Synergistic effect of drought and rainfall events of different patterns on watershed systems

Observations of enhanced rainfall variability in Kenya, East Africa

Understanding local patterns of rainfall variability is of great concern in East Africa, where agricultural productivity is dominantly rainfall dependent. However, East African rainfall climatology is influenced by numerous drivers operating at multiple scales, and local patterns of variability are not adequately understood. Here, we show evidence of substantial variability of local rainfall patterns between 1981 and 2021 at the national and county level in Kenya, East Africa. Results show anomalous patterns of both wetting and drying in both the long and short rainy seasons, with evidence of increased frequency of extreme wet and dry events through time. Observations also indicate that seasonal and intraseasonal variability increased significantly after 2013, coincident with diminished coherence between ENSO (El Nino Southern Oscillation) and rainfall. Increasing frequency and magnitude of rainfall variability suggests increasing need for local-level climate change adaptation strategies.

Pollutant removal in an experimental bioretention cell situated in a northern Chinese sponge city

To assess the viability and effectiveness of bioretention cell in enhancing rainwater resource utilization within sponge cities, this study employs field monitoring, laboratory testing, and statistical analysis to evaluate the water purification capabilities of bioretention cell. Findings indicate a marked purification impact on surface runoff, with removal efficiencies of 59.81% for suspended solids (SS), 39.01% for chemical oxygen demand (COD), 37.53% for ammonia nitrogen (NH3-N), and 30.49% for total phosphorus (TP). The treated water largely complies with rainwater reuse guidelines and tertiary sewage discharge standards. Notably, while previous research in China has emphasized water volume control in sponge city infrastructures, less attention has been given to the qualitative aspects and field-based evaluations. This research not only fills that gap but also offers valuable insights and practical implications for bioretention cell integration into sponge city development. Moreover, the methodology and outcomes of this study serve as a benchmark for future sponge city project assessments, offering guidance to relevant authorities.

Assessing the quality of drinking water from selected water sources in Mbarara city, South-western Uganda

This study assessed the physical, chemical, and microbiological quality with emphasis on risk score, source apportionment, geochemistry, feacal coliforms and water quality index of drinking water from selected water sources. A cross-sectional study was conducted in six villages in Mbarara city, south-western Uganda. Each selected source was inspected using a WHO-adopted sanitary inspection questionnaire. Each source's risk score was calculated. Thirty-seven samples were taken from one borehole, nine open dug wells, four rain harvest tanks, and twenty-three taps. The values for apparent color and phosphate were higher than the permissible level as set by the World Health Organization and Ugandan standards (US EAS 12). The isolated organisms were Klebsiella spp. (8.11%), Citrobacter divergens (62.16%), Citrobacter fluendii (2.7%), E. coli (35.14%), Enterobacter aerogenes (8.11%), Enterobacter agglomerus (5.4%), Proteus spp. (2.7%), Enterobacter cloacae (13.5%), and Proteus mirabilis (2.7%). Twelve water sources (32.4%) had water that was unfit for human consumption that was unfit for human consumption (Grade E), Five sources (13.5%) had water that had a very poor index (Grade D), nine (24.3%) had water of poor index (Grade C), eight (21.6%) had water of good water index (Grade B), and only three (8.1%) had water of excellent water quality index (Grade A). The piper trilinear revealed that the dominant water type of the area were Mgso4 and Caso4 type. Gibbs plot represents precipitation dominance. PCA for source apportionment showed that well, tap and borehole water account for the highest variations in the quality of drinking water. These results suggest that drinking water from sources in Mbarara city is not suitable for direct human consumption without treatment. We recommend necessary improvements in water treatment, distribution, and maintenance of all the available water sources in Mbarara City, South Western Uganda.

