Accurate precipitation, temperature, and evapotranspiration maps at landscape scales are needed for many applications in agriculture, climate forecasting, irrigation schemes, and water provisioning. These climatic maps are important in ecological studies because precipitation, temperature, and evapotranspiration strongly influence the transfer of moisture between the surface and the atmosphere at local and regional levels. Precipitation is the main source of water in the terrestrial water cycle, while evapotranspiration returns about 65% of precipitation into the atmosphere, depending on the vegetation cover [1
]. The sun as a black body emits energy at 5530 °C, averaged over the year, and of all surfaces of the earth this amounts to 342 W m−2
. Some amount of the solar energy is used for all plant physiological processes and sets up large-scale climatic conditions and patterns.
Precipitation and temperature are mostly measured at meteorological stations. Evapotranspiration is commonly assessed indirectly by either (i) considering the energy balance at land surface [2
]; (ii) by measuring eddy covariance at some distance above the land surface [3
]; (iii) by a water balance approach for watersheds when precipitation, change in storage, and stream discharge are known [2
]; or (iv) by estimating reference evapotranspiration (ETo
) from a hypothetical surface of green grass cover of uniform height of 0.12 m adequately watered with surface resistance of 70 s m−1
and albedo of 0.23 [4
Precipitation and temperature patterns on the earth’s surface are determined by the combination of geographic factors such as altitude, latitude, aspect and exposure, atmospheric circulations, characteristics of ocean currents, and effects of continentality [5
]. Mountain climates are controlled by the same factors, with their hydrological and ecological systems being sensitive to climate variability [6
], confounded by local variety of combinations created by orientation, spacing, and steepness of slopes, along with the presence of complex patterns of snow patches, shade, vegetation, and soil. By acting as a barrier, mountains themselves affect local and regional climates and modify passing storms. When mountain ranges are oriented perpendicular to the prevailing winds, forced ascent of air is usually most effective; the more exposed the slope, the more rapidly air will be forced to rise and cool, which results in precipitation. Great variations in precipitation and temperature occur over relatively short distances; one slope may be excessively wet with more precipitation at higher elevations, while another is relatively dry [7
Different interpolation or extrapolation methods can be conceived to map climate variables in a spatially explicit way. Over the last few decades, geostatistic interpolation methods [8
] became commonly used and recognized to have several advantages [9
] over non-geostatistic methods such as Thiessen polygon, inverse distance weighting, or isohyetal methods.
Many research studies have used geostatistic techniques which consider topographic variations in mapping climatological variables on mountains terrain. Studies exemplifying these approaches are [10
] for precipitation and [11
] for evapotranspiration. However, most of these interpolation techniques do not take into account the effect of relief and other geographic factors. For that reason, interpolation techniques should take into account the potential effects of topographical factors on the spatial distribution of climatic variables. Such interpolation techniques (universal techniques) use geographic information systems (GIS) and digital elevation models (DEMs) for spatial analyses [12
Several researchers have demonstrated the potential of universal techniques on mapping precipitation [13
], temperature [14
], and evapotranspiration [15
]. In these regression-based techniques, geographic and topographic factors that control the spatial distribution of climate are used as independent variables [16
], and dependence models are created between the climate data and independent variables. The main advantage of this technique is that maps are compiled from weather stations and auxiliary information that describe geographic and topographic variables which improves the accuracy and spatial detail of the maps. A goal of the present study is to apply universal interpolation methods and GIS technologies in mapping precipitation, temperature, and evapotranspiration of the southern Mkomazi River Basin, an East-African mountainous region including parts of the Pare and Usambara mountains. The region is typical for remote East-African rural areas, where most of the population settles on the slopes of the mountains and in the vicinity of the river, whereas the semi-arid plains are scarcely populated.
The climate of the southern Mkomazi River Basin is characterized by two distinct rainfall seasons. Long-rains in March–May are commonly abundant [17
], whereas short-rains in October–December reveal more interannual variability [18
]. This bimodal pattern is largely related to the seasonal migration of the inter-tropical convergence zone (ITCZ) across the equator [19
There are two essential phenomena influencing the interannual rainfall variability in this region: (i) the El Niño-Southern Oscillation (ENSO) [20
]; and (ii) the Indian Ocean dipole (IOD) [21
] or Indian Ocean zonal model (IOZM) [22
]. Both extreme weather events can bring large floods [23
] or strong droughts [24
], which severely affect the livelihoods of the people.
Therefore, better knowledge of the spatial distribution of precipitation, temperature, and evapotranspiration is required, particularly in areas with strong variations in topography and elevation [13
]. To address this, the present study uses regression-based techniques and GIS knowledge to construct monthly maps of precipitation, temperature, and evapotranspiration, accounting for major topographic influences, particularly elevation, surface orientation, and obstruction by surrounding topographic features.
Unfortunately, the number of meteorological stations where precipitation, air temperature, wind speed, humidity, and solar radiation are observed is limited in many parts of the globe, particularly in developing countries. Many sub-Saharan countries continue to experience difficulties with the availability of long-term climatic data, and available information is sparse with numerous prolonged gaps both in time and space. These limitations in the quantity and quality of site observations impose substantial constraints on studies of the climatic variability, particularly in the southern Mkomazi River Basin in Tanzania. Therefore, our study involved additional efforts of data correction and dealing with missing data.
