







































Georgian Geographical Journal 

 

Hydrological Modeling in Studies of 

Mountain River Basins of Central Asia 
Olga Kalashnikova1,* , Jafar Niyazov 2  Aliya Nurbatsina3  
1
Department of the “Climate, Water and Geoecology”/ Central-Asia Institute for 

Applied Geoscience, Bishkek, Kyrgyz Republic 
2 

Laboratory of Climatology, Glaciology and modeling of water resources / 

National Academy of Tajikistan Institute of water problems, hydropower and 

ecology, Dushanbe, Republic of Tajikistan 
 

3 
Laboratory of Water Resources/Institute of Geography and Water Security, Committee of Science of the Ministry of 

Education and Science of the Republic of Kazakhstan, Almaty, Republic of Kazakhstan
 

* Corresponding author: o.kalashnikova@caiag.kg 

 

 

 

 

 

 

 

 

 

 

 

 

 

Introduction 

  Central Asia is particularly vulnerable to climate change, as this region is mainly irrigated 

agriculture, which depends on river runoff formed mainly by snowmelt and glacial waters.  

Mathematical modeling in Central Asia has been developed since the 1970s at SANIGMI (Central 

Asian Scientific Research Hydrometeorological Institute) in Tashkent (Borovikova, 1972). 

Mathematical modeling made it possible to move from the use of indirect characteristics of runoff-

forming factors used in physical-statistical methods to approximate calculations of these factors. 

Water reserves in snow cover, their distribution by height, and other factors influencing the formation 

of mountain river runoff began to be determined by mathematical expressions.  

  Studies have shown that in the Tien Shan, snow accounts for up to 70 % of the total precipitation 

and provides 60% of the total river runoff (Aizen, 1995). The use of remote sensing data, AVHRR 

satellite images (Baumgartner, 1988), and MODIS has provided information on snow reserves in 

inaccessible mountainous areas of Central Asia (Gafurov & Bárdossy, 2009). Over the following 

decades, hydrological models were developed by various scientific institutes and applied to the 

different Central Asian river basins. Research work has been carried out for the Syr Darya and Amu 

Georgian Geographical Journal, 2024, 4(2) 38-47 

© The Author(s) 2024 

 
This article is an open access article distributed under 

the terms and conditions of the Creative Commons 

Attribution (CC BY) license (https:// 

creativecommons.org/licenses/by/ 4.0/). 

DOI: 

https://journals.4science.ge/index.php/GGJ 

Abstract 

Nowadays, hydrological modeling is widely used both in the scientific and 

practical studies of mountain river basins. The purpose of this study is to 

review the developed and adapted hydrological models in Central Asian 

countries. The models have different methodological approaches and 

peculiarities in data requirements, which the authors describe in more detail 

in the paper. Model calibration and testing results are given for mountain 

rivers located in Kyrgyzstan, Kazakhstan, and Tajikistan. For the high-

mountain river basins with small river basin areas (in our study, the average 

catchment elevation is 2,652-4,170 m.a.s.l. and river basin area starts from 

6.6 to 13.7 thousand km2) and availability of the representative 

meteorological stations, the use of whole hydrological models such as HBV 

light and HBV-EHT show good quality (NSE=0.65-0.94, R=0.82-0.97) and 

practical applicability for rivers runoff estimation. However, such models 

have a loss of accuracy as they consider the basin a single unit. The 

Snowmelt Runoff Model (SRM) also performed well (R = 0.71), but 

requires additional input information on snow cover area from satellite 

images. The Soil and Water Integrated Model (SWIM), as a distributed type 

of model, uses a partitioning of the basin into hydrotopes, which 

complicates the calibration of the river basin model but allows a more 

accurate description of the processes in its basin (has good calibration 

quality for a river basin NSE = 0.88). The Water Evaluation and Planning 

system (WEAP) has a user-friendly interface and good calibration quality 

(NSE = 0.61, R = 0.88) for large river catchments (in our case 52.2 thousand 

km2) and can be applied for water management purposes both at national 

and regional levels. The paper outlined the main conclusions on the 

application of these models for research purposes.   

