Annual Journal of Hy draulic Engineering, JSCE, Vol.5 9, 2015, February
INVESTIGATING THE HYDROLOGIC RESPONSE OF CURRENT DAM OPERATION SYSTEM TO FUTURE CLIMATE IN A SNOWY RIVER BASIN (YATTAJIMA) OF JAPAN
Maheswor SHRESTHA1, Patricia Ann JARANILLA-SANCHEZ2, Lei WANG 3 and Toshio KOIKE4
1,2Member of JSCE, Ph. D., Research Associate, Dept. of Civil Eng., the University of Tokyo (Bunkyo-ku, Tokyo, 113-8656, Japan) 3Member of JSCE, Ph. D., Professor, Inst. of Tibetan Plateau Research, Chinese Academy of Sciences (Beijing, 100085, China) 4Fellow of JSCE, Dr. Eng., Professor, Dept. of Civil Eng., the University of Tokyo (Bunkyo-ku, Tokyo, 113-8656, Japan)
A distributed biosphere hydrological model with energy balance based multilayer snow physics (WEB-DHM-S) was implemented to investigate the hydrologic response of the current dam operation system to future climate in a snowy river basin (Yattajima basin) of Japan considering the impacts on flood, drought and dam behavior. Dynamic downscaled data (6 km and hourly) for past and future climate were archived from Data Integration and Analysis System (DIAS). Dam operation modules for 6 dams were formulated based on observed patterns of dam inflow/outflow and were validated in 2001-2005. The climatological average analysis of past (1981-2010) vs future (2081-2110) simulation results indicate that a remarkable decrease in runoff in May at Yagisawa, Naramata, Fujiwara and Aimata dam is observed due to shift of snowmelt towards April. Top 20 flood analysis reveals that future peak flow will increase in all gauges and dams except Fujiwara dam. Flood risk is reduced by Fujiwara dam due to the projected changes in snow seasonality. Future low flow will increase reducing the likeliness of the drought; however the number of days with low water level in Yagisawa dam will certainly increase in future.
Key Words: Climate change, dam operation module, WEB-DHM-S, Yattajima, snowmelt runoff
1. INTRODUCTION approach whether current dam regulation system would be valid in the future or not. Seasonal snow cover is an important component In Japan, the Tone river basin is considered one of the mountain hydrology as it acts as a temporary of the most complex basin system where IWRM is buffer for the release of precipitation in liquid form. highly practiced. Several studies have been carried From a hydrological point of view, the temporal out in the Tone river basin for flood management4), variability of spring snowmelt runoff (onset date for study of water and energy cycle using distributed and magnitude) at the basin outlet greatly governs biosphere hydrological model5), for optimization of the water management policies in the snowy river dam outflow to meso-scale quantitative precipitation basin where many multipurpose dams are forecast6) and for climate change impact constructed to control the dam outflow. It had been assessment7). Regarding climate change impact concluded that the snow hydrology of cold region studies, global climate model (GCM) output offers and high elevation mountainous basins are expected greater uncertainty in snowy basins as local to be more susceptible to climate change as topography affects snow cover strongly. The use of significant air temperature warming affects the regional climate model (RCM) with a fine seasonality of runoff1,2,3). Thus it is utmost necessary resolution grid can address this issue and thus the to study the hydrologic response to future climate in applicability of Pseudo-Global-Warming (PGW) integrated water resources management (IWRM) method, one of the methods to produce future climate data through dynamically downscaling (a) (b) DEM (m) Legend ± (! Yagisawa ± using the RCM, has shown its materialistic High : 2509 Stream gauge $ $ Dam 8,9) $ Naramata prevalence . Furthermore, the capability of River Low : 42 $ hydrological model in simulating the snow cover, Basin Fujiwara Aimata $ Sonohara snow melt runoff, low flow and high flow in $ !