Your search found 5 records
1 Assefa, A.; Haile, Alemseged Tamiru; Dhanya, C. T.; Walker, D. W.; Gowing, J.; Parkin, G. 2021. Impact of sustainable land management on vegetation cover using remote sensing in Magera micro Watershed, Omo Gibe Basin, Ethiopia. International Journal of Applied Earth Observation and Geoinformation, 103:102495. [doi: https://doi.org/10.1016/j.jag.2021.102495]
Sustainable land management ; Normalized difference vegetation index ; Watershed management ; Remote sensing ; Satellite imagery ; Datasets ; Land cover mapping ; Hydrological factors ; Rain / Ethiopia / Omo Gibe Basin / Magera Watershed
(Location: IWMI HQ Call no: e-copy only Record No: H050722)
https://www.sciencedirect.com/science/article/pii/S0303243421002026/pdfft?md5=adc6f5caeb7b85ee841a993c82269f8c&pid=1-s2.0-S0303243421002026-main.pdf
https://vlibrary.iwmi.org/pdf/H050722.pdf
https://vlibrary.iwmi.org/pdf/H050722.pdf
(11.20 MB) (11.2 MB)
The hydrological impact of many expensive investments on watershed interventions remains unquantified due to lack of time series data. In this study, remote sensing imagery is utilized to quantify and detect vegetation cover change in Magera micro-watershed, Ethiopia, where sustainable land management interventions have been implemented. Normalized difference vegetation index (NDVI) values were retrieved for the period 2010 to 2019, which encompasses before, during and after the interventions. Mann-Kendal trend test was used to detect temporal trends in the monthly NDVI values. In addition, multiple change-point analyses were carried out using Pettitt’s, Buishand’s and Standard Normal Homogeneity (SNH) tests to detect any abrupt changes due to the watershed interventions. The possible influence of rainfall on changes in vegetation cover was investigated. A significant increasing trend (from 1.5% to 33%) was detected for dense vegetation at the expense of a significant reduction in bare land from 40.9% to 0.6% over the analysis period. An abrupt change in vegetation cover was detected in 2015 in response to the interventions. A weak and decreasing correlation was obtained between monthly rainfall magnitude and NDVI values, which indicates that the increase in vegetation cover is not from rainfall influences. The study shows that the sustainable land management has an overall positive impact on the study area. The findings of this research support the applicability of remote sensing approaches to provide useful information on the impacts of watershed intervention investments.
2 Kchouk, S.; Melsen, L. A.; Walker, D. W.; van Oel, P. R. 2022. A geography of drought indices: mismatch between indicators of drought and its impacts on water and food securities. Natural Hazards and Earth System Sciences, 22(2):323-344. [doi: https://doi.org/10.5194/nhess-22-323-2022]
Drought ; Water security ; Food security ; Indicators ; Monitoring ; Early warning systems ; Meteorological factors ; Precipitation ; Evapotranspiration ; Soil moisture ; Vegetation index ; Socioeconomic environment
(Location: IWMI HQ Call no: e-copy only Record No: H051048)
https://nhess.copernicus.org/articles/22/323/2022/nhess-22-323-2022.pdf
https://vlibrary.iwmi.org/pdf/H051048.pdf
https://vlibrary.iwmi.org/pdf/H051048.pdf
(6.55 MB) (6.55 MB)
Drought monitoring and early warning systems (DEWSs) are seen as helpful tools to tackle drought at an early stage and reduce the possibility of harm or loss. They usually include indices attributed to meteorological, agricultural and/or hydrological drought: physically based drought drivers. These indices are used to determine the onset, end and severity of a drought event. Drought impacts, like water and food securities, are less monitored or even not included in DEWSs. Therefore, the likelihood of experiencing these impacts is often simply linearly linked to drivers of drought. The aim of this study is to evaluate the validity of the assumed direct linkage between drivers of drought and water and food insecurity impacts of drought. We reviewed scientific literature on both drivers and impacts of drought. We conducted a bibliometric analysis based on 5000+ scientific studies in which selected drought indices (drivers) and drought-related water and food insecurities (impacts) were mentioned in relation to a geographic area. Our review shows that there is a tendency in scientific literature to focus on drivers of drought, with the preferred use of meteorological and remotely sensed drought indices. Studies reporting drought impacts are more localised, with relatively many studies focusing on sub-Saharan Africa and Australasia for impacts with regard to food security and water security, respectively. Our review further suggests that studies of food and water insecurity impacts related to drought are dependent on both the physical and human processes occurring in the geographic area, i.e. the local context. With the aim of increasing the relevance and utility of the information provided by DEWSs, we argue in favour of additional consideration of drought impact indices oriented towards sustainable development and human welfare.
