Coupled application of R and WetSpa models for assessment of climate change impact on stream ﬂ ow of Werie Catchment, Tigray, Ethiopia

This research assesses the stream ﬂ ow response of Werie River to climate change. Baseline (1980 – 2009) climate data of precipitation, maximum and minimum temperature were analyzed using delta based statistical downscaling approach in R software packages to predict future 90 years (2010 – 2099) periods under two emission scenarios of Representative Concentration Pathways (RCP) 4.5 and RCP 8.5, indicating medium and extremely high emission scenarios respectively. Generated future climate variables indicate Werie will experience a signi ﬁ cant increase in precipitation, and maximum and minimum air temperature for both RCPs. Further, Water and Energy Transfer between Soil, Plants, and Atmosphere (WetSpa) was applied to assess the water balance of Werie River. The WetSpa model reproduced the stream ﬂ ow well with performance statistics values of R 2 ¼ 0.84 and 0.85, Nash – Sutcliffe ef ﬁ ciency ¼ 0.72 and 0.72, and model bias ¼ – 0.14 and – 0.15 for the calibration data set of 1999 – 2010 and validation data of 2011 – 2014 respectively. Finally, by taking the downscaled future climate variables as input, WetSpa future prediction shows that there will an increase in the Werie catchment mean annual stream ﬂ ow up to 29.6% for RCP 4.5 and 35.6% for RCP 8.5 compared to the baseline period. (cid:129) Application of a fully distributed hydrological WetSpa;Water and Energy Transfer between Soil, Plants, and Atmosphere, model in Werie catchment revealed that the model performs well to reproduce the observed stream ﬂ ow values, hence can be used in similar climatic and hydrological conditions. (cid:129) In the 21st century stream ﬂ ow in Warie catchment in response to climate change is expected to experience an increase during the rainy summer (June to September) season and a decrease in the dry (October to May) season.


INTRODUCTION
According to the International Panel on Climate Change (IPCC , ) scientific assessment report, global average temperature will rise between 1.4 and 4.0 C by 2100, relative to 1980-1990, with the doubling of the CO 2 concentration in the atmosphere. The term climate change refers to any change in climate over time, whether due to natural causes or as a result of human activities (IPCC ).
Changes in average climate, frequency and intensity of extreme weather events are likely to have a major impact on natural and human systems (Aerts & Droogers ). A major effect of climate change is alterations in the hydrologic cycle and changes in water availability. Changes in evaporation and precipitation characteristics have the potential to affect runoff, frequency and intensity of floods and droughts, soil moisture, and water supplies for domestic use, irrigation and hydroelectric generation.
The IPCC () findings indicate that developing countries such as Ethiopia will be more vulnerable to climate change and may have far reaching implications for various reasons; mainly the economy largely depends on agriculture, and a large part is highly prone to desertification and drought (Alemayehu & Fantahun ).
Particularly in Africa, climate change has the potential to impose additional pressures on water availability and water demand by 2100, dryland crop net revenues could rise by 51% if future warming is mild and wet but fall by 43% if future climates are hot and dry (Seo & Mendelsohn ).
In Ethiopia, both instrumental and proxy records have shown significant variations in the spatial and temporal patterns of climate. According to NMA (), the country experienced ten wet years and 11 dry years over the past The average temperature of Ethiopia has risen a little more than 1 F during the past 100 years or so (Gene Jiing-Yun You ). Similarly, the results of IPCC's midrange emission scenario as per the NMA-NAPA () show that compared to , the average mean annual temperature across Ethiopia will increase by between 0.9 and 1.1 C by the year 2030 and from 1.7 to 2.1 C by the year 2050. The temperature across the country could rise by between 2.7 and 3.6 C by 2080. Unlike the temperature trends, it is very difficult to detect long-term rainfall trends in Ethiopia due to the high inter-annual and inter-decadal variability. A small increase in annual precipitation is expected over the country (NMA-NAPA ).
Therefore, the projection of climate change impact on water resources of any catchment in the country should not be neglected in the future development plan.
Climate change impacts can vary between catchments even within relatively small areas. This is due to local climate depending on specific watershed processes and the difference in geophysical characteristics of watersheds.
Modeling climate change impacts on hydrology on a local and national scale is needed to infer reliable estimates of the climate change impact on hydrology. The current study investigates impacts of climate change on streamflow in Werie catchment with the coupled application of AgMIP climate downscaling and project script in R-programming and the WetSpa hydrological model. The Werie river is one of the few rivers providing a source of water in Tigray regional state, northern Ethiopia, where different water users compete, hence, there is a need to model its streamflow response to climate change. Potential impacts of climate change on water resources are usually assessed by applying climate projections (temperature and precipitation) derived from global circulation models (GCMs) using a hydrologic model (Kopytkovskiy et al. ). Therefore, this research tries to address how the future climate such as precipitation, maximum temperature, and minimum temperature will change compared to the present conditions and subsequently investigate the potential future impacts of climate change on the streamflow of Werie catchment. Our current study analyzes changes in precipitation, temperature, and streamflow using current GCM and downscaling approaches coupled with a watershed model. This gives great importance to understand how future conditions of local water resources may change and will enable the decision-makers to enforce appropriate adaptive strategies of the water resources.

