b
Submitted by
Nepal Development Research Institute (NDRI)
Shree Durbar Tole, Pulchowk, Lalitpur, Kathamandu Nepal
www.ndri.org.np
Submitted to
Commonwealth Scientific and Industrial
Research Organization (CSIRO)
Rainfall Runoff Modelling Using GR4J Model in Source: A Pilot Study in Bagmati Basin, Nepal
July, 2015
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Research Team
Team Leader: Laxmi P. Devkota, D.Eng.
Research Associate: Anita Khadka
Published by
Nepal Development Research Institute
P.O. Box: 6975, EPC 2201, Lalitpur, Nepal
Telephone: +977-1-5537362, 5554975
Email: [email protected]
Web: http://www.ndri.org.np
Copyright © 2015
Nepal Development Research Institute (NDRI)
All rights reserved.
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Table of Contents
1. Introduction ................................................................................................................................................................. 1
2. Study area .................................................................................................................................................................. 2
3. Model description ...................................................................................................................................................... 4
3.1. Description of the model ................................................................................................................................. 4
3.2. Evaluation Criteria ........................................................................................................................................... 7
4. Model Development .................................................................................................................................................. 9
4.1. Data Input .......................................................................................................................................................... 9
4.1.1. Spatial data ............................................................................................................................................. 9
4.1.2. Temporal data ...................................................................................................................................... 10
4.2. Model Calibration and Validation ............................................................................................................ 13
5. Result and discussion .............................................................................................................................................. 15
5.1. Rainfall and Runoff characteristics............................................................................................................. 15
5.2. Calibration and validation results .............................................................................................................. 18
5.3. Conclusion........................................................................................................................................................ 30
6. References ................................................................................................................................................................ 31
7. Annex ........................................................................................................................................................................ 32
List of Tables
Table 1: Basin Features ..................................................................................................................................................... 3 Table 2: Thiessen weighted factor for precipitation data according to sub-catchments .................................. 11
Table 3: Evaporation stations ....................................................................................................................................... 13 Table 4: Gauging stations ............................................................................................................................................. 13 Table 5: Model run in different setting for stations 589 and 581 ........................................................................ 14
Table 6: Rainfall-Runoff statistics ................................................................................................................................. 16 Table 7: Model results for individual runs at station 589 and 581 without calibration weighting ................ 24 Table 8: Regression relationship of parameter x1 to x2 and x3 ......................................................................... 29 Table 9: Model results with different calibration weighting option at station 589 and 581 .......................... 29
List of Figures
Figure 1: Bagmati study basin ......................................................................................................................................... 3
Figure 2: Node link structure of the model in Bagmati basin .................................................................................... 4 Figure 3: Structure of GR4J model ................................................................................................................................ 6 Figure 4: Reclassified landuse map of the study area ........................................................................................... 10 Figure 5: Thiessen polygon area in the basin ........................................................................................................... 12 Figure 6: Isohyteal map based on annual total rainfall ......................................................................................... 12
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Figure 7: Trend analysis of discharge at Pandheradivan (Stn589) and Bhorleni (Stn581) station ............... 15 Figure 8: Trend analysis of rainfall at Pandheradivan (Stn589) and Bhorleni (Stn581) station ................... 16 Figure 9: Rainfall-Runoff relationship (200-2008) at Pandheradovan (Stn589) and Bhorleni(Stn581) ....... 17 Figure 10: Hydrographs of maximum, minimum and average flows .................................................................... 17
Figure 11: Rainfall-runoff hydrograph in a catchment area of Pandheradovan (Stn589) and Bhorleni
(Stn581) for the period 0f 2000 to 2008 ................................................................................................................. 17 Figure 12: Probability of exceedance for historical data of flow (a) and rainfall (b) at Pandheradovan
and Bhorleni stations ....................................................................................................................................................... 18 Figure 13: Scatter plots of daily observed versus simulated flow for calibration period of 2000 to 2004,
using four objective function - NSE, NSE-Bias, NSE-FDC, NSE-logFDC represented in a clockwise direction at
Pandheradovan gauging station (without joint calibration) .................................................................................... 19 Figure 14: Daily hydrograph of observed and simulated flow for calibration (2000-2004) and validation
(2005-2008) period at Pandheradovan gauging station (without joint calibration) ........................................ 20 Figure 15: Daily hydrograph of observed and simulated flow for calibration (2000-2004) and validation
(2005-2008) period at Bhorleni gauging station (without joint calibration)....................................................... 21 Figure 16: Daily flow hydrograph comparing base flows at Pandheradovan (Stn589) and Bhorlnei
(Stn581) gauging station with the compound objective weighting at a range of 0.3 to 0.8 (NSE-FDC, NSE-
logFDC) .............................................................................................................................................................................. 26 Figure 17: Comparison of daily hydrograph of observed versus simulated flow for a 38 day period (1July
to 7 Aug of 2002) based on GR4J and GeoSRM model results ........................................................................... 27 Figure 18: Daily hydrograph of observed and simulated flow for calibration (2000-2004) and validation
(2005-2008) period at Pandheradovan gauging station (with joint calibration) ............................................. 28
Annex
Annex 1: Distribution of land based on its utilization in the basin ......................................................................... 32 Annex 2: Sub-catchment wise distribution of land .................................................................................................... 32 Annex 3: List of Hydro-meteorological stations......................................................................................................... 32
Annex 4: Rainfall-runoff relationship during calibration and validation period at Pandheradovan (Stn589)
and Bhorleni (Stn581) gauging station ....................................................................................................................... 33
Annex 5: Scatter plots of daily observed versus simulated flow for calibration period of 2000 to 2004,
using four objective function - NSE, NSE-Bias, NSE-FDC, NSE-logFDC represented in a clockwise direction at
Bhorleni gauging station (without joint calibration) .................................................................................................. 34 Annex 6:Scatter plots of daily observed versus simulated flow for validation period of 2005 to 2008,
using four objective function - NSE, NSE-Bias, NSE-FDC, NSE-logFDC represented in a clockwise direction at
Pandheradovan gauging station (without joint calibration) .................................................................................... 35 Annex 7:Scatter plots of daily observed versus simulated flow for validation period of 2005 to 2008,
using four objective function - NSE, NSE-Bias, NSE-FDC, NSE-logFDC represented in a clockwise direction at
Bhorleni gauging station (without joint calibration) ................................................................................................. 35
Annex 8: Monthly hydrograph of observed and simulated flow for calibration (2000-2004) and validation
(2005-2008) period at Pandheradovan (stn589) and Bhorleni (Stn 581) gauging station (without joint
calibration) ........................................................................................................................................................................ 36 Annex 9: Daily hydrograph of observed and simulated flow for calibration (2000-2004) and validation
(2005-2008) period at Pandheradovan and Bhorleni gauging station with joint calibration ........................ 37
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Annex 10: Monthly hydrograph of observed and simulated flow for calibration (2000-2004) and
validation (2005-2008) period at at Pandheradovan (stn589) and Bhorleni (Stn 581) gauging station
(with joint calibration) ..................................................................................................................................................... 37 Annex 11: Flow duration curves for Pandheradovan (Stn589) gauging stations during calibration period
(without joint calibration) ................................................................................................................................................ 38 Annex 12: Flow duration curves for Pandheradovan (Stn589) gauging stations during validation period
(without joint calibration) ................................................................................................................................................ 39 Annex 13: Flow duration curves for Bhorleni (Stn581) gauging station during calibration period (without
joint calibration) ............................................................................................................................................................... 39
Annex 14: Flow duration curves for Bhorleni (Stn581) gauging station during validation period (without
joint calibration) ............................................................................................................................................................... 40 Annex 15: Daily hydrograph of observed flow for calibration (2000-2004) and validation (2005-2008)
period at Bhorleni (Stn581) gauging station (with joint calibration) ..................................................................... 41 Annex 16: Statistical analysis for Pandheradovan station (Stn589) during monsoon season-Jun to Sep (for
calibration and validation period) ............................................................................................................................... 42 Annex 17: Statistical analysis for Bhorleni station (Stn581) during monsoon season-Jun to Sep (for
calibration and validation period) ............................................................................................................................... 43 Annex 18: Statistical analysis for Pandheradovan station (Stn589) during calibration (2000-2004) and
validation (2005-2008) period ................................................................................................................................... 44
Annex 19: Statistical analysis for Bhorleni station (Stn589) during calibration (2000-2004) and validation
(2005-2008) period ....................................................................................................................................................... 45
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Abbreviation
DEM Digital Elevation Model
DHM Department of Hydrology and Meteorology
FDC Flow Duration Curve
GR4J Ge´nie Rural a` 4 parame`tres Journalier
HEC-HMS Hydrologic Engineering Center's Hydrologic Modelling System
ICIMOD International Center for Integrated Mountain Development
IWM Institute of Water Modelling
MODIS Moderate Resolution Spectroradiometer
NSE Nash Sutcliffe Efficiency
RVE Relative Volume Error
SCE Shuffle Complex Evolution
SMA Soil Moisture Accounting
SRTM Shuttle Radar Topographic Mission
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1. INTRODUCTION
Water resource management activities are crucial from the prospect of development to harness economic
solidity and for sustainability of natural resources and well being. These actions are only achieved through
better understanding of the hydrological system of the area, for which a reliable model and sets of data is
required. Several hydrological models have been developed and used to analyze the functioning and for
the prediction of the response of the hydrological system, that can eventually help policy makers for smart
decisions making on water resources planning and management. These hydrological models have
generally, been classified into three types, namely, a) empirical models, b) conceptual models and c)
physically based models. Physically based model, though considered to be more accurate of all, are very
data intensive and time consuming that require conceptual models are the most widespread used model
that neglects the spatial variability of the state variables and parameters (Zang and Savenije, 2005).
Having their own pros and cons, all these models share a common problem of parameter identification or
accuracy and model equifinality. There are even several uncertainties created in these models associated
with the a) input of data, b) model structure and c) model parameters. Therefore, there is no rainfall-runoff
model that precisely reflects the real situation. Also it is impossible to specify the initial and boundary
conditions required by the model with complete accuracy (Lu et al., 2009). Sometimes too many
parameters in the model (over-parameterization) and systematic errors of input data are also the source
of equifinality (Lu et al., 2009). Moreover, increasing model complexity does not necessarily provide
higher performance for a given catchment. Conceptual hydrological models have been found to perform
better on larger and/or wetter catchments than on smaller and/or drier catchments (van Esse, 2013).
Against this backdrop, this study uses a simplified conceptual rainfall-runoff model of 'Source' which tries to
simulate the flow in a more reliable and accurate way. It is a framework which provides an interface for
input, modelling, and output of flow and water resource related information.
Source has widely been applied from catchment scale, generating evidence based decision making for
water allocation and planning process. With its best practices in catchments like Great Barrier Reef of
Queensland, Lake Tai of China, Drain L catchment of Australia, Murray Darling Basin of Australia etc., for
varied issues of irrigation for crop water use and allocation for decision making, water quality for
environmental flows or wetlands restoration, hydropower etc.1 Source has proven to ease the water based
planning process through its simplified rainfall-runoff models. With this successful approach in these
countries, Source is been applied in Nepal as a pilot study to understand how the model responses in a
catchment with different climatic zone or variations. The output from this research is expected to open up
new avenues to extensively utilize the model in other catchments of Nepal and assist in solving water
resources problems associated with flooding, drought, agriculture and industrial uses for sustainable
utilization of water resources, which is further likely to strengthen the decision making process. This research
is conducted by Nepal Development Research Institute in collaboration with Commonwealth Scientific
Industrial and Research Organization (CSIRO) of Australia. The major objective of this study is to build a
hydrological model in Bagmati basin for better estimation of stream flow in the basin.
Bagmati River is considered as an important river among all rivers of Nepal due to its lowest water
availability per population (Sharma and Shakya, 2006). Several studies have been conducted in Bagmati
basin by numerous authors with their different purpose. Chen and Shrestha (2006) used TANK model at
Pandheradovan gauging station in Bagmati basin and investigated the uncertainty of the model output
resulting from parameters based on three simulation techniques viz., Markov Chain Monte Carlo, Monte
1 Adams, Geoffrey."eWater Source Case Studies" (presentation lecture, New Horizon India Limited, New Delhi, 4th December,
2014)
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Carlo simulation and Latin Hypercube simulation. Their result indicated that the model uncertainty to be
more important than the parameter uncertainty. Shrestha et al. (2008) simulated stream flow for the
period of 2002 to 2004 by comparing with observed rainfall data and with a satellite rainfall estimate
(RFE), using a semi-distributed hydrological model, GeoSFM. The timing and magnitude of flow was found
to be well simulated by the model when observed data were used, while it yielded poor results with the
RFE data. Sharma and Shakya (2006) assessed the changes in hydrology and its probable future
implication in Bagmati basin. The result indicates decrease in flood magnitude but increase in frequency
and duration along with the decrease tendency in hydropower production projected for the period of
2010, 2020 and 2030. Babel et al. (2014) applied five GCM data (CGCM3, CCSR, BRB, ECHAM4,
HadCM3 and CSIRO-MK2) and HEC-HMS model to study the potential hydrological impact in Bagmati
basin in changing climate. The result based on 30 years baseline (1970-1999) compared to three future
period from 2010-2099 (for SRES A2 and B2 scenarios) showed an increase in annual water availability,
with more rise at the end of century varying between 10.82 to 12.84%. Crop models like CROPWAT have
also been applied in the basin to quantify crop yield in different climate change scenarios (Shrestha,
2007). Flood forecasting and flood risk mapping was conducted by IWM institute of Bangladesh (2011)2
using MIKE11 in Bagmati basin. This research recommends that the rating curve at Pandheradovan
gauging station should be updated with the recent data, making them more representatives for high flood
condition.
The runoff simulations reported herein aims at investigating the capability of the Source using GR4J model,
in simulating the stream flow of the Bagmati basin.
