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An Atmospheric–Hydrologic Forecasting Scheme for the Indus River Basin

KRISTOFER Y. SHRESTHA,PETER J. WEBSTER, AND VIOLETA E. TOMA School of Earth and Atmospheric Sciences, Georgia Institute of Technology, Atlanta, Georgia

(Manuscript received 2 April 2013, in ﬁnal form 3 December 2013)

ABSTRACT

South Asia is vulnerable to extreme weather events, and the Indus River basin (IRB) is subject to flood risk. Flooding occurred in Pakistan during the monsoon seasons from 2010 to 2012, causing major economic damage and significant loss of life. The nature of slow-rise discharge on the Indus and overtopping of riverbanks in the IRB indicates the need for extended warning time (1–10 days). Daily mean streamflow at Tarbela Dam, a major control structure on the Indus River, is reproduced by using numerical weather prediction initialization data from the ECMWF to drive the Variable Infiltration Capacity macroscale hydrologic model. A one-dimensional routing model conducts base flow and surface runoff from each grid cell through a stream network. Comparisons of reconstructions with inflow data at Tarbela Dam over a 3-yr period yield an 2 R2 correlation of 0.93 and RMSE of 692 m3 s 1. From 2010 to 2012, 276 daily ensemble hindcasts are generated. In comparing the ensemble mean of 10-day hindcasts to reconstructed inflow, the R2 correlation was .0.85 for 2010, .0.70 for 2011, and .0.70 for 2012. The RMSE was ,24% of mean streamflow for 2010, ,28% for 2011, and ,34% for 2012. A method to translate this type of probabilistic streamflow forecast data into communicable information is described. A two-dimensional model is described to simulate movement of overflow water into the floodplain.

1. Introduction Pakistan has made it one of the most vulnerable regions to extreme hydrometeorological events (Vorosmarty The unanticipated variability of monsoon rainfall in et al. 2000). In addition to human casualties, the loss of the current and future climate can have devastating crops, farm animals, and purchased agricultural inputs consequences for the developing world, where societies from flood damage condemns successive generations to are centered on agrarian pursuits (Webster and Jian ‘‘a treadmill of poverty’’ (Webster et al. 2010, p. 1494). 2011). Ensembles of extended-range weather forecasts In 2010, massive flooding directly affected nearly 20 are now being used as forcing data for hydrological model million people (Webster et al. 2011; Houze et al. 2011). simulations to determine the risk posed by potential Though not as pervasive and destructive, flooding in weatherextremes(Cloke and Pappenberger 2009). 2011 and 2012 also caused significant damage in south- For example, hydrological forecasts and flood warn- ern and central portions of the country. ings are routinely provided to Bangladesh authorities out The Indus River originates on the Tibetan Plateau to 10-day lead times (RIMES 2013) by the Regional In- and flows to the west through India before entering tegrated Multi-Hazard Early Warning System for Asia Pakistan as shown with the National Aeronautics and and Africa (RIMES), located in Bangkok, Thailand, Space Administration (NASA) Shuttle Radar To- using methodologies described in Hopson and Webster pography Mission (SRTM) data in Fig. 1.Theriver (2010), Webster et al. (2010),andWebster and Jian turns south in northern Pakistan and moves southwest (2011). through the center of the country before emptying The continuing expansion of settlements and agri- into the Arabian Sea ;3200 km later. The major culture in the Indus River basin (IRB) floodplain in tributaries—the Jhelum, Beas, Ravi, Chenab, and Satluj Rivers—conduct runoff occurring in western India and eastern Pakistan westward to the Indus. These Corresponding author address: K. Y. Shrestha, School of Earth and Atmospheric Sciences, Georgia Institute of Technology, 311 rivers meet with the Indus northeast of Sindh Province Ferst Dr., Ford ES&T Building, Atlanta, GA 30318. and add to flows originating in northern and central E-mail: [email protected] Pakistan.

DOI: 10.1175/JHM-D-13-051.1

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can store 500 days of runoff, and dams in India can store between 120 and 220 days of runoff (Qureshi 2011). Since 2006, the Pakistan Water and Power Develop- ment Authority (WAPDA) has launched several major initiatives such as the building of the Mirani Dam (completed in 2007; 3.7 3 108 m3), the raising of Mangla Dam (completed in 2009; 3.58 3 109 m3), and the building of Gomal Zam Dam (completed in 2011; 1.41 3 109 m3; WAPDA 2012). Diamer-Bhasha Dam is also underway (estimated completion in 2023; 1 3 1010 m3). Despite these improvements and commit- ments, water management in Pakistan remains tenu- ous, as suggested by flooding in each of the last 3 years. b. Aims of this work There is a great need for extended horizon (5–10 day) streamflow forecasts in Pakistan to allow for advanced FIG. 1. Map of SRTM data: green (red) indicates low (high) elevation. The IRB (dark blue outline) covers much of Pakistan preparation/mitigation of flooding and to optimize wa- and parts of Afghanistan, China, and India (thick black borders). ter resource management. The basic aim of this work is Large rivers are indicated by blue text. The location of Tarbela to produce a hydrologic forecasting scheme for the IRB Dam and Sukkur Barrage are indicated by yellow and purple to support an operational framework. The IRB irriga- circles, respectively. tion system is sufficiently advanced that with enough warning time, water could perhaps be diverted into ca- nal systems in advance of large flood pulses, thereby a. Challenges for water resource management ensuring the safety of expensive barrage structures. Water resources in Pakistan are focused around three Preserving the barrage infrastructure is integral to the major rivers: the Indus, the Jhelum, and the Chenab. success of agricultural operations dependent on the re- The flows of these rivers generally peak in spring and lated water diversions (PMPIU 2011). summer, with much lower overall discharge in fall and winter (Kahlown and Majeed 2003). Water is stored in 2. Overview of 2010–12 a number of dams and barrages during the high-flow period and is released during the dry season. These We first analyze the storage situation at Tarbela Dam storages benefit hydropower, irrigation, and flood con- (8.26 3 109 m3; Fig. 1) during the 2010–12 monsoon sea- trol. The end of the monsoon season—typically late sons. The catchment upstream of Tarbela, covering an August or early September—is the most critical time for area of ;170 000 km2, is unregulated and predominately storage decisions, that is, exactly how much water to a barren, mountainous, and glaciated landscape (Asianics retain, how much to release, and when. Agro Dev International 2000). We should expect a rea- Flood risk in Pakistan is exacerbated by the annual sonable correlation between modeling efforts based on threat of drought, and officials must strike a perilous bal- ‘‘naturalized’’ simulations and observed inflows. Natu- ance between maximizing retention in dams while main- ralized or unregulated simulations are projections of river taining enough storage capacity to attenuate potential routing without any man-made diversions or storage. flood waves that begin farther upstream. To complicate Tarbela Reservoir, along with the numerous barrages matters further, the holding capacities of existing struc- and headworks capable of modifying flow on the Indus tures are declining. Since their initial construction, the and its tributaries, can be a critical resource if danger- combined active storage of the three large hydropower ous flow conditions on the Indus are anticipated. dams—Tarbela, Mangla, and Chashma—in Pakistan has Overbank floodwater from the main stem of the Indus degraded by ;22% because of silting, leaving 2.29 3 River can spread rapidly to large areas, especially in the 1010 m3 to supply the Indus basin irrigation system south. Sindh Province is often at great risk for floods and (PMPIU 2011). The major dams are capable of storing is home to the country’s second largest economy, with barely a month’s worth of flow in total (Briscoe and Qamar a substantial agricultural base along the Indus River. An 2009). In contrast, the dams on the U.S. Colorado and the example of flooding is shown in Fig. 2, which is a visual Australian Murray–Darling Rivers can store 900 days of interpretation (Haq et al. 2012) of the inundated areas river runoff, dams on the South African Orange River based on imagery captured by the NASA Moderate

