The temporal trend and spatial distribution of reference evapotranspiration was investigated across Madagascar for the period of 1980-2010. Air temperature, relative humidity temperature, relative humidity, solar radiation, and wind speed were collected from 22 weather stations across the country and were used to estimate daily reference evapotranspiration (ET₀) by the Penman-Monteith equation. Monthly average daily ET₀ and the total annual ET₀ were estimated. The Mann-Kendall test was used for the temporal trend analysis in monthly average daily ET₀ and the total annual ET₀ and the Sen’s method was used to estimate the rate of change in ET₀ during the study period. The spline interpolation method was used for spatial interpolation of the variation in annual and monthly average ET₀. The results showed southwest-north East trend in ET₀. Reference evapotranspiration was higher at the western semiarid region than the humid eastern region. ET₀ peaked during the period of September-October. Annual total ET₀ varied from 1,081 mm in Andapa at northeast to 2,239 mm in Antsohihy at northwestern coastal region. Overall, there was an increasing trend in annual total ET₀; however, the upward trend was significant only at 7 out of 22 weather station sites while monthly ET₀ did not show consistent trends. This is one of the first comprehensive studies that investigate spatial and temporal dynamics of ET₀ in Madagascar, which can aid in developing appropriate adaptation strategies to improve crop water use and evaporative losses estimates for maintaining or increasing food production while enhancing water use efficiency in the western semiarid regions of Madagascar.

Keywords: Penman-Monteith, Reference evapotranspiration, Spatio-temporal trend, Madagarcar

