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Ethiopia is a largely agrarian country with nearly 85% of its employment coming from agriculture. Nevertheless, it is not known how much land is under cultivation. Mapping land cover at finer resolution and global scales has been particularly difficult in Ethiopia. The study area falls in a region of high mapping complexity with environmental challenges which require higher quality maps. Here, remote sensing is used to classify a large area of the central and northwestern highlands into eight broad land cover classes that comprise agriculture, grassland, woodland/shrub, forest, bare ground, urban/impervious surfaces, water, and seasonal water/marsh areas. We use data from Landsat spectral bands from 2000 to 2011, the Normalized Difference Vegetation Index (NDVI) and its temporal mean and variance, together with a digital elevation model, all at 30-m spatial resolution, as inputs to a supervised classifier. A Support Vector Machines algorithm (SVM) was chosen to deal with the size, variability and non-parametric nature of these data stacks. In post-processing, an image segmentation algorithm with a minimum mapping unit of about 0.5 hectares was used to convert per pixel classification results into an object based final map. Although the reliability of the map is modest, its overall accuracy is 55%-encouraging results for the accuracy of agricultural uses at 85% suggest that these methods do offer great utility. Confusion among grassland, woodland and barren categories reflects the difficulty of classifying savannah landscapes, especially in east central Africa with monsoonal-driven rainfall patterns where the ground is obstructed by clouds for significant periods of time. Our analysis also points out the need for high quality reference data. Further, topographic analysis of the agriculture class suggests there is a significant amount of sloping land under cultivation. These results are important for future research and environmental monitoring in agricultural land use, soil erosion, and crop modeling of the Abay basin. [ABSTRACT FROM AUTHOR]

Climate resilience in subsistence agricultural communities depends strongly on the robustness and effective management of the agricultural natural resource base. For this reason, adaptation planning efforts frequently focus on natural resource conservation as the primary motivation for and primary outcome of adaptation activities. Here, we present an analysis of the sustainability of community based adaptation (CBA) activities in 20 community based organizations (CBO) that were established in the Blue Nile Highlands of Ethiopia in order to promote resilience to climate change. CBA sustainability was assessed through multi-criteria analysis using the Analytic Hierarchy Process (AHP). Sustainability was considered for social, institutional, technical, financial, and environmental dimensions, with second-order indicators or factors defined for each dimension. According to this analysis, CBA efforts of two thirds of the COBs studied were found to be unsustainable in all dimensions and CBA efforts of the remaining CBOs were found to be at risk of unsustainability. A number of barriers to CBA sustainability were identified, including inadequacies in community participation, training of local community members, local government commitment, farmer capacity, and bureaucratic efficiency. Participatory evaluation of CBA, however, revealed that many of these barriers can be attributed to the decision to use conservation of natural resources as the primary framework for CBA activities. Based on this evaluation, new efforts have been developed that use markets as the entry and exit points for sustainability activities. Lessons learned in this project are relevant for CBA efforts in other agricultural regions of the developing world. [ABSTRACT FROM AUTHOR]

Understanding the relationship between topography, hydrological processes, and runoff source areas is essential in engineering design, such as predicting floods and implementing effective watershed management practices. This relationship is not well defined in the highlands with a monsoon climate and needs further study. The objective of this study is to relate topographic position and hydrological response in tropical highlands. The research was conducted in the Debre Mawi watershed in the northwest sub-humid Ethiopian highlands. In the monsoon rain phase of 2017 and 2018, groundwater depth, infiltration rate, and surface runoff were monitored at the upslope, midslope, and downslope positions. Surface runoff rates were measured in farmer fields through distributed V-notch weirs as estimates of positional runoff. Average water table depths were 30 cm deep in the downslope regions and 95 cm in the upslope position. The water table depth affected the steady-state infiltration rate in the rain phase. It was high upslope (350 mm h−1), low midslope (49 mm h−1), and zero downslope. In 2017, the average runoff coefficients were 0.29 for the upslope and midslope and 0.73 downslope. Thus, topographic position affects all aspects of the watershed hydrology in the humid highlands and is critical in determining runoff response. [ABSTRACT FROM AUTHOR]

