The impact of potential future climate change scenarios on the irrigation water requirements (IRRs) of two major agricultural crops (coffee and seed corn) in Hawai'i was studied using the Irrigation Management System (IManSys) model. In addition to IRRs calculations, IManSys calculates runoff, deep percolation, canopy interception, and effective rainfall based on plant growth parameters, site specific soil hydrological properties, irrigation system efficiency, and long-term daily weather data. Irrigation water requirements of two crops were simulated using historical climate data and different levels of atmospheric CO2 (330, 550, 710 and 970 ppm), temperature (+1.1 and +6.4 °C) and precipitation (±5, ±10 and ±20%) chosen based on the Intergovernmental Panel on Climate Change (IPCC) AR4 projections under reference, B1, A1B1 and A1F1 emission scenarios. IRRs decreased as CO2 emission increased. The average percentage decrease in IRRs for seed corn is higher than that of coffee. However, runoff, rain canopy interception, and deep percolation below the root zone increased as precipitation increased. Canopy interception and drainage increased with increased CO2 emission. Evapotranspiration responded positively to air temperature rise, and as a result, IRRs increased as well. Further studies using crop models will predict crop yield responses to these different irrigation scenarios.

INTRODUCTION

Population growth, climate change, and anthropogenic land-use changes trigger severe negative effects on the global water resources. The impacts of these changes have more pronounced effects on small elevation-variant islands, e.g., Hawaiian Islands. These islands have been experiencing prevailing spatio-temporal variability of the major water cycle components, e.g., rainfall, evapotranspiration, and to a less extent temperature. Human activities are altering the global climate (Hansen et al. 2006) by raising the concentration of the greenhouse gases especially methane, nitrous oxide and carbon dioxide (CO2). Atmospheric CO2 is expected to increase from its reference (330 ppm) concentration used in this study to between 550 and 970 ppm by 2,100 (IPCC 2007). Increased greenhouse gas concentrations are likely to raise the earth's mean temperature and influence precipitation, storm patterns and sea level (IPCC 2007). However, in general, the magnitude of these changes will depend on future anthropogenic activities as well as on technological and economic development. Over the past years of the 21st century, the global average surface temperature has increased by approximately 0.6 °C and is projected to rise by an additional 1.1–6.4 °C (IPCC 2007).

Timm & Diaz (2008) analyzed the precipitation variations over the Hawaiian Islands during most of the first decade of the 21st century using the AR4 A1B emission scenario. They predicted a 5–10% reduction of wet season precipitation and 5% increase during the dry season. Karl et al. (1996) reported an increase of the mean air temperature by 2.5 °C and 20% decrease in precipitation between 1,900 and 1,990 based on analysis of observed air temperature and precipitation at Honolulu International Airport, Hawai'i. Giambelluca & Luke (2007) reported an increasing trend in the mean annual temperature between 0.12 and 0.23 °C per decade across the Hawaiian Islands between 1905 and 2006. Although the magnitude of change in temperature and precipitation varied temporally and spatially, the anticipated impact on the hydrology and thus, irrigation water requirements for the crops in the mountainous Hawaiian watersheds is a serious matter for sustainable future water yields.

Anticipating changes in the hydrologic cycle is particularly important for regions with limited water supplies (Ficklin et al. 2009) such as the Hawaiian Islands. The Hawaiian Islands are the most isolated oceanic island group on earth and rely solely on precipitation and subsequent groundwater recharge for their fresh water needs (Mair & Fares 2010). Streams in Hawai'i supply more than 50% of irrigation water and a fair amount of drinking water in some places (Oki 2003). Examining the sensitivity of hydrologic responses to climate across different geomorphologic regions is important to formulate appropriate water management policies and irrigation water allocations for local responses (Qi et al. 2009).

An increase in average daily temperature due to climate change will increase reference evapotranspiration (ETo) resulting in an increased use of irrigation water whereas increase in atmospheric CO2 will increase crop water use efficiency (WUE, which is the ratio of CO2 uptake to evapotranspiration) resulting in less water needed for irrigation (Ficklin et al. 2010). The likely net effect of increased temperature and CO2 will be insignificant changes in ETo within the next 30 years (Hatfield et al. 2008).

Elevated atmospheric CO2 often reduces plant stomatal conductance (Nederhoff et al. 1992; Rodaglou et al. 1992) and plant transpiration per unit leaf area (Goudriaan & Unsworth 1990; Dugas et al. 1997). Reduced transpiration increases the leaf temperature which can further increase photosynthesis (Acock 1990). The effect of higher levels of CO2 on stomatal conductance and transpiration is observed in both C3 and C4 species (Dugas et al. 1997). Morison & Gifford (1984) found that stomatal conductance was reduced by 36% while transpiration was reduced by 21% for several plant types, the difference being attributed to the higher leaf temperatures. Similar average values of 34% for stomatal conductance and 23% for transpiration were found in a literature survey by Cure & Acock (1986). An increase in photosynthesis and a decrease in transpiration result in an increase in the plant's water use efficiency.