Study area
The Werie river is one of the largest sources of water in Tigray regional state, Ethiopia, and it is mainly used for public water supply and irrigation purposes. This river, with Tsedia, Chemit and Meseuma rivers as its main tributaries, drains into the Tekeze River basin which in turn joins the Nile in Sudan. The catchment is located at 13 43 0 0″-14 4 0 0″ North and 35 50 0 0″-39 20 0 0″) East ( Figure 1).
Topographically, the catchment is highly vulnerable to soil erosion by water and has become eroded due to steep land features. The undulating terrain and steep slopes, fragile environment, erratic precipitation, and sparse vegetation coverage characterize it, which in turn facilitates soil erosion by water. The elevation of the catchment ranges from 1,079 to 2,494 meters above sea level (MASL) (1) Gathering of baseline data (current climates corresponding to WorldClim from www.worldclim.org/).
(3) Calculation of 30-year running averages for present day simulations  and three future periods.
(4) Using Equations (1) and (2) calculation of anomalies as the ratio of absolute difference between future values in each of the three variables to be interpolated (minimum and maximum temperature, and total precipitation) against 30-year running averages for present day simulations: where ΔX i is the delta change, XC i the 30-year mean of the variable in the current climate, and XF i the 30-year (5) Interpolation of these anomalies using centroids of GCM cells as points for interpolation.
(6) Addition of anomalies to the interpolated surfaces baseline climates from WorldClim to get the downscaled future, using absolute sum for temperatures, and addition of relative changes for precipitation.
where X OBSi is the current climate from observations (i.e. WorldClim); ΔX Ii is the interpolated anomaly (delta); and X DCi is the downscaled future climate of each GCM in the time i.
(7) Calculation of mean temperature as the average of maximum and minimum temperatures.  According to the IPCC (), these two scenarios indicated that in the future there will be medium and extremely high emissions of greenhouse gas, respectively. The model simulates several physical processes at the raster cell level such as interception, depression storage, evapotranspiration, runoff, interflow, groundwater recharge and groundwater flow (at sub-catchment level). The simulated hydrological system consists of four control items: plant canopy cover, the soil surface, the root zone, and the saturated groundwater aquifer. Figure 3 shows schematically an overview of the model water balance at the cell level.
The total water balance for each raster cell is composed where P is the total precipitation in the watershed over the where ui(t) is the cell impulse response function (1/s), and l i is cell size (m). Two parameters ci and d i are needed to define the cell response function, which can be estimated using the relation of Manning as (Henderson ): where L is the size of a grid cell, which is a constant for available spatial maps. The summations presented in Equation (8) where R i is the average hydraulic radius of cell i (m), S i is the cell slope (m/m), and v i is the flow velocity of the cell i (m/s).
The hydraulic radius is determined by a power law relationship with an exceeding probability (Molnar & Ramirez 1998), which relates hydraulic radius to the controlling area and is seen as a representation of the average behavior of the cell and the channel geometry: where A i is the drained area upstream of the cell (km 2 ), which can be easily determined by the flow accumulation routine in ArcView GIS, a p (-) is a network constant and b p (-) a geometry scaling exponent, both depending on the discharge frequency. The flow velocity is calculated by the Manning's equation as: where n i is the Manning's roughness coefficient (-), which depends upon land use categories and the channel characteristics, R is the river channel hydraulic radius (m) and S is longitudinal slope of the river bed (-). The velocity calculated by Equation (11) CR3 -Nash-Sutcliffe efficiency. The Nash-Sutcliffe efficiency (Nash & Sutcliffe ) varies from a negative value to 1, with 1 indicating a perfect fit between observed and simulated hydrographs. When CR3 is below zero, it indicates that the average observed streamflow would have been as good a predictor as the modeled streamflow: CR4model efficiency (low flows). CR4 is logarithmic Nash-Sutcliffe efficiency for evaluating the ability of reproducing the time evolution of low flows (Nash & Sutcliffe ). Similar to CR3, a perfect value of CR4 is 1: CR5model efficiency (high flows). CR5 is an adapted version of the Nash-Sutcliffe criterion for evaluating the ability of reproducing the time evolution of high flows (Nash & Sutcliffe ). A perfect value of CR5 is 1: where Q o,i and Q s,i are, respectively, the observed and simulated river discharge at time step i, N is the number of time steps over the simulation period, and the bar above the variables means the average for the simulation period, and ε is an arbitrary chosen small value introduced to avoid problems with nil observed or simulated river discharges.
In this study the physically GIS-based fully distributed

