Investigating the causes of reduced dissolved oxygen concentrations in Kickapoo Creek, TX.

Excessive organic matter and nutrients can depress dissolved oxygen concentration (DOC) in streams. The aim of this study was to understand the depressed DOC of Kickapoo Creek in Texas, USA, which is an impaired stream; identify the possible sources causing the depressed DOC and elevated Escherichia coli levels; and identify possible remedial measures. Monthly grab water quality data was monitored for nine stations in the watershed. For three of the nine stations, 24-h DOC was also monitored for a comparison with the minimum and average DOC criteria. Correlational, graphical, spatial, and temporal analyses were carried out for DOC concentration with other water quality variables which have the potential to depress DOC in the stream. The correlational analyses show a weak to moderate correlation for DOC with nutrient and oxygen-demanding substance concentrations present in the stream. However, there are spatial and temporal trends in DOC data that can be attributed to the nutrient influx into the stream. A pattern of increasing nutrient concentrations from upstream to downstream partially explains the decreasing dissolved oxygen (DO) concentrations observed towards the lower reaches. Visual interpretations of riparian vegetation and sediment influx also support the spatial patterns in DO concentrations. The majority of the depressed DOC occurs in the summer months when streamflow is at a minimum. The depressed DOC during summer is partially explained by the increasing daily temperatures for the summer months as revealed by the trend analysis of the daily temperature data from 1981 to 2020 using the modified Mann-Kendall test, Pettitt test, and Sen's slope.

Spatiotemporal variability and trends in rainfall and temperature extremes using ERA5 reanalysis and CORDEX-Africa model in Southern Ethiopia.

Rainfall and temperature are characterized by spatial and temporal variability in Ethiopia. However, less attention was given for the analysis of climate variability using advanced techniques and multiple sets of data. This study was conducted to examine spatiotemporal variability and trends in rainfall and temperature extremes in Ghibe III Dam watershed. Observational, ERA5, and regional simulation model data sets were used. The coefficient of variation (CV) and precipitation concentration index (PCI) were employed. The trends in rainfall and temperature extremes were examined using the modified Mann-Kendall test and the Sen Slope estimator in R-ClimDex in R 4.2.2 software. The warmest days exhibited temperature from 24.6°C to 40°C in Bele, 28.2°C to 35.43°C in Wolaita Sodo, 33.6 °C-44 °C in Areka, 31.64 °C-36.8 °C in Gesuba, and 29.19 °C-36.15 °C in Gena Bosa. The warmest nights showed temperature ranging from 14 °C to 18.74 °C in Bele and Gena Bosa, respectively. Annual warm days (TX90p) ranges from 11.34 to 57.1 days, with higher heating in the southern parts. The cool days (TX10p) range from 2.79 to 8.41, while the cool nights (TN10p) range from 0.04 to 8.26 days. The areal average temperature maximum and minimum range between 26.37 °C and 13.81 °C, respectively, with mean precipitation of 1446.92 mm.The rainfall extremes indices showed increasing and decreasing trends. The extreme temperature indices showed an overall warming trend. Based on ERA estimates, the rainfall in winter showed higher variability (CV = 72.4%-99.3 %) than the annual rainfall (CV = 33%-79.8 %). PCI showed a moderately (12 %) to very erratic (19.4 %) rainfall distribution. The climate model estimate showed high variability (CV = 20.65 %) in Climate Limited Area Modeling Community (CCLM) under representative concentration paths (RCP) 4.5 and 8.5 and extremely high variability (CV = 93.49 %) in the Regional Atmospheric Climate Model (RACMO) under RCP 4.5. Policymakers should design appropriate adaptation strategies applicable to farmers.

Grid-based climate variability analysis of Addis Ababa, Ethiopia.