Keywords: hydrological modelling, Central Asia, mountain river, SRM, 

HBV, WEAP, SWIM 

Citation: Kalashnikova, O.; Niyazov, J.; 
Nurbatsina, A. Hydrological Modeling in 

Studies of Mountain River Basins of Central 
Asia. Georgian Geographical Journal 
2024, 4(2). 38-47 
https://doi.org/10.52340/ggj.2024.04.02.05 

 

mailto:o.kalashnikova@caiag.kg


Kalashnikova et al. 2024 4(2) 

39 
 

Darya river basins, ranging from small river basins such as the Ala-Archa (Hagg, 2006) to rivers with 

significant basin areas such as the Tarim (Duethmann, 2016) and Ak-Shyirak in the High Tien Shan 

(Hagg, 2018). Modeling has been applied both to calculate intra- and inter-annual variability of the 

streamflow (Gafurov, 2017) and to calculate individual streamflow components, mainly glacial runoff 

(Hagg, 2007). In addition to research purposes, some hydrological models are used by state 

organizations (national hydrometeorological services) for practical purposes. For example, HBV light 

is actively used for river basins in Kazakhstan (Bolatova, 2018), and in the 2000s SRM for rivers in 

Uzbekistan (Baumgartner, 2000). 

  Several different types of hydrological models have been created so far. These models can predict 

changes in streamflow with varying levels of accuracy and consider both natural and human-caused 

changes in the environment.  

  This paper presents hydrological models implemented and tested in various organizations and 

institutions in Central Asia (national hydrometeorological services, research institutions, etc.). The 

authors evaluated the success of these hydrological models on the example of Central Asian river 

basins.  The presented hydrological models were tested by the authors in previous studies and 

published in scientific journals and reports for the river basins of Kyrgyzstan (Kalashnikova, 2023), 

Kazakhstan (Nurbatsina, 2019) and Tajikistan (Niyazov, 2020). The publication presents the main 

methodological approaches, a brief characterization of hydrological models, and the main conclusions 

and recommendations obtained by the authors when using them. 

Methods and Materials 

  River basins of Central Asia with basin area from 6,602 km2 to 52,187 km2 with an average 

catchment elevation of 240 - 4170 m.a.s.l. were selected as the main study sites. Most of the studied 

rivers (Naryn, Gunt, and Chatkal) have the snow-glacial type of feeding, with a glaciation area of 0.6 - 

4.6 % of the basin area as estimated by Konovalov in 1985 (Konovalov, 1985) and Shabunin in 2018 

(Shabunin, 2018), with flood peak in June. In addition, two river basins were selected, the headwaters 

of the Naryn River up to the Naryn and Kyzyl-Suu (western, headwaters of the Vakhsh River), with 

glacial-snow type of feeding, with a glaciation area of 7.7-11.9 % of the basin area (Konovalov, 1985) 

with flood peak in July-August, the Oba and Buktyrma rivers are belonging to the basin of the Ertis 

River (Irtysh). Basic information on the river basins is presented in Table 1.  

Table 1. Basic information about the river basins under study*. 

 

 

№ Name of the gauging station 
Catchment area 

in km2 

Mean elevation 

in m.a.s.l. 

Glacier area Mean annual 

water discharge 

in m3/s in km2 percentage of 

the basin area 

1 Naryn river – tributary to the 

Toktogul reservoir. 

52,187 2,851 1063* 2.0 404 

2 Gunt river – Khorog town 13,700 4,170 634 4.6 104 

3 Naryn river - Naryn town 10,500 3,570 511* 4.9 92.9 

4 Oba – Shemonaiha village 8,550 1,228 - - 173 

5 Kyzyl-Suu river - Dombrachi 8,470 3,540 578* 6.8 76.2 

6 Bukhtyrma – Lesnaya Pristan  10,700 - - - 218 

7 Chatkal river – Khudoydodsay 6,602 2,652 42.3* 0.6 110 

Note: *- glacier area according to Landsat images for 2013-2016  (Shabunin, 2018).  

  The most widely known hydrological model is HBV. Hydrological model HBV light 2.0 were used 

for runoff model calibration for the upper Naryn (Naryn town) and Gunt river basins. Also were used 

hydrological model HBV3-ETH9 for runoff model calibration for the Kyzyl-Suu and Chatkal river 

basins. The HBV model was developed in 1973 at the Swedish SMHI (Swedish Meteorological and 

Hydrological Institute) (Bergstörm, 1992), has been tested for regions of Switzerland (Braun, 1992), 

Alpine mountain basins (Hottelet, 1993) and further it was improved at the ETH Zurich.  The model 

has been modified many times, and there are various versions in many countries. Currently, the model 

has a user-friendly interface that allows to visualize data and retrieve them both in graphical form and 

in the form of tables.  The “runoff – precipitation” model HBV3-ETH9 we use to calculate runoff of 

high mountain rivers is an advanced model HBV, which allows us to calculate the daily runoff 

hydrograph based on meteorological and hydrological ground observation data. The HBV3-ETH9 

model has a simple non-deterministic structure and does not require a large set of meteorological 



Kalashnikova et al. 2024 4(2) 

40 
 

parameters. Another model that we use is HBV light 2.0 with semi-distributed parameters, which 

includes subroutines for meteorological interpolation, calculation of snow accumulation and 

snowmelt, total evaporation, soil moisture, and runoff generalization to calculate the transformation of 

water movement across rivers and lakes.  

