Understanding the hydrological system ... - Wiley Online Library

HYDROLOGICAL PROCESSES Hydrol. Process. 28, 1329–1344 (2014) Published online 9 January 2013 in Wiley Online Library (wileyonlinelibrary.com) DOI: 10.1002/hyp.9627

Understanding the hydrological system dynamics of a glaciated alpine catchment in the Himalayan region using the J2000 hydrological model S. Nepal,*,† P. Krause, W.-A. Flügel, M. Fink and C. Fischer Department for Geoinformatics, Hydrology and Modelling, Friedrich Schiller University of Jena, Jena, Germany

Abstract: This paper provides the results of hydrological modelling in a mesoscale glaciated alpine catchment of the Himalayan region. In the context of global climate change, the hydrological regime of an alpine mountain is likely to be affected, which might produce serious implications for downstream water availability. The main objective of this study was to understand the hydrological system dynamics of a glaciated catchment, the Dudh Kosi River basin, in Nepal, using the J2000 hydrological model and thereby understand how the rise in air temperature will affect the hydrological processes. The model is able to reproduce the overall hydrological dynamics quite well with an efficiency result of Nash–Sutcliffe (0.85), logarithm Nash–Sutcliffe (0.93) and coefficient of determination (0.85) for the study period. The average contribution from glacier areas to total streamflow is estimated to be 17%, and snowmelt (other than from glacier areas) accounts for another 17%. This indicates the significance of the snow and glacier runoff in the Himalayan region. The hypothetical rise in temperature scenarios at a rate of +2 and +4 C indicated that the snowmelt process might be largely affected. An increase in snowmelt volume is noted during the premonsoon period, whereas the contribution during the monsoon season is significantly decreased. This occurs mainly because the rise in temperature will shift the snowline up to areas of higher altitude and thereby reduce the snow storage capacity of the basin. This indicates that the region is particularly vulnerable to global climate change and the associated risk of decreasing water availability to downstream areas. Under the assumed warming scenarios, it is likely that in the future, the river might shift from a ‘melt-dominated river’ to a ‘rain-dominated river’. The J2000 model should be considered a promising tool to better understand the hydrological dynamics in alpine mountain catchments of the Himalayan region. This understanding will be quite useful for further analysis of ‘what-if scenarios’ in the context of global climate and land-use changes and ultimately for sustainable Integrated Water Resources Management in the Himalayan region. Copyright © 2012 John Wiley & Sons, Ltd. KEY WORDS

hydrological system dynamics; snow and glacier melt; climate change; Himalayan region; hydrological modelling, sensitivity and uncertainty analysis

Received 20 September 2011; Accepted 8 October 2012

INTRODUCTION The mountainous areas play a key role in hydrology because they usually receive high amounts of precipitation, which may be stored in the form of snow and glaciers. These areas provide essential freshwater for populations living in both upstream and downstream areas (Viviroli et al., 2003; Nepal, 2012). The glaciated alpine catchment of the Himalayan region serves the lives and livelihoods for millions of people living downstream (Eriksson et al., 2009). The snow and glacier melt have particular significance for downstream communities during the dry season (Immerzeel et al., 2010; Nepal, 2012). In the context of global climate change, a major concern is how this water storage and release (in both amount and timing) will impact the hydrological balance and the future availability of water resources in the region (Immerzeel et al., 2010). Therefore, understanding the existing hydrological regimes of these

*Correspondence to: Santosh Nepal, Department for Geoinformatics, Hydrology and Modelling, Friedrich Schiller University of Jena, Jena, Germany. E-mail: [email protected] † Current Address: International Centre for Integrated Mountain Development, Kathmandu, Nepal. E-mail: [email protected] Copyright © 2012 John Wiley & Sons, Ltd.

river systems is vital. This requires better insight into the relationship between different watershed components (such as soil, groundwater, glacier and snow) so that streamflows may be quantitatively evaluated (Armstrong, 2011; Alford, 1992). Hydrological models are an important tool to understand the characteristics of a catchment and its response to streamflow (Beven, 2001a; Krause, 2002; Nepal, 2012). The following factors complicate the analysis of hydrological dynamics of Himalayan river systems using a hydrological model: • lack of representative data (Alford, 1992; Sharma et al., 2000; Kattelmann, 1987), due to a low-density station network and a particular location (low altitude and valleys) where it is difficult to adequately characterize the precipitation dynamics of mountainous areas; • uncertainties due to absence of long-term data records and due to practical difficulties in maintaining data quality (e.g. remoteness and lack of accessibility; Kattelmann, 1987); • inability of a model to capture the hydrological dynamics of the Himalayan rivers in general terms, for example, greater floods during the monsoon season and low flows

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during the winter season, and to represent the several runoff components, such as glacier, snowmelt and baseflow. Therefore, a robust and dynamic hydrological model, which can address the previously described limitations as much as possible in mesoscale and macroscale catchments where the heterogeneity of the catchment parameters increases with a decrease in data accuracy, is required (Krause, 2002; Alford, 1992). The application of hydrological models to understand the hydrological dynamics in the Himalayan region is a relatively new approach. As a result, in some cases, modelling applications have been conducted without using glacier information (Sharma et al., 2000; Gosain et al., 2011). However, in recent years, the relative importance of melt runoff has been discussed in the context of climate change (Immerzeel et al., 2010; Eriksson et al., 2009). In the Himalayan region, snow and glacier melt have been estimated by using empirical approaches, for example, snowmelt based on ablation gradient (Alford and Armstrong, 2010), threshold air temperature (Sharma et al., 2000), and a degree-day factor (Immerzeel et al., 2010). Immerzeel et al. (2012) recently used an enhanced degree-day factor, considering aspects and debris-covered glaciers, which also replicate the glacial ice-melt process. However, detailed information about the integrated hydrological system analysis is still limited, as comprehensive knowledge and data about system dynamics, such as runoff components and snow and glacier ice melt, are not available. In this study, the process-oriented distributed J2000 hydrological model (Krause, 2001) is used to better understand the hydrological system dynamics of the study area. The glacier model has been integrated into the J2000 hydrological model to simulate snow and glacier melt using an enhanced degree-day approach by considering factors such as temperature, radiation, aspects and debriscovered glaciers. The J2000 hydrological model has been adapted and implemented in the context of specific catchment requirements of the Himalayan region. The application of the model provides important knowledge of watershed characteristics (such as runoff components, snow and glacier melt, and evapotranspiration) that are prerequisite information for sustainable water resources management. To the end, the impact of climate variability on different hydrologic-cycle components (such as evapotranspiration and snowmelt) is assessed by applying different temperature change scenarios. STUDY AREA The Dudh Kosi River basin, located in the eastern part of the Nepalese Himalaya, has been selected for this study. The basin is one of the subcatchments of the Kosi River basin, which is located in the Himalayas, extending from Tibet to the Gangetic Plains of India. The total area of the Dudh Kosi basin above the gauging station at Rabuwabazaar (460 m a.s.l.) is 3712 km2. The basin is characterized by very steep topography and young and Copyright © 2012 John Wiley & Sons, Ltd.