Reference evapotranspiration (ET₀) is one the most important parameters in agricultural, hydrological, and environmental studies. Despite the availability of numerous estimation methods, the Penman Monteith reference evapotranspiration method is the most accurate and being adopted worldwide and is recommended as a standardized method for ET₀ estimation under different climatic conditions.^1‒2 Hourly, daily, seasonal, and annual ET₀ are used for water resources planning, irrigation scheduling, rainfed agriculture, and the wetlands management. Evapotranspiration constitutes the main source of water losses at field, watershed and basin level as it is defined as the sum of the water loss by evaporation from various surfaces and transpiration from plant leaves. With the rising air temperatures on a global scale, an increase in ET₀ is expected as revealed by number of studies. Increasing trend in annual ET₀ was reported 70% of examined weather station in Iran with slopes varying from 2.30 to 11.28 mm/year.³ Similar trend in ET₀ was found in Serbia.⁴ Upward trend in ET₀ was reported at the rate of 1.4 mm per year during the 1957-2008 period in the Southern Italy.⁵ Significant increase in annual ET₀ was reported for the Southern Senegal for the period of 1950-2000 at the rate of 2.43,4.08,0.55 and 1.85 mm/year at Tambakounda, Kedougou, Kolda and Ziguinchor, respectively,⁶ In Southwest China, Feng et al.⁷ found a declining trend in annual ET₀ at a rate of 0.15mm/year during the 1954-2013 period. While ET₀ is showing an increasing trend in some parts of the world, it has been reported to decline in other parts. Song et al.⁸ reported significant decreasing trend in annual ET₀ across the North China Plain at the rate of 1.19 mm/year for the period of 1961–2006. Similarly, annual and seasonal ET₀ showed significant decreasing trend in North-East India [9]. Irmak et al [10] reported decrease in ET₀ with a rate of 0.3596 mm/year for the period of 1893 to 2008 in the Platte River Basin, central Nebraska-USA and they attributed this decrease to an increase in precipitation with a rate of about 0.90mm/year) that significantly reduces the available energy. Huo et al.¹¹ also found a decreasing trend in annual ET₀ at the rate of 3 mm/year in the North West China for the period of 1955–2008 due to the increase in precipitation as reported by Irmak et al.¹⁰ Zhang et al.¹² reported that annual ET₀ significantly declined at the rate of 1.29 mm/year from 1961 to 2012 in the Yellow River Basin, China. Xu et al.¹³ reported a significant decreasing trend in ET₀ mainly caused by a significant decrease in the net radiation and a significant decrease in wind speed in Changjiang (Yangtze River) watershed. Liu & Zhang¹⁴ indicated that in the driest Northwest region of China ET₀ decreased from 1960 to 1993 at the rate of 2.34 mm/year and increased thereafter up to 2010 at the rate of 4.80 mm/year. Xing et al.¹⁵ reported decadal variations in the ET₀. Gao et al.¹⁶ reported a decreasing trend in ET₀ at 46.7% of the weather station sites and an increasing trend in ET₀ at 53.3% of the sites for the period of 1960 to 2012 in the arid and semi-arid area of the West Liao River basin of China. They indicated that larger ET₀ was recorded in the plains area, which gradually decreased towards the surrounding areas and was smaller for the mountain area. Yin et al.¹⁷ also reported a decreasing trend in ET₀ in most regions across China during the period 1961–2008; however, an increasing trend in ET₀ was observed in the cold temperate humid region and the tropical humid region in China. Inter-annual variation in precipitation has a dramatic impact on the vulnerable rainfed agriculture in the developing countries such a Madagascar with increasing effects of drought in the dry years and flooding in the wet years, exposing the population to famine and other socioeconomic disasters^18,19 as well as challenges in water management in agricultural and natural resources settings. The seasonal and spatial distribution of the rainfall in Madagascar is affected by the county relief and associated landscape and topographical characteristics as the central massif along eastern Madagascar and Warm Western Indian Ocean sea surface temperature would result in enhanced moisture evaporation, latent heat transport and convection, leading to greater rainfall in the western Indian Ocean.¹⁹ About 70% of the Madagascar population is smallholder farmers whose livelihood fully depends on agriculture^20,21 with almost no water management and conservation practices due to numerous reasons, including lack of information and data availability on crop water use. Recently, USDA^22\ revealed that rice production in 2017/2018 seasons in Madagascar is estimated at 3.5 million metric tons, representing 11% reduction as compared to the 5-year average due to a severe drought in the central and northern regions of the country where nearly 80% of Madagascar’s rice is grown. Moreover, seasonal rainfall during the first half of the rice growing season (November 2016 through February 2017) was the lowest in the past 36 years. The drought in the central and northern parts of the country reduced land area cultivated with rice and significantly affected crop yields. In addition, cropland was flooded in the north and northeast regions when Cyclone Enawo, the largest cyclone (Category 4) stroke Madagascar since 2004, which made landfall in early March 2017.²² These statistics and their implications to the population and agricultural and natural resources are alarming and there is a pressing need to investigate water management at regional or country level to aid in mitigating the effect of climate change on food production and project some adaptation strategies to climate change to assure decent harvest yields and increase resilience across Madagascar. Understanding the spatial and temporal variability of ET₀ can aid in decision making regarding managing agricultural activities under irrigated and rainfed production systems. The present knowledge shows very limited data and information on the magnitude and location related ET₀ across Madagascar. The objective of this study was to evaluate the spatial and temporal variation in monthly average and annual total grass-reference evapotranspiration (ET₀) across Madagascar for water resources planning, management, and projections for environmental and agricultural projects.

Study area and meteorological data used

The study covers Madagascar, which is the largest African Island. Madagascar is roughly situated between 110 and 260 latitudes south and 420 and 500 longitudes east (Figure 1) and typically characterized as a low plateau and plains in the west, a plateau in central part, and coastal strip in the east. Madagascar is dominated by two seasons: hot and wet period from November to April and dry season from May to October. Climate datasets, including, daily average air temperature, relative humidity, solar radiation, and wind speed, that were collected at 22 weather stations across Madagascar for the period of 1980-2010 were used in the analyses. The long-term average climatic conditions are summarized in Table 1.