  Data preparation, work with the model, and model optimization were carried out according to the 

HBV ligh 2.0t user manual (Seibert, 2005). The authors of the article used the allowable ranges of the 

model parameters given in the manual and the materials of previous studies for the mountain rivers of 

Kazakhstan (Bolatova, 2018). Data preparation, work with the model HBV3-ETH9, and model 

optimization were also carried out according to the HBV3-ETH9 user guide (Konz, 2003). Parameter 

ranges for model optimization were taken from previous studies prepared for the Enylchek of the 

Central Tian-Shan River (Mayr, 2014). 

  The Snowmelt Runoff Model (SRM) was developed by J. Martinec (1975) first for small European 

basins and is intended for modeling and predicting daily runoff in mountain basins where snowmelt is 

a major factor in the runoff. The SRM has been successfully tested at WMO for runoff modeling 

(WMO, 1986) and is partially used for simulated conditions of real-time runoff forecasts (WMO, 

1992). The WinSRM provides the user with a complete modeling environment in which snowmelt is 

simulated for mountain basins where snowmelt provides a significant contribution to that runoff. 

  The SRM can be used for the following purposes:  

1. Modeling of daily runoff in a snowmelt season, a year, or a series of years. Results can be 

compared with measured runoff to evaluate model performance and verify model parameter values. 

Modeling can also serve to evaluate runoff patterns in unmeasured basins and under hypothetical 

climate change. 

2. The short-term and seasonal runoff forecast. The microcomputer program includes the formation 

of upgraded recession curves, which are the ratio of snow-covered areas and cumulative snowmelt 

depths when SRM is calculated.  

These curves provide the ability to extrapolate snowpack area values manually by the user several 

days ahead from temperature forecasts so that this input of values is available for runoff forecasts. 

Modernized recession curves can also be used to estimate snow resources for seasonal runoff 

forecasts. Model performance may deteriorate if predicted air temperature and precipitation differ 

from actual values, but errors can be reduced by periodic upgrades. 

The SRM calibration parameters include: runoff coefficient from snow, runoff coefficient from 

rainfall, degree-day factor (swarming factor), temperature gradient, critical temperature, rain coverage 

area, regression coefficient, and run-up time. 

  The SRM parameters are not calibrated or optimized with historical data. They can be obtained 

either by measurement or estimated by hydrologic judgment, considering basin characteristics, 

physical laws, and theoretical or regression relationships. Random successive fits should never exceed 

the range of hydrologically or physically acceptable estimates. 

The German hydrological model SWIM (Soil and Water Integrated Model), which was developed at 

the Potsdam Institute for Climate Impact Research (Germany) by Krysanova et al. in 1996, was tested 

in this study (Krysanova, 1996). The hydrological model SWIM performs calculations using the 

GRASS GIS software, which is freely available online (Geographic Resource Analysis Support 

System GRASS GIS, 2023). The user manual GRASS 4.1 was used for working with hydrological 

modeling (GRASS 4.1 Reference Manual, 1993). At the heart of the calculations is the division of the 

entire study basin into hydrotopes - areas with relatively homogeneous conditions and flow indicators, 

including unified characteristics of soils and land use.  

  In order to put the model into operational practice, it is necessary to thoroughly test it and verify 

the quality of the results. The following tasks were addressed during the testing: adaptation of the 

SWIM hydrological model to the conditions of the studied river; obtaining predictive meteorological 

information and processing data format in RStudio; conducting model calibration and verification; 

development of automatic procedures for making forecasts; evaluation of forecast quality. 

The following meteorological parameters were used in SWIM for hydrological modeling: 

maximum, minimum, and average air temperature, precipitation amount, relative humidity, total solar 

radiation, and average wind speed. 

The main methodological approach for modeling hydrology in WEAP (Water Evaluation and 

Planning system) is the soil moisture method. This method uses a one-dimensional equation that 

calculates the water balance for the surface soil profile (upper bucket, shallow soil zone) and the deep 



Kalashnikova et al. 2024 4(2) 

41 
 

soil profile (lower bucket, deep soil zone) (User's Manual WEAP, Integrated water and energy 

modeling for the Syrdarya River basin). 

  The method considers the effect of soil water-holding capacity on runoff, evapotranspiration (ET), 

intermediate runoff, percolation (infiltration), and baseflow. Each watershed is delineated based on 

land use, soil types, and topography. Automatic watershed delineation separates catchments based on 

land use and elevation. Land use type and climatic parameters in the soil moisture method together 

determine how much water infiltrates into the ground, evaporates, or drains to the river. 