Many studies estimated future changes in irrigation water requirements without considering a decrease in stomatal conductance due to elevated CO2 in calculating ETo. These studies calculated ETo using temperature-based equations, e.g. Modified Hamon equation (Lee & Huang 2014), Priestley-Taylor method (Doll 2002). Several investigators, e.g., Strzepek et al. (1999), Hatch et al. (1999), and Rosenzweig & Iglesias (1998), used the Priestley-Taylor method to calculate ETo; in addition, they incorporated the effect of elevated CO2 on stomatal closure and leaf area index and consequently, their impact on potential transpiration using the ratio (Peart et al. 1989) of transpiration under elevated CO2 conditions to that under ambient conditions. However, very few researchers have attempted to estimate future changes in irrigation water requirements based on projected climate changes from GCMs, and estimates of decreased stomatal conductance due to elevated CO2 in ETo (e.g., Allen et al. 1991; Izaurralde et al. 2003). Thus, in this study we used a modified version of the FAO Penman-Monteith ETo equation to account for the effects of elevated atmospheric CO2 concentration and increase in temperature on ETo.

In this study, the connections between anticipated climate change and hydrologic processes were studied to better understand the climate change impact on irrigation water requirements for two major crops in the islands of Hawai'i. The specific objectives of this research are to evaluate the potential impact of climate change on: (i) irrigation water requirements for a short cropping season annual crop, seed corn, and a perennial crop, coffee; and (ii) the major water cycle components, e.g., evapotranspiration, canopy interception, runoff, and excess water losses/groundwater recharge below the rootzone under Hawaiian conditions.

MATERIALS AND METHODS

Study area

Climate change sensitivity assessment was performed based on long-term historical weather data collected at Honolulu International Airport (Station ID–GHCND: USW00022521) on the Island of Oahu, Hawai'i, USA. The latitude and longitude of this station are 21.323 ° and −157.929 °, respectively. The station is located at an elevation of 2.1 m AMSL. Daily climate data (precipitation, wind speed, maximum and minimum temperature) from 1984/01/01 to 2010/10/31 (27 years) were downloaded from the Global Historical Climatology Network (GHCN) of the National Climatic Data Center on the National Oceanic and Atmospheric Administration's (NOAA) website (http://www.ncdc.noaa.gov/cdo-web/).

Irrigation Management System (IManSys) model

The IManSys is a numerical simulation model; it calculates irrigation water requirements (IRRs) for different crops based on the water balance approach and using site-specific data and historical weather data. Water budget models have been used for irrigation scheduling and crop water requirement estimation (Smajstrla & Zazueta 1988; Obreza & Pitts 2002; Fares et al. 2008). Smajstrla & Zazueta (1988) reported on the Agricultural Field Scale Irrigation Requirements Simulation (AFSIRS) model that uses a water balance approach with a layered soil column to simulate soil water infiltration, redistribution, and extraction by evapotranspiration as steady state processes on a daily basis. The AFSIRS model simulates the irrigation requirements for a crop based on plant physiology, soil, irrigation system, growing season, climate and basic irrigation management practice. The AFSIRS does not account for runoff neither for canopy interception nor does it multiple crops. It addition, it cannot calculate ET based on other weather data, nor does it have databases for soils other than those found in Florida. Another major deficiency is that AFSIRS does not account for climate change effects on IRRs calculation. Based on the deficiencies of the current water balance models, e.g. AFSIRS, it was necessary to develop a new model that builds on the water balance approach used by AFSIRS and offers new functions needed to calculate IRRs across different climates and environmental conditions where runoff, canopy interception, and climate change are important. Such a new model should also be able to calculate ET data.

Thus, IManSys was developed based on AFSIRS’ approach (Smajstrla & Zazueta 1988); in addition to IRRs calculations, IManSys calculates runoff, canopy interception, effective rainfall, ET and IRRs for multiple crops based on crop growth parameters, soil properties, irrigation system efficiency, and long-term weather data (precipitation and evapotranspiration). IManSys accounts for climate change effects and water management practices on IRRs. If ET data is not available, IManSys calculates evapotranspiration using limited weather data, e.g. temperature-based model ETM (Fares & Mansell 1996) or using complete weather data, e.g. FAO Penman-Monteith method (Allen et al. 1998). IManSys uses multiple databases, e.g., soil and plant growth parameters, irrigation system efficiencies, and canopy interception. IManSys was implemented in JAVA object oriented language. IManSys output includes detailed net and gross IRRs, and all water budget components at different time scales (daily, weekly, biweekly, monthly, and annually) based on non-exceedance drought probability which is calculated from a conditional probability model that uses the type I extreme value distribution for positive non-zero irrigation values (Fares & Fares 2012).