fragile mountains. The study area is indicated on the map in Figure 1. The average slope of the basin is 26 . Nearly 45% of the land is located higher than this slope. The highest peak of the world, Mt. Everest (8848 m a.s.l.), is also located in the basin that includes other peaks higher than 7000 m a.s.l. About 30% of the land is located higher than 5000 m, and 32% of the land area is between 3000 and 5000 m a.s.l. In recent decades, the glaciers have been retreating at a higher rate, leading to the formation of many glacial lakes in the Himalayan region (Kattelmann, 2003; Mool et al., 2001). The outburst from these unstable lakes is a major concern because these flash floods cause significant damage to life, property and livelihoods (Shrestha et al., 2010). According to a study based on remote sensing images in the Dudh Kosi basin, nearly 12% of total glacier area retreated between 1976 and 2000 (Bajracharya and Mool, 2009). The lower altitude soil is dominated by Cambisol, Umbrisol and Regosol types, all of which are characterized by medium to fine texture materials. The higher elevation areas are mostly dominated by Regosol, which is comprised of very weakly developed minerals soils with unconsolidated materials (Nepal, 2012). The basin is characterized by subtropical to temperate climate at lower altitudes. The higher altitude areas exhibit subalpine and alpine climates associated with low temperature (MoFSC, 2002). The precipitation pattern of the Himalayan region is greatly influenced by the Indian monsoon system. From June to September, the moist air from the Bay of Bengal approaches the Himalayan mountains. During this seasonal period, the region receives intense rainfall, resulting in floods and widespread damage to property and life. The Kosi River basin experiences floods every year, which impact eastern Nepal and the Gangetic Plains. About 14% of the catchment area is covered by glaciers as shown in Figure 1. The Dudh Kosi River basin, which comprises 273 glaciers with an ice volume of 51 km3, is one of the most densely glaciated regions of Nepal. The glacier map was collected from International Centre for Integrated Mountain Development (ICIMOD), which was prepared by using a topographic map, remote sensing images and was further verified during field visits in 1999 (Mool et al., 2001). About 50% of the glacier areas are located below 5500 m a.s.l.; which are sensitive to recent global warming. Nearly 41% of the basin is covered by forests including deciduous, coniferous and mixed forest types. Other land-use types include agriculture (11%), grassland (4%), shrubland (3%), bare land (25%) and rock and water bodies (2%). The GlobeCover land cover product (May 2005–April 2006) (Defourny et al., 2006) was used to derive the land-cover information of the study area.

THE MODELLING DESCRIPTION AND SET-UP The section provides a short overview of the different modules of the J2000 hydrological model, the modelling strategy applied for this study and the preparation of the Hydrol. Process. 28, 1329–1344 (2014)

HYDROLOGICAL SYSTEM DYNAMICS OF HIMALAYA RIVERS

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Figure 1. Dudh Kosi River basin (inset: location of the Dudh Kosi River in Nepal)

input data required to run the model. The detailed description of the modules and modelling system can be found in Krause (2001), language German, and Nepal (2012), language English. The J2000 model

J2000 is a distributed, process-oriented hydrological model for hydrological simulations of mesoscale and macroscale catchments (Krause, 2001, 2002). It is implemented in the Jena Adaptable Modelling System (JAMS) framework (Kralisch and Krause, 2006; Kralisch et al., 2007), which is a software framework for component-based development and application of environmental models. The model describes the hydrological processes as encapsulated or independent process modules. A glacier module is integrated into the standard J2000 modelling system to simulate the glacier runoff in the study area (Nepal, 2012). The J2000 model comprises modules to represent the important hydrological processes. A short description of Copyright © 2012 John Wiley & Sons, Ltd.

these modules is provided in the succeeding sections. All the modules contain a number of calibration parameters that have to be adapted during the model application. The list of the calibration parameters is provided in Table I. The detailed description of these parameters and of the modules which they are related to is provided in Nepal (2012). The spatial heterogeneity of the watershed was distributed into Hydrological Response Units (HRUs) as suggested by Flügel (1995). These HRUs were selected as a modelling entity and described in later section. The principle layout of the model components is shown in Figure 2. The modelling system produces four different runoff components according to their specific origin as provided in Figure 2. The component (RD1) with the fastest response is the direct runoff. It consists of runoff from impermeable areas, saturation and infiltration access runoff, and snow and glacier ice melt from glacier areas. The slow direct runoff (RD2; also known as interflow 1) can be regarded as outflow similar to the lateral subsurface flow Hydrol. Process. 28, 1329–1344 (2014)

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Table I. Calibration parameters of the J2000 hydrological model Parameters Precipitation distribution Trs Interception module a_rain a_snow Snow module snowCritDens snowColdContent baseTemp t_factor r_factor g_factor Glacier module meltFactorIce alphaIce kIce kSnow kRain debrisFactor tbase Soil module soilMaxDPS soilLinRed soilMaxInfSummer soilMaxInfWinter soilMaxInfSnow soilImpLT80 SoilDistMPSLPS SoilDiffMPSLPS soilOutLPS soilLatVertLPS soilMaxPerc soilConcRD1Flood soilConcRD1Floodthreshold soilConcRD1 soilConcRD2 Groundwater module gwRG1RG2dist gwRG1Fact gwRG2Fact gwCapRise Reach routing flowRouteTA

Descriptions

Actual value

Range

Base temperature

0

1 to +1

Interception storage for rain Interception storage for snow

1.0 1.28

0–5 0–5

Critical density of snowpack Cold content of snowpack Threshold temperature for snowmelt Melt factor by sensible heat Melt factor by liquid precipitation Melt factor by soil heat flow

0.381 0.0012 0 2.84 0.21 3.73

0–1 0–1 5 to +5 0–5 0–5 0–5

Melt factor for ice melt Radiation melt factor for ice Routing coefficient for ice melt Routing coefficient for snowmelt Routing coefficient for rain runoff Debris factor for ice melt Threshold temperature for melt Maximum depression storage Linear reduction coefficient for actual evapotranspiration Maximum infiltration in summer Maximum infiltration in winter Maximum infiltration in snow cover areas Infiltration for areas lesser than 80% sealing MPS–LPS distribution coefficient MPS–LPS diffusion coefficient Outflow coefficient for LPS Lateral vertical distribution coefficient Maximum percolation rate to groundwater Recession coefficient for flood event Threshold value for soilConcRD1Flood Recession coefficient for overland flow Recession coefficient for interflow