2. STUDY AREA
The study is conducted in Bagmati Basin of Nepal as shown in Figure 1. It is a rainfall dominated basin
which ranges at an elevation of 121 to 2913 m. The catchment area of the basin at Pandheradovan is
2827 Km2 and can extend to 3550 Km2 up to the national territory i.e. Nepal-India border. The river
originates in the Mahabharat range of mountains and drains out the hills, then to Terai and finally drains
out of Nepal into India. The basin is covered by eight districts viz. Bhaktapur, Lalitpur, Kathmandu,
Kavrepalanchowk, Makwanpur, Sindhuli, Rautahat and Sarlahi. The basin is divided into three parts viz.
upper, middle and lower. The upper part of the basin where the capital of county is situated is covered by
Kathmandu, Lalitpur and Bhaktapur districts while the middle part of the basin is partially covered by
Makwanpur, Kavrepalanchowk, Sindhuli districts. The lower part lies in Rautahat and Sarlahi districts. The
study area up to Pandheradovan falls in upper and middle part of the basin. Approximately 73% of the
basin lies at an elevation range of <500m to 1500m while 27% of it is situated above 1500 m as given
in Table 1. The length of the main channel is about 195 km within Nepal and 134 km above the
Pandheradovan gauging station. Of the total area approximately 5% of the area is covered by
settlements and that is mainly in the upper part of the basin. Based on landuse map of MODIS satellite
data, majority of the basin i.e. 2/3rd of the study area has forest while 1/4th of the basin is practiced for
agriculture.
There are five gauging stations located at Sundarijal, Gaurighat, Khokana, Bhorleni and Pandheradovan
from the upstream to the downstream of the basin. The basin also harbors the first and second oldest
hydropower station of Nepal with a capacity of 500 KWh and 640 KW in Pharping and Sundarijal
respectively. Along with this, there is a single ever storage type hydropower of Nepal -Kulekhani
hydropower, with a capacity of 92 MW in Makwanpur districts. Besides hydropower, water has
2 http://hydrology.gov.np/new/hydrology/_files/efa57c381519c85c482428163535f05a.pdf
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extensively been extracted from Bagmati River and its tributaries for dirinking purpose in Kathmandu
valley. About 159 km2 of the area in the upper part of the basin has also been gazetted as Shivapuri
Nagarjun National Park. Two 24m and 64m dam are also to be constructed in the Dhap and Nagmati
River, a major tributary of the Bagmati River, respectively to augment the dry season flow in the Bagmati
River. A Bagmati irrigation canal has also been constructed at the downstream of the Pandheradovan
gauging station in the basin.
Figure 1: Bagmati study basin
Table 1: Basin Features
District Coverage within the basin
District Area (Km2) Percentage
Bhaktapur 121.58 4.34
Kathmandu 370.56 13.23
Kavrepalanchowk 342.51 12.23
Lalitpur 393.57 14.05
Makwanpur 626.26 22.36
Sindhuli 946.62 33.79
Area elevation of the basin
Elevation Zone Area (Km2) Percentage
<500 m 768.6 27.19
500-1000 m 556.9 19.71
1000-1500 m 740.3 26.19
1500-2500 m 747.8 26.46
>2500 m 12.6 0.45
Total Area 2827.2
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3. MODEL DESCRIPTION
3.1. Description of the model
GR4J is simple parsimonious conceptual, lumped, water balance model based on node-link network
(Figure 2), which translates inputs of daily rainfall
and potential evapo-transpiration data into runoff.
This is a four parameter model which has evolved
from previous three parameter model after myriad
testing and refinement in varied catchments. With
its numerous experiments conducted in France and
other countries, the model has proven to provide
better results than other rainfall runoff models like
Tank model, IHACRES, HBV, SMAR, TOPMODEL,
Xinanjiang etc (cited in Harlan et al., 2010). GR4J
model has thus been successfully applied in several
countries and used by different authors in various
hydrological studies (Servat and Dezetter 1993;
Yang and Parent, 1996; Kuczera and Parent,
1998; Yang and Michel, 2000; cited in
Andreassian et al)
In this model, the catchment is divided into number of
sub-catchments, where the flow from each sub-
catchment is computed through lumped simulation and then routed to the outlet of the catchment. As
illustrated in Figure 3, potential evapo-transpiration (E) is subtracted from rainfall (P) to determine the net
precipitation (Pn) or net evaporation (En). When rainfall is greater than potential evapo-transpiration, net
rainfall is P-E and net evapo-transpiration is considered to be zero. If rainfall is less than potenial evapo-
transipiration, net evpo-transpiration is the difference between E and P, where net rainfall is considered to
be zero. If Pn is not zero, it is partitioned between two components: production storage (S) and the channel
routing. The flow component (Pr) contributed from the combined effect of percolated flow (Pperc) from the
production storage and the rainfall component (Pn-Ps) is divided into two parts: 10% of this rainfall is
routed via a single unit hydrograph, while 90% is routed via a unit hydrograph and a non linear routing
store (R). And finally water gain or loss function (F) is applied to both flow components, representing
ground water exchange (eWater Ltd, 2013a; Harlan et al., 2010, van Esse, et al., 2013).
The key feature of the model is that it simply consists of two stores and few parameters as explained
below:
a. Production store (mm) : Also known as Soil moisture accounting (SMA) store that determines the maximum capacity of this
store. It is storage in the surface of soil which can store rainfall, where its storage capacity highly
depends on the soil porosity. As illustrated in the model diagram (Figure 3), the process of evapo-
transpiration and percolation is prevalent in this storage (Harlan et al., 2010; Luis et al.).
Numerous modeling results indicate the parameter to provide best result at a range of 100-1200
mm with 80% confidence interval (eWater Ltd, 2013a).
b. Water exchange coefficient:
Figure 2: Node link structure of the model in Bagmati basin
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It is a function of groundwater exchange which influences the routing store. This parameter can
either be positive or negative and ranges from -5 to 3 at 80% confidence interval.
Usually a zero value indicates no water exchange with the routing store. A positive value of
indicates that the water is imported into the routing store. This happens when there is not enough
rainfall in the basin that leads to more addition of water into the store so as to balance the rainfall
data error. This also occur when the stream flow is very high, where there is not enough rainfall to
produce such flows, thereby leading to more import in the routing store. Larger exchange
coefficient indicates larger level in the routing store. While in case of negative value, instead of
importing water to routing store, water is exported to deep aquifer.
c. Routing store (mm): This represents the amount of water that can be stored in soil porous and their value depends on
the type and the humidity of soil (Harlan et al., 2010). About 90% of the total quantity of water
(Pr) produced after percolation from production storage plus the amount of rainfall entering
besides production store is routed through non-linear process in the model. The parameter ranges
from 20-300 mm at 80% confidence interval.
d. Unit Hydrograph time base /Time lag : It is the time when peak of the flood hydrograph is created in GR4J modelling, where 90 % of
flow is slow flow that infiltrates into the ground and 10% of flow is fast flow that flows on the soil
surface (Harlan et al., 2010). The parameter ranges from 1.1-2.9 at 80% confidence interval
e. K and C: GR4J also includes a base flow component. Using these parameters in the model differentiates
slow flow and quick flow. However, a use of zero value deactivates the base flow component and
all flow is produced as quick flow.
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E Potential areal evapo-transipiration
Net evapo-transipiration capacity
Actual evaporation rate
P Precipitation
Per Percolation
Net rainfall
Total quantity of water to reach routing functions
-
Amount of net rainfall that goes directly to the Routing functions
Amount of net rainfall that goes directly to the production store
Direct flow
Routed flow
UH1 Unit Hydrograph 1
UH2 Unit Hydrograph 2
Interception
Land surface E
P
-
Per
S
= +
UH1 UH2
0.9 0.1
Exchange, X2
R
Figure 3: Structure of GR4J model
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3.2. Evaluation Criteria
Source offers different objective function to measure how well the flow data has been predicted by the
model with respect to the observed data by the process of calibration and validation of these data.
Besides the statistical value of the varied objective function, the performance of the model is also assessed
through visual inspection of the flow data in the form of hydrograph, scatter plots, cumulative plots, flow
duration curves, logarithmic plots etc. which is also available in the model. Generally, if the simulated data
is closely aligned with the observed data, then the model is strongly able to reflect the real situation of the
hydrological system and hence there is high confidence in analyzing the model results. The key objective
functions implemented within the Source are Nash Sutcliffe Efficiency (NSE), Nash Sutcliffe Efficiency and
Bias Penalty (NSE-Bias), Nash Sutcliffe Efficiency and Flow Duration Curve (NSE-FDC) and Nash Sutcliffe
Efficiency and log Flow Duration Curve (NSE-log FDC) and measuring volume bias. Use of these objective
functions depend on the objective of the research. The purpose of each objective function is elaborated as
follows:
a. Nash-Sutcliffe Coefficient of Efficiency (NSE)
It is a widely used objective function in rainfall-runoff modeling to measure the goodness of fit
between the measured and the observed discharge data. It could be used to measure the
efficiency of daily and monthly flows. The value ranges from -∞ to +1. Closer the simulated flow
values to the observed one, better is the performance of the model. A NSE of 1 corresponds to the
perfect match of the modeled discharge with the observed data; a NSE of 0 depicts that the
simulated discharge data are more close to the mean of the observed discharge data; a NSE of
<0 indicates that the simulated data is not satisfactory for the further analysis i.e. the observed
mean of discharge data is better predictor than the model simulated data. The daily NSE tends to
capture high and moderate flows very well but often produce poor fits to low flows. It also tends
to provide a good match to the timing and shape of runoff events (Vaze et al., 2011).
Mathematically it is expressed as,
Where, S is the simulated flow, Q is the observed flow data, is the mean observed flow
b. Nash-Sutcliffe Coefficient of Efficiency and Bias Penalty (NSE-Bias)
This objective function can also be used for daily and monthly flows. Parameters in this process are
calibrated by optimizing NSE along with maintaing low bias in stream flow. It generally improves
the low flow performance and is strongly influenced by moderate and high flows and by the
timing of runoff events, which can still often result in poor fits to low flows (Vaze et al., 2011)
c. Combined Nash-Sutcliffe Coefficient of Efficiency and Flow Duration Curves (NSE-FDC); Nash-Sutcliffe Coefficient of Efficiency and logFlow Duration Curves (NSE-logFDC)
This objective function is more focused on creating a best fit for high and moderate flows. It
performs under a weighted combination of two different calculations of the NSE. The first
calculation is based on the daily data of observed and predicated flows. The second calculation is
the NSE on the points in the flow duration curves of both the observed and predicted flow. More
importantly, this second calculation has the effect of optimizing for the distribution of flow
magnitudes from the observed data, rather than the timing of flows (eWater Ltd, 2013b). NSE-FDC
tries to produce a good fit to peak flows while a log of this objective function produces to low
flows (Vaze et al., 2011). Source also offers a "Compound Objective Weighting" option, where a
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user can define weights so as to put more emphasis on either of the objective function (i.e. NSE-
FDC, NSE-logFDC).
d. Relative Volume Error (RVE) It measures the differences in the total volume of simulated discharge data to the observed
discharge data. The closer the deviation to zero, more precise is the model simulation. Also, a
negative and positive value signifies an underestimation and overestimation of simulated flow
respectively. For better evaluation of the model results, there should be low bias in the flow
volume along with the NSE close to 1.
Mathematically, the deviation of runoff volume is expressed as,
x 100
Where, indicates relative volume error, is the volume of simulated discharge, is the
volume of observed discharge
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4. MODEL DEVELOPMENT
4.1. Data Input
4.1.1. Spatial data
a. DEM
Digital Elevation Model (DEM) in source model helps in defining sub-catchments according to the user
defined minimum or maximum area of the sub-catchments. These sub-catchments are further processed
using ArcGIS interface to get the desired number of sub-catchments where the user choice rainfall runoff
model could be applied. Hence, as a prime input for delineating catchment boundaries and also to define
a customize sub-catchments; a Shuttle Radar Topographic Mission (SRTM) DEM with a spatial resolution of
90m x90m was used in this study. The number of sub-catchments defined in this study is 11 (Figure 2). A
node link network was created to route the flow from each of the sub-catchments to the outlet of the basin.
A node in the model represents the gauging stations located at or near to the confluence point and links
between these nodes is created to channel the flow to the downstream as shown in Figure 2.
b. Land use data
Land use in the study area is defined using Moderate Resolution Imaging Spectroradiometer (MODIS)
satellite data of the year 2010 which has a spatial resolution of 250 m x 250 m and the data was
accessed from International Center for Integrated Mountain Development (ICIMOD). ICIMOD has classified
the land use into 11 different categories and they are: Needle leaved closed forest, Needle leaved open
forest, Broad leaved closed forest, Broad leaved open forest, Shrubland, Grassland, Agriculture land,
Bare area, Builtup area, River and a unclassified category. However, land use in this study has been
categorized into four functional units viz., forest, agriculture land, bare area and built up area as depicted
in Figure 4, so as to reduce the complexity. Approximately 2/3rd of the land is covered by forest in the
basin followed by agriculture land which is about 1/4th of the total land area. The detail areal
percentage of land distribution according to elevation and sub-catchments is provided in the Annex (1 &
2). Source also has the capability to assign different models to different functional units (land use) in the
sub-catchments.
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Figure 4: Reclassified landuse map of the study area
4.1.2. Temporal data
To calibrate the model, daily series of precipitation, evaporation and discharge data are required. Daily
records of these data were collected for the period of 1972 to 2008 from the Department of Hydrology
and Meteorology (DHM), Nepal.
a. Precipitation data
All together, 23 rainfall stations were selected (Annex 3) after assessing the quality of the rainfall data to
ensure better performance of the model. Hence, precipitation stations suffering from missing records were
interpolated by following arithmetic method from proximate stations known as 'Normal ratio Method'. This
method is used when normal annual precipitation of any index station differs from that of the interpolation
station by more than 10%. When the annual precipitation of the index stations were less than 10% a
simple arithmetic mean method was employed.
