Medium-range ensemble prediction of hydrological droughts Felix Fundel, Stefanie Jörg-Hess, Massimiliano Zappa NRP-61 Drought-CH NFP61 sustainable water management Are we prepared for droughts?

Motivation Who could be interested in drought forecasts? Hydro power generation, water quality, tourism, agriculture, power plant cooling etc. Every interest group is affected differently, i.e. numerous drought indicators exist. Here, only hydrological droughts are considered.

Hydrological droughts runoff time threshold severity duration Hydrological droughts are defined by looking on runoff only From the runoff you can derive a number of drought indices Therefore you define a threshold you consider as representative for a drought You have a drought event whenever the runoff falls below the threshold For each event you can calculate duration, severity (deficit) or magnitude (not independent of course) Common drought characteristics (e.g. Tallaksen et al., 1997; Fleig et al., 2006) Duration = length of longest forecast period below threshold Severity = cumulative runoff deficit w.r.t. threshold Magnitude = severity/duration

Study Site Area 1750 km2 Altitude 370m – 2350m runoff climatology Gurtz et al., 1999 Hydrol. Proc. Area 1750 km2 Altitude 370m – 2350m runoff climatology (1991-2008)

Forecast system Validation ECMWF VarEPS PREVAH Meteorological forcing Reforecast 1991-2008 4+1 members, weekly, lead-time 32 days downscaling bilinear interpolation (t, td, rr, rh, rad, v, ssd, alb) lapse rate correction (t, td) PREVAH Hydrological model Hydrotope units 500m x 500m Initial states from observation based reference run Gauge observations Runoff forecast Validation Verification of 5 member daily mean forecast

Forecast quality Is this good (skillful)? At 32 d? Only every other week Those forecasts are quite common in operational hydrology today. Maybe not so much on time scales of 32 days, and there are good reasons for that: Is this good (skillful)? At 32 d?

Forecast quality Limited predictability of peak-flow This probabilistic verification of the forecast shown before as function of lead-time and runoff quantile form 0 to 1. The score used is the 2AFC (two alternatives forced choice), a convenient measure, reflecting the potential forecast skill. Convenient because of its intuitive interpretation (.5 better than climatological guess), 1 perfect And it can be used for probabilistic, continuous, ensemble , deterministic … forecasts. You see that forecasts of peak-flow lose their skill rapidly, after day 10 there is hardly skill left. The situation is better for low flow, where the predictability is extended to longer lead-times. Now this is a verification on a daily basis, by using drought indices as described the forecast complexity is further reduced VarEPS/PREVAH ensemble runoff forecast skill drops with lead-time low-flow has higher predictability at longer lead-times 2AFC: Mason and Weigel 2009

Drought Indices Example 32 days runoff prediction  low-flow index lead-time is no longer involved For this study only the longest event within the 32d period was considered Low-flow threshold 15th quantile (seasonal)

Low-Flow Indices Gray areas give the ensemble range for each forecast, the red line shows the observed index in the 32d period of each forecast. The resemblance is visible to the naked eye, especially the 2003 drought is standing out here. Maybe underestimation in case of severity and magnitude Why did I choose the 15th quantile as threshold? Of course every other threshold might have been picked and be interesting for a specific user group. Howerer a analysis of different the forecast quality of different thresholds revealed…

Dependence on choice of threshold Forecast quality Dependence on choice of threshold 2AFC for low-flow index forecasts Mean forecast vs. observed index Q15th (varying seasonal) Good compromise of forecast quality and event frequency The quality of low-flow index forecasts depends on the chosen threshold and seems to peak at around the 15th quantile. Also picking lower thresholds had impact on the robustness of the scores.

Forecast quality For the drought threshold of 15th quantile the properties of the drought index forecasts are as follows Read points The post-processing should at least correct for the underestimation Not perfect but significant correlation of observation and forecast mean Severity and magnitude forecasts underestimate Duration forecasts seem to be reliable Potential for statistical post-processing

Economic Value Relative improvement in economic value when making forecast based decisions Considering the cost/loss ratio of the forecast user Considering different event thresholds (50th to 98th quantile of the low-flow indices) 1: Forecasts are most valuable for users whose C/L ratio is close to the climatological event frequency One remaining question concerns the timing of the event within the forecast. E.g. a 10 day event could befrom day 1-10 in the forecast and day 21-30 in the observations. Obviously forecasts are most valuable for users with C/L around freqclim Valuable forecasts of all low-flow indices are possible for all users

Timing of forecast events observed Yes No 42.5% 24.5% 7.5% 25.5% Contingency table for drought forecast & obs. forecast Only 42% of hits do overlap Forecast users should allow for some temporal fuzziness

2006 Drought extremely hot period but shorter than 2003 Temp. anomaly mean min max Rebetez et al., 2009, Theor. Appl. Climatol. Temp. anomaly July 2006 July temp. distribution (1895-2008, France < 500m) The 2006 heat wave was described and classified by Rebetez. In parts of Europe the monthly temperatures (min and max) were amongst the highest ever recorded. As at this time the 51 member monthly forecast system of ECMWF already was operational we took the opportunity to compare the runoff forecast during this event with those of the reforecasts. The questions were: was there an event? Was it forecast? What is the added value of having 51 members compared to only 5 in this case? extremely hot period but shorter than 2003

Forecasts using the operational 51 member VarEPS, the runoff is inverted to emphasis low-flow, also the runoff was normed so that the threshold (15th) equals 1. Forecasts start at end of June and are then issued each successive week, whereas the start of the reforecast covering the event is a couple of days later. Of course, forecasts and reforecasts are not based on the same model version. Which is why the predictions are not strictly comparable.

Summary Predictability of surface peak runoff is limited Low-flow events are predictable longer in advance Forecasting low-flow indices (averaging over several days) additionally enhanced predictability Monthly low-flow index ensemble forecasts provide a valuable tool for decision making Predictability of surface runoff is limited: depending on catchment an forecast system but generally not much longer than 10 days in advance Low-flow events are predictable longer in advance: initial conditions get more important than the role of the meteorological forcing Forecasting low-flow indices (averaging over several days) additionally enhanced predictability: by averaging the complexity is reduced and also skill from shorter lead-times is assigned to longer lead-times Monthly low-flow index ensemble forecasts can be useful for decision making: monthly forecast of course are mainly interesting or sectors vulnerable on such short scales like power production or water quality management.