Measurement and modelling of evaporation from a coastal wetland in Maputaland, South Africa

Abstract. The surface renewal (SR) method was used to determine the long-term (12 months) total evaporation (ET) from the Mfabeni Mire with calibration using eddy covariance during two window periods of approximately one week each. The SR method was found to be inexpensive, reliable and with low power requirements for unattended operation. Despite maximum ET rates of up to 6.0 mm day−1, the average summer (October to March) ET was lower (3.2 mm day−1) due to early morning cloud cover that persisted until nearly midday at times. This reduced the daily available energy, and the ET was lower than expected despite the available water and high average wind speeds. In winter (May to September), there was less cloud cover but the average ET was only 1.8 mm day−1 due to plant senescence. In general ET was suppressed by the inflow of humid air (low vapour pressure deficit) and the comparatively low leaf area index of the wetland vegetation. The accumulated ET over 12 months was 900 mm. Daily ET estimates were compared to the Priestley-Taylor model results and a calibration α = 1.0 (R2 = 0.96) was obtained for the site. A monthly crop factor (Kc) was determined for the standardised FAO-56 Penman-Monteith. However, Kc was variable in some months and should be used with caution for daily ET modelling. These results represent not only some of the first long-term measurements of ET from a wetland in southern Africa, but also one of the few studies of actual ET in a subtropical peatland in the Southern Hemisphere. The study provides wetland ecologists and hydrologists with guidelines for the use of two internationally applied models for the estimation of wetland ET within a coastal, subtropical environment and shows that wetlands are not necessarily high water users.

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Measurement and modelling of evaporation from a coastal wetland in Maputaland, South Africa

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-9-1741-2012 ◽

2012 ◽

Vol 9 (2) ◽

pp. 1741-1782 ◽

Cited By ~ 2

Author(s):

A. D. Clulow ◽

C. S. Everson ◽

M. G. Mengistu ◽

C. Jarmain ◽

G. P. W. Jewitt ◽

...

Keyword(s):

Water Balance ◽

Animal Species ◽

Coastal Wetland ◽

Surface Renewal ◽

Site Specific ◽

Combination Of Methods ◽

A Site ◽

Sr Method ◽

Subtropical Environment

Abstract. The contribution of freshwater supply from the Mfabeni Mire to Lake St. Lucia during dry periods is important to the survival of certain plant and animal species in the iSimangaliso Wetland Park. This freshwater supply is mainly dependent on the variability of the major components of the water balance, namely rainfall and total evaporation (ET). Attempts to quantify the water balance have been limited through uncertainties in quantifying ET from the Mfabeni Mire. Despite advances in evaporation measurement and modelling from wetlands, there still exists some doubt as to which methods are best suited to characterise wetland ET with most authors suggesting a combination of methods. In this study, the surface renewal (SR) method was successfully used to determine the long-term ET (12 months) from the Mfabeni Mire with calibration using eddy covariance during two window periods of approximately one week each. The SR method was found to be inexpensive, reliable and with low power requirements for unattended operation. The annual ET was lower (900 mm yr−1) than expected, due to cloud cover in summer and low atmospheric demand throughout the year, despite the available water and high windspeeds. Daily ET estimates were compared to the Priestley-Taylor results and a site specific calibration α = 1.0 was obtained for the site. The Priestley-Taylor results agreed well with the actual ET from the surface renewal technique (R2 = 0.96) throughout the 12 month period. A monthly crop factor (Kc) was determined for the standardised FAO-56 Penman-Monteith. However, Kc was variable in some months and should be used with caution for daily ET modelling. These results represent not only some of the first long-term measurements of ET from a wetland in Southern Africa, but also one of the few studies of actual ET in a subtropical peatland in the Southern Hemisphere. The study provides wetland ecologists and hydrologists with guidelines for the use of two internationally applied models for the estimation of wetland ET within a coastal, subtropical environment.

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Consistent Long-Term Monthly Coastal Wetland Vegetation Monitoring Using a Virtual Satellite Constellation

Remote Sensing ◽

10.3390/rs13030438 ◽

2021 ◽

Vol 13 (3) ◽

pp. 438

Author(s):

Subrina Tahsin ◽

Stephen C. Medeiros ◽

Arvind Singh

Keyword(s):