( Iwamoto( combination with dam operation schemes also (! Murakami modulate the assessment strategy. Although many previous studies7,10,11) focused on climate change Maebashi (! impact studies on the Tone river basin, the response (! Yattajima of the basin to future climate using the RCM output $ in distributed multilayered energy balance based Shimokubo 0 10 20 40 Km snowmelt model integrated with dam operation module has not been performed yet. Fig. 1 (a) Digital elevation model (b) Dams and stream gauges In this study, water and energy balance based in Yattajima basin distributed biosphere hydrological model with multilayer snow physics (WEB-DHM-S)12,13) was 3. MODEL, DATA AND METHODS implemented to investigate the operationability of the current dam operation system in future climate in the (1) Distributed Snow Model (WEB-DHM-S) Yattajima basin (Tone river) of Japan by utilizing the WEB-DHM-S is the improved version of dynamically downscaled meteorological forcing from WEB-DHM7) in its snow physics in the distributed the Weather Research Forecasting (WRF) model. The biosphere hydrological modeling framework. hydrological model is set up for 500 m spatial WEB-DHM-S incorporates SiB214) land surface resolution and at hourly time step. First, dam scheme, three layered energy balance scheme for operation modules were formulated based on snowpack with prognostic albedo formulation, observed dam inflow and outflow data and operation multilayer soil model with vertical and horizontal rules were validated in current scenario (2001-2005). transport of water fluxes and groundwater model in a Secondly, long term simulations were carried out for hill slope hydrologic unit with kinematic runoff 30 years past (1981-2010) and 30 years future routing scheme. The point scale12) and basin scale13) (2081-2110). Thirdly, hydrologic response of the evaluation of WEB-DHM-S at wide regions showed basin to future climate was analyzed and applicability that the model was able to simulate the variability of of the current dam operation system in future climate snow density, snow depth and snow water equivalent, was investigated. snow albedo, snow layer temperature, snow cover area and runoff. Details of the model equations and 2. STUDY AREA snow physics can be found in Shrestha et al.12).
The Yattajima basin is located in the Upper Tone (2) Dataset River, northeast of Tokyo, Japan. There are 6 dams The Digital Elevation Model (DEM; see Fig. 1a) (Yagisawa, Naramata, Fujiwara, Aimata, Sonohara and land use data are acquired from the Japan and Shimokubo dams). Four stream gauges Geographical Survey Institute and the soil map is (Iwamoto, Murakami, Maebashi and Yattajima) are processed from the Gunma Prefecture geological considered for hydrological simulation (see Fig. 1). map. The static vegetation parameters for different The elevation of the basin ranges from 42 to 2509 m landuse type are defined following Sellers et al.14). above mean sea level. The climatic condition of this Soil static parameters are taken from Yang et al.5). It basin is wet and humid. The basin receives light is assumed to have a homogeneous land use and soil snowfall in November and heavy snowfall in type for each model grid. The dynamic vegetation December to February due to a northwest monsoon parameters such as Leaf Area Index (LAI) and wind from the Sea of Japan. Snowmelt runoff is very Fraction of Photosynthetically Active Radiation important in this region as Upper Tone River is the (FPAR) are obtained from the MODIS Terra satellite key source of water supply, irrigation and power for the period after 2000 and LAI/FPAR are obtained generation for the Tokyo Metropolitan area and from AVHRR satellite for the period before 2000. surroundings. The water management in this basin is Soil data, DEM, land-use and LAI/FPAR for future largely affected by the change in upstream water simulations are considered the same as that in the activities and hydro-climatic nature. The existence of past. Dam behavior data, dam inflow, outflow and several multi-purpose dams plays a key role in water level data and stream gauge discharge data are integrated river basin management by regulating obtained from the Ministry of Land, Infrastructure spring snowmelt runoff and summer flood. and Transportation (MLIT), Japan.
