A Study on Flood Forecasting in the Upper Indus Basin Considering Snow and Glacier Meltwater

Paper: A Study on Flood Forecasting in the Upper Indus Basin Considering Snow and Glacier Meltwater

Tong Liu∗,†, Morimasa Tsuda∗, and Yoichi Iwami∗∗

∗International Centre for Water Hazard and Risk Management (ICHARM) under the auspices of UNESCO, Public Works Research Institute 1-6 Minamihara, Tsukuba, Ibaraki 305-8516, Japan †Corresponding author, E-mail: [email protected] ∗∗Public Works Department, Nagasaki Prefectural Government, Nakagaki, Japan [Received June 10, 2016; accepted May 11, 2017]

This study considered glacier and snow meltwater by dropower generated from the Indus River at Tarbela Dam using the degree–day method with ground-based air contributes approximately 30% of the total energy de- temperature and fractional glacier/snow cover to sim- mand of the country [3]. ulate discharge at Skardu, Partab Bridge (P. Bridge), Flooding in the monsoon season, from the middle of and Tarbela Dam in the Upper Indus Basin during the June to the end of September, is a challenging problem monsoon season, from the middle of June to the end in the Indus River. A massive flood occurring in July– of September. The optimum parameter set was deter- August 2010 in Pakistan, one of the greatest water-related mined and validated in 2010 and 2012. The simulated disasters in the country’s history, affected more than 20 discharge with glaciermelt and snowmelt could cap- million people and almost one-fifth of Pakistan’s land ture the variations of the observed discharge in terms area [4]. Since 2010, Pakistan has experienced severe of peak volume and timing, particularly in the early monsoon floods annually. Although the floods in 2011, monsoon season. The Moderate Resolution Imaging 2012, and 2013 were relatively less widespread, they still Spectroradiometer (MODIS) daily and eight-day snow caused damage across vast areas. A flood forecasting cover products were applied and recommended with system can mitigate flood damages by alerting officials proper settings for application. This study also investi- of an imminent flood occurrence. Therefore, for further gated the simulations with snow packs instead of daily strengthening of the flood management capacity in Pak- snow cover, which was found to approach the maxi- istan and for accurate early warning of flood and disaster mum magnitude of observed discharge even from the management in the Indus Basin, it is imperative to up- uppermost station, Skardu. grade and improve the accuracy and reliability of the ex- This study estimated the glacier and snow meltwater isting flood forecasting systems. contribution at Skardu, Partab Bridge, and Tarbela The operational flood early warning system, known as 43.2–65.2%, 22.0–29.3%, and 6.3–19.9% of aver- as Indus-Integrated Flood Analysis System (IFAS), was age daily discharge during the monsoon season, re- established by the International Centre for Water Haz- spectively. In addition, this study evaluated the main ard and Risk Management (ICHARM) through a United source of simulation discrepancies and concluded that Nations Educational, Scientific and Cultural Organiza- the methodology proposed in the study worked well tion (UNESCO) project in conjunction with Japan Inter- with proper precipitation. national Cooperation Agency (JICA). IFAS provides the Flood Forecasting Division (FFD) of the Pakistan Me- Keywords: flood, hydrological modeling, glaciermelt, teorological Department (PMD) with scientifically based snowmelt, Indus River flood forecast information along the main stream of the Indus River and the Kabul River, which originates in Afghanistan and is known as one of its main tribu- 1. Introduction taries [5–9]. This system can forecast the peak discharge with hours of lead time with both ground-observed and The Indus River, one of the largest rivers in the world, satellite-based rainfall as input. However, Indus-IFAS has emerges from the Tibetan Plateau and the Himalayas. The several unresolved technical challenges, particularly the Indus Basin covers 1.12×106 km2 in Pakistan (47%), In- lack of glacier and snow meltwater. Glaciated headwa- dia (39%), China (8%), and Afghanistan (6%) and has a ter and snowmelt along with monsoon runoff and ground- total population of more than 300 million [1]. The In- water are the major water sources for the Indus sys- dus Basin Irrigation System (IBIS), which is the largest tem [10]. The Upper Indus Basin (UIB) is reported to con- contiguous irrigation system in the world, provides water tain 11,413 glaciers with a total area of 15,061 km2.Most for all sectors of the economy. In particular, it provides glaciers occur at elevations of 4,000 to 7,000 m above 90% of agricultural production, which contributes about sea level (a.s.l.). The highest elevation, 8,566 m a.s.l., is 22% to Pakistan’s gross domestic product (GDP) [2]. Hy- found in the Shigar Sub-Basin, and the lowest, 2,409 m

Journal of Disaster Research Vol.12 No.4, 2017 793 Liu, T., Tsuda, M., and Iwami, Y.