Vegetation Index ◽

Coastal Wetland ◽

Thermal Emission ◽

Mean Squared Error ◽

Wetland Vegetation ◽

Coefficient Of Determination ◽

Landsat 8 ◽

Vegetation Monitoring ◽

Time Space

Long-term monthly coastal wetland vegetation monitoring is the key to quantifying the effects of natural and anthropogenic events, such as severe storms, as well as assessing restoration efforts. Remote sensing data products such as Normalized Difference Vegetation Index (NDVI), alongside emerging data analysis techniques, have enabled broader investigations into their dynamics at monthly to decadal time scales. However, NDVI data suffer from cloud contamination making periods within the time series sparse and often unusable during meteorologically active seasons. This paper proposes a virtual constellation for NDVI consisting of the red and near-infrared bands of Landsat 8 Operational Land Imager, Sentinel-2A Multi-Spectral Instrument, and Advanced Spaceborne Thermal Emission and Reflection Radiometer. The virtual constellation uses time-space-spectrum relationships from 2014 to 2018 and a random forest to produce synthetic NDVI imagery rectified to Landsat 8 format. Over the sample coverage area near Apalachicola, Florida, USA, the synthetic NDVI showed good visual coherence with observed Landsat 8 NDVI. Comparisons between the synthetic and observed NDVI showed Root Mean Squared Error and Coefficient of Determination (R2) values of 0.0020 sr−1 and 0.88, respectively. The results suggest that the virtual constellation was able to mitigate NDVI data loss due to clouds and may have the potential to do the same for other data. The ability to participate in a virtual constellation for a useful end product such as NDVI adds value to existing satellite missions and provides economic justification for future projects.

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The Impacts of the Geographic Distribution of Manufacturing Plants on Groundwater Withdrawal in China

Water ◽

10.3390/w13091158 ◽

2021 ◽

Vol 13 (9) ◽

pp. 1158

Author(s):

Yanting Zheng ◽

Huidan Yang ◽

Jinyuan Huang ◽

Linjuan Wang ◽

Aifeng Lv

Keyword(s):

Manufacturing Industry ◽

Tap Water ◽

Demand Management ◽

Spatial Regression ◽

High Water ◽

Groundwater Withdrawal ◽

Ecological Environment ◽

Manufacturing Plants ◽

Water Users ◽

Water Transmission

The overexploitation of groundwater in China has raised concern, as it has caused a series of environmental and ecological problems. However, far too little attention has been paid to the relationship between groundwater use and the spatial distribution of water users, especially that of manufacturing factories. In this study, a factory scatter index (FSI) was constructed to represent the spatial dispersion degree of manufacturing factories in China. It was found that counties and border areas between neighboring provinces registered the highest FSI increases. Further non-spatial and spatial regression models using 205 provincial-level secondary river basins in China from 2016 showed that the scattered distribution of manufacturing plants played a key role in groundwater withdrawal in China, especially in areas with a fragile ecological environment. The scattered distribution of manufacturing plants raises the cost of tap water transmission, makes monitoring and supervision more difficult, and increases the possibility of surface water pollution, thereby intensifying groundwater withdrawal. A reasonable spatial adjustment of manufacturing industry through planning and management can reduce groundwater withdrawal and realize the protection of groundwater. Our study may provide a basis for water-demand management through spatial adjustment in areas with high water scarcity and a fragile ecological environment.

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Cloud Cover and Aurora Contamination at Dome A in 2017 from KLCAM

Monthly Notices of the Royal Astronomical Society ◽

10.1093/mnras/staa3824 ◽

2020 ◽

Author(s):

Xu Yang ◽

Zhaohui Shang ◽

Keliang Hu ◽

Yi Hu ◽

Bin Ma ◽

...

Keyword(s):

Cloud Cover ◽

Emission Lines ◽

Austral Winter ◽

Site Testing ◽

Dome A ◽

Astronomical Observations ◽

Auroral Emission ◽

Long Term Monitoring ◽

Term Monitoring

Abstract Dome A in Antarctica has many characteristics that make it an excellent site for astronomical observations, from the optical to the terahertz. Quantitative site testing is still needed to confirm the site’s properties. In this paper, we present a statistical analysis of cloud cover and aurora contamination from the Kunlun Cloud and Aurora Monitor (KLCAM). KLCAM is an automatic, unattended all-sky camera aiming for long-term monitoring of the usable observing time and optical sky background at Dome A. It was installed at Dome A in January 2017, worked through the austral winter, and collected over 47,000 images over 490 days. A semi-quantitative visual data analysis of cloud cover and auroral contamination was carried out by five individuals. The analysis shows that the night sky was free of clouds for 83 per cent of the time, which ranks Dome A highly in a comparison with other observatory sites. Although aurorae were detected somewhere on an image for nearly 45 per cent of the time, the chance of a point on the sky being affected by an aurora is small. The strongest auroral emission lines can be filtered out with customized filters.

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Development and Verification of the Available Number of Water Intake Days in Ungauged Local Water Source Using the SWAT Model and Flow Recession Curves

Water ◽

10.3390/w13111511 ◽

2021 ◽

Vol 13 (11) ◽

pp. 1511

Author(s):

Jung-Ryel Choi ◽

Il-Moon Chung ◽

Se-Jin Jeung ◽

Kyung-Su Choo ◽

Cheong-Hyeon Oh ◽

...

