LOGYHYDRStress testing: recharge scenarios to quantify streamflow drought sensitivityMichael Stoelzle1,2, Maria Staudinger3, Markus Weiler2, and Kerstin Stahl1 EGU2018-3391: Session “Hydrological extremes: from droughts to floods” (HS2.2.1)

Reference fromoriginal HBV simulation

Stress test assessment(Reference day: 1st June)

(1) Environmental Hydrological Systems, Faculty of Environment and Natural Resources, University of Freiburg, Germany (3) Department of Geography, University of Zurich, Switzerland (2) Hydrology, Faculty of Environment and Natural Resources, University of Freiburg, Germany Contact: michael.stoelzle@hydro.uni-freiburg.de Poster-Download: researchgate.net/profile/Michael_Stoelzle

Poster highlights in 1 minute12x wettest

months

Long-term average

Sorting the climate forcing according to decreasing recharge from wet to dry conditions (Staudinger et al., 2015).

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12x 2nd wettest months

12x averagemonths

12x 2nd driest months

12x driest months

Temperature series is permuted according to precipitation series

DRY-UPFrom wet to totally dry

NAT-VARNatural variability

01-Jun 2003

20012000

2004

…

1999

2005…

↺2011

20092008

2012

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2007

2013…

↺20132012

2016

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2011

2017…

200519851976

31-May

1976

1977

…2001

20032004…

20142015

Further drought years…

Change initial conditions for historical droughtyears

2015↺

Streamflow years

50:50 drier/wetter years

What would have happened in a drought year if the preceding recharge year was changed?

More years drier

More yearswetter

ReferencesReading list

▪ Vormoor et al., 2017, Climatic Change▪ Staudinger et al., 2015, Hydrology and

Earth System Sciences▪ Stoelzle et al., 2014, Geophysical

Research Letters▪ github.com/tidyverse▪ github.com/clauswilke/ggridges/

STRE

SS T

EST

SCEN

ARIO▪ Stress testing scenarios are a

valuable alternative to conventional climate scenarios.

▪ “Stress” means recharge reduction before drought, “Test” means a quantification of ”Stress”-effect on the drought year (e.g. deficit, recovery, minimum flow).

▪ Scenarios are embedded in a HBV model framework to compare streamflow series from scenario runs with streamflow from reference runs.

▪ NAT-VAR stress tests elucidate that drought is catchment- and event-specific, thus improved stress tests should consider more event-specific characteristics (< monthly scale).

▪ Drought propagation is controlled by short-term and long-term recharge reduction and by recharge timing.

▪ DRY-UP stress test filters which catchments are more sensitive to progressive drying (dry weather).

▪ Successful identification of catchments with higher drought sensitivity.

HBV

refe

renc

eH

BV s

cena

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Runoff ratio (Q/P) during DRY-UP:Test catchments’ sensitivity to progressive drying

snow-dominatedrain-dominated hybrid

Z-score (Q/P)

Drie

r las

t dec

ade

Intensity

Deficit

RecoveryReference

D Terms & Framework

▪ “Stress” is defined as systematic decrease of recharge, “Test” is defined as a framework to quantify the streamflow response to this changed main control.

▪ Stress test scenarios are not about hydrological prediction, but identify catchments’ sensitivity to drought.

▪ All scenarios are compared against the HBV reference run (never against Qobs)! HBV recharge is percolation water from soil box into GW box (incoporates also snow melt)

▪ All scenarios focus on recharge reduction or decreased recharge rates, but for scenario simulation P and T is changed (HBV input)

▪ 40 meso-scale (1-1000km2) catchments grouped into rainfall-, hybrid- and snowmelt-dominated regimes (<800m, 800-1600m, >1600m).

▪ Daily data 1971-2015: Precipitation (gridded, Rhires), Temperature (gridded, MeteoSwiss) and observed streamflow (FOEN)

Why stress testing??Climate change studies often fail to distinguish between inherent climate variability and projected climate change signal. For example, different temporal structures of future climate input can affect low flows, but the sequencing of simulated wet and dry spells is not altered (Vormoor et al., 2017). Climate change scenarios bring huge uncertainties to hydrological assessment of future streamflow droughts and low flow events.

Instead of future climate, we use historical data ▪ to alter antecedent water deficit

conditions while preserving catchment-specific climate variability

▪ to quantify how sensitive catchments are to such alterations (“Stress”), i.e. under decreased recharge before drought.

Through alteration of antecedent water defict a new sequencing in drought propagation can be tested.

Data & Catchments1101001011

HBV model▪ Scenario development is based on

GAP-calibrated HBV reference run▪ Calibration 1980-2000 with 70%

adapted KGE and 30% MARE.▪ Model spin-up with 5 average years▪ P and T gradients based on input data

0 time 1

~0

0.5~1

0 R

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0 time 1

Recharge timinglike Gini-coefficient and

Lorenz curve

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1983

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2003

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2010

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2013

1976 1985 2003 2005 2011 Drought year

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Recharge in scenario year (%)

drierwetter

Scenario year(1975-2015)

Drought yearrepeated

6

13

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<1

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1

7

8

16

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22

<1

15

Ratio (%) of recharge year as “minimum scenario year” for all drought years.