a.s.l., is found in the Hunza Sub-Basin [11]. Previous studies have reported that about 50–80% of the total average river flows in the Indus system are fed by snow and glacier meltwater in the Hindu–Kush–Karakoram (HKK) region of the Great Himalayas [2, 12, 13]. Accordingly, the snow cover in the Upper Indus Basin shrinks approximately 1.5 × 105 to 2.5 × 104 km2 from April to Au- gust every year [14]. Moreover, the northern mountain- ous areas are particularly susceptible to early monsoon floods caused by the rapid melting of snow and outflow from glacial lakes [15, 16]. In combination with specific weather conditions such as excessive rainfall on melting snow, rapid snowmelt can trigger floods, landslides, and debris flows. In addition, the temporal and spatial variability of snowfall and the changes in snow and glacier meltwater can be amplified by the changing climate, which has implications for managing basin water resources. Fig. 1. Map of the Indus River Basin including meteorological and gauging stations. The current Indus-IFAS relies mainly on observed discharge from upstream as input and includes the glacier and snow meltwater component to obtain reliable simu- Table 1. Global Land Cover by National Mapping Organi- lated discharge in the downstream region. The objective zations (GLCNMO) land cover classification and Integrated of this study is to improve the simulated discharge by the Flood Analysis System (IFAS) surface tank parameter class current flood Indus-IFAS, particularly in the Upper Indus [19]. Basin, by developing and applying a method for simulat- ing glacier and snow meltwater reliably with limited ob- Global Map Categories IFAS Parameter served meteorological data. Indus-IFAS is improved by 1 Broadleaf Evergreen Forest considering glacier and snow meltwater calculated with 2 Broadleaf Deciduous Forest 3 Needle leaf Evergreen Forest ground-based air temperature, snow cover from Moder- 1 ate Resolution Imaging Spectroradiometer (MODIS), and 4 Needle leaf Deciduous Forest 5 Mixed Forest glacial coverage combined with ground-based rainfall ob- 6 Tree Open servations. 7 Shrub 8 Herbaceousv 9 Herbaceous with Sparse Tree/Shrub 2 2. Study Region 10 Sparse vegetation 16 Bare area consolidated (gravel, rock) This study focuses on the Upper Indus Basin, which is 17 Bare area unconsolidated (sand) defined as the catchment area upstream of Tarbela Dam 11 Cropland with an area of 277,075 km2 [14] (Fig. 1). As the river 12 Paddy field leaves the mountains, Tarbela Dam is the first major struc- 13 Cropland/ Other vegetation Mosaic 3 ture on the Indus River and supplies irrigation for the agri- 14 Mangrove cultural lands. The management of the Tarbela Dam de- 15 Wetland 18 Urban 4 pends greatly on the summer inflow contribution of the 19 Snow/Ice snow- and glacier-fed tributaries [14]. 20 Water bodies 5 During the monsoon season, from the middle of June to the end of September, monsoon rainfall causes peak floods together with snowmelt runoff in short duration. Major tributaries of the Indus upstream of Tarbela Dam at 1 km resolution. are shown in Fig. 1. Three water gauging stations at River channels were generated and corrected to match Skardu, Partab Bridge (P. Bridge), and Tarbela Dam are Google Earth images for mainstreams of Indus [5]. investigated in this study. The land use distribution is based on GLCNMO Global Map Land Cover data, which provides global coverage 3. Data at 1 km resolution. Twenty land use categories were included in the modeling and were summarized into five pa- 3.1. Topology, Land Use, and Soil rameter classes, as shown in Table 1. The parameters of the unsaturated tank were deter- The topographic data applied in this study were ob- mined according to soil texture and depth distribution tained from the Global Map elevation data by the Interna- available from the Harmonized World Soil Database v. tional Steering Committee for Global Mapping (ISCGM) 1.2 [17]. For the Upper Indus Basin, 11 soil textural

794 Journal of Disaster Research Vol.12 No.4, 2017 A Study on Flood Forecasting in the Upper Indus Basin Considering Snow and Glacier Meltwater

Table 2. Meteorological stations of the Pakistan Meteoro- logical Department (PMD) in the Indus River main stream.