Normal Ratio Method
Where,
Where is the missing precipitation for any storm at the
interpolation station 'x', is the precipitation for the
same period at the "ith" station of a group of index
stations, the normal annual precipitation value for the
'x' station and the normal annual precipitation value
for 'ith' station.
Arithmetic Mean Method
Where,
n is the number of stations, is the missing station, is
the precipitation at the ith station
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As the Bagmati basin is divided into 11 sub-catchments, precipitation in these sub-catchments were
allocated by creating a polygon based on the existing number of rainfall stations in the basin known as
Thiessen Polygon. Polygons in the basin were developed based by the nearest-neighbour method, where it
assumes that the precipitation at one location resembles to the precipitation at the other location. A
weighted factor was generated based on area of sub-catchment to the area covered by a particular
polygon in that sub-catchment. The method is simple, robust and assumes that the data are error free
(eWater Ltd, 2013a). Figure 5 and Table 2 below illustrates the sub-catchment wise polygon area in the
Bagmati basin. The isohyetal map in Figure 6 shows that most part of the catchment receives rainfall in
range of 1500 to 1800 mm. There is exception of highest rainfall >2400 mm in the Bhorleni premises. Few
pockets of less rainfall in the range of 934-1200 mm are observed at the uppermost part of catchment
and also at the bottom of the south-western part of the catchment.
Table 2: Thiessen weighted factor for precipitation data according to sub-catchments
SC Area (Km2)
Station Thiessen Area
Zone Proportion
SC Area (Km2)
Station Thiessen Area
Zone Proportion
0
350.34
1117 204.6 X 0.584 5
448.07
1117 128.9 X 0.288
0 1115 145.4 W 0.415 5 1049 21.8 O 0.049
0 1049 0.6 O 0.002 5 1075 99.6 V 0.222
1
27.12
1049 5.9 K 0.216 5 1060 1.7 R 0.004
1 1074 16.9 U 0.622 5 1022 129.6 H 0.289
1 1071 4.4 S 0.162 5 919 45.0 D 0.100
2
502.52
1049 2.6 O 0.005 5 904 21.7 A 0.048
2 1075 11.2 V 0.022 6
350.04
910 9.1 B 0.026
2 1007 13.4 F 0.027 6 1117 147.2 X 0.420
2 1035 32.8 K 0.065 6 1001 192.6 E 0.550
2 1073 7.3 T 0.015 6 919 1.4 D 0.004
2 1060 50.9 R 0.101 7
548.11
1117 212.0 X 0.387
2 1029 52.9 I 0.105 7 1115 327.6 W 0.598
2 1022 49.4 H 0.098 7 1001 7.5 E 0.014
2 1015 37.4 G 0.075 7 1121 1.3 Y 0.002
2 1074 2.2 U 0.004 8
213.73
1117 38.5 X 0.180
2 1071 43.1 S 0.086 8 1001 16.7 E 0.078
2 1059 61.6 Q 0.123 8 1121 158.7 Y 0.743
2 1052 15.2 P 0.030 9
43.61
1035 0.2 K 0.004
2 1039 66.5 M 0.132 9 1029 1.0 I 0.023
2 1038 3.0 L 0.006 9 1074 20.5 U 0.469
2 1030 23.0 J 0.046 9 1071 2.4 S 0.056
2 1043 30.4 N 0.061 9 1052 7.2 P 0.165
3
91.98
1075 42.7 V 0.465 9 1039 0.3 M 0.006
3 1073 45.1 T 0.491 9 1030 12.1 J 0.276
3 1060 3.1 R 0.034 10
114.22
1015 13.2 G 0.115
3 1029 1.0 I 0.011 10 915 98.2 C 0.860
3 1015 0.0 G 0.000 10 904 2.1 A 0.018
4
85.14
1075 21.2 V 0.249 10 1038 0.9 L 0.008
4 1073 5.5 T 0.065
4 1015 10.6 G 0.125
4 915 30.7 C 0.361
4 904 17.1 A 0.201
*SC is Sub-Catchments
Page | 12
Figure 5: Thiessen polygon area in the basin
Figure 6: Isohyteal map based on annual total rainfall
Page | 13
b. Potential evapo-transpiration data
There are only two stations in the Bagmati basin where the records of evaporation data are available and
they are located in Tribhuvan International Airport (1030) and in Khumlatar (1029) (Table 3). The
evaporation data used in this study is from Khumaltar station, which consists of a Class-A PAN data.
Numerous methods have been developed to estimate potential evapo-transipiration from the pan
evaporation data. Pan has been used successfully to estimate evapo-transpiration by observing the water
loss from the pan and using empirical coefficients to relate pan evaporation to evapo-transpiration.
However, due to many missing records, evaporation data of station 1029 was assumed to be as potential
evapo-transpiration as an input for the model.
Table 3: Evaporation stations
Station Station Name Evap Sunshine Elevation
1029 Khumaltar 2002-2009 2001-2012 1350
1030 Ktm airport 2000-2010 1991-2013 1337
c. Discharge data
The discharge data was also acquired from DHM. There are five hydrological stations in the basin located
at different altitudes. A daily series of data was collected for the stations Pandheradovan (589), Bhorleni
(581), Khokana (550.5), Gaurighat (530) and Sundarijal (505) along with staff gauge reading of the
respective stations. The details of these stations are depicted in the Table 4.
Table 4: Gauging stations
Station Site Name Daily Q Stage data Yearly. Max.
instantaneous
Yearly Min.
instantaneous Data period Data period
505 Sundarijal 1963-2010 1984-2009 1963-2010 1963-2010
530 Gaurighat 1991-2013 1991-2014 1991-2009 1991-2009
550.05 Khokana 1992-2013 1992-2013 1992-2011 1992-2011
581 Bhorleni 2000-2010 2000-2014 2000-2011 2000-2011
589 Pandheradovan 1979-2013 1979-2014 1979-2013 1979-2013
4.2. Model Calibration and Validation
Calibration is the process of optimising the model parameters to get the best estimate of the real data i.e.
observed flow data. While model validation is process of justifying the suitability of this calibrated model
i.e. without changing the value of parameters estimated during the calibration process to predict the flow,
tested beyond the calibration period. The four parameter GR4J (Ge´nie Rural a` 4 parame`tres
Journalier) model can be calibrated with simple techniques. Source allows user to select the optimisation
function either in manual calibration mode or in auto-calibration mode. There are three different auto-
calibration processes in build within source and they are Shuffle Complex Evolution (SCE), Uniform Random
Sampling, Rosenbrock, SCE then Rosenbrock. This user choice optimisation function tries to provide best
suitable parameters through number of iterative process for a given catchment. The model in this study is
calibrated through auto-calibration process using a global optimisation algorithm known as "Shuffle
Complex Evolution and Rosenbrok". In this process, the best set of metaparameters is used as an initial
parameter set or "seed" for fine tuning the maetaparameter range using the Rosenbrock optimiser. The
SCE algorithm involves selecting a number of sets of metaparameter values as random. The model is run
and the objective function is calculated for each of these sets of metaparameter values. The sets of
metaparameter values are formed into a number of groups or complexes (eWater Ltd, 2013b).
Page | 14
Considering the flow data availability, a period of 2000 to 2008 was selected for rainfall-runoff
modelling in this study. The time series of flow at Pandheradovan and Bhorleni, was divided into three
periods viz., 'warm up', 'calibration' and 'validation' periods. Source recommends a minimum of 3 months to
1 year as a warm up period before the start of the calbiration and validation year, however, a two year
warm up period i.e. 01/01/1998 to 12/31/1999 was considered. A long warm up period was taken into
account so as to ensure the well representation of the soil moisture and ground water stores in the basin.
Moreover, the initial five years, 2000 to 2004 were chosen for calibration and the rest four years, 2005-
2008 was used for validation. A four shuffle and hundred iterative process was carried out to allow the
model to genrate the best set of the parameters. Starting from average parameter values, the procedure
searches an optimum parameter set in the parameter space by maximizing the objective function (eWater
Ltd, 2013b). As the research is a pilot study, all four objective functions availabe in the source was
assessed in this study, to understand how the flow data responds to each of them. Therefore, GR4J model
was applied under following setting as illustrated in Table 5.
Table 5: Model run in different setting for stations 589 and 581
Objective function Runs Weights Run with Calibration
weighting from station 589 to 581
NSE daily Individual None 1:1 2:1 1:2
NSE daily & bias penalty Individual None 1:1 2:1 1:2
NSE daily & Flow Duration Curve Individual 0.3 to 0.8
NSE daily & log Flow Duration Curve Individual 0.3 to 0.8
This rainall-runoff model was applied to only two gauging stations in the basin, that is at Pandheradovan
(station 589) and Bhorleni (Station 581). The length of the river is about 38 Kms from Pandheradvoan to
Bhorleni gaguing station. The GR4J model was calbirated individually without any joint calibration to these
nodes. Moreover, for objective funtion like "NSE daily and Flow Duration Curve" and "NSE daily and
logFlow Duration Curve" different weights in the range of 0.3 to 0.8 were used to test the outputs between
the component of these two objective function. Model calbirated through this process generates
independent set of parameters for each catchment. Furthermore, a joint calibration was also performed by
providing different weights to these nodes i.e. by providing equal weights of 1 (1:1) to both gauges;
giving more weights toPandheradovan station than to Bhorleni station (2:1) and vise versa using only two
objective function as shown in Table 5. This process, however, generates single set of parameters for all of
the catchments.
Page | 15
5. RESULT AND DISCUSSION
5.1. Rainfall and Runoff characteristics
Dicharge data at Pandheradovan and Bhorleni was used for hydrologic analysis. The mean annual flow at
Pandheradovan for the period of 1979 to 2008 is 133.66 m3/s. The daily maximum and minimum flow
measured at this station during this period are 7550 and 5.1 m3/s respectively. However, the annual
average flow in the study period i.e. 2000 to 2008 is 110.57 m3/s (Table 6). The maximum and minimum
flow ranges from 5600 to 5.2 m3/s during this period. It indicates that both the average and high flow to
be decreasing over the years at Bagmati basin. A trend in discharge, depicted in Figure 7, clearly shows
that the flow is decreasing at the rate of 1.16 m3/s pr year. However, this trend is more pronounced in the
study period as the slope of the trend line is 4.6 m3/s per year.
The mean annual flow at Bhorleni gauging station for the study period (2000 to 2008) is 81.42 m3/s. The
maximum and minimum flow ranges from 2250 to 3.13 m3/s at this station. A tendency of decrease in
measured flow at a rate of -5.62 m3/s was found for this station too. This result validates the overall
decrease of water resources in the study area.
Figure 7: Trend analysis of discharge at Pandheradivan (Stn589) and Bhorleni (Stn581) station
Since rainfall is the main driver of hydrological process in Bagmati basin, the flow characteristics in the
basin are evaluated comparing the characteristics of the rainfall data. Basin average annual rainfall,
upstream of Pandheradovan gauging station (with a catchment area of 2775 Km2) for 1972-2008 is
1688 mm while its value, upstream of Bhorleni gauging sation (with a catchment area of 1663 Km2) is
1515 mm. However, these rainfall values of Pandheradovan and Bhorleni catchments for the study period
were found to be increased by 9% and 6% respectively as shown in Table 6.
Trend analysis of basin average annual rainfall of Bhorleni and Pandheradovan catchments from the
period of 1979 to 2008 in Figure 8 shows a slight increasing trend. However, a decreasing trend in
rainfall is observed at a rate of -41.22 mm and -35.32 mm for the study period (2000 to 2008) in
Pandheradovan and Bhorelni catchment respectively, which is also in correspondence to decreasing trend
in flow during this period ( Figure 7 and Figure 8-highlighted part). This phenomena is attributed to the
higher values of rainfall during the period between 1985 and 2000.
A steady drop in both annual average flow and rainfall were observed from 2002-2005 (Figure 7 and
Figure 8) in both basins. Further, the scatter plots of rainfall-runoff of these catchments in Figure 9 shows
that runoff clearly followed the rainfall characteristics, i.e. annual flow in the river is high when the rainfall
y = -1.1588x + 151.92
0
50
100
150
200
250
300
1979
1981
1983
1985
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
2007
An
nu
al av
era
ge f
low
, m
3/s
Panhderadovan_Trend
y = -5.6294x + 109.56
0
20
40
60
80
100
120
140
2000
2001
2002
2003
2004
2005
2006
2007
2008
Bhorleni_Trend
Page | 16
is high and vice versa, in the Bagmati basin. It assures the consistency of the rainfall and runoff data used
for simulation in the study.
Figure 8: Trend analysis of rainfall at Pandheradivan (Stn589) and Bhorleni (Stn581) station
Runoff coefficient (C) is the function of interaction among precipitation, evapo-transipiration, land use/land
cover, soil characteristics and slope. The runoff coefficients estimated in the study area are given in Table
6. It is higher in the Bhorleni catchment with an average of 0.96. Given the area is mainly forested
catchment, such a high runoff coefficient is unexpected and it may indicate that the measured values of
rainfall are either underestimated or discharge are overestimated. On the other hand, the value of C for
Pandheradovan located further downstream of Bhorleni is 0.69, which is reasonable. It is because of more
rainfall losses in terms of either evapo-transpiration, interception, percolation etc.