IManSys’ simulated soil profile depth is assumed to be equal to the crop root zone depth. The plant root zone is divided into irrigated (upper 50%) and non-irrigated (lower 50%) zones based on the common practice of irrigating only the upper portions of the crop root zone where most of the roots are located (Smajstrla 1990; Smajstrla & Zazueta 1988). It is assumed that 70% and 30% of crop ET are extracted from the irrigated and non-irrigated zones, respectively, when water is available (SCS 1970). This pattern of water extraction is typically assumed for well-irrigated plants on non-restrictive soil profiles (Smajstrla & Zazueta 1988).

The plant specific irrigation water requirements are calculated based on plant physiology, soil hydrological properties, irrigation system efficiency, growing season, climate and basic irrigation management practices. The daily water balance equation for the soil column defined by the crop root zone expressed in terms of equivalent water depth per unit area (cm) is: 
formula
1
where ΔS is the change in soil water storage expressed as equivalent water depth (cm), P is the gross rainfall (cm), Gw is the groundwater contribution (cm) from shallow water table, IRRnet is the net irrigation water requirement (cm), (QD + QR) is summation of groundwater drainage and surface water runoff (cm), ETc is the plant evapotranspiration (cm) and I is canopy rainfall interception (cm).

The water storage capacity is the amount of water that is available for plant uptake. It is calculated as the equivalent water between field capacity and permanent wilting point for a given soil multiplied by the depth of the root zone. Irrigation is scheduled based on an allowable level of soil water depletion from the root zone. Irrigation amounts are calculated to restore the soil water content to field capacity.

Assuming a negligible groundwater contribution to the rootzone (Gw = 0) and since gross IRRs is IRRs=IRRnet/fi and the detailed equation is as follows: 
formula
2
where fi is the efficiency of the irrigation system used.

The irrigation requirement is calculated as the depth of water required to replenish the soil water content to field capacity in the irrigated crop root zone. Irrigation events were assumed to start when the available water for plant uptake decreases to a predetermined minimum allowable level, termed allowable soil water depletion (AWD) percentage. Allowable soil water depletion values were determined from the literature and are fractions of the available soil water storage capacity that can be allowed to be depleted without significant reduction in crop yield. Allowable soil water depletion values for the annual and perennial crops are user specific and can be provided with crop information. The model uses AWD equals to 0.50 as the default value for all perennial crops. A value of 0.50 means that 50% of the available water in the irrigated crop root zone is allowed to be depleted between two consecutive irrigation events.

More details about the IManSys model can be found in Fares (2008), Fares (2009), and Fares & Fares (2012). The theoretical and computational methods used in IManSys are the same as those used in IWREDSS (Fares 2008). Details about the IWREDSS model and its validation are given in Fares (2008).

Modification of FAO Penman-Monteith equation

Different methods to calculate potential evapotranspiration were implemented in IManSys. The FAO Penman-Monteith equation was modified to incorporate climate change scenarios of different CO2 emissions and temperature rises in future ET calculations.

Reference evapotranspiration (ETo) based on the FAO Penman-Monteith method (Allen et al. 1998), is calculated using the following equation: 
formula
3
where ETo is the reference evapotranspiration (mm day−1), Rn is the net radiation at the crop surface (MJ m−2 day−1), G is the soil heat flux density (MJ m−2 day−1), T is the air temperature at 2 m height (°C), rs is the bulk surface resistance (s m−1), ra is the aerodynamic resistance (s m−1), es is the saturation vapour pressure (kPa), ea is the actual vapour pressure (kPa), es–ea is the saturation vapour pressure deficit (kPa), Δ is the slope vapour pressure curve (kPa °C−1), γ is the psychrometric constant (kPa °C−1), and u2 is the wind speed at height 2 m (m s−1).

The ratio rs/ra in the FAO Penman-Monteith equation is a function of wind speed at 2 m height (0.34 u2). We modified the value of ratio rs/ra according to variations in CO2 concentration. An increase in CO2 concentration not only causes increases in air temperature but also will result in a reduction of the leaf stomatal conductance (Saxe et al. 1998; Medlyn et al. 2001; Wullschleger et al. 2002) and an increase in LAI because of enhanced photosynthesis (Pritchard et al. 1999; Wand et al. 1999). The stomatal conductance of reference grass surface is adjusted using a similar approach to that used in the Soil and Water Assessment Tool (SWAT) (Arnold et al. 1998). Similar to Eckhardt & Ulbrich (2003), we used a smaller increase (7%) in LAI with a doubling of CO2 concentration.