Latitude Longitude Altitude Station Name (◦CN) (◦CE) (m) ASTORE 35.32 74.86 2200 BUNJI 35.64 74.64 1400 CHILAS 35.42 74.09 1250 CHITRAL 35.84 71.78 1500 GILGIT 35.92 74.33 1450 GUPIS 36.23 73.45 2150 SKARDU 35.34 75.54 2300 ISLAMABAD 33.68 73.06 525 BALAKOT 34.55 73.35 1000 DIR 35.21 71.88 1400 DROSH 35.57 71.8 1350 G.DUPATTA 34.22 73.62 825 KALAM 35.48 72.58 2000 MALAMJABBA 34.8 72.57 2450 Fig. 2. Sub-basins of the Upper Indus Basin. MURREE 33.91 73.39 2000 MUZAFFARABAD 34.37 73.47 750 SAIDUSHARIF 34.75 72.36 950 of the Indus River Basin causes difficulties in collecting hydrometeorological data from neighboring countries. types excluding clay were identified by using the United States Department of Agriculture (USDA) soil texture tri- 3.3. Glacier and Snow Data angle [18]. The soil physical properties were determined The glacial coverage in the Upper Indus Basin was by field survey conducted by the Pakistan Council of Re- obtained from the glacier inventory of ICIMOD [11]. search in Water Resources (PCRWR). Two types of snow cover data were used in this study. To apply the model to a large extent considering the MODIS/Terra Snow Cover Daily L3 Global 500 m Grid, calculation time for real-time forecasting, the topographic Version 6 (MOD10A1) identifies daily snow cover by us- data, land use, and soil data were upscaled to 5 km to ing the Normalized Difference Snow Index (NDSI) [22]. construct IFAS model for the Upper Indus Basin. MODIS/Terra Snow Cover eight-day L3 Global 500 m Grid, Version 6 (MOD10A2) [23] provides the maximum snow cover extent generated by reading eight days of 3.2. Ground-Based Air Temperature, Precipitation, MOD10A1 data. Four tiles from each database were mo- and Discharge saicked to cover the entire Upper Indus Basin. Figure 1 shows the locations of gauging stations in The glacial coverage and mosaicked MODIS scenes the Upper Indus Basin. Six hourly discharge datasets were projected to the World Geodetic System 1984 recorded during monsoon months were obtained from (WGS-84) coordinate system and were upscaled to the the Water and Power Development Authority (WAPDA) IFAS grid system of 5 km. Fractional glacier and snow through the PMD for model calibration and validation. cover amounts were then extracted to assess the percent- Ground-based daily maximum (Tmax), minimum (Tmin), age of glacier and snow cover in each grid. and average temperature (Tave) were observed by the PMD at 17 meteorological stations located in northern areas of Pakistan (Table 2 and Fig. 1). The Thiessen polygon 4. Methodology method was applied to interpolate the data values, and the results were adjusted with elevation to calculate snowmelt The Upper Indus Basin is a large basin divided into in each 5 km cell [20]. three sub-basins with scarce ground gauges (Fig. 2). In Figure 1 shows the locations of 92 rain gauges in previous studies, upstream station discharges for each Pakistan with 15 rain gauges in the Upper Indus Basin, sub-basin are given as the boundary conditions to indi- which cover more than 277,075 km2 corresponding to rectly account for glacier- and snow-induced runoff [5, 9]. an average density of 1.8 × 104 km2 per station. This The present study includes meltwater from glacier and value significantly exceeds the World Meteorological Or- snow cover. ganization (WMO) recommendations of one station every As demonstrated in the flowchart in Fig. 3, elevation- 250 km2 for mountains or 575 km2 for hilly and plain adjusted air temperature was used to separate snowfall areas [21]. However, the geography of the Upper Indus and rainfall from precipitation. Snowfall was then ap- Basin, with a maximum elevation of more than 7,000 m, plied to calibrate fractional snow cover detected on each creates challenges in accessing, installing, and maintain- grid by MODIS snow products. If the grid was covered ing rain gauges. Moreover, the transboundary character with snow, only snowmelt was calculated. If no snow was

Journal of Disaster Research Vol.12 No.4, 2017 795 Liu, T., Tsuda, M., and Iwami, Y.

Fig. 3. Flowchart used in the present study.

detected on a grid, glacier coverage was judged. After Table 3. Four-tank Public Works Research Institute- Dis- that glacier and snow meltwater was calculated by us- tributed Hydrological Model (PWRI-DHM) [19]. ing the degree-day method for each grid with elevation- adjusted air temperature and glacier/snow coverage. The Model Function obtained glacier and snow meltwater amounts were even- • Infiltration to unsaturated layer tually input into the IFAS together with rainfall to sim- • surface runoff ulate discharge during the monsoon season. The IFAS Surface tank model • surface storage simulated runoff was evaluated afterward by comparison • evapotranspiration with the observed discharge for calibration and validation • rapid unsaturated subsurface flow at Skardu, P. Bridge, and Tarbela Dam. Because this study • Infiltration to aquifer simulated glacier and snow meltwater by coverage type, Subsurface tank model • subsurface runoff snow depth and the condition of snow on a glacier surface (for the 3 tank model) • subsurface storage • were not investigated. slow unsaturated subsurface flow • Outflow from aquifer Aquifer tank model • aquifer loss • 4.1. IFAS River course tank model River course discharge IFAS, a Graphic User Interface for building a distributed rainfall-runoff analysis model, was released by ICHARM at Public Works Research Institute (PWRI), by using the data of 2010 at the gauging stations (Fig. 1) Japan [19]. The PWRI-Distributed Hydrological Model and was validated with the data of 2012 [5]. (PWRI-DHM) is the main model of IFAS. In this study, a 5-km mesh model was built in IFAS for the mainstream of the Upper Indus River. PWRI-DHM is based on the 4.2. Glacier and Snow Meltwater Runoff Module tank model concept in a distributed four-tank configura- The complexity of existing glacier/snowmelt models tion including surface, subsurface, and river course tanks varies significantly, ranging from simple degree-day mod- (Table 3) [19, 24]. els [26–30] to advanced physically based energy balance- A surface layer tank was presented by five classes of based models [31–34]. The physical approach is usu- parameters according to land use based on GLCNMO ally data-driven and requires complex calculations of vari- Global Map Land cover (Table 1). An unsaturated layer ables with a high spatial and temporal resolution database. tank and an aquifer layer tank were parameterized based Thus, construction of an entire database for such models on soil texture, whereby flows go directly or indirectly is challenging in regions with scarce data. Moreover, the into the river course tank. A kinematic wave river routing main task of IFAS is to efficiently and accurately forecast model was the engine in the river course tank represented floods during the monsoon season when limited data are by a set of 10 parameters [25]. The initial conditions were available. Simplicity is crucial for both data preparation obtained by running the model several times with forcing and operation by local technicians. data until hydrological equilibrium was reached. In this study, the degree-day method, which requires The model except for the snow module was calibrated only air temperature, was applied to calculate the glacier

