138	0.152591	0.020047
sar_runoff_2017_std	0.233307	-0.162994	0.219698	0.020435	0.104022	0.098107	0.005084	-0.060842	0.135886	-0.036869	0.058864	-0.011855	-0.084682	0.113317	0.105532	0.179000	0.081281	1.000000	-0.054654	0.267696	0.039859	0.082758	-0.002868	0.173414	-0.006073	0.256503	-0.036251	0.134349	0.038865	0.129484	0.006999	-0.050310	-0.025012	0.052216	-0.034880	0.035415	0.011368	0.029498	0.032289	0.502620
sar_runoff_2018	-0.075801	0.274470	-0.066826	-0.070594	0.124706	0.090105	0.267109	0.263360	0.194105	0.173771	0.140170	0.235571	0.078910	0.045835	0.141226	-0.017214	0.159751	-0.054654	1.000000	0.113255	0.216051	-0.082191	0.330906	-0.092873	0.404173	0.051766	0.317879	-0.008592	0.129004	0.056351	-0.022958	0.136142	-0.724341	-0.057523	-0.125904	-0.247207	-0.034084	-0.054858	-0.089792	-0.004032
sar_runoff_2018_std	0.268945	-0.337485	0.188648	0.018229	-0.083282	-0.105163	-0.172699	-0.246220	-0.028582	-0.170768	-0.123457	-0.175035	-0.179938	0.102235	0.155015	0.217257	-0.030338	0.267696	0.113255	1.000000	-0.021982	0.103375	-0.054165	0.173027	-0.107052	0.250199	-0.074924	0.232295	0.215016	0.063805	-0.205252	-0.124833	-0.283314	-0.007239	-0.106902	-0.011881	-0.106403	0.107035	0.093898	0.580359
sar_runoff_2019	0.282690	-0.075981	0.147355	0.029860	0.157430	0.250717	0.220047	0.190263	0.274109	0.250882	0.103205	0.180047	0.187109	-0.033246	0.275121	-0.038178	0.289898	0.039859	0.216051	-0.021982	1.000000	-0.045542	0.350095	0.008376	0.263815	0.195184	0.296462	0.061710	0.140939	0.001205	-0.021704	-0.022933	-0.015914	-0.744602	-0.100384	-0.161929	-0.091458	0.091192	0.129809	0.031807
sar_runoff_2019_std	0.001767	0.012653	0.078995	0.012997	0.016371	0.005919	-0.030209	-0.036022	-0.015733	-0.052219	0.039217	0.037043	-0.053683	-0.019209	-0.040557	0.093680	0.013248	0.082758	-0.082191	0.103375	-0.045542	1.000000	-0.001008	0.117554	-0.037015	0.116782	-0.093840	0.162363	-0.072380	0.053339	0.027616	-0.021818	0.046514	0.037116	-0.040220	0.075908	0.093968	0.142981	0.117606	0.380147
sar_runoff_2020	0.133856	0.056169	0.127311	0.036316	0.250430	0.232445	0.315227	0.259225	0.236428	0.257734	0.179846	0.277347	0.244621	-0.026265	0.304717	-0.205832	0.347363	-0.002868	0.330906	-0.054165	0.350095	-0.001008	1.000000	-0.026149	0.472585	-0.002365	0.352509	-0.037777	0.068642	0.046457	-0.052528	0.064201	-0.097583	-0.178700	-0.624894	-0.281794	-0.013253	0.041267	0.027399	-0.090669
sar_runoff_2020_std	0.131848	-0.128019	0.114719	0.039293	-0.016160	-0.032017	-0.098325	-0.118116	-0.026164	-0.053534	-0.046255	-0.111351	-0.083123	0.177212	0.034096	0.247028	0.027287	0.173414	-0.092873	0.173027	0.008376	0.117554	-0.026149	1.000000	-0.040752	0.245355	-0.037006	0.162503	0.011552	0.046453	-0.057715	-0.101868	-0.033613	-0.041203	-0.014895	0.008889	-0.051892	0.140512	0.145548	0.526986
sar_runoff_2021	0.073790	0.158653	0.050287	-0.009371	0.233265	0.234939	0.334156	0.325665	0.187072	0.206737	0.166825	0.308962	0.283914	-0.049991	0.339404	-0.117913	0.288632	-0.006073	0.404173	-0.107052	0.263815	-0.037015	0.472585	-0.040752	1.000000	-0.009386	0.367237	-0.053333	0.059172	-0.006670	-0.076235	0.103912	-0.138361	-0.122950	-0.223727	-0.741483	-0.039339	0.092585	0.076566	-0.105282