3 Tedla, H. Z.; Haile, Alemseged Tamiru; Walker, D. W.; Melesse, A. M. 2022. Evaluation of factors affecting the quality of citizen science rainfall data in Akaki Catchment, Addis Ababa, Ethiopia. Journal of Hydrology, 612(Part C):128284. [doi: https://doi.org/10.1016/j.jhydrol.2022.128284]
Citizen science ; Rain ; Weather data ; Data quality ; Catchment areas ; Monitoring ; Principal component analysis / Ethiopia / Addis Ababa / Akaki Catchment
(Location: IWMI HQ Call no: e-copy only Record No: H051572)
(4.66 MB)
Citizen Science can fulfill the quest for high-quality and sufficient environmental data, such as rainfall. However, the factors affecting the quality of rainfall data collected by the citizen scientists are not well understood. In this study, we examined the effect of citizen scientists’ attributes on the quality of rainfall data. For this purpose, Principal Component Analysis (PCA), stepwise regression and Multiple Linear Regressions (MLR) were used. A quality control procedure was developed and applied for daily observed rainfall data collected in the summer rainy season of 2020. Attributes of the citizen scientists’ were gathered for those who collected rainfall data in the urban and peri-urban Akaki catchment which is located in the Upper Awash sub-basin, Ethiopia. We found that easy-to-detect errors, which were identified during the initial stage of quality control, formed most of the errors in the rainfall data. The PCA and the stepwise regression results revealed that four dominant attributes (education level, gauge relative location, use of smartphone app, and supervisor’s travel distance) highly affected the rainfall data quality. The MLR model using these four prominent dominant variables performed very well with R2 value of 0.98. The k-fold cross validation result showed that the developed model can be used to predict the relationships between data quality and attributes of citizen scientists with high accuracy. Hence, the PCA technique, stepwise regression and MLR model can provide useful information regarding the influence of citizen scientists’ attributes on rainfall data quality. Therefore, future studies should carefully consider citizen scientists’ attributes when engaging and supervising citizen scientists, with a comprehensive data quality control while monitoring rainfall.
4 Tedla, H. Z.; Taye, E. F.; Walker, D. W.; Haile, Alemseged Tamiru. 2022. Evaluation of WRF model rainfall forecast using citizen science in a data-scarce urban catchment: Addis Ababa, Ethiopia. Journal of Hydrology: Regional Studies, 44:101273. [doi: https://doi.org/10.1016/j.ejrh.2022.101273]
Rain ; Weather forecasting ; Models ; Citizen science ; Urban areas ; Catchment areas ; Weather data ; Monitoring / Ethiopia / Addis Ababa / Akaki Catchment / Awash River Basin
(Location: IWMI HQ Call no: e-copy only Record No: H051575)
https://www.sciencedirect.com/science/article/pii/S2214581822002865/pdfft?md5=22730ccbb29c100b7f9cc8989888849f&pid=1-s2.0-S2214581822002865-main.pdf
https://vlibrary.iwmi.org/pdf/H051575.pdf
https://vlibrary.iwmi.org/pdf/H051575.pdf
(9.02 MB) (9.03 MB)
Study region: The Akaki catchment is found in the Upper Awash River Basin in Ethiopia.
Study focus: Understanding the accuracy of rainfall forecasts in the data-scarce urban catchment has a multitude of benefits given the increased urban flood risk caused by climate change and urbanization. In this study, accuracy of the weather research and forecasting (WRF) model rainfall forecast was evaluated using citizen science data. Categorical and continuous accuracy evaluation metrics were used beside gauge representativeness effect.