2.5 0.2 10 5 5 3 1

0–5 0–5 0–50 0–50 0–50 0–10 5 to +5

2 0.6 60 75 40 0.5 0.27 0.1 0.3 0.5 10 1.3 300 2.8 3

0–10 0–10 0–200 0–200 0–200 0–1 0–10 0–10 0–10 0–10 0–100 0–10 0–500 0–10 0–10

RG1–RG2 distribution coefficient Adaptation for RG1 flow Adaptation for RG2 flow Capillary rise coefficient

2.1 0.3 0.5 0.01

0–5 0–10 0–10 0–10

Flood routing coefficient

1.3

0–10

MPS, middle pore storage; LPS, large pore storage.

within soil zone and reacts slightly more slowly than RD1. In addition, two further baseflow-runoff components can be distinguished from groundwater. The relatively fast baseflow-runoff component (RG1; also known as interflow 2) simulates the runoff from the upper part of an aquifer, which is more permeable because of weathering compared with the lower zone of the aquifer. On the other hand, the slow baseflow-runoff component (RG2) characterizes outflow from the saturated groundwater aquifers. First, the precipitation is distributed between rain and snow, depending upon the air temperature. To determine the amount of rain and snow, it is assumed that temperatures below a certain threshold result in total snow precipitation Copyright © 2012 John Wiley & Sons, Ltd.

and that exceeding a second threshold results in total rainfall. Within the range between those threshold temperatures, mixed precipitation occurs. Interception module. The interception module uses a simple storage approach as described in Dickinson (1984). It calculates a maximum interception storage capacity, on the basis of the Leaf Area Index of the respective land cover in connection with calibration parameters. Any precipitation higher than the storage capacity is passed to the next module as throughfall. It is assumed in the model that the interception storage is depleted by evaporation only. Hydrol. Process. 28, 1329–1344 (2014)


Figure 2. Principal layout of the J2000 model concept. MPS, middle pore storage; LPS, large pore storage; DPS, depression storage; ET, Evapotranspiration. Source: adapted from (Krause, 2001)

Snow module. The snow module calculates the different phases of snow accumulation, metamorphosis and snowmelt. The snow module is processed according to Knauf (1980). Two different types of Snow Water Equivalent (SWE) are calculated: The first one describes the SWE of ‘dry’ snow, which is actually frozen, and the related density (p_dry). The second one is total SWE including the liquid water stored in a snowpack and the related density. In this way, the total SWE, which is the product of stored liquid water and dry SWE, is known. The potential snowmelt is calculated by providing energy associated with air temperature (temperature factor), soil heat flux (ground factor), rainfall (rain factor) in the form of calibration parameters. The associated melt are considered as potential melt rate (potMR), as described by Knauf (1976). This potential snowmelt is considered as liquid water and is stored into the snowpack. The snowpack can store liquid water in its pores up to a certain critical density. This storage capacity is lost almost completely when a certain amount of liquid water in relation to total SWE is reached according to Bertle (1966). In the model, the snowmelt runoff from snowpack then is passed to the soil water module. Glacier module. The glacier module is treated as a separate module in the J2000 model where snow and ice melt runoff directly provide output to a stream as a direct runoff (RD1). The snowmelt from glacier areas is calculated in the same way as described in ‘Snow Module’. For ice melt, the approach suggested by Hock (1999) was adapted and implemented. This approach considers ice melt by using an enhanced day-degree factor by taking temperature and radiation into account. In Copyright © 2012 John Wiley & Sons, Ltd.

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addition to this, slope, aspect and debris-covered factor of glaciers are also included. The glacier area is provided as a GIS layer, which is treated during HRU delineation, which provides a unique land-cover ID for glaciers. All the processes that occur in glacier areas are treated separately, on the basis of the unique ID. First, the seasonal snow occurs on top of glacier (glacier HRU). The model first treats the snowmelt as described previously. When seasonal snow cover on glaciers has melted (i.e. snow storage is zero), the ice melt begins. With this approach, the glacier runoff can be well simulated for the present condition; however, for the long-term estimation of glacier runoff in the context of climate change, the module is less suitable because it does not account for the changing spatial extent of glacier areas. The debris-covered factor is implemented by segregating the glaciers with and without debris covered. The glaciers in the study area generally are debris covered in the valley areas; therefore, a simple segregation method is applied on the basis of land slope. If the slope for a given HRU is higher than 30 , the gravels, stones and pebbles are rolled down, and glacier is regarded as a clean glacier. The slope lower than this threshold is suitable for accumulation of debris on top of glaciers. By this approach, about 77% of the glaciers are estimated as debris-covered glaciers. According to Mool et al. (2001), about 70% of the glaciers in the Dudh Kosi River basin are valley types. In general, valley glaciers are debris covered in the Himalayan region (Fujji and Higuchu, 1977; Sakai et al., 2000). It can be assumed that the debriscovered glacier areas estimated by this approach are fairly representative. When glacier is covered by debris, the melt is reduced only for the ice melt because the fresh snow is stored on top of debris. From the calibration parameter, the effects of debris cover on melt can be controlled. The melt from glaciers, which is the combination of snowmelt, ice melt and rain on top of glaciers, is considered as glacier runoff. In reality, snow is stored in the accumulation zone of high-altitude areas. The snow is transported to low-altitude areas by wind, avalanches and gravity. As snow becomes buried under new snow, it is gradually converted into firn and eventually into glacier ice. This ice flows by gravity downstream towards the ablation zone as glaciers (Jansson et al., 2003). However, such dynamic processes of snow transformation and transportation are not included in the glacier module of the J2000 model. Therefore, some part of the precipitation is always stored as snow in the accumulation zone of high-altitude areas. To compensate for this long-term storage process, a constant glacier layer is used in the model as a surrogate, which provides melting from glacier ice. Soil water module. The central and most complex component of the J2000 model is the soil water module (Figure 2) of the unsaturated zone, which controls the regulation and distribution of water movement. The soil zone of each response unit (HRU) is represented by two storage areas according to the specific pore volumes of the soil. The middle pore storage (MPS) represents the pores Hydrol. Process. 28, 1329–1344 (2014)