sar_runoff_2021_std	0.280766	-0.195474	0.249394	0.027959	0.066103	0.098900	-0.022215	-0.055931	0.079033	0.047586	0.015737	-0.039880	0.040705	0.122883	0.104580	0.200640	0.105241	0.256503	0.051766	0.250199	0.195184	0.116782	-0.002365	0.245355	-0.009386	1.000000	0.092081	0.266864	0.172984	0.032695	-0.015241	-0.081506	-0.095601	-0.129384	0.038768	0.008364	-0.111776	0.035035	0.006635	0.579066
sar_runoff_2022	0.001313	0.147524	-0.042745	0.021052	0.145503	0.112220	0.245424	0.200480	0.203588	0.163384	0.167984	0.227179	0.216351	0.025133	0.296145	-0.098543	0.290073	-0.036251	0.317879	-0.074924	0.296462	-0.093840	0.352509	-0.037006	0.367237	0.092081	1.000000	-0.146333	-0.008533	-0.032577	-0.127642	0.035748	-0.114138	-0.144370	-0.127927	-0.207900	-0.581057	0.104433	0.093081	-0.094649
sar_runoff_2022_std	0.172179	-0.131339	0.142293	-0.022815	0.020884	0.030982	-0.070054	-0.017123	0.117623	0.079959	0.001269	-0.060705	-0.018293	0.082399	0.049647	0.085811	0.030798	0.134349	-0.008592	0.232295	0.061710	0.162363	-0.037777	0.162503	-0.053333	0.266864	-0.146333	1.000000	0.169607	0.019778	-0.030541	-0.090512	-0.041794	0.004566	0.072155	0.015403	0.052496	0.022386	0.020274	0.556153
sar_runoff_pixel_count	0.178913	-0.169680	-0.016107	-0.066203	-0.071635	-0.092550	0.008730	-0.064297	0.055059	-0.023264	-0.021380	-0.044697	0.066925	0.144853	0.124535	0.085795	0.089881	0.038865	0.129004	0.215016	0.140939	-0.072380	0.068642	0.011552	0.059172	0.172984	-0.008533	0.169607	1.000000	-0.106837	-0.169479	-0.070780	-0.176649	-0.100562	-0.100080	-0.058094	-0.052745	0.025517	0.018943	0.200980
offset_2015	0.360923	-0.062874	0.345837	nan	0.766510	0.398776	0.367036	0.288167	0.451711	0.359015	0.457412	0.346785	-0.609445	-0.235796	0.054473	-0.055825	-0.043395	0.129484	0.056351	0.063805	0.001205	0.053339	0.046457	0.046453	-0.006670	0.032695	-0.032577	0.019778	-0.106837	1.000000	0.268044	0.363292	0.162264	0.310946	0.244520	0.311898	0.314576	-0.273842	-0.365555	-0.003007
offset_2016	0.069264	0.200931	0.087881	nan	0.269839	0.661322	0.323163	0.423750	0.403834	0.460751	0.414498	0.379072	-0.083688	0.022132	-0.643390	-0.100068	-0.107774	0.006999	-0.022958	-0.205252	-0.021704	0.027616	-0.052528	-0.057715	-0.076235	-0.015241	-0.127642	-0.030541	-0.169479	0.268044	1.000000	0.369458	0.327553	0.299766	0.410927	0.343118	0.427984	-0.493456	-0.517209	-0.085034
offset_2017	0.027953	0.245605	-0.035576	nan	0.377800	0.383056	0.718455	0.494348	0.415827	0.423942	0.489331	0.479179	-0.092591	-0.045288	-0.104256	-0.117727	-0.484804	-0.050310	0.136142	-0.124833	-0.022933	-0.021818	0.064201	-0.101868	0.103912	-0.081506	0.035748	-0.090512	-0.070780	0.363292	0.369458	1.000000	0.228852	0.310062	0.299245	0.236663	0.368808	-0.412412	-0.422503	-0.144422