▪ 66% of all mimimum recharge years in 2013, 2010, 2006 and 1983

▪ Highest average recharge reduction with 2005 (-53%) compared to 2003 (-19%)

▪ Repeated drought years are less or not effecive

▪ Wet decade 1992-2002▪ More driest recharge years after

2002 (~70%) than before 1992 (~30%)

Germany

Italy

Austria

France

▪ Rsce / Rref in the 2 months before drought▪ Rsce / Rref in the year before drought▪ Recharge timing (0=early, 1=late) in the year before

drought (relative to Rref)

▪ ∆Qabs Qsce — Qref▪ ∆Qrel Qsce / Qref▪ Trec Recovery time (until ∆ < ± 2%)▪ ∆NQ NQsce / NQref

Cause (before 1th June)

Effe

ct (a

fter 1

st J

une)

Only sc

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Data: 2

003,

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Correlat

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hybridrain−dominatedsnow−dominated

Rsce / Rref (during Scenario)Q

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)

4461

22 29 2437

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%

94% 72% 53% 70% 66% 48%Streamflow related to long-term average (Jun-Nov)

Drought year gets due to NATVAR scenariodrierwetter

40 catchments

Drought intensification / attenuation due to permuted antecedent recharge years

Effect of minimum “worst-case” recharge year before a typical drought year

RESU

LTS

STRE

SS T

EST

SCEN

ARIO

RESU

LTS

StationID

40 years of progressive dry-up

Recharge sum (—1 year)

Recharge timing (—1 year)

Less annual Q/P variation More annual Q/P variation

2468

_SIT

2609

_ALP

2635

_GRO

2305

_GLA

2374

_NEC

2487

_KLE

2283

_RAP

2300

_MIN

2251

_RO

T26

04_B

IB21

59_G

UE23

08_G

OL

2282

_SPE

2414

_RIE

2603

_ILF

2343

_LAN

2608

_SEL

2412

_SIO

2386

_MUR

2179

_SEN

2034

_BRO

2471

_MUR

2070

_EM

M23

12_A

AC24

50_W

IG26

29_V

ED24

37_P

AR23

27_D

IS23

69_M

EN21

55_E

MM

2126

_MUR

2232

_ALL

2356

_RIA

2497

_LUT

2461

_MAG

2321

_CAS

2366

_PO

S22

44_K

RU

∆NQTrec∆Qrel∆Qabs

∆NQTrec∆Qrel∆Qabs

StationID

Recharge sum (—2 months)

∆NQTrec∆Qrel∆Qabs

Pearson’s rank correlation coef.-0.7 +0.7

p < 0.2p < 0.01

▪ Correlation between short-term recharge and drought characteristics suggest smaller catchment-storage and shorter storage memory.

▪ Correlation between long-term recharge and drought characteristics suggest larger catchment-storages (e.g. 2343_LAN, 2308_GOL)

▪ For a faster recovery the recharge amount is more important than the recharge timing.

Snow-dominated catchments in bold font type, here snowpack- and snowmelt-effects (before June) must be considered.

21 Which recharge years should be used to test the “Stress”-effect on drought years?

-2 0 +2 2 0 2 2 0 22437_PAR

2369_MEN2155_EMM2070_EMM2450_WIG

2461_MAG2282_SPE

2603_ILF2497_LUT2179_SEN2386_MUR2312_AAC2321_CAS2471_MUR2034_BRO

2414_RIE2126_MUR2409_EMM2608_SEL2487_KLE2412_SIO2604_BIB

2629_VED2343_LAN2283_RAP2374_NEC2159_GUE2305_GLA2468_SIT

2609_ALP2635_GRO

2327_DIS2232_ALL

2605_VER2366_POS2300_MIN2244_KRU2356_RIA

▪ Monthly Q/P from Jun-Nov▪ Catchments sorted along 90th

percentile of Z-score of the driest decade (in yellow)

▪ Stronger shifts to negative Z-scores suggest higher sensitivity of catchments to drying

▪ Snowmelt influence in higher-elevation catchments

▪ Earlier / later tipping points

Wettest Decade

Driest decade

Relationship between antecedent recharge- and following drought-characteristics

Effect of driest recharge years (x-axis) on streamflow (y-axis) for 4 drought years:

▪ Antecedent recharge leads to wettest reference in 2003, driest reference in 2011

▪ Streamflow reduction is smaller for snow-dominated catchments

▪ Hybrid catchments often more sensitive to reduced recharge (e.g. 1976, 2011)

▪ Analysis supports the “drought year”- and “catchment”-specific approch

snow-dominated (> 1600 m asl)hybridrain-dominated (< 800 m asl)

▪ Severity of summer streamflow droughts is different

▪ Changing antecedent conditions leads to wetter and drier drought periods

▪ Thus, a catchment- and event-specific approach is needed

Data