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a. 2010 In 2010, intense rainfall in the upland catchment began in July. Figure 3 indicates the locations and magnitudes of significant rainfall measured at gauging stations in various areas of Pakistan (PMD 2013). On 27–30 July 2010, Risalpur, Cherat, Saidu Sharif, Peshawar, Mianwali, and Kohat all recorded new records with 415, 372, 338, 333, 271, and 262 mm, respectively. The reservoir level and mean daily discharge at Tarbela Dam were also recorded by the Flood Forecasting Division (FFD) of the Pakistan Meteorological Department (PMD). Figure 4 shows the daily level and corresponding inflow/outflow from 1 July to 30 September 2010. The maximum level is 472.44 m (1550 ft), as indicated by the plateau in late August. The gray overlay indicates the time window of significant rainfall events as reported by gauges. Historically unprecedented flooding occurred throughout the country along the Indus main stem and in places where the typical courses of flow were altered (due to FIG. 2. NASA MODIS data colored and plotted over topography to show inundated area in Sindh Province (southern Pakistan) on breaching of artificial levees). Syvitsky and Brakenridge 15 Sep 2011. Visual interpretation was used to classify stagnant (2013) recently conducted an analysis of multitemporal water as dark blue and flowing water as light blue (Haq et al. 2012). remote sensing observations and topography. They indicated that much of the 2010 flood damage was caused Resolution Imaging Spectrometer (MODIS) instrument. by dam- and barrage-related backwater effects, reduced MODIS bands 1 (620–670 nm), 2 (841–876 nm), and water and sediment conveyance capacity, and multiple 7 (2105–2155 nm) were used here. Stagnant water is given failures of irrigation system levees. These findings agreed as a dark blue color and flowing water as a light blue with depositions to the Pakistan Supreme Court during its color. investigation of the flood.

FIG. 3. Total rainfall (mm) recorded at gauging stations in Pakistan on 27–30 Jul 2010.

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FIG. 6. (top) Reservoir level (m) and (bottom) inflow/outflow FIG. 4. (top) Reservoir level (m) and (bottom) inflow/outflow 2 2 3 1 (m3 s 1) at Tarbela Dam from 1 Jul to 31 Sep 2010. Time frame of (m s ) at Tarbela Dam from 1 Aug to 31 Oct 2011. Time frame of significant rainfalls is indicated by overlay. significant rainfalls indicated by overlay. b. 2011 Barrage (in northern Sindh) is approximately 8 days (PMD 2013). Even if forecasts for the latter part of this From 1 to 30 September 2011, Mithi, Mirpur Khas, time window had been communicated 10 days in ad- and Nawabshah all recorded new records with 760, 603, vance, Tarbela Reservoir would have been either nearly and 353 mm, respectively (PMD 2013), shown in Fig. 5. or completely full. Figure 6 shows the daily level and corresponding inflow/ outflow from 1 August to 31 October 2011. Major c. 2012 flooding was reported in Sindh in September. The gray overlay indicates the time window of significant rainfall From 8 to 11 September 2012, Jacobabad, Khanpur, events as reported by gauges. The travel time of water and Shorkot all recorded new rainfall records with 457, along the Indus River from Tarbela Dam to Sukkur 290, and 152 mm, respectively (PMD 2013), shown in

FIG. 5. Total rainfall (mm) recorded at gauging stations in Pakistan for 1–30 Sep 2011.

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FIG. 7. Total rainfall (mm) recorded at gauging stations in Pakistan for 8–11 Sep 2012.

Fig. 7. Despite indications at the end of August that 2012 b. Land surface model would turn out to be a low water year, the monsoon Land surface models (LSMs) describe the terrestrial again persisted through September. Capacity at Tarbela water cycle. These models provide implementations in advance of these rainfalls was higher than in 2011 of our current understanding of hydrological systems (Fig. 8), but the reservoir still reached capacity by the (Gudmundsson et al. 2012). Adding complexity to any middle of September. Had rainfalls in the south ex- hydrologic model increases the risk of overcalibration, tended for a longer period of time, as in 2011, much and overcalibration is an unavoidable circumstance of more flood damage could have occurred. more comprehensive efforts. Overparameterization of models (particularly distributed models) relative to 3. Methodology a. Challenges for streamflow modeling Despite the intricacy of irrigation processes and the progress of hydropower development in the IRB, it is an unfortunate reality that little historical discharge data from the river network is publicly available. From a modeling standpoint, this means that we are forced to treat these vulnerable regions as essentially ungauged. Streamflow prediction is particularly difficult in these circumstances (Wagener and Wheater 2006), but not im- possible. Hopson and Webster (2010), for example, described a multimodel framework to model water-balance physics explicitly (evapotranspiration, soil storage, etc.). This forecasting methodology was used to predict the severe flooding events on the Brahmaputra River in 2004, 2007, and 2008 despite the absence of upstream river discharge data from India. Discharge forecasts as a result FIG. 8. (top) Reservoir level (m) and (bottom) inflow/outflow 2 of this scheme were able to significantly outperform both (m3 s 1) at Tarbela Dam from 1 Aug to 31 Oct 2012. Time frame of climatological and persistence forecasts. significant rainfalls indicated by overlay.

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FIG. 10. NOAA CMORPH vs EC-DET precipitation data in 2011, spatially aggregated and averaged over the region 248–278N, 688–728E. FIG. 9. Framework for an operational hydrologic prediction system. budgets balance at every time step. The VIC model has been used in several large international projects, such the information content of observational data available as the Project for Intercomparison of Land-Surface for calibration of parameter values is a common issue Parameterization Schemes (PILPS; Henderson-Sellers in hydrologic modeling (Beck 1987). Models strive to et al. 1995). One of the main goals of PILPS was to find an optimal means of fitting simulated outputs to evaluate the ability of current land surface schemes to observational data, but the information context is such reproduce measured energy and water fluxes over multiple that no approach, by definition, can yield a single un- seasonal cycles across a climatically diverse, continental- ambiguous mathematical solution to the identification scale basin (Wood et al. 1998). When modeled latent problem. Cornwell and Harvey (2007),forexample, heat fluxes were compared to those estimated from ra- critically reviewed several LSMs typically used in global diosonde data, VIC reproduced the atmospheric budget circulation models (GCMs) to simulate soil moisture and evapotranspiration to within the approximated estimated evaporation and found that soil moisture was treated and errors of 5% and 10% for the annual and monthly warm even defined differently in several different models. season means (Liang et al. 1998). In the North American Instead of making the case that a selected model Land Data Assimilation System (NLDAS) project, VIC provides the ‘‘best’’ method of environmental parameterization, it may be more reasonable to adhere to an equifinality principle (Beven 2006), that is, the idea that two models may lead to an equally acceptable representation of the observed natural processes and that there is much to be gained from an analysis of both methodologies. Here we employ the Variable Infiltration Capacity (VIC) model (Liang et al. 1994; Bohn et al. 2013), a research model that has been tested in a variety of watersheds with several recent applications in the western United States and southeastern Australia (Tang et al. 2010; Shukla et al. 2011; Zhao et al. 2012; Li. et al. 2013). VIC balances both the water and surface energy budgets within a grid cell; its subgrid variations are captured statistically. By comparing model-simulated runoff to observations over large river basins, Maurer et al. (2002) described the benefits of generating a dataset of land surface states and fluxes in which both the land surface water and energy FIG. 11. The 2010–12 inflow reconstructions at Tarbela Dam.