Bulk surface resistance (rs) is defined by using the following equation: 
formula
4
where rl is the bulk stomatal resistance of the well-illuminated leaf (s m−1), LAIactive is the active (sunlit) leaf area index (m2 (leaf area) m−2 (soil surface)).
Stomatal resistance and LAI for different CO2 emission scenarios can be calculated using the following equations: 
formula
5
where gCO2 is the modified leaf conductance (Neitsch et al. 2011) and g is the conductance without the effect of CO2 for the reference crop, grass, 0.01 m s−1.
LAI for different CO2 emission scenarios (LAICO2) based on assumption of 7% increase in LAI with a doubling of CO2 emission is given by the following equation, 
formula
6
For clipped grass, a general equation for leaf area index (LAI) is: 
formula
7
where h is the height of reference crop, grass (0.12 m), 
formula
8
For reference crop, grass, 
formula
9
The expression for bulk surface resistance (rs) can be modified to Equation (10) using Equations (4)–(9), 
formula
10
The ratio rs/ra can be calculated for different CO2 emission scenarios (Table 1) using Equation (10) and ra (=208/u2) for a grass reference surface (Allen et al. 1998).
Table 1

rs/ra for different CO2 emissions

CO2 emission (ppm) rs/ra 
550 0.435 u2 
710 0.573 u2 
970 1.311 u2 
330 0.340 u2 
CO2 emission (ppm) rs/ra 
550 0.435 u2 
710 0.573 u2 
970 1.311 u2 
330 0.340 u2 

Note: u2 is the wind speed at height 2 m (m s−1).

ETo is calculated for different scenarios of temperature rise and CO2 emission using the modified FAO Penman-Monteith equation (Figure 1). The details of climate change scenarios are given in the subsequent section. ETo increased with increase in temperature and decreased with increase in CO2 emission (Figure 1).
Figure 1

ETo for different scenarios of temperature increase and CO2 emission.

Figure 1

ETo for different scenarios of temperature increase and CO2 emission.

The remaining terms are estimated; following Allen et al. 1998 using observed or increased daily maximum and minimum temperature in different scenarios.

Climate change scenarios

In this study, a total of 24 climate change scenarios (Table 2) were generated on the basis of the IPCC Special Reports on Emission Scenarios (IPCC 2001), local trend analysis and GCM downscaling (Timm & Diaz 2008) similar to the climate change scenarios used by Ficklin et al. (2009) and Safeeq & Fares (2012). On the basis of the IPCCs projections of greenhouse gas emissions for the 21st century, atmospheric concentration of CO2 is expected to increase between 550 (B1 emission scenario) and 970 ppm (A1F1 emission scenario) from its reference emission scenario of 330 ppm. The two extreme scenarios represent a future world of a very rapid economic growth (A1F1 emission scenario–970 ppm) or a future world with low economic growth and fossil fuel independency (B1 emission scenario–550 ppm).

Table 2

Climate change sensitivity scenarios based on different potential emission scenarios

Scenarios CO2 emission (ppm) Temperature (°C) Precipitation variation (%) Emission scenario 
Reference 330 Reference emission scenario 
330 1.1   
330 6.4   
330 −5   
330 −10   
330 −20   
330   
330 10   
330 20   
550 1.1 B1 emission scenario 
10 550 1.1 −5   
11 550 1.1 −10   
12 550 1.1 −20   
13 550 1.1   
14 550 1.1 10   
15 550 1.1 20   
16 970 6.4 A1F1 emission scenario 
17 970 6.4 −5   
18 970 6.4 −10   
19 970 6.4 −20   
20 970 6.4   
21 970 6.4 10   
22 970 6.4 20   
23 710 −5% in wet and +5% in dry season A1B emission scenario 
24 710 −10% in wet and +5% in dry season   
Scenarios CO2 emission (ppm) Temperature (°C) Precipitation variation (%) Emission scenario 
Reference 330 Reference emission scenario 
330 1.1   
330 6.4   
330 −5   
330 −10   
330 −20   
330   
330 10   
330 20   
550 1.1 B1 emission scenario 
10 550 1.1 −5   
11 550 1.1 −10   
12 550 1.1 −20   
13 550 1.1   
14 550 1.1 10   
15 550 1.1 20   
16 970 6.4 A1F1 emission scenario 
17 970 6.4 −5   
18 970 6.4 −10   
19 970 6.4 −20   
20 970 6.4   
21 970 6.4 10   
22 970 6.4 20   
23 710 −5% in wet and +5% in dry season A1B emission scenario 
24 710 −10% in wet and +5% in dry season   

To bracket the range of changes in precipitation over the Hawaiian Islands, four arbitrary precipitation scenarios with respect to the reference level (i.e., 0, ±5, ±10 and ±20%) were selected. Two additional precipitation-based scenarios were generated on the basis of six GCM predictions for Hawai'i under AR4 A1B emission scenario. Temperature-based scenarios were generated by increasing the temperature by 1.1 and 6.4 °C. All the climate change scenarios were generated using the ‘delta method’ (Hamlet & Lettenmaier 1999) on observed precipitation and air temperature data from the Honolulu Airport Weather Station for the period between 1984 and 2010. Daily precipitations were increased or decreased relative to the original observed data; thus, the latter data were multiplied by a scaling factor. A scaling factor with a value of 1.05 means a 5% increase in precipitation compared with historical data. One of the reasons for choosing two cropping seasons for the annual seed corn crop is to cover the potential impact climate change might have on rainfall distribution across the year. Changes in air temperature were made by adding the required 1.1 or 6.4 °C to observed temperature, half of each of these values is added to the maximum daily temperature and the other half to the minimum daily temperature.

