SWAT model (Sediment and Water Assessment Tool) was used to evaluate the impacts of climate change on water resources in Al-Adhaim Basin which is located in north east of Iraq. Al-Adhaim River is the main source of fresh water to Kirkuk City, one of the largest cities of Iraq. Recent studies have shown that blue and green waters of the basin have been manifesting increasing variability contributing to more severe droughts and floods apparently due to climate change. In order to gain greater appreciation of the impacts of climate change on water resources in the study area in near and distant future, SWAT (Soil and Water Assessment Tool) has been used. The model is first tested for its suitability in capturing the basin characteristics, and then, forecasts from six GCMs with about half-a-century lead time to 2046-2064 and one-century lead time to 2080-2100 are incorporated to evaluate the impacts of climate change on water resources under three emission scenarios: A2, A1B and B1. The results showed worsening water resources regime into the future.
The water resources of any basin are influenced by several variables including precipitation, soil, land use and natural calamities such as cyclones, and induced catastrophes such as bushfires. Climate change has significant effects on the supply and demand balance of water resources universally [
Iraq is classified as arid or semi-arid with less than 150 mm of annual rain and high evaporation rate. Its water balance is relatively delicate threatened by water scarcity that can significantly aggravate due to climate change [
In Northern Iraq, Al-Adhaim River is one of the fife tributaries of Tigris River. Al-Adhaim is the source of surface water for Kirkuk city. This basin has been suffering from water scarcity and pollution due to its extreme dry weather [
Al-Adhaim or Nahr Al Uzaym (
The Soil and Water Assessment Tool (SWAT) model [
semi-distributed and physically based continuous time (daily computational time step) model for analyzing hydrology and water quality at various watershed scales with varying soils, land use and management conditions on a long-term basis. Details of the model are well described by [
Land and routing phases are the two models used in this model. The land phase predicts the hydrological components (surface runoff, evapotranspiration, groundwater, lateral flow, ponds, tributary channels and return flow) while the routing phase is the movement of water, sediments, nutrients and organic chemicals via the channel network of the basin to the outlet. The estimation of surface runoff is done through two methods. The first is the SCS curve number method which is an empirical method to estimate the surface runoff based on studies of different rainfall-runoff relationships for small rural watersheds, then developed for different types of soils and land use [
Model input
A great amount of input data is essential for SWAT model to accomplish the tasks envisaged in this research. They are: digital elevation model (DEM), land use map, soil map, weather data and discharge data. These data were compiled from different sources. DEM data was obtained from Queensland Department of Natural Resources and Mines (https://data.qld.gov.au/), and land cover map from Queensland Government Data (https://www.dnrm.qld.gov.au/) and the soil map from the global soil map of the Food and Agriculture Organization of the United Nations [
Model setup, calibration and evaluation
The watershed is divided into sub-basins based on the elevation model (DEM). Thereafter, sub-basins are further delineated by Hydrologic Response Units (HRUs) which are defined as packages of land that have a unique slope, soil and land use area within the borders of the sub-basin. To calibrate the model, the sequential uncertainty fitting algorithm application (SUFI-2) embedded in the SWAT-CUP package [
where
To find out how well the plot of the observed against the simulated values fits the 1:1 line ENC value was used.
For sensitivity analysis, which is computed based on the Latin Hypercube and multiple regression analysis. The multiple regression equation is defined as below.
where g is the value of evaluation index for the model simulations, α is a constant in multiple linear regression equation, β is a coefficient of the regression equation, b is a parameter generated by the Latin hypercube method and m is the number of parameters.
For more details see [
General Circulation Model (GCM) inputs
For climate change prediction, six GCMs (CGCM3.1/T47, CNRM-CM3, GFDL- CM2.1, IPSLCM4, MIROC3.2 (medres) and MRI CGCM2.3.2) were used under a very high emission scenario (A2), a medium emission scenario (A1B) and a low emission scenario (B1) for two future periods (2046-2064) and (2080-2100). The projected temperatures and precipitation were then input to the SWAT model to compare water resources in the basin with the baseline period (1980-2010) (
Sensitivity analysis
Sensitivity analysis has been carried out for 25 parameters related to stream flow (
CN2 was the dominant SWAT calibration parameter for the Al-Adhaim. In most SWAT applications in different watersheds, CN2 was found to be the most sensitive parameter [
Group | Parameter | Description | Unit |
---|---|---|---|
Soil | SOL_ALB | Moist soil albedo | - |
SOL_AWC | Available water capacity | mm∙mm−1 | |
SOL_K | Saturated hydraulic conductivity | mmh−1 | |
SOL_Z | Depth to bottom of second soil layer | mm | |