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FIG. 12. (a) Location of Tarbela Dam (Loc. 1), a point on the Indus main stem in southwest Punjab province (Loc. 2), and a point on the Indus main stem in southwest Sindh province (Loc. 3). Cumulative volume by day from 1 July to 30 September for streamflow reconstruction and observed streamflow for (b) 2010, (c) 2011, and (d) 2012. outputs were used to drive an independent routing model. stream networks were obtained from the U.S. Geological A comparison of modeled runoff to observed flow showed Survey’s (USGS) HYDRO1K data (Verdin and Greenlee a bias of less than 5% and a correlation of 0.9 over a 2-yr 1996). Elevations at 3-arc-s resolution based on void- period (Lohmann et al. 2004). filling interpolation methods (Reuter et al. 2007)for SRTM data were obtained from the Consultative Group c. Hydrological system framework for International Agricultural Research (CGIAR) Con- A flow diagram for the streamflow modeling frame- sortium for Spatial Information. work centered on the VIC model is shown in Fig. 9. This The basin delineation (Fig. 1, dark blue outline) is diagram indicates the different types of geospatial da- created from an aggregation of smaller catchments. tabases used to produce the soil, vegetation, and routing Spatial soil water–holding properties are estimated by parameters necessary to transform estimates of tem- employing soil textures (Reynolds et al. 2000). These perature and precipitation to streamflow. Soil textures at texture classes are linked to literature surveys of hy- 0.258 resolution were obtained from Food and Agricul- draulic soil properties (Rawls et al. 1982). In each grid tural Organization data (FAO 1998). Vegetation types cell, the FAO soil texture is used to estimate charac- and land cover information at 30-in. resolution were teristics such as saturated hydraulic conductivity, bub- obtained from University of Maryland (UMD) land bling pressure, quartz content, bulk density, particle cover data (Hansen et al. 2000). Drainage basins and density, field capacity, and wilting point. UMD land

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FIG. 13. (a) Region over which precipitation was averaged: 308–368N, 708–748E. (b) Forecast lead time diagram of the probability that the statistically corrected 2010 ECMWF forecast for the region highlighted in green in (a) ex- ceeds the observed CMORPH climatology plus one standard deviation. The blue line represents the observed CMORPH rainfall averaged for the same region and time period. (c) As in (a), but for 248–278N, 688–728E. (d) Forecast lead time diagram for the statistically corrected 2011 ECMWF forecast in the green region in (c) vs July– August climatology. (e) As in (a), but for 258–308N, 688–728E. (f) Forecast lead time diagram for the statistically corrected 2012 ECMWF forecast in the green region in (e) vs July–September climatology.

cover classes are used similarly to estimate overstory, VIC, we use a large-scale river routing algorithm designed architectural resistance, leaf area index, shortwave to be coupled to land surface parameterization schemes albedo, roughness length, displacement, and radiation (Lohmann et al. 1996). We have selected this routing attenuation factor. Fractional characterization of land approach because of its history of use with the VIC model cover within each grid cell is performed by subdivision and its computational efficiency. The application of this into tiles. The seasonal dependence of some parame- model allows for the comparison of predicted and mea- ters is based on literature surveys about the Northern sured streamflow data as an integrated spatiotemporal Hemisphere (Tian et al. 2004). Elevations are used to quantity, or ‘‘semidistributed’’ model. Base flow and produce flow directions and accumulations. Path lengths runoff from each upstream grid cell are routed to each are created from flow directions. station based on one-dimensional (in-channel only) flow. Meteorological variables are used as forcings for the The 1D routing model is predicated on assumptions of LSM in 6-h, energy balance mode. To create represen- linearity and time invariance and requires the specifica- tations of streamflow from output fluxes generated by tion of a set of parameters estimated independently from

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FIG. 14. The 10-day streamflow hindcasts at Tarbela Dam, based on raw QPF, for (a) 27 Jul, (b) 1 Aug, (c) 13 Aug, and (d) 23 Aug 2010. the land surface scheme. It uses base flow separation precipitation forcings. Quantitative precipitation data at techniques (Linsley et al. 1975) to determine the pa- high spatial (0.258) and temporal (6 h) resolution were rameters for the routing within each grid cell. This type considered for this study. Emphasis was placed on real- of horizontal routing process is suitable for macroscale time products. Reanalysis products such as Interim Eu- (10–300 km) effects. ropean Centre for Medium-Range Weather Forecasts A simplified routing strategy can have limitations. For (ECMWF) Re-Analysis (ERA-Interim) were not used example, Li et al. (2013) note that although impulse because of their coarser spatial resolution (1.58); the LSM response function (IRF) methods can perform well is run here at 0.258. Several different high-resolution across a wide range of resolutions, they view the runoff satellite precipitation products are operationally avail- transport system as linear and stationary. These methods able, for example, the National Oceanic and Atmospheric may require significant calibration, and representation Administration (NOAA) Climate Prediction Center of flows from a particular spatial/temporal regime may (CPC) morphing technique (CMORPH; Joyce et al. not translate effectively to others. The IRF method also 2004) and the Tropical Rainfall Measuring Mission implicitly incorporates the subtleties of overland flow (TRMM) Multi-satellite Precipitation Analysis (TMPA; (travel time across hill slopes and through finescale Huffman et al. 2007). Each is available quasi-globally at stream networks), so these types of dynamics may be 3-h temporal and 0.258 spatial resolutions. Passive mi- inaccurately modeled. crowave (PMW)-based estimates of instantaneous precipitation are subject to significant uncertainties, and d. Quantitative precipitation forcings each of these products has strengths and weaknesses that The VIC model processes time series of subdaily me- can vary both spatially and temporally. teorological drivers such as precipitation, air tempera- A comparison of CMORPH versus TRMM 3B42, ture, and wind speed. The model is most sensitive to version 6 (3B42V6), in the continental United States for

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FIG. 15. (a) Reconstructed vs observed inﬂow at Tarbela Dam in 2010, based on raw QPF. (b) Ensemble mean of raw QPF hindcasts vs reconstruction by lead day for July–September 2010.

