Phan Cao Duong - International Conference on the Mekong, Salween and Red Rivers: Sharing Knowledge and Perspectives Across Borders

Phan Cao Duong  - International Conference on the Mekong, Salween and Red Rivers:  Sharing Knowledge and Perspectives Across Borders

Phan Cao Duong - International Conference on the Mekong, Salween and Red Rivers: Sharing Knowledge and Perspectives Across Borders

16:41 - 16/03/2018

RESEARCH ON THE SCIENTIFIC AND PRACTICAL BASIS TO HARMONISE WATER ALLOCATION WITH WATER TREATMENT FOR IRRIGATION SYSTEMS IN THE RED RIVER DELTA
Community based water quality monitoring: a multi-benefit approach to water governance in the Red river basin, Vietnam
Small-scale irrigation – effective solution for sloping land areas
Assessment of climate change impacts on river flow regimes to support decision-making in water resources management in The Red River Delta, Vietnam – A case study of Nhue-Day River Basin
Impact of existing water fee policy in the Red River Basin, Vietnam

It is very likely that climate change impacts will be a primary concern for human being, environment and ecosystems. To develop a strategy for adaptation to response climate change effects is very important.

Assessment of climate change impact on river flow regimes to support decision-making in water resources management in The Red River Delta, Vietnam – A case study of Nhue-Day River Basin

 

By Phan Cao Duong

 

Presented at:

International Conference on the Mekong, Salween and Red Rivers:

Sharing Knowledge and Perspectives Across Borders

 

Faculty of Political Science, Chulalongkorn University,

12th November 2016

 


Abstract

Global warming has caused dramatic changes in regional climate variavbility, particularly regarding the fluctuation in temperature and rainfall. Thus, it is predicted that river flow regimes will be accordingly altered. The purpose of this paper is to present the results of modelling such changes by simulating discharge with the HEC_HMS model. The precipitation projection of super-high resolution multiple climate models (20 km resolution) with newly updated emission scenarios used as input for a Hec-Hms model for flow analysis at the Red River Basin in the northern area of Vietnam. The findings showed that climate change impact on the flow river regimes towards to a decrease in dry season and a longer duration of flood flow. A slight runoff reduction is simulated for November while a considerable runoff increase is modelled for July and August amounting to 30% and 25% respectively. The presented discharge scenarios do serve as a basis for water managers to develop suitable adaptation methods and responses at the river basin scale.

 

Introduction

Climate change is believed to be one of the predominant challenges for mankind in the 21st century. It has been resulting in immense adverse effects on human and natural systems over the world. Meanwhile, many fields are being impacted by climate change. For example, a decline of agriculture production and heightening risk of animal and plant extinction are created by rising temperature; destruction of infrastructure and loss of lives are led by severe flood events; besides, severe droughts occurring in dry seasons probably lead to water conflict. A regional assessment of climate change on mankind, to some extent, was addressed in the Fifth Assessment Report by the Intergovernmental Panel on Climate Change (IPCC, The Physical Science Basic, 2014).

The key factors of climate change are the growth of temperature and variability of precipitation. According to observed data showed that the last decade has been recorded as the warmest years in the last hundred years. The globally averaged surface temperature calculated by a linear trend show a warming of 0.85 oC over the period 1880 to 2012 (IPCC, The Physical Science Basic, 2014). The global mean surface temperature change for the period 2016-2035 relative to 1986-2005 will likely be in the range of 0.3 to 0.7oC which is based on the simulation of the new Representative Concentration Pathway scenarios. The new Representative Concentration Pathway of radiation by the end of this century include 2.6 W/m2 (CPR 2.6/CS1), 4.5 W/m2 (CPR 4.5/CS2), 6.5 W/m2 (CPR 6.5/CS3), 8.5 W/m2 (CPR 8.5/CS4) (IPCC, The Physical Science Basic, 2014). Increases in temperature are likely led to change in hydrological cycles, particularly the growth of spatiotemporal variation of rainfall. It is very likely expected that river flow regimes would be fluctuate. Flow in most tropical areas is predicted to rise because of the higher frequency of extreme precipitation. In the same time, more serious drought events during dry seasons might lead to water shortage and further inland salinity intrusion.