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with a diameter 0.2–50 mm in which water is held against gravity but can be reduced by plant transpiration during the calculation of evapotranspiration. If the water is filled to its capacity in the MPS, it is regarded as its field capacity. The large pore storage (LPS) represents the macropores (>50 mm) which cannot hold water against gravity. The information about the soil properties, in terms of waterholding capacity, is provided in a soil-parameter file. The inputs for the soil water module are from snowmelt and rain. First, infiltration is calculated by an empirical approach, on the basis of actual soil moisture. Water that is not able to infiltrate is stored at the surface as depression storage up to a certain amount, and any surplus is treated as surface runoff (RD1) and routed to the next (downstream) HRU. Infiltrated water is distributed between the MPS and the LPS; this allocation depends upon the actual water saturation of the MPS. Water stored in the MPS is reduced by the plant transpiration determined by the potential evapotranspiration (PET) and the actual water saturation of the MPS. Water in the LPS is distributed into a lateral component (outflow as interflow 1) and a vertical component (percolation), depending on the slope. The percolation is conveyed to the groundwater module. The interflow is routed to the next HRU or a connected to river reach. Groundwater module. The groundwater module of the J2000 model follows a simple storage concept of groundwater storage for each response unit (HRU). The storage in the upper groundwater zone (RG1) can be considered as the weathered layer on top of bedrock. Similarly, the storage in the lower groundwater zone (RG2) represents saturated groundwater aquifers (Figure 2). Input water from percolation is distributed between the RG1 and RG2, depending on the slope of the response unit. The water discharge from the RG1 and RG2 is carried out according to the current storage amount in the form of a linear-outflow function using storage retention coefficient for the storage. Routing. The J2000 model has two routing components. The lateral routing in HRUs describes water transfer within a flow cascade from one HRU to another HRU from the upper catchment areas until the receiving stream, which is explained in the next section. The reach routing describes flow processes in a stream channel by using the commonly applied kinematic wave approach and the calculation of velocity according to Manning and Strickler (Krause, 2001). The only model parameter that has to be estimated by the user is a routing coefficient, which influences the ‘run time’ of the runoff waves that move in the channel until it reaches the catchment outlet. Modelling entities: Hydrological Response Units

Hydrological Response Units (HRUs) are applied as model entities for the J2000 hydrological model. HRUs are distributed, heterogeneously structured entities with a common climate, land-cover and underlying pedo-topogeological associations controlling their hydrological Copyright © 2012 John Wiley & Sons, Ltd.

dynamics (Flügel, 1995). In this study, HRUs were delineated from spatially distributed information of topography, land cover and soil type. The Digital Elevation Model (DEM) data with 90-m resolution were collected from the Shuttle Rader Topographical Mission (SRTM). The soil information was derived from the Soil and Terrain datase (SOTER), at a scale of 1:1 million. The GlobCover land cover product (May 2005–April 2006) (Defourny et al., 2006) was used for land cover information. A detailed description of these input data can be found in Nepal (2012). All these maps were reclassified at 250-m resolution. The HRUs were delineated by overlaying the maps in a GIS arc info environment using a process developed by Pfennig and Wolf (2007). A total of 3799 HRUs were delineated with varying sizes, ranging between 0.06 and 18 km2. To preserve the heterogeneity of some specific landuse types (such as glacier in this case) and distinct from neighbouring land-use types, a glacial area is merged with nearby areas of the same land use (i.e. glacier) in delineating the HRUs. Such a process retains the land-cover properties of glacier surface close to the glacier map, which otherwise might be changed, primarily by merging with neighbouring land-cover type(s). These HRUs were topologically connected for lateral routing of flows to simulate lateral water-transport processes between HRUs. These HURs were further connected to the nearby reach for reach routing. A specific protocol is that HRU conveys water to adjacent HRUs having lower elevations. This information is stored in each HRU parameter file, which is created at the end of the HRU delineation process (Pfennig and Wolf, 2007). The distribution concept of HRUs retains the heterogeneous nature of the catchment by keeping the high spatial resolution with many small polygons in higher hydrological dynamics (in areas such as steep slope). In contrast, regions with lower dynamics (e.g. relatively flat land) comprise a smaller number of larger polygons. This allows an efficient simulation of the hydrological dynamics with a minimum amount of redundancy. Because of the lack of good geological dataset for this watershed, the geological information was derived from basic geological characters of the region that control the maximum percolation rates and groundwater storage. Three classes of geological information were derived on the basis of the information from soil data and literature: glacier, higher Himalayas and lower Himalayan areas. In glacier areas, no infiltration to the soil is assumed to occur. The J2000 model has separate parameter files for land use, soil, geology, HRU and reach. The required information, stored in each parameter files, is provided in Nepal (2012). The interactions between sets of the parameter files involve the relationships among the soil, land-use and hydrogeological descriptors in a given HRU parameter file and respective descriptors in the other files. These parameter files are spatially distributed but temporally static descriptors (spatial attributes), which describe the spatial heterogeneity of a catchment. The second group of spatial and Hydrol. Process. 28, 1329–1344 (2014)


temporal static calibration parameters is used for the adaptation of the model response (Krause et al., 2006). The list of these parameters for this case study and general ranges are provided in Table I. The calibration parameter and their connection to modules they are related to are described in Nepal (2012). Hydrometeorological input data

The input data required for the modelling were obtained from the observed stations in the Dudh Kosi River basin. The data are collected and managed by the Department of Hydrology Meteorology, Nepal. Data for six precipitation stations and one climate station were used for this study. The density of stations is as one station per 618 km2, which indicates the data scarcity that is common in the region. The datasets include air temperature, precipitation, sunshine hour, wind speed and relative humidity. The quality of each dataset was statistically tested for homogeneity by using double-mass analysis. The double-mass analysis has been used as being useful for assessing homogeneity in weatherrelated variables (Raghunath, 2006; Allen et al., 1998). It is a useful tool for checking the consistency of climatic variables where the error is due to various causes, such as change in environment (or exposure) of stations and instruments. No abnormal behaviour of the data was found for this case study during the model-run phase. The specific climate-input data from the observed stations are adapted to conditions at each modelling entity (i.e. the HRUs) using the regionalization approach of the J2000 model. This involves a combination of inverse distance weighting (IDW) and vertical (regression) variation (Krause, 2001; Nepal, 2012). Air temperature (maximum, average and minimum) was interpolated from the data of a base climatological station (1720 m a.s.l.) by using a summer and winter lapse rate (Nepal, 2012). Using air temperature data in low-altitude areas, we found that the lapse rate during summer season is higher than during other months. Hence, in the model, two seasonal lapse rates – summer (June–September) and winter (October–May), which had respective values of 0.55 and 0.6 C per 100 m – were provided. The low-altitude temperature stations were used because of the lack of stations located in high-altitude areas. As a consequence, the estimates are biased to conditions at low altitude. Sakai et al. (2004) also found a lapse rate of 0.5 C per 100 m during the monsoon season in the Central Nepal. A similar variation of the lapse rate was observed in the Tibetan Plateau (Du et al., 2007) and mountains of the USA (Minder et al., 2010). The regionalized climate data were then used for the computation of the hydrological processes for each HRUs. From these input data, PET is calculated according to the Pennman– Monteith approach using the method presented in Allen et al. (1998). The description of the approach in a modelling context is provided in Krause (2001) and Nepal (2012).