2003–06 (Tian et al. 2007) indicates that CMORPH has the hydrologic integration process, possibly owing to the season-dependent biases, with overestimation in sum- difficulties of precipitation estimations at high latitudes/ mer and underestimation in winter. This led to TRMM complex topography. 3B42V6 showing a lower bias and higher correlation We note similar problems with utilizing such real-time with gauge measurements when aggregated to annual or PMW data to initialize our hydrological model in South seasonal time scales. On daily time scales, however, Asia and thus present a different approach in this CMORPH correlated slightly better with ground-based work. To produce real-time streamflow forecasts using measurements than TRMM 3B42V6. The authors rec- high-resolution (0.1258) quantitative precipitation and ommended the use of 3B42V6 for long-term and clima- temperature estimates, we chose the first two time steps tological studies and the use of CMORPH for short-term of the ECMWF deterministic (EC-DET) forecasting applications because of its higher probability of detection system. EC-DET forecasts are initialized twice per day of rainfall events. (0000 and 1200 UTC) with globally assimilated obser- The 3B42V6 is typically available at significant delay, vational data. The first time step of each forecast is an ;10–15 days after the end of each month. A real-time ‘‘analysis’’ field that represents the observed state of the version of TRMM (3B42RT) is available. While both atmosphere at the time of integration. To obtain forc- CMORPH and 3B42RT have significant potential for ings every 6 h, we use the first two time steps of con- global hydrological application and research, both secutive forecasts each day (0000 and 1200 UTC). This display errors in high-latitude and complex-terrain leads to the use of a forecast product for two of the four regions. A study over the Laohahe basin in northern daily time steps, but this is currently unavoidable— China for 2003–08 (Jiang et al. 2010) indicated that precipitation analyses at 6-h, 0.1258 resolution are 3B42V6 had the best correspondence with surface obser- not available. This does have the advantage of main- vations, and CMORPH performed better than 3B42RT. taining continuity between the precipitation-driven Comparing the daily real-time products, CMORPH had hydrological model initializations and precipitation fore- better correlation, lower bias, and lower mean error. cast variables. For total precipitation, minimum tempera- Yong et al. (2010) evaluated 3B42RT and 3B42V6 for ture, and maximum temperature, four time steps per day a high-latitude hydrological application. When used to are then aggregated spatially from 0.1258 to 0.258. drive the VIC model and generate a daily streamflow The quality of daily EC-DET precipitation assimilated series, 3B42V6 produced Nash–Sutcliffe coefficient effi- in this manner was evaluated by comparing magnitudes ciency (NSCE) 5 0.55 with 12.78% bias. The 3B42RT, on over the basin to an observational dataset. For afore- the other hand, produced NSCE 5218.39 with 327.14% mentioned reasons, CMORPH data were selected as bias. Both model-parameter error analysis and hydro- observations. A comparison of EC-DET to CMORPH logic application suggested that VIC cannot tolerate the precipitation after spatial aggregation over the region nonphysical overestimation behavior of 3B42RT through 248–278N, 688–728EisshowninFig. 10. EC-DET tends

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FIG. 16. Streamflow hindcast data at Tarbela Dam in 2010, based on raw QPF. (a) Scatterplots of ensemble mean vs reconstructed inflow at 1, 3, 5, and 7 lead days, with 1-to-1 (dashed) and linear regression (solid) lines. (b) Linear regressions of data by lead day. (c) Correlation of ensemble mean by lead day (solid) and correlation of persistence (dashed). (d) RMSE of ensemble mean vs reconstructed inflow (solid) and correlation of persistence (dashed) with the mean of reconstructed inflow (92 days). to underestimate the magnitude of rainfall pulses in is later used to statistically adjust the precipitation fields relation to CMORPH. To minimize systematic model in forecast mode. bias differences, we implement a quantile-to-quantile e. 2010–12 reconstructions (q to q) mapping technique following Hopson and Webster (2010, their Fig. 3). Daily EC-DET and CMORPH The hydrological framework was used to simulate the precipitation values from 2011 are binned into quantiles, inflow to Tarbela Dam for 2010–12. As previously men- creating a cumulative distribution function (CDF) for tioned, because of the naturalized catchment upstream of each 0.258 cell. EC-DET values are mapped onto the this location, reconstructed flows should match reasonably CMORPH precipitation fields by identifying the EC- well to observed gauge data at Tarbela. Measurements of DET CDF bin corresponding to the estimate and then average daily inflow at Tarbela Dam in 2010–12 were replace the estimate with the value found in the same provided by the PMD through the World Bank. In a cali- CMORPH CDF bin. This creates a nonlinear statistical bration phase, tuning parameters were modified to pro- correction for each grid cell. Note that the same technique duce optimum fit between simulated flow and gauge

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FIG. 17. Uncertainty of uncorrected streamflow hindcasts vs reconstruction at Tarbela Dam in 2010, by lead day, based on raw QPF. Spread of (a) 35th–65th, (b) 25th–75th, (c) 15th–85th, and (d) 5th–95th percentiles. measurements. These parameters include the fraction of cumulative sum of each series at a daily time step, the the maximum base flow where nonlinear base flow begins total flow moving through Tarbela at the end of the

Ds (0–1); the fraction of the maximum soil moisture 3 months is overestimated by 0.46% in 2010, over- where nonlinear base flow occurs Ws (0–1); a de- estimated by 6.58% in 2011, and underestimated by scription of the VIC binfilt (0–0.4); and the depths of 18.71% in 2012. each soil layer d1–d3 (0.1–2.0 m). The results are shown in Fig. 11. Over the 3-yr period, R2 5 0.93 and RMSE 5 2 4. Probabilistic forcings 692 m3 s 1. Note the significant difference in hydro- graphs when comparing 2010 to 2011 and 2012; even The meteorological community is now capable of though the latter two were both flood years, 2010 caused leveraging numerical models that rely on real-time, flooding in nearly 20% of the country. satellite-dominated global observing systems involving In Fig. 12, daily mean flows in 2010, 2011, and 2012 the atmosphere, ocean, land, and cryosphere (AMS are quantitatively compared to observational data 2013; Webster 2013). Observed values from both remote from July to September. In 2010, 2011, and 2012, R2 sensing and direct measurements are synthesized to ini- was 0.87, 0.78, and 0.81, respectively. Calculating the tialize the models.

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FIG. 18. The 10-day streamﬂow hindcasts at Tarbela Dam, based on adjusted QPF, for (a) 10 Jul, (b) 1 Aug, (c) 13 Aug, and (d) 23 Aug 2010. a. Probabilistic meteorological forecasting method (Toth and Kalnay 1993) is based on the argu- background ment that fast-going perturbations develop naturally in a data assimilation cycle and will continue to grow The atmosphere is inherently a nonlinear and complex as short- and medium-range forecast errors. A Monte system; small errors in the initial conditions of a weather Carlo–type procedure can also be adopted, for example, forecast can cause signiﬁcant divergence in pairs of near by generating initial conditions through assimilation of analogs (Lorenz 1969). For numerical weather prediction randomly perturbed observations using different model (NWP) systems, predictability is limited by model errors versions in a number of independent data assimilation linked to the approximate simulation of atmospheric cycles (Houtekamer and Derome 1995). Here we will use processes. It follows that a single (deterministic) numer- forecasts from the ECMWF Variable Resolution En- ical forecast may not provide the best estimate of the true semble Prediction System (VarEPS), which are created via state of the atmosphere. Ensemble methods seek to ap- selective sampling procedures. Singular vector-based proximate the probability density function of the initial methods are used to identify the directions of fastest state. growth (Buizza and Palmer 1995; Molteni et al. 1996). There are a number of ways to describe the distribution Finite-element representations of the dynamics and of initial errors and to subsequently sample the distribu- physics of the atmosphere introduce further uncertain- tion (Buizza et al. 2005). The bred-vector perturbation ties, for example, the uncertainty of parameters describing

