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The standardized precipitation index (SPI) is a fundamental indicator of meteorological, hydrological, and agricultural droughts in the world. This study aims to evaluate different timescales, 3 months (SPI-3), 6 months (SPI-6), 9 months (SPI-9), and 12 months (SPI-12) indices from meteorological data in quantifying drought characterization in lower eastern counties of Kenya from 1990 to 2018 for observed data and from 1986 to 2018 for Climatic Research Unit Time Series (CRU) data. Precipitation in-situ data (annual) and high-resolution (0.5 x 0.5 degree grid) monthly-scale precipitation data were sought from Kenya Meteorological Department and CRU TS, respectively. Z-Score (SPI) was computed for each year (in-situ data) and month (CRU TS data) using the SPI algorithm, expressed as the departure from the mean in standard deviation units. Quality control of CRU TS data was done by checking outlier values and comparing the data with precipitation data obtained from the meteorological department as well as ERA5 reanalysis data. Results showed that extreme to mild drought was experienced across the Kenyan counties for both annual in-situ and monthly gridded data. Machakos county experienced a year of extreme drought, while Makueni and Taita-Taveta have had 2 and 4 years of severe droughts, respectively. The monthly SPI indices of 3, 6, 9, and 12 months showed a remarkably consistent behavioral pattern detecting extreme droughts across the counties. Considering the uncertainties, unpredictability, and shifting of the long and short rainy seasons in Kenya, results were obtained related to dry and wet episodes and to their relationship with agricultural production as well as water availability and environmental management. [ABSTRACT FROM AUTHOR]

The extreme drought in Europe in 2022 also hit hard the Great Hungarian Plain. In this short overview article, we summarize the natural environmental conditions of the region and the impact of river control works on the water-retention capacity of the landscape. In this respect, we also review the impact of intensive agricultural cultivation on soil structure and on soil moisture in light of the meteorological elements of the 2022 drought. The most important change is that the soil stores much less moisture than in the natural state; therefore, under the meteorological conditions of summer 2022, the evapotranspiration capacity was reduced. As a result, the low humidity in the air layers above the ground is not sufficient to trigger summer showers and thunderstorms associated with weather fronts and local heat convection anymore. Our proposed solution is to restore about one-fifth of the area to the original land types and usage before large-field agriculture. Low-lying areas should be transformed into a mosaic-like landscape with good water supply and evapotranspiration capacity to humidify the lower air layers. Furthermore, the unfavorable soil structure that has resulted from intensive agriculture should also be converted into more permeable soil to enhance infiltration. [ABSTRACT FROM AUTHOR]

We propose a formal definition of backtestability for a statistical functional of a distribution: a functional is backtestable if there exists a backtest function depending only on the forecast of the functional and the related random variable, which is strictly monotonic in the former and has zero expected value for an exact forecast. We discuss the relationship with elicitability and identifiability which turn out being necessary conditions for backtestability. The variance and the expected shortfall are not backtestable for this reason. We compare (absolute) model validation in the context of hypothesis tests via backtest functions, versus (relative) model selection between competing forecasting models, via scoring functions. We define a backtest to be sharp when it is strictly monotonic with respect to the real value of the functional and not only to its forecast. This decides whether the expected value of the backtest determines also the prediction discrepancy and not only its significance. We show that the quantile backtest is not sharp and in fact it provides no information whatsoever on its true value. The expectile is also not sharp; we provide bounds for its true value, which are looser for outer confidence levels. We then introduce the notion of ridge backtests, applicable to particular non{backtestable functionals, such as the variance and the expected shortfall, which coincide with the attained minimum of the scoring function of another elicitable auxiliary functional (the mean and the value at risk, respectively). This permits approximated sharp backtests up to a small and one{sided sensitivity to the prediction of the auxiliary variable. The ridge mechanism explains why the variance has always been de{facto backtestable and allows for similar efficient ways to backtest the expected shortfall. We discuss the relevance of this result in the current debate of financial regulation (banking and insurance), where value at risk and expected shortfall are adopted as regulatory risk measures. [ABSTRACT FROM AUTHOR]

This study uses Sentinel-3 SLSTR data to analyze short-term drought events between 2019 and 2021. It investigates the crucial role of vegetation cover, land surface temperature, and water vapor amount associated with drought over Kenya's lower eastern counties. Therefore, three essential climate variables (ECVs) of interest were derived, namely Land Surface Temperature (LST), Fractional Vegetation Cover (FVC), and Total Column Water Vapor (TCWV). These features were analyzed for four counties between the wettest and driest episodes in 2019 and 2021. The study showed that Makueni and Taita Taveta counties had the highest density of FVC values (60–80%) in April 2019 and 2021. Machakos and Kitui counties had the lowest FVC estimates of 0% to 20% in September for both periods and between 40% and 60% during wet seasons. As FVC is a crucial land parameter for sequestering carbon and detecting soil moisture and vegetation density losses, its variation is strongly related to drought magnitude. The land surface temperature has drastically changed over time, with Kitui and Taita Taveta counties having the highest estimates above 20 °C in 2019. A significant spatial variation of TCWV was observed across different counties, with values less than 26 mm in Machakos county during the dry season of 2019, while Kitui and Taita Taveta counties had the highest estimates, greater than 36 mm during the wet season in 2021. Land surface temperature variation is negatively proportional to vegetation density and soil moisture content, as non-vegetated areas are expected to have lower moisture content. Overall, Sentinel-3 SLSTR products provide an efficient and promising data source for short-term drought monitoring, especially in cases where in situ measurement data are scarce. ECVs-produced maps will assist decision-makers with a better understanding of short-term drought events as well as soil moisture loss episodes that influence agriculture under arid and semi-arid climates. Furthermore, Sentinel-3 data can be used to interpret hydrological, ecological, and environmental changes and their implications under different environmental conditions. [ABSTRACT FROM AUTHOR]