Keyword(s):

Water Supply ◽

Water Intake ◽

Water Resource Management ◽

Assessment Tool ◽

Swat Model ◽

High Water ◽

Coefficient Of Determination ◽

Severe Drought ◽

Regional Government

Climate change significantly affects water supply availability due to changes in the magnitude and seasonality of runoff and severe drought events. In the case of Korea, despite high water supply ratio, more populations have continued to suffer from restricted regional water supplies. Though Korea enacted the Long-Term Comprehensive Water Resources Plan, a field survey revealed that the regional government organizations limitedly utilized their drought-related data. These limitations present a need for a system that provides a more intuitive drought review, enabling a more prompt response. Thus, this study presents a rating curve for the available number of water intake days per flow, and reviews and calibrates the Soil and Water Assessment Tool (SWAT) model mediators, and found that the coefficient of determination, Nash–Sutcliffe efficiency (NSE), and percent bias (PBIAS) from 2007 to 2011 were at 0.92, 0.84, and 7.2%, respectively, which were “very good” levels. The flow recession curve was proposed after calculating the daily long-term flow and extracted the flow recession trends during days without precipitation. In addition, the SWAT model’s flow data enables the quantitative evaluations of the number of available water intake days without precipitation because of the high hit rate when comparing the available number of water intake days with the limited water supply period near the study watershed. Thus, this study can improve drought response and water resource management plans.

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Long-Term Changes in Floristic Diversity as an Effect of Transforming the Lake into a Retention Reservoir

Sustainability ◽

10.3390/su13147642 ◽

2021 ◽

Vol 13 (14) ◽

pp. 7642

Author(s):

Joanna Sender ◽

Danuta Urban ◽

Monika Różańska-Boczula ◽

Antoni Grzywna

Keyword(s):

Drainage System ◽

High Water ◽

Diversity Indices ◽

Habitat Diversity ◽

Peat Bogs ◽

Ecological Index ◽

Natural Lakes ◽

Lighting Conditions ◽

Low Reaction Temperature

The Łęczna-Włodawa Lake District is one of the most valuable natural regions in Europe. It is an area of numerous lakes, peat bogs, swamps and forests, which has been undergoing intensive transformation for decades. Among the largest projects were the creation of the Wieprz Krzna Canal system along with the drainage system and the transformation of natural lakes into retention reservoirs. Among the transformed lakes is Lake Wytyckie. The land was used for analyses near the lake, and floristic and habitat analyses were carried out within the boundaries of the contemporary embankment. The studies were carried out from the 1950s, when the lake functioned as a natural reservoir, through to the 1980s (the transformation of the lake), to the 2020s. Lake Wytyckie was transformed into a retention reservoir by increasing its size and flooding the areas inhabited mainly by peat bog, meadow and forest vegetation, which contributed to the impoverishment of both species and habitat diversity of the area, while it increased the nutrient richness of the water. This was reflected both in the decline in the value of individual diversity indices as well as in the ecological index numbers. In the first period of the research, the area was dominated by wetlands, not drained, with a large variety of species that preferred good lighting conditions. Additionally, the habitat was characterized by low reaction, temperature and trophic values. In the following period, there was an increase in the depth of the water of the reservoir, characterized by high water visibility values, which contributed to the presence of protected species, as did the low moisture content of the areas within the embankment and a neutral pH. The factors currently influencing the formation of the vegetation structure are the high humidity of the entire embankment area, the increase in pH, and the significant increase in the share of built-up areas in the immediate vicinity.

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Long-term changes of river discharge regime in Latvia

Hydrology Research ◽

10.2166/nh.2008.033 ◽

2008 ◽

Vol 39 (2) ◽

pp. 133-141 ◽

Cited By ~ 5

Author(s):

Maris Klavins ◽

Valery Rodinov

Keyword(s):

Climate Variability ◽

River Discharge ◽

Water Resource Management ◽

Regional Climate ◽

Hydrological Regime ◽

High Water ◽

Regime Changes ◽

Long Term Changes ◽

Discharge Regime

The study of changes in river discharge is important for regional climate variability characterization and for development of an efficient water resource management system. The hydrological regime of rivers and their long-term changes in Latvia were investigated. Four major types of river hydrological regimes, which depend on climatic and physicogeographic factors, were characterized. These factors are linked to the changes observed in river discharge. Periodic oscillations of discharge, and low- and high-water flow years are common for the major rivers in Latvia. A main frequency of river discharge regime changes of about 20 and 13 years was estimated for the studied rivers. A significant impact of climate variability on the river discharge regime has been found.

