This research evaluates the quality of water and surface sediment in the Bistrica River, addressing the growing environmental challenges in Kosovo caused by extensive human activities. Contamination of these resources poses significant threats to aquatic ecosystems and human health. To assess this, we analysed the levels of potentially toxic elements (PTEs) in the samples using inductively coupled plasma optical emission spectrometry (ICP-OES). The elements examined included Fe, Pb, Ni, Mn, Cu, Zn, Al, and Co. Samples were collected from various sites along the Bistrica River during both high-flow and low-flow seasons in October 2023. The degree of PTE contamination was assessed using several pollution indices (contamination factor (CF), contamination degree (CD), pollution load index (PLI), enrichment factor (EF), geoaccumulation index (Igeo) and ecological risk index (ERI)), indicating that both water and surface sediment exhibited moderate to high levels of contamination. Results revealed that pollution in water samples exceeded the guidelines set by the U.S. Environmental Protection Agency (EPA) and World Health Organization (WHO). Additionally, statistical analysis and contamination clusters, primarily originating from agricultural fields and grazing areas within the catchment. To reduce these risks and safeguard both the aquatic ecosystem and human health, it is crucial to maintain regular monitoring and enforce effective management strategies.
Even during normal hydrometeorological conditions, water management in dam reservoirs requires special measures and difficult operational decisions. The situation becomes even more complicated when high or even extreme surges occur. The study, which focused on four newly constructed dam reservoirs, identified key issues that may result in inappropriate operational assumptions being adopted. These include: (1) uncertainty in the values of characteristic flows – this is particularly true for the Krosnowice reservoir, where calculations were based on only one empirical method, (2) uncertainty in the capacity of the discharge devices – the capacity for bottom outlet of the Szalejów Górny reservoir was shown to be 19.5 m3 ∙ s−1 higher than assumed, (3) consequences of attempts to absolutely maintain permitted outflow – for the analysed reservoirs, in the matter of control flow, it ultimately results in exceeding permitted outflow by values ranging from 123.86% (Roztoki Bystrzyckie reservoir) to 2000% (Krosnowice reservoir), (4) considering the cooperation of facilities located in the same catchment – for the wave of the design flow, delaying the outflow from Szalejów Górny reservoir would allow to reduce the total wave in Kłodzko by 41.37%, (5) the need to prepare the multi-purpose reservoirs for the surge – in the event of a design flow surge it would allow to reduce the surge in Kłodzko from 242 to 101.5 m3 ∙ s−1, however it would require a difficult decision to anticipate emptying the facilities in the interval from 18 h before the surge for Szalejów Górny to 4 h before the surge for Boboszów.
The research was based on a field experiment on light soil. The sampling used for row plantings was Catalpa bignonioides. The reason for this choice was the species recommended for row plantings due to its attractive appearance, long flowering and relatively good resistance to changing climatic conditions. The research aimed to determine water needs: field water consumption of C. bignonioides in row plantings on light soil under subsurface drip irrigation conditions. Water needs identified with field water consumption of C. bignonioides in row plantings on light soil under optimal soil moisture conditions during the growing season were variable. They depended on the variants of the experiment and the course of precipitation and thermal conditions in all growing seasons. The values of total water consumption of C. bignonioides in the growing seasons ranged from 241.3 (2019) to 428.7 mm (2022) for the W1 variant (irrigation performed when soil moisture dropped to –40 kPa). In the W2 variant objects (irrigation performed when soil moisture dropped to –20 kPa), the values of seasonal water consumption were higher and ranged from 266.5 (2019) to 458.8 mm (2022). Daily water consumption increased with the growth of C. bignonioides, regardless of the experimental variant. During each year of the experiment, higher values of daily water consumption were characteristic of the W2 variant. Cultivating C. bignonioides, growing on light soils, enables implementing subsurface drip irrigation technology, which, while ensuring optimal soil moisture conditions, will allow for the undisturbed growth and development of this species in row plantings.
Flooding in Jakarta is a multifaceted issue influenced by a combination of geographical, social, economic, and environmental factors. This study focuses on predicting floods by comparing automatic rain gauge (ARG) ground station data and Climate Hazards Group InfraRed Precipitation (CHIRPS) satellite data using the Adaptive Neurofuzzy Inference System (ANFIS) integrated with principal component analysis (PCA). The dataset includes precipitation measurements from both ARG and CHIRPS along with water level data spanning from 2014 to 2020. ARG provides precise local rainfall data, while CHIRPS offers extensive regional precipitation coverage. To enhance data quality, preprocessing techniques such as mean imputation, data normalisation, and the interquartile range (IQR) method were employed. The ANFIS-PCA model, which integrates fuzzy logic and neural network training, was applied using an 80:20 split for training and validation. When trained with ARG ground station data and water level measurements, the ANFIS-PCA model demonstrated superior accuracy, achieving a root mean square error (RMSE) of 0.13, mean absolute error (MAE) of 0.12, and R2 of 0.82. In contrast, the ANFIS model without PCA yielded higher errors, with RMSE 6.3, MAE 6.2, and R2 0.74. Training with CHIRPS satellite data resulted in significantly higher errors (RMSE 30.14, MAE 24.05, R2 0.42). These findings underscore the superiority of ground-based measurements for flood prediction, given the reduced precision and higher susceptibility to errors in satellite-derived data. While CHIRPS satellite data offers broader spatial coverage, its limitation in precision and higher susceptibility to errors reduce its effectiveness for accurate flood prediction.
Journal of Water and Land Development List of reviewers 2024