2 Meteorological forcing data (surface air discussed here. Daily Qout in Jan-Mar is prescribed temperature, wind speed at 10m, specific humidity, at 11.67, 20, 20 m3s-1 respectively if 816.8≤WL≤850 surface air pressure, downward shortwave and is satisfied. If WL≤816.8, Qout = 0.0 and if WL≥850, longwave radiation, and precipitation) for the past Qout= Qin. In April, Qout is made zero to store (1980-2010) and future (2080-2110) at 6km spatial snowmelt runoff till WL reaches 850. In May, Qout= resolution and at hourly time step were obtained Qin since WL reaches 850. The WL is made lower from the output of the numerical weather prediction by the end of June to cope flood in Jul-Sep. Qout is modeling with pseudo global warming (PGW) made maximum of inflow or prescribed discharge in technique, carried out by the Japan Agency for corresponding months of summer season. Marine-Earth Science and Technology (JAMSTEC). 1000 As described by Hara et al.8), the PGW method 900
) 800
1
- s
allows “comparison of the climate in the present 3
year and that in a PGW year, which is similar to the 600 800 control year in terms of the inter-annual variation 400 Discharge (m Discharge 300 but includes future climatology”. Weather Research 200 and Forecast (WRF) model was used as the regional 100 0 4 8 12 16 20 24 28 32 climate model. Future forecast data are produced by Time (hour) using the PGW method where the climate change component estimated from 4 GCMs (miroc3_2_hires, gfdl_cm2_1, mri_cgcm2_3_2a, csiro_mk3_0) ensemble mean is added as the difference between past and future. The dataset is made available through Data Integration Analysis System (DIAS –http://dias-dss.tkl.iis.u-tokyo.ac.jp/ Fig. 2 (a, b) Yagisawa dam characteristics and design discharge at flood respectively (from MLIT) (c,d) Observed and simulated ddc/finder). In this study, no bias correction was 3 -1 attempted to the meteorological data. All 6km WRF water level (m) and dam outflow (m s ) respectively averaged during 2001-2005. data were disaggregated into 500 m model grid using linear interpolation method. 2500
(3) Formulation of Dam operation module 2350 )
1 2000
-
s
800 3 In the study region, the release of water during 1550 1500 flood follows regulation rule of MLIT as defined in 1000 Fig. 2,3b but dam outflow during low flow and normal (m Discharge 500 flow season depends on operation manager and varies 0 0 5 10 15 20 25 30 35 40 45 each year according to water demand and Time (hour) precipitation. Thus dam operation modules were formulated in this study based on observed patterns of dam inflows, dam outflow, water level and boundary conditions of dam behavior (maximum, minimum water level). A simple storage function approach was used to express the change in water volume in a dam within a time interval Δt, as the inflow minus outflow Fig. 3 Same as Fig. 2 for Sonohara dam. from a dam. Then, the reservoir storage at the current time step V can be obtained following Yang et al.5) t 4. RESULTS AND DISCUSSION Qt Qt1 in in t (1) Vt Vt1 Qoutt (1) Validation of dam operation module 2 To confirm applicability of the model with dam where the subscript t and (t-1) refers to the current operation module, the model is first run with the and previous time step, V is the reservoir volume, observed dam inflow from 2000-2005. The validation Qin and Qout are dam inflow and outflow results for reconstructed dam operation module is respectively. First, Qin is calculated from shown in Fig 2 and 3 for Yagisawa dam and hydrological simulation at the flow interval just Sonohara dam respectively (other dams are not upstream of the dam, provided that Vt-1 needs to be shown here). Fig 2a shows the dam behavior for set as initial condition. Then, water level (WL) is Yagisawa dam which describes that water storage calculated using the h-v curve. Based on boundary between WL 816.8m and 850m is for the water use condition for WL and proposed dam operation rule, whereas storage between 850m and 854.5m is for the Qout is calculated. Qout estimation at Yagisawa is flood control. Fig 2b shows the design discharge