796 Journal of Disaster Research Vol.12 No.4, 2017 A Study on Flood Forecasting in the Upper Indus Basin Considering Snow and Glacier Meltwater

Table 4. Experiment designed in the study. Degree-day factor Lapse rate Snow cover Glacial coverage (α,mm◦C-1d-1)(γ, ◦C per 100 m) (%) (%) 5.1 Degree-day factor (α) 4.0 0.65 Daily N/A • 0.65 5.2 Lapse rate (γ)4.0 • Monthly Daily N/A • 0.65 • Daily 5.3 Snow cover 4.0 • Monthly • 8-day N/A • Snow: 4.0 • Daily ICIMOD 5.4 Glacial coverage • Glacier: 4.0 & 9.0 Monthly • 8-day

Fig. 4. Sensitivity analysis of degree-day factor and lapse rate with snowmelt in 2010.

and snow meltwater amounts from the glacial and snow 5.1. Sensitivity Analysis of Degree-Day Factor coverage: Sensitivity analysis of the degree-day factor (α)for M = α (T − T ) f snow was conducted by comparing the simulated and ob- cal cri glacier/snow ..... (1) M = 0, Tcal ≤ Tcri served discharge at Tarbela, P. Bridge, and Skardu from June 15 to September 30, 2010 (Fig. 4). The lapse = γ ( − )+ Tcal Hobs H T ...... (2) rate (γ) remained constant at 0.65◦C per 100 m. The α ◦ -1 −1 MODIS daily snow cover product (MOD10A1) was ap- where is the degree-day factor (mm C d ); Tcal is ◦ ◦ plied. The critical temperature Tcri was set at 4.0 C. the air temperature adjusted with elevation ( C); Tcri is the ◦ The melting temperature was suggested by Pakistani re- critical temperature ( C); fglacier/snow is the glacial coverage (%) of the grid determined by ICIMOD glacier in- searchers. Since the dynamics of snowpack or ice were ventory and snow cover (%) of the grid obtained from not addressed, this value may to some extent represent γ the waring and ripening phase of snowpack. the MODIS snow products, is the lapse rate, in which ◦ −1 −1 0.65◦C per 100 m is generally used [35]; T is the air tem- A degree-day factor of 3.4 mm C d was reported perature observed at the nearest station (◦C); H is the during June–August 1975 for the Batura glacier in the Up- obs per Indus Basin in China [36]. An additional degree-day elevation of the nearest station (in 100 m); and H is the ◦ −1 −1 elevation of the grid (in 100 m). factor of 4.0 mm C d was obtained near Gilgit in the Upper Indus Basin in Pakistan [37]. Compared with the observed discharge at the three stations in 2010, the ◦ −1 −1 5. Results and Discussion degree-day factor of 4.0 C d was relatively reasonable. To improve the simulated discharge in the Upper In- From the end of July to August 2010, Pakistan ex- dus Basin by the current flood forecasting system, Indus- perienced the most disastrous flood of past decades. IFAS, this study calculated glacier and snow meltwater Peak flooding was observed in the Upper Indus Basin with air temperature, snow cover, and glacial coverage. at Skardu, the uppermost station, as shown by circles The experiments designed in the study are shown in Ta- in Fig. 4. This flooding was later intensified through ble 4. P. Bridge to Tarbela. The simulated discharge with snowmelt, labeled as “with snow” in Fig. 4, performed significantly better than that without snowmelt, particularly before the observed heavy rainfall at the end of July

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Table 5. Monthly lapse rate (γ, ◦C·km−1) in the Upper Indus Basin [38].

Month Jun Jul Aug Sep γ Tmax 7.3 6.1 5.6 6.7 γ Tmin 5.9 5.9 5.6 6.2 γ Tave 6.0 5.7 5.7 6.2

2010. However, the main ﬂood peak was still missing, which was considered due to snow cover and precipitation uncertainty.