Table 6: Rainfall-Runoff statistics
Pandheradovan Bhorleni
Area (Km2) 2774.89 1663.01
1979-2008 2000-2008 1979-2008 2000-2008
Average discharge, Qav (m3/s) 133.66 110.57 81.41
Precipitation-Total (mm) 1667.58 1819.20 1514.91 1600.90
Runoff coefficient, C 0.90 0.69 0.96
Pearson correlation coefficient, r (Between Q and P)
0.51 0.73 0.86
y = 6.4954x + 1665.2 R² = 0.0488
y = 6.9962x + 1452.9 R² = 0.0695
0
500
1000
1500
2000
2500
19
79
19
80
19
81
19
82
19
83
19
84
19
85
19
86
19
87
19
88
19
89
19
90
19
91
19
92
1993
19
94
19
95
19
96
19
97
1998
19
99
20
00
20
01
20
02
2003
20
04
20
05
20
06
20
07
20
08
Ann
ual Rain
fall,
mm
R_stn589 R_stn581
y = 0.6755x + 27.732 R² = 0.5295
0
500
1000
1500
2000
2500
1000 1300 1600 1900 2200 2500
Flo
w in
mm
Annual Rainfall, mm
Pandheradovan_2000-2008
y = 1.3014x - 539.4 R² = 0.7424
0
500
1000
1500
2000
2500
1000 1500 2000 2500
Flow
in
mm
Annual Rainfall, mm
Bhorleni_2000-2008
Page | 17
Figure 9: Rainfall-Runoff relationship (200-2008) at Pandheradovan (Stn589) and Bhorleni(Stn581)
Hydrograph analysis (for the historical data) in Figure 10 suggests, maximum flow events during the
month of July and August at both gauging station with an exception of maximum flow also during the
month of September at Pandheradvoan station. Likewise, the low flows are more distinct during March and
April in both stations. Approximately, 75-80 % of the total flow is contributed during monsoon season (Jun
to Sept) at both gauging stations. Also, plots of monthly average rainfall (Figure 11) indicates the peak
rainfall during the month of July and about 80% of total rainfall occurs during monsoon season, which is
followed by post-monsoon season (Oct-Nov).
Figure 10: Hydrographs of maximum, minimum and average flows
Figure 11: Rainfall-runoff hydrograph in a catchment area of Pandheradovan (Stn589) and Bhorleni
(Stn581) for the period 0f 2000 to 2008
Figure 12 depicts the probability of exceedance for flow and rainfall of two basins. A magnitude of 0.1%
and 1% probability of exceedance flows at Pandheradvoan station is approximately 2.5 times higher
than the Bhorleni station. Minimum flows at 99 % exceedance are observed to be 7.3 m3/s and 4.96m3/s
for Pandheradovan and Bhorleni stations respectively. Similarly, at 0.1 % to 50 % probability of
exceedance, both stations exhibit nearly similar magnitude of rainfall in both catchments. However, at
0.01%, magnitude of rainfall in Pandherdovan catchment is 1.3 times higher than that of Bhorleni.
0
200
400
600
800
1000
1200
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Flo
w, m
3/s
Stn589_Pandheradovan
Qav Qmax Qmin
0
100
200
300
400
500
600
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Stn581_Bhorleni
Qav Qmax Qmin
0
100
200
300
400
500
600
0
100
200
300
400
500
600
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Av.
flo
w, m
m
Stn589_Av. Rainfall, mm QStn589,mm
0
100
200
300
400
500
600
0
100
200
300
400
500
600
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Ra
infa
ll, m
m
Stn581_Av. Rainfall Qstn581, mm
Page | 18
Figure 12: Probability of exceedance for historical data of flow (a) and rainfall (b) at Pandheradovan and Bhorleni stations
5.2. Calibration and validation results
a. Without Joint Calibration
Table 7 demonstrates the results based on Pearson correlation coefficient (r), biases in total flow volume
and efficiency for each objective function applied at two gauging stations (Pandheradovan and Bhorleni)
to evaluate the model performance. Based on the results in Table 7 and from the scatter plots, daily and
monthly hydrographs of observed and simulated discharge (Figure 13 to 15 & Annex 5 to 8); it is quite
evident that NSE and NSE-Bias objective function outperformed the other objective function for both
gauging stations. A satisfactory relationship was found between observed and simulated flow data with an
efficiency of 0.80 and 0.75 when NSE and NSE-bias objective function were used at Pandheradovan and
Bhorleni gauging station respectively in the calibration period. Pearson correlation coefficients and relative
volume error (RVE) are also slightly better for these objectives functions and for both gauging stations. It is
noted here that the r and efficiency values are in a close range for both stations (r for Panderadocan:
0.86 to 0.90; Bhorlnei: 0.86 to 0.87; efficiency of Pandheradovan: 0.72 to 0.80; Bhorleni: 0.73 to 0.75).
However, RVE manifested quite different and peculiar characteristics. Flows in overall, at Pandheradovan
gauging station is underestimated by approximately 10% by the model for all objective function except
for NSE-logFDC, where it is overestimated by 11 to 20 % depending on the weights used. Likewise, flows
at Bhorleni gauging station is slightly overestimated at a range of 1 to 4 % for all objective function
except for NSE-logFDC where flows are underestimated by 11to 17 %. Compare to all objective function,
NSE-logFDC shows poorest performance in terms of volume bias for both stations. However, combined
comparisons of correlation coefficient, volume bias and efficiency results (Table 7) indicate that the model
provided better estimate of simulated flow when NSE-Bias was used. Numerous calibration process using
all the objective function in large number of catchments in Australia has also proven to provide better
estimate of daily flows, timing and volume ratios when NSE-Bias objective function was used (Vaze et al.,
2011).
Values of r, RVE and efficiency for each objective functions applied at both gauging stations have yielded
poor results in the validation period. The range of r and efficiency are close but that of the RVE is quite
high. It is interesting to note that RVE is switched from underestimation in calibration to overestimation in
validation for Bhorleni station for NSE-logFDC and the performance is better in validation than in
5600
3950
2250
1510
0
1000
2000
3000
4000
5000
6000
0.01 0.1 1 5 10 25 40 50 75 90 95
Dis
charg
e, m
3/s
Exceedance probability, %
a) Pandheradovan_Flow Bhorleni_Flow
168.78
87.99
37.81
0
30
60
90
120
150
180
0.01 0.1 1 5 10 25 40 50 75 90 95 99
Rain
fall, m
m
Exceedance in %
b) Pandheradovan_Rainfall, mm Bhorleni_Rainfall, mm
Page | 19
calibration for this objective function in absolute terms. For Pandheradovan, flows simulated in the
validation period are less, in relative terms, while it is opposite for Bhorleni.
The flow duration curve in Annex 11 and Annex 12 also shows that the ability of the model to capture
flow above 2500 m3/s in the calibration period at Pandheradovan gauging station but, it fails to simulate
peak flows above 2500 m3/s in the validation period. A similar case is observed at Bhorleni station, where
flows above 1000 m3/s have not been well simulated by the model. Therefore, more deficits in water
volume and an even more increased in water volume are observed in validation period at Pandheradovan
and Bhorleni gauging station respectively.
Figure 13: Scatter plots of daily observed versus simulated flow for calibration period of 2000 to 2004, using four objective function - NSE, NSE-Bias, NSE-FDC, NSE-logFDC represented in a clockwise
direction at Pandheradovan gauging station (without joint calibration)
y = 0.7792x + 14.878 R² = 0.8017
0
1000
2000
3000
4000
5000
6000
0 1000 2000 3000 4000 5000 6000
Sim
ula
ted
flo
w, m
3/s
Observed flow, m3/s
Pandheradovan_NSE daily
y = 0.8018x + 21.196 R² = 0.7978
0
1000
2000
3000
4000
5000
6000
0 1000 2000 3000 4000 5000 6000
Sim
ula
ted
flo
w, m
3/s
Observed flow, m3/s
Pandheradovan _NSE-Bias
y = 0.7982x + 13.613 R² = 0.7987
0
1000
2000
3000
4000
5000
6000
0 1000 2000 3000 4000 5000 6000
Sim
ula
ted
flo
w, m
3/s
Observed flow, m3/s
Pandheradvoan_NSE-FDC (0.5)
y = 0.7246x + 54.435 R² = 0.7592
0
1000
2000
3000
4000
5000
6000
0 1000 2000 3000 4000 5000 6000
Sim
ula
ted
flo
w, m
3/s
Observed flow, m3/s
Pandheradovan _NSE-logFDC (0.5)
Page | 20
Figure 14: Daily hydrograph of observed and simulated flow for calibration (2000-2004) and validation (2005-2008) period at Pandheradovan gauging station (without joint calibration)
0
1000
2000
3000
4000
5000
6000
2000 2001 2002 2003 2004
Dis
cha
rge, m
3/s
Pandheradovan_Calibration
Obs_Stn589 Stn589 _NSE daily Stn589 _NSE daily & bias penalty
0
1000
2000
3000
4000
5000
2005 2006 2007 2008
Dis
charg
e, m
3/s
Pandheradvoan_Validation
Obs_Stn589 Stn589_NSE daily Stn589_NSE daily & bias penalty
Page | 21
Figure 15: Daily hydrograph of observed and simulated flow for calibration (2000-2004) and validation (2005-2008) period at Bhorleni gauging station (without joint calibration)
0
500
1000
1500
2000
2500
3000
2000 2001 2002 2003 2004
Disch
arg
e, m
3/s
Bhorleni_Calibration
Obs_Stn581 Stn581_NSE daily Stn581_NSE daily & bias penalty
0
300
600
900
1200
1500
1800
2005 2006 2007 2008
Disch
arg
e, m
3/s
Bhorleni_Validation
Obs_Stn581 Stn581_NSE daily Stn581_NSE daily & bias penalty
Page | 22
These differences in volume at Pandheradovan and Bhorleni could be because of variations in flow in
calibration and validation period (Annex 4). The average flow in the calibration period is 121.9 m3/s
while it is only 96.4 m3/s in validation period. Similarly, the average flow in the calibration period of
Bhorleni is 95.8 m3/s while it is only 63.4 m3/s in validation period. Further, the average monsoon flows
(Jun to Sep) is 302.67 m3/s and 241.41 m3/s (Annex 16) during calibration and validation period for
Pandheradovan, while it is 235.99 m3/s and 140.20 m3/s for Bhorleni during calibration and validation
period. Moreover, average rainfall for the calibration and validation periods are 1962 mm and 1641 mm
for Pandheradovan and it is 1710 mm and 1465 mm for Bhorleni. These statistics shows different rainfall-
runoff ratios in these periods at both stations. However, this ratio is smaller for Pandheradovan. It thus
explains the differences in RVE, i.e. % of change of RVE of validation period with respect to calibration
period in Bhorleni, which is more than that at Pandheradovan. Also, a distinct decrease in rainfall and
runoff is observed in the validation period especially during monsoon and pre-monsoon period at both
gauging stations as shown in Annex 4. Figure in Annex 4 also shows a sharp decline in rainfall and runoff
from Jul to Oct during calibration period at Pandheradovan. However, the decline for both rainfall and
runoff is smooth in the validation period. In addition to these characteristics, it is clearly depicted in Annex
4 that flows during monsoon season at Bhorleni is higher than rainfall during the calibration period with a
reverse phenomena being observed at validation period. Therefore, the parameters calibrated to capture
these different phenomena during the calibration period might not have been able to produce the flow
properly in the validation period as briefly discussed below.
Parameter , being the function of groundwater exchange, influences the routing store. During calibration
period, for Pandheradovan, rainfall is higher than flow (Annex 4). It indicates that the flow should be
negative (away for the routing store) to match the observed flow by the simulated ones. The negative
values of for objective functions NSE daily and NSE-FDC is thus calibrated by the model. For NSE-bias,
however a very small exchange (0.7) was observed. Further, for NSE-logFDC, since the higher values of
flow are moderated by the function used; positive values of in the expected range might have been
possible. The phenomenon is reverse in validation period, which has resulted in relatively poor
performance of the model in that period.
For Bhorleni, to capture the higher flow during monsoon and post monsoon season with low rainfall (Annex
4), must be positive and of high in magnitude (Table, > 3). Thus a higher value of exchange was
calibrated. The phenomenon is reverse in the validation period thereby resulting poor results in this period.
The negative and positive values of RVE for Pandherdovan and Bhorleni and their magnitude further justify
this explanation. It shows that the climatic condition for calibration and validation and for its application
should be similar i.e. the parameters derived in a wetter (or drier) period cannot be used in a drier (or
wetter) period.
Different weighting options for NSE-FDC and NSE-logFDC objective function ranging from 0.3 to 0.8 were
applied to understand the response of flow simulated in the catchment. Table 7 clearly describes the
differences with the changes in weights. Increasing weights from 0.3 to 0.8 was found to increase the
efficiency of the model in both calibration and validation period for both NSE-FDC and NSE-logFDC
objective function. Results based on NSE-logFDC objective function however, opposes the result of other
three objective functions. A less biases in water volume is observed in the validation period at both
gauging stations despite decrease in model efficiency. This objective function was found to balance more
base flows as could be clearly seen from Figure 16 (b and d). However, the medium flows at
Pandheradovan gauging station was found to be less estimated by the model from the year 2003 to
Page | 23
2004 when NSE-logFDC was used. Nevertheless, the model has adequately captured the base flows at
both gauging station while using NSE-logFDC. These findings are in good agreement with the results of
Vaze et al., 2011.
Table 7: Model results for individual runs at station 589 and 581 without calibration weighting
Node
S.N.