3 during flood describing the distribution of dam temperature and less amount of snowfall. It outflow for prescribed dam inflow. The comparison ultimately affects the discharge in May at of 5 year averaged simulated and observed water downstream gauges as shown in Fig. 4 (d,e,f); level and dam outflow are shown in Fig 2c and 2d. however, runoff in Jan-Mar is increased. Maebashi Both model outputs are well simulated. The dam and Yattajima gauge results show that the discharge operation rule for Yagisawa dam incorporates the will increase in the months except in April and May. constant daily outflow of 11.67, 20, 20 m3s-1 in Jan, Top 20 daily discharge(m3/s) 400 300 4000 Feb and Mar. Starting from April 1, all the dam Past Future 200 inflow is stored unless the WL reaches 850m. The 200 2000 100 dam outflow is designed in such a way that the WL (Yagisawa inflow) (Yagisawa outflow) (Iwamoto) 0 0 0 never exceeds 850m so that the flood water can be 1 Rank 20 1 Rank 20 1 Rank 20 stored easily if necessary. The similar 4 sub figures 300 100 8000 200 are shown in Fig. 3 for the case of Sonohara dam. 50 4000 100 Sonohara dam is designed mainly for flood control in (Naramata inflow) (Naramata outflow) (Maebashi) Jul-Sep and for water use in Oct-Jun, however, some 0 0 0 400 1 Rank 20400 1 Rank 20 1 Rank 20 15000 Past - Natural allocation for flood and water use are provided in Past-with dam 10000 Future-Natural both seasons. Simulated WL and dam outflow are 200 200 Future-with dam 5000 comparable to the observed values (see Fig. 3c, d). (Fujiwara inflow) (Fujiwara outflow) (Yattajima) 0 0 0 (2) Long term simulation in past and future 1 Rank 20 1 Rank 20 1 Rank 20 A series of simulations have been designed to Fig. 5 Top 20 daily discharge in past and future 30 years investigate the effect of dams in the past Lowest 50 climatological average daily discharge (m3/s) 4 4 (1980-2010) and future (2080-2110) climate by (Yagisawa in) (Yagisawa out) 40 (Iwamoto) 2 2 performing simulations in “natural flow” and “with 20 dam” condition. Top 20 daily flood discharge, low 0 0 0 2 315 340 36560 315 340 365 50 climatological average daily discharge, 2 315 340 365 (Maebashi) Days(Naramata in) (NaramataDays out) Days climatology of dam inflow, outflow, WL, snow depth 40 1 1 and discharge at stream gauges are analyzed. 20 0 0 0 15 315 340 36515 315 340 36580 315 340 365 Days(Fujiwara in) (FujiwaraDays out) Days(Yattajima) 10 10 60 40 Past - Natural 5 5 Past-with dam 20 Future-Natural 0 0 0 Future-with dam 315 340 365 315 340 365 315 340 365 Days Days Days Fig. 6 Lowest 50 climatological average of daily discharge for past and future simulation (b) Top 20 daily discharge Fig. 5 presents the top 20 daily discharge for past and future simulations. At stream gauges, the results of natural flow simulation are also presented. The future peak flow trend is slightly larger at Yagisawa and Naramata dam inflows, whereas it is just opposite at Fujiwara dam inflow where these two dam outflow serve as inflow. Provided the current dam operation
Fig. 4 Climatology of monthly averaged discharge (m3s-1) for rule, Yagisawa dam outflow peaks were largely past and future simulations at Yagisawa, Naramata, Fujiwara reduced which caused decrease of inflow to Fujiwara. dam inflows and Iwamoto, Maebashi and Yattajima gauges. At Iwamoto gauge, there is considerable difference in the past and future flood for both natural and dam (a) Climatology of discharge in past vs future control simulations. At Maebashi station, about 50% The climatology of monthly averaged discharge of the discharge come from the basin area without the for past and future simulations at selected dams and dam control which amplifies the likeliness of future gauges are shown in Fig. 4. Yagisawa and Naramata flood to a greater extent and it is ultimately carried dam receives a large amount of runoff in April-June over to Yattajima station. due to snow melt and outflow of these dams served (c) Low 50 climatological average daily discharge partly to Fujiwara dam inflow. In future, a The climatological average of the lowest 50 daily considerable decrease in runoff in May at Yagisawa discharge for past and future are given in Fig. 6. Future and Naramata dam is observed due to shift of low inflow will increase in Yagisawa and Naramata snowmelt towards April. This is because of rise in
4 due to increase in precipitation. Qout from these dams (e) Climatology of dam water level will also increase due to shift of snowmelt runoff. The Fig. 5 presents the past and future climatology of th climatological averaged 355 rank of the past daily daily water level for 6 dams. In future, Yagisawa and discharge simulation was used as the basis of drought Naramata dam (see Fig. 8a,b) suffers with shift of WL discharge (Qdrought). The analysis reveals that the low remarkably in April. WL is simulated quite low at flow, in general, will increase in the future suggesting Yagisawa in summer that provides enough space to less chance of drought compared to the past (see Table accommodate future flood but lower WL in winter 2). However, this basin suffered with severe drought in declines water availability considerably. In contrast, the past (1987, 1994-1996), and the future discharge winter WL at Naramata dam is simulated higher than follows the similar trend of the past. Qdrought at Fujiwara that in