Estimation of reference evapotranspiration

Daily grass-reference ET (ET₀) was computed using the standardized ASCE form of the Penman-Monteith (ASCE-PM) equation.² The Penman-Monteith reference evapotranspiration equation with fixed stomatal resistance values for grass surface is:

$ETo=\frac{0.408\Delta \left(Rn-G\right)+\gamma Cn u2/\left(T+273\right)\left(es-ea\right)}{\Delta +\gamma \left(1+Cd u2\right)}$     (1)

where, ET₀ is reference evapotranspiration (mm/day), Δ is the slope of saturation vapor pressure versus air temperature curve (kPaoC^-1), Rn, net radiation at the crop surface (MJ m^-2 d^-1); G, soil heat flux density at the soil surface (MJ m^-2 d^-1); T, mean daily air temperature at 1.5-2.5m height (^oC); u2, mean daily wind speed at 2 m height (ms^-1); es, the saturation vapor pressure (kPa); ea, the actual vapor pressure (kPa); es-ea, saturation vapor pressure deficit (kPa); γ, psychrometric constant (kPa oC^-1); Cn, numerator constant that changes with reference surface and calculation time step (900°C mm s3 Mg⁻¹ d⁻¹ for 24 h time steps for the grass-reference surface), γ is the psychrometric constant (kPa C⁻¹); Cd, denominator constant that changes with reference surface and calculation time step (0.34sm⁻¹ for 24h time steps). All parameters necessary for computing ET₀ were computed according to the procedure developed in FAO-56 by Allen et al.¹

Spatial trend analysis

The predicted values of monthly and annual total ET₀ based on 30 years (from 1980 to 2010) of historical data were computed using the spline interpolation (Radial Basis Function) method which is an advanced, computationally intensive, geo-statistical estimation method.²³ Spline interpolation is a deterministic interpolation method that fits a mathematical function through input data to create a smooth surface. The functions allow users to decide between smooth curves or tight straight edges between measured points. It can generate the accurate surfaces from only few sampled points. In each station, the estimation of the fitted surface, and the mean square error was calculated. The mean squared error calculations are repeated for a range of values of a smoothing parameter and the value that minimizes the mean squared error was used to determine the optimum smoothing. This process is called minimizing the generalized cross‐validation (GCV) or “leave one out” technique. In spline interpolation, surface is achieved through weights (γ_j) and number of points (N). We used regularized spline with maximum of five and minimum of three neighboring stations. The weight parameter defines the weight of the third derivatives of the surface in the curvature minimization. A higher weight creates a smoother gridded surface. We used the interpolation procedures following Sharma et al.²⁴

$S \left(x, y\right)=T\left(x, y\right)+{\displaystyle {\sum }_{j=1}^N{\lambda }_{j}R\left(r_j\right)}$      (2)

where, T is the constant trend, r_j is the distance from point (x, y) to the jth point, R is a weighted function of the distance between the interpolated point and jth data point (j = 1,2,3 …N), N is the number of known point and λ_j is the unknown weight for the measured values at the jth location. For regularized spline interpolation, T and r is defined as:

$T \left(x, y\right)= a_1+a_{2}x+a_{3}y$     (3)

$R\left(r\right)=\frac{1}{2\pi }\left\{\frac{r^2}{4}\left[\ln \left(\frac{2}{2\pi }\right)+c-1\right]+ {\tau }^2\left[K_o\left(\frac{r}{\tau }\right)+c+\text{ln }\left(\frac{r}{2\pi }\right)\right]\right\}$    (4)

where, τ is a weight parameter of the third derivatives of the surface in the curvature minimization expression, r is the distance between the point and the sample, K_o is a modified Bessel function, and c is a constant (0.577). Coefficient a₁,a₂&a₃ are found by the solution of a system of linear equations. The weight parameter was optimized, indicating the smoothness of the interpolant.