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FIG. 19. As in Fig. 15, but for adjusted QPF. subgrid-scale cumulus convection in a global model value for many users was reliant on the choice of a prob- (Leutbecher and Palmer 2008). These two types of er- ability threshold appropriate to the decision-making sit- rors are not definitively separable because the estima- uation. Pappenberger et al. (2008) conducted a case study tion of the initial conditions involves a forecast model, of a flood event in Romania and indicated that awareness and thus, initial condition errors are affected by model could have been raised as early as 8 days before the event. errors. Ensemble forecasting is a response to the limi- The authors concluded that the forecasts could have tations imposed by the inherent uncertainties in the provided increasing insight into the range of possible prediction process. A deterministic forecast can be flood conditions. Boucher et al. (2011) compared the complemented by utilizing a range of simulations asso- performance of ensemble and deterministic fore- ciated with the complex boundary conditions governing casts in six subcatchments of the Gatineau River ba- the atmosphere. The ECMWF VarEPS forecasts consist sin. In terms of continuous ranked probability score of 51 ensemble members initialized twice per day (0000 and mean absolute error, ensemble forecasts out- and 1200 UTC), and each ensemble member has a performed the higher-resolution deterministic fore- 10-day forecast horizon. The horizontal resolution of the casts at each lead time, except for the first forecasting model is ;25 km from 0 to 10 days. horizon. Meteorological input uncertainty is usually assumed b. Ensemble prediction systems for streamflow to represent the largest source of uncertainty for hy- forecasting drological prediction systems. However, an increase in The adoption of ensembles of NWPs to drive opera- the skill of meteorological models does not always tional streamflow forecast models is becoming adopted consequently lead to similar improvement in hydrolog- more frequently (Cloke and Pappenberger 2009), sug- ical modeling (Pappenberger et al. 2011). Despite the gesting that we can project weather forecast uncertainty availability of quasi-global precipitation forecasts, it is successfully onto hydrologic forecasts for a variety of important to test how well perceived meteorological uses. Gouweleeuw et al. (2005) indicated that opera- skill translates to hydrological signals in each probabi- tional flood forecasting has appeared to follow a shift listic forecasting application. from a single-solution or ‘‘best guess’’ deterministic c. Skill of precipitation forecasts in South Asia approach to an ensemble-based probabilistic approach. Ensemble-based streamflow predictions in the Meuse Webster et al. (2011) indicated that the heavy rainfall and Odra River basins provided complementary infor- events of July–August 2010 affecting regions of northern mation in the medium forecast range, both in the case Pakistan were skillfully predictable in both magnitude of under- and overprediction. In a case study of two and timing 6–8 days in advance (Fig. 13b). The result Belgian catchments, Roulin (2007) showed that hydro- motivated Galarneau et al. (2012) to examine the further logical ensemble predictions had greater skill than de- use of ECMWF Ensemble Prediction System forecasts terministic ones, owing to the fact that greater economic to determine synoptic-scale features modulating the

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FIG. 20. As in Fig. 16, but for adjusted QPF. production of heavy rainfall. In the 96–144-h forecast exceed the observed CMORPH climatology plus one initialized at 0000 UTC 24 July, the probability of pre- standard deviation. The blue line represents the ob- cipitation (PoP) .50 mm during 28–29 July reached served CMORPH rainfall averaged for the same region 60% over northern Pakistan, confirming the Webster and time period. For 2011 rainfall events, the forecast et al. (2011) study. The region of highest PoP over lead time diagram (Fig. 13d) shows high probabilities northern Pakistan agreed with the observed TRMM- for rainfall pulses ;8 days in advance of the events. For derived precipitation analysis. 2012 events, the diagram (Fig. 13f) shows the lead time Forecast lead time diagrams for 2011 and 2012 were for a pulse starting on 3 September exceeding 10 days. created using a similar procedure. Statistically cor- A significant pulse on 8 September 2012 was not well rected precipitation data from the ECMWF VarEPS predicted by VarEPS, however. were used to analyze critical areas of the IRB. In Fig. 13, We will now show a case study intended to evalu- the ensemble forecasts are evaluated against observed ate integrations of spatially and temporally varying data from the NOAA CMORPH system. Forecasts precipitation/temperature forecasts over the IRB. Using a for the regions highlighted in green (Figs. 13a,c,e)are semidistributed hydrological model, can we translate spa- considered. The white-to-red color scheme corre- tially and temporally varying precipitation/temperature sponds to the probability that the ECMWF forecasts effects into skillful streamflow forecasts?

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FIG. 21. Forecast uncertainty of streamﬂow hindcasts vs reconstruction at Tarbela Dam in 2010, by lead day, based on adjusted QPF. Spread of (a) 35th–65th, (b) 25th–75th, (c) 15th–85th, and (d) 5th–95th percentiles.

5. Hindcast verifications updated with the latest analysis information in every cycle. To create a streamflow prediction system capable of a. Forecast spinup and reduction in ensemble producing daily forecasts available 2 h after receipt of members NWP initializations, computational resource limitations To spin up the hydrological model (propagating currently necessitate that we condense the number of moisture/energy storages forward), we use the first two 6-h simulated ensembles to 11, or approximately one-fifth time steps of every EC-DET forecast prior to the forecast of the 51 ensembles provided by ECMWF VarEPS. date up until 1 January of the previous year. The method VarEPS forecasts consist of one control member and 50 is the same as that used to create the streamflow re- other members resulting from perturbed initial condi- constructions (section 3d). For example, to simulate a tions. We simulate the control member unaltered. A re- hindcast for 1 July 2010, we assimilate EC-DET data from duction in the other 50 members is performed by ranking 1 January 2009 to 30 June 2010. To simulate a hindcast for each of the ensembles by precipitation magnitude and av- 2 July 2010, we assimilate EC-DET data from 1 January eraging the meteorological variables in each five-member 2009 to 1 July 2010, and so on. This scheme mimics op- ‘‘block.’’ This leaves us with 1 1 10 or 11 total sets of erational use, where the hydrological initializations are variables with which to force the hydrological model.

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FIG. 22. The 10-day streamflow hindcasts at Tarbela Dam, based on adjusted QPF, for (a) 20 Jul, (b) 20 Aug, (c) 3 Sep, and (d) 14 Sep 2011. b. 2010 hindcasts (raw QPF) inflow from 1 July to 30 September 2010 is plotted versus the ensemble mean at each lead time. The ensemble Figure 14 shows four 10-day streamflow hindcasts at mean consistently underestimates the reconstruction, Tarbela Dam: 27 July, 1 August, 13 August, and 23 and the magnitude of the underestimation generally August 2010. The blue vertical line indicates the first day increases with lead day for most values of streamflow. of the forecast, the ensemble mean is plotted as a bold Figure 16a shows scatterplots of the ensemble mean green line, and each ensemble member (11 total) is versus reconstructed inflow at 1, 3, 5, and 7 lead days, plotted as a thin green line. The reconstructed stream- for 92 hindcasts (from 1 July through 30 September). flow is plotted as a bold black line, and the black hori- Figure 16b shows linear regressions of each data series zontal lines indicate the 70% and 95% levels of the and indicates a trend of nonlinear biases. Figure 16c shows 2 expected overflow threshold (22 643 m3 s 1, or the ‘‘ex- the correlation and Fig. 16d shows the RMSE as a function tremely high’’ flood limit as given by PMD). We will of lead day. A persistence hindcast was also generated on refer to this set of simulations as ‘‘raw quantitative each hindcast day; this refers to the practice of using the precipitation forecast (QPF)’’ hindcasts. river streamflow on day t to forecast the discharge on day

