Adaptation and mitigation as complementary tools for reducing the risk of climate impacts

Abstract

This paper uses the likelihood of flooding along Brahmaputra and Ganges Rivers in India to explore the hypothesis that adaptation and mitigation can be viewed as complements rather than sustitutes. For futures where climate change will produce smooth, monotonic and manageable effects, adopting a mitigation strategy is shown to increase the ability of adaptation to reduce the likelihood of crossing critical threshold of tolerable climate. For futures where climate change will produce variable impacts overtime, though, it is possible that mitigation will make adaptation less productive for some time intervals. In cases of exaggerated climate change, adaptation may fail entirely regardless of how much mitigation is applied. Judging the degree of complementarity is therefore an empirical question because the relative efficacy of adaptation is site specific and path dependent. It follows that delibrations over climate policy should rely more on detailed analyses of how the distributions of possible impacts of climate might change over space and time.

Appendix—The hydrologic models [as Described in Yohe and Strzepek (2004)]

1.1 Uncertainties in the historical climate record

The COSMIC scenario generator provides a base year of 1990, but does not provide any information on the statistics of climate record for the country. It is nonetheless necessary to have data on the moments and probability distributions of the hydro-climatic variables to perform a flood frequency analysis. To supplement the COSMIC scenario data for Nepal, we employed historical climate data gathered by the Tyndall Center for Climate Change Research and recorded in their TYN CY 1.1 data set. Mitchell et al. (2004) report that the TYN CY 1.1 data provide a summary of the climate of the 20th century for 289 countries and territories including monthly time series data for seven climate variables for the 20th century (1901–2000). Interestingly, the data set creators provide the following warning: “This data set is intended for use in trans-boundary research, where it is necessary to average climatic behaviour over a wide area into statistics that are representative of the whole area.” This warming endorses the use of TYN CY 1.1 and COSMIC data for Nepal as appropriate for this modeling approach.

The TYN CY1.1 monthly time series data for the 20th century (1901–2000) show that mean annual temperature in Nepal varies very little with a COV of 0.04 and a lag-one correlation of 0.47. By way of contrast, precipitation exhibits variability at the total annual level. More importantly for predicting the likelihood of flooding events, though, maximum monthly precipitation per year is even more variable and strongly (positively) skewed with a high coefficient of variation.

The flooded area in Bangladesh varies greatly from year to year. Flood risk is characterized by the probability that a certain level of flood will occur each year. The risk factor is generally express as a return period of T = 1/(probability of occurrence). The return period is determined from the cumulative density function of flood frequency. For flood frequency analyses, FAP (1992) recommends using the Gumbel Type I distribution (EV1) for the major rivers in Bangladesh; it is defined by

$$ \begin{array}{c} F(x)=\exp \left[ {-\exp \left( {\frac{x-u}{\alpha }} \right)} \right]-\infty < x < \infty \\ \alpha =\frac{\sqrt{6}}{\pi }S \\ u=\hbox{X}- 0.5772\alpha. \end{array} $$

where S is the standard deviation and 7 is the mean. The mean and standard deviation of the flood peak as well as the parameters of the EV1 distribution were determined using 100 year time series of climate data with the rainfall runoff model. Using these statistics and the EV1 distribution, flood flows for the 2, 10, 50, and 100 year return periods were calculated.

1.2 Flooded area and severity

High river flows themselves are not a problem unless they overtop their banks and flood area in the adjoining flood plain. The determination of flood flows used the science of hydrology, while determining the extent of and depth of flooding was based on the science of hydraulics. Mirza et al. (2003) reported on the application of the MIKE11-GIS hydrodynamic model for Bangladesh to determine flooded area as a function of peak flood flows in the Brahmaputra–Ganges–Meghna rivers system. Their work supports a non-linear relationship that was develop between peak flow and flooded area with results in an R² of .59:

$$\hbox{Flooded area (million of hectares)} = 4.3095* \hbox{ln[Flow (cms)]} -45.906$$

With a relationship between peak flow and flooded area, we have created a link between climate variables and the extent of flooding. Subsequent analysis of climate change will examine the impact of potential climate change on flooding in Bangladesh with full recognition of the possibility that this impact may not symmetric with respect to all levels of flood risk.

