Statistical Downscaling and Inflows Projections of an Arid Watershed

Study Case: Hassan Addakhil Dam the Way Forward

Ismail Elhassnaoui

, Wafae El Harraki

, Ahmed Bouziane

, Driss Ouazar

and Moulay Driss Hasnaoui

2 e

Mohammadia School of Engineers, Mohammed V University in Rabat, Morocco

Ministry of Equipment, Transport, Logistics and Water, Rabat, Morocco

Keywords: Climate change, HEC-HMS, RCP, Spatial Downscaling.

Abstract: This study aims to assess future inflows of Hassan Addakhil dam in front of hydroclimatic variation under

climate change. The Coupled Model Intercomparison Project Phase 5 (CMIP5) precipitation data was

downscaled for Foum Tilicht and Foum Zaabel rain gauge stations at a daily time-scale, using SDSM software

considering the baseline period 1983-2012. Future precipitation projections over the period 2013-2100, were

generated for three Representative Concentration Pathway, namely RCP 2.6, RCP4.5 and RCP8.5. Besides,

the future hydrologic projection was conducted through HEC-HMS. The results show that the sum of

precipitations would increase for Foum Zaabel compared to the reference period by 14% for RCP2.6, 11%

for 4.5%, and 14% for RCP8.5 in the 2050s. For the 2080s these changes would move to 11%, 14%, and 15%

for RCP2.6, RCP4.5, and RCP 8.5 respectively. Concerning Foum Tilicht, this increase is estimated at 22%,

19%, and 20% for RCP 2.6 and RCP4.5 and RCP8.5 in the 2050s, whereas in the 2080s it's around 21% for

and 22%. The Future projection of the Hassan Addakhil dam inflow, under RCP 2.6, RCP 4.5 and RCP 8.5,

shows that the maximum and the minimum inflow is likely to occur during October and July respectively.

1 INTRODUCTION

The anthropogenic factor increased significantly the

greenhouse emissions, which, unfortunately,

accentuated global warming (IPCC, 2014). Climate

change has led to the heterogeneity of the spatial and

temporal distribution of precipitation, shifting of

rainfall trend, snowmelt conditions change and

increasing of extreme event frequency (Trenberth,

2011). Indeed, the hydro-climatic condition tends to

change in the sense that some areas will experience

increasing precipitation while other areas will

experience an opposite trend (IPCC, 2014; Kaito et

al., 2000).

Therefore, Climate change is considered as the

principal trigger for precipitation heterogeneity in

terms of variation of its magnitude, frequency, and

intensity; which can lead to a socio-economic and

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environmental crisis (Dong, 2020; Sillmann et al.,

2013; Sisco et al., 2017).

Along with climate change, water demands

(irrigation, public supply, industry…) are

continuously increasing, which would put more

pressure on water resources (Karmaoui et al., 2020).

Consequently, the increase in water demand and the

spatiotemporal heterogeneity in hydro-climatic

conditions will certainly affect dams performance

(Okkan & Kirdemir, 2018). Dam is a hydraulic

structure often used to meet multiple purposes

comprising: drinking water, irrigation, hydropower,

flood mitigation and environmental streamflow

requirements (L. Zhang, 2000). Indeed, more than

47000 dams are constructed worldwide (Shah &

Kumar, 2008). Due to the spatiotemporal variability

of precipitation, and the increasing demands and the

severity of the extreme events, dam policy becomes a

tool for guaranteeing water security as a concept

combining water availability, water accessibility,

water safety and quality, and water management

(Gain, 2016). Morocco is one of the countries highly

concerned by water security problems. To avoid

water shortages, Morocco has adopted a dam policy

since 1960’s and has implemented more than 140

dams to meet drinking water demand and enhance

agriculture investments. However, climate change

and extreme events affect directly sustainable

management of these reservoirs. Several studies over

the world have assessed the behaviour of dams under

climate change. Ehsani et al., 2017 used neural

network algorithm to assess the dam's behaviour

under climate change and to optimize releases to meet

downstream demands. Yang et al., 2019 investigated

the impact of dam’s construction under climate

change on hydroecological behaviour and natural

hazard risk. The results showed that the construction

of the dam helps to overcome drought conditions and

mitigate flood risks. Yang et al., 2019 assessed

streamflow variation due to climate change and dams

construction using the Global Environmental Flow

Calculator while the dam’s management was

conducted through HEC-ResSim. The results showed

that the multi-functional dams can enhance the

performance of streamflow regularization. Okkan &

Kirdemir, 2018 investigated the evolution of the

projected crop water demands, hydro-meteorological

changes, and dam performance under climate change.