Multi-scale response relationship between water quality of rivers entering lakes from different pollution source areas and land use intensity: a case study of the three lakes in central Yunnan

As the main supply source of lakes, the water quality of the rivers entering the lakes directly determines the water safety and sustainable development of the lakes. Human activities are the direct cause of changes in the water quality of rivers entering lakes, and land use intensity is the direct manifestation of human activities on the land surface. Although significant progress has been made in studying the relationship between land use changes and water quality in lakes, there is still a lack of research on exploring the relationship between land use intensity and water quality at multiple scales, especially in comparative studies of different pollution source areas. To address this problem, this study used Pearson's correlation analysis and land use intensity index method to explore the response relationship between river water quality and land use intensity at different spatial and temporal scales and different pollution source areas using three lakes in central Yunnan as examples. The results showed that land use intensity was generally positively correlated with water quality, but the response relationship between land use intensity and different water quality indicators was significantly different at different scales and for different pollution source areas. Compared to non-urban areas, the impact of land use intensity on water quality is more significant in urban areas. Compared to the rainy season, the correlation between CODNa, TP, and NH3-N values and land use intensity is stronger during the dry season, while the correlation between COD, TN, and land use intensity is weaker during the dry season. When viewed at different scales, different water quality indicators have different scale effects, but overall, the larger the scale, the stronger the correlation. Therefore, in the work of lake water environmental governance, it is necessary to consider comprehensively from multiple scales and perspectives and adopt measures that are more suitable for regional water pollution prevention and control.

Classification of precipitation types in Poland using machine learning and threshold temperature methods

The phase in which precipitation falls-rainfall, snowfall, or sleet-has a considerable impact on hydrology and surface runoff. However, many weather stations only provide information on the total amount of precipitation, at other stations series are short or incomplete. To address this issue, data from 40 meteorological stations in Poland spanning the years 1966-2020 were utilized in this study to classify precipitation. Three methods were used to differentiate between rainfall and snowfall: machine learning (i.e., Random Forest), daily mean threshold air temperature, and daily wet bulb threshold temperature. The key findings of this study are: (i) the Random Forest (RF) method demonstrated the highest accuracy in rainfall/snowfall classification among the used approaches, which spanned from 0.90 to 1.00 across all stations and months; (ii) the classification accuracy provided by the mean wet bulb temperature and daily mean threshold air temperature approaches were quite similar, which spanned from 0.86 to 1.00 across all stations and months; (iii) Values of optimized mean threshold temperature and optimized wet bulb threshold temperature were determined for each of the 40 meteorological stations; (iv) the inclusion of water vapor pressure has a noteworthy impact on the RF classification model, and the removal of mean wet bulb temperature from the input data set leads to an improvement in the classification accuracy of the RF model. Future research should be conducted to explore the variations in the effectiveness of precipitation classification for each station.

Effects of rainfall characteristics on runoff quality parameters within an industrial sector in Tennessee, USA

The relationship between rainfall characteristics and pollutant discharge has rarely been investigated in industrial sectors. To address this need, we investigated the pollutant concentrations of surface runoff and the correlation between pollutant discharge and rainfall characteristics using the self-reported stormwater quality data collected under the Tennessee Multi-Sector Permit program for two industrial facilities in West Tennessee. The variation of certain stormwater quality parameters over this period was utilized as an indicator to evaluate the effectiveness of control measures implemented at these two facilities. Furthermore, the Water Quality Index (WQI) as an indicator to assess the temporal changes in stormwater quality at industrial facilities was determined using the Weighted Sum (WSM) and Canadian Council of Ministers of the Environment (CCME) methods. The principal component analysis (PCA) and Pearson correlation coefficient were utilized to understand the correlation between runoff quality parameters, rainfall characteristics, and the sources of pollutants. The results demonstrated lower WQI indices using the WSM method compared to the CCME method. The data analysis revealed that 93.1%, 100%, 86.2%, and 48.3% of Al, Mg, Cu, and Fe experienced a concentration greater than the benchmark level, respectively. There was a significant relationship between Total suspended solids (TSS) and Al, Chemical Oxygen Demand (COD), Fe, oil and grease (O&G), and Zn concentrations. As a result, TSS could be a priority pollutant for designing various best management practices (BMPs) and low impact developments (LIDs). The result of the PCA and Pearson correlation coefficient showed that Al concentration made a significant correlation with the rainfall depth and rainfall duration. This analysis also illustrated that biochemical oxygen demand (BOD₅), COD, and O&G concentrations were highly correlated with antecedent dry days (ADDs). However, pH was more related to rainfall depth and rainfall intensity. This study informs both regulatory agencies and industry stakeholders regarding the importance of evaluating self-reported stormwater quality data.