offset_2018	0.043037	0.084405	0.067613	nan	0.231653	0.327613	0.196931	0.474341	0.222809	0.289347	0.307220	0.283608	0.030706	-0.110514	-0.094837	-0.145353	-0.070312	-0.025012	-0.724341	-0.283314	-0.015914	0.046514	-0.097583	-0.033613	-0.138361	-0.095601	-0.114138	-0.041794	-0.176649	0.162264	0.327553	0.228852	1.000000	0.182375	0.314602	0.329638	0.323840	-0.210484	-0.177384	-0.173071
offset_2019	0.035349	0.092604	0.045803	nan	0.269034	0.206777	0.239490	0.206352	0.437840	0.182900	0.332138	0.246495	-0.151451	-0.010357	-0.187367	-0.047504	-0.129289	0.052216	-0.057523	-0.007239	-0.744602	0.037116	-0.178700	-0.041203	-0.122950	-0.129384	-0.144370	0.004566	-0.100562	0.310946	0.299766	0.310062	0.182375	1.000000	0.292529	0.329892	0.315491	-0.352051	-0.415317	-0.025771
offset_2020	0.106157	0.093301	0.067996	nan	0.187790	0.349687	0.206431	0.287924	0.285606	0.593278	0.379217	0.284840	-0.142301	-0.011732	-0.172050	0.062666	-0.153660	-0.034880	-0.125904	-0.106902	-0.100384	-0.040220	-0.624894	-0.014895	-0.223727	0.038768	-0.127927	0.072155	-0.100080	0.244520	0.410927	0.299245	0.314602	0.292529	1.000000	0.449956	0.339914	-0.439889	-0.424589	-0.003294
offset_2021	0.060244	0.029970	0.093660	nan	0.198184	0.187726	0.121436	0.133114	0.264968	0.268611	0.537871	0.240344	-0.225396	0.002156	-0.254102	-0.004892	-0.173178	0.035415	-0.247207	-0.011881	-0.161929	0.075908	-0.281794	0.008889	-0.741483	0.008364	-0.207900	0.015403	-0.058094	0.311898	0.343118	0.236663	0.329638	0.329892	0.449956	1.000000	0.363029	-0.251284	-0.242608	0.030868
offset_2022	0.074893	0.204911	0.130860	nan	0.322872	0.408688	0.357099	0.445661	0.346131	0.421155	0.490488	0.660579	-0.072220	-0.109882	-0.127787	-0.099833	-0.066456	0.011368	-0.034084	-0.106403	-0.091458	0.093968	-0.013253	-0.051892	-0.039339	-0.111776	-0.581057	0.052496	-0.052745	0.314576	0.427984	0.368808	0.323840	0.315491	0.339914	0.363029	1.000000	-0.288342	-0.297117	-0.081323
median_offset	-0.080771	-0.112779	-0.042383	nan	-0.220959	-0.372035	-0.314467	-0.354716	-0.357196	-0.492370	-0.253474	-0.272268	0.151984	0.009323	0.245838	0.088829	0.166138	0.029498	-0.054858	0.107035	0.091192	0.142981	0.041267	0.140512	0.092585	0.035035	0.104433	0.022386	0.025517	-0.273842	-0.493456	-0.412412	-0.210484	-0.352051	-0.439889	-0.251284	-0.288342	1.000000	0.943363	0.131205
mean_offset	-0.144865	-0.078920	-0.092861	nan	-0.278872	-0.425454	-0.337072	-0.358080	-0.393670	-0.489415	-0.260993	-0.286512	0.220646	0.041949	0.225145	0.100086	0.152591	0.032289	-0.089792	0.093898	0.129809	0.117606	0.027399	0.145548	0.076566	0.006635	0.093081	0.020274	0.018943	-0.365555	-0.517209	-0.422503	-0.177384	-0.415317	-0.424589	-0.242608	-0.297117	0.943363	1.000000	0.127306
mean_std	0.267050	-0.276511	0.198294	0.026022	-0.035862	-0.025247	-0.155467	-0.205486	0.023141	-0.089667	-0.071491	-0.157486	-0.038520	0.466035	0.075761	0.550566	0.020047	0.502620	-0.004032	0.580359	0.031807	0.380147	-0.090669	0.526986	-0.105282	0.579066	-0.094649	0.556153	0.200980	-0.003007	-0.085034	-0.144422	-0.173071	-0.025771	-0.003294	0.030868	-0.081323	0.131205	0.127306	1.000000

