Accessing the Climate change

Published on Jun 27, 2019 | Bikesh Bade | Shirisa Timilsina

Accessing the Climate change in the hydrology of the Indrawati river basin


1. Introduction

Nepal is largely a mountainous country. The climate in Nepal varies from the tropical to the arctic within the 200km span from south to north.  National mean temperatures hover around 15 °C and increase from north to south except for mountain valleys. The average rainfall is 1,500 mm, with rainfall increasing from west to east.  Rainfall also varies by altitude; areas over 3,000 m experience a lot of drizzles while heavy downpours are common below 2,000 m (Team–Nepal, 1997). The climate in the Indrawati basin is primarily governed by the interaction of the South Asian monsoon system and the Himalayas. Heavy rainfall, relatively high temperatures, and humidity characterize the summer months from roughly mid-May to mid-October; nearly half the total annual rainfall occurs in the months of July and August. Temperatures range from 5 degrees to 32.5 degrees Centigrade (Sharma C., 2002).


2. Study Area

The Indrawati river basin is located in central Nepal, approximately 50 km northeast from the capital city of Nepal in Kathmandu and is the part of the larger Koshi basin. The Indrawati River originates in the high Nepali Himalayas, eventually joining the Sun Koshi. The Indrawati river basin is an important river basin due to its significaIndrawati Rivernce in water diversion into the Kathmandu Valley. The river basin is situated in the mid-hills of Nepal and has a high variation in altitude.

The altitude ranges from 595 to 5838 m above sea level (MSL). Both snowmelts and spring sources contribute to the base flow of the river. The basin lies within the latitude 27°27’11” N–28°10’12” N and longitude 85°45’21” E–85° 260’6” E with a total drainage area of 1230 km2. The main tributaries of the Indrawati River are Melamchi, Yangri, Larke, Mahadev, Chaa, Handi, and Jhyangri. Among all the tributaries, water from the  Melamchi, Yangri, and Larke Rivers is planned for diversion into the Kathmandu Valley (Bhattarai, 2002).


3. Data and Research Methodology

The overall methodology of the study is represented in the diagram below. Spatial and spatial-temporal data were acquired, prepared and used as inputs, and the processes were implemented to meet the objectives of the study.


3.1 Topographical Data

The topography of the Indrawati watershed basin was defined by the ASTER GDEM v2.0. It has spatial resolution of 30m. A DEM represents the 3- dimensional topographic features of the study area. The DEM used in this study is of 30 m x 30 m grid size. Subbasin parameters such as slope gradient, slope length of the terrain and the stream network characteristics such as slope length and width were derived from DEM.


3.2 Land Use

The Landsat derived land use map with a spatial resolution of 30m obtained from International Centre for Integrated Mountain Development (ICIMOD)’s geo-portal was used for the study. The reclassification of the land use map was done to represent the land use according to the specific land use/cover types and the respective parameters defined in the SWAT database. land use map is reclassified to represent the respective parameters that were selected from SWAT database. A lookup table that identifies the 4-letter SWAT code for the different categories of land cover/land use was prepared to relate the grid values to SWAT land cover/land use classes. SWAT calculated the area covered by each land use.


3.3 Soil Data

The SWAT model requires different soil textural and physical-chemical properties such as soil texture, available water content, hydraulic conductivity, bulk density and organic carbon content for different layers of soil. These data were obtained mainly from the FAO soil properties database (FAO, 2002) and other sources. An FAO Soil map was downloaded and clipped for the Indrawati river Basin using ArcGIS spatial analyst tools. 


3.4 Meteorological Data 

 Meteorological data is needed by the SWAT model to simulate the hydrological conditions of the basin. The meteorological data required for this study were collected from the Department of   Hydrology and Meteorology (DHM) of Nepal. The meteorological data collected were daily precipitation, daily maximum, and minimum temperature. Data from 8 stations, which are within and around the study area, were collected. For all the stations, climatic records during the years (1990 – 2014) were obtained.


3.5 Hydrological Data

The hydrological data was required for two purposes, first for performing sensitivity analysis, calibration and validation of the model, and the latter one was to define inlet discharge points for the basin. The hydrological data for both purposes were collected from the hydrology section of the DHM. 


4. Result and Discussion

4.1 Catchment Characteristics of Indrawati Basin

The watershed delineation and HRU definition in the Indrawati Basin gave an effective watershed area 1130 km2 which resulted in 21 sub-basins with 555 HRUs. As observed in the given table, the major portion of the watershed is covered by the Forest area which accounts for 48.28% of the total watershed area. Agricultural land constitutes the second largest portion of the watershed with 32.21%.


4.2 Model Calibration

Manual Calibration was done by changing the sensitive parameter, after the Manual-calibration, best fit parameter values which have an effect on the runoff were obtained. The parameter values thus obtained were used for the climate data and future projection of the Indrawati basin. For best-fit parameter values, CN_2 was calibrated to adjust the surface flow, and it was decreased by 40%because of its higher potential to contribute to surface runoff. Another parameter to adjust the same flow component was ESCO, which accounts for the easiness with which water from lower layers is available for evaporation. Lower value accounts for higher evapotranspiration.  