Fig. 5. Comparison of snow cover of the Upper Indus 5.2. Sensitivity Analysis of Lapse Rate Basin by Moderate Resolution Imaging Spectroradiometer It’s reported that lapse rate in Hunza Basin in the Up- (MODIS) snow products. per Indus Basin is 0.48–0.76◦C per 100 m depending on elevation zones, whereas 0.65◦C per 100 m is the general lapse rate in the region [35]. A study in the Karako- maximum snow extent during the eight days. Moreover, rum region reported that the lapse rate varies from 0.65 to ◦ the snow cover was counted as 1 or 0 instead of percent- 0.75 C per 100 m [12]. age. As a result, there was an obvious gap between the Lapse rates have been analyzed and obtained in previ- overall snow coverage in the basin deduced by the two ous research using ground-based air temperature observed datasets. by the PMD [38]. Observed air temperature, including The simulated discharge at the three stations had sim- the daily maximum, minimum, and average, were plotted ilar patterns as that simulated with daily snow product against altitudes of the stations, from which slopes were MOD10A1 (Fig. 5). However, the eight-day snow cover obtained as monthly lapse rates (Table 5). Because this performed better during the flood peak time except for a study focuses on the monsoon season, monthly lapse rates deficit on August 10. obtained by average daily air temperatures recorded from Discharge generated from the three sub-basins is the middle of June to the end of September were used. strongly affected by snowmelt that is proportional to snow Sensitivity analysis of the lapse rate (γ) was conducted coverage. Therefore, snow area in the monsoon season by comparing the simulated and observed discharge at should be carefully calibrated and evaluated. As shown Tarbela, P. Bridge, and Skardu from June 15 to September in Fig. 6, snow coverage detected in each sub-basin by 30, 2010, with the degree-day factor (α) for snow set as eight-day snow cover was at least twice that by daily snow ◦ −1 −1 4.0 mm C d . The critical temperature Tcri was set as cover. However, even though the snow coverage was ex- 4.0◦C for this analysis. ◦ tremely enlarged, the main peak that occurred around Au- The lapse rate, 0.65 C per 100 m, and monthly lapse gust 10 was still missing (P. Bridge in Fig. 5). There are rate (Table 5) were analyzed, and the simulated and ob- two reasons for this, namely, possible glacier contribution served discharge values were compared. The simulated and uncertainties due to precipitation. discharge with the monthly lapse rate better captured the variations, particularly the peaks, compared with that sim- ◦ 5.4. Meltwater from Glacial Coverage ulated with a lapse rate of 0.65 C per 100 m (Fig. 4). This study calculated the meltwater from glacial coverage by using the air temperature and fractional glacier 5.3. Snow Cover cover obtained from ICIMOD glacier inventory (Figs. 3 This study calculated the snowmelt by using air temper- and 7). Meltwater generated from the three sub-basins is ature and fractional snow cover obtained from the daily proportional to the glacial coverage. As shown in Fig. 7, MODIS snow product MOD10A1. Because MOD10A1 numerous glacial areas were detected in the sub-basins, cannot detect snow cover under cloud coverage, the snow particularly in Skardu, compared with the snow cover on cover can be underestimated during intensive precipi- July 27. tation with extensive cloud coverage. To investigate To investigate the contribution of glaciers to the dis- whether the missing flood peak that occurred in August charge from no-snow areas and after snow melt, both 2010 is related to the potential underestimation of snow daily and eight-day snow cover were tested (Figs. 8 and cover, eight-day MODIS snow product MOD10A2 was 9). For parameter setting, the degree-day factor (α)for applied with the same methodology and parameters as glaciers was set at 4.0 mm ◦C−1 d−1 (labeled as “snow those described in the previous sections. 4.0, glacier 4.0”) and 9.0 mm ◦C−1 d−1 (labeled as “snow As shown in Fig. 5, the percentage obtained from eight- 4.0, glacier 9.0”) to simulate the discharge during the day data was much larger than that obtained from daily monsoon in 2010. The critical temperature Tcri was set data. The eight-day snow product MOD10A2 selected the at 4.0◦C.

798 Journal of Disaster Research Vol.12 No.4, 2017 A Study on Flood Forecasting in the Upper Indus Basin Considering Snow and Glacier Meltwater

Fig. 6. Simulation with ground-based precipitation including the eight-day maximum snow extent in 2010.

Fig. 7. Glacier and snow coverage of the Upper Indus Basin.

Fig. 8. Simulation with ground-based precipitation and daily trials of glacier and snow melt in 2010.

Figure 8 shows that with daily snow cover, the sim- ing the missing ﬂood peak occurring in August 2010. ulated discharge at all three stations increased after the However, only a slight change was observed when the inclusion of glaciermelt. The simulated discharge showed eight-day snow cover was applied (Fig. 9). The simu- that the meltwater contribution from glacial coverage was lated and observed discharge agreed well during the early signiﬁcant through the monsoon season, particularly dur- monsoon, which demonstrates that the meltwater from

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Fig. 9. Simulation with ground-based precipitation and eight-day trials of glacier and snow melt in 2010.