Temporal trend analysis

The Mann–Kendall test^25,26 a non-parametric method for trend analysis, was used for the analysis of temporal trend in annual and monthly ET₀. The Mann-Kendall test is a statistical test widely used for the analysis of trends in climatologic and hydrologic time series,^27,28 which has two advantages:

1. It is a nonparametric test and does not require the data to be normally distributed and
2. The test has low sensitivity to abrupt breaks due to inhomogeneous time series.²⁹

According to this test, the null hypothesis (H₀) is that there is no trend (the data is independent and randomly ordered) and the null hypothesis is tested against the alternative hypothesis (H₁), which assumes that there is a trend. The Mann-Kendall test statistic S is given as follows:

$\text{S}={\displaystyle {\sum }_{\text{j}=1}^{\text{n}-1}{\displaystyle {\sum }_{\text{i}=\text{j}+1}^{\text{n}}\text{sign }\left(\text{xi}-\text{xj}\right)}}$     (5)

Where, xi and xj are the data values at time i and j, n is the length of the dataset and sign( ) is the sign function which can be computed as:

$\text{sign}\left(\text{xi}-\text{xj}\right)=\{\begin{array}{c}1 if \left(xi-xj\right)>0\\ 0 if \left(xi-xj\right)=0\\ -1 if \left(xi-xj\right)<0\end{array}$      (6)

For n > 10, the test statistic Z approximately follows a standard normal distribution:

&$\text{Z}=\{\begin{array}{c}\frac{\text{S}-1}{\sqrt{\text{Var}\left(\text{S}\right)}} if S>0\\ 0 if S=0\\ \frac{\text{S}+1}{\sqrt{\text{Var}\left(\text{S}\right)}} if S<0\end{array}$      (7)

In which Var(S) is the variance of statistic S.

A positive value of Z indicates that there is an increasing trend and a negative value indicates a decreasing trend. The null hypothesis, Ho, that there is no trend in the records is either accepted or rejected depending on whether the computed Z statistics is less than or more than the critical value of Z statistics obtained from the normal distribution table at the 5 % significance level. If $\left|Z\right|>Z\left(1-\alpha /2\right)$ , the null hypothesis of no autocorrelation and trend in time series is rejected, in which $Z\left(1-\alpha /2\right)$ is corresponding to the normal distribution with α being the significance level. If a time series has a trend, the magnitude of the trend can be denoted by the trend slope ß.³⁰

$\text{β}=\text{Median}\left(\frac{\text{xi}-\text{xj}}{\text{i}-\text{j}}\right)$ $\forall j<I$       (8)

Where, xi and xj are data values at the time ti and tj (i> j), respectively.

Linear regression analysis was applied for analyzing trends in the time series. The main statistical parameter drawn from regression analysis is the slope which indicates the mean temporal change in the variable under study. A positive slope indicates an increasing trend, while a negative slope indicates a decreasing trend.

Figure 1 Map of Madagascar showing the weather stations under consideration in this study.

Spatial variation of the long-term average annual and monthly reference evapotranspiration

Annual ET₀ varied from 1081 mm to 2239 mm and averaged 1620mm/year when all 22 weather stations were combined. ET₀ showed spatial patterns across Madagascar (Figure 2). The highest value range of the long-term average annual ET₀ (1891-2111 mm/year) was obtained in the western coast between Morombe and Bekily and at the northwestern coast that cover an area from Marovoay to Arrachart (Figure 2). The rest of the low plateau and western plains, covering Beroroha, Tolagnaro, Morndava, Maintiranom Besalampy Maevatanana up to the northeast, had high annual average ET₀, ranging from 1724 to 1890 mm/year. The western plateau and low plains were experiencing the highest annual evapotranspiration while the central plateau showing long-term average annual ET₀ that ranged from 1362 to 1723mm/year. The lowest and medium average annual ET₀ were obtained in the eastern coastal strip, covering Farafangana, Fianarantsoa, Ambbohibary, Mahanoro, Toamasina, Ambohirsilaozana, Maoantsetra, Amtsirabato, and Andapa, which ranged from 1140 to 1361 mm/year (Figure 2). The high annual ET₀ values at the western coast might be due to the dry climate along the western region with low relative humidity that results in increased evaporative demand of the local atmosphere. The extreme southern region had relatively high values of annual ET₀ and that region correspond to the arid semi-desertic landscape.³¹ The eastern coastal region with the lowest annual ET₀ is dominated by the equatorial climate with the highest precipitation. There were temporal and spatial variations in monthly average ET₀ from January to December across Madagascar (