RESULTS AND DISCUSSION

Different scenarios were simulated using IManSys for 27 years after adjusting precipitation, temperature, leaf conductance, and LAI. Other climate variables (i.e., solar radiation, wind speed, and relative humidity) were kept constant for all scenarios. Seed corn growing seasons and their corresponding lengths are given in Table 3. The growing seasons used for all simulated scenarios are the same for both crops. The dry and wet seasons for corn start in June and December, respectively. The irrigation system used in all simulations is the drip irrigation system which has an irrigation efficiency of 0.85. Simulation results are presented on a monthly and an annual basis; they were then compared with those of the reference scenarios.

Table 3

Growing seasons selected for simulating irrigation water requirements

Crop Season Sowing Harvesting Length of season 
Coffee   January 1 December 31 365 
Seed corn Dry June 1 September 6 98 
Seed corn Wet December 1 March 8 98 
Crop Season Sowing Harvesting Length of season 
Coffee   January 1 December 31 365 
Seed corn Dry June 1 September 6 98 
Seed corn Wet December 1 March 8 98 

Gross irrigation water requirements and crop evapotranspiration

Irrigation water requirements decreased with increased CO2 emission (Figure 2) for both crops. The decrease level for coffee was 6.4% and 42.2% for CO2 emission levels of 550 ppm and 970 ppm, respectively. The corresponding average decrease in IRRs for seed corn was 8% and 50.1%, respectively. Similarly, gross irrigation water requirement has an inverse relationship with precipitation, i.e., it decreased with rise in precipitation but increased with fall of precipitation. The average annual gross rainfall for the reference scenario is 430 mm for this study area.
Figure 2

Gross irrigation water requirement for different scenarios of temperature increase and CO2 emission.

Figure 2

Gross irrigation water requirement for different scenarios of temperature increase and CO2 emission.

Evapotranspiration increased with increase in temperature for the reference scenario of CO2 emission (CO2 = 330 ppm and ΔP = 0); thus, gross irrigation water requirement also increased (Figure 3). However, the combined effect of temperature and CO2 emission as illustrated in scenarios B1 and A1F1, resulted in a decrease of all monthly ET and gross IRRs (Figure 4).
Figure 3

Effect of changes in temperature on gross IRR and ET (CO2 = 330 ppm and ΔP = 0).

Figure 3

Effect of changes in temperature on gross IRR and ET (CO2 = 330 ppm and ΔP = 0).

Figure 4

Monthly variation of gross IRRs and ET for different CO2 emission scenarios (coffee).

Figure 4

Monthly variation of gross IRRs and ET for different CO2 emission scenarios (coffee).

Based on an extensive review of the literature, the authors did not find any previous studies on the potential impact of climate change on IRRs of crops in Hawai'i; however, there were other studies under non-tropical environments. Projections of future irrigation requirement for field corn in Georgia, USA calculated using a climate change scenario derived from the HadCM2 (Johns et al. 1997) global circulation model showed a decrease in IRRs by 20% by 2030 and 50% by 2090 due to the combined effect of precipitation increase and shortening of the growing seasons (Hatch et al. 1999). Hatch et al. (1999) used the Priestley-Taylor equation to calculate reference evapotranspiration; they also incorporated the effect of elevated CO2 on stomatal closure on potential transpiration using the ratio (Peart et al. 1989) of transpiration under elevated CO2 conditions to that under ambient conditions. In general, results of the current study concur with their prediction of an IRR decrease. However, results of IRRs across the US corn-belt, under three climate change scenarios showed mixed result (Strzepek et al. 1999). In their study they also used a similar approach to calculate ETo; the calculated potential transpiration reflects the effects of elevated CO2. Allen et al. (1991) studied the effects of CO2-induced climatic changes on IRR in the Great Plains region (Nebraska, Kansas, Oklahoma and Texas) using GCMs data and a water balance model. They also used the Penman-Monteith equation to calculate ETo; they simulated the effect of elevated CO2 level transpiration by increasing the value of reference crop bulk stomatal resistance (+20, +40, +60 and +80%). They reported a decrease in IRR for corn at all locations for increased bulk stomatal resistance of 40–80% under the GISS (Goddard Institute for Space Studies) scenario; however, IRR increased at all locations for increased bulk stomatal resistance of 20–60% except for Texas under the GFDL (Geophysical Fluid Dynamics Laboratory of Princeton University) scenario. The IRRs may increase or decrease depending on the spatial location of the study area and climate change scenarios used.

