Long-term water quality assessments under changing land use in a large semi-arid catchment in South Africa

Mararakanye, N.; Roux, J. Le; Franke, A.C.

doi:10.1016/j.scitotenv.2021.151670

Cited by 24 publications

(5 citation statements)

References 49 publications

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“…Although P leaching rates were generally low, the observed leaching of 51 kg P ha −1 (Field 11) does indicate a risk of P leaching in case P is applied through fertigation. The results suggest that N leaching from irrigated cropped fields may contribute considerably to eutrophication of surface waters in the area, while the impacts from P leaching may be limited (Mararakanye et al, 2022).…”

Section: Discussionmentioning

confidence: 94%

In-field assessment of the variability in water and nutrient use efficiency among potato farmers in a semi-arid climate

Franke,

Machakaire,

Mukiibi

et al. 2023

Front. Sustain. Food Syst.

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Precise water management is imperative for sustainable crop production under irrigation in semi-arid regions with increasingly variable rainfall. Water use efficiencies achieved by farmers often differ widely, even within regions with homogenous agro-ecological conditions. To better understand the drivers of this variability and derive management recommendations, detailed measurements of water and associated nutrient dynamics were conducted on 20 potato farms in two regions of South Africa. Crop characteristics and typical growing conditions in South Africa make potato cultivation sensitive to water and nutrient losses. Water and nutrient inputs and losses through drainage and plant uptake were measured in potato and the follow-up crop, along with yield and weather variables. Seasonal mean irrigation and evapotranspiration were lower in winter crops (240 mm irrigation, 236 mm evapotranspiration) than in summer crops (598 mm irrigation, 608 mm evapotranspiration). Seasonal field water balances were often close to zero, indicating that estimations of evapotranspiration and drainage were representative for the greater field. Lysimeter observations however appeared more representative in sandy unstructured soils than in heavier structured soils. Rainfall during cropping reduced demand for irrigation and increased water use efficiency based on irrigation water, but caused substantial drainage. Leaching of N and K was considerable (on average 30 kg N, 55 kg K ha−1, much higher in some fields), while P leaching was negligible, with the exception of one field (51 kg P ha−1). Variation in water and nutrients use efficiencies was the result of management decisions and environmental factors, such as rainfall and soil type. Without substantial rainfall, application of irrigation scheduling tools can keep drainage and nutrient losses to a minimum, as was observed on almost half the fields, even on sandy soils with a low water holding capacity. Potato yields greatly varied among farmers (9.1–24.7 t dry matter tuber ha−1). Efficient farmers included those who used water sparsely and had minimal drainage, but also farmers who used inputs judiciously and obtained excellent productivity. The role of follow-up crops as users of surplus nutrients and water left behind by the potato crop was not always evident.

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Section: Discussionmentioning

confidence: 94%

In-field assessment of the variability in water and nutrient use efficiency among potato farmers in a semi-arid climate

Franke,

Machakaire,

Mukiibi

et al. 2023

Front. Sustain. Food Syst.

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“…Adjacent to cropland in the further north, about 9.92% of the plantation area cover was observed. The areas dominated by croplands and plantations are known for having low water quality [8,79] due to the inputs used to enhance plant life. The residuals remain in the soil and are eventually channelled to the waterbodies, impacting the freshwater ecosystem [78,79].…”

Section: Discussionmentioning

confidence: 99%

“…The areas dominated by croplands and plantations are known for having low water quality [8,79] due to the inputs used to enhance plant life. The residuals remain in the soil and are eventually channelled to the waterbodies, impacting the freshwater ecosystem [78,79]. In light of these environmental risks, water resource managers must ensure that control measures are in place to promote sustainable agriculture and forestry.…”

Section: Discussionmentioning

confidence: 99%

“…Driven by societal needs [7,8,76], it comprises a range of infrastructure types, as explained in Table 1. The built-up class creates impermeable surfaces with low water infiltration, raising water run-off [80], hiking the non-point pollutants to the rivers [8,63,79,81] and ultimately increasing the stream rate flow [82] with implications for water quality and hydrological structures. For such reasons, practical measures to control urban sprawling must be developed, as numerous studies have suggested [76,83].…”

Section: Discussionmentioning

confidence: 99%

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Evaluating Machine-Learning Algorithms for Mapping LULC of the uMngeni Catchment Area, KwaZulu-Natal