In agronomic studies, calculations of reference evapotranspiration (ETo
) following the Hargreaves and Samani (HS) equation are generally performed using values of extraterrestrial radiation (Ra
) calculated assuming a planar surface and solely as a function of latitude, according to the method described by Reference [4
], which does not take relief into account. This study reveals the potential of regression-based models, digital elevation models (DEM), and geographic information systems (GIS) modelling techniques to map ETo
, precipitation and temperature—the climate variables that are important in many environmental and water resources studies [1
]. We have modelled Ra
using DEM and ArcGIS. The usefulness of this approach might not be for flat terrain, in which relief does not significantly affect Ra
. However, in complex terrain, for high-resolution ETo
maps used for ecological and water resources management, spatial variations in relief are commonly very important and significantly affect the values of Ra
estimates because radiation flux are especially dependent on the geometry of terrain [15
], and this has a significant effect on local ETo
The dataset was collected from many institutions, and cleaned for inhomogeneous data. Obvious outliers were removed by means of traditional methods based on the mean and standard deviation and a predefined limit. The largest risk of making a type I error (i.e., erroneously removing good data) was in the subjective decision to remove too high rainfall in dry months for dry years.
There was an overall relationship between elevation and both rainfall and temperature, as expected. The results of the precipitation models showed that, for the long-rains season in March–May, R2
values for the period March–April increased for leeward and windward, and were great in April for both sides, in which rainfall peaked in April. In contrast, for short-rains season in October–December, in which rainfall peaked in December, R2
values decreased and were very low in December. From the constructed rainfall maps and the analysis of temporal rainfall variability, precipitation patterns regimes agreed well with results from equatorial East African studies showing that the rainfall is abundant in most areas for the long-rains season [17
], while short-rains season reveal more interannual variability [18
]. High and low R2
values corresponded with the temporal patterns in rainfall variability.
Rainfall was modelled in a linear form assuming that condensed water falls immediately to the ground and no influence of horizontal movements was included, which is somewhat violating mountain wave theory for modelling orographic precipitation [36
]. It does not include the physical elements such as airflow dynamics, advection and fallout, condensed water convection, and downslope evaporation [46
]. However, it is a usable assumption to obtain a relationship between elevation and precipitation in most situations, an example showing such an assumption is shown in the study by References [13
]. In addition to general usable assumptions on climatic model development, one has to increase the complexity and performance of such models upon availability of other parameters.
An attempt to improve R2
values for rainfall was to exclude stations at the ridge (station 9 in this case) for analysis of elevation–rainfall relationship and seasonal trends. The results showed significant improvement of R2
values for windward-side, particularly for the short-rains season in comparison to the long-rains season. In contrast, there was no significant improvement of R2
values for the leeward-side, particularly in December. Also, the elevation-rainfall relationship for the long-rains season for both windward and leeward models showed no significant R2
differences with or without station 9. Low R2
values indirectly indicate climate mechanisms for rainfall distribution in particular for the period November to January. The rainfall distribution form during this period is yet unknown. However, the climate of the Mkomazi River Basin is largely influenced by equatorial East African climate systems. Slingo and others [19
] noted that over the Western Indian Ocean the inter-tropical convergence zone (ITCZ) makes its greatest North–South excursions, dominated by the Asian monsoon with its reversals of the wind from northeasterly in December–February to southwesterly in June–August periods. As such, during the transition periods in March–May as the northeast monsoon relaxes, the ITCZ from its southernmost position over the southern Western Indian Ocean progresses northwards bringing equatorial East Africa long-rains season, and as the Asian summer monsoon retreats in September–November, the ITCZ progresses South again bringing equatorial East Africa short-rains season. Therefore, we considered local prevailing winds, especially their trend and strength, associated with the ITCZ excursions, also to be an important variable for rainfall patterns.
We used the HS method to map ETo
for the reason that, to construct reliable maps, it is necessary to use a dense dataset of the climatic variables. Although the most accurate method is the one that is physically based on the Penman–Monteith equation, it is impossible to produce reliable ETo
maps using the Penman–Monteith equation in the area of data scarcity because of a relatively high data demand of such an equation. Apart from our specific study area, this may be the case for many Africa regions. In addition, numerous researchers (e.g., [41
]) have demonstrated that, for ETo
estimates for periods longer than one week, the HS method provides similar results to those obtained using the Penman–Monteith equation.
As a caveat to our study, we note that the lack of climate stations and the sometimes discontinuous maintenance of the existing stations pose a serious obstacle to the derivation of climate maps from data. A small number of stations and the unavailability of additional climate parameters besides rainfall decrease the reliability of the regression functions that relate rainfall and temperature to elevation. Climate maps derived from sparse data must therefore considered with care. This problem will likely persist in the near future in many parts of sub-Saharan Africa.
Our study has demonstrated the potential use of linear-regression-based, digital elevation models (DEM), and geographic information systems (GIS) techniques in modelling and construction of reliable climate maps. These maps were made on a monthly basis for rainfall, temperatures, and evapotranspiration.
Both rainfall and temperature showed a linear correlation form with elevation. Temperature linear correlation form with elevation was stronger than that showed by rainfall with elevation. These temperature stations were located on two different altitudes (low and high), which supported the linear form strongly. For rainfall, the linear form was more pronounced for the long-rains seasons than for the short-rains season. For the long-rains season, the rainfall-elevation relationships showed no significant changes in R2 values, both for the leeward- and windward-side, when the station at the ridge was not included for the analysis of rainfall–elevation relationship. In contrast, for the short-rains season, R2 values improved substantially when station at the ridge was not included into rainfall models. Therefore, rainfall distribution for the southern Mkomazi River Basin particularly for the short-rains season deserves further attention, when other variables affecting rainfall distribution (e.g., wind speed and direction) become available.
The constructed maps for reference evapotranspiration (ETo), rainfall, and temperatures can be useful for environment and water resources studies in the region as climate variability affects river flows, which has in turn implications on livelihoods of the people which depend directly or indirectly on rain-fed agriculture.