The negative correlation between precipitation and gross IRRs for these scenarios was consistent during all months (Figure 5); thus, gross IRRs increased by 7.7–17.2% for a temperature rise of 6.4 °C at the reference CO2 level (330 ppm). The variation in gross IRRs during the wet season (November–April) is higher than during the dry season (May–October). Gross IRRs almost were consistently lower than those of the reference case for all scenarios of B1 emission (CO2 = 550 ppm) except during 4 months of scenario 12 (which has only 80% of the reference precipitation) (Figure 5). The highest monthly decrease in the gross IRRs of 20.4% is during February for scenario 15 which has the highest precipitation increase (20%). Decrease in gross IRRs varies between 13.7 and 22.6% for the two scenarios of A1B emission (CO2 = 710 ppm). Decrease in the gross irrigation water requirements is more pronounced for the A1F1 emission scenario where the minimum decrease was 33.9% (Scenario 18) and the maximum decrease reached 55.7% (Scenario 22).
Figure 5

Change in average monthly gross IRRs relative to the reference scenario (coffee).

Figure 5

Change in average monthly gross IRRs relative to the reference scenario (coffee).

Rainfall, runoff and canopy interception

In IManSys, surface runoff is calculated using the SCS curve number method (Fares & Fares 2012). Thus, runoff is a function of precipitation, soil hydrologic group, land use, and management practices. Runoff is not influenced by the CO2 emission level; thus, for this case it only responds to precipitation levels (Figure 6). For each crop and at a given precipitation level, runoff is the same for all CO2 emissions. Runoff represents about 18.3% of the gross rainfall for the perennial coffee crop and about 7% for the dry and 18.9% for the wet seasonal seed corn crop.
Figure 6

Runoff for different scenarios of temperature increase and CO2 emission.

Figure 6

Runoff for different scenarios of temperature increase and CO2 emission.

IManSys calculates precipitation canopy interception as a function of the maximum leaf area index (LAImax) of the crop; it assumes that LAImax will increase by 7% with doubling of CO2 emission for all crops. Thus, interception is higher for higher CO2 emission scenarios. Canopy precipitation interception increases with increases in gross rainfall (Figure 7). About 19 to 22% of the gross rainfall was lost as precipitation canopy interception under the coffee crop. However, precipitation canopy interception represents 10–12% of the gross precipitation under seed corn.
Figure 7

Interception for different scenarios of temperature increase and CO2 emission.

Figure 7

Interception for different scenarios of temperature increase and CO2 emission.

Drainage

Drainage and precipitation are positively correlated (Figure 8) for both crops; thus, drainage increased with increase in precipitation and decreased with a decrease in precipitation. However, the magnitude and the rate of drainage increase are higher for seed corn than for coffee. This drainage increase represents 8% of the gross rainfall for coffee and 20% for seed corn. Similarly, the rate of drainage rise as a result of precipitation increase is equal to 0.82 cm cm−1 of precipitation for seed corn and 0.18 cm cm−1 for the coffee crop. The lower predicted drainage rates under the coffee crop is due to a combination of several factors including LAI, and difference in the cropping season as coffee is a perennial crop and seed corn is a short growing season annual crop.
Figure 8

Drainage for different scenarios of temperature increase and CO2 emission.

Figure 8

Drainage for different scenarios of temperature increase and CO2 emission.

CONCLUSIONS AND RECOMMENDATIONS

This work investigated the impact of potential future climate change scenarios on the gross IRRs of two major agricultural crops (corn and coffee) in Hawai'i and the subsequent water budget components (runoff, drainage, and canopy interception) using IManSys.

It was found that an increase in temperature resulted in an increase in ET demand and consequently in an increase of IRR for both crops; however, IRR is higher for coffee (>10%) than for that of seed corn. As expected, gross IRR is negatively correlated with precipitation; however, there is a direct correlation between drainage and precipitation. Irrigation water requirement decreased with increased CO2 emission. Results of the study indicated that the impact of climate change on two major crops grown in a major Hawaii Island (seed corn and coffee) is very sensitive to future climate scenarios generated as predicted by different GCMs. The average percentage decrease in IRRs for coffee is 6.4% and 42.2% for CO2 emission levels of 550 ppm and 970 ppm, respectively. Whereas the corresponding average decrease in IRRs for seed corn are 8% and 50.1%, respectively. The average percentage decrease in IRR for seed corn is comparatively higher than that of coffee. Runoff increased with a precipitation rise, but it did not show any change in response to CO2 level variations. Canopy interception and drainage increased with increased CO2 emission. However, the magnitude and the rate of drainage increase are higher for seed corn than for coffee. Evapotranspiration increased for rising temperatures, and as a result the gross irrigation water requirement was also increased. Changes in rainfall variability and increased evaporation will directly impact rain-fed agriculture and reduce water availability for irrigation.

Results of this study as well as those obtained from other studies suggest that as the CO2 content in the atmosphere continues to rise, terrestrial plants will likely display reductions in transpirational water loss. As a result, most plants might be able to better deal with periodic water shortages and warmer temperatures, possibly even could expand their ranges into areas where it was too dry for them to successfully live and reproduce in the recent past.

This study only focused on IRR calculations for two main crops grown on Hawai'i. Further studies on predicting crop yield responses to these different irrigation scenarios using crop modeling are essential to quantify the impact of climate change on the yield of these two crops in Hawai'i.