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Cost determination and sustainable financing for rural water services in sub-Saharan Africa

Water Policy ◽

10.2166/wp.2007.012 ◽

2007 ◽

Vol 9 (4) ◽

pp. 373-391 ◽

Cited By ~ 26

Author(s):

Peter A. Harvey

Keyword(s):

Sub Saharan Africa ◽

Water Services ◽

Innovative Strategies ◽

Water Users ◽

Sub Saharan ◽

The Cost ◽

National Governments ◽

Cost Determination ◽

Sustainable Financing

Access to safe, sufficient and affordable water in rural Africa will not increase unless sustainable financing strategies are developed which ensure the sustainability of existing water services. There is a strong need for international donors and national governments to confront the true costs associated with sustained service provision in order to develop practicable long-term financing mechanisms. This paper presents a systematic approach that can be applied to determine the overall cost of service delivery based on respective cost estimates for operation and maintenance, institutional support, and rehabilitation and expansion. This can then be used to develop a tariff hierarchy which clearly indicates the cost to water users of different levels of cost recovery, and which can be used as a planning tool for implementing agencies. Community financing mechanisms to ensure sustained payment of tariffs must be matched to specific communities and their economic characteristics; a blanket approach is unlikely to function effectively. Innovative strategies are also needed to ensure that the rural poor are adequately served, for which a realistic, targeted and transparent approach to subsidy is required.

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The use of artificial neural networks in the problem of classifying cloud types in wide-angle images of the visible hemisphere of the sky.

10.5194/egusphere-egu21-14470 ◽

2021 ◽

Author(s):

Nikita Veremev

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Cloud Cover ◽

Human Factor ◽

Wide Angle ◽

Reliable Determination ◽

Air Masses ◽

Key Aspects

Within the framework of meteorology and oceanology, the importance of the cloud mass and the type of clouds cannot be underestimated. When describing and studying weather, precipitation and the movement of air masses over the ocean, the amount and type of clouds determines the flows of precipitation, their intensity, helps to predict the weather and the content of various impurities in the air, which makes the study of the properties of cloud cover one of the key aspects of meteorological and oceanological research.The types of clouds are determined by the specialist, visually comparing the picture of the sky over the ocean with the guideline documents, the use of which reduces the possibility of the human factor affecting the determination of these parameters.For an accurate study, study of the dynamics and dependence of climatic models on the conditions of cloud types, long-term measurements of the same type and the continuity of their methods are required. However, all these data are very unevenly distributed over the Earth's surface, and the number of ship observations is greatly reduced.Thus, taking into account the importance of reliable determination of data related to cloudiness and the problems of their accuracy, the relevance and need to automate the determination of cloud types are obvious.As a result of the work, an algorithm was obtained that allows classifying cloud types based on photographs taken during long-term sea expeditions.

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Beyond Cloud: A Fused Optic and SAR Based Solution to Monitor Crop Health

10.5194/egusphere-egu21-15589 ◽

2021 ◽

Author(s):

Brianna Pagán ◽

Adekunle Ajayi ◽

Mamadou Krouma ◽

Jyotsna Budideti ◽

Omar Tafsi

Keyword(s):

Machine Learning ◽

Satellite Imagery ◽

Cloud Cover ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Learning Approaches ◽

Area Index ◽

Spatial And Temporal Scales ◽

Field Level ◽

Crop Health

The value of satellite imagery to monitor crop health in near-real time continues to exponentially grow as more missions are launched making data available at higher spatial and temporal scales. Yet cloud cover remains an issue for utilizing vegetation indexes (VIs) solely based on optic imagery, especially in certain regions and climates. Previous research has proven the ability to reconstruct VIs like the Normalized Difference Vegetation Index (NDVI) and Leaf Area Index (LAI) by leveraging synthetic aperture radar (SAR) datasets, which are not inhibited by cloud cover. Publicly available data from SAR missions like Sentinel-1 at relatively decent spatial resolutions present the opportunity for more affordable options for agriculture users to integrate satellite imagery in their day to day operations. Previous research has successfully reconstructed optic VIs (i.e. from Sentinel-2) with SAR data (i.e. from Sentinel-1) leveraging various machine learning approaches for a limited number of crop types. However, these efforts normally train on individual pixels rather than leveraging information at a field level.&#160;Here we present Beyond Cloud, a product which is the first to leverage computer vision and machine learning approaches in order to provide fused optic and SAR based crop health information. Field level learning is especially well-suited for inherently noisy SAR datasets. Several use cases are presented over agriculture fields located throughout the United Kingdom, France and Belgium, where cloud cover limits optic based solutions to as little as 2-3 images per growing season. Preliminary efforts for additional features to the product including automated crop and soil type detection are also discussed. Beyond Cloud can be accessed via a simple API which makes integration of the results easy for existing dashboards and smart-ag tools. Overall, these efforts promote the accessibility of satellite imagery for real agriculture end users.&#160;

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