). The highest January average ET₀ (> 5 mm/day) was observed in the southwest from Morombe to Bekily which is another region with hot and dry climatic characteristics in the Beroroha region while the lowest ET₀, ranging from 3.27 to 3.7 mm/day, which was observed in the central eastern region to the extreme northeast region and along the extreme southern coast. The other part of the country showed medium monthly average ET₀ during January. In February, the highest ET₀ coverage expanded and the lowest ET₀ area was reduced and was observed only in the extreme northeast, the central eastern region, covering an area from Ambohibary to Fianarantsoa and the surrounding region of Toamasina. Larger coverage of high ET₀ (>5mm/day) was observed in March. The south central regions and all of the western coastal region experienced the highest monthly average ET₀ and the central plateau region showed medium average daily ET₀ (Figure 3) and the lowest ET₀ was observed along the east coast region from Fianarantsoa up the extreme northeast. In the central plateau, ET₀ was within the range of 3.7-4.5 mm/day. Similar spatial variability in ET₀ in March was observed in April; however, the southern and all of the eastern regions showed the lowest daily average ET₀ range that continued expanding into May, June, and July. The medium ET₀ range was diminishing with the expansion of both high and low ET₀ areas. While ET₀ increased from the west coastal region towards inland, ET₀ had a decreasing trend towards the inland plateau. Reduced band in the central plateau showed daily average ET₀ range of 3.71-4.5 mm/day. In August, daily ET₀ higher than 5 mm/day was observed in the most part of the western and central regions of Madagascar and covered all of the western and central regions as well as the extreme southern regions in September. The minimum ET₀ range was observed in the eastern coastal regions in and around Mananjary, Toamasina, Andana, Amtrirabato and Moroantsetra (Figure 3). The increase in daily average ET₀ continued from the west towards east with more than 75% of the country having daily ET₀ of higher than 5mm/day. In December, the situation changed and there was a reduction in the daily average ET₀ from the eastern coast towards the inland regions. The lowest ET₀ range of 3.27-3.7 mm/day expanded to the regions between Fianaramtsoa and Ambohibary in the central eastern region and along the northeastern coastal region (Figure 3). Overall, the greatest coverage of the highest daily average ET₀ was during September and October while the greatest coverage of the lowest monthly ET₀ was in May and June. The results of this study are in agreement with those reported by Hobeichi et al.³² who indicated that the evapotranspiration had higher values in Sahel from September to November and in the high plateau of Madagascar from December to May.