In Fig. 15a, the 2010 reconstructed streamﬂow at t 1 tf. The ensemble-mean correlation outperforms per- Tarbela Dam (black dashed line) is plotted versus the ob- sistence at all lead times—R2 . 0.90 throughout the 1– served inﬂow (green line). In Fig. 15b, the reconstructed 10-day time window—but the ensemble-mean RMSE is

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FIG. 23. (a) Reconstructed vs observed inflow at Tarbela Dam in 2011, based on adjusted QPF. (b) Ensemble mean of adjusted QPF hindcasts vs reconstruction by lead day for July–September 2011. worse than persistence for days 5–10. The uncertainty August, and 23 August are shown in Fig. 18; we will boundaries resulting from streamflow hindcasts in 2010 refer to these simulations as ‘‘adjusted’’ QPF hindcasts. are shown in Fig. 17. The four plots show 30%, 50%, Figure 19b shows the reconstructed inflow from 1 July to 70%, and 90% of the total distribution created by the 30 September 2010 plotted versus the ensemble mean at ensemble members. each lead time. Note that the ensemble mean at each Though the data correlation is favorable, the RMSE lead day is better centered about the reconstruction. values are too large (at five lead days, the error is Figures 20a and 20b illustrate the new data distributions 2 ;3000 m3 s 1 or 40% of mean flow from 1 July to 30 by lead day via scatterplots and linear regressions. The September). Error values must at least show improve- fittings by lead day are closer to the 1-to-1 line, but there ment over persistence for this system to be useful. The is still slight underestimation, especially at lower values 2 underestimation trends displayed in Figs. 16a and 16b of flow (,7500 m3 s 1). Figure 20c shows that the cor- suggest that the process could benefit from some form of relation resulting from the adjusted QPF hindcasts is statistical postprocessing. We undertake procedures to slightly worse compared to the raw QPF simulations but minimize the error of the ensemble mean versus the is still .0.85 at all lead times and better than persistence streamflow reconstruction while attempting to preserve on all lead days. Figure 20d shows that the RMSE is now the correlation. Ideally, an automatic process would be better than persistence at all lead days, an improvement implemented that ‘‘recenters’’ the data distributions compared to raw QPF simulations. The RMSE of the shown in Fig. 16a. As previously mentioned, the LSM ensemble mean versus reconstructed streamflow is now 2 tends to be most sensitive to precipitation uncertainty; less than 1750 m3 s 1 at all lead days; this is ,24% of a systematic underestimation of forecasted precipita- mean streamflow from 1 July to 30 September 2010. This tion typically leads to a systematic underestimation of approach of minimizing the ensemble-mean error is not streamflow. Because the biases are nonlinear, a q-to-q without drawbacks, however; a comparison of Figs. 17 correction of the precipitation forcings was again used. and 21 shows that the upper bounds of the 5th–95th Similar to the model initializations described in section percentiles, at longer lead times (.3 days), may increase 3d,aq-to-q mapping of VarEPS precipitation data was to an extent too large to be practically useful for eval- created from daily operational forecasts in 2011—in uating uncertainty. As a result, a qualitative analysis of each grid cell and by each lead day—to CMORPH a particular hindcast (after correction) may need to be precipitation data. constrained to the ensemble mean and the middle 70% of the distribution (Figs. 21a–c). c. 2010 hindcasts (adjusted QPF) d. 2011 hindcasts (adjusted QPF) These modified precipitation forcings are used to create a new series of 92 hindcasts from 1 July to 30 Figure 22 shows corrected 10-day streamflow hindcasts September 2010. Hindcasts from 27 July, 1 August, 13 from 20 July, 20 August, 3 September, and 14 September

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FIG. 24. Streamflow hindcast data at Tarbela Dam in 2011, based on adjusted QPF. (a) Scatterplots of ensemble mean vs reconstructed inflow at 1, 3, 5, and 7 lead days, with 1-to-1 (dashed) and linear regression (solid) lines. (b) Linear regressions of data by lead day. (c) Correlation of ensemble mean by lead day (solid) and correlation of persistence (dashed). (d) RMSE of ensemble mean vs reconstructed inflow (solid) and correlation of persistence (dashed) with the mean of reconstructed inflow (92 days).

2011. Figure 23b shows the reconstructed inflow from 1 uncertainties generated by the ensemble spread at the July to 30 September 2011 plotted versus the ensemble upper quantiles at longer lead times may again be too mean at each lead time. In Fig. 24b, linear regressions of large to be practically useful, shown in Fig. 25d. the data by lead time show generally good agreement e. 2012 hindcasts (adjusted QPF) with the 1-to-1 line, but higher values of streamflow 2 (.5000 m3 s 1) at longer lead times (.5 days) tend to be Figure 26 shows corrected 10-day streamflow hindcasts 2 overestimated. Lower values of streamflow (,5000 m3 s 1) from 3 August, 20 August, 27 August, and 3 September tend to be underestimated at longer lead times (.2days). 2012. Figure 27b shows the reconstructed inflow from Figure 24c shows that the ensemble-mean correlation 1 July to 30 September 2012 plotted versus the ensemble outperforms persistence at all lead times, with R2 . 0.7. mean at each lead time. In Fig. 28b, linear regressions Figure 24d shows that RMSE outperforms persistence of the data by lead time show that higher values of 2 on all lead days. The error magnitudes were ,28% of streamflow (.5000 m3 s 1) tend to be underestimated at mean streamflow from 1 July to 30 September 2011. The longer lead times (.2 days). Lower values of streamflow

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FIG. 25. As in Fig. 21, but for 2011.

2 (,5000 m3 s 1) tend to be underestimated at longer lead this point are only model projections. In Fig. 30, dis- times (.2 days). Figure 28c indicates that the correlation charge is shown from the three locations on the main outperforms persistence at all lead times, with R2 . 0.7. stem of the Indus River (Fig. 12a): Tarbela Dam, green; Figure 28c indicates that the RMSE outperforms persis- southern Punjab province, blue; and southern Sindh tence at all lead times. The error magnitudes were ,34% province, red. Daily flows are plotted. The probabilities of mean streamflow from 1 July to 30 September 2012. The generated from the evolution of the ensemble distribu- uncertainty generated by the ensemble spread for the tion can be used to create alert states for each day of the upper quantiles, at longer lead times, may be too large to 10-day forecast. Figure 30 shows these alerts for the be practically useful, shown in Fig. 29d. three main stem locations based on river threshold estimations from the PMD FFD. The two black horizontal f. Alert states lines correspond to 70% and 95% of estimated bankfull Streamflow can be forecasted at any location in the discharge. In the case that at least four ensembles break basin (0.258 resolution). It should be noted, however, a discharge threshold, the alert state is increased to that we currently do not have access to daily historical either watch (yellow) or warning (red). If this condi- data to the south of Tarbela Dam and simulations past tion is not met, the alert state is normal (white). Three