New hydrological insights for the region: The rainfall forecasts performance accuracy is high for 1–3- days lead-time but deteriorates for 4–5-days lead-time. The WRF model captured the temporal dynamics and the rainfall amount according to the estimated KGE values. The model has relatively higher detection performance for no rain and light rain events (< 6 mm/day), but it has lower performance for moderate and heavy rain events (> 6 mm/day). Use of data from a single rain gauge misrepresents the accuracy level of the rainfall forecast in the study area. The gauge representativeness error contributed a variance of 28.08–83.33 % to the variance of WRF-gauge rainfall difference. Thus, the use of citizen science rainfall monitoring program is an essential alternative source of information where in-situ rainfall monitoring is limited that can be used to understand the “true” accuracy of WRF rainfall forecasts.
Study focus: Understanding the accuracy of rainfall forecasts in the data-scarce urban catchment has a multitude of benefits given the increased urban flood risk caused by climate change and urbanization. In this study, accuracy of the weather research and forecasting (WRF) model rainfall forecast was evaluated using citizen science data. Categorical and continuous accuracy evaluation metrics were used beside gauge representativeness effect.
New hydrological insights for the region: The rainfall forecasts performance accuracy is high for 1–3- days lead-time but deteriorates for 4–5-days lead-time. The WRF model captured the temporal dynamics and the rainfall amount according to the estimated KGE values. The model has relatively higher detection performance for no rain and light rain events (< 6 mm/day), but it has lower performance for moderate and heavy rain events (> 6 mm/day). Use of data from a single rain gauge misrepresents the accuracy level of the rainfall forecast in the study area. The gauge representativeness error contributed a variance of 28.08–83.33 % to the variance of WRF-gauge rainfall difference. Thus, the use of citizen science rainfall monitoring program is an essential alternative source of information where in-situ rainfall monitoring is limited that can be used to understand the “true” accuracy of WRF rainfall forecasts.
5 Walker, D. W.; Oliveira, J. L.; Cavalcante, L.; Kchouk, S.; Neto, G. R.; Melsen, L. A.; Fernandes, F. B.; Mitroi, V.; Gondim, R. S.; Martins, E. S. P. R.; van Oel, P. R. 2024. It's not all about drought: What “drought impacts” monitoring can reveal. International Journal of Disaster Risk Reduction, 103:104338. [doi: https://doi.org/10.1016/j.ijdrr.2024.104338]
Drought ; Monitoring ; Vulnerability ; Risk reduction ; Mitigation ; Infrastructure ; Hydrometeorology ; Crop losses ; Socioeconomic aspects ; Water resources ; Rainfall ; Water supply / Brazil
(Location: IWMI HQ Call no: e-copy only Record No: H052730)
https://www.sciencedirect.com/science/article/pii/S2212420924001006/pdfft?md5=7cf44ae1a35e680dcbaa259211242a6f&pid=1-s2.0-S2212420924001006-main.pdf
https://vlibrary.iwmi.org/pdf/H052730.pdf
https://vlibrary.iwmi.org/pdf/H052730.pdf
(5.06 MB) (5.06 MB)
Drought impacts monitoring has been called the missing piece in drought assessment. The potential to improve drought management is high but uncertain due to rare analyses of impacts datasets, predominantly because there are few impacts monitoring programmes to generate the datasets. Drought impacts monitoring is conducted on the ground in much of Brazil by local observers at monthly and municipality scale to support the Brazilian Drought Monitor. In Ceará state, within drought-prone semiarid northeast Brazil, over 3600 drought impacts reports were completed by agricultural extension officers from 2019 to 2022. We investigated, through manual coding and observer interviews, the reported drought impacts and impact drivers. Analysis provided a catalogue of the experienced impacts and showed that impacts still occur, and are often normalised, during non-drought periods, sometimes as lingering effects of previous droughts. The impact drivers were predominantly non-extreme hydrometeorological conditions or a result of socio-technical vulnerabilities such as insufficient water infrastructure. The normalisation of “impacts” included, in particular: a generally accepted high level of crop losses and consistently low reservoir levels around which the agricultural and domestic systems are adapted. Conventional drought indices often did not align with experienced impact severity, highlighting the limitations of relying solely on these indices for emergency response. Continual impacts monitoring could be extremely valuable anywhere in the world for identifying vulnerabilities and informing proactive measures to reduce drought and other hazard risk, in addition to guiding targeted mitigation efforts.
Powered by DB/Text WebPublisher, from