Tropical highland regions are experiencing rapid climate change. In these regions the adaptation challenge is complicated by the fact that elevation contrasts and dissected topography produce diverse climatic conditions that are often accompanied by significant ecological and agricultural diversity within a relatively small region. Such is the case for the Choke Mountain watersheds, in the Blue Nile Highlands of Ethiopia. These watersheds extend from tropical alpine environments at over 4000 m elevation to the hot and dry Blue Nile gorge that includes areas below 1000 m elevation, and contain a diversity of slope forms and soil types. This physical diversity and accompanying socio-economic contrasts demand diverse strategies for enhanced climate resilience and adaptation to climate change. To support development of locally appropriate climate resilience strategies across the Blue Nile Highlands, we present here an agroecosystem analysis of Choke Mountain, under the premise that the agroecosystem-the intersection of climatic and physiographic conditions with agricultural practices-is the most appropriate unit for defining adaptation strategies in these primarily subsistence agriculture communities. To this end, we present two approaches to agroecosystem analysis that can be applied to climate resilience studies in the Choke Mountain watersheds and, as appropriate, to other agroecologically diverse regions attempting to design climate adaptation strategies. First, a full agroecoystem analysis was implemented in collaboration with local communities. It identified six distinct agroecosystems that differ systematically in constraints and adaptation potential. This analysis was then paired with an objective landscape classification trained to identify agroecosystems based on climate and physiographic setting alone. It was found that the distribution of Choke Mountain watershed agroecosystems can, to first order, be explained as a function of prevailing climate. This suggests that the conditions that define current agroecosystems are likely to migrate under a changing climate, requiring adaptive management strategies. These agroecosystems show a remarkable degree of differentiation in terms of production orientation and socio-economic characteristics of the farming communities suggesting different options and interventions towards building resilience to climate change. [ABSTRACT FROM AUTHOR]

There is an increasing demand for a land surface temperature (LST) dataset with both fine spatial and temporal resolutions due to the key role of LST in the Earth's land–atmosphere system. Currently, the technique most commonly used to meet the demand is thermal infrared (TIR) remote sensing. However, cloud contamination interferes with TIR transmission through the atmosphere, limiting the potential of space-borne TIR sensors to provide the LST with complete spatio-temporal coverage. To solve this problem, we developed a two-step integrated method to: (i) estimate the 10-km LST with a high spatial coverage from passive microwave (PMW) data using the multilayer perceptron (MLP) model; and (ii) downscale the LST to 1 km and fill the gaps based on the geographically and temporally weighted regression (GTWR) model. Finally, the 1-km all-weather LST for cloudy pixels was fused with Aqua MODIS clear-sky LST via bias correction. This method was applied to produce the all-weather LST products for both daytime and nighttime during the years 2013–2018 in South China. The evaluations showed that the accuracy of the reproduced LST on cloudy days was comparable to that of the MODIS LST in terms of mean absolute error (2.29–2.65 K), root mean square error (2.92–3.25 K), and coefficients of determination (0.82–0.92) against the in situ measurements at four flux stations and ten automatic meteorological stations with various land cover types. The spatial and temporal analysis showed that the MLP-GTWR LST were highly consistent with the MODIS, in situ, and ERA5-Land LST, with the satisfactory ability to present the LST pattern under cloudy conditions. In addition, the MLP-GTWR method outperformed a gap-filling method and another TIR-PMW integrated method due to the local strategy in MLP and the consideration of temporal non-stationarity relationship in GTWR. Therefore, the test of the developed method in the frequently cloudy South China indicates the efficient potential for further application to other humid regions to generate the LST under cloudy condition. [ABSTRACT FROM AUTHOR]

Mapping and quantifying land degradation status is important for identifying vulnerable areas and to design sustainable landscape management. This study maps and quantifies land degradation status in the north Gojjam sub-basin of the Upper Blue Nile River (Abbay) using GIS and remote sensing integrated with multicriteria analysis (MCA). This is accomplished using a combination of biological, physical, and chemical land degradation indicators to generate a comprehensive land degradation assessment. All indicators were standardized and weighted using analytical hierarchy and pairwise comparison techniques. About 45.3% of the sub-basin was found to experience high to very high soil loss risk, with an average soil loss of 46 t ha−1yr−1. More than half of the sub-basin was found to experience moderate to high level of biological degradation (low vegetation status and low soil organic matter level). In total, 80.2% of the area is characterized as having a moderate level of physical land degradation. Similarly, the status of chemical degradation for about 55.8% and 39% of the sub-basin was grouped as low and moderate, respectively. The combined spatial MCA of biological, chemical, and physical land degradation indicators showed that about 1.14%, 32%, 35.4%, and 30.5% of the sub-basin exhibited very low, low, moderate, and high degradation level, respectively. This study has concluded that soil erosion and high level of biological degradation are the most important indicators of land degradation in the north Gojjam sub-basin. Hence, the study suggests the need for integrated land management practices to reduce land degradation, enhance the soil organic matter content, and increase the vegetation cover in the sub-basin. [ABSTRACT FROM AUTHOR]