the past. Naramata WL in May and June is shows that the future base flow is relatively decreased simulated quite low but there is no remarkable change where it is stable at Maebashi. Moreover, standard in July-December. Fujiwara dam is slightly affected by deviation of Qdrought will be increasing at all sites. the WL from Yagisawa and Naramata dams. Table 2 Summary of low flow analysis (Qdrought is the Remarkable lowering of WL in June and July is th average of 355 rank) for dam inflows and gauges simulated. Comparatively, increased future flood nd Qdrought ± σ 2 lowest value in caused increased WL at Aimata, Sonohara and 30 yrs of Qdrought Shimokubo dams (see Fig. 8d,e,f). The individual year Past Future Past Future analysis at Yagisawa shows that future flood will be Yagisawa 1.26±0.31 1.79±0.45 0.98 1.058 accommodated by current dam operation system but Naramata 0.79±0.18 1.03±0.27 0.574 0.614 low WL in the future will reduce the water availability Fujiwara 8.8±1.86 7.77±1.86 5.56 4.607 which may be needed for various water use. The Iwamoto 17.2±5.95 23.8±6.70 14.64 14.75 integrated responses to WL, low and high inflow and Maebashi 38.3±9.05 38.3±10.7 23.71 23.72 outflow due to the shift of snowmelt runoff in Yattajima 49.7±10.7 52.7±12.7 29.33 34.66 Yagisawa dam are discussed in detail below. (d) Climatology of snow depth (a) Yagisawa dam waterlevel (m) (d) Aimata dam waterlevel (m) Only 4 dam sub-basins; Yagisawa, Naramata, 860 570 850 560 Aimata and Sonohara basins receive a substantial 840 550 830 amount of snow in Yattajima basin. Basin averaged 540 climatology of snow depth in the past and future in 820 810 Past 530 Future these 4 sub basins is shown in Fig. 7. It is found that 800 520 (b)J NaramataA damJ waterlevelO (m) (e)J SonoharaA damJ waterlevelO (m) there will be a temporal shift and decrease in 900 570 maximum snow depth (MSD) in all 4 basins. At 565 880 Yagisawa, the MSD is reduced to 2.54 m (1 March in 560 860 555 the future) from 3.5 m (21 March in the past) which 550 840 is shifted to 20 days earlier; reducing the width of 545 snow cover days by 25 days in June. Naramata basin 820 540 (c)J FujiwaraA dam waterlevelJ O (m) (f) JShimokuboA damJ waterlevelO (m) naively follows Yagisawa basin response with the 655 320 reduction of MSD to 1.62 from 2.3 m. At Aimata, 650 300 280 MSD is shifted by 14 days earlier with large 645 260 640 reduction of snow depth from 1.04 m to 0.55 m; 240 however snow cover days are reduced by 4 days 635 220 only. Sonohara dam basin follows the Aimata basin 630 200 J A J O J A J O pattern as Sonohara and Aimata basins receive less Fig. 8 Inter-annual variability of simulated dam water level snowfall compared to Yagisawa and Naramata. along with climatological average for past and future simulation (a) Yagisawa - Avg. snowdepth (m) (b) Naramata - Avg. snowdepth (m) 4 2.5 Past (f) Integrated analysis at Yagisawa dam basin 3 Future 2 Fig. 9 discusses representative case for an 1.5 2 integrated analysis of inflow, outflow and water level 1 1 0.5 in the past (2005-2006) and future (2105-2106). For 0 0 past simulations, WL attains its maximum level about (c)O Aimata J- Avg. snowdepthA J (m) (d) OSonoharaJ - Avg.AsnowdepthJ (m) 1.2 1.5 one month in May according to operation rule so that 1 outflow equals to inflow. As a result, the dam outflow 0.8 1 0.6 generally gets peak in May since flood in Jul-Sep is 0.4 0.5 stored well by the dam. For future simulations, the 0.2 0 0 inflow is shifted earlier due to rise in temperature. O J A J O J A J Provided past dam operation rule, operation manager Fig. 7 Climatology of snow depth for past and future.
5 need to release more water in May by reducing the ACKNOWLEDGMENT: This study was WL since there would be less availability of inflow as implemented in the Research Program on Climate discussed earlier. Rapid reduction of WL in May-June Change Adaptation (RECCA). M. Fujita, M. Hara causes decline in bandwidth of maximum WL and F. Kimura (JAMSTEC) and Y. Wakazuki providing more space in dam for flood control. Thus, it (University of Tsukuba) are acknowledged for PGW can be concluded that the projected changes in snow simulated data. The DIAS data set is archived and seasonality tend to reduce the flood risk in the future. provided under the framework of the RECCA, WL(m) PAST Q(m3/s) through the Ministry of Education, Culture, Sports, 860 300 2005 2006 Science and Technology (MEXT). 840 200 Water level REFERENCES 820 Dam outflow 100 Dam inflow 1) Barnett, T.P., Adam, J. C., and Lettenmaier, D.P.: Potential 800 0 impacts of a warming climate on water availability in 860 1/1 4/1 6/30 9/28 12/27 3/27 6/25 9/23 12/22300 2105 FUTURE 2106 snow-dominated regions, Nature, 438, 303-309, 2005.