snow cover on the ground was the main source of the glacier meltwater, whereas eight-day snow cover can be discharge occurring during the early monsoon in the Up- applied without considering glaciers. In practical appli- per Indus Basin. Contribution from glaciermelt became cation, eight-day snow cover can be utilized for real-time more significant when the snow cover was diminished ow- forecasting; however, the maximum snow cover overesti- ing to snowmelt. In the case of 2010, the simulated dis- mates the actual snow area to some extent. charge approached the magnitude of observed discharge considering glaciermelt and snowmelt; however, the main peak around August 10, 2010, was missing, particularly at 5.6. Meltwater Considering Snow Depth Skardu and P. Bridge. It is believed that the missing peak As demonstrated in the flowchart (Fig. 3), glacier originated from the upstream region owing to the uncer- and snowmelt were calculated by using the degree-day tainty of the observed precipitation during that period. method for each grid including the elevation-adjusted air temperature and glacier/snow coverage. The glacial cov- 5.5. Validation of Parameters erage was fixed, whereas the snow cover was obtained The optimum combination of parameters was cali- from daily or eight-day MODIS snow products. Because brated and validated with ground-based precipitation and snowmelt depends strongly on snow cover, the daily snow air temperature excluding observed upstream discharge as area was at times underestimated, as discussed in the pre- boundary conditions in 2012 (Fig. 10). For parameter set- vious sections. Therefore, this analysis considered initial ting, degree-day factors (α) for glacier and snow were snow depth rather than daily snow cover. ◦ −1 −1 setat4.0mm C d , the critical temperature Tcrifor Snow cover obtained on June 15, 2010, was used to melting was set at 4.0◦C, and the lapse rate varied on a define the initial snow cover for this analysis. Uniform monthly basis (Table 5). depth of snow was applied for each grid here. Snowfall in The year 2012 was considered with normal floods when this case was accumulated as snow packs in water equiva- the maximum daily precipitation was observed to be less lent, whereas snowmelt was extracted from the snow pack than 50 mm. Accordingly, the meltwater from snow and while melting. The glacial coverage was fixed, and the glacier cover was the main contributor to the discharge for depth was defined as infinite. such normal years. Discharge simulated with daily snow The obtained glacier and snow meltwater was eventu- cover captured the variations of the observed discharge in ally input into the IFAS together with rainfall to simulate general, as shown in the upper images in Fig. 10.How- discharge at Skardu, P. Bridge, and Tarbela Dam during ever, the discharge without glacier meltwater was not suf- the monsoon season in 2010. The experimental combina- ficient compared with the discharge simulated with eight- tion of parameters is given in Table 6. The critical temper- ◦ day snow cover in terms of observed magnitude and tim- ature Tcri was set at 4.0 C for calculating snow and glacier ing, as shown in the bottom images in the figure. The meltwater. The IFAS-simulated runoff was evaluated af- simulated discharge with eight-day maximum snow cover terward by comparison with the observed discharge. obtained by the two methods showed almost the same re- The initial snow depth values were applied and eval- sults. These results agreed well with the variations of the uated. In Fig. 11, the dotted line labeled as “snow m, observed discharge during the entire monsoon period, par- glacier 0.0, Tm 4, LR m (snow pack: 0.5 K)” shows the ticularly at P. Bridge and Skardu. simulated discharge with the initial snow pack as 500 mm It can be concluded that the snow and glacier meltwater without glaciers; “snow m, glacier m, Tm 4, LR m (snow methods are effective when precipitation is properly ob- pack: 0.5 K)” shows the simulated discharge with the served. Daily snow cover performed better by including initial snow pack as 500 mm but includes glaciers. A

800 Journal of Disaster Research Vol.12 No.4, 2017 A Study on Flood Forecasting in the Upper Indus Basin Considering Snow and Glacier Meltwater

Fig. 10. Validation of glacier and snow meltwater-included simulations of 2012 with daily (upper images) and eight-day Moderate Resolution Imaging Spectroradiometer (MODIS) (bottom images) snow products.

comparison of the two lines revealed a signiﬁcant dis- Table 6. Optimum combination of parameters for glacier charge contribution by glaciers. The simulation without and snow meltwater in the Upper Indus Basin. glaciermelt presented a reasonable magnitude in the early monsoon season, whereas the remainder of the season re- Month Jun Jul Aug Sep mained at low discharge. Moreover, the simulation with Degree-day factor α glaciermelt presented relatively steady discharge through- (glacier) glacier 2.0 3.0 6.0 2.0 out the monsoon. The other two simulations with ini- Degree-day factor α tial snow depths as 1000 mm and 2000 mm approached (snow) snow 2.0 3.0 6.0 2.0 the maximum magnitude of the observed discharge from Lapse Rate γ Tave 6.0 5.7 5.7 6.2 Skardu, the uppermost station. It can be concluded from this experiment that snow pack might be able to eliminate the effects of underes- cesses should not be eliminated. As shown in Fig. 12,the timated snow cover owing to cloudiness. However, ob- snow cover obtained by MODIS tended to increase after taining accurate snow depth as an initial condition could a sudden drop in air temperature in late August, which be challenging. is the time at which the simulated discharge exceeded the observation. It is assumed that it occurred when the temperature difference between day and night was sufﬁ- 5.7. Overestimation During Late Monsoon ciently large so that the air temperature at night could be Simulated discharge was generally overestimated dur- less than 0◦C at high altitudinal regions. Precipitation was ing the late monsoon season in late September after the therefore in the form of snowfall, whereas snow cover on snowmelt was included, whereas the simulations without the ground increased. Moreover, as shown in Fig. 12,the snowmelt were in accord with the observations (Fig. 10). snow cover varied in a shallow U-shape, which correlates However, the average daily air temperature remained with the seasonal variations of insolation. As reported higher than 10◦C, which demonstrated that melting pro- previously, radiation accounts for 66–90% of the energy

Journal of Disaster Research Vol.12 No.4, 2017 801 Liu, T., Tsuda, M., and Iwami, Y.