Z. Zhang et al., 2015 assessed the streamflow

variability response to climate change in a coastal

Chinese watershed, using the ecohydrological

analysis and environmental flow factor. The results

showed that a cascade dam’s development can

mitigate the effect of climate change on hydrologic

behaviour. Raje & Mujumdar, 2010 evaluated the

performance of a significant reservoir in India under

climate scenarios by employing alternative operating

policy. Raje & Mujumdar, 2010 investigated the

response of multipurpose reservoirs to climate

change.

In terms of climate change assessment, Global

climate models (GCM) and the Regional climate

models (RCM) are the primary sources for studying

historical trends for precipitation and predict likely

future rainfall (Chokkavarapu & Ravibabu, 2019). A

set of available GCMs for climate change projection,

namely the Coupled Model Intercomparison Project

Phase 5 (CMIP5), basically used for the fifth IPCC

Assessment Report (Lutz et al., 2016), was presented

by (Chokkavarapu & Ravibabu, 2019). Furthermore,

to explore the future climate change variation and its

impact, four representative concentration pathways

have been set: RCP2.6 as a mitigation scenario

(Vuuren et al., 2011), RCP4.5/RCP6 as medium

stabilization scenarios and RCP8.5 as a pessimistic

scenario. These scenarios were performed for the

CMIP5 to predict future global change (Taylo et al.,

2012; Xin et al., 2013). However, the spatial

resolution of GCM and RCM are respectively

between [250Km, 600 km] and [30Km, 90Km]

(Chokkavarapu & Ravibabu, 2019). Hence, the GCM

and RCM are too coarse to assess local environmental

factors (Chokkavarapu & Ravibabu, 2019; Diallo et

al., 2012; Zeng et al., 2016). To overcome the GCM

and RCM data uncertainty, global and regional

precipitation data are downscaled at a finer scale

resolution (IPCC, 2014). Downscaling coarse

precipitation data aims to assess the likely future

rainfall data with less uncertainty. The most used

methods in this sense are: delta/ratio, stochastic

downscaling, statistical downscaling, and dynamic

downscaling (Chokkavarapu & Ravibabu, 2019).

Statistical downscaling has the advantage to be less

computationally demanding, cheaper and more

convenient for users. Among the developed tools in

this sense, the Statistical Downscaling Model SDSM,

which has been efficiently used by several researchers

to downscale GCM coarse data. Indeed, (Y. Zhang et

al., 2016) used SDSM to downscale GCM products,

based on CanESM2 predictors over the Xin River

Basin. S. Samadi et al (Samadi et al., 2011) used

SDSM for downscaling HadCM3 Global Circulation

Model data based on NCEP/NCAR reanalysis, over

Iran for a baseline period between 1964 and 2001. M.

M. Gulacha et al (Gulacha & Mulungu, 2017)

generated climate change scenarios for precipitation

and temperature using SDSM in Wami-Rivu river

basin Tanzania. The SDSM was used to downscale

HadCM3 under A1 and A2 scenarios. Tukimat et al.,

2019 analyzed the accuracy of projected precipitation

at ungauged rainfall stations using SDSM and

CanESM2 projected data under RCP4.5 and RCP 8.5.

In this study, we used the second-generation

Canadian Earth System Model (CanESM2)

developed in the fifth version of Coupled Model

Intercomparison Project (CMIP5) in the Statistical

Downscaling Model (SDSM) software to downscale

precipitations in Ziz watershed controlled by Hassan

Addakhil reservoir . These projections were used

afterwards in assessing future inflows of Hassan-

Addakhil dam in front of hydroclimatic variation

under climate change. The measured precipitation

recorded for Foum Zaabel, and Foum tilicht rain

gauge stations in this watershed were used in SDSM

for calibration and validation considering 1983-2012

as a baseline period and (2035-2064) ,(2065-2094) as

future periods to compare with. After successful

calibration and validation of historical data compared

to the modeled one, projected precipitations for the

two future horizons 2050 and 2080, were modeled in

HEC-HMS to generate future inflows of Hassan

Addakhil dam.

2 MATERIALS AND METHODS

2.1 Study Area

The study is carried out for the watershed of Hassan

Addakhil dam (Figure 1), located in the southeastern

of Morocco.

The measured precipitation data for Foum tilicht

and Foum Zaabel rain gauge stations, located

upstream the dam, were provided by the Hydraulic

Basin Agency of Ziz-Guir-Rheriss. The daily

maximum rainfall data were provided over the period

1983-2012 (the most available data) of the rain

stations of Foum Zaabel and Foum Tillicht. Over the

period ranging from 1983-2012, the station of Foum

Zaabel registered a maximum and minimum daily

precipitation of 64 mm and 0 mm respectively, with a

mean and standard deviation respectively of 0.41mm

and 2.57. On other hand, the station of Foum Tilicht

is characterized by a maximum and minimum average

daily precipitation of 50.2 mm and 0 mm

respectively, with a mean and standard deviation

respectively of 0.33mm and 1.90.