R and WetSpa models integration
The assessment of streamflow response to climate change in Werie catchment is performed by using both coupled application of R-software package for climate projection and the  Figure 4.

Data used
The basic data sets that are required to develop an input database for the two models are climate, streamflow, and Additionally, hydrological daily streamflow data was required for calibrating and validation of the WetSpa model.

Statistical downscaling
The GCM output was downscaled with the delta method

Predicted future temperature
The maximum temperature projection result indicated that in Werie catchment's future there will be higher maximum temperatures compared to the baseline period under both RCP 8.5 and RCP 4.5 scenarios.
The graphical trend of the maximum temperature predictions is depicted in Figure 7. It is found that under the RCP 4.5 scenario, maximum temperature will rise by 3.5 C (13%) in near-future, by 4.4 C (16%) in mid-future and by 4.8 C (17%) in end-future, with respect to the baseline value. Similarly, under the RCP 8.5 scenario compared to the baseline period an increase in maximum temperature by 3.6 C (13%), 5 C (18%) and 8 C (28%) in the near-future, midfuture and end-future periods respectively is expected.
Moreover, the minimum temperature projection result indicated that similar to the maximum temperature there will also be an increase in minimum temperature compared to the baseline period under both RCP 4.5 and RCP 8.5 scenarios.
As is depicted in Figure 8, it was found that under RCP 4.5 scenario minimum temperature will rise by 5.8 C in near-future, 6.8 C in mid-future and 8.8 C in end-future with respect to the baseline value. Similarly, under the RCP 8.5 scenario compared to the baseline period, an increase in minimum temperature by 3.6 C (13%), 5 C (19%) and 8 C (28%) in the near-future, mid-future and end-future periods respectively is expected.

Future changes in average temperature
According to the temperature scenarios generated (Figure 9), a change of the average temperature in percent showed an increase in all stations. Results from both scenarios revealed that future temperatures will generally increase consistently across the groundwater storage, surface runoff exponent and rainfall intensity threshold, which are relevant to the study area (Table 2). Threshold melt temperature, melt-rate factor and rainfall melt-rate factors, which are related to snowmelt parameters, were not considered in the calibration process as such processes do not exist in the Werie catchment. In  From Table 4 we can understand that the water balance in the catchment for the calibration period of the    flow, respectively.

Streamflow prediction under climate change
Annual mean streamflow for all time periods and scenarios considered showed that the flow will increase in the future.

Wetspa model performance
WetSpa model evaluation on the catchment, as indicated in Table 3 above, for the calibration period the model performs

Streamflow response to climate change
In this paper, the response of streamflow to climate change has been assessed for Werie catchment in northern Ethiopia. Future percentage changes in the monthly mean of river flow, depicted in Figure 12, indicate the flow of the river will increase in the summer wet season which extends from June to September, whereas it will predominantly decrease in the dry season (October-May). This is consistent with the findings of similar studies at upper Guder catchment in north Ethiopia (Fentaw ). In many regions, changing precipitation or melting snow and ice are altering water resources in terms of quantity and quality. The result of climate change prediction clearly shows there will be an increment of precipitation over the Werie catchment with both RCP 4.5 and RCP 8.5 scenarios. Precipitation is higher in RCP 8.5 than RCP 4.5.  Werie will experience an increase in streamflow during the rainy summer season (June-September) and a decrease in the dry season (October-May). Most of the annual flow occurs in the wet summer season, hence, the streamflow in Werie catchment will increase due to future climate change, which includes an increase in both precipitation and air temperature.