Meteorological parameters of the weather stations were used in WEAP for hydrological modeling: 

average air temperature, precipitation amount, relative humidity, and average wind speed.  

  The following optimization parameters were considered when evaluating the model calibration for 

the basin:  

1. Pearson’s correlation coefficient (R) is an index of the degree of linear relationship between 

observed and simulated data. It ranges from −1 to 1. If R = 0, no linear relationship exists. If R = 1 or 

−1, a perfect positive or negative linear relationship exists;  

2. Pearson’s coefficient of determination (R2) also compares simulated and measured data; it 

describes the proportion of the variance in measured data explained by the model. R2 ranges from 0 to 

1, with higher values indicating less error variance; typically, values greater than 0.5 are considered 

acceptable. 

3. The Nash-Sutcliffe Efficiency (NSE) coefficient is commonly used in hydrological modeling to 

evaluate how well modeled stream flow matches observed streamflow. The ideal value is 1, Values of 

0.5 are acceptable; most modelers aim for values that are at or above 0.7.  

4. The ratio of the root mean squared error to the standard deviation (RSR) is a measure of how 

much simulated flows deviate from observed hydrographs. An ideal value is zero, but values less than 

0.7 are considered acceptable. 

5. Percent bias (PBIAS) is a measure of the model’s ability to match the total volume of flow. An 

ideal value is zero, but PBIAS of plus or minus 25% of the observed streamflow is considered 

acceptable. 

  In our study, first of all, the main two parameters were taken into account to evaluate the 

calibration results of all presented hydrological models for streamflow - Nash-Sutcliffe Efficiency 

(NSE) and Pearson's correlation coefficient (R). Additionally, two morindicators—therratioio of 

throotomeanasquaredeerroror to the standard deviation (RSR) anpercentnt bias (PBIAS) are 

considered. The ideal values and interpretation of these ten embedded statistics are summarized in 

Table 2.  

 
Table 2. Optimize calibration based on visual and statistical assessment. 

Statistical measure Ideal and 

acceptable values Interpretation 

Statistical measure Ideal and 

acceptable values Interpretation 

Statistical measure Ideal and acceptable 

values Interpretation 

Pearson’s correlation 

coefficient, R 

If R = 1 a perfect positive linear 

relationship exists 

describes the degree of collinearity between 

simulated and measured data 

Pearson’s coefficient of 

determination, R2 

The ideal value is 1, values of 0.5 to 

1 are considered acceptable 

is an index of the degree of linear 

relationship between observed and 

simulated data 

Nash-Sutcliffe Efficiency 

(NSE) 

The ideal value is 1; values > 0.5 

= acceptable > 0.6 = desired > 0.7 

= good > 0.8 = very good 

how well modeled stream flow matches 

observed streamflow 

Ratio of the Root Mean 

Squared Error to the standard 

deviation (RSR) 

An ideal value is zero, but values 

less than 0.7 are considered good. 

a measure of how much simulated flows 

deviate from observed hydrographs 

Percent bias (PBIAS) 
An ideal value is zero, but PBIAS 

of plus or minus 25% of is 

considered acceptable 

tendency of consistent over or 

underestimation of flows / match of 

simulated to observed total volume 

 

Result  

  The calibration of the HBV-light model. The authors have used data for the Naryn river (Naryn 

town) period2010-201919 and for the Gunt river (Khorog), 2012-2016For thehe calibration of the 



Kalashnikova et al. 2024 4(2) 

42 
 

HBV-EHmodell, for the Chatkal and Kyzyl-Suu rivers we used data periods 2012-2016. The data from 

gauging stations Khudoydodsay for Chatkal river, Dombrachi for Kyzylsu river, Khorog for Gunt 

riv,er and Naryn town for Naryn river were used as input data. The weather stations Tien-Shan, 

Javshangoz, Chatkal and Sary-Tash, which were used focalibration, wereon mainly located in the 

upper reaches of the rivers. The gauging stations should be located at the exit from the mountain 

gorge, as well as located above the points of water intake for irrigation and communal services.  

  The data for the calculations were obtained from the historical data archive of the national 

hydrometeorological services under existing agreements with scientific institutes. Preparation of data 

in GIS required a digital elevation model with a spatial resolution of 30 meters 

(https://earthexplorer.usgs.gov) and shapefiles of modern glaciation, digitized for 2013-2016 using 

Landsat 8 images and Randolph Glacier Inventory 6.0 (2017) (Shabunin, 2018). There was a division 

into altitudinal zones for all watersheds above 200 meters. Model HBV3-ETH9 calculates in 

MATLAB and has the same set of input data as well as glacier mass balance data. We used input data 

on the simulated mass balance of the Abramov glacier for 2012-2016 (Barandun, 2018). 