Objective function Weights
Calibration Validation Eff. of objective function
Parameters
r Volume Efficiency r Volume Efficiency x1 x2 x3 x4
(Pand
hera
dova
n_Stn
58
9)
1 NSE daily 0.90 -9.89 0.800 0.82 -13.42 0.66 0.80 24.68 -1.23 62.60 0.50
2 NSE-Bias 0.89 -2.44 0.798 0.82 -8.42 0.67 0.80 138.91 0.70 53.72 0.50
1
NSE-FDC
0.3 0.892 -8.796 0.795 0.81 -14.155 0.653 0.91 54.93 -0.47 30.46 0.5
2 0.4 0.893 -8.872 0.796 0.813 -14.267 0.656 0.89 55.95 -0.49 35.05 0.5
3 0.5 0.894 -9.018 0.798 0.815 -14.468 0.658 0.88 57.40 -0.52 39.77 0.50
4 0.6 0.894 -9.193 0.799 0.816 -14.532 0.659 0.86 54.32 -0.59 44.35 0.5
5 0.7 0.895 -9.455 0.799 0.817 -14.763 0.659 0.85 53.25 -0.66 48.88 0.5
6 0.8 0.90 -9.54 0.80 0.82 -14.24 0.66 0.83 40.57 -0.84 53.99 0.50
1
NSE-logFDC
0.3 0.855 11.455 0.72 0.756 7.087 0.557 0.87 654.90 3.64 55.73 0.5
2 0.4 0.868 14.568 0.746 0.777 11.404 0.593 0.85 549.22 3.03 38.13 0.5
3 0.5 0.871 17.114 0.754 0.783 14.334 0.604 0.84 488.42 3.18 40.95 0.50
4 0.6 0.873 20.369 0.757 0.797 16.618 0.63 0.82 379.38 2.84 35.05 0.5
5 0.7 0.88 20.269 0.769 0.798 18.486 0.63 0.81 377.59 2.75 31.27 0.5
6 0.8 0.88 19.70 0.77 0.80 17.91 0.64 0.80 343.98 2.52 27.97 0.50
(Bho
rleni
_Stn
58
1)
1 NSE daily 0.87 1.99 0.748 0.83 28.18 0.63 0.75 512.83 5.00 43.20 1.10
2 NSE-Bias 0.87 0.899 0.748 0.83 26.52 0.64 0.75 511.68 5.00 44.10 1.10
1
NSE-FDC
0.3 0.865 1.991 0.726 0.839 27.456 0.6 0.91 296.26 5.00 49.34 1.12
2 0.4 0.866 1.863 0.734 0.838 27.416 0.611 0.88 334.32 5.00 48.02 1.11
3 0.5 0.866 4.194 0.737 0.838 31.337 0.609 0.86 361.06 4.67 39.72 1.12
4 0.6 0.867 1.867 0.743 0.836 27.644 0.623 0.83 395.52 5.00 46.05 1.11
5 0.7 0.867 1.813 0.746 0.835 27.723 0.629 0.81 429.11 4.94 44.17 1.11
6 0.8 0.866 1.929 0.747 0.833 27.930 0.631 0.79 456.60 5.00 44.42 1.10
1
NSE-logFDC
0.3 0.862 -16.604 0.733 0.828 0.265 0.675 0.87 483.10 4.57 58.24 1.09
2 0.4 0.863 -15.033 0.735 0.83 2.433 0.673 0.85 453.19 4.73 60.39 1.10
3 0.5 0.863 -13.874 0.736 0.83 3.94 0.67 0.84 437.12 5.00 65.43 1.09
4 0.6 0.863 -13.392 0.736 0.83 4.651 0.669 0.82 433.17 5.00 64.71 1.10
5 0.7 0.864 -12.395 0.738 0.832 6.308 0.67 0.80 439.07 4.82 58.42 1.10
6 0.8 0.864 -11.432 0.739 0.831 7.638 0.667 0.78 442.72 5.00 60.66 1.09
Page | 25
0
100
200
300
400
01/01/2000 01/01/2001 01/01/2002 01/01/2003 01/01/2004 01/01/2005 01/01/2006 01/01/2007 01/01/2008
Disch
arg
e, m
3/s
a)
Pa
nd
hera
dvoan_N
SE-
FD
C
Obs_Stn589_NSE-FDC Sim_Stn589 (0.3)_NSE-FDC Sim_Stn589 (0.8)_NSE-FDC
0
100
200
300
400
01/01/2000 01/01/2001 01/01/2002 01/01/2003 01/01/2004 01/01/2005 01/01/2006 01/01/2007 01/01/2008
Disch
arg
e, m
3/s
b)
Pa
nd
hera
dovan_N
SE
-log
FD
C
Obs_Stn589_NSE-logFDC Sim_Stn589 (0.3)_NSE-logFDC Sim_Stn589 (0.8)_NSE-logFDC
Page | 26
Figure 16: Daily flow hydrograph comparing base flows at Pandheradovan (Stn589) and Bhorlnei (Stn581) gauging station with the
compound objective weighting at a range of 0.3 to 0.8 (NSE-FDC, NSE-logFDC)
0
100
200
300
01/01/2000 01/01/2001 01/01/2002 01/01/2003 01/01/2004 01/01/2005 01/01/2006 01/01/2007 01/01/2008
Dis
hca
rge, m
3/s
a)
Bho
rleni_
NSE-
FD
C
Obs_Stn581_NSE-FDC Sim_Stn581 (0.3)_NSE-FDC Sim_Stn581 (0.8)_NSE-FDC
0
100
200
300
01/01/2000 01/01/2001 01/01/2002 01/01/2003 01/01/2004 01/01/2005 01/01/2006 01/01/2007 01/01/2008
Dis
hca
rge, m
3/s
b)
Bho
rleni_
NSE-
log
FD
C
Obs_Stn581_NSE-logFDC Sim_Stn581 (0.3)_NSE-logFDC Sim_Stn581 (0.8)_NSE-logFDC
After calibrating parameters for daily flows for the whole year, priority was given to fitting high flows
during monsoon season which is, from 1st of June to 31st of August for the period of 2000 to 2004.
Comparison was made using different objective function with their respective efficiency criteria,
percentage bias and visually inspecting the graphs to measure the goodness of fit. The statistical summary
in Annex 18 and Annex 19 clearly shows that the average flow during the calibration period has been
adequately represented by the model. The maximum peak event of 5600 m3/s in 07/09/2004 has also
been well captured by the model using NSE daily. This magnitude of this flow has been slightly
overestimated when 'NSE-Bias' and 'NSE-FDC' objective functions were used. Conversely, the low flows
during this season have been better simulated when 'NSE-logFDC' objective function was used.
Comparison of the results based on GeoSFM by ICIMOD (Shrestha et al., 2008) and GR4J model by
NDRI, for the period of 1 July to 7 August of 2002 shows that the GR4J model is slightly more efficient in
producing the flows close to observed ones. A high degree of close relationship was found between
observed and simulated flow as indicated by NSE of 0.91 and 0.92 using GeoSFM and GR4J model
respectively. Figure 17 clearly portrays the peak event of 5380 m3/s on 23rd July that has been highly
captured by the both model. GR4J model seems slightly better in simulating the rising and recession limb
of this peak event. Nevertheless, both models are equally capable of simulating high flows with better
accuracy.
a) Flow simulation using GR4J model
b) Flow simulation using GeoSFM model by ICIMOD (Shrestha et al., 2008)
Figure 17: Comparison of daily hydrograph of observed versus simulated flow for a 38 day period
(1July to 7 Aug of 2002) based on GR4J and GeoSRM model results
b. With Joint Calibration
The model simulation result based on individual gauges have specified two important objective function viz.
NSE and NSE-bias, that had provided the better estimate of overall flow considering the efficiency and
the volume bais. Hence, this two objective function were applied for joint calibration process using three
different criteria as explained in Table 5. Table 9 displays the evaluation criteria for different weighting
process at Pandheradovan and Bhorleni gauging station. Of all the weighting process, more weighing at
Pandheradovan gauging station was able to yield better result for Pandheradovan (Figure 18). On the
other hand, more weights on Bhorelni was able to provide better estimate of flow at Bhorleni. Using NSE-
Bias at Pandheradovan, was able to reproduce flows with better efficiency (0.77) and less volume (13 %)
of all weightage option and also for both calibration and validation period. Conversely, NSE-daily was
able to function better at Bhorleni gauging station.
0
2000
4000
6000
8000
10000
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37
Dis
charg
e, m
3/s
Obs_Stn589 Stn589 _NSE daily
Figure 18: Daily hydrograph of observed and simulated flow for calibration (2000-2004) and validation (2005-2008) period at Pandheradovan gauging station (with joint calibration)
0
1000
2000
3000
4000
5000
6000
01/01/2000 01/01/2001 01/01/2002 01/01/2003 01/01/2004 01/01/2005 01/01/2006 01/01/2007 01/01/2008
Dis
cha
rge m
3/s
Pandheradovan_NSE daily
Obs_Stn589 Sim_Stn589 (PtoB, 2:1) Sim_Stn589 (PtoB,1:2) Sim_Stn589 (PtoB, 1:1)
0
1000
2000
3000
4000
5000
6000
01/01/2000 01/01/2001 01/01/2002 01/01/2003 01/01/2004 01/01/2005 01/01/2006 01/01/2007 01/01/2008
Dis
cha
rge, m
3/s
Pandheradovan_NSE-Bias
Obs_Stn589 Sim_Stn589 (wt2:1) Sim_Stn589 (wt1:2) Sim_Stn589 (wt1:1)
The parameters sets generated for independent calibration and with joint calibration process provided
different range of values however, these parameters were found to lie within the range as provided in
Source Scientific Reference Guide for 80% confidence interval. Based on independent calibration process,
the production store and the ground water exchange coefficient at Pandhearadovan gauging station was
found to vary greatly, however depending on the objective function. The production storage capacity and
exchange rates increased when NSE-logFDC was used. These two parameters might play a significant role
in contributing and balancing the base flows. On the other hand, these two parameters were more
consistent for Bhorleni.
Harlan et al. (2010) conducted sensitivity analysis by changing the four parameters including the various
boundary value combination and comparing this with the results based on NSE and Relative Volume Error
(RVE) to obtain optimum range of parameters in Citarum Hulu River Basin. The large deviation in volume
during validation period in Bagmati basin could possibly due to error in discharge data or also in rainfall
data. The location of gauges at Pandherdvoan was found to be in appropriate, since no flow was
observed at this location when it was inspected during the month of April. Hence, the quality of discharge
data in this basin is questionable leading to more uncertain results. Different sets of parameters were
generated for different process involved. Thus, the relationship between the parameters is tested using a
regression equation, more specifically sensitivity of x1 to x2 and x3. The regression analysis for
independent calibration process between parameter x1 and x2 (Table 8) showed an inverse relationship
for NSE-FDC and NSE-logFDC. This signifies that a less exchange is observed in a catchment with larger
production stores.
Table 8: Regression relationship of parameter x1 to x2 and x3
Gauges
x2 (slope) x3 (slope)
Stn589 NSE-FDC -1.0479 0.0209
NSE-logFDC -0.0261 0.003
Stn581 NSE-FDC -0.0261 0.0002
NSE-logFDC -0.1047 -0.1047
Table 9: Model results with different calibration weighting option at station 589 and 581
Nodes
S.N.
Objective function
Calibration weigh
ts
Calibration Validation Eff. of Objectiv
e function
Parameters
r Vol. Eff. r Vol. Eff. x1 x2 x3 x4
(Stn
589)
1 NSE daily (1:1) 0.87 30.72 0.74 0.78 31.00 0.59 0.73 404.91 3.46 35.43 1.04
2 NSE-bias (1:1) 0.87 19.69 0.75 0.78 17.01 0.60 0.64 364.42 3.52 54.08 1.04
3 NSE daily (2:1) 0.88 15.97 0.77 0.80 12.84 0.63 0.74 341.55 2.90 44.07 1.01
4 NSE-bias (2:1) 0.88 12.55 0.77 0.80 8.57 0.63 0.67 314.62 2.86 54.59 1.00
5 NSE daily (1:2) 0.86 39.27 0.71 0.77 40.99 0.56 0.73 439.17 4.33 43.97 1.05
6 NSE-bias (1:2) 0.86 27.12 0.73 0.77 26.40 0.58 0.64 439.01 3.58 40.34 1.06
(Stn
581)
1 NSE daily (1:1) 0.86 -15.24 0.72 0.84 3.05 0.69
2 NSE-bias (1:1) 0.86 -23.53 0.71 0.84 -9.75 0.69
3 NSE daily (2:1) 0.85 -26.03 0.68 0.84 -13.12 0.68
4 NSE-bias (2:1) 0.84 -28.52 0.67 0.84 -16.96 0.68
5 NSE daily (1:2) 0.86 -9.22 0.74 0.84 11.55 0.67
6 NSE-bias (1:2) 0.86 -18.07 0.73 0.84 -1.25 0.69
Page | 30
5.3. Conclusion
A GR4J rainfall-runoff model in Source was successfully applied in Bagmati basin of Nepal. This study
found that the simple model structure like GR4J with few parameters can simulate discharge with
adequate accuracy. Though the model was satisfactorily efficient in simulating flows, increased biases in
flow volume especially during validation periods, signify some error in input data i.e. both rainfall and
runoff. The four parameters generated through auto-calibration process were found to vary with each
objective function and with their weighting process. Nevertheless, all the parameters were found to lie
within the standard range. However, based on r, RVE and efficiency, independent calibration process is
better suited than joint calibration process. Among the four objective functions, NSE and NSE-Bias was able
to produce better results. Parameters like ground water exchange and production store was found to
differ with respect to the objective function used, however, it is still difficult to define the optimum set of
parameters when the model is calibrated in auto-calibration mode. Therefore, there is a need for
analyzing the sensitivity of these parameters to minimize the uncertainty. This model could be of potential
use if a hybrid combination of different method of calibration and optimisation could be used that could
generate better results.
Page | 31
6. REFERENCES
Andréassian, V., Parent, Eric and Michel, C., undefined. Using a Parsimonious Rainfall-Runoff Model to Detect Non-
stationarities in the Hydrological Behavior of Watersheds.