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up into 1986–1991 for the calibration and 1992–1997 for the validation period. The first year, 1985, is considered as an initialization period. Model quality was quantified by the data from the validation period, which was not used during the model calibration. By the split-sample test (Klemes, 1986), evidence is given that the model calibration parameter set represents the hydrological process dynamics adequately not only during the calibration period but also using independent data during the validation period. The calibration was carried out using the combination of manual trial and error and of automatic or numerical parameter optimization (Gupta et al., 2005). The efficiency of the model performance was measured using objective functions: Nash–Sutcliffe (ENS), logarithm Nash–Sutcliffe (LNS) and coefficient of determination (r2). The ENS is sensitive to high peaks, whereas the LNS is sensitive to low flow conditions. The r2 represents the overall trend between observed and simulated discharges (Krause et al., 2005). For the model calibration, all 36 calibration parameters (Table I) were used. From these, 16 parameters were selected for sensitivity and uncertainty analysis, which is described in the following sections.

RESULTS AND DISCUSSIONS Hydroclimatic conditions

In the study area, about 81% of the precipitation occurs during the monsoon season. Figure 3 shows the observed precipitation and discharge of the Dudh Kosi River basin between 1985 and 1997. The streamflows begin to increase in June and reach their peak flow in August. During the premonsoon period (March–May) when the temperatures start rising, the snow and glacier melt also contribute to the streamflow. The melt runoff period coincides with the monsoon season (June–September) and continues until the postmonsoon period (October–November) when temperatures are relatively high. The rainfall–runoff ratio increases during the last 2 months of the monsoon season, producing a greater amount of runoff compared with the initial 2 months. During the postmonsoon period, flows are higher than might be expected from precipitation amounts. This condition indicates that the streamflow is contributed also from soil storage and groundwater during this period. Regarding the temperature, the average maximum and minimum tempera-

Modelling strategy

The model was applied from 1985 to 1997 on a daily basis. The entire time series data for this period were split Copyright © 2012 John Wiley & Sons, Ltd.

Figure 3. Average monthly observed precipitation and discharge of the Dudh Kosi basin from 1985 to 1997 Hydrol. Process. 28, 1329–1344 (2014)

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tures are 21 and 12.6 C at the Okhaldhunga station located at 1720 m a.s.l. The temperature is low during the winter season (December to February) and gradually started to rise from the premonsoon period. During the period between1985 and 1997, the average observed precipitation of the five stations in the study area is 1934 mm, and the average discharge flow at Rabuwabazaar station is 1602 mm. The simulated average annual precipitation of the Dudh Kosi River basin using the IDW method is 2145 mm. The elevation correction factor was not applied because of the lack of any precipitation station in the northern part of the basin. The regionalisation of the precipitation data is always a challenging task in a mountainous catchment, especially in the Himalayan region, because of the lack of a high density network of precipitation stations. However, the IDW method applied here provided reasonable results, on the basis of the modelling outcomes, which will be discussed in the subsequent section. By this approach, the average annual precipitation on HRUs with average elevation between 4000 and 6000 m is nearly 2150 mm. These HRUs are normally located in the middle and upper parts of the watershed, as shown in Figure 1. As the model simulates daily precipitation values, the modelling results are based on the average value for the whole catchment. However, because of the lack of high station density network, the average simulated precipitation value might be biased on spatial locations. The station (Chaurikhark, 2660 m) located to the north (Figure 1), close to the middle of the basin, is the only station that represents the conditions of the upstream (northern) part of the watershed. Few short–term data on high-altitude areas (including the central Himalaya of Nepal) indicate that the high-altitude areas in general receive lower amounts of precipitation (Shiraiwa et al., 1992) than the stations located on the low-altitude areas. However, the studies conducted by Higuchi et al. (1982) and Yasunari and Inoue (1978) in the Dudh Kosi area suggested that there is high variation among the stations in high-altitude areas located in valley sides and mountains and ridges. The limited few years of data from this study suggested that the river valleys stations at 4200 m a.s.l. received about 500 mm precipitation, whereas the stations in mountains and ridges at 5000–5500 m received four to five times more precipitation than valley stations during the monsoon season. Similarly, precipitation decreases northward and attitudinally along the major river valleys in the region. Because most of the stations located in high-altitude areas are situated in valley locations (Barry, 2008), because of ease in accessibility and in maintenance points of view, it is likely that those stations provides low precipitation, which might not be representative relative to conditions in nearby mountains and ridges. The results of simulated precipitation by this study support other findings (Higuchi et al., 1982; Yasunari and Inoue, 1978) that conclude that the mountains of the high-altitude areas might receive a higher amount of precipitation than the stations located in the river valleys. However, in general, the precipitation is reported low in high-altitude areas because the resultant data may be biased because of the stations being located in the river valleys for Copyright © 2012 John Wiley & Sons, Ltd.

accessibility. Despite this condition, there is a high variation in the spatial distribution of precipitation in the high-altitude areas (Yasunari and Inoue, 1978) between mountains and river valleys, and such variation is difficult to simulate, especially because of the low density of climatological stations. Nepal (2012) also conducted hydrologic modelling in the Tamor River basin, a sub-basin of the Kosi River basin, using the IDW method for precipitation simulation. The modelling results represent the hydrological process dynamics fairly well and suggested that the precipitation simulation by the IDW method is acceptable for this modelling application. Sensitivity analysis

As part of an optimization procedure of the J2000 model parameters, a sensitivity analysis is conducted. The sensitivity analysis is the study of how uncertainty in the output of a model (numerical or otherwise) can be apportioned to different sources of uncertainty in the model input factors (Saltelli and Sobol, 1995). The sensitivity analysis helps to understand the nature of model parameters in terms of their influence on the total model outputs and potentially useful in all phases of the model application process (McCuen, 2005). Therefore, for any model, it is necessary to find out which parameters are more sensitive to model outputs and therefore should be taken into account during a calibration process (Jansson et al., 2003). Sixteen effective model parameters, out of 36, were selected for the sensitivity analysis. They are principally based on a study by Bäse (2005) and experiences from the trial-and-error method during the calibration process. All the parameters are provided in Table I, and the selected 16 parameters for this analysis are indicated in bold emphasis. The same 16 parameters were also used for the uncertainty analysis; this aspect will be discussed in the later sections. The other parameters were not included in this analysis because their sensitivity was considered to be quite low during the trial-and-error process. A Monte Carlo analysis procedure was conducted using all 16 parameters to produce 1600 simulations. A uniform random sampling method was used where the values of the parameters were chosen within the range provided. The actual values and the normal range of the parameters are provided in Table I. For sensitivity and uncertainty analyses, the range was even reduced to include only behavioural simulations. The objective function ENS was used for judging the results of the sensitivity analysis. The regional sensitivity analysis (RSA) (Hornberger and Spear, 1981) has been used to analyse the sensitivity of the model parameters. This method estimates the impact of a parameter and its interactions with model outputs. The RSA is a method of assessing the sensitivity of model parameters where sensitivity is defined as the effect of the parameters on the overall model performance as indicated by objective functions. The general concept of applying the RSA is to split the various model samples into good (behavioural) and bad (nonbehavioural) populations as explained by Freer and Beven (1996) and to compare their distribution functions in the objective function space. If the model performance is Hydrol. Process. 28, 1329–1344 (2014)