ACKNOWLEDEGMENTS

This project was partially supported by USDA National Institute of Food and Agriculture, Hatch Act Formula Grant in University of Hawai'i. Partial funding was also provided by the National Institute of Food and Agriculture of the United States Department of Agriculture Evans-Allen funds in Prairie View A&M University. The authors wish to thank Kyle Mulleady for his assistance in updating the IManSys program. The authors also wish to thank Hla Htun for his assistance in the early stages of this study.

REFERENCES

REFERENCES
Acock
B.
1990
Effects of carbon dioxide on photosynthesis, plant growth and other processes
. In:
Impact of Carbon Dioxide, Trace Gases and Climate Change on Global Agriculture
(
Kimball
B. A.
Rosenberg
N. I.
Alien
L. H.
Jr
, eds).
Special Publication no 53. ASA
,
Madison, WI
,
USA
, pp.
45
60
.
Allen
R. G.
Gichuki
F. N.
Rosenzweig
C.
1991
CO2-induced climatic changes and irrigation-water requirements
.
Journal of Water Resources Planning and Management
117
,
157
178
.
Allen
R. G.
Pereira
L. S.
Raes
D.
Smith
M.
1998
Crop Evapotranspiration
.
FAO Irrigation and Drainage Paper 56. FAO
,
Rome
.
Arnold
J. G.
Srinivasan
R.
Muttiah
R. S.
Williams
J. R.
1998
Large area hydrologic modeling and assessment-Part I: model development
.
Journal of American Water Resource Association
34
,
73
89
.
Cure
J. D.
Acock
B.
1986
Crop responses to carbon dioxide doubling: a literature survey
.
Agricultural and Forest Meteorology
38
,
127
145
.
Dugas
W. A.
Prior
S. A.
Rogers
H. H.
1997
Transpiration from sorghum and soybean growing under ambient and elevated CO2 concentrations
.
Agricultural and Forest Meteorology
83
,
37
48
.
Fares
A.
2008
Water Management Software to Estimate Crop Irrigation Requirements for Consumptive Use Permitting In Hawai'i. Final Report 71 pp. Submitted to State of Hawaii Department of Land and Natural Resources Commission of Water Resources Management. http://hawaii.gov/dlnr/cwrm/publishedreports/PR200808.pdf
.
Fares
A.
2009
GIS-IManSys, A User-Friendly Computer Based Water Management Software Package
.
GCC Conference and Exhibition, 2009 5th IEEE, Kuwait City
,
2009
, pp.
1
4
. doi:10.1109/IEEEGCC.2009.5734338.
Fares
A.
Fares
S.
2012
Irrigation Management System, IMANSYS, a User-Friendly Computer Based Water Management Software Package
. In:
Proceedings of the Irrigation Show and Education Conference
,
Orlando, FL
,
2–6 November 2012
.
Fares
A.
Mansell
R. S.
1996
A potential evapotranspiration prediction submodel for multi-dimensional water flow in soils
.
Crop Science Society Florida Proceeding
55
,
36
45
.
Fares
A.
Dogan
A.
Parsons
L. R.
Obreza
T.
Morgan
K. T.
2008
Effects of citrus canopy interception on effective rainfall calculations using Water Budget Analysis and TR-21 Method
.
Soil Science Society of American Journal
72
,
578
585
.
Ficklin
D. L.
Luo
Y.
Luedeling
E.
Zhang
M.
2009
Climate change sensitivity assessment of a highly agricultural watershed using SWAT
.
Journal of Hydrology
374
,
16
29
.
Giambelluca
T. W.
Luke
M. S. A.
2007
Climate change in Hawai'i’s mountains
.
Mountain Views
1
,
13
18
.
Hamlet
A. F.
Lettenmaier
D. P.
1999
Effects of climate change on hydrology and water resources of the Columbia River basin
.
Journal of the American Water Resources Association
35
,
1597
1624
.
Hansen
J.
Sato
M.
Ruedy
R.
Lo
K.
Lea
D. W.
Medinaelizade
M.
2006
Global temperature change
.
Proceedings of the National Academy of Sciences of the United States of America
103
,
14 288
14 293
.
Hatch
U.
Jagtap
S.
Jones
J.
Lamb
M.
1999
Potential effects of climate change on agricultural water use in the Southeast U.S
.
Journal of the American Water Resources Association
35
,
1551
1561
.
Hatfield
J.
Boote
K.
Fay
P.
Hahn
L.
Izaurralde
C.
Kimball
B. A.
Mader
T.
Morgan
J.
Ort
D.
Polley
W.
Thomson
A.
Wolfe
D.
2008
Agriculture
.
In:
The Effects of Climate Change on Agriculture, Land Resources, Water Resources, and Biodiversity in the United States
(M. Walsh, ed.).
A Report by the U.S. Climate Change Science Program and the Subcommittee on Global Change Research
,
Washington, DC
,