Figure 1: Ziz watershed Location

2.2 Climatic Data and Downscaling

In this study, the second-generation Canadian Earth

System Model (CanESM2) integrated in the Coupled

Model Intercomparison Project Phase 5 (CMIP5) was

used to investigate future changes in Ziz watershed in

terms of precipitation under different representative

concentration pathway (RCPs). Indeed, CanESM2,

which was developed by the Canadian Centre for

Climate Modelling and Analysis (CCCma) of

Environment and Climate Change Canada is freely

available in the portal: http://climate-

scenarios.canada.ca/?page=pred-canesm2. 26

atmospheric variables (predictors) from the National

Centre for Environmental Prediction (NCEP)

reanalysis were used during calibration and validation

(1983-2005), while CanESM2 predictors data (of a

spatial resolution of 2.81°) were used for projections

under the intermediate and pessimistic scenarios

RCP4.5 and RCP8.5 (2013-2100).

The downscaling steps were conducted in SDSM

software developed by (Wilby et al., 2002). This

software enables reducing the uncertainty of GCMs

at a local region using statistical downscaling leading

to climate change scenarios at a daily time-scale.

SDSM model is based on multiple regression and

stochastic weather models. For the correlation

between independent variables and the dependent

variable, SDSM uses empirical statistical techniques.

Indeed, the predictor variables provide daily data at

the large-scale atmosphere and the predictand

describes the conditions of local climatic conditions.

A stochastic algorithm is used to calibrate the

predicted precipitations to fit the observed ones in the

best way. (Pervez & Henebry, 2014). Based on the

GCM independent variables, the stochastic weather

generator algorithm is also carried out for

precipitation prediction. (Figure 2)

Figure 2: Climate scenario generation using SDSM (Wilby

& Dawson, 2007)

2.3 Hydrological Modeling

The goal of the current phase is to estimate the future

inflows of Hassan Addakhil dam under climate

change scenarios. The projected future hydrologic

inflows resulted from modelling projected

precipitations using HEC-HMS software. Several

scenarios have been modelled. Indeed, two future

horizons were considered for each RCP, which has

led to six future inflows series. HEC-HMS

(Hydrologic Engineering Center - Hydrologic

Modeling System) is a hydrology software developed

by engineers from the United States Army (U.S.

Army Corps of Engineers). The hydrologic model is

based on the Soil Conservation Curve Number

method (SCS-CN) (USDA, 1986). Besides, the

Hydrological model has been calibrated and validated

for the study are of the present paper by (Elhassnaoui

et al., 2019).

3 RESULTS AND DISCUSSION

3.1 Screening of Predictors

Precipitation over the period 1983-2012 was taken as

a baseline period. Quality control was first

undertaken in SDSM to check the total number of

observed values as well as of missing values.

Following this, “Screen variables” is a primary step

that allows identifying more sensible predictors to the

available daily measured data. Correlation analysis

and scatter plots lead to finding the most suitable

parameters for building calibration relationships.

More correlated parameters correspond to low P-

value with higher Partial r. Three predictors were

found more sensible for Foum Zaabel and two for

Foum Tillicht (Table 1).

Table 1: Selected predictors for the two rain gauge stations

Station Screened predictors

Foum Zaabel

 Ncepp850gl

 Nceps850gl



cepp1z

Foum Tillicht



Ncepp1zghl



cepp8

3.2 Calibration and Validation in

SDSM

Calibrating observed precipitation using the selected

predictors consist of developing relationships

between predictor variables and predictand variables.

This process, which has been conducted for the period

1983-2005, led to satisfactory results, where R-square

ranges from 0.73 to 0.82. Indeed, Comparison

between observed series and generated ones using

weather generator indicated approximate values.

(Figure 3)

Figure 3: Graphical comparison between observed and

modeled precipitation (1983-2005) (upper part for Foum

Zaabel and lower part for Foum Tillicht station)

3.3 Scenario Generator: Climate

Projections

Precipitation has been downscaled using outputs

from CanESM2 for the three RCPs: 2.6, 4.5, and 8.5.

Projections have been then compared to the chosen

baseline period 1983-2012. Two horizons of

projections are studied: 2050 (2035-2064) and 2080

(2065-2094).

Projections for the two stations showed different

changes. For Foum Zaabel, mean precipitation would

tend to decrease in the 2050s from November to

March with a percentage ranging from -5% to-39%

for RCP 2.6, -1% to -42% for RCP 4.5 and -7% to -

45% for RCP 8.5. This period would know the same

trend in the 2080s with decrease between -2% and -

34% for RCP 2.6, -6% and -41% for RCP 4.5, -11%

to -56% for RCP 8.5. In parallel with that, mean

precipitation projections for the period from April to

October tend to increase. This can be explained by

extreme events and summer storms that would be

intensified in the area due to climate change. Months

of June and October showed the highest increase, as

shown in the tables below. The sum of monthly

values for Foum Zaabel was in line with mean values

changes. (Table 2, Table 3 and Figure 4)

Table 2: Future variation of monthly mean precipitation at

Foum Zaabel station

Table 3: Future variation of monthly sum of precipitation at

Foum Zaabel station

On the contrary, Foum Tilicht station showed

fewer trends to decrease and more ones to increase.