  The HBV-light and HBV-EHT models were calibrated for the selected basins. The calibration 

showed the following results for river basins (Fig. 1): Naryn NSE = 0.84, R = 0.91 (R2 = 0.83); Gunt 

NSE = 0.65, R = 0.82 (R2 = 0.67); Chatkal NSE=0.94, R=0.97 (R2 = 0.95); Kyzyl-Suu NSE = 0.74, R 

= 0.91 (R2 = 0.82). 

 

  

  

Figure 1. The HBV-light model calibration for the Naryn river (SG Naryn town) and for Gunt river  

(SG Khorog); the HBV-EHT model calibration for Chatkal river (SG Khudoydodsay) and Kyzyl-Suu River (SG Dombrachi). 

 

  To work with the Snowmelt Runoff Model (SRM) necessary to prepare the basin area and altitude 

zones, for this purpose the basin boundary is defined by the location of the stream gauge on the river 

and the watershed is identified from the topographic map. For meteorological parameters are needed 

the air temperature, precipitation and snow cover area data, and for hydrological parameters is needed 

a water discharge. For the SRM model calibrati,on we used hydrological data of the river Oba (SG 

Shemonaiha) and meteorological data (MSs Leninogorsk and Ust-Kamenogorsk) of the period 2014 - 

2017. The result of calibration is R = 0.71 (fig. 2). 

 



Kalashnikova et al. 2024 4(2) 

43 
 

  
The comparison of March 2017 flood volumes using the 

annual analog method. 

The comparison of March 2017 flood volumes by Fourier 

analysis 

Figure 2. The calibration of the SRM model for the Oba river basin. 

 

  To prepare input information for Soil and Water Integrated Model (SWIM), maps of elevation zones, 

soil types, land use, and vegetation were prepared. A digital elevation model SRTM with a spatial 

resolution of 30m was used to prepare the map of elevation zones. The soil types map was prepared 

based on data from the FAO Soils Portal (Food and Agriculture Organization of the United Nations, 

2023). The land use/vegetation maps were based on data from the "Global Land 30" project for 2014, 

which used satellite images from the Landsat project with 30 m resolution for a multi-year series for 

the period 2009-2011. In digital format, as of the end of 2015 (www.globeland30.org) land use maps 

became fully available and contained more detailed division into classes (Globeland30. Publicly 

available global geoinformation product, 2023). To calibrate the model SWIM, we used hydrological 

data of the river Bukhtyrma (SG Lesnaya Pristan) and meteorological data (MSs Katon-Karagai, 

National Park Markakol, Ulken Naryn, Leninogorsk, Seleznevka) for the period 2001 - 2010. The 

calibration result is a NSE = 0.88 (fig. 3). 

 

 

 

Figure 3. The SWIM model calibration for the Bukhtyrma river basin  

   

  To build the model for the Naryn River in the WEAP software, initial data from the Stockholm 

Environment Institute (SEI) from the water-energy model for the entire Syr Darya River basin and 

Kyrgyzhydromet data for the Naryn River basin were used. The basis for modeling was spatial data 

from the Stockholm Environment Institute (SEI) - the HydroSHEDS digital elevation model with a 

spatial resolution of 500 meters, glacier area from Randolph Glacier Inventory 6.0 (2017), glacier 

extent and land use data from the European Space Agency (ESA).  



Kalashnikova et al. 2024 4(2) 

44 
 

  Climatic data for the Naryn River basin were interpolated using a temperature and precipitation 

gradient from weather stations located at the three altitude zones of Toktogul (at 983 m.a.s.l.), Naryn 

(2039 m.a.s.l.), and Tien Shan (3614 m.a.s.l.). Water inflow to the Toktogul reservoir, data on water 

discharge and volume were used according to the data of the site of JSC “Electric Power Plants”. 

Calibration of the model was carried out in PEST tools.  

  There are four charts we recommend considering in the process of calibration:  

1) The monthly time series for observed and modeled streamflow. This chart allows us to observe 

how well the modeled flow matches base flows, wet weather flows, and seasonality for a range of dry 

and wet years.  

2) Annual total of observed and modeled streamflow. The annual total provides a good sense of the 

general fit of the model over time and of how well the modeled total annual volume of runoff matches 

observed values. 3) Monthly average of observed and modeled streamflow. The monthly average 

shows how the modeled flow values deviate from the historical record, on average, each annual cycle.  

4) Exceedance Probability for observed and modeled streamflow. The exceedance probability chart 

ranks each flow measurement by value, the lowest on the right and the highest on the left, for both the 

modeled and observed streamflow. The values on the x-axis show the percentage of flows that exceed 

the values of flow. The modeled and observed streamflow should show a close match of exceedance 

percentages for the two flow records.  