The assessment of climate change impacts on hydrology has been addressed for years before. It has been constantly revised thanks to the improvement of climate model output regarding spatiotemporal resolution and projection capability. Most estimations have been based chiefly upon coupling method between global atmospheric general circulation models (GCMs), which are set up to simulate past and current climate and then used to project the future state of global climate with specifically greenhouse gas emission scenarios, and hydrological models. Although climate models could be expected to project correctly trends, different climate models might give different outputs. In other words, application of various climate model output often results in the discrepancies of runoff simulations. Assessment of climate change impacts with multi-climate models has been exhibited a cost-effective method to determine the scope of project in the Coupled Model Inter-comparison Project (CMIP).

Up to date, a large number of impact assessments on flow regime and water resources have been conducted at river basin scales as result of changes in rainfall, temperature, and evapotranspiration (Nam, Do Hoai, 2012a). Typical studies are, for example, the fourth assessment report on climate change (IPCC, Climate Change 2014: Synthesis Report, 2014), impact assessments at river basin scales in Canada (Jason R. Wesmacott, 1997), in America (Wilby R.L., 2008), in Germany (Menzel L., 2002), in Japan (Kaoru Takara, 2009),  in Australia  (Francis H. S. Chiew, 2002), Mekong River basin (Francis H. S. Chiew, 2002), Srepok River  (Do Hoai Nam, 2012b), and Thu Bon River (Do Hoai Nam, 2011) etc. These studies mostly focused on the  analyses  of  inter-annual  or inter-seasonal  variation  of  stream flow.  Studies  on  changes  in  frequency  and intensity of  rainfall have been  insufficiently performed due  to  the limited  capability  of  climate  models  for  intense  rainfall  projections.  These climate models were  developed with  very  coarse  spatial  resolution  (approx. 300 km grid distance), thus, they demonstrated no ability to diagnose extreme phenomena  occurred  at  scales  much  smaller  than  the  computational  grid distance

The variation of river flow regimes in some largest river basins were estimated in the latest valuation. The Fifth Assessment Report conducted by the IPCC (2013) is an example, which employs advanced climate models developed by leading modelling organizations around the world (CMIP4). The report showed that a significantly decline trend in river flow is expected during dry seasons. Besides, increasing temperature and quick population growth in most these river basins are confronted severe water shortage by the middle of this century. Other research demonstrated that flood flow during wet periods is forecast to have increased frequency by most climate change scenarios (Gellens and Roulin 1998). However, it is expected that hydrological responses are totally dissimilar in each  particular river basin because of the distinction in topography and weather patterns.

Vietnam is one of the most influenced nations by climate change which has been considered as a primary challenge in recent decades. In respect of adaptation strategies to climate change, valuations of river flow change within a river basin scale can provide decision makers and exposed communities with essential information for the better development of water resources management. This study presents a projection of short-term runoff change in Nhue-Day river basin as a case study. The precipitation prediction during period of 2026-2035 under different scenarios simulated by multi-climate models is used as input for a distributed hydrological model to estimate flow fluctuation.Data and Methodology

Study Area

The Nhue-Day river basin, a sub-basin of the Red River Basin in Vietnam, has been chosen as a case study for assessing changes in flow regimes. This basin is approximately 114 kilometres in length, covering five northern provinces of Phu Tho, Vinh Phuc, Hoa Binh, Ha Nam and Hanoi, with a drainage area of 7.665 km2. The basin is often adversely affected by tropical cyclones from the northwest Pacific Ocean to South China Sea. As observed data have shown that flood and drought frequencies have increased dramatically in the recent years.

Figure 1.1:           Map of the Nhue-Day river basin and locations of hydro-meteorological stations.

Data

The changes of flow regime are governed by some factors, such as rainfall, evaporation, topography, geography, land cover and so on. This study, however, considered several significant points, namely rainfall, topography, geography, and land cover.

SRTM90 digital elevation data developed by The CGIAR Consortium of Spatial Information was used to extract topograhic factors such as elevation and slope. These factors was used to assess hydrological variables such as flow direction and accumulation.

Regarding geographic parameters, the FAO-UNESCO Soil Map of the World_2003 was used. For the vegetation, global land cover classification collected by the University of Maryland Department of Geography was applied. Imagery from the AVHRR satellite accquired between 1981 and 1994 were analyzed to distinguish fourteen land cover classes. This product is available at three spatial scales : 1 degree, 8 kilometer and 1kilometer pixel resolutions.

Table 1.1:                 Code Values for 1km and 8km data

Value

Label

RGB Red

RGB Green

RGB Blue

0

Water

068

079

137

1

Evergreen Needleleaf Forest

001

100

000

2

Evergreen Broadleaf Forest

001

130

000

3

Deciduous Needleleaf Forest

151

191

071

4

Deciduous Broadleaf Forest

002

220

000

5

Mixed Forest

000

255

000

6

Woodland

146

174

047

7

Wooded Grassland

220

206

000

8

Closed Shrubland

255

173

000

9

Open Shrubland

255

251

195

10

Grassland

140

072

009

11

Cropland

247

165

255

12

Bare Ground

255

199

174

13

Urban and Built

000

255

255

Meteorological data included daily mean rainfall, daily mean evaporation and daily mean discharge data. The daily mean rainfall was extracted from APHRODITE project (1970-2007) and nine gauges (1962-2010) from National Center for Hydro-meteorological Forecasting, namely Son Tay, Lam Son, Ha Dong, Lang, Ha Noi, Ha Nam, Phu Ly, Hung Thi, and Ninh Binh. The data of the nine stations was used to varify the APHRODITE data. The APHRODITE data was the data that collected from meteorological gauges and then downscaling to precipitation points with the resolution of 0.25 degree. The daily mean evaporation was collected from Lang station while the daily mean discharge data was gathered from three sattions namely Lam Son, Hung Thi and Ba Tha (1970-1978).

Regarding climate change scenarios, there are four representative concentration pathways (RCPs) namely RCP 2.6, 4.5, 6.5 and 8.5. But, this study chose the first three scenarios. Meanwhile, it was to be assumed that the amount of emission will gain the highest level in the near future. The precipitation simulated by three climate models (AGCM3-2H, MIROC-4H, and GFDL-HIRAM-C360) corresponding with the three emission scenarios was aaplied to assess change of flow regimes. These data was extracted from the Coupled Model Intercomparison Project.

Methods

1.1. General Circulation Model and Climate Model Selection

With regard to the contribution of the Coupled Model Inter-comparison Project, some leading climate modelling centres in Europe, America and Asia have built a large quantity of GCMs. These models commonly give experimental simulation of global climate with comparatively coarse spatiotemporal resolution; its output is often on monthly basic for a grid cell distance of 2 to 5 degree. As specific purpose of simulated output, each model generates its own simulation result depending on computational capability. The models thus might be various regarding physical parameterisation, time slice and spatiotemporal resolution.

Climate modelling centres participating in CMIP5 have given approximately 40 climate models and more than 60 simulations. Most these models, as a result of increase in computational capability, have much better spatial resolution than those in the past. These spatial resolutions shift from 20 to 500 km and some of which have even finer spatial resolutions. The fine spatial resolution of climate model could simulate extreme phenomena more correct than the model with coarse spatial resolution does.

This study analyses the predictions of rainfall, runoff and discharge from multiple climate models with the spatial resolution of 20 to 50 km experimented by leading climate modelling centres over the world. The climae models employed in this research include MRI-AGMC3.2H and MRI-AGMC3.2S models (Meteorological Research Institute of Japan), MIROC4h model (Japan Agency for Marine-Earth Science and Technology, Atmosphere and Ocean Research Institute (The University of Tokyo), and National Institute for Environmental Studies), and GFDL-HIRAM-C360 (Geophysical Fluid Dynamics Laboratory - USA).

Hydrological Model

In order to assess climate change impacts on flow river regimes, it is suitable to employ a less complex hydrological model, which can accommodate the insufficient information of a future catchment, could be the most preferable (citation). In this research, a HEC-HMS is applied to perform hydrological analysis in the river. This model is considered a valuable tool for climate change impact estimation because of the simplicity of the model structure and the nearly calibration-free feature of the model parameters. In actual application, the HEC-HMS model has demonstrated great ability in precipitation-runoff analysis across a wide range of spatial scales (citations).

The HEC-HMS model is developed to simulate the rainfall-runoff processes of dendritic catchment systems. Hydrographs created by the program can be used directly or conjunction with other software for studies of water availability, flow forecasting and so on. This program includes some model components which are used to simulate the hydrologic response in a watershed. The primary components are basin models, meteorologic models, control specifications and input data components. A simulation calculates the rainfall-runoff response in the model provided input from the meteorologic model, whereas the period and time step of the simulation run are defined by the control specifications.

With regard to the structure of HEC-HMS model (Figure 1.2), the sub-basin storage consist of some components. River water is supplied from two sources namely precipitation and base-flow. Precipitation falling on land is divided three parts. A part lose due to infiltration and evaporation, other part infiltrate in deep land to supply water for groundwater which then supplies a certain quantity to the river as the base-flow, and the other generates direct runoff to pour into the river.

Figure 1.2:   Schematic overview of the HEC-HMS model (US Army Corps of Engineers, March 2000).

The estimation of runoff model performance is based on the Nash Sutcliffe Index (NSI), or so-called coefficient of model efficiency. This is detailed in Eq. 1 follows.

 
(1.1)    

Where, Qobs = observed monthly streamflow; obs = observed monthly mean streamflow; Qcal = calculated monthly streamflow.

Model Calibration

  • Theory of rainfall-runoff model

One of the greatest benefit of the HEC-HMS model is its nearly calibration-free parameters. There are three components are required for model calibration including computing runoff volumes, modelling direct runoff, and modelling basefow. In this study, these parameters are selected as follows.

To compute runoff volumes (Eq. 1.2), a loss method of initial and constant was chosen. The initial and constant-rate model, in fact, includes one parameter (the constant rate) and initial condition (the initial loss). These respectively represent physical properties of the catchment soils and land use and the antecedent condition.

(1.2)    

Where, Pet is the runoff volume; Ia is the initial loss; pt is the MAP depth during a time interval t to t + ∆t; fc is constant throughout an event.

In order to simulate direct runoff (Eq. 1.3), Snyder Unit Hydrograph Model is be employed. To apply this transform method, two parameters need to be defined, namely the lag tp and the peak coefficient Cp. While Cp ranges from 0.1 to 0.8 as suggestions of Bedient and Huber (1992), the lag time tp is calculated as following equation:

tp = CCt(LLc)0.3

(1.3)    

Where Ct = basin coefficient; L = length of the main stream from the outlet to the divide; Lc = length along the main stream from the outlet to a point nearest the watershed centroid; and C = a conversion constant (0.75 for SI and 1.00 for foot-pound system).

Baseflow is determined by Exponential Recession Model. The recession model has been used often to explain the drainage from natural storage in watershed (Linsley et al, 1982). It defines the relationship of Qt, the baseflow at any time t, to an initial value as:

Qt = Q0.kt

(1.4)    

Where Q0 = initial baseflow (at time zero); and k = an exponential decay constant.

  • Model calibration and validation

Based on input data namely the physical properties of the catchment soils and land use, the antecedent condition, observed precipitation and flow discharge, these above parameters are defined. However, because of these model parameters are not measured parameters, they are best determined by calibration.

Figure 1.3:   Schematic overview of calibration procedure (US Army Corps of Engineers, March 2000)

The calibration procedure begins with data collection. For rainfall-runoff models, the required data are precipitation and discharge time series. The next step is to select initial estimates of the parameters. In this study, after the step of initial valuation of the parameters, historical rainfall and flow data of during the period 1972 to 1974 are used for calibration. Model validation is performed for the period 1976 to 1978.

Results

2.1. HEC-HMS Model Calibration

Results showed that the simulated hydrograph fits very well with the observed hydrograph (Fig. 1.4); most high flow periods are caught by runoff model. It can be seen in Fig. 1.4 and 1.5 that the simulated discharge exhibites a very good agreement with observed discharge; it is comparable to those reproduced employing precipitation value from rain stations. Coefficient of model efficiency (NSI = 0.86) is attained for the overall model performance. Hence, validation of model runoff demonstrates a high level of confidence for the application of this calibrated model in future climate and runoff analysis.

Table 1.2:                 Candidate predictor variables for model calibration.

Model

Parameter

Value

Initial and constant-rate loss

Initial loss

Constant loss rate

= 5.00 mm

= 0.20 mm/hr

Snyder’s Unit Hydrograph

Lag

Cp

= 20 hr

= 0.15

Baseflow

Initial discharge

Recession constant

Flow

= 30 m3/s

= 0.96

= 25 m3/s

Muskingum routin

K

X

Number of steps

= 1.67

= 0.15

= 10 step

Figure 1.4:   Time-series of observed and simulated discharge for the model calibration (1972-1974).

Figure 1.5:   Time-series of observed and simulated discharge for the model validation (1976-1978).

2.2. Evaluation of future variability in Climate change scenarios

The following section shows the short-term projections of variation in monthly mean precipitation relative to the baseline (1979-2003).

Table 1.3:             The variation percentage of predicted monthly mean precipitation relative to the baseline (1979-2003), (AGCM3 model).

Scenarios

Jan

Feb

Mar

Apr

May

Jun

Jul

Aug

Sep

Dec

SC1_AGCM3

-25.50

29.37

-11.03

-17.69

6.48

3.13

4.29

10.46

75.98

-43.78

SC2_AGCM3

-18.70

15.13

20.10

-4.44

34.96

37.93

15.00

-4.96

12.34

25.03

SC3_AGCM3

-5.34

12.13

-1.02

18.23

-3.10

-26.16

-22.97

-19.99

-6.41

31.82

Figure 1.6:   The short-term predictions of variations in monthly mean rainfall relative to the baseline, 1979-2003 (AGCM3 model).