USA
,
362
pp.
IPCC
2001
Special Report on Emissions Scenarios
.
Cambridge University Press, Cambridge, UK, 612 pp.
IPCC
2007
Climate Change 2007: The Physical Science Basis
. In:
Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change
(
Solomon
S.
Qin
D.
Manning
M.
, eds).
Cambridge University Press
,
Cambridge
,
UK and New York, USA
.
Johns
T. C.
Carnell
R. E.
Crossley
J. F.
Gregory
J. M.
Mitchell
J. F. B.
Senior
C. A.
Tett
S. F. B.
Wood
R. A.
1997
The second Hadley Centre coupled ocean-atmosphere GCM: Model description, spinup and validation
.
Climate Dynamics
13
,
103
134
.
Karl
T. R.
Knight
R. W.
Easterling
D. R.
Quayle
R. G.
1996
Indices of climate change for the United States
.
Bulletin of the American Meteorological Society
77
,
279
292
.
Medlyn
B. E.
Barton
C. V. M.
Broadmeadow
M. S. J.
Ceulemans
R.
De Angelis
P.
Forstreuter
M.
Freeman
M.
Jackson
S. B.
Kellomaki
S.
Laitat
E.
Rey
A.
Roberntz
P.
Sigurdsson
B. D.
Strassemeyer
J.
Wang
K.
Curtis
P. S.
Jarvis
P. G.
2001
Stomatal conductance of forest species after long-term exposure to elevated CO2 concentration: a synthesis
.
New Phytologist
149
,
247
264
.
Neitsch
S. L.
Arnold
J. G.
Kiniry
J. R.
Williams
J. R.
2011
Soil and Water Assessment Tool: Theoretical Documentation Version 2009
.
Texas Water Resources Institute Technical Report No. 4066
.
Texas Water Resources Institute
,
TX
.
Obreza
T. A.
Pitts
D. J.
2002
Effective rainfall in poorly drained microirrigated citrus orchards
.
Soil Science Society American Journal
66
,
212
221
.
Oki
D. S.
2003
Surface Water in Hawai'i
.
U.S. Geological Survey Fact Sheet, USGS
,
VA
.
Peart
R. M.
Jones
J. W.
Curry
R. B.
Boote
K.
Allen
L. H.
Jr
1989
Impact of climate change on crop yield in the southeastern USA
. In:
The potential Effects of Global Climate Change on the United States, Appendix C–Agriculture
(
Smith
J. B.
Tirpak
D. A.
, eds).
US Environmental Protection Agency, EPA-230-05-89-050
,
Washington, DC
, pp.
21
254
.
Pritchard
S. G.
Rogers
H. H.
Prior
S. A.
Peterson
C. M.
1999
Elevated CO2 and plant structure: a review
.
Global Change Biology
5
,
807
837
.
Qi
S.
Sun
G.
Wang
Y.
McNulty
S. G.
Myers
J. A. M.
2009
Streamflow response to climate and landuse changes in a coastal watershed in North Carolina
.
Transactions of the ASABE
52
,
739
749
.
Rodaglou
K. M.
Aphalo
P.
Jarvis
P. G.
1992
Response of photosynthesis, stomatal conductance and water use efficiency to elevated CO, and nutrient supply in acclimated seedlings of Phaseolus vu1garis L
.
Annals of Botany
70
,
257
264
.
Rosenzweig
C.
Iglesias
A.
1998
The use of crop models for international climate change impact assessment
. In:
Understanding Options for Agricultural Production
(
Tsuji
et al.  
eds)
Kluwer Academic Publishers
,
Dordrecht
, pp.
267
292
.
Saxe
H.
Ellsworth
D. S.
Heath
J.
1998
Trees and forest functioning in an enriched CO2 atmosphere
.
New Phytologist
139
,
395
436
.
SCS
1970
Irrigation Water Requirements
.
Technical Release No. 21
.
USDA Soil Conservation Service
,
Washington, DC
.
Smajstrla
A. G.
1990
Technical manual: Agricultural Field Scale Irrigation Requirements Simulation (AFSIRS) Model
.
University of Florida
,
Gainesville, FL
.
Smajstrla
A. G.
Zazueta
F. S.
1988
Simulation of irrigation requirements of Florida agronomic crops
.
Soil Crop Science Society Florida Proceeding
47
,
78
82
.
Strzepek
K. M.
Major
D. C.
Rosenzweig
C.
Iglesias
A.
Yates
D. N.
Holt
A.
Hillel
D.
1999
New methods of modelling water availability for agriculture under climate change: the U.S. Cornbelt
.
Journal of the American Water Resources Association
35
,
1639
1655
.
Wullschleger
S. D.
Gunderson
C. A.
Hanson
P. J.
Wilson
K. B.
Norby
R. J.
2002
Sensitivity of stomatal and canopy conductance to elevated CO2 concentration interacting variables and perspectives of scale
.
New Phytologist
153
,
319
331
.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Licence (CC BY-NC-ND 4.0), which permits copying and redistribution for non-commercial purposes with no derivatives, provided the original work is properly cited (http://creativecommons.org/licenses/bync-nd/4.0/)