Babel, M. S., Bhusal, S. P., Wahid, S. M., 2014. Climate Change and Water Resources in the Bagmati River Basin,
Nepal. Theor Appl Climatol, Vol. 115, Pg. 639-654. (DOI 10.1007/s00704-013-0910-4)
Chen, C. and Shrestha, D. L., 2006. Comparison of Methods for Uncertainty Analysis of Hydrological Models. In: 7th
International Conference on Hydroinformatics, HIC 2006, Nice, France.
eWater Ltd (2013a, October 11). Source Scientific Reference Guide (v3.5.0) (Online: Available:
https://ewater.atlassian.net/wiki/display/SD35/Source+Scientific+Reference+Guide)
eWater Ltd, 2013b. Source User Guide (v3.5.0). (Online: Available:
https://ewater.atlassian.net/wiki/display/SD35/ Source+User+Guide)
Harlan, D., Wangsadipura, M. and Munajat, C. M., 2010. Rainfall-Runoff Modeling of Citarum Hulu River Basin by
Using GR4J. In: Proceedings of the World Congress on Engineering, Vol. 2. ISBN: 978-988-18210-7-2. ISSN: 2078-
0958 (Print); ISSN: 2078-0966 (Online)
Lobligeois, F., Andréassian, V., Perrin, C., Loumagne, C., 2012. Can we improve stream flow simulation by using
higher resolution rainfall information? The Seventh European Conference on Radar in Meteorology and Hydrology
(ERAD, 2012)
Lu, L., Jun, X., Chong-Yu, X., Jianjing, C., Rui, W., 2009. Analyze the sources of equifinality in hydrological model using
GLUE methodology. Hydroinformatics in Hydrology, Hydrogeology and Water Resources (Proc. of Symposium JS.4 at
the Joint IAHS & IAH Convention, Hyderabad, India, September 2009). IAHS Publ. 331, 2009
Luis A. Alfaro Casas, A., José Herrera Quispe, B., Juan Carlos Gutiérrez, C. , Jorge L. Suaña Ch., D., Henry
Gallegos Velgara, E., undefined. Optimal Calibration of Parameter of a Conceptual Rainfall-Runoff Model Using
Genetic Algorthim.
Sharma, R. H. and Shakya, N. M., 2006. Hydrological changes and its impact on water resources of Bagmati
watershed, Nepal. Journal of Hydrology, vol. 327, pg. 327-322.
Shrestha, M. S., Artan, G.A., Bajracharya, S. R., Sharma, R. R., 2008. Using satellite-based rainfall estimates for
streamflow modeling in Bagmati Basin. Journal of Flood Risk Management, 1 (2008), 89-99.
Shrestha, R. K., 2007. Impact of Climate Change on Crop Water Use and Productivity: A Case Study of Bagmati
River Basin, Nepal. A Master Thesis submitted to UNESCO-IHE Institute for Water Education, Delft, the Netherlands.
van Esse, W. R., Perrin, C., Booij, M. J. Augustijn, D. C. M., Fenicia, F., Kavetski, D., Lobligeois, F., 2013. The influence
of conceptual model structure on model performance: a comparative study for 237 catchments. Hydrol. Earth Syst.
Sci., Vol. 17., Pg. 4227-4239. doi:10.5194/hess-17-4227-2013.
Vaze, J., Jordan, P., Beecham, R., Frost, A., Summerell, G. (eWater Cooperative Research Centre 2011). Guidelines
for Rainfall-Runoff Modelling: Towards Best Practice Model Application.
Zang, G. P. and Savenije, H. H. P., 2005. Rainfall-runoff modelling in a catchment with a complex groundwater flow
system: application of the Representative Elementary Watershed (REW) approach. Hydrology and Earth System
Sciences, Vol. 9, pg. 243-261.
Page | 32
7. ANNEX
Annex 1: Distribution of land based on its utilization in the basin
S.N. LC_type Area (sq. km.) % of Area
1 Forest 1838.165 66.20
2 Agriculture 737.3705 26.56
3 Bare area 74.01093 2.67
4 Built up area 127.0141 4.57
Total area 2776.561 100
Annex 2: Sub-catchment wise distribution of land
SC LC_Type SC_area LC_area Area% SC LC_Type SC_area LC_area Area%
0 Forest
350.3
292.7 83.5 6 Forest
350.04
214.78 61.3
Agriculture 54.8 15.6 Agriculture 109.04 31.2
Bare area 3.0 0.9 Bare area 26.43 7.6
Builtup area 0 0 Builtup area 0 0
1 Forest
27.1
25.2 93.0 7 Forest
548.11
391.3 71.4
Agriculture 1.9 7.0 Agriculture 133.2 24.3
Bare area 0 0 Bare area 24.0 4.3
Builtup area 0 0 Builtup area 0 0
2 Forest
502.5
163.4 32.5 8 Forest
213.73
183.6 85.9
Agriculture 224.4 44.6 Agriculture 22.0 10.3
Bare area 3.6 0.7 Bare area 8.1 3.7
Builtup area 111.4 22.2 Builtup area 0.2 0.1
3 Forest
92.0
41.9 45.5 9 Forest
43.61
12.0 27.4
Agriculture 45.5 49.4 Agriculture 19.3 44.3
Bare area 1.4 1.6 Bare area 0.2 0.5
Builtup area 3.2 3.5 Builtup area 12.1 27.7
4 Forest
85.1
64.7 76.0 10 Forest
114.22
81.5 71.3
Agriculture 19.5 22.8 Agriculture 32.6 28.5
Bare area 1.0 1.2 Bare area 0.2 0.2
Builtup area 0 0 Builtup area 0 0
5 Forest
448.1
367.1 81.9
Agriculture 75.2 16.7
Bare area 6.0 1.4
Builtup area 0 0
Annex 3: List of Hydro-meteorological stations
S.N. Index Station District Lat. Long. Elevation
(m) Data period Annual
ppt DNA %
1 904 Chisapani Gadhi Makwanpur 27.55 85.13 1706 1957-2009 2156 1
2 910 Nijgadh Bara 27.28 85.16 244 1955-2009 1925 2.1
3 915 Markhugaun Makwanpur 27.62 85.15 1530 1972-2013 1443 0
4 919 Makwanpur Gadhi Makwanpur 27.42 85.16 1030 1975-2009 2274 3
5 1001 Timure Rasuwa 27.23 85.42 1900 1957-2008 934 3.1
6 1007 Kakani Nuwakota 27.8 85.25 2064 1972-2008 2811 4
7 1015 Thankot Kathmandu 27.68 85.2 1630 1967-2013 1838 0.3
8 1022 Godavari Lalitpur 27.58 85.4 1400 1953-2008 1706 10.3
9 1029 Khumaltar Lalitpur 27.67 85.33 1350 1967-2013 1184 2.7
10 1030 Kathmandu Airport Kathmandu 27.7 85.37 1337 1968-2013 1453 0
11 1035 Sankhu Kathmandu 27.75 85.48 1449 1971-2008 2017 0.2
Page | 33
12 1038 Dhunibesi Dhading 27.71 85.18 1085 1971-2008 1529 3.3
13 1039 Panipokhari Kathmandu 27.73 85.33 1335 1971-2013 1372 7
14 1043 Nagarkot Bhaktapur 27.7 85.52 2163 1971-2013 1828 4.3
15 1049 KhopasI(Panauti) Kabhre 27.59 85.51 1517 1971-2008 1396 2.1
16 1052 Bhaktapur Bhaktapur 27.73 85.42 1330 1971-2012 1450 2.4
17 1059 Changunarayan Bhaktapur 27.7 85.42 1543 1974-2013 1682 2.1
18 1060 Chapagaun Lalitpur 27.6 85.33 1448 1976-2012 1221 0
19 1071 Buddhanilakantha Kathmandu 27.78 85.37 1350 1987-2013 1799 14.4
20 1107 SINDHULI GADHI Sindhuli 27.28 85.96 1463 1955-2008 2581 9.8
21 1115 Nepalthok Sindhuli 27.45 85.81 1098 1950-2008 945 1.6
22 1117 Hariharpur Gadhi Valley Sindhui 27.33 85.5 250 1978-2013 2431 0
23 1121 Karmaiya Sarlahi 27.12 85.47 131 1984-2009 1782 3.6
Annex 4: Rainfall-runoff relationship during calibration and validation period at Pandheradovan (Stn589) and
Bhorleni (Stn581) gauging station
0
100
200
300
400
500
600
700
0
100
200
300
400
500
600
700
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Calibration Validation
Av
. R
ain
fall,
mm
Flo
w, m
m
Stn589_Av. Rainfall QStn589,mm
0
100
200
300
400
500
600
0
100
200
300
400
500
600
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Calibration Validation
Av.
Ra
infa
ll, m
m
Flow
, m
m
Stn581_Av. Rainfall Qstn581, mm
Page | 34
Annex 5: Scatter plots of daily observed versus simulated flow for calibration period of 2000 to 2004, using four objective function - NSE, NSE-Bias, NSE-FDC, NSE-logFDC represented in a clockwise direction at Bhorleni
gauging station (without joint calibration)
y = 0.7732x + 23.642 R² = 0.7494
0
500
1000
1500
2000
2500
3000
0 500 1000 1500 2000 2500 3000
Sim
ula
ted f
low
, m
3/s
Observed flow, m3/s
Bhorleni _NSE daily
y = 0.7733x + 22.584 R² = 0.7492
0
500
1000
1500
2000
2500
3000
0 500 1000 1500 2000 2500 3000
Sim
ulate
d f
low
, m
3/s
Observed flow, m3/s
Bhorleni_NSE-Bias
y = 0.8427x + 19.094 R² = 0.7496
0
500
1000
1500
2000
2500
3000
0 1000 2000 3000
Sim
ula
ted
flo
w, m
3/s
Observed flow, m3/s
Bhorleni _NSE-FDC (0.5)
y = 0.79x + 6.8243 R² = 0.745
0
500
1000
1500
2000
2500
3000
0 500 1000 1500 2000 2500 3000
Sim
ula
ted
flo
w, m
3/s
Observed flow, m3/s
Bhorleni _NSE-ogFDC (0.5)
y = 0.6011x + 25.514 R² = 0.6637
0
1000
2000
3000
4000
5000
0 1000 2000 3000 4000 5000
Sim
ula
ted
flo
w, m
3/s
Observed flow, m3/s
Pandheradovan_NSE daily
y = 0.6124x + 29.258 R² = 0.6739
0
1000
2000
3000
4000
5000
0 1000 2000 3000 4000 5000
Sim
ula
ted
flo
w, m
3/s
Observed flow, m3/s
Pandheradovan_NSE-bias
Page | 35
Annex 6:Scatter plots of daily observed versus simulated flow for validation period of 2005 to 2008, using
four objective function - NSE, NSE-Bias, NSE-FDC, NSE-logFDC represented in a clockwise direction at
Pandheradovan gauging station (without joint calibration)
Annex 7:Scatter plots of daily observed versus simulated flow for validation period of 2005 to 2008, using
four objective function - NSE, NSE-Bias, NSE-FDC, NSE-logFDC represented in a clockwise direction at Bhorleni
gauging station (without joint calibration)
y = 0.6228x + 22.418 R² = 0.6637
0
1000
2000
3000
4000
5000
0 1000 2000 3000 4000 5000
Sim
ula
ted
flo
w, m
3/s
Observed flow, m3/s
Pandheradovan_NSE-FDC (0.5)
y = 0.5541x + 56.806 R² = 0.613
0
1000
2000
3000
4000
5000
0 1000 2000 3000 4000 5000
Sim
ula
ted
flo
w, m
3/s
Observed flow, m3/s
Pandheradovan_NSE-logFDC (0.5)
y = 0.7746x + 31.403 R² = 0.6668
0
500
1000
1500
2000
0 500 1000 1500 2000
Sim
ula
ted
flo
w, m
3/s
Observed flow, m3/s
Bhorleni_NSE daily
y = 0.7742x + 30.386 R² = 0.6667
0
500
1000
1500
2000
0 500 1000 1500 2000
Sim
ula
ted
flo
w, m
3/s
Observed flow, m3/s
Bhorleni_NSE-Bias
y = 0.8527x + 27.973 R² = 0.6824
0
500
1000
1500
2000
0 500 1000 1500 2000
Sim
ula
ted
flo
w, m
3/s
Observed flow, m3/s
Bhorleni_NSE-FDC
y = 0.776x + 15.894 R² = 0.6678
0
500
1000
1500
2000
0 500 1000 1500 2000
Sim
ula
ted
flo
w, m
3/s
Observed flow, m3/s
Bhorleni_NSE-logFDC (0.5)
Page | 36
Annex 8: Monthly hydrograph of observed and simulated flow for calibration (2000-2004) and validation