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FIG. 26. The 10-day streamflow hindcasts at Tarbela Dam, based on adjusted QPF, for (a) 3 Aug, (b) 20 Aug, (c) 27 Aug, and (d) 3 Sep 2012. probability states are also associated with the alert state: needed: 1) regulatory information for the highly man- if less than 40% of the ensembles break a threshold, aged IRB, 2) daily historical flows in central and southern this indicates low confidence; 40%–80% breaking a parts of Pakistan, and 3) measurements of specific cross- threshold indicates medium confidence; and 80%–100% sectional areas leading to overbank thresholds. In the breaking a threshold indicates high confidence. The re- absence of this information, we can make projections of spective ensemble means are plotted as bold lines. For naturalized flow into the floodplain resulting from a static the 19 July 2010 hindcast, simulations projected a watch overflow threshold throughout the basin. Our main focus state at location 2 on 21 July and a warning state for is to indicate areas affected by ‘‘worst case’’ overflows 22–28 July. Simulations projected a warning state at (i.e., in the absence of any regulation) on the main stem location 3 from 24–28 July. of the Indus River, so we have selected a threshold 2 2 of 22 650 m3 s 1 (8 3 105 ft3 s 1) for each cell in the basin delineation. In the future, we plan to make use of more 6. Floodplain projections realistic overflow thresholds by incorporating lower Currently, there are numerous limitations to the thresholds on each tributary and at specific levels at analysis of flood prediction in Pakistan. This is in large critical structures (e.g., Jinnah, Chashma, Taunsa, Guddu, part due to data availability for researchers outside of Sukkur, and Kotri Barrages). Locations 2 and 3 (Figs. 12a, Pakistan. More detailed information on the following is 30) in the ‘‘alert state’’ image described in the previous

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FIG. 27. (a) Reconstructed vs observed inflow at Tarbela Dam in 2012, based on adjusted QPF. (b) Ensemble mean of adjusted QPF hindcasts vs reconstruction by lead day for July–September 2012. section are an indication of the potential for this forecast travel times specified by the 1D routing solutions. The scheme; we can simulate daily streamflow at any point in sequence continues until the flood volume has either the basin. The 2D model operates at relatively coarse moved past the basin outlet or beyond the image spatial spatial resolution (0.258); it is intended to preserve the extents, and it is repeated for each subsequent image in volumes of water overtopping the main stem of the Indus thetimeseries. and to give a general assessment of worst-case inundation We now have iteratively deconvolved the flood wave extent based on overages in each cell. responses from the 1D, or in-channel, image series. The The output of the ‘‘naturalized’’ routing algorithm in series of overflow images serves as the basis for raw each grid cell is imported to create a three-dimensional water volumes to be modified by floodplain attributes. As matrix; its dimensions are latitude, longitude, and time. far as 2D expansion into adjacent cells, we make three Because discharge is simply volume per time, this matrix simplifying assumptions: 1) flow overtops the riverbanks is a set of images describing the average volume of water omnidirectionally, 2) the velocity of overflow is much moving through each cell at each time step. These water slower than the typical river flow, and 3) the reflow ve- volumes serve as the raw inputs for a 2D flow module. locity of floodwater back into the river channel (related The 2D module simulates flood-affected areas because to persistence time) is even slower. This is not a fully of flow overages in image cells (pixels). hydrodynamic solution and thus faces limitations; for First, a threshold image is populated (the expected example, the physical mechanisms driving interactions river threshold in each cell). We also populate an over- between lateral and channelized flow are significantly flow image (the volume exceedance in each grid cell), more complicated than described here. We have made a modifiable 1D image (same as the raw set of images, simplifications to support operational calculations. but capped at the channel saturation limits in each cell), For each cell in the overflow image, the surrounding a flow direction image (direction water moves out of total storage on the floodplain is calculated by estimat- each cell outlet), a flow distance image (the length of the ing the volume of a cone based on the elevations image. flow path in each cell), an elevation image (the average The overflow water progresses radially outward into the terrain elevation in each cell), and a 2D image (de- floodplain according to the overflow velocity for one scribing flood-affected areas). time step. If overflow volume of this particular cell is The following sequence is iterated for each simulated fully contained by the estimated floodplain storage, the day. First, an overflow image is generated based on the outward movement of water stops, and reflow back into water volumes exceeding each cell threshold. A modifi- the channel takes over (as long as the channel is not able 1D image is created by capping the raw volumes for currently saturated). If the overflow volume is greater this day at saturation. For each nonzero cell in the over- than the estimated floodplain storage, we move to the flow image, the volume of water is removed from the next next time step and so on until the storage condition has day’s modifiable 1D image according to the cell-to-cell been met and reflow can take place.

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FIG. 28. As in Fig. 24, but for 2012.

We are left with two major outputs: 1) the modified Figure 31 also shows four spatial maps of flood-affected 1D image describing the in-channel flows and 2) the areas simulated by the model on four different days. modified 2D image describing the floodplain flows. Each of these images is a daily ‘‘snapshot’’ of the flow Evaporation of water from the floodplain is not cur- magnitudes in each cell in the basin network. Dark blue rently considered, but we plan to address this in future colors correspond to low discharge, and dark red colors work. The combination of these images creates a se- to high discharge. On 16 July 2010, the naturalized quence that simulates the sequence of overflow and model simulates in-channel flow for all major rivers. On drainage in the basin. Early results from this algorithm 23 July 2010, the model simulates overbank flow just development were made in a report to the World Bank south of the confluence between the Indus main stem (Webster and Shrestha 2013). and its major tributaries (southern Punjab). On 29 July Figure 31 shows the 2010 reconstruction. Three time 2010, flooding in northern part of the country is simu- series are plotted corresponding to three locations lated and flood-affected areas have increased in size. By (Fig. 12a) on the Indus main stem. The observed inflow 26 August 2010, the model simulates an intensification at Tarbela Dam is plotted as a black series. Note that the of flooding in the south central area of the country. 2 red and blue series plateau at 22 650 m3 s 1; this was our Figure 32 shows the 2011 reconstruction. On 28 July assumption for the mean daily flow level leading to 2011, the model simulates in-channel flow for all major bankfull discharge at points on the Indus main stem. rivers. On 1 August 2011, the model simulates overbank

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FIG. 29. As in Fig. 21, but for 2012.

flow in southern Sindh. This simulated flood pulse arrived heavy rains in northern India. The model simulates Sindh earlier than news reports and remote sensing suggest (the flooding in early September and intensification through- Sindh floods began in mid-August 2011). As indicated by out the month. Fig. 1, a significant portion of the Indus River basin is Figure 33 shows the 2012 reconstruction. On 25 affected by rains over India, which would ultimately drain August 2012, the model simulates overbank flow in westward into Pakistan in an unregulated scenario with- southern Punjab. This first flood pulse shows a ‘‘false out withholding by dams and barrages. However, because positive’’ in comparison to flooding reports, but we of political agreements between the two countries, India again need to consider the mitigating properties of the has exclusive control of the Sutlej, Beas, and Ravi Rivers, large eastern dams (in India), which are not simulated. while Pakistan controls the Indus, Jhelum, and Chenab. These images illustrate the benefits of heavy regulation This means that major dams in India such as the Bhakra throughout Pakistan and India and what might occur in (8.63 3 109 m3), the Nathpa Jhakri (3.08 3 109 m3), and a worst-case scenario. The progression of the projected the Pong (7.4 3 109 m3), along with many other man- flood wave in early September is shown. Dangerous flows made diversion effects throughout Pakistan, significantly on the main stem Indus enter southern Punjab starting attenuate flows into Pakistan. Figure 31 shows the un- 6 September 2012 and do not abate until 15 September regulated model projections in Pakistan shortly after 2012.