Indeed, except February, April and December, other

months were characterized generally by rising mean

and sum precipitation values. The highest rises were

remarked in March and October.

Overall, projections indicated that the sum of

precipitations would increase for Foum Zaabel

compared to the reference period by 14% for RCP2.6,

11% for 4.5%, and 14% for RCP8.5 in the 2050s. For

the 2080s these changes would move to 11%, 14%,

and 15% for RCP2.6, RCP4.5, and RCP 8.5

respectively. Concerning Foum Tilicht, this increase

is estimated at 22%, 19%, and 20% for the 2050s,

whereas in the 2080s it's around 21% for RCP 2.6 and

RCP4.5 and 22% for RCP8.5. (Table 4, Table 5 and

Figure 5)

Table 4: Future variation of monthly mean precipitation at

Foum Tilicht station

Table 5: Future variation of monthly sum of precipitation at

Foum Tilicht station



Figure 4: Future variation of monthly mean and sum of

precipitation at Foum Zaabel station under RCP 2.6, RCP

4.5 and RCP 8.5



Figure 5: Future variation of monthly mean and sum

precipitation at Foum Tilicht station under RCP 2.6, RCP

4.5 and RCP 8.5

3.4 Hydrological Modeling: Climate

Projections

The Future projection of the Hassan Addakhil dam

inflow, under RCP 2.6, RCP 4.5 and RCP 8.5 for the

2050 horizon, shows that the maximum inflow is

likely to occur during October. The average monthly

dam inflow during October is 1442 m

/s for RCP 2.6,

1359 m

/s for RCP 4.5 and 1367 m

/s for RCP 8.5. In

the other hand the minimum monthly dam inflow, for

the 2050 horizon, is likely to occur during July. The

average dam inflow during July is 216 m

/s for RCP

2.6, 222 m

/s for RCP 4.5 and 211 m

/s for RCP 8.5.

For the 2080 horizon, the monthly maximum inflow

is likely to occur during October, for RCP 2.6 and

RCP 4.5, however the simulation under RCP 8.5

shows that the monthly maximum inflow will occur

during September. The average monthly dam inflow

during October is 1107 m

/s for RCP 2.6. In other

hand the average dam monthly inflow during

September is 1224 m

/s for RCP 8.5. Furthermore, the

minimum monthly dam inflow, for the 2050 horizon,

is likely to occur during July. The average monthly

dam inflow during July is 274 m

/s for RCP 2.6, 260

/s for RCP 4.5 and 256 m

/s for RCP 8.5.

Figure 6: The Future projection of the Hassan Addakhil

dam inflow, under RCP 2.6, RCP 4.5 and RCP 8.5 for the

2050 horizon

Figure 7: The Future projection of the Hassan Addakhil

dam inflow, under RCP 2.6, RCP 4.5 and RCP 8.5 for the

2080 horizon

4 CONCLUSIONS

Daily rainfall data for Foum Tilicht and Foum Zaabel

rain gauge stations were provided by the hydraulic

agency of Ziz-Guir-Rheriss for the period 1983-2012.

The Coupled Model Intercomparison Project Phase 5

(CMIP5) precipitation data was downscaled for these

stations at a daily time-scale, using CanESM2 data in

SDSM software. Future precipitation projections over

the period 2013-2100, were generated for three

Representative Concentration Pathway, namely

RCP2.6, RCP4.5 and RCP8.5 and compared with the

baseline period 1983-2012. HEC-HMS was then used

considering these projected precipitations to evaluate

future inflows of Hassan-Addakhil dam. Trends

towards increase were more remarked for the two

stations compared to months with decreasing

precipitations. As an overall assessment of the sum

precipitation, Foum Zaabel showed a trend to

increase around 14% for the three RCP while Foum

Tilicht precipitation would tend to increase up to

20%. This could be interpreted by the likely tendency

to floods because of climate change effects.

Afterwards, Future inflows of the Hassan Addakhil

dam were assessed in HEC-HMS, for the three

scenarios RCP 2.6, RCP 4.5 and RCP 8.5 which has

showed that the maximum and minimum inflow are

likely to occur during October and July respectively.

ACKNOWLEDGEMENTS

The authors would like to acknowledge the support

through IRIACC Initiative sponsored by IDRC under

the project Number 106372-013.

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