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Environmental Hydrology

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Browsing Environmental Hydrology by Author "Bulcock, Hartley Hugh."

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    An assessment of canopy and litter interception in commercial and indigenous forests in the KwaZulu-Natal Midlands, South Africa.
    (2011) Bulcock, Hartley Hugh.; Jewitt, Graham Paul Wyndham.
    Understanding of the hydrological cycle and processes such as interception span as far back as the times of the Renaissance, when Leonardo da Vinci (1452-1519) first described it. However, there remains a gap in the knowledge of both canopy and litter interception in South African forest hydrology. Interception is typically considered to constitute only a small portion of total evaporation and in some models is disregarded or merely lumped with total evaporation, and not considered as a separate process. Interception is a threshold process, as a certain amount of water is required before successive processes such as infiltration and runoff can take place. Therefore an error introduced in modelling interception, especially disregarding it, will automatically introduce errors in the calibration of subsequent models/processes. In this study, field experiments to assess these two poorly understood hydrological processes, viz. canopy and litter interception were established for the three main commercial forestry genera in South Africa, namely, Pinus, Acacia and Eucalyptus as well as an indigenous Podocarpus henkelii stand, thus, accounting for interception of “broad leaf”, “compound leaf” and “needle leaf” trees in order to provide further insight into these processes. The study took place at two locations in the KwaZulu-Natal Midlands over a period of three years. The first site is the Two Streams catchment, located in the Seven Oaks area, about 70km north-east of Pietermaritzburg where the study on the commercial plantation species took place. The second site was the Podocarpus henkelii stand in Karkloof near Howick, 40km north of Pietermaritzburg. From the field data collected (cf. Chapter 2) it was observed that canopy storage capacity, an important parameter governing interception, was not constant and changed with rainfall intensity, with lower intensity events resulting in a higher storage capacity. Building on these findings, a physically based canopy interception model that is based on the well known Gash model was developed, and is referred to herein as the “variable storage Gash model”. While canopy interception is dependent on many factors including the storage capacity, potential evaporation, rainfall intensity and rainfall duration, the litter interception is largely dependent on the storage capacity due to the evaporative drivers under the canopy such as radiation, temperature and wind speed being moderated by the above canopy. From these finding, a litter interception model based on idealised drying curves from litter samples collected at the study sites was also developed (cf. Chapter 3). From the field data, it was found that the canopy interception for Eucalyptus grandis, Acacia mearnsii and Pinus patula was 14.9, 27.7 and 21.4% of mean annual precipitation (MAP) respectively. The simulated canopy interception using the “variable storage Gash model” was 16.9%, 26.6% and 23.3% for E. grandis, A. mearnsii and P. patula respectively. The litter interception measured for E. grandis, A. mearnsii and P. patula was found to be 8.5, 6.6 and 12.1% of MAP respectively, while the simulated litter interception using the idealised drying curve model corresponded well with the measured results and were 10.1%, 5.4% and 13.4% for E. grandis, A. mearnsii and P. patula respectively. The idealised drying curve model is site and species specific and is therefore not transferable to other locations. Conversely, the “variable storage Gash model” is transferable as it is not site and species specific, and relies on readily measureable and available information. Building on field studies, this was then used to simulate the canopy interception for Eucalyptus, Acacia mearnsii and Pinus in South Africa (including Lesotho and Swaziland) for all quinary catchments in which commercial forestry could be grown, i.e. a mean annual precipitation of greater than 600 mm.year-1 (cf. Chapter 4). It was found that, depending on the location and genus, canopy interception loss can be as high as 100 to 300 mm per year or approximately 10% to 40% of MAP. This relates to a mean interception loss of between 1.0 and 3.0 mm per rainday, highlighting the spatial variability of canopy interception. To further investigate the spatial variability of canopy interception, at various spatial scales, remote sensing technology was applied to estimate leaf area index (LAI) for use in modelling/estimating canopy storage capacity and canopy interception (cf. Chapter 6). The NDVI, SAVI and Vogelmann 1 vegetation indices were used in the estimation of the LAI. It was found the Vogelmann 1 index produced the best results. As models to estimate canopy interception typically require LAI and storage capacity, it was calculated that the ability to estimate these parameters over large areas is valuable for water resources managers and planners. An often neglected consideration of canopy and litter interception is its role in determining the water use efficiency (WUE) of a forest stand (cf. Chapter 5). This component of the study was undertaken in an indigenous Podocarpus henkelii stand as well as a commercial Pinus patula stand in Karkloof in the KwaZulu-Natal Midlands. The sap flow (transpiration) was measured in both the P. henkelii and P. patula stands using the using the Heat Pulse Velocity (HPV) technique in order to determine the productive green water use. The canopy and litter interception was measured in the P. henkelii site, but was modelled in the P. patula site using the “variable storage Gash” and idealised drying curve models, in order to estimate the non-productive green water use. It was found that the canopy and litter interception for P. henkelii was 29.8% and 6.2% respectively, while the modelled canopy and litter interception for P. patula was 22.1% and 10.7% respectively. If only the productive green water use (transpiration) is considered, then the water use efficiency of P. henkelii and P. patula was found to be 7.14 g.mm-1 and 25.21 g.mm-1 respectively. However, from a water management perspective it is important to consider the total green water use efficiency (transpiration + interception), which reveals a significantly lower water use efficiency of 3.8 g.mm-1 and 18.8 g.mm-1 for P. henkelii and P. patula respectively. To extend the study to a globally relavent issue, the possible impact of climate change on canopy interception was investigated, as forests growth is critically linked to climate (cf. Chapter 7). To achieve this, the CABALA model was used to model LAI and transpiration of Eucalyptus grandis and Pinus patula under 9 different climate change scenarios, including changes in temperature, rainfall and atmospheric CO2. The simulated LAI values from the CABALA model for all 9 climate scenarios were then used to simulate canopy interception using the “variable storage Gash model”. Results show that LAI may increase by as much as 24% and transpiration may decrease by as much as 13%, depending on the scenario, location and tree species. However, it was found that canopy interception does not change greatly, leading to the conclusion that under climate change conditions, canopy interception may not become a more dominant component of the hydrological cycle than it currently is as the changes under climate change are likely to be less than the natural variability from year to year. However, canopy interception remains an important consideration for water resources management and planning both currently and in the future.