(2005-2008) period at Pandheradovan (stn589) and Bhorleni (Stn 581) gauging station (without joint
calibration)
0
100
200
300
400
500
600
700
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Dis
hca
rge, m
3/s
Pandheradovan_calibration
0
100
200
300
400
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov
Pandheradovan_Validation
0
50
100
150
200
250
300
350
400
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Dis
charg
e, m
3/s
Bhorleni_Calibration
0
50
100
150
200
250
300
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Bhorleni_Validation
Obs_Stn581
Stn581_NSE daily
Stn581_NSE-Bias
Stn581 (0.5)_NSE-FDC
Stn581 (0.5)_NSE-logFDC
0
1000
2000
3000
4000
5000
6000
2000 2001 2002 2003 2004
Dis
cha
rge, m
3/s
Pandheradovan_Calibration
2005 2006 2007 2008
Pandheradovan_Validation
Page | 37
Annex 9: Daily hydrograph of observed and simulated flow for calibration (2000-2004) and validation (2005-2008) period at Pandheradovan and Bhorleni gauging station with joint calibration
Annex 10: Monthly hydrograph of observed and simulated flow for calibration (2000-2004) and validation
(2005-2008) period at at Pandheradovan (stn589) and Bhorleni (Stn 581) gauging station (with joint calibration)
0
500
1000
1500
2000
2500
3000
3500
4000
2000 2001 2002 2003 2004
Dis
cha
ge, m
3/s
Bhorleni_calibration
2005 2006 2007 2008
Bhorleni_validation
Obs_Stn581
Stn581 (1:1)_NSE
Stn581 (2:1)_NSE daily
Stn581 (1:2)_NSE daily
Stn581 (1:1)_NSE-Bias
Stn581 (2:1)_NSE-Bias
Stn581 (1:2)_NSE-Bias
0
100
200
300
400
500
600
700
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Dis
charg
e, m
3/s
Pandheradovan_Calibration
0
100
200
300
400
500
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Pandheradovan_Validation
0
50
100
150
200
250
300
350
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Dis
charg
e, m
3/s
Bhorleni_Calibration
0
50
100
150
200
250
300
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Bhorleni_Validation
Obs_Stn581
Stn581 (1:1)_NSE
Stn581 (2:1)_NSE daily
Stn581 (1:2)_NSE daily
Stn581 (1:1)_NSE-Bias
Stn581 (2:1)_NSE-Bias
Stn581 (1:2)_NSE-Bias
Page | 38
Annex 11: Flow duration curves for Pandheradovan (Stn589) gauging stations during calibration period
(without joint calibration)
0
1000
2000
3000
4000
5000
6000 0
5
9
14
18
23
28
32
37
41
46
51
55
60
64
69
74
78
83
87
92
97
Disch
arg
e, m
3/s
Stn589_Obs_NSE
stn589 _Sim _NSE
0
4
8
13
17
21
25
30
34
38
42
46
51
55
59
63
67
72
76
80
84
88
93
97
Stn589_Obs_NSE-Bias
stn589 _Sim _NSE-Bias
0
1000
2000
3000
4000
5000
6000
0
5
9
14
18
23
28
32
37
41
46
51
55
60
64
69
74
78
83
87
92
97
Disch
arg
e, m
3/s
Stn589_Obs_NSE-FDC (0.5)
stn589 _Sim _NSE-FDC (0.5)
0
4
8
13
17
21
25
30
34
38
42
46
51
55
59
63
67
72
76
80
84
88
93
97
Stn589_Obs_NSE-logFDC (0.5)
stn589 _Sim _NSE-logFDC (0.5)
0
500
1000
1500
2000
2500
3000
3500
4000
4500
5000
0
4
8
13
17
21
25
29
33
38
42
46
50
54
58
63
67
71
75
79
84
88
92
96
Stn589_Obs_NSE
stn589 _Sim _NSE
0
4
8
13
17
21
25
29
33
38
42
46
50
54
58
63
67
71
75
79
84
88
92
96
Stn589_Obs_NSE-Bias
stn589 _Sim _NSE-Bias
Page | 39
Annex 12: Flow duration curves for Pandheradovan (Stn589) gauging stations during validation period (without
joint calibration)
Annex 13: Flow duration curves for Bhorleni (Stn581) gauging station during calibration period (without joint
calibration)
0
500
1000
1500
2000
2500
3000
3500
4000
4500
5000
0
4
8
13
17
21
25
29
33
38
42
46
50
54
58
63
67
71
75
79
84
88
92
96
Stn589_Obs_NSE-FDC (0.5)
stn589 _Sim _NSE-FDC (0.5)
0
4
8
13
17
21
25
29
33
38
42
46
50
54
58
63
67
71
75
79
84
88
92
96
Stn589_Obs_NSE-logFDC (0.5)
stn589 _Sim _NSE-logFDC (0.5)
0
500
1000
1500
2000
2500
3000
0
4
9
13
18
22
26
31
35
39
44
48
53
57
61
66
70
74
79
83
88
92
96
Stn581_Obs_NSE
Stn581 _Sim _NSE
0
4
8
13
17
21
25
30
34
38
42
46
51
55
59
63
67
72
76
80
84
89
93
97
Stn581_Obs_NSE-Bias
Stn581 _Sim_NSE-Bias
0
500
1000
1500
2000
2500
3000
3500
0
4
8
12
16
19
23
27
31
35
39
43
47
51
54
58
62
66
70
74
78
82
86
89
93
97
Stn581_Obs_NSE-FDC (0.5)
Stn581 _Sim _NSE-FDC (0.5)
0
4
7
11
1
4
18
22
25
29
3
3
36
4
0
43
4
7
51
5
4
58
6
1
65
6
9
72
7
6
79
8
3
87
9
0
94
9
8
Stn581_Obs_NSE-logFDC (0.5)
Stn581 _Sim _NSE-logFDC (0.5)
Page | 40
Annex 14: Flow duration curves for Bhorleni (Stn581) gauging station during validation period (without joint
calibration)
0
200
400
600
800
1000
1200
1400
1600
1800
0
4
8
13
17
21
25
29
33
38
42
46
50
54
58
63
67
71
75
79
84
88
92
96
Stn581_Obs_NSE
Stn581 _Sim _NSE
0
4
8
13
17
21
25
29
33
38
42
46
50
54
58
63
67
71
75
79
84
88
92
96
Stn581_Obs_NSE-Bias
Stn581 _Sim_NSE-Bias
0
200
400
600
800
1000
1200
1400
1600
1800
0
4
8
13
17
21
25
29
33
38
42
46
50
54
58
63
67
71
75
79
84
88
92
96
Stn581_Obs_NSE-FDC (0.5)
Stn581 _Sim _NSE-FDC (0.5)
0
4
8
12
16
20
24
28
32
36
40
44
48
53
57
61
65
69
73
77
81
85
89
93
97
Stn581_Obs_NSE-logFDC (0.5)
Stn581 _Sim _NSE-logFDC (0.5)
Page | 41
Annex 15: Daily hydrograph of observed flow for calibration (2000-2004) and validation (2005-2008)
period at Bhorleni (Stn581) gauging station (with joint calibration)
0
500
1000
1500
2000
2500
3000
3500
4000
01/01/2000 01/01/2001 01/01/2002 01/01/2003 01/01/2004 01/01/2005 01/01/2006 01/01/2007 01/01/2008
Dis
cha
rge, m
3/s
Obs_Stn581 Sim_Stn581 (PtoB, 2:1) Sim_Stn589(PtoB:1:2) Sim_Stn581 (PtoB, 1:1)
0
500
1000
1500
2000
2500
3000
3500
4000
01/01/2000 01/01/2001 01/01/2002 01/01/2003 01/01/2004 01/01/2005 01/01/2006 01/01/2007 01/01/2008
Dis
cha
rge, m
3/s
Obs_Stn581 Sim_Stn581 (PtoB, 2:1) Sim_Stn589(PtoB:1:2) Sim_Stn581 (PtoB, 1:1)
Annex 16: Statistical analysis for Pandheradovan station (Stn589) during monsoon season-Jun to Sep (for calibration and validation period)
Monsoon_Stn589_Calibration year r NSE_Monsoon RVE S.d_obs Skewness Volume
ratio Obs_Stn589 302.67 5600.00 15.50 556.77 5.53
Stn589 _NSE daily 303.34 0.88 0.778 0.22 5620.50 24.97 462.94 6.31 1.002
Stn589 _NSE daily & bias penalty 336.08 0.88 0.771 11.04 5693.05 5.11 468.31 6.18 1.110
Stn589 (0.5)_NSE daily & FDC 311.82 0.88 0.775 3.03 5678.46 11.45 474.68 6.10 1.030
Stn589 (0.3)_NSE daily & FDC 312.33 0.88 0.772 3.19 5685.86 12.80 479.33 5.99 1.032
Stn589 (0.4)_NSE daily & FDC 312.17 0.88 0.773 3.14 5682.64 12.14 477.06 6.05 1.031
Stn589 (0.6)_NSE daily & FDC 310.87 0.88 0.775 2.71 5672.01 11.90 472.52 6.15 1.027
Stn589 (0.7)_NSE daily & FDC 309.86 0.88 0.776 2.38 5664.89 11.88 470.22 6.19 1.024
Stn589 (0.5)_NSE daily & logFDC 367.15 0.86 0.717 21.31 4989.81 18.32 414.79 5.73 1.213
Stn589 (0.3)_NSE daily & logFDC 342.71 0.84 0.678 13.23 4453.44 17.04 375.29 5.54 1.132
Stn589 (0.4)_NSE daily & logFDC 356.11 0.86 0.708 17.66 4791.20 18.82 400.02 5.67 1.177
Stn589 (0.6)_NSE daily & logFDC 379.39 0.87 0.735 25.35 5315.20 17.72 440.26 5.81 1.253
Stn589 (0.7)_NSE daily & logFDC 381.35 0.87 0.735 26.00 5318.53 19.91 440.93 5.80 1.260
Stn589 (1:1)_NSE daily 400.57 0.86 0.699 32.35 4984.93 27.83 426.99 5.59 1.323
Stn589 (2:1)_NSE daily 377.57 0.87 0.733 24.75 5275.18 12.62 441.82 5.81 1.247
Stn589 (1:2)_NSE daily 415.92 0.85 0.673 37.42 4880.20 33.88 421.77 5.47 1.374
Stn589 (1:1)_NSE & bias penalty 384.98 0.86 0.710 27.20 5097.38 14.08 433.88 5.70 1.272
Stn589 (2:1)_NSE & bias penalty 371.54 0.87 0.739 22.76 5358.55 9.23 445.79 5.90 1.228
Stn589 (1:2)_NSE & bias penalty 392.00 0.85 0.688 29.52 4816.76 24.20 417.34 5.53 1.295
Monsoon_Stn589_Validation year r NSE_Monsoon RVE S.d_obs Skewness Volume
ratio
Obs_Stn589 241.41
4600.00 13.70 403.75 6.53 Stn589 _NSE daily 230.00 0.78 0.596 -4.73 2439.03 3.85 270.19 3.76 0.953
Stn589 _NSE daily & bias penalty 245.70 0.79 0.608 1.78 2294.60 2.04 265.73 3.46 1.018
Stn589 (0.5)_NSE daily & FDC 233.05 0.78 0.599 -3.47 2536.37 2.38 281.82 3.73 0.965
Stn589 (0.3)_NSE daily & FDC 234.48 0.77 0.593 -2.87 2631.29 1.98 290.22 3.74 0.971
Stn589 (0.4)_NSE daily & FDC 233.87 0.78 0.596 -3.12 2585.78 2.19 286.10 3.74 0.969
Stn589 (0.6)_NSE daily & FDC 232.34 0.78 0.599 -3.76 2500.70 2.58 278.66 3.72 0.962
Stn589 (0.7)_NSE daily & FDC 231.33 0.78 0.600 -4.18 2458.64 2.75 275.08 3.71 0.958
Stn589 (0.5)_NSE daily & logFDC 264.79 0.75 0.538 9.68 2056.76 14.79 242.02 3.08 1.097
Stn589 (0.3)_NSE daily & logFDC 239.46 0.72 0.482 -0.81 1881.94 13.39 217.23 2.90 0.992
Stn589 (0.4)_NSE daily & logFDC 254.64 0.74 0.525 5.48 2012.28 15.15 233.90 3.12 1.055
Stn589 (0.6)_NSE daily & logFDC 278.43 0.77 0.566 15.33 2173.72 14.12 257.76 3.18 1.153
Page | 43
Stn589 (0.7)_NSE daily & logFDC 280.95 0.77 0.567 16.37 2206.97 16.15 259.15 3.22 1.164
Stn589 (1:1)_NSE daily 297.15 0.75 0.527 23.09 2002.32 24.25 248.89 2.71 1.231
Stn589 (2:1)_NSE daily 276.00 0.77 0.563 14.32 2063.85 9.65 254.26 2.92 1.143
Stn589 (1:2)_NSE daily 309.11 0.73 0.495 28.04 1962.76 30.35 246.58 2.51 1.280
Stn589 (1:1)_NSE & bias penalty 280.76 0.75 0.533 16.30 1963.23 11.27 247.80 2.63 1.163
Stn589 (2:1)_NSE & bias penalty 270.11 0.77 0.566 11.88 2050.95 6.78 253.32 2.91 1.119
Stn589 (1:2)_NSE & bias penalty 287.71 0.74 0.511 19.17 1924.94 20.79 241.89 2.59 1.192
Annex 17: Statistical analysis for Bhorleni station (Stn581) during monsoon season-Jun to Sep (for calibration and validation period)
Monsoon_Stn581_Calibration year
r NSE_Monsoon RVE S.d_obs Skewness Volume ratio
Obs_Stn581 235.99 2250.00 8.99 219.52 4.01
Stn581_NSE daily 219.57 0.79 0.599 -6.96 2828.06 28.69 200.05 5.77 0.93