Climate change is an intricate global environmental concern. However, its impact is more pervasive in developing nations such as Ethiopia. Hence, this manuscript examines temperature variability and the magnitude of change over 38 years in the specific case of Addis Ababa, Ethiopia. Gridded meteorological data consisting of minimum and maximum temperatures on a monthly time scale ranging from 1981 to 2018 was obtained from the National Meteorological Agency of Ethiopia. The coefficient of variation (CV) and standardized anomaly index (SAI) were used to examine the rate and extent of temperature anomalies. Geostatistical models, particularly ordinary kriging, are presented as a means of spatially interpolating temperature data. Modified Mann-Kendall test (MMK), Sen's Slope (SS) estimator, principal component analysis (PCA), and T-test were employed to determine the monthly, annual, and seasonal trends using Geospatial technologies, "R" programming, and statistical software. The findings revealed substantial spatial and temporal variation in Addis Ababa's annual and seasonal maximum and minimum temperatures. The long-term mean annual maximum and minimum temperatures were 25.8 °C and 12.6 °C, respectively. The monthly, annual, and seasonal temperatures accrued significantly except in the months of January and September. It is noteworthy that the decadal maximum temperature has risen by 2.7 °C, while minimum temperatures have displayed comparatively minor fluctuations. Moreover, the findings also exhibited that the average maximum and minimum temperatures increased by 1.88 °C and 1.72 °C, correspondingly and the highest temperature occurred during the spring (Belg) season. The first two PCAs (Annual and Kiremt Tmax) account for 90% of the temperature variation. In conclusion, the findings underscore the pressing need for the implementation of climate adaptation strategies and policy measures, which can strengthen the city's resilience to imminent climate change-induced hazards. The mounting temperature presents substantial challenges across various sectors within the city, emphasizing the urgency of preemptive actions to mitigate potential repercussions.

Influence of short- and long-term persistence on identification of rainfall temporal trends using different versions of the Mann-Kendall test in Mizoram, Northeast India.

Investigating the temporal dynamics of rainfall in a changing climate, especially in rainfed agriculture regions, is crucial for analyzing climate-induced changes and offering adaptation options. Since Mizoram experiences unfavorable impacts of rain nearly every year, the region rainfall has been altering over the years, and vital climatic activity is becoming uncontrollable. The current study is primarily concerned with the changing trend of rainfall over Mizoram, which includes both short-term persistence (STP) and long-term persistence (LTP) of rainfall in seasonal and annual time series of rainfall overseeing for the period of 25 years of daily average rainfall from 1996 to 2020 collected collectively from the seven stations over the study area of Mizoram. Four different Mann-Kendall method iterations were used to analyze rainfall trends: the original or conventional method (without autocorrelation) (MnKn1), removing lag-1 autocorrelation (trend-free pre-whitening), considering multiple lag autocorrelation (more than lag-1 autocorrelation) (MnKn3), and Hurst coefficient or LTP (MnKn4). In the analysis, the study found that during monsoon, station Lawngtlai (LT) observed the highest rainfall having a Z value of 1.986, increased by 0.466 cm/year, while station Serchhip (SC) observed the lowest rainfall having Z value of -2.282, decreased by -0.163 cm/year. After applying modified MnKn4, we observed LTP of rainfall in winter at station Lawngtlai (LT) with an increasing trend and other stations observing STP in almost all seasons either increasing or decreasing trend. Therefore, possible climate change adaptation measures should be made to optimize rainfall use for various applications for the states of Mizoram.

Drought evolution indicated by meteorological and remote-sensing drought indices under different land cover types in China.

Remote sensing freely provides many processed image products such as moderate resolution imaging spectroradiometer (MODIS), and long-term data record (LTDR), for the investigation of drought evolution. Our objectives are to investigate drought evolution and spatiotemporal variations from 1982 to 2017 based on two remote-sensing indices, namely, the normalized difference vegetation index (NDVI) and the vegetation condition index (VCI), and a popular meteorological index-standardized precipitation index (SPI)-under four different land cover types, cropland, forestland, grassland, and desertland in China. The modified Mann-Kendall test was used to detect the significance of a trend. The Pearson correlation method was used to find the relationship between NDVI anomaly, VCI, precipitation, and SPI. The results revealed that (a) both mean monthly and yearly precipitation had a general land cover type rank of forestland > grassland ≈ cropland > desertland. (b) A positive correlation was found between drought indices (NDVI anomaly, VCI, SPI) and precipitation for different land cover types. The NDVI anomaly and VCI were well correlated with 3-month SPI for cropland and were well correlated with 6-month SPI for forestland. VCI performed better than NDVI anomaly when correlating with SPI. (c) The coefficient of determination (R2) was obtained for precipitation and VCI in the driest (2011) and wettest (2016) years. The R2 values for desert and grassland ranged from 0.70 to 0.90 and for cropland and forestland were lower (0.54-0.69). (d) Only precipitation, SPI, and VCI of cropland had significant increasing trends. The spatial distribution patterns of precipitation, NDVI, and VCI increased with the decreased elevation. The study revealed that desert and grassland had been regularly exposed to moderate or extreme droughts conditions and confirmed that desert and grassland are more sensitive to short-term drought.