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    The effect of spatial resolution in remote sensing estimates of total evaporation in the uMgeni catchment.
    (2014) Shoko, Cletah.; Clark, David John.; Bulcock, Hartley Hugh.; Mengistu, Michael Ghebrekidan.
    The estimation of total evaporation plays a vital role in water resources monitoring and management, especially in water-limited environments. In South Africa, the increasing water demand, due to population growth and economic development, threatens the long-term water supply. This, therefore, underscores the need to account for water by different consumers, for well-informed management, allocation and future planning. Currently, there are different methods (i.e. ground-based and remote sensing-based methods), which have been developed and implemented to quantify total evaporation at different spatial and temporal scales. However, previous studies have shown that ground-based methods are inadequate for understanding the spatial variations of total evaporation, within a heterogeneous landscape; they only represent a small area, when compared to remotely sensed methods. The advent of remote sensing therefore provides an invaluable opportunity for the spatial characterization of total evaporation at different spatial scales. This study is primarily aimed at estimating variations of total evaporation across a heterogeneous catchment in KwaZulu-Natal, South Africa, using remote sensing data. The first part provides an overview of total evaporation, its importance within the water balance and consequently in the management of water resources. It also covers various methods developed to estimate total evaporation, highlighting their applications, limitations, and finally, the need for further research. Secondly, the study determines the effect of sensor spatial resolution in estimating variations of total evaporation within a heterogeneous uMngeni Catchment. Total evaporation estimates were derived, using multispectral 30 m Landsat 8 and 1000 m MODIS, based on the Surface Energy Balance (SEBS) model. The results have shown that different sensors, with varying spatial resolutions, have different abilities in representing variations of total evaporation at catchment scale. It was found that Landsat-based estimates were significantly different (p < 0.05) from MODIS. The study finally estimates spatial variations of total evaporation from Landsat 8 and MODIS datasets for the uMngeni Catchment. It was found that the Landsat 8 dataset has greater potential for the detection of spatial variations of total evaporation, when compared to the MODIS dataset. For instance, MODIS-based daily total evaporation estimates did not show any significant difference across different land cover types (One way ANOVA; F1.924 = 1.412, p= 0.186), when compared to the 30 m Landsat 8, which yielded significantly different estimates between different land cover types (One way ANOVA; F1.993= 5.185, p < 0.001). The validation results further indicate that Landsat-based estimates were more comparable to ground-based eddy covariance measurements (R2 = 0.72, with a RMSE of 32.34 mm per month (30.30% of the mean)). In contrast, MODIS performed poorly (R2 = 0.44), with a RMSE of 93.63 mm per month (87.74% of the mean). In addition, land cover-based estimates have shown that, not only does the land cover type have an effect on total evaporation, but also the land cover characteristics, such as areal extent and patchiness. Overall, findings from this study underscore the importance of the sensor type, especially spatial resolution, and land cover type characteristics, such as areal extent and patchiness, in accurately and reliably estimating total evaporation at a catchment scale. It is also evident from the study that the spatial and temporal variations in SEBS inputs (e.g., LAI, NDVI and FVC) and energy fluxes (e.g., Rn) calculated by SEBS for the two sensors can affect the spatial and temporal variations in total evaporation estimates. For instance, spatial variations in total evaporation reflected similar spatial variations in Rn. Areas with high NDVI, FVC and LAI (which denotes dense vegetation cover) tend to have higher total evaporation estimates, compared to areas with lower vegetation cover. In addition, the MODIS sensor at 1000 m spatial resolution showed lower estimates of SEBS inputs with less variability across the catchment. This resulted in lower total evaporation estimates, with less variability, compared to the 30 m Landsat 8. In addition, with regard to inputs derived from remote sensing, it was found that the spatial variations in total evaporation are not determined by individual variables (e.g., LST), but are influenced by a combination of many biophysical variables, such as LAI, FVC and NDVI. These findings lay a foundation for a better approach to estimate total evaporation using remote sensing for use in the management and allocation of water.