Fig. 11. Simulation with ground-based precipitation and trials of snow pack in 2010.

data are less affected by cloudiness. According to the analysis, glaciermelt and snowmelt contribution at Skardu was estimated to be 43.2–65.2% of the average daily discharge during the monsoon season in 2005–2012. Glacier and snow meltwater occurring between Skardu and P. Bridge was estimated to be 22.0–29.3% of the average daily discharge observed at P. Bridge. The glaciermelt and snowmelt originating from sub-catchments between P. Bridge and Tarbela were calculated to be 6.3–19.9% of the average daily discharge observed at Tarbela. Scarce ground-gauge precipitation records are another reason for the missing ﬂood peaks. After the increased contribution of glaciermelt and snowmelt, deﬁcits remained that are believed to originate from the upstream Fig. 12. Comparison of snow cover and air temperature in owing to the lower amount of observed precipitation in the Upper Indus Basin in 2010. the upstream region (Fig. 10).

available for melt [39]. Physically, three basic phases of 6. Conclusion snowmelt occur in a snowpack – warming, ripening, and output – which are driven by the internal energy budget Indus-IFAS, the current flood analysis system utilized [40]. The overestimation of snowmelt in September indi- in the Indus River Basin in Pakistan, does not explicitly cates that the region received less insolation as the main consider glaciermelt or snowmelt processes. This study energy source for melting, as shown by the decrease in improved the system by including glacier and snow melt- air temperature and increase in snow cover area (Fig. 12). water in the Upper Indus Basin. These parameters were This phenomenon can be further investigated by examin- calculated by using the degree-day method with ground- ing the energy balance within the snowpack. However, based air temperature and glacier/snow cover obtained by energy balance was not addressed in this study. MODIS daily and eight-day products. The values were It can be concluded that the simulated discharge with then input with ground-based rainfall into IFAS to sim- glacier and snow meltwater substantially improved the ulate discharge at Skardu, P. Bridge, and Tarbela Dam model performance in the Upper Indus Basin, particularly during the monsoon season, from the middle of June to September. The optimum parameter set was determined during the early monsoon. However, discrepancies re- ◦ −1 −1 sulted from the uncertainties of precipitation during mas- when the degree-day factor, 4.0 mm C d ,usedfor sive flooding. The simulations with snow packs were both glacier and snow meltwater and the lapse rate, was therefore investigated, which could approach the maxi- used as the monthly value. This result was validated in mum magnitude of the observed discharge even from the 2010 and 2012. The simulated discharge with glacier- uppermost station, Skardu. It is believed that snow pack melt and snowmelt remarkably improved the model per- might be able to eliminate the effects of underestimated formance in the Upper Indus Basin, particularly during snow cover owing to cloudiness or overestimation in the the early monsoon. eight-day maximum snow cover. However, obtaining ac- During normal flood years such as 2012, the simulated curate snow depth as an initial condition can be challeng- discharge with snowmelt could capture the variations of ing. Therefore, eight-day maximum snow cover can be the observed discharge in terms of peak volume and tim- practically applied for real-time forecasting because the ing, particularly with eight-day snow cover. The eight-

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[31] E. A. Anderson, “A point energy and mass balance model of a snow cover,” Energy, Vol.114, No.D24, p. 150, 1976. Name: [32] N. Rutter, R. Essery, J. Pomeroy, N. Altimir, K. Andreadis, I. Baker, Tong Liu A. Barr, P. Bartlett, A. Boone, H. Deng, H. Douville, E. Dutra, K. Elder, C. Ellis, X. Feng, A. Gelfan, A. Goodbody, Y. Gusev, D. Gustafsson, R. Hellstrom, Y. Hirabayashi, T. Hirota, T. Jonas, V. Affiliation: Koren, A. Kuragina, D. Lettenmaier, W.-P. Li, C. Luce, E. Mar- Research Specialist, International Centre for Wa- tin, O. Nasonova, J. Pumpanen, R. D. Pyles, P. Samuelsson, M. ter Hazard and Risk Management (ICHARM) Sandells, G. Schadler, A. Shmakin, T. G. Smirnova, M. Stahli, R. under the auspices of UNESCO, Public Works Stockli, U. Strasser, H. Su, K. Suzuki, K. Takata, K. Tanaka, E. Thompson, T. Vesala, P. Viterbo, A. Wiltshire, K. Xia, Y. Xue, Research Institute and T. Yamazaki, “Evaluation of forest snow processes models (SnowMIP2),” J. Geophys. Res., Vol.114, No.D6, p. D06111, Mar. 2009. Address: [33] J. Pomeroy, M. MacDonald, C. DeBeer, and T. Brown, “Model- ing Alpine Snow Hydrology in the Canadian Rocky Mountains,” in 1-6 Minamihara, Tsukuba, Ibaraki 305-8516, Japan Western Snow Conf., 2009. Brief Career: [34] M. Shrestha, L. Wang, T. Koike, Y. Xue, and Y. Hirabayashi, “Im- 2014- Research Specialist at ICHARM, PWRI proving the snow physics of WEB-DHM and its point evaluation 2010-2013 Ph.D. Candidate at Tokyo Institute of Technology at the SnowMIP sites,” Hydrol. Earth Syst. Sci., Vol.14, No.12, 2007-2009 Research Specialist at the Place Pharmaceutical Co., Ltd in pp. 2577-2594, Dec. 2010. China [35] A. A. Tahir, P. Chevallier, Y. Arnaud, and B. Ahmad, “Snow 2006-2007 Master student at the University of Leeds in UK cover dynamics and hydrological regime of the Hunza River basin, Selected Publications: Karakoram Range, Northern Pakistan,” Hydrol. Earth Syst. Sci., • Vol.15, No.7, pp. 2259-2274, 2011. T. Liu, T. Kinouchi, J. Mendoza, and Y. Iwami, “Glacier mass balance and catchment-scale water balance in Bolivian Andes,” J. of Disaster [36] Y. Zhang, S. Liu, and Y. Ding, “Observed degree-day factors and their spatial variation on glaciers in western China,” Ann. Glaciol., Research, Vol.11, No.6, pp. 1040-1051, 2016. Vol.43, No.1, pp. 301-306, 2006. • T. Liu, T. Kinouchi, and F. Ledezma, “Characterization of Recent [37] C. Mayer, A. Lambrecht, C. Mihalcea, M. Belo, G. Diolaiuti, C. Glacier Decline in the Cordillera Real by LANDSAT,” ALOS, and ASTER Smiraglia, and F. Bashir, “Analysis of Glacial Meltwater in Bagrot Data, Remote Sensing of Environment, Vol.137, pp. 158-172, 2013. Valley, Karakoram: Based on Short-term Ablation and Debris • T. Liu, T. Kinouchi, and F. Ledezma, “Spatial Distribution of Glacier Cover Observations on Hinarche Glacier,” Mt. Res. Dev., Vol.30, Melt Deduced from Solar Radiation Mapping with Refined Atmospheric No.2, pp. 169-177, 2010. Parameters,” J. of Japan Society of Civil Engineers, Ser. B1 (Hydraulic [38] M. Afzal, “Estimation of snowmelt contribution to discharge in Up- Engineering), Vol.69, No.4, pp. 181-186, 2013. per Indus Basin in Pakistan using degree day method,” National Academic Societies & Scientific Organizations: Graduate Institute for Policy Studies (GRIPS), Tokyo, Japan, 2014. • American Geophysical Union (AGU) [39] D. Marks and J. Dozier, “Climate and energy exchange at the snow surface in the alpine region of the Sierra Nevada 2. Snow Cover En- ergy Balance,” Water Resour. Res., Vol.28, No.11, pp. 3043-3054, 1992. [40] D. H. Male and R. J. Granger, “Snow surface energy exchange,” Water Resour. Res., Vol.17, No.3, pp. 609-627, 1981. Name: Morimasa Tsuda