Groundwater | ALPHA_BF | Base flow Alpha factor | days |
GW_DELAY | Groundwater delay | days | |
GW_REVAP | Groundwater “revap” coefficient | - | |
GWQMN | Threshold depth of water in the shallow aquifer for return flow to occur | mm H2O | |
REVAPMN | Threshold depth of water in the shallow aquifer for “revap” to occur | mm H2O | |
Subbasin | TLAPS | Temperature laps rate | ˚C∙km−1 |
HRU | EPCO | Soil evaporation compensation factor | - |
ESCO | Plant uptake compensation factor | - | |
CANMX | Maximum canopy storage | mm H2O | |
SLSUBBSN | Average slope length | m | |
Routing | CH_N2 | Manning’s n value for the main channel | - |
CH_K2 | Effective hydraulic conductivity in main channel alluvium | mm∙h−1 | |
Management | BIOMIX | Biological mixing efficiency | - |
CN2 | Initial SCS runoff curve number for moisture condition II | - | |
General data basin | SFTMP | Snowfall temperature | ˚C |
SMFMN | Minimum melt rate for snow during year | mm H2O∙˚C−1 day−1 | |
SMFMX | Maximum melt rate for snow during year | mm H2O∙˚C−1∙day−1 | |
TEMP | Snow pack temperature lag factor | - | |
SURLAG | Surface runoff lag time | days | |
BLAI | Maximum potential leaf area index for land cover/plant | - | |
SLOPE | Slope | - |
Parameter | Al-Adhaim | Initial values | Fitted values |
---|---|---|---|
CN2 | 1 | −0.2 - 0.2 | −0.10 |
ESCO.hru | 2 | 0 - 0.2 | 0.89 |
SOL_AWC | 3 | −0.2 - 0.4 | 0.305 |
ALPHA_BF | 4 | 0 - 1 | 0.37 |
SURLAG | 5 | 0.05 - 24 | 14.4 |
GW_DELAY | 6 | 50 - 450 | 75.5 |
HRU_SLP | 7 | 0 - 0.2 | 0.017 |
SFTMP | 8 | −5 - 5 | −3.1 |
GWQMN | 9 | 0 - 2 | 0.07 |
SLSUBBSN | 10 | 0 - 0.2 | 0.005 |
CH_K2 | 11 | 5 - 130 | 53.87 |
GW_REVAP | 12 | 0 - 0.2 | 0.17 |
which directly affects the evapotranspiration losses from the watershed (
Calibration and validation
SWAT was calibrated and validated for AlAdhaim at the solo discharge station in the basin, Injanasation, on a monthly scale. The model was calibrated for thirteen years (1979-1991) and validated for six years (1992-1997) at the Injana discharge station. The first three years was set as a warm up.
The results of monthly discharge calibration and validation for the station showed good agreement with observed data as shown in
Using the calibrated model, annual precipitation, blue water (summation of water yield and deep aquifer recharge) and green water storage (soil water content) were estimated during the last three decades to identify the impacts of climate change on the water
cycle components. Blue water is the freshwater humans can access for instream use or withdrawal. Green water storage does not provide direct access to humans but sustains natural flora and rain-fed agriculture. Green water flow is actual evapotranspiration. The model outputs matched observations.
Blue water and green water storage in the Al-Adhaim basin decreased from east to west (
Rate of relative change in the last three decades | |||
---|---|---|---|
Water component | 1990s vs 1980s | 2000s vs 1990s | 2000s vs 1980s |
Precipitation | −0.24 | −0.25 | −0.43 |
Blue water | −0.40 | −0.52 | −0.70 |
Green water storage | −0.17 | −0.17 | −0.31 |
Green water flow | −0.12 | −0.13 | −0.11 |
The calibrated model was used for water scarcity analysis. Among a large number of water scarcity indicators, the most widely applied and accepted is the water stress threshold [
Mean annual temperature and precipitation outputs from the six GCMs identified earlier were processed for the Al-Adhaim basin under three scenarios (A2, A1B, B1).
GCM names predicting changes in mean annual temperature (˚C) | ||||||
---|---|---|---|---|---|---|
Period | CGCM3.1/T47 | CNRM-CM3 | GFDL-CM2.1 | PSLCM4 | MIROC3.2 | MRI CGCM2.3.2 |
A2 | ||||||
2046-2064 | 1.9 | 3.4 | 3.65 | 2.45 | 1.65 | 1.15 |
2080-2100 | 5.2 | 5.5 | 5,4 | 5.2 | 4.3 | 3.8 |
A1B | ||||||
2046-2064 | 1.5 | 2.5 | 2.9 | 2.7 | 1.3 | 1.1 |
2080-2100 | 4.2 | 5 | 4.8 | 4.2 | 3.3 | 2.78 |
B1 | ||||||
2046-2064 | 1.5 | 2.7 | 2.9 | 0.9 | 1.1 | 1 |
2080-2100 | 3.8 | 3.2 | 4 | 3.2 | 3 | 2.3 |
century projection (2046-2064) showed a decrease in blue water under all emission scenarios for the whole basin. A2 scenario projected the highest reduction (62%) followed by A1B (34%) and then B1 (23%). In the one-century future, the reduction will increase to 66%, 37% and 27% under A2, A1B and B1 emission scenarios, respectively. Similarly, green water storage will decrease under the three emission scenarios for the two future periods, which is captured in
SWAT model was successfully applied for the Al-Adhaim basin at monthly time steps. The model was calibrated and validated at Injana hydrological station. The calibration and validation results showed good performance of the model in simulating hydrological processes. The calibrated model was used to identify the impacts of climate change on blue and green water over last three decades. It was also used to project blue and green water and deep aquifer recharge for near future (2046-2064) and far future (2080-2100) under three emission scenarios (A2, A1B, B1) using six GCMs. All models under three emission scenarios predicted that whole basin will be extremely dry in near and far future. The results of this study may enable decision makers to find a suitable water resources management and crop production for future.
Abbasa, N., Wasimia, S.A. and Al-Ansari, N. (2016) Assessment of Climate Change Impacts on Water Resources of Al-Adhaim, Iraq Using SWAT Model. Engineering, 8, 716-732. http://dx.doi.org/10.4236/eng.2016.810065