1.3 A hydrologic model for the rivers

Mirza et al. (2003) examined the potential climate change impacts for river discharges in Bangladesh using an empirical model to analyze changes in the magnitude of floods of the Ganges, Brahmaputra and Meghna Rivers. The present analysis uses a conceptual rainfall-runoff model, WATBAL, to analyze changes in the magnitude of floods for the same watershed. Yates (1997) describes the model. It has been applied in over forty country studies of climate change impact on runoff including the Nile River Basin, a river basin of the same spatial scale as the GBM basin.

More specifically, the WATBAL model predicts changes in soil moisture according to an accounting scheme based on the one-dimensional bucket conceptualization. Yates and Strzepek (1994) compared this relatively simple formulation to more detailed distributed hydrologic models and found them in close agreement with absolute and relative runoff. The advantage of this lumped water balance model lies in its use of continuous functions of relative storage to represent surface outflow, sub-surface outflow, and evapotranspiration in the form of a differential equation [see Kaczmarek (1993) or Yates (1996)]. The monthly water balance contains two parameters related to surface runoff and subsurface runoff. A third model parameter, maximum catchment water-holding capacity (S _max), was obtained from a global dataset based on the work of Dunne and Willmott (1996).

The precise structure of WATBAL is easily described. To begin with, the monthly soil moisture balance is written as:

$$S_{\max} \frac{dz}{dt}=R_s(P,z,t)-R_{ss}(z,t)-Ev(Pet,z,t)$$

(1)

where

• P _eff  = effective precipitation (length/time),

• R _s  = surface runoff (length/time),

• R _ss  = sub-surface runoff (length/time),

• E _v  = evaporation (length/time),

• S _max = maximum storage capacity (length), and

• z = relative storage (1 ≥  z ≥  0).

A non-linear relationship describes evapotranspiration based on Kaczmarek (1993):

$$E_v(z,Pet,t)=Pet\left({\frac{5z-2z^{2}}{3}} \right)$$

(2)

Following Yates (1996), surface runoff is described in terms of the storage state and the effective precipitation according to

$$R_s (z,P,t)=z^{\varepsilon}(P_{eff})$$

(3)

where ɛ is a calibration parameter that allows for surface runoff to vary both linearly and non-linearly with storage. Finally, sub-Surface runoff is a quadratic function of the relative storage state:

$$R_{ss} =\alpha z^2$$

(4)

where α is the coefficient for sub-surface discharge.

In certain regions, snowmelt represents a major portion of freshwater runoff and greatly influences the regional water availability. Ozga-Zielinska et al. (1994) provide a two parameter, temperature based snowmelt model was used to compute effective precipitation and to keep track of snow cover extent. Two temperature thresholds define accumulation onset through the melt rate (denoted mf _i ). If the average monthly temperature is below some threshold T _s , then the all the precipitation in that month accumulates. If the temperature is between the two thresholds, then a fraction of the precipitation enters the soil moisture budget and the remaining fraction accumulates. Temperatures above some higher threshold T _l give a mf _i value of 0, so all the precipitation enters the soil moisture zone. If there is any previous monthly accumulation, then this is also added to the effective precipitation.

$$Peff_{i}= mf_{i}(A_{i-l}+ Pm_{i})$$

(5)

where,

$$ mf_i =\left\{ {\begin{array}{lll} 0&{\rm for}&T_i \leq T_s\\ 1&{\rm for}&T_i \geq T_l\\ \frac{(T_i-T_s)}{(T_l -T_s)}&{\rm for}&T_s < T_i < T_l\\ \end{array} } \right. $$

(6)

and snow accumulation is written as,

$$ A_{i}=(1-mf_{i})(A_{i-1}+ Pm_{i}) $$

(7)

In writing equations (5) through (7),

• mf _i  = melt factor,

• A _i  = snow accumulation,

• Pm _i  = observed precipitation,

• Peff _i  = effective precipitation,

• T _l  = upper temperature threshold at which precipitation is all liquid (°C),

• T _s  = lower temperature threshold at which precipitation is all solid (°C), and

• i = month.

The model was calibrated from the TYN CY 1.1 data for the Ganges and Brahmaputra separately over using data from monthly flow from the 1970 and 1980 and produced R² statistics of .89 and .87 for the Brahmaputra and Ganges, respectively. Since the climate change scenarios in COSMIC begin with a base year of 1990, the COSMIC base had to be correlated with the TYN CY 1.1 average data.