Bhungeni,

Ramjatan,

Gebreslasie

2024

Remote Sensing

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Analysis of land use/land cover (LULC) in catchment areas is the first action toward safeguarding freshwater resources. LULC information in the watershed has gained popularity in the natural science field as it helps water resource managers and environmental health specialists develop natural resource conservation strategies based on available quantitative information. Thus, remote sensing is the cornerstone in addressing environmental-related issues at the catchment level. In this study, the performance of four machine learning algorithms (MLAs), namely Random Forests (RFs), Support Vector Machines (SVMs), Artificial Neural Networks (ANNs), and Naïve Bayes (NB), were investigated to classify the catchment into nine relevant classes of the undulating watershed landscape using Landsat 8 Operational Land Imager (L8-OLI) imagery. The assessment of the MLAs was based on a visual inspection of the analyst and commonly used assessment metrics, such as user’s accuracy (UA), producers’ accuracy (PA), overall accuracy (OA), and the kappa coefficient. The MLAs produced good results, where RF (OA = 97.02%, Kappa = 0.96), SVM (OA = 89.74%, Kappa = 0.88), ANN (OA = 87%, Kappa = 0.86), and NB (OA = 68.64%, Kappa = 0.58). The results show the outstanding performance of the RF model over SVM and ANN with a significant margin. While NB yielded satisfactory results, its sensitivity to limited training samples could primarily influence these results. In contrast, the robust performance of RF could be due to an ability to classify high-dimensional data with limited training data.

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“…Four significant non-optical parameters, TN, TP, NH 3 -N, and NO 3 -N, have been extensively investigated to represent the eutrophication of rivers and lakes, which will cause a critical water pollution issue in many countries like degrading functioning and endangering water security (X. Chen et al, 2018;Liang et al, 2018;Lv & Wu, 2021;Mararakanye et al, 2022). Traditionally, in-situ measurements and the collection of water samples are the major approaches for monitoring water quality.…”

Section: Introductionmentioning

confidence: 99%

Non-optical Water Quality Retrieval from Zhuhai-1 OHS Hyperspectral Images in Taipu River

Yukun

Lin

et al. 2022

Preprint

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Hyperspectral remote sensing is thought to be a useful technology for assessing the condition of inland waters. However, non-optically active water quality parameters are rarely explored in hyperspectral remote sensing applications, despite they are highly valued in the aquatic environment condition. This study intends to evaluate the performance of non-optically active water quality parameters using Zhuhai-1 hyperspectral imagery. Focusing on total nitrogen (TN), total phosphorus (TP), ammonia nitrogen (NH3-N) and nitrate-nitrogen (NO3-N) in Taipu River, we constructed empirical models to evaluate the precision of water quality inversion from OHS by comparing with Sentinel-2, and determined the sensitive bands of different water quality parameters. The final results showed that the polynomial model based on OHS had the greatest potential in retrieving TN, TP and NH3-N concentration, and the R2 was 0.9678, 0.7924, 0.7682 respectively. The combination of R(510)/R(820) and R(700)/R(806), R(940)/R(820) and R(806)/R(926), R(709)/R(806) and R(746)/R(620) were most sensitive to TN, TP and NH3-N respectively. The OHS and Sentinel-2 both had potential in retrieving NO3-N. The R2 was 0.9791 from OHS and was 0.9513 from Sentinel-2. The sensitive bands of NO3-N were R(596)/R(665) and R(466)/R(580) from OHS, and Red Eage3/Blue and SWIR1/Blue from Sentinel-2. We also analyzed the drivers of the spatial distribution of water quality in Taipu River, the results showed negative impacts of farmland and urban land on water quality, and beneficial impacts of forest land on water quality. This study represented a promising step in hyperspectral remote sensing for retrieving inland non-optically active water quality parameters utilizing Zhuhai-1.

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Long-term water quality assessments under changing land use in a large semi-arid catchment in South Africa

Cited by 24 publications

References 49 publications

In-field assessment of the variability in water and nutrient use efficiency among potato farmers in a semi-arid climate

In-field assessment of the variability in water and nutrient use efficiency among potato farmers in a semi-arid climate

Evaluating Machine-Learning Algorithms for Mapping LULC of the uMngeni Catchment Area, KwaZulu-Natal

Non-optical Water Quality Retrieval from Zhuhai-1 OHS Hyperspectral Images in Taipu River

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