Temporal trends of annual and monthly reference evapotranspiration

Except a non-significant decreasing trend in annual ET₀ at Tolagnaro and Bekily, increasing trend in annual ET₀ was observed at 91% of the weather station sites with increasing ET₀ rates that varied from 0.07 to 3.15 mm/year. However, the variation in annual ET₀ was significant only at 7 weather station sites (32% of the stations) [Amhbohibary, Amtsirabato, Toamasina, Mahanoro, Mananjary, Ivato, and Fianarantsoa (Table 2)]. The linear regression between annual ET₀ and year at these stations are presented in Figure 4. The greatest rate of change in annual ET₀ was observed at Mahanoro at the eastern humid coastal region. Across the country, there was an increase in annual ET₀ by a rate of 0.88 mm/year, which represents a total increase of 31 mm in ET₀ from 1980 to 2010. Similarly, increasing trends in ET₀ were reported for the Semiarid southern Senegal,⁶ in Burkina Faso,³³ in Togo,²⁹ in Benin.³⁴ The increasing trend in annual ET₀ can imply a projected increase for demand water resources for food production across Madagascar. The results of this study are in agreement with those reported by Tabari et al.³⁵ who observed overall increasing trend in annual ET₀ at higher rate varying from 8.36 to 31.68 mm/year with statistically significant rates at 30% of the stations under their study in Iran. Liu et al.³⁶ reported increase in annual ET₀ in the upper and middle Yellow River Basin in China due to significant increase in air temperature and decrease in the relative humidity, which result in increasing evaporative demand. In contrast, decreasing trend in annual ET₀ in reported in the arid land of Northwestern China by Zheng & Wang.³⁷ Wang et al.³⁸ indicated significant decrease in annual ET₀ at a rate of 0.68 mm/year across China. Li et al.³⁹ reported increase in annual ET₀ at 75% of the weather station sites they studied while across the Pearl River Basin (China), Zhang et al.⁴⁰ found a decreasing trend in ET₀. While total annual ET₀ might be very useful for annual and seasonal irrigation and water management planning, monthly ET₀ might have more relevance in practical applications when agricultural crop production and irrigation practices are considered in terms of seasonal and in season water management for sustainable crop production across Madagascar where rainfed production is dominant and subject to in season drought spell and rainfall anomalies under climate change. Non-significant decreasing trend in monthly ET₀ was observed at 68% of the weather station sites while February is dominated by non-significant increasing trend in monthly ET₀, except in Maevatenana where monthly ET₀ showed significant increasing trend (Table 3). Similar observations were made for March ET₀ values, which showed an increasing trend at Ambohirsiloazanam Toamasina, Mahhanoro, and Ivato. Eight station sites showed significant increasing trend in April ET₀ and non-significant trend was observed in May across all weather station sites. ET₀ significantly increased only at Besalampy in June and only at Andapa in July. Monthly ET₀ showed more significant variability in August (36% of stations), October (50% of stations), and December (41% of stations) (Table 3). At the weather station level, monthly ET₀ showed significant variability at Mahanoro (March, April, August, October-December), Ivato (March-April, August and October-November) while other stations were slightly affected by variability in monthly ET₀. At the country level, monthly ET₀ showed decreasing trend in January, May, and December at the rates of 0.12,0.001&0.099 mm/month, respectively. Increased trend in the monthly ET₀ was observed in other months. Variability in available surface energy, relative humidity, and wind speed may greatly affect the variability of the ET₀ as the vapor pressure deficit constitutes the main driving force for evaporation demand between the crop system and the surrounding atmosphere. Mcvicar et al.⁴¹ reported that reference evapotranspiration was affected by land topography in the Loess Plateau, China. In Mongolia ET₀ is influenced by the ocean, land cover, and topography.⁴² In the semiarid region of northeastern China, the peak daily evapotranspiration occurred in August for the degraded grassland and cropland land surface China⁴³ The results of this study are in agreement with those reported by Davis⁴⁴ who observed an increase in evapotranspiration across southern Africa, including Madagascar.