sufficiently sensitive to a particular parameter, there will be a large difference between the cumulative frequency distributions; that is, the parameter has a significant effect on the model output (Wagener et al., 2001). The 1600 simulations were analysed using the RSA method. Examples of the RSA of high and low sensitive parameters are provided in Figure 4(a, b). The red line indicates the cumulative distribution function of the behavioural parameter set, and the blue line indicates the nonbehaviour set. Figure 4(a), which shows a larger difference between the parameter set, indicates the higher sensitivity of the parameter to the model performance and vice versa for Figure 4(b). The RSA of 16 parameters with different objective functions can be found in Nepal (2012). Figure 5 provides a comprehensive depiction of the sensitivity of the 16 parameters on the basis of RSA with ENS efficiency criteria. The parameter ranking was done by the normalization of each parameter, on the basis of its sensitivity as suggested by Fischer et al. (2012). The

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more sensitive parameters obtain higher stakes in the figure. For example, as indicated in Figure 5, the model parameter soilLatVertLPS is the most sensitive parameter on the basis of the efficiency criteria ENS, where this parameter explained about 23% of the variation in model results. The parameters LatVertDistLPS and soilConCRD1 are the most sensitive parameters with ENS. Similarly, parameters soilConcRD2, gwRG2Fact, baseTemp, soilLinRed and soilConcRD1flood are moderately sensitive. Finally, the other parameters remaining in this analysis are less sensitive. Model calibration and validation

The model results during the calibration (1986–1991) and validation (1992–1997) periods are provided in Figures 6 and 7, respectively. The red and blue lines represent simulated and observed streamflow discharges, respectively. The daily mean precipitation (grey) is given in upper panel. During the calibration and validation

(a) The RSA of the soilLatVertLPS parameter with Nash-Sutcliffe efficiency

(b) The RSA of the soilMaxInfWinter parameter with Nash-Sutcliffe efficiency

Figure 4. The regional sensitivity analysis of the selected parameters Copyright © 2012 John Wiley & Sons, Ltd.

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Figure 5. Sensitivity of the selected calibration parameters with the Nash–Sutcliffe efficiency criterion

Figure 6. Observed and simulated discharge during the calibration period (1986–1991) in the Dudh Kosi river basin

Figure 7. Observed and simulated discharge during the validation period (1992–1997) in the Dudh Kosi river basin

periods, the model is able to reproduce overall hydrological dynamics fairly well, on the basis of results given in the graphical and statistical evaluations. During the calibration period, the actual and simulated low flow conditions are fairly comparable, although overprediction and underprediction can be seen in Copyright © 2012 John Wiley & Sons, Ltd.

comparison during the initial years. Similarly, the rising limbs are also captured fairly well, which signifies the role of snow and glacier melt during the early monsoon period. The recession limbs of the seasonal hydrographs are well simulated by the model in most of the years. The simulated hydrograph for higher flow (flood) periods are Hydrol. Process. 28, 1329–1344 (2014)

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reproduced well but in some cases are underpredicted (e.g. in 1987, 1990 and 1991). The highest flood peak in 1990 is well reproduced by the model. The model has also predicted the streamflows during an extreme precipitation event in October 1987. Normally, during this month, the precipitation amount is low; however, the relatively higher soil moisture condition prevails as the monsoon occurs until September. During the model validation period, the low flows are simulated well during most of the years. Both rising and recession limbs are also well captured. Regarding the flood periods, the model tends to underpredict streamflows during flood events in 1993. The floods from 1995 to 1997 in general are well represented by model-simulated flows, although overprediction can be noted during the early monsoon periods of 1996 and 1997. The simulated streamflows by the model have been overpredicted for most of the rainy season in 1994. The possible reason could be that from February to mid-March, the observed hydrograph gradually decreased from 33 to about 27 m3/s. However, during the premonsoon season (March–May), the hydrograph gradually increases, because of additional melt runoff coming from snow and glacier melt. This gradual decrease may be the result of some adjustment in the observed streamflow measurement. The absolute volume error for this year is about 15%. The comparison of average monthly simulated and observed runoff (Figure 5) during the calibration and validation periods indicates a reasonably good fit, as shown in Figure 8(a, b). The model provides better and consistent simulated flows during low flow periods than during higher flow. For the calibration period, the low flows are slightly overestimated, whereas high flows are slightly underestimated except for the month of July. The validation period resulted in better flow simulations during low flow periods. However, flows during the initial rainy months (June to August) are overestimated. Figure 8(c, d) indicates

the daily scatter plot of observed versus simulated flows for the calibration and validation periods, respectively. The comparison in general appears good, except for some outliers during the high flow periods. The coefficient of determination (r2) is better during the validation period (0.88) than for the calibration period (0.85). The efficiency results of the model also indicate good performance between observed and simulated discharges as displayed in Table II. This confirms that the model is equally good in simulating flows both during the calibration and validation periods, with a slightly better result during the validation period. The volume error is zero and +2% during the calibration and validation periods, respectively. Uncertainty analysis

The hydrological model incorporates uncertainty, which may arise from different sources. Beven (2001b), Beven and Freer (2001) and Walker et al. (2003) describe uncertainty on the basis of its location within the whole model complex. Uncertainty analysis as methodological tools provide a general basis for evaluation of the model performance (Weichel et al., 2007; Crosetto and Tarantola, 2001). The first source of uncertainty involves the model context (i.e. the implemented processes and algorithms in the model structure), as it is always a simplified description of the processes as compared with nature. The second source of uncertainty is due to the input data used to drive the model,

Table II. Efficiency results of the calibration and validation periods Objective functions ENS LNS r2

Calibration

Validation

0.84 0.90 0.85

0.87 0.95 0.88

Figure 8. Comparison of observed and simulated results Copyright © 2012 John Wiley & Sons, Ltd.