The value of ESCO was set to 2 for forest areas and was set to 1.5 for agricultural land. Maximum canopy storage was increased to 8.5mm for forested areas and 6.5 for agricultural land. For adjusting the subsurface flow, GWQMN water depth in the shallow aquifer was adjusted. The value of GWQMN was increased to 3500 mm. The default value of 0.014 for Ch_N2 is, of course, unrealistic; hence, it was increased to 0.05, to account for the overall roughness of the channel bed. Parameter Ch_K2 adjusts the water exchange from groundwater to the river and was found to be very sensitive to adjust the shape of the hydrograph, especially for low flows. The value of Ch_K2 was increased to 110 mm/hr. ALPHA_BF was also used to smoothen the shape of the hydrograph, especially for the recession period, and it was increased to 0.1. The SURLAG coefficient was replaced to 14. Hence was essential in adjusting the base flow component of the hydrograph.


4.3 Calibration Period (1994 – 1999)

For the calibration period, the simulation of flow with the observed values suggested that the model had a strong predictive capability with ENS = 0.901 and R^2 = 0.951. 


4.4 Validation Period (2001-2006)

Validation for the model was done for the independent validation data set of six years (2001-2006). The model was again found to have a strong predictive capability with the flow simulation. The ENS value was found to be 0.906 and the R^2 value as 0.937.


5. Discussions

The SWAT model was calibrated and validated against streamflow where parameters were adjusted based on the sensitivity analysis. Keeping the model parameters at reasonable ranges minimized the uncertainty in the simulations. By tuning all the sensitive parameters, the calibration and validation of the SWAT model for a catchment like the Indrawati Basin were carried out. The parameter CN2, which assists in the contribution of runoff, was found to be the most sensitive for flow whereas Manning’s Roughness factor (CH_N2) was sensitive in case of sediment simulation, in such catchment. In general, there was good agreement between measured and simulated monthly streamflow (NSE = 0.901) for the calibration period. The corresponding simulation efficiencies for the validation period were 0.906 respectively. As can be read from the figure, higher precipitation is clearly reflected by the response on water. However, the simulation of dry season flow was slightly underestimated but overall, the agreement between the observed and simulated streamflow was acceptable. The groundwater parameters presented some difficulties in the calibration exercise, as there was not adequate information on their estimates for this region. The peak flows and peak sediment loads were also not adequately simulated, which could be attributed to inadequate representation of the spatial variability of rainfall. However, the statistical and graphical evaluations of the model performance showed that it could be reliably used for simulating hydrology in catchments with readily available observed data.


5.1 Change in monthly flow

The characteristic peak flow month in the historical scenario is August but the RegCM4-LMDZ4 led simulated flows to suggest a shift in monthly peak to October suggesting a decrease in monsoon flows and a subsequent significant increase in flows from October to January. It can be incurred that from the water availability point of view, climate change is not going to impact greatly the basin water resources. However, monthly variations in flow are quite high and a significant decrease in monsoon flows can have a major impact on the operation of many water resources projects in the region.


5.2 % Change in flow relative to a historical monthly average

Possible water withdrawals from the river under no storage condition or the storage reservoir capacity and/or operation of the reservoir depending on the monthly availability of the flow and its variation. To evaluate this aspect of flow, the percentage change in the average of monthly flows for the projected periods versus the baseline average monthly values were calculated and are depicted in figure 8. The changes in monthly flows vary from decrement up to 75% in August to increments by more than 303% relative to the historical monthly average in January. Most of the flow is increased during the lean period and decreasing in June, July, August, and September. Changes in the operating rules moreover become a necessity to deal with these changes in monthly flows ultimately.


6. Conclusion

The watershed simulation model robust hydrological model Soil and Water Assessment Tool (SWAT) was applied to Indrawati Basin at Dolalghat to simulate the discharge at the outlet. Likewise, The RegCM4-LMDZ4 projected data was used in the SWAT model to assess the possible climatic impacts on the hydrology of the Indrawati River Basin. Given the complexities of a watershed and a large number of interactive processes taking place simultaneously and consecutively at different times and places within a watershed, it is quite remarkable that the simulated results comply with the measurements to the degree.  There was good agreement between measured and simulated monthly streamflow (NSE = 0.901) for the calibration period. The corresponding simulation efficiencies for the validation period was   0.906. The fair matching of the hydrographs and the graphs following the trend of precipitation shows a good predictive capability of the model.


The comparison of model-simulated historical and projected flows suggests that the historical trend of flow is decreasing at the rate of 0.55 cumecs/year. According to RegCM4-LMDZ4 simulations, the trend is going to continue but at a flatter rate. The decreasing trend is observed to be very less. The results with the GCM led simulations in this study have shown a peculiar result. The results suggest drop-in monsoon flows and an increase in dry season flows thereby suggesting a shift in peak flows from August to October. This is an atypical result as compared to the ongoing research in the river basins in Nepal. This can arise due to the inability of the GCM to project the future as per the hydrological conditions in the area. It has been well documented that the GCM led projections can have a very high degree of uncertainty associated with them.


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