Comparison of Meteorological- and Agriculture-Related Drought Indicators across Ethiopia

Tsige, Dawit Teweldebirhan; Uddameri, Venkatesh; Forghanparast, Farhang; Hernandez, E. Annette; Ekwaro-Osire, Stephen

doi:10.3390/w11112218

Cited by 17 publications

(12 citation statements)

References 64 publications

(82 reference statements)

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“…As PDSI is a long-term indicator, the results indicate that moisture-holding compartments (e.g., soil moisture) have very little resilience to combat the impacts of reduced rainfall and increased temperature. Accurate soil moisture measurements are critical to rigorously validate the trend projections of PDSI, and the absence of soil moisture networks is a major limiting factor to assess drought impacts on agriculture systems in Ethiopia [58].…”

Section: Trends Across Ethiopia During Bega Seasonmentioning

confidence: 99%

“…In addition to changes in intensities, the patterns of wet-dry spells and transitions between these climate states is also important to fully understand drought characteristics [69]. The reader is referred to a complementary study presented in [58] that addresses this aspect. The available datasets limit the evaluation of drought characteristics on a monthly timescale.…”

Section: Closing Remarksmentioning

confidence: 99%

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Long-Term Drought Trends in Ethiopia with Implications for Dryland Agriculture

Temam

Uddameri

Mohammadi

et al. 2019

Water

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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.

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Section: Trends Across Ethiopia During Bega Seasonmentioning

confidence: 99%

Section: Closing Remarksmentioning

confidence: 99%

Long-Term Drought Trends in Ethiopia with Implications for Dryland Agriculture

Temam

Uddameri

Mohammadi

et al. 2019

Water

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“…Agricultural drought indices, especially those using soil moisture measurements, could serve as better surrogates for capturing irrigation dynamics. However, ground truthing soil-moisturebased agriculture drought indicators is often challenging due to the paucity of the data, and these soil moisture indices do not include irrigation water applications (Tsige et al, 2019). Therefore, these indicators may not be full substitutes for the lack of groundwater pumping data.…”

Section: Evaluation Of Forecasting Capabilities Of the Ist-ann Model mentioning

confidence: 99%

Physics-inspired integrated space–time artificial neural networks for regional groundwater flow modeling

Ghaseminejad

Uddameri

2020

Hydrol. Earth Syst. Sci.

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Abstract. An integrated space–time artificial neural network (ANN) model inspired by the governing groundwater flow equation was developed to test whether a single ANN is capable of modeling regional groundwater flow systems. Model-independent entropy measures and random forest (RF)-based feature selection procedures were used to identify suitable inputs for ANNs. L2 regularization, five-fold cross-validation, and an adaptive stochastic gradient descent (ADAM) algorithm led to a parsimonious ANN model for a 30 691 km2 agriculturally intensive area in the Ogallala Aquifer of Texas. The model testing at 38 independent wells during the 1956–2008 calibration period showed no overfitting issues and highlighted the model's ability to capture both the observed spatial dependence and temporal variability. The forecasting period (2009–2015) was marked by extreme climate variability in the region and served to evaluate the extrapolation capabilities of the model. While ANN models are universal interpolators, the model was able to capture the general trends and provide groundwater level estimates that were better than using historical means. Model sensitivity analysis indicated that pumping was the most sensitive process. Incorporation of spatial variability was more critical than capturing temporal persistence. The use of the standardized precipitation–evapotranspiration index (SPEI) as a surrogate for pumping was generally adequate but was unable to capture the heterogeneous groundwater extraction preferences of farmers under extreme climate conditions.

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“…Agricultural drought indices, especially those using soil moisture measurements, could serve as better surrogates to capture irrigation dynamics. However, ground-truthing soil moisture-based agriculture drought indicators is often challenging due to paucity of data , and these soil moisture indices do not include irrigation water applications (Tsige et al, 2019). Therefore, these indicators may not fully substitute for the lack of groundwater pumping data.…”

Section: Independent Testing Of Ist-ann Modelmentioning

confidence: 99%

Physics-inspired integrated space-time Artificial Neural Networks for regional groundwater flow modeling

Ghaseminejad¹,

Uddameri²

2020

Preprint

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An integrated space-time Artificial Neural Network (ANN) model inspired by the governing groundwater flow equation was developed to test whether a single ANN is capable of modeling regional groundwater flow systems. Modelindependent entropy measures and random forest (RF) based feature selection procedures were used to identify suitable inputs for ANNs. L2 regularization, 5-fold cross-validation, and adaptive stochastic gradient descent (ADAM) algorithm led to a parsimonious ANN model for a 30,691 km 2 agriculturally intensive area in the Ogallala Aquifer of Texas. The model testing at 5 38 independent wells during the 1956-2008 calibration period showed no overfitting issues and highlighted the model's ability to capture both the observed spatial dependence and temporal variability. The forecasting period (2009)(2010)(2011)(2012)(2013)(2014)(2015) was marked by extreme climate variability in the region and served to evaluate the extrapolation capabilities of the model. While ANN models are universal interpolators, the model was able to capture the general trends and provided groundwater level estimates that were better than using historical means. Model sensitivity analysis indicated that pumping was the most sensitive process.

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Comparison of Meteorological- and Agriculture-Related Drought Indicators across Ethiopia

Cited by 17 publications

References 64 publications

Long-Term Drought Trends in Ethiopia with Implications for Dryland Agriculture

Long-Term Drought Trends in Ethiopia with Implications for Dryland Agriculture

Physics-inspired integrated space–time artificial neural networks for regional groundwater flow modeling

Physics-inspired integrated space-time Artificial Neural Networks for regional groundwater flow modeling

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