In [38]:

snotel_gf.describe()

Out[38]:

	elevation_m	latitude	longitude	max_swe_2015	max_swe_2016	max_swe_2017	max_swe_2018	max_swe_2019	max_swe_2020	max_swe_2021	...	offset_2016	offset_2017	offset_2018	offset_2019	offset_2020	offset_2021	offset_2022	median_offset	mean_offset	mean_std
count	473.000000	473.000000	473.000000	433.000000	435.000000	435.000000	433.000000	435.000000	434.000000	436.000000	...	430.000000	435.000000	421.000000	435.000000	434.000000	436.000000	409.000000	437.000000	437.000000	473.000000
mean	2362.120096	42.558749	-113.065845	73.935335	88.928736	91.655172	96.078522	98.818391	96.905530	91.942661	...	-17.254651	-9.400000	-1.342043	-15.729885	-7.109447	-10.332569	-8.182152	20.344966	23.274865	17.570013
std	598.696246	3.308625	4.900410	28.761318	24.341881	25.114405	16.289576	17.642811	18.500055	17.398082	...	31.342508	27.726607	22.509376	25.418502	22.895891	25.566311	31.673258	12.442188	11.892380	4.641499
min	515.112000	33.691441	-123.457474	32.000000	32.000000	32.000000	32.000000	48.000000	32.000000	32.000000	...	-128.000000	-119.000000	-79.000000	-114.000000	-122.000000	-100.000000	-111.000000	4.000000	4.250000	2.562500
25%	1961.692749	39.677120	-116.122330	61.000000	88.000000	68.000000	85.000000	85.000000	93.000000	85.000000	...	-31.875000	-29.000000	-12.000000	-27.000000	-18.000000	-27.000000	-25.000000	12.500000	15.937500	14.184456
50%	2438.399902	42.562481	-111.709999	66.000000	91.000000	94.000000	100.000000	104.000000	98.000000	87.000000	...	-15.500000	-9.000000	-4.000000	-14.000000	-7.000000	-11.000000	-8.500000	17.500000	20.875000	18.389199
75%	2812.694336	45.077068	-109.878151	104.000000	96.000000	119.000000	108.000000	107.000000	109.000000	102.000000	...	4.500000	8.750000	12.000000	-2.000000	5.000000	5.000000	12.000000	24.500000	27.437500	21.222068
max	3541.775879	48.980751	-103.976883	147.000000	140.000000	143.000000	207.000000	155.000000	137.000000	160.000000	...	54.000000	71.000000	97.000000	62.000000	58.000000	74.500000	92.000000	108.000000	103.625000	24.955974

8 rows × 39 columns

In [ ]:

In [54]:

f,ax=plt.subplots(figsize=(10,10))
#snotel_gf_projected = gpd.GeoDataFrame(snotel_gf,geometry=gf.geometry).to_crs('EPSG:32611')
snotel_gf[snotel_gf.max_swe_2019<90].plot(ax=ax,legend=True)
snotel_gf[snotel_gf.max_swe_2019>90].plot(ax=ax,legend=True)

ax.axis('equal')
ax.set_title('Checking out the bimodal distribution of 2017 peak SWE')
ctx.add_basemap(ax=ax, crs=snotel_gf.crs, source=ctx.providers.Stamen.Terrain, attribution=False)

In [55]:

f,ax=plt.subplots(figsize=(10,10))
#snotel_gf_projected = gpd.GeoDataFrame(snotel_gf,geometry=gf.geometry).to_crs('EPSG:32611')
#snotel_gf[snotel_gf.max_swe_2017<80].plot(ax=ax,x='elevation_m',y='max_swe_2017')
#snotel_gf[snotel_gf.max_swe_2017>80].plot(ax=ax,x='elevation_m',y='max_swe_2017')
ax.scatter(snotel_gf[snotel_gf.max_swe_2019<90].elevation_m,snotel_gf[snotel_gf.max_swe_2019<90].max_swe_2019)
ax.scatter(snotel_gf[snotel_gf.max_swe_2019>90].elevation_m,snotel_gf[snotel_gf.max_swe_2019>90].max_swe_2019)

ax.set_ylim([0,200])
#ax.axis('equal')
ax.set_title('2017 SNOTEL DOY of Peak SWE vs Elevation')
#ctx.add_basemap(ax=ax, crs=snotel_gf.crs, source=ctx.providers.Stamen.Terrain, attribution=False)

Out[55]:

Text(0.5, 1.0, '2017 SNOTEL DOY of Peak SWE vs Elevation')

In [ ]: