risk management along the mekong tributaries
DESCRIPTION
Presentation by Guillaume Lacombe, Somphasith Douangsavanh, Richard Vogel, Matthew McCartney, Yann Chemin, Lisa Rebelo, Touleelor Sotoukee at the International conference “Sustainability in the Water-Energy-Food Nexus” 19-20 May 2014, Bonn, GermanyTRANSCRIPT
Uniting agriculture and nature for poverty reduction
to predict flow metrics for water resource and risk management along the Mekong tributaries
Guillaume Lacombe, Somphasith Douangsavanh, Richard Vogel, Matthew McCartney, Yann Chemin, Lisa Rebelo, Touleelor Sotoukee
Simple power-law models
International conference “Sustainability in the Water-Energy-Food Nexus” 19-20 May 2014, Bonn, Germany
Uniting agriculture and nature for poverty reduction
Introduction
• Increased vulnerability to stream-flow variability,
• Prediction models are:– complex (use by practitioners is limited),– mainstream-focused (away from poorest
populations),– Physically-based (assumed physical
processes, high data requirement).
One dot = 1 village
Uniting agriculture and nature for poverty reduction
Objectives
• To define simple relationships to predict flow metrics from catchments characteristics in the Lower Mekong River
• To assess the effect of land- cover (forest, paddy, wetlands) on the flow metrics and downstream water resources
Uniting agriculture and nature for poverty reduction
• Multivariate power-law equation to predict flow (Q) from catchment characteristics (Xi)
mmXXXQ 210
21exp
• Logarithmic transformation solved by weighted least square regression (multiple linear regressions)
)ln(...)ln()ln()ln( 22110 mm XXXQ
Method
Uniting agriculture and nature for poverty reduction
Method
• Daily flow metrics– 11 flow percentiles, annual mean, min
and max– Data from Mekong River Commission
(MRC): 65 gauging stations with 1 to 41 years of daily record
• Catchment characteristics– rainfall, geomorphology, geography,
soil, land-cover (forest, paddy and wetlands)
– Data from Aphrodite, HydroSHEDS, MRC
Uniting agriculture and nature for poverty reduction
Method
• Selection of variables:– Combined use of “best- subsets” and
“step-wise” regressions– P-value < 0.05– Constraints:
• Homoscedasticity of residual,• Independence of variables,• Outliers removed (Cook D)
• Leave-one-out cross-validations to maximize the prediction R-squared
Uniting agriculture and nature for poverty reduction
Results m
mXXXQ 21021exp
Q m3/s 0 Explanatory variables (j, j>0) Prediction
R2 (%) Rain Peri Elev Area Drai Slop Lati Padd Fore
Max 1.870 -0.796 0.668 2.694 0.798 -1.423 89.09
0.05 -14.434 2.376 0.862 2.016 94.14 0.10 -21.435 2.608 0.970 93.45 0.20 -23.087 2.742 0.988 94.34 0.30 -24.135 2.519 0.335 0.992 91.78 0.40 -29.234 2.603 1.789 0.566 92.53 0.50 -31.247 2.529 1.798 0.714 0.262 92.13 0.60 -24.521 2.289 1.600 0.963 -1.526 -0.155 92.44 0.70 -24.023 2.307 1.469 1.074 -1.820 -0.155 90.72 0.80 -25.761 2.582 1.411 1.080 -1.852 -0.189 92.16 0.90 -28.562 2.613 1.467 0.844 -1.706 0.587 -2.503 89.53 0.95 -27.857 2.698 1.436 0.966 -1.291 -0.285 90.49 Min -32.951 3.027 1.416 0.803 -2.684 0.535 -2.598 89.13 Mean -18.989 2.543 0.883 1.089 94.71
Uniting agriculture and nature for poverty reduction
Results
Q m3/s 0 Explanatory variables (j, j>0) Prediction
R2 (%) Rain Peri Elev Area Drai Slop Lati Padd Fore
Max 1.870 -0.796 0.668 2.694 0.798 -1.423 89.09
0.05 -14.434 2.376 0.862 2.016 94.14 0.10 -21.435 2.608 0.970 93.45 0.20 -23.087 2.742 0.988 94.34 0.30 -24.135 2.519 0.335 0.992 91.78 0.40 -29.234 2.603 1.789 0.566 92.53 0.50 -31.247 2.529 1.798 0.714 0.262 92.13 0.60 -24.521 2.289 1.600 0.963 -1.526 -0.155 92.44 0.70 -24.023 2.307 1.469 1.074 -1.820 -0.155 90.72 0.80 -25.761 2.582 1.411 1.080 -1.852 -0.189 92.16 0.90 -28.562 2.613 1.467 0.844 -1.706 0.587 -2.503 89.53 0.95 -27.857 2.698 1.436 0.966 -1.291 -0.285 90.49 Min -32.951 3.027 1.416 0.803 -2.684 0.535 -2.598 89.13 Mean -18.989 2.543 0.883 1.089 94.71
Q5% = e-14.43 × annual rainfall2.38 × area0.86 × drainage density2.02
Uniting agriculture and nature for poverty reduction
Results m
mXXXQ 21021exp
Q m3/s 0 Explanatory variables (j, j>0) Prediction
R2 (%) Rain Peri Elev Area Drai Slop Lati Padd Fore
Max 1.870 -0.796 0.668 2.694 0.798 -1.423 89.09
0.05 -14.434 2.376 0.862 2.016 94.14 0.10 -21.435 2.608 0.970 93.45 0.20 -23.087 2.742 0.988 94.34 0.30 -24.135 2.519 0.335 0.992 91.78 0.40 -29.234 2.603 1.789 0.566 92.53 0.50 -31.247 2.529 1.798 0.714 0.262 92.13 0.60 -24.521 2.289 1.600 0.963 -1.526 -0.155 92.44 0.70 -24.023 2.307 1.469 1.074 -1.820 -0.155 90.72 0.80 -25.761 2.582 1.411 1.080 -1.852 -0.189 92.16 0.90 -28.562 2.613 1.467 0.844 -1.706 0.587 -2.503 89.53 0.95 -27.857 2.698 1.436 0.966 -1.291 -0.285 90.49 Min -32.951 3.027 1.416 0.803 -2.684 0.535 -2.598 89.13 Mean -18.989 2.543 0.883 1.089 94.71
Rainfall elasticity of streamflow
If mean annual rainfall increases by 10%, mean annual flow increases by 1.12.543=1.27 (27%)
Uniting agriculture and nature for poverty reduction
Results m
mXXXQ 21021exp
Q m3/s 0 Explanatory variables (j, j>0) Prediction
R2 (%) Rain Peri Elev Area Drai Slop Lati Padd Fore
Max 1.870 -0.796 0.668 2.694 0.798 -1.423 89.09
0.05 -14.434 2.376 0.862 2.016 94.14 0.10 -21.435 2.608 0.970 93.45 0.20 -23.087 2.742 0.988 94.34 0.30 -24.135 2.519 0.335 0.992 91.78 0.40 -29.234 2.603 1.789 0.566 92.53 0.50 -31.247 2.529 1.798 0.714 0.262 92.13 0.60 -24.521 2.289 1.600 0.963 -1.526 -0.155 92.44 0.70 -24.023 2.307 1.469 1.074 -1.820 -0.155 90.72 0.80 -25.761 2.582 1.411 1.080 -1.852 -0.189 92.16 0.90 -28.562 2.613 1.467 0.844 -1.706 0.587 -2.503 89.53 0.95 -27.857 2.698 1.436 0.966 -1.291 -0.285 90.49 Min -32.951 3.027 1.416 0.803 -2.684 0.535 -2.598 89.13 Mean -18.989 2.543 0.883 1.089 94.71
Two land-use variables are related to low flow: Paddy and Forest
If paddy area doubles, the 0.95 flow percentile will be
reduced by 18%(2-0.285=0.82)
Uniting agriculture and nature for poverty reduction
Results m
mXXXQ 21021exp
Q m3/s 0 Explanatory variables (j, j>0) Prediction
R2 (%) Rain Peri Elev Area Drai Slop Lati Padd Fore
Max 1.870 -0.796 0.668 2.694 0.798 -1.423 89.09
0.05 -14.434 2.376 0.862 2.016 94.14 0.10 -21.435 2.608 0.970 93.45 0.20 -23.087 2.742 0.988 94.34 0.30 -24.135 2.519 0.335 0.992 91.78 0.40 -29.234 2.603 1.789 0.566 92.53 0.50 -31.247 2.529 1.798 0.714 0.262 92.13 0.60 -24.521 2.289 1.600 0.963 -1.526 -0.155 92.44 0.70 -24.023 2.307 1.469 1.074 -1.820 -0.155 90.72 0.80 -25.761 2.582 1.411 1.080 -1.852 -0.189 92.16 0.90 -28.562 2.613 1.467 0.844 -1.706 0.587 -2.503 89.53 0.95 -27.857 2.698 1.436 0.966 -1.291 -0.285 90.49 Min -32.951 3.027 1.416 0.803 -2.684 0.535 -2.598 89.13 Mean -18.989 2.543 0.883 1.089 94.71
Uniting agriculture and nature for poverty reduction
Results m
mXXXQ 21021exp
Q m3/s 0 Explanatory variables (j, j>0) Prediction
R2 (%) Rain Peri Elev Area Drai Slop Lati Padd Fore
Max 1.870 -0.796 0.668 2.694 0.798 -1.423 89.09
0.05 -14.434 2.376 0.862 2.016 94.14 0.10 -21.435 2.608 0.970 93.45 0.20 -23.087 2.742 0.988 94.34 0.30 -24.135 2.519 0.335 0.992 91.78 0.40 -29.234 2.603 1.789 0.566 92.53 0.50 -31.247 2.529 1.798 0.714 0.262 92.13 0.60 -24.521 2.289 1.600 0.963 -1.526 -0.155 92.44 0.70 -24.023 2.307 1.469 1.074 -1.820 -0.155 90.72 0.80 -25.761 2.582 1.411 1.080 -1.852 -0.189 92.16 0.90 -28.562 2.613 1.467 0.844 -1.706 0.587 -2.503 89.53 0.95 -27.857 2.698 1.436 0.966 -1.291 -0.285 90.49 Min -32.951 3.027 1.416 0.803 -2.684 0.535 -2.598 89.13 Mean -18.989 2.543 0.883 1.089 94.71
93%
90%
Uniting agriculture and nature for poverty reduction
Conclusions
• Highly-predictive & simple tools to assess high and low flows in ungauged areas– water resources planning, flood risks
assessment, hydropower potential, storage design
• A range of applications– Assessment of effect of paddy area
expansion on downstream low-flow– Prediction of climate change impact on
basin water yields