  The monthly time series for observed and modeled streamflow were NSE=0.61 and R=0.88. Figure 

4 shows the results of WEAP model calibration. 

  

A B 

  

C D 

 
Figure 4. Calibration of the WEAP model for the Naryn river basin – inflow to Toktogul reservoir.  

A: The monthly time series for observed and modeled streamflow, B: Annual Total of observed and modeled streamflow, C: 

Monthly average of observed and modeled streamflow; D: Exceedance probability for observed and modeled streamflow. 

Blue color: modeled, red color: observed. 

  Thus, the study covered 5 main hydrological models (HBV light, HBV-EHT, SRM, SWIM and 

WEAP) with different methodological and model calibration approaches for the river basin.   

 



Kalashnikova et al. 2024 4(2) 

45 
 

Discussion 

  The choice of hydrological models HBV light 2.0 and HBV-EHT was due to the fact, that they are 

freely available and do not require a license. The model has a simple interface and a small set of input 

data: daily data on air temperature, precipitation and evaporation from a weather station representative 

of the watershed, and average daily water discharge from a gauging station with natural flow. To 

prepare the data in HBV-EHT, also are needed data on the mass balance of glaciers and the area of 

modern glaciation in the shape file. Calibration of models for small river basins (up to 13,000 km2) 

with representative meteorological stations in Monte-Carlo and Gap-Optimization tools appears to be 

the simplest and can be successfully supplemented by manual calibration.  

  In contrast to HBV models, the SWIM hydrological model requires a large amount of incoming 

spatial data and complete calibration but is flexible in interpreting different situations introduced 

during scenario preparation. For example, changes in land cover, land use, etc. can be additionally 

considered. 

  The SRM hydrological model, like the HBV models, considers the river basin (as a whole), but 

requires input information on the spatial distribution of snow cover. The creation of the MODSNOW-

Tool program (Gafurov, 2016) greatly simplifies the acquisition of this data for elevation zones of 200 

meters or more. The SRM model has a simple interface and does not require a license.  

  The WEAP software has a user-friendly interface and models river runoff well for large river 

basins (in our case 52,000 km2), which cannot be modeled in other simpler models, such as HBV, 

showing low statistical calibration performance. The WEAP software works in integration with 

QUAL2K, MODFLOW, MODPATH, PEST, Excel, and GAMS programs. It is currently actively 

integrating with LEAP, NEMO, MABIA, and MACRO programs. This integration allows for an 

integrated approach to the planning of water resources management activities, considering the full 

range of possibilities of their multipurpose use.   

Conclusion  

  The main task of hydrological modeling is to obtain reliable hydrological forecasts of future water 

resource change. In the arid conditions of Central Asia, when irrigated agriculture requires significant 

water resources in the summer, advance forecasting is important for planning water allocation 

between upstream and downstream countries. An advance forecast of low water availability during 

the growing season, to carry out preventive measures for rational water use by water and energy 

companies, is the most important.  

  Assessment of hydrological models’ efficiency, carried out by the authors based on long-term 

experience, allows using in practice the best models depending on the goals set by users 

(stakeholders).    

  Water availability forecasts based on the developed methods in the presented hydrological models 

and software, which have good quality and high efficiency, should be used in hydrometeorological 

services and water management. 

  Reliable and early forecasts will allow ministries and agencies to plan water use measures for the 

growing season, as well as to take timely preventive measures to avoid consequences of low water 

(water shortage, hydrological drought) and high water (mudflows/floods).  

Authors’ contribution 

Kalashnikova O.Yu.: Conceptualization, methodology, formal analysis, writing original draft, 

visualization, writing, reviewing and editing, data collection, investigation. Niyazov J.B.: 

conceptualization, methodology, writing original draft, visualization, investigation, data processing. 

Nurbatsina A.A.: conceptualization, methodology, formal analysis, writing original draft, writing, 

reviewing and editing, data collection, investigation.  

ORCID iD 

Olga Kalashnikova https://orcid.org/0000-0002-6920-8067 

Jafar Niyazov https://orcid.org/0000-0001-7723-3142 

Aliya Nurbatsina https://orcid.org/0000-0002-4978-5377 

 



Kalashnikova et al. 2024 4(2) 

46 
 

Reference 

Aizen V.B., Aizen E.M., Melak Zh. A. (1995). Climate, snow cover, glaciers and runoff in the Tien Shan. 

Bulletin of water resources. No. 31(6).  