Stn581_NSE daily & bias penalty 218.50 0.79 0.598 -7.41 2830.97 27.62 200.17 5.78 0.93
Stn581 (0.5)_NSE & FDC 231.78 0.79 0.580 -1.78 3074.22 27.39 219.40 5.92 0.98
Stn581 (0.3)_NSE & FDC 235.93 0.79 0.559 -0.03 3225.35 22.08 228.94 6.01 1.00
Stn581 (0.4)_NSE & FDC 232.84 0.79 0.572 -1.33 3156.44 23.24 223.95 5.97 0.99
Stn581 (0.6)_NSE & FDC 228.10 0.79 0.587 -3.34 3034.50 25.21 215.58 5.89 0.97
Stn581 (0.7)_NSE & FDC 225.22 0.79 0.593 -4.56 2974.36 26.37 210.95 5.86 0.95
Stn581 (0.5)_NSE & logFDC 204.79 0.79 0.575 -13.22 2967.55 13.05 207.71 6.01 0.87
Stn581 (0.3)_NSE & logFDC 197.47 0.79 0.570 -16.32 2861.12 12.83 200.71 5.98 0.84
Stn581 (0.4)_NSE & logFDC 201.79 0.79 0.573 -14.49 2921.38 13.02 205.05 6.00 0.86
Stn581 (0.6)_NSE & logFDC 205.77 0.79 0.575 -12.80 2954.13 13.30 208.03 5.97 0.87
Stn581 (0.7)_NSE & logFDC 206.66 0.79 0.578 -12.43 2954.84 14.52 207.68 5.98 0.88
Stn581 (1:1)_NSE 201.51 0.79 0.552 -14.61 3273.58 14.69 216.30 6.65 0.85
Stn581 (2:1)_NSE daily 188.59 0.78 0.491 -20.08 3577.50 6.14 227.39 7.30 0.80
Stn581 (1:2)_NSE daily 209.61 0.79 0.576 -11.18 3157.90 18.37 211.81 6.38 0.89
Stn581 (1:1)_NSE & bias penalty 192.23 0.79 0.531 -18.54 3369.04 7.14 219.74 6.89 0.81
Stn581 (2:1)_NSE & bias penalty 185.19 0.78 0.476 -21.53 3656.76 4.36 229.79 7.51 0.78
Stn581 (1:2)_NSE & bias penalty 196.19 0.79 0.560 -16.87 3109.59 12.76 208.96 6.45 0.83
Monsoon_Stn581_Validation year r NSE_Monsoon RVE S.d_obs Skewness Volume ratio
Obs_Stn581 140.20
1620.00 7.19 126.75 4.81
Stn581_NSE daily 166.69 0.73 0.475 18.90 1053.31 28.54 110.23 2.34 1.19
Stn581_NSE daily & bias penalty 165.56 0.73 0.478 18.09 1052.22 27.46 110.21 2.34 1.18
Page | 44
Stn581 (0.5)_NSE & FDC 179.09 0.75 0.426 27.73 1109.54 27.34 118.99 2.28 1.28
Stn581 (0.3)_NSE & FDC 182.18 0.75 0.399 29.94 1131.80 21.99 123.20 2.22 1.30
Stn581 (0.4)_NSE & FDC 179.16 0.75 0.420 27.79 1118.35 23.11 120.95 2.23 1.28
Stn581 (0.6)_NSE & FDC 174.61 0.75 0.445 24.54 1096.26 25.05 117.25 2.26 1.25
Stn581 (0.7)_NSE & FDC 172.03 0.74 0.458 22.70 1083.82 26.21 115.13 2.29 1.23
Stn581 (0.5)_NSE & logFDC 151.00 0.73 0.509 7.70 1042.41 12.52 111.34 2.26 1.08
Stn581 (0.3)_NSE & logFDC 144.69 0.73 0.517 3.20 1022.83 12.27 108.01 2.35 1.03
Stn581 (0.4)_NSE & logFDC 148.56 0.73 0.514 5.96 1036.11 12.49 110.10 2.31 1.06
Stn581 (0.6)_NSE & logFDC 151.95 0.73 0.507 8.38 1043.99 12.78 111.64 2.26 1.08
Stn581 (0.7)_NSE & logFDC 153.22 0.74 0.508 9.29 1048.85 14.03 111.75 2.29 1.09
Stn581 (1:1)_NSE 151.05 0.75 0.533 7.74 1065.76 14.44 114.31 2.59 1.08
Stn581 (2:1)_NSE daily 138.67 0.76 0.546 -1.09 1067.66 5.75 115.93 2.75 0.99
Stn581 (1:2)_NSE daily 157.49 0.75 0.515 12.33 1068.12 18.08 113.52 2.45 1.12
Stn581 (1:1)_NSE & bias penalty 141.01 0.75 0.546 0.57 1059.22 6.71 113.29 2.56 1.01
Stn581 (2:1)_NSE & bias penalty 135.12 0.76 0.549 -3.63 1065.57 4.05 115.09 2.76 0.96
Stn581 (1:2)_NSE & bias penalty 145.37 0.75 0.538 3.69 1046.00 12.37 110.90 2.54 1.04
Annex 18: Statistical analysis for Pandheradovan station (Stn589) during calibration (2000-2004) and validation (2005-2008) period
Stn589_Calibration year r Vol bias Eff. S.d_obs Skewness Volume ratio
Obs_Stn589 121.9
5600 9.0 347.2 9.0 Stn589 _NSE daily 109.9 0.90 -9.89 0.80 5620.5 0.4 302.1 9.1 0.90
Stn589 _NSE daily & bias penalty 118.9 0.89 -2.44 0.80 5693.0 1.1 311.7 8.6 0.98
Stn589 (0.5)_NSE daily & FDC 110.9 0.89 -9.02 0.80 5678.5 0.3 310.1 8.9 0.91
Stn589 (0.3)_NSE daily & FDC 111.2 0.892 -8.796 0.795 5685.9 0.2 312.8 8.8 0.91
Stn589 (0.4)_NSE daily & FDC 111.1 0.893 -8.872 0.796 5682.6 0.3 311.5 8.8 0.91
Stn589 (0.6)_NSE daily & FDC 110.7 0.894 -9.193 0.799 5672.0 0.3 308.7 8.9 0.91
Stn589 (0.7)_NSE daily & FDC 110.4 0.895 -9.455 0.799 5664.9 0.4 307.3 9.0 0.91
Stn589 (0.5)_NSE daily & logFDC 142.8 0.871 17.114 0.754 4989.8 13.3 288.7 7.4 1.17
Stn589 (0.3)_NSE daily & logFDC 135.9 0.855 11.455 0.72 4453.4 11.6 263.2 7.1 1.11
Stn589 (0.4)_NSE daily & logFDC 139.7 0.868 14.568 0.746 4791.2 13.3 278.6 7.3 1.15
Stn589 (0.6)_NSE daily & logFDC 144.9 0.873 20.369 0.757 5315.2 12.7 304.7 7.6 1.19
Stn589 (0.7)_NSE daily & logFDC 146.6 0.88 20.269 0.769 5318.5 14.5 305.1 7.5 1.20
Stn589 (1:1)_NSE daily 159.3 0.87 30.72 0.74 4984.9 22.4 301.2 7.0 1.31
Page | 45
Stn589 (2:1)_NSE daily 141.4 0.88 15.97 0.77 5275.2 8.8 306.1 7.5 1.16
Stn589 (1:2)_NSE daily 169.8 0.86 39.27 0.71 4880.2 28.4 301.0 6.7 1.39
Stn589 (1:1)_NSE & bias penalty 145.9 0.87 19.69 0.75 5097.4 10.4 303.5 7.3 1.20
Stn589 (2:1)_NSE & bias penalty 137.2 0.88 12.55 0.77 5358.5 6.2 307.2 7.7 1.13
Stn589 (1:2)_NSE & bias penalty 155.0 0.86 27.12 0.73 4816.8 19.2 295.1 6.9 1.27
Stn589_Validation year r Vol bias Eff. S.d_obs Skew_obs Volume ratio
Obs_Stn589 96.4
4600 5.2 255.6 10.0 Stn589 _NSE daily 83.5 0.82 -13.4 0.66 2439.0 0.5 188.6 5.4 0.87
Stn589 _NSE daily & bias penalty 88.3 0.82 -8.4 0.67 2294.6 1.2 190.6 4.9 0.92
Stn589 (0.5)_NSE daily & FDC 82.5 0.815 -14.5 0.658 2536.4 0.4 195.4 5.4 0.86
Stn589 (0.3)_NSE daily & FDC 82.8 0.81 -14.2 0.653 2631.3 0.3 200.0 5.5 0.86
Stn589 (0.4)_NSE daily & FDC 82.7 0.813 -14.3 0.656 2585.8 0.3 197.7 5.5 0.86
Stn589 (0.6)_NSE daily & FDC 82.4 0.816 -14.5 0.659 2500.7 0.4 193.6 5.4 0.85
Stn589 (0.7)_NSE daily & FDC 82.2 0.817 -14.8 0.659 2458.6 0.5 191.6 5.4 0.85
Stn589 (0.5)_NSE daily & logFDC 110.2 0.783 14.3 0.604 2056.8 13.8 180.9 4.1 1.14
Stn589 (0.3)_NSE daily & logFDC 103.2 0.756 7.1 0.557 1881.9 12.5 162.5 3.9 1.07
Stn589 (0.4)_NSE daily & logFDC 107.4 0.777 11.4 0.593 2012.3 13.9 174.2 4.2 1.11
Stn589 (0.6)_NSE daily & logFDC 112.4 0.797 16.6 0.63 2173.7 13.0 192.4 4.3 1.17
Stn589 (0.7)_NSE daily & logFDC 114.2 0.798 18.5 0.63 2207.0 14.7 193.3 4.3 1.18
Stn589 (1:1)_NSE daily 126.3 0.78 31.0 0.59 2002.3 22.6 191.2 3.7 1.31
Stn589 (2:1)_NSE daily 108.8 0.80 12.8 0.63 2063.8 9.0 191.2 4.0 1.13
Stn589 (1:2)_NSE daily 135.9 0.77 41.0 0.56 1962.8 28.7 191.9 3.4 1.41
Stn589 (1:1)_NSE & bias penalty 112.8 0.78 17.0 0.60 1963.2 10.7 189.1 3.7 1.17
Stn589 (2:1)_NSE & bias penalty 104.7 0.80 8.6 0.63 2051.0 6.4 189.8 4.0 1.09
Stn589 (1:2)_NSE & bias penalty 121.9 0.77 26.4 0.58 1924.9 19.5 186.1 3.6 1.26
Annex 19: Statistical analysis for Bhorleni station (Stn589) during calibration (2000-2004) and validation (2005-2008) period
Stn589_Calibration year r Vol bias Eff. S.d_obs Skew_obs Volume ratio
Obs_Stn589 95.8
2250 3.1 162.4 5.0
Stn589 _NSE daily 97.7 0.87 1.99 0.748 2828.1 27.2 145.0 6.8 1.02
Stn589 _NSE daily & bias penalty 96.7 0.87 0.90 0.748 2831.0 26.1 145.1 6.8 1.01
Stn589 (0.5)_NSE daily & FDC 99.8 0.866 4.194 0.737 3074.2 26.1 158.1 7.0 1.04
Stn589 (0.3)_NSE daily & FDC 97.7 0.865 1.991 0.726 3225.4 20.8 165.1 7.1 1.02
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Stn589 (0.4)_NSE daily & FDC 97.6 0.866 1.863 0.734 3156.4 22.0 161.5 7.0 1.02
Stn589 (0.6)_NSE daily & FDC 97.6 0.867 1.867 0.743 3034.5 23.9 155.7 6.9 1.02
Stn589 (0.7)_NSE daily & FDC 97.6 0.867 1.813 0.746 2974.4 25.0 152.4 6.9 1.02
Stn589 (0.5)_NSE daily & logFDC 82.5 0.863 -13.874 0.736 2967.6 11.8 148.6 7.2 0.86
Stn589 (0.3)_NSE daily & logFDC 79.9 0.862 -16.604 0.733 2861.1 11.5 143.4 7.2 0.83
Stn589 (0.4)_NSE daily & logFDC 81.4 0.863 -15.033 0.735 2921.4 11.7 146.6 7.2 0.85
Stn589 (0.6)_NSE daily & logFDC 83.0 0.863 -13.392 0.736 2954.1 12.1 149.0 7.2 0.87
Stn589 (0.7)_NSE daily & logFDC 84.0 0.864 -12.395 0.738 2954.8 13.2 148.8 7.2 0.88
Stn589 (1:1)_NSE daily 81.2 0.86 -15.24 0.72 3273.6 13.4 151.8 8.1 0.85
Stn589 (2:1)_NSE daily 70.9 0.85 -26.03 0.68 3577.5 5.2 155.9 9.2 0.74
Stn589 (1:2)_NSE daily 87.0 0.86 -9.22 0.74 3157.9 17.0 150.7 7.6 0.91
Stn589 (1:1)_NSE & bias penalty 73.3 0.86 -23.53 0.71 3369.0 6.2 152.8 8.5 0.76
Stn589 (2:1)_NSE & bias penalty 68.5 0.84 -28.52 0.67 3656.8 3.7 156.7 9.6 0.71
Stn589 (1:2)_NSE & bias penalty 78.5 0.86 -18.07 0.73 3109.6 11.4 147.3 7.9 0.82
Stn589_Validation year (2005-2008)
r Vol bias Eff. S.d_obs Skew_obs Volume ratio
Obs_Stn589 63.4
1620 6.2 93.3 5.7
Stn589 _NSE daily 81.2 0.83 28.18 0.63 1053.3 27.2 90.1 3.1 1.28
Stn589 _NSE daily & bias penalty 80.2 0.83 26.52 0.64 1052.2 26.1 90.1 3.1 1.27
Stn589 (0.5)_NSE daily & FDC 83.3 0.838 31.337 0.609 1109.5 26.1 98.3 3.0 1.31
Stn589 (0.3)_NSE daily & FDC 80.8 0.839 27.456 0.6 1131.8 20.9 102.7 2.9 1.27
Stn589 (0.4)_NSE daily & FDC 80.8 0.838 27.416 0.611 1118.3 22.0 100.4 3.0 1.27
Stn589 (0.6)_NSE daily & FDC 80.9 0.836 27.644 0.623 1096.3 24.0 96.7 3.0 1.28
Stn589 (0.7)_NSE daily & FDC 81.0 0.835 27.723 0.629 1083.8 25.1 94.6 3.0 1.28
Stn589 (0.5)_NSE daily & logFDC 65.9 0.83 3.94 0.67 1042.4 12.1 90.2 3.1 1.04
Stn589 (0.3)_NSE daily & logFDC 63.6 0.828 0.265 0.675 1022.8 11.8 86.9 3.1 1.00
Stn589 (0.4)_NSE daily & logFDC 64.9 0.83 2.433 0.673 1036.1 12.0 88.9 3.1 1.02
Stn589 (0.6)_NSE daily & logFDC 66.3 0.83 4.651 0.669 1044.0 12.3 90.5 3.1 1.05
Stn589 (0.7)_NSE daily & logFDC 67.4 0.832 6.308 0.67 1048.8 13.5 90.7 3.1 1.06
Stn589 (1:1)_NSE daily 65.3 0.84 3.05 0.69 1065.8 13.5 91.2 3.4 1.03
Stn589 (2:1)_NSE daily 55.1 0.84 -13.12 0.68 1067.7 5.4 90.5 3.6 0.87
Stn589 (1:2)_NSE daily 70.7 0.84 11.55 0.67 1068.1 17.2 91.7 3.2 1.12
Stn589 (1:1)_NSE & bias penalty 57.2 0.84 -9.75 0.69 1059.2 6.4 89.7 3.4 0.90
Stn589 (2:1)_NSE & bias penalty 52.6 0.84 -16.96 0.68 1065.6 3.8 89.5 3.7 0.83
Stn589 (1:2)_NSE & bias penalty 62.6 0.84 -1.25 0.69 1045.995 11.6 88.5 3.3 0.99
