Intraseason and seasonal drought trends in Ethiopia were studied using a suite of drought indicators—standardized precipitation index (SPI), standardized precipitation evapotranspiration index (SPEI), Palmer drought severity index (PDSI) and Z-index for Meher (long-rainy), Bega (dry), and Belg (short-rainy) seasons—to identify drought-causing mechanisms. Trend analysis indicated shifts in late-season Meher precipitation into Bega in the southwest and southcentral portions of Ethiopia. Droughts during Bega (October–January) are largely temperature controlled. Short-term temperature-controlled hydrologic processes exacerbate rainfall deficits during Belg (February–May) and highlight the importance of temperature- and hydrology-induced soil dryness on production of short-season crops such as tef. Droughts during Meher (June–September) are largely driven by precipitation declines arising from the narrowing of the intertropical convergence zone (ITCZ). Increased dryness during Meher has severe consequences on the production of corn and sorghum. PDSI is an aggressive indicator of seasonal droughts suggesting the low natural resilience to combat the effects of slow-acting, moisture-depleting hydrologic processes. The lack of irrigation systems in the nation limits the ability to combat droughts and improve agricultural resilience. There is an urgent need to monitor soil moisture (a key agro-hydrologic variable) to better quantify the impacts of meteorological droughts on agricultural systems in Ethiopia.

The performance of four tree-based classification techniques-classification and regression trees (CART), multi-adaptive regression splines (MARS), random forests (RF) and gradient boosting trees (GBT) were compared against the commonly used logistic regression (LR) analysis to assess aquifer vulnerability in the Ogallala Aquifer of Texas. The results indicate that the tree-based models performed better than the logistic regression model, as they were able to locally refine nitrate exceedance probabilities. RF exhibited the best generalizable capabilities. The CART model did better in predicting non-exceedances. Nitrate exceedances were sensitive to well depths-an indicator of aquifer redox conditions, which, in turn, was controlled by alkalinity increases brought forth by the dissolution of calcium carbonate. The clay content of soils and soil organic matter, which serve as indicators of agriculture activities, were also noted to have significant influences on nitrate exceedances. Likely nitrogen releases from confined animal feedlot operations in the northeast portions of the study area also appeared to be locally important. Integrated soil, hydrogeological and geochemical datasets, in conjunction with tree-based methods, help elucidate processes controlling nitrate exceedances. Overall, tree-based models offer flexible, transparent approaches for mapping nitrate exceedances, identifying underlying mechanisms and prioritizing monitoring activities.Water 2020, 12, 1023 2 of 27 worldwide [5,6]. Intensification of agricultural activities for both food and energy will further increase the risks of nitrate contamination in aquifers across the world [7][8][9].Nitrate is mobile and fairly recalcitrant, especially in shallow groundwater systems that typically tend to be under oxidizing conditions. Nitrate exhibits the ability to spread over large areas and cannot be treated in-situ using conventional plume scale treatment technologies [10]. Therefore, individual homeowners are often required to install costly point-of-use treatment systems to mitigate nitrate risks arising from the ingestion of contaminated groundwater [11,12]. However, as nitrate is colorless and odorless, many people do not realize the risk of nitrate contamination and are unwittingly exposed to elevated levels of nitrate over long periods of time [13]. Therefore, nitrate contamination must be prevented through proper land management practices. Additionally, areas with a high susceptibility to nitrate pollution must be carefully delineated, with the goal of increasing public awareness regarding elevated health risks arising from nitrate exposures. Such an effort is also useful to prioritize monitoring activities and ensure that the limited fiscal and logistic resources are being used in a prudent manner.Mapping the susceptibility of aquifers to nitrate contamination is an essential step in mitigating and managing nitrate contamination. Multi-criteria decision making (MCDM) methods, such as DRASTIC [14], have been widely used to map aquifer vulnerabili...

Predicting streamflow in intermittent rivers and ephemeral streams (IRES), particularly those in climate hotspots such as the headwaters of the Colorado River in Texas, is a necessity for all planning and management endeavors associated with these ubiquitous and valuable surface water resources. In this study, the performance of three deep learning algorithms, namely Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and Self-Attention LSTM models, were evaluated and compared against a baseline Extreme Learning Machine (ELM) model for monthly streamflow prediction in the headwaters of the Texas Colorado River. The predictive performance of the models was assessed over the entire range of flow as well as for capturing the extreme hydrologic events (no-flow events and extreme floods) using a suite of model evaluation metrics. According to the results, the deep learning algorithms, especially the LSTM-based models, outperformed the ELM with respect to all evaluation metrics and offered overall higher accuracy and better stability (more robustness against overfitting). Unlike its deep learning counterparts, the simpler ELM model struggled to capture important components of the IRES flow time-series and failed to offer accurate estimates of the hydrologic extremes. The LSTM model (K.G.E. > 0.7, R2 > 0.75, and r > 0.85), with better evaluation metrics than the ELM and CNN algorithm, and competitive performance to the SA–LSTM model, was identified as an appropriate, effective, and parsimonious streamflow prediction tool for the headwaters of the Colorado River in Texas.

14Ethiopian agriculture is not only affected by precipitation declines (meteorological droughts) but 15 also soil dryness caused by temperature increases and associated long-term hydrological changes. 16Meteorological drought indicators (e.g., SPI), do not fully capture the water deficits in agricultural 17 systems (i.e., agricultural droughts). An Ethiopia-wide assessment of meteorological and 18 agricultural drought trends was carried out to characterize century-scale (1902 -2016) changes in 19droughts. SPI and SPEI calculated using two-month accumulation and the Palmer Z-index were 20 used for assessing intra-season drought trends. SPI and SPEI at six-month accumulations and 21PDSI were used to define full season droughts. Detrended variance corrected Mann-Kendall test 22was used for trend analysis during Bega (dry), Belg (short-rainy) and Meher (long-rainy) seasons. 23The SPEI-2 and PDSI were most aggressive in characterizing intra-season and seasonal-drought 24 trends. There is on average 1% -6% annual increase in dryness with the lower estimate based on 25 precipitation declines and the upper end accounting for seasonal soil moisture dynamics. The area 26 between 37.5 o E -42.5 o E denotes a climate hot-spot. Precipitation declines in Belg along the 27 Ethiopia-South-Sudan/Sudan border during Belg and along Eretria-Ethiopia border during Meher 28have the potential to exacerbate transboundary water conflicts and further threaten the food 29 security of the region. 30 31