Assessment of rain event effects on source water quality degradation and subsequent water treatment operations

Heavy rainfall events can lead to the runoff of large amounts of dissolved and particulate matter into surface water sources that may represents challenges for drinking water treatment, such as membrane fouling, increases in chemical demands, and formation of various disinfection by products (DBPs) after disinfection, such as trihalomethanes (THM) and haloacetic acids (HAA). In this study, a framework is defined for analyzing water quality data in relation to climatic variables (rainfalls). The effects of 22 different rain events were assessed on an organic matter proxy (UV absorbance), and on different key water quality parameters for the coagulation step in a drinking water treatment plant. Extended impacts of rewetting events after long term dry period on source water quality were identified, with significant increases in raw water UV 254 nm that last almost 3 weeks. A significant effect on filtered water quality was also noticed and the potential impacts on finished waters quality was confirmed by HAA modelling results. Future studies could focus on the monitoring and modelling of other regulated DBPs such as THM as well as simulations of different scenarios of climate change to estimate the variability of DBPs and its precursors such as organic matter.

Drought impacts on hydrology and water quality under climate change

The Intergovernmental Panel on Climate Change (IPCC) has predicted that droughts are projected to affect global hydrology and water quality in varying ways, resulting in a considerable challenge to water availability for society, environment, and ecosystems. This study employed the Soil and Water Assessment Tool to evaluate how drought affects hydrology and water quality in the Miyun Reservoir watershed, coupled with bias-corrected climate projections in the Representative Concentration Pathway 8.5 scenario, accommodating the intercoupling effects of precipitation shifts and rising temperatures. The standardized precipitation index (SPI), standardized runoff index (SRI), and standardized soil moisture index (SSWI) were used to characterize meteorological, hydrological, and agricultural droughts that occur in the different phases in the hydrological cycle. Climate change had the most significant impact on agricultural drought. SSWI were projected to considerably increase in intensity, frequency, and duration in most subbasins by up to 15 %, 55 %, and 45 %, respectively, and showed a strong correlation with meteorological and hydrological droughts (correlation coefficients r = 0.54, 0.57, and 0.60 with SPI for the baseline, near future and far future periods, and 0.91, 0.87, and 0.89 with SRI for the three periods, respectively). Hydrological components, sediment export, and nutrient loss were highly correlated with changes in drought indexes, with r ranging between -0.68 and 0.34 in the near future period and -0.62 and 0.53 in the far future period. Drought conditions of surface runoff and soil water dominated the changes in sediment export, and hydrological drought was the major cause for reduced nutrient loads. In addition to drought impacts, the synergistic effects of increasing precipitation and rising temperature led to a certain degree of increase in sediment and nutrient exports. The results of this study emphasize the need to enhance the resilience of watershed systems to the predicted increases in the intensity, frequency, and duration of droughts.

A simpler statistical approach to estimate the allowable effluent discharge into a low monitored river network

The goal of the study was to illustrate how the variation in streamflow patterns affects the magnitude of pollutants that can be loaded into the hydrography in a more straightforward manner. For that, two basins in the Doce River basin (Brazil) were evaluated in order to estimate the variation in the allowed wastewater in streamflow units (m³s^-1) into the hydrography without impairing the rivers' water quality. The main water quality parameters of the basin were considered for the analysis: E. coli, total phosphorus (P), nitrate‑nitrogen (N-NO₃^-), and biochemical oxygen demand (BOD). Because the already existing E. coli in the system is too high, the allowable streamflow, considering this pollutant, was null. For N-NO₃^-_, the model was not valid because the concentration of it the wastewater is smaller than the allowed in the legislation. For last, considering P and BOD, it was observed that, for most gauges, it was possible to load more wastewater in the hydrography during wet months, especially when the effluent goes through some treatment. For example, considering P, a significant difference between the allowable pollutants in the wet season in comparison to the dry season, indicating that for one gauge, more wastewater is allowed in the dryer season. For BOD, contrarily, the allowable wastewater in the hydrography increased by about 50% in both seasons, without much variation. With that, we conclude that the dilution capacity in the waters is variable, so should be the allowed amount of pollutants into the river network throughout the year.