Potential effects of the variability of ET₀ on crop production across madagascar

The ET₀ patterns observed might be influenced by the topography and trade wind circulations as it significantly impacted rainfall and temperature patterns in the country.^45‒46 The eastern coastal region has a humid climate covered by rainforests. The middle plateau is sub-humid while the central and north west has a dry climate and the south and the southwest region has spiny desert and are in a sub-ari d zone Dry forest and some mangroves occur in the west and northwest.⁴⁷ Annual precipitation, mean annual temperature, precipitation seasonality, actual and reference evapotranspiration, topographic range, geological heterogeneity, vegetation-type heterogeneity, the human influence index, and average historical human population density were positively correlated to the climate.⁴⁸ Recent study revealed a decrease in precipitation in the eastern coastal area of Madagascar from 15ºS to 25ºS after early 1990s, with the southern area experiencing a particularly large reduction in rainfall bands.⁴⁹ Large variability of climate across Madagascar is due to its geographical position in the Indian Ocean, the large range of altitudes that create microclimates. The peak ET₀ does not coincide with rainy season (November-April) while rainfall occurs at the southern and eastern coasts during the winter (May - October).⁴⁶ This can have a large impact on seasonal rainfall across Madagascar and while the northeastern region receives 3500 mm of annual rainfall, the western region receives very low rainfall,⁴⁶ creating large discrepancies in rainfall among regions and impact land cover and food production. However the projections by Hewitson & Crane⁵⁰ as well as Tadross et al.⁴⁶ projected an increase in summer (January-April) rainfall, and a decrease in winter (July–September) along the southeast coast by 2050 and projected wetter seasons elsewhere. The increase in ET₀ vs. reduced rainfall in the western regions of Madagascar might create severe aridity and drought condition, which can be prejudicial to food production and livestock.^6,28 Under rainfed as well as irrigated crop production, the annual and monthly ET₀ might help deciding on the crop choice, decision on planting period and crop management practices to meet crop water demand via utilizing precipitation when the producers are aware of the season or annual precipitation forecast. Conservation agriculture, mulching practices and, minimum tillage practices should also help on managing the available soil water in relation to crop evapotranspiration. The combined effect of increase in ET₀ and decrease in precipitation can create significant challenges for food production via increasing aridity index.^6,28 Drought-tolerant varieties can be adopted to mitigate the impact of drought and maintain acceptable level of crop production. Some adaptation measures and strategies for increasing resilience and coping with risks as changing usual crops to less water demanding one or short season crops.^28,51,52 Also drip irrigation can provide additional benefits over sprinkler and surface irrigation systems since it considerably or significantly reduces surface water evaporation.⁵³ Even though the management of drip irrigation could be slightly more complicated than sprinkler irrigation, efforts need to be made by agriculture agencies to train farmers on this technology, which can aid in mitigating aforementioned potential challenges. The adaptation strategies to the increase in ET₀ can range from allocating more water demanding crop to less water demanding ones (new crops, new varieties), adopting agriculture conservation practices such as minimum tillage, cover crop incorporation into well documented cropping systems, efficient irrigation water and rainfall water management to cope with high water productivity.^28,52Rainwater harvesting is another approach to mitigate the drought during the drought spell periods of the cropping season. ^52,54‒56 Moreover, harvested water could serve in environmental, industrial and domestic use.^54,57 Rain water harvested and used in agriculture increased crop productivity⁵⁸ by 30-50% in South Africa,⁵⁹ in Kenya,^60‒61 and in Malawi.⁶² This technic had improved water management under semiarid climate and rainfed and irrigated agriculture elsewhere.^63‒65

Figure 2 Spatial variation in long-term average annual ET₀ across Madagascar.

Spatial and temporal variability in monthly average and annual total reference evapotranspiration (ET₀) estimated using Penman-Monteith method from daily climatic variables across Madagascar was analyzed for the period of 1980-2010. The required climatic variables such as minimum and maximum air temperature, minimum and maximum relative humidity, solar radiation and wind speed were collected from 22 weather stations for the study period. The Mann-Kendall test and the Sen’s method were used for temporal trend analysis in monthly average and annual total ET₀ while the spline interpolation method was used to map the spatial variation in annual and monthly average ET₀ across Madagascar. The results showed three main patterns in spatial distribution of ET₀ with south to north direction: the western semiarid regions with the highest ET₀ value, the humid eastern regions with the lowest ET₀ and the central plateau with the medium ET₀ values. The spatial distribution of ET₀ seemed to be impacted by the landscape and topography. The highest daily ET₀ occurs during September and October with the high evapotranspiration progressing from the west coastal regions towards the plateau. There was a wide variation in the annual total ET₀ over the three topographic landscapes. Annual total ET₀ varied from 1081 at Andapa in the northeast region to 2239mm at Antsohihy in the northwestern coastal region. There was a significant increasing trend in annual total ET₀ at 32% of the weather station sites and there was a wide variation in the monthly average daily ET₀ among the weather station sites. The results of this study could serve as a guideline and could be used by agricultural and environmental project managers, hydrologists, agronomists, irrigation professionals, students, and university researchers to improve water management for better water productivity under the Madagascar agricultural, environmental conditions. The upward trend in ET₀ should be considered with regards to some water conservations practices, cropping system, crop and varietal choice, planting time and plant density for resilience and better water management for enhancing the sustainability of irrigated and rainfed agriculture in Madagascar.

None.

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