Afﬁliation: Senior Researcher, Dr. Eng, International Cen- tre for Water Hazard and Risk Management (ICHARM) under the auspices of UNESCO, Public Works Research Institute

Address: 1-6 Minamihara, Tsukuba, Ibaraki 305-8516, Japan Brief Career: 2005- Engineering Center, Japan Water Agency 2007- Shikoku Regional Head Office, Japan Water Agency 2010- Administration, Operation and Maintenance Department, Japan Water Agency 2012- Kansai Regional Head Office, Japan Water Agency 2014- Senior Researcher, ICHARM, PWRI Selected Publications: • A. Sugiura, S. Fujioka, S. Nabesaka, M. Tsuda, Y. Iwami, “Development of a flood forecasting system on upper Indus catchment using IFAS,” J. Flood Risk Manage., Vol.9, Issue 3, pp. 265-277, 2016. • M. Tsuda, S. Nishida, M. Irie, “Quantification of the adverse effects of drought caused by water supply restrictions considering the changes in household water consumption characteristics,” Water Sci. Technol.: Water Supply, Vol.14, No.5, pp. 743-750, 2014. Academic Societies & Scientific Organizations: • Japan Society of Civil Engineers (JSCE)

804 Journal of Disaster Research Vol.12 No.4, 2017 A Study on Flood Forecasting in the Upper Indus Basin Considering Snow and Glacier Meltwater

Name: Yoichi Iwami

Afﬁliation: Director, Public Works Department, Nagasaki Prefectural Government

Address: 1-6 Minamihara, Tsukuba, Ibaraki 305-8516, Japan Brief Career: 2004- Senior Advisor on River Management, Mekong River Commission Secretariat 2006- Director of River & National Road Ofﬁce in Kochi, Shikoku D.B., MLIT 2010- Director of 3rd Research Department, Water Resources Environment Center 2012- Head of River Environment Research Division, NILIM, MLIT 2013- Chief Researcher on Hydrology and Hydraulics, ICHARM, PWRI 2017- Director of Public Works Department, Nagasaki Prefectural Government Selected Publications: • Y. Kwak, A. Yorozuya, and Y. Iwami, “Disaster Risk Reduction using Image Fusion of Optical and SAR Data Before and After Tsunami,” IEEE Aerospace2016, IEEE, DOI: 978-1-4673-7676-1/16, March 2016. • A. Hasegawa, M. A. Gusyev, T. Ushiyama, and Y. Iwami, “Drought assessment in the Pampanga River basin, the Philippines – Part 2: A comparative SPI approach for quantifying climate change hazards,” In T. Weber, M. J. McPhee, and R. S. Anderssen (Eds.), MODSIM2015, 21st Int. Congress on Modelling and Simulation. Modelling and Simulation Society of Australia and New Zealand, pp. 2388-2394, December 2015 :ISBN: 978-0-9872143-5-5. • A. Sugiura, S. Fujioka, S. Nabesaka, M. Tsuda, and Y. Iwami “Development of a Flood Forecasting System on Upper Indus Catchment Using IFAS,” Proc. of 6th Int. Conf. on Flood Management (ICFM6), ICFM6, Sept 2014. Academic Societies & Scientiﬁc Organizations: • Japan Society of Civil Engineers (JSCE) • Japan Society of Hydrology and Water Resources (JSHWR)

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