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FIG. 30. The 10-day streamﬂow hindcast from 19 July 2010 and associated alert image to communicate forecast probabilities at three locations.

7. Conclusions hindcasts to reconstructed inflow, the R2 correlation was .0.85 for 2010, .0.70 for 2011, and .0.70 for 2012. The We have described a methodology for generating RMSE was ,24% of mean streamflow for 2010, ,28% 10-day probabilistic streamflow forecasts by driving the for 2011, and ,34% for 2012. In terms of these metrics, VIC semidistributed macroscale hydrologic model with the ensemble-mean hindcasts outperformed persistence gridded ECMWF atmospheric forecasts. The forecast hindcasts at all lead times. scheme is intended to be run daily, and computations are Previous efforts to develop rapid forecasting and warn- designed to complete 2 h after receipt of NWP data. The ing in places such as Bangladesh (Hopson and Webster LSM operates at a 6-h time step and 0.258 spatial reso- 2010; Webster et al. 2010) have demonstrated the utility of lution, balances the surface energy budget within each experimental systems based on probabilistic forecasting. grid cell, and creates estimates of output fluxes in each The Bangladesh system, which was first used experimen- grid cell. Base flow and runoff from each upstream cell tally in 2004 and became operational in 2007, was de- are combined based on one-dimensional flow routing. veloped with support from the U.S. National Science This type of horizontal routing process is suitable for Foundation, the U.S. Agency for International Develop- large-scale (10–300 km) effects. The uncertainty of me- ment, and the humanitarian agency Cooperative for teorological forecast variables is projected onto dis- Assistance and Relief Everywhere (Webster 2013). We charge estimations. This type of approach, as opposed to have presented a methodology based on ensemble simu- one reliant on physical gauges, is uniquely suited to sit- lations that may be capable of providing similar early uations where real-time data are suspect. One major warnings (Fig. 30) for aid organizations. Operational hy- advantage is the capability of the process to remain drologic ensemble forecasting is relatively new, and details functional even in the case that extreme floods result in on these types of macroscale hydrometeorological en- the loss of upstream equipment and/or data transmission semble approaches are just starting to emerge (Alfieri et al. failure from gauges (Cloke and Pappenberger 2009). An 2012; Demargne et al. 2014). alert system is described that communicates alert states Limitations and future work and the confidence of each forecast. Comparisons of streamflow reconstructions with ob- Currently, there are numerous limitations to the served inflow data at Tarbela Dam over a 3-yr period analysis of flood prediction in Pakistan. This is in large 2 yield an R2 correlation of 0.93 and RMSE of 692 m3 s 1. part due to data availability for researchers outside of Probabilistic meteorological forcings were used to drive the country. More detailed information is needed on the hydrological modeling framework, generating 276 regulatory information for the highly managed IRB, ten-day streamflow hindcasts from 2010 to 2012 (July– historical flows in central and southern parts of Pakistan, September). In comparing the ensemble mean of these and measurements of specific cross-sectional areas leading

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FIG. 31. The 2010 reconstructed inflow and model projections for flood-affected areas. to overbank thresholds. In the meantime, we have de- ;1 165 000 km2, and projections of floodwater are typi- scribed a fully automated operational process that is cally most critical at southern locations in Pakistan. capable of operating without any of these require- Specific quantitative analyses at locations far to the south ments. The creation of a mechanism to obtain this clari- are not included in this paper because of a lack of his- fying information—especially in a real-time context—is torical data. As soon as data become available, we will ongoing. repeat the hindcast analyses at other critical points in the We have sought to make quantitative comparisons to basin. gauge information where available. A case study was A 2D routing scheme was described to project over- described at Tarbela Dam, which is particularly relevant flows onto the floodplain. The simulations of routing given the complex nature of water management de- mechanics described here are relatively coarse in spatial cisions in the IRB. This methodology can be used to scale (0.258), owing to the logistics associated with per- forecast flow at any point in the basin, and we have noted forming daily ensemble forecasts. Simulations of more that forecast skill in the IRB (in comparison to our re- finescale effects are highly desirable, and recent reconstructions) appears to increase as catchment size in- search indicates that this is possible. For example, Neal creases. Although a substantial amount of area is drained et al. (2012) present a computationally efficient hy- at Tarbela (;170 000 km2), the IRB in its entirety drains draulic model for simulating the spatially distributed

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FIG. 32. The 2011 reconstructed inflow and model projections for flood-affected areas. dynamics of water surface elevation, wave speed, and ensembles. Operational forecasting systems are now inundation extent over large spatial domains. The scheme being run on a daily basis with global coverage (Alfieri was shown to be numerically stable and scalable in test et al. 2012; Wu et al. 2012), indicating that it is possible to cases such as the Niger River in Mali. Li et al. (2013) design a computationally efficient structure supporting present the Model for Scale Adaptive River Transport large-scale LSM processes. Code optimizations follow- (MOSART), a physically based runoff routing model ing these methodologies are being pursued. There are for simulating routing processes through hill slopes, sub- also recent advancements to the VIC model that have networks, and main channels. The model was shown to not yet been leveraged, for example, improved daily be capable of capturing the seasonal variation of both long-/shortwave estimations in the presence of snow, streamflow and channel velocities at different spatial subdaily humidity estimations, and computation of soil resolutions. It may be possible to either couple outputs layer average temperatures (University of Washington from the 1D scheme described in this work with subgrid- 2012). scale inundation modeling or replace the model in its In each of the three study years, corrected streamflow entirety with a new paradigm. hindcasts tended to underestimate low flow values. In For future work, we plan to increase computational 2011 and 2012, higher values of flow tended to be over- resources in order to increase the number of streamflow estimated. Ideally, we would like to always bound the

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FIG. 33. The 2012 reconstructed inflow and model projections for flood-affected areas. solution by the ensemble spread. In some cases (espe- World Bank, the late Dr. Kurt Frankel from Georgia cially early July hindcasts), the solution was not contained Tech, Dr. Thomas Hopson from the National Center for by the 70% interpercentile bounds. This suggests that Atmospheric Research, Dr. Dennis Lettenmaier from the quantile matching procedure is still not sufficient to the University of Washington, and Raghav Vemula from address systematic forecast errors/biases. For computa- ESRI for their help in this work. We would also like to tional simplicity, the quantile maps in each grid cell op- thankallthoseatECMWFwhoworktoprovidethenu- erated on a fixed temporal scale; that is, they were not merical weather prediction data critical to this research. updated on a real-time basis. We plan to update these quantile maps in the future on a dynamic basis, that is, by ingesting real-time CMORPH or TMPA data. Research REFERENCES is ongoing to determine whether such improvements to Alfieri, L., P. Burek, E. Dutra, B. Krzeminski, D. Muraro, existing framework yield improved estimates for medium- J. Thielen, and F. Pappenberger, 2012: GloFAS—Global en- range streamflow forecasting. semble streamflow forecasting and flood early warning. Hy- drol. Earth Syst. Sci. Discuss., 9, 12 293–12 332, doi:10.5194/ hessd-9-12293-2012. Acknowledgments. This research is supported by the American Meteorological Society, cited 2013: Annual theme, 2013 National Science Foundation under Award 0965610. We AMS Annual Meeting. [Available online at http://annual. would like to specifically thank Mr. Javaid Afzal of the ametsoc.org/2013/index.cfm/programs-and-events/theme/.]

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