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which have inherent systematic or unsystematic errors. The data used to validate the model results also bring another source of uncertainty. The third source of uncertainty is model parameter uncertainty, which involves the data and methodology used to calibrate the model parameters. The general likelihood uncertainty estimation (GLUE) method (Beven and Binley, 1992) is applied for the model’s uncertainty estimation. The GLUE method is a procedure for uncertainty assessment based on Monte Carlo simulations. It has been widely applied in hydrology and environmental modelling to estimate the uncertainty associated with model outputs and parameters estimates (Beven and Binley, 1992; Beven and Freer, 2001; Montanari, 2005). The advantage of the GLUE method compared with other methods mainly reside the fact that the uncertainty accounts for all sources of uncertainty, that is, input uncertainty, structural uncertainty and parameter uncertainty. It is because the likelihood measure value is associated with a parameter set and reflects all these sources of error and any effects of the covariance of parameter values of model performance implicitly (Beven and Freer, 2001). Figure 9 gives the uncertainty band resulting from 1600 model runs using the selected 16 calibrated parameters during the 1988–1989 period using the GLUE method. The uncertainty analysis was conducted for the entire period; however, a typical period (1988–1989) is presented here as an example. The analysis for other years can be found in Nepal (2012). The uncertainty band comprises 0.95 percentile by using each simulations higher than 0.7 of ENS. In this way, nearly 3% of the simulations were omitted from the analysis. The uncertainty analysis indicates that the observed hydrograph falls within the range of uncertainty bands, mainly during low flow and recession periods as shown in Figure 9. During the flood periods, observed streamflows tend to be towards the lower range of uncertainty bands and in some

cases outside the bands indicating data or structural uncertainty. Similarly, there is a fairly good agreement between the observed runoff and the ensemble mean flows on the basis of results of the 1600 simulations. The coefficient of determination between these two variables is 0.85. This indicates that the process description within the J2000 modelling system is generally acceptable for the purpose of this model’s application. Moreover, as suggested by Beven (2001b) and Beven and Freer (2001), there may be many different model structures or parameter sets that could be considered as acceptable in simulations suggesting equifinality. In this study, the uncertainty band in Figure 9 can be considered as equifinality resulting from model parameter uncertainty. The available input data also contain a source of uncertainty. Because of the low-density network of stations for input data, the precipitation input data are also a source of uncertainty. Especially, because of the lack of precipitation stations to the north (high-altitude areas of the river basin), the model relies upon the most northern station (Chaurikhark) to simulate precipitation in upstream areas. The higher precipitation value at this station is likely to result in overprediction of the runoff hydrograph. For example, on 27 June 1996, Chaurikhark station received 82 mm of precipitation, and the other five stations on average received 20 mm of precipitation. The modelled streamflow was overestimated by about 570 m3/s higher than the observed value (an overestimation by about 100%). Because of the low-density network of stations, and the lack of stations in higher altitude areas, the regionalisation of precipitation input data might be a source of uncertainty. The validation data (discharge) are also a source of uncertainty especially during high flow periods. The discharge volume is estimated by using a stage-discharge rating curve. These rating curves are calculated, commonly on the basis of a few measurements favouring low flow periods in the range of 30–500 m3/s

Figure 9. Results of the uncertainty analysis using the general likelihood uncertainty estimation method (1988–1989).The grey band represents ensemble values from 1600 simulations. The blue and red lines are the observed runoff and the mean of ensemble values Copyright © 2012 John Wiley & Sons, Ltd.

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(associated level of river of 1–4 m). The rating curve for the Rabuwabazaar gauging station that includes the lowest and highest measured flow values can be found in Nepal (2012). Any values higher than this are normally estimated through extrapolation of the rating curve. As suggested by Kattelmann (1987), in the Himalayan region, a stage-discharge rating curve may be based on an inadequate number of flow measurements; hence, the rating curve of higher flows inherently has a major source of uncertainty. Therefore, on the basis of the existing discharge measurement process, there is high confidence on the lower values of discharge compared with greater uncertainty for higher streamflow values. Hydrological systems analysis

The results from the model application provide insight into the hydrological dynamics of the study area. The results are discussed herein using the average of values between the years 1986 and 1997. The actual evapotranspiration (AET) of the basin, as simulated by the J2000 model, is 428 mm per year, whereas the PET was calculated in the model to average 566 mm annually. Sharma (1997) developed an equation for Nepal for relating Class A pan evaporation with elevation. According to this equation, the PET value for the elevation similar to Okhaldhunga station (1720 m a.s.l.) is close to the PET values obtained by the J2000 model at this elevation. Therefore, estimated PET values for the study area can be assumed to be representative. The water balance analysis indicates that the average input into the system from precipitation and ice runoff are 2114 and 74 mm per year respectively between 1986 and 1997. Of these inputs, 72% results in output in the form of streamflow, and 20% is estimated to be lost to the system as AET. Nearly 8% of the precipitation is stored in the basin as snow. Most of the snow above a ‘mean equilibrium altitude’ is stored as snow where the mean temperature is below zero throughout the year. The simulation of snow and glacier movement from higher to lower altitudes is not simulated in the model as explained in the glacier module. To compensate this long-term storage of snow in high-altitude areas, the model uses constant glacier layers, which allow for melting from ice. The other water storages are available in soil, groundwater and interception components. The model also generates four different runoff components originating from different sources as described in Figure 2. The relative average contributions of different runoff components on average are provided in Figure 10. The figure indicates that the overland flow (RD1) is the most dominating component of the hydrograph, contributing about 50% of the total runoff. This is due to the high intensity rainfall predominantly during the monsoon season, which results in saturated soil moisture condition, and most of the rainfall is discharged from the catchment as surface runoff. In addition to this factor, steep topography, rocky mountains and bare land surface in high-altitude areas also provide favourable conditions for the overland flow. Copyright © 2012 John Wiley & Sons, Ltd.

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Figure 10. Runoff components from simulated runoff (1986–1997)

Similarly, the model’s interflow components (RD2 and RG1) together contribute 30% of the total runoff. The baseflow component (RG2) contributes about 20% of the total streamflow runoff, which suggests that the baseflows are an important part of the streamflow, which provides sustained flow during the dry season. Contribution from snow and glacier melt

The application of the glacier module in the J2000 modelling system has provided important insight regarding snow and glacier melt processes in the study catchment. The total contribution from snow and glacier melt to the streamflow is about 34%. Of this amount, glacier melt alone contribute about 17% (including 5% from glacier ice melt). Figure 11 indicates the monthly contribution of glacier melt to the streamflow. Similarly, snowmelt that occur, other than glaciers areas, contributes nearly 17%, of which more than 50% is contributed from rain falling on the snow surface areas. Rain-on-snow is a phenomenon that is relatively significant in lower altitude areas, and the proportion becomes lower with higher altitude areas. The contribution of snow and glacier melt during the rainy season (June–September) is about 39% and also includes the contribution from rain-on-snow. The contribution in April and May is about 69% and 79%, respectively, which indicates the significance of glacier and snowmelt during the premonsoon period. The contribution from melt water during this period is primarily from the melt process associated with higher temperature. Immerzeel et al. (2010) estimated that the

Figure 11. Monthly contribution of glacier melt to the streamflow (1986–1997) Hydrol. Process. 28, 1329–1344 (2014)