840 200 2) Immerzeel, W. W., van Beek, L. P. H. and Bierkens, M. F. P.: Climate Change Will Affect the Asian Water Towers, Science, 820 100 328(5984), 1382–1385, 2010. 3) Lemke, P., and coauthors: Observations: Changes in Snow, 800 0 Ice and Frozen Ground. In: Climate Change 2007: The 1/1 4/1 6/30 9/28 12/27 3/27 6/25 9/23 12/22 Fig. 9 Representative case for Yagisawa dam inflow, outflow Physical Science Basis, IPCC(AR4-WG1), 2007. and water level in 2005-2006 in past and 2105-2106 in future 4) Yang, D., Koike, T., and Tanizawa, H.: Application of a distributed hydrological model and weather radar 5. CONCLUSIONS observations for flood management in the upper Tone River In this study, distributed hydrological model with of Japan, Hydrol. Process., Vol. 18, pp.3119–3132, 2004. multilayer snow physics (WEB-DHM-S) was 5) Wang, L., and coauthors: Assessment of a distributed implemented to investigate the operationability of the biosphere hydrological model against streamflow and MODIS current dam operation system in future climate at land surface temperature in the upper Tone River Basin, J. Yattajima basin (Tone River) of Japan by utilizing Hydrol., 377, 21–34, 2009. 6) Saavedra Valeriano, O. C., and coauthors: Decision support the dynamically downscaled meteorological forcing for dam release during floods using a distributed biosphere using PGW technique. Formulation of dam operation hydrological model driven by quantitative precipitation modules was accomplished based on observed forecasts, Water Resour. Res., 46, W10544, 2010. patterns of dam inflow/outflow and were validated in 7) Kim, S., and coauthors: Climate change impact on river flow 2001-2005. The climatological average analysis of of the tone river basin, Japan, Ann. J. Hydraul. Eng. (JSCE), past (1981-2010) vs future (2081-2110) runoff results Vol. 55, pp. S85-S90, 2011. indicate that a considerable decrease in runoff in May 8) Hara M, and coauthors: Estimation of the impact of global at Yagisawa, Naramata dam is observed due to shift warming on snow depth in Japan by the Pseudo of snowmelt towards April. Maebashi and Yattajima -Global-Warming method, Hydrol. Res. Lett. 2: 61–64, 2008. gauge results show that the discharge will increase in 9) Ma, X. and coauthors: Simulating river discharge in a snowy the months except in April and May. Future low flow region of Japan using output from a regional climate model, will increase reducing the likeliness of the drought; Adv. Geosci., 35, 55–60, 2013. however the number of days with low water level in 10) Islam, M. S., Arakami, T., and Hanki, K.: GCM-based Yagisawa dam will certainly increase in future. Top analysis of water availability along the Tone River, Reg 20 flood analysis reveals that future peak flow will Environ Change, 7, 15–26, 2007. increase in all gauges and dams except Fujiwara dam. 11) Takara K, and coauthors: Assessing climate change impact on It should be noted that overall control of all dams water resources in the Tone River basin, Japan, using is 6000 m3/s whereas design flood for river is 16000 super-high-resolution atmospheric model output, J. Disaster m3/s. Although dam operation system controls 6000 Res., 4(1), 12–23, 2009. 12) Shrestha, M., and coauthors: Improving the snow physics of m3/s out of 22000 m3/s at Yattajima, its compounded WEB-DHM and its point evaluation at the SnowMIP sites, impact cannot be neglected. Besides the improvement Hydrol. Earth Syst. Sci., Vol. 14, pp.2577–2594, 2010. of current dam operation rules, special focus should 13) Shrestha, M., and coauthors: Correcting basin-scale snowfall in a be given to channel improvement planning in low mountainous basin using a distributed snowmelt model and lying regions also to cope with future flood. remote sensing data, Hydrol. Earth Syst. Sci., 18, 747-761, 2014. Nevertheless, careful consideration should be taken 14) Sellers, P. J., and coauthors: A revised land surface in hydrologic simulations by accounting possible parameterization (SiB2) for atmospheric GCMs, Part I: range of deviations and biases in the WRF output Model formulation, J. Climate, Vol. 9, pp.676-705, 1996. before drawing firm conclusions. (Received September 30, 2014)
6