LOGY HYDR Stress testing: recharge scenarios to quantify streamflow drought sensitivity Michael Stoelzle 1,2 , Maria Staudinger 3 , Markus Weiler 2 , and Kerstin Stahl 1 EGU2018-3391: Session “Hydrological extremes: from droughts to floods” (HS2.2.1) Reference from original 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: [email protected] Poster-Download: researchgate.net/profile/Michael_Stoelzle Poster highlights in 1 minute 12x wettest months Long-term average Sorting the climate forcing according to decreasing recharge from wet to dry conditions (Staudinger et al., 2015). Precepitation Streamflow JFMAMJJASOND JFMAMJJASOND JFMAMJJASOND Monthly recharge JFMAMJJASOND 12x 2nd wettest months 12x average months 12x 2nd driest months 12x driest months Temperature series is permuted according to precipitation series DRY-UP From wet to totally dry NAT-VAR Natural variability 01-Jun 2003 2001 2000 2004 … 1999 2005 … ↺ 2011 2009 2008 2012 … 2007 2013 … ↺ 2013 2012 2016 … 2011 2017 … 2005 1985 1976 31-May 1976 1977 … 2001 2003 2004 … 2014 2015 Further drought years… Change initial conditions for historical drought years 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 years wetter References Reading 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/ STRESS TEST SCENARIO ▪ 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 reference HBV scenario Runoff ratio (Q/P) during DRY-UP: Test catchments’ sensitivity to progressive drying snow-dominated rain-dominated hybrid Z-score (Q/P) Drier last decade Intensity Deficit Recovery Reference 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 (1-1000km 2 ) 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 & Catchments 11010 01011 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 Recharge amount 1 0 time 1 Recharge timing like Gini-coefficient and Lorenz curve 1975 1983 1986 1988 1989 1991 2003 2005 2006 2008 2010 2011 2013 1976 1985 2003 2005 2011 Drought year Catchment-specific minimum recharge scenario year 25 50 75 100 125 Recharge in scenario year (%) drier wetter Scenario year (1975-2015) Drought year repeated 6 13 2 <1 7 1 7 8 16 2 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) Effect (after 1st June) Only scenarios with drier recovery (compared to reference) Data: 2003, 2005, 2015 Correlation analysis JFMAMJJASOND Recharge uniformly distributed ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 2005 2011 1976 2003 0.0 0.5 1.0 0.0 0.5 1.0 0 1 2 0 1 2 ratio Rsce / Rref (during Scenario) Qsce / Qref (during Recovery) 44 61 22 29 24 37 0 25 50 75 100 1976 1985 2003 2005 2011 2015 % 94% 72% 53% 70% 66% 48% Streamflow related to long-term average (Jun-Nov) Drought year gets due to NATVAR scenario drier wetter 40 catchments Drought intensification / attenuation due to permuted antecedent recharge years Effect of minimum “worst-case” recharge year before a typical drought year RESULTS STRESS TEST SCENARIO RESULTS Station ID 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_ROT 2604_BIB 2159_GUE 2308_GOL 2282_SPE 2414_RIE 2603_ILF 2343_LAN 2608_SEL 2412_SIO 2386_MUR 2179_SEN 2034_BRO 2471_MUR 2070_EMM 2312_AAC 2450_WIG 2629_VED 2437_PAR 2327_DIS 2369_MEN 2155_EMM 2126_MUR 2232_ALL 2356_RIA 2497_LUT 2461_MAG 2321_CAS 2366_POS 2244_KRU ∆NQ Trec ∆Qrel ∆Qabs ∆NQ Trec ∆Qrel ∆Qabs Station ID Recharge sum (—2 months) ∆NQ Trec ∆Qrel ∆Qabs Pearson’s rank correlation coef. -0.7 +0.7 p < 0.2 p < 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. 2 1 Which recharge years should be used to test the “Stress”-effect on drought years? -2 0 +2 2 0 2 2 0 2 2437_PAR 2369_MEN 2155_EMM 2070_EMM 2450_WIG 2461_MAG 2282_SPE 2603_ILF 2497_LUT 2179_SEN 2386_MUR 2312_AAC 2321_CAS 2471_MUR 2034_BRO 2414_RIE 2126_MUR 2409_EMM 2608_SEL 2487_KLE 2412_SIO 2604_BIB 2629_VED 2343_LAN 2283_RAP 2374_NEC 2159_GUE 2305_GLA 2468_SIT 2609_ALP 2635_GRO 2327_DIS 2232_ALL 2605_VER 2366_POS 2300_MIN 2244_KRU 2356_RIA ▪ Monthly Q/P from Jun-Nov ▪ Catchments sorted along 90 th 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) hybrid rain-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