Barandun M., Huss M., Usubaliev R., Azisov E., Berthier E., Kääb A., Bolch T. and Hoelzle M. (2018). Multi-

decadal mass balance series of three Kyrgyz glaciers inferred from modelling constrained with repeated 

snow line observations. The Cryosphere, 12, 1899–1919. doi.org/10.5194/tc-12-1899-2018 

Baumgartner, M. F. (1988). Snowmelt runoff simulation based on snow cover mapping using digital Landsat-

MSS and NOAA/ AVHRR data, USDA-ARS, Hydrology Lab. Tech. Rep.  

Baumgartner M., Spreafico M., Weiss H. (2000) Operational snowmelt runoff forecasting in the Central Asian 

mountains. Remote Sensing and Hydrology 2000 (Proceedings of a symposium held at Santa Fe, New 

Mexico, USA, April 2000). IAHS-AISH Publication. No. 267. P. 66–71. 

Bergstörm S. (1992). The HBV model – it’s structure and applications. SMHI Reports Hydrology. Norrkoping, 

Sweden, № 4.  

Bolatova A.A., Tillakarim T.T., Raymzhanova M.N., Serikbay N.T., Bagitova B.E., Bolatov K.M. (2018). HBV 

hydrological model calibration in the Kazakhstan mountain rivers. Hydrometeorology and ecology. № 3. 

110-124. 

Borovikova L.N., Denisov Y.M., Trofimova E.B., Shentsis I.D. (1972). Mathematical modeling of the flow of 

mountain rivers. Proceedings of SANIGMI, issue 61 (76), Leningrad, Hydrometeoizd  

Braun L.N., Renner C.B. (1992). Application of a conceptual runoff model in different physiographic regions of 

Switzerland // Hydrological Sciences-Journal. № 37, 3.  

Duethmann Doris, Menz Christoph, Jiang Tong, Vorogushyn Sergiy. (2016). Projections for headwater 

catchments of the Tarim River reveal glacier retreat and decreasing surfacewater availability but 

uncertainties are large. Environmental Research Letter. 11. doi:10.1088/1748-9326/11/5/054024. 

Food and Agriculture Organization of the United Nations. (2023). http://www.fao.org/soils-portal/soil-

survey/soil-maps-and-databases/faounesco-soil-map-of-the-world/en/ 

Gafurov A., Bárdossy A. (2009). Cloud removal methodology from MODIS snow cover products.  Hydrology 

and Earth System Sciences. № 13(7). P. 1361–1373. doi: 10.5194/hess-13-1361-2009 

Gafurov A., Duethmann D., Kriegel D., Unger-Shayesteh K., Huss M., Farinotti D., Vorogushyn S. (2017). 

Climate impact assessment on water resources and glacierization in the Naryn, Karadarya and Zerafshan 

basins, Central Asia. EGU Poster.  

Gafurov A., Lüdtke S., Unger-Shayesteh K., Vorogushyn S., Schöne T., Schmidt S., Kalashnikova O., Merz B. 

MODSNOW-Tool: an operational tool for daily snow cover monitoring using MODIS data. 

Environmental Earth Science. 2016. № 75(14), 1078. doi: 10.1007/s12665-016-5869-x 

Globeland30. Publicly available global geoinformation product. (April 2023). 

http://www.globeland30.org/GLC30Download/index.aspx, 

https://landcover.usgs.gov/global_climatology.php. 

GRASS4.1 Reference Manual (1993). US Army Corps of Engineers. Construction Engineering Research 

Laboratories, Champaign, Illinois. 

Hottelet Ch., Braun L.N., Leibundgut Ch., Rieg A. (1993). Simulation of Snowpack and Discharge in an Alpine 

Karst Basin // IHS Publication, No. 218. 

Hagg W., Braun L.N., Weber M. and Becht M. (2006). Runoff modelling in glacierized Central Asian 

catchments for present-day and future climate // Nordic Hydrology. Vol 37 (2). 

Hagg Wilfried, Hoelzle Martin, Wagner Stephan, Mayr Elisabeth, Klose Zbynek. (2013). Glacier and runoff 

changes in the Rukhk catchment, upper Amu-Darya basin until 2050 // Global and Planetary Change 110. 

Hagg Wilfried et al. (2018). Future Climate Change and Its Impact on Runoff Generation from the Debris-

Covered Inylchek Glaciers, Central Tian Shan, Kyrgyzstan // Water, 2018, 10, 153. Doi: 

10/3390/w10111513. 