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snow and glacier melt to the total streamflow is about 10% in the Ganges River basin. The lower value of glacier melt contribution is because the study was applied for the entire Ganges basin. The contribution becomes lower if the melt runoff is estimated for the entire catchment or the gauging stations located to downstream regions. In this study, the estimation is based on the studies that are relatively located in upstream areas where the contribution is normally high. Alford and Armstrong (2010) estimated the glacier melt in the Dudh Kosi River basin. The study estimated the melt runoff from the relationship between mean specific runoff of altitude and energy exchange (or ablation) gradient. By this approach, the contribution of the glacier melt is estimated to be about 18%, which is very close to the percentage obtained from this study. Temperature change scenarios

A distributed nature of the J2000 hydrological model readily allows an assessment of the hypothetical temperature change scenarios. Incremental or synthesis scenarios (IPCC, 1994) describe a technique that particular climate elements are changed incrementally by plausible though random amounts (e.g. +1, +2, and +3 C change from baseline air temperature, and +10% and +20% change from the baseline precipitation; IPCC, 2001). In this study, incremental climate scenarios of +2 C (Scenario 1) and +4 C (Scenario 2) from the baseline temperature conditions are applied to understand the effect of temperature change in the snowmelt pattern and associated runoff. The rise in the temperature in the modelling context means increase in maximum, minimum and average temperatures. A similar range of rise in temperature has been reported in the region in different studies including some based on Regional Climate Model results (Nepal, 2012; Kumar et al., 2006; New et al., 2002; IPCC, 2007; Dobler et al., 2011; Gao et al., 2012). The focus of this analysis is given on the effect of assumed increase in temperature on the resultant snowmelt runoff pattern. The analysis of scenarios indicates that the snowmelt pattern will be affected significantly. The snowmelt amount will be decreased by 31% and 60% in Scenarios 1 and 2, respectively, as given in Figure 12. After May, the snowmelt volume will be decreased substantially in both scenarios; however, the magnitude is higher in

Figure 12. Change in snowmelt pattern with 2 and 4 C rise in temperature Copyright © 2012 John Wiley & Sons, Ltd.

Scenario 2. On the average, during the monsoon season, the snowmelt amount will be decreased to 40% and 73% in Scenarios 1 and 2, respectively. The reasons for these results are twofold. First, higher temperature will tend to shift the snowline to higher altitudes and thereby decrease the snow storage capacity of the basin. Second, precipitation in the form of rain will be higher than snow because of higher temperatures. The rainy season coincides with the summer period, and there will be less snow occurrence in the basin, especially within the lower altitude areas. During the premonsoon period (March– May), Scenario 1 would produce slightly higher snowmelt than in the baseline condition. However, in Scenario 2, the snowmelt would increase until April and thereafter would tend to gradually decrease. This is because the snow is stored in the catchment during the winter season, which is then melted and contributes to streamflows during the premonsoon period. As the months progress towards the summer season, the temperature is higher, and less snow is available for melting. The analysis suggests that under the assumed warming scenarios, the Dudh Kosi River might shift from a ‘melt-dominated river’ to a ‘rain-dominated river’ in the future. The ice melt from glacier is not included in the snowmelt scenarios (Figure 12) because the feedback effect of glaciers (i.e. a change in spatial distribution of glacier area due to change in temperature) is not included in the J2000 model. Over a short period, the feedback process has less effect on the overall streamflow discharge; however, the influence increases with higher temperature. The temperature change scenarios are also likely to affect the evapotranspiration process largely. The AET values would be increased by 25% and 53% in Scenarios 1 and 2, respectively, on the basis of the results of model simulations. The impact of climate warming on glacier discharge is relatively complex due to glacier ice storage (Hock, 2005). Because of the rise in temperature, on the one hand, more of the glaciers’ surface areas are exposed to melting due to the upward shift of snowline. On the other hand, the glacier area would shrink in the ablation zone. Therefore, the glacier ice melt is directly affected by both the unit of glacier area for melt as well as the glacier shrinkage. As suggested by many studies (Immerzeel et al., 2012; Prasch, 2010), it is likely that the melt runoff associated with glacier ice would increase initially during the first few decades because of larger glacier melt rates. However, when the storage is decreased, the glacier ice melt is likely to decrease during later periods. The study by Nepal et al. (2011) indicates that the average annual streamflow would be increased by 10% and 18% under 2 and 4 C rise in temperature in the Dudh Kosi River basin. Similarly, Singh and Kumar (1997) also reported increase in streamflow and glacier melt runoff with increasing temperature scenarios in the western Himalayan region. However, these studies based on modelling applications did not take into account the change in glacier areas under warming scenarios. Because of the lack of this approach, the modelling applications result higher glacier ice melt resulting from the rise in temperatures. Hydrol. Process. 28, 1329–1344 (2014)


CONCLUSIONS AND OUTLOOK The J2000 hydrological model is able to represent hydrological dynamics of the Dudh Kosi River basin fairly well. Additionally, the model provides insights into the hydrological system dynamics as it is also able to distinguish between different runoff components (surface, subsurface, baseflow) including snow and glacier melt. Therefore, the study shows basis that the J2000 hydrological model can be used in a monsoon-dominated alpine-glaciated catchment of the Himalayan region. The hypothetical rise in temperature scenarios indicated that the snowmelt runoff amount will be decreased in both scenarios. However, the premonsoon period will witness a slightly increase in snowmelt and heavy decrease during the monsoon season. In evaluating the snowmelt contribution to streamflow, any river basin in a high alpine mountain region is judged to be vulnerable to global climate change. This occurs primarily because the higher temperature will shift the snowline to areas of higher altitude and thereby reduce the snow storage capacity of a river basin. Second, because of high temperature, precipitation will occur more in the form of rain than of snow, resulting in shifting timing of higher runoff flows. As a result, in the future, under warming scenarios, the Dudh Kosi River might shift from a ‘meltdominated river’ to a ‘rain-dominated river’. This might have an adverse impact on the water availability and use in downstream areas. These analyses can provide vital information for the planning of more sustainable strategies using Integrated Water Resources Management principles in the Himalayan region. Similar studies should be carried out in the other parts of the Himalayan region to understand hydrological system dynamics and potential impact from climate change. ACKNOWLEDGEMENTS

The author would like to acknowledge the support from the Federal Ministry of Education and Research (BMBF), Germany, for providing funds for the PhD research within International Postgraduate Studies in Water Technologies (IPSWaT). I am grateful to Dr. Timothy D. Steele, a former research scholar at the FSU-Jena supported by the Alexander von Humboldt Stiftung, for providing his valuable inputs and suggestions on the manuscript. Thanks are also due to the Department of Hydrology and Meteorology (DHM), Nepal, and the International Centre for Integrated Mountain Development (ICIMOD) for providing dataset required for the study. Finally, I am thankful to the reviewers for the critical inputs and comments on the manuscript.

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