Table 1.4: The variation percentage of predicted monthly mean precipitation relative to the baseline (1979-2003), (GFDL-HIRAM-C360 model).

Scenarios

Jan

Feb

Mar

Apr

May

Jun

Sep

Oct

Nov

Dec

SC1_C360

8.87

-47.39

-34.50

-28.02

-7.69

6.87

17.23

258.27

-75.38

-13.95

SC2_C360

-3.61

10.76

9.89

-34.98

25.91

21.27

3.47

20.20

-30.91

-5.46

Figure 1.7:   The short-term predictions of variations in monthly mean rainfall relative to the baseline, 1979-2003 (GFDL-HIRAM-C360 model).

Table 1.5:                 The variation percentage of predicted monthly mean precipitation relative to the baseline (1979-2003), (MIROC model).

Scenarios

Jan

Feb

Mar

Apr

May

Jun

Jul

Aug

Sep

Dec

SC1_MIROC

-21.89

33.95

2.02

12.03

36.67

17.81

9.57

12.52

44.20

6.97

SC2_MIROC

-43.16

0.43

18.08

37.19

-11.23

24.83

16.94

23.93

9.68

-4.07

SC3_MIROC

19.53

23.63

-33.39

0.85

-1.24

25.79

27.33

27.75

13.37

-15.47

Figure 1.8:   The short-term predictions of variations in monthly mean rainfall relative to the baseline, 1979-2003 (MIROC model).

         The changes in projected monthly mean precipitation are discribed from Fig. 1.6 to Fig. 1.8. The amount of projected monthly mean rainfall varies according to climate models, scenarios and the periods, but increasing trends are seen in most simulated results. The quantity of rainfall simulated by AGCM3 and GFDL-HIRAM-C360 models undergo slight flutuations from -40% to 40% during most month of the year, except for October and November (rainy season). This period, the predicted monthly mean precipitation is expected to increase by more than 100 % relative to those of the baseline (1979-2003). Likewise, rise tendencies in future rainfall forecast by the MIROC climate model is shown on Fig. 1.8.

Projection of Runoff Variation

To estimate the potential runoff reactions to climatic changes, the short-term predictions (2026 to 2035) of rainfall based on outputs of multiple climate models are used to simulate river regimes. Simulated discharges are then compared to those of the baseline during the period 1979 to 2003.

Based on the input precipitaton data, the model calculated the created discharge with a period of fifteen minutes through water loss, transforming and baseflow. Water loss was computed by the method of initial and constant loss while Snyder Unit Hydrograph method was used for figuring how water transforming. The method of recession was applied to identify baseflow. The mean discharge of fifteen minutes was used to calculate the daily mean discharge based on the weighting average method.

The changes of precipitation are regionally dependent on the increase in surface temperature which has been remarked for the entire globe (IPCC, The Physical Science Basic, 2014). As a result of surface temperature increase, evapotranspiration is likely to rise so that a higher evapotranspiration rate in future is expected. I could directly affect flow creation routing. Thus, the variation of evapotranspiration rate plays an important role for flow estimation. However, the projected period of runoff in this research is short-term from 2026 to 2035 so that surface temperature increase is insignificant which is projected to be about 0.4 oC (IPCC, The Physical Science Basic, 2014). Meanwhile, the shift of simulated runoff is insignificantly impacted by the variability of evapotranspiration proportion so that it might remain unchanged during process of projection flow. Therefore, the procedure of flow response to climate change impacts is mainly based on the variability of projected precipitation from the output of three climate models with three scenarios as mentioned on section 1.2.2.

The simulations of runoff response to the three climate scenarios are conducted by using HEC-HMS rainfall-runoff model. Simulated hydrographs during the period 2026 to 2035, which are illustrated in Fig. 1.9 to 1.11, are compared to the observed discharge in the baseline period (1979-2003).

Table 1.6:                 The changes of projected monthly mean discharge (2026-2035) relative to the baseline (1979-2003), (simulated by AGCM3 model).

 

Scenario

Jan

Feb

Mar

Apr

May

Jun

Jul

Aug

Sep

Oct

Nov

Dec

SC1_AGCM3

-7.41

-16.83

-24.89

-16.62

-3.08

7.42

2.75

16.76

58.45

45.61

39.77

30.10

SC2_AGCM4<