Hagg, W., L.N. Braun, M. Kuhn, T.I. Nesgaard (2007) Modelling of hydrological response to climate change in 

glacierized Central Asian catchments. Journal of Hydrology (2007) 332, 40– 53  

Integrated water and energy modeling for the Syrdarya River basin 

https://www.riverbp.net/community_of_practice/hub/weap_leap/   

Kalashnikova O. Yu., Niyazov J., Nurbatsina A., Kodirov S., Radchenko Y., Kretova Z. (2023). Kyrgyz 

transboundary rivers’runoff assessment (Syr-darya and Amu-darya river basins) in climate change 

http://www.fao.org/soils-portal/soil-survey/soil-maps-and-databases/faounesco-soil-map-of-the-world/en/
http://www.fao.org/soils-portal/soil-survey/soil-maps-and-databases/faounesco-soil-map-of-the-world/en/
http://www.globeland30.org/GLC30Download/index.aspx
https://landcover.usgs.gov/global_climatology.php
https://www.riverbp.net/community_of_practice/hub/weap_leap/


Kalashnikova et al. 2024 4(2) 

47 
 

scenarios. Central Asian Journal of Water Research (CAJWR). No. 9(1): 59-88. https://water-

ca.org/article/kyrgyz-transboundary-rivers-runoff-assessment-syr-darya-and-amu-darya-river-basins-in-

climate-change-scenarios  

Kalashnikova O.Yu., Nurbatsina A.A. and Niyazov J.B. (2023). Assessment of flood and flood risks for 

sustainable development of the Zhabai River basin (Kazakhstan). https://cajscr.com/ru/paper/flood-and-

flash-flood-risk-assessment-for-sustainable-development-in-the-zhabay-river-basin-kazakhstan 

Konovalov V.G. (1985). Melting and runoff from glaciers in the river basins of Central Asia. L: ed.: 

Gidrometeoizdat. 

Konz M. (2003). User Manual HBV3-EHT9. Commission for Glaciology of the Bavarian Academy of Sciences 

and Humanities.  

Krysanova, V., Müller-Wohlfeil, D.I., Becker, A., (1996) Integrated Modelling of Hydrology and Water Quality 

in mesoscale watersheds. PIK Report No. 18, July 1996, Potsdam Institute for Climate Impact Research 

(PIK), Germany, 32p. 

Mayr E., Juen M., Mayer Cr., Usubaliev R., Hagg W. (2014). Modeling runoff from the Inylchek glaciers and 

filling of ice‐dammed lake Merzbacher, Central Tian Shan // Geografiska Annaler: Series A, Physical 

Geography, 96:4, 609-625.  doi.org/10.1111/geoa.12061 

Niyazov J.B., Kalashnikova O.Yu., Gafurov A.A. (2020). MODIS Imagery Based Water Content Forecasting 

Methodology for Mountain Rivers in Central Asia. Central Asian Journal of Water Research (CAJWR). 

No. 6(2). – С.26-37. doi: 10.29258/CAJWR/2020-R1.v6-2/26-37.rus  

Nurbatsina A., Pak A., Hamidov V., Lobanova A., Didovets I. (2019). Climate Change and Hydrology in 

Central Asia: A Survey of Selected River Basins. Report. Zoï Environment Network, Geneva.  

Seibert J. (2005). User Manual HBV light 2.0. Stockholm University, University of Oregon, Uppsala University. 

Shabunin A.G. (2018). Catalog of glaciers in Kyrgyzstan. Bishkek. 

www.caiag.kg/phocadownload/projects/Catalogue%20%20%20of%20glaciers%20Kyrgyzstan%202018.p

df 

Water resources of Kazakhstan: assessment, forecast, management (2012). Water availability of the Republic of 

Kazakhstan: state and prospects vol. 21. Almaty: Arko LLP. 

User's Manual WEAP https://weap21.org/index.asp?action=208  

 

https://water-ca.org/article/kyrgyz-transboundary-rivers-runoff-assessment-syr-darya-and-amu-darya-river-basins-in-climate-change-scenarios
https://water-ca.org/article/kyrgyz-transboundary-rivers-runoff-assessment-syr-darya-and-amu-darya-river-basins-in-climate-change-scenarios
https://water-ca.org/article/kyrgyz-transboundary-rivers-runoff-assessment-syr-darya-and-amu-darya-river-basins-in-climate-change-scenarios
https://cajscr.com/ru/paper/flood-and-flash-flood-risk-assessment-for-sustainable-development-in-the-zhabay-river-basin-kazakhstan
https://cajscr.com/ru/paper/flood-and-flash-flood-risk-assessment-for-sustainable-development-in-the-zhabay-river-basin-kazakhstan
https://doi.org/10.29258/CAJWR/2020-R1.v6-2/1-25.rus
http://www.caiag.kg/phocadownload/projects/Catalogue%20%20%20of%20glaciers%20Kyrgyzstan%202018.pdf
http://www.caiag.kg/phocadownload/projects/Catalogue%20%20%20of%20glaciers%20Kyrgyzstan%202018.pdf
https://weap21.org/index.asp?action=208