The objective of this study was to evaluate the performance of satellite rainfall estimates (Climate Hazards Group Infrared Precipitation with Stations version 2 (CHIRPSv2) and Multi-Source Weighted-Ensemble Precipitation version 2 (MSWEPv2) from 1981 to 2018 for monthly meteorological drought analysis over the Upper Blue Nile (UBN) basin. The reference for the performance evaluation was rainfall measured in situ selected with reference to the elevation zones of the basin: Highland, midland, and lowland. Both the measured and estimated rainfall datasets were aggregated by month at a spatial resolution of 10 km × 10 km with a temporal coverage of 38 years from 1981 to 2018 and evaluated with respect to raw precipitation statistics and the standardized precipitation index (SPI). The values of SPI were validated with reference to documented meteorological drought records of the country. The mean bias, correlation coefficient, probability of bias (PBias, %), mean error (ME, mm), and root mean square error (RMSE, mm) values across the elevation zones for CHIRPSv2 were found to be 1.07, 0.91, 6.75, 7.74, and 122.34, respectively. The corresponding values were 1.19, 0.87, 18.56, 19.54, and 130.26 for MSWEPv2. Based on this result, CHIRPSv2 was employed to analyze the magnitude of drought in the different elevation zones of the UBN. The magnitude (SPI) of monthly meteorological drought over the entire UBN basin from 1981 to 2018 ranged from 0 to −3.74. The strongest negative SPI value (−3.74) was observed in August 1984 in midland areas. The highest magnitude of drought was −3.0 in July 2015 over the highland and −3.03 in June 2015 over the lowland during 2014–2017. The observed drought was characterized by extreme, severe, and moderate levels. The mean frequency of severe/extreme meteorological drought in the 38-year period over the highland, midland, and lowland parts of the UBN ranged from 7% to 11%. The average of severe/extreme drought events in each of the elevation zones of the basin was 9%, that is, drought occurred almost every 10 years for all elevation zones of the basin. Over the 38-year period, severe/extreme drought occurred at the onset and/or offset time of rainy season over all elevation zones of the basin. The UBN is characterized as a drought-prone basin. However, the frequency and magnitude of drought could neither be described as a decreasing nor as an increasing linear trend. Thus, the farming practices in the basin need to be enhanced with an improved early warning system and drought-resistant seed technologies. [ABSTRACT FROM AUTHOR]

The Ethiopian highlands suffer from severe land degradation, including erosion. In response, the Ethiopian government has implemented soil and water conservation practices (SWCPs). At the same time, due to its economic value, the acreage of eucalyptus has expanded, with croplands and pastures converted to eucalyptus plantations. The impact of these changes on soil loss has not been investigated experimentally. The objective of this study, therefore, is to examine the impacts of these changes on stream discharge and sediment load in a sub-humid watershed. The study covers a nine-year period that included installation of SWCPs, a three-fold increase from 1.5 ha in 2010 to 5 ha in 2018 in eucalyptus, and the upgrading of an unpaved to the paved road. Precipitation, runoff, and sediment concentration were monitored by installing weirs at the outlets of the main and four nested watersheds. A total of 867 storm events were collected in the nine years. Runoff and sediment concentration decreased by more than half in nine years. In the main watershed W5, we estimated that evapotranspiration by eucalyptus during the dry phase (November to May) increased approximately from 30 mm a−1 in 2010 to 100 mm a−1 in 2018. In watershed W3 it increased from 2 mm a−1 to 400 mm a−1, requiring more rainfall before saturation excess runoff began in the rain phase. The reduction in runoff led to a decreased sediment load from 70 Mg ha−1 a−1 in 2010 to 2.8 Mg ha−1 a−1 in 2018, though the reduction in discharge may have negative impacts on ecology and downstream water resources. SWCPs became sediment-filled and minimally effective by 2018. This indicates that these techniques are either inappropriate for this sub-humid watershed or require improved design and maintenance. [ABSTRACT FROM AUTHOR]

The association between elevation (agro-climatic zones, ACZs) and the mean annual total rainfall (MATRF) is not straightforward in different parts of the world. This study sought to estimate the amount of MATRF across four elevation zones of Jema watershed, which is situated in the northwestern highlands of Ethiopia, by employing an appropriate interpolation method. The elevation of the watershed ranges from 1895 to 3518 m a.s.l. For the sake of this study, 34 sample MATRF data were extracted from satellite and nearby gauge stations that were recorded from 1983 to 2010. These data sources were reconstructed by International Research Institute for Climate and Society at Columbia University, USA, at a scale of 10 km by 10 km. An elevation data set generated from a digital elevation model with 30-m resolution (DEM 30 m) was considered as a covariable to estimate the MATRF. To identify the optimal interpolation model, mean errors were computed using cross-validation statistics. The root-mean-square error (RMSE) analysis showed that ordinary cokriging (OCK) was the most accurate model with a predictive power of 87.3%. The root-mean-square standardized (RMSSE) analysis showed that the best precision value (0.72) occurred in OCK. Stable and Gaussian trend lines together with local polynomial types of trend removal, and an elliptical neighborhood search function could perform best to maximize the accuracy and the precision of estimating MATRF. Elevation, as a covariable, enhanced the degree of accuracy and precision of estimation. The value of the trend line function (least square) between the MATRF and elevation was very weak (R2 = 0.07), whereas the value of trend line function (least square) between the MATRF and the longitude coordinates (east–west direction) was medium (R2 = 0.34). The estimated MATRF for the entire watershed under study ranged from 1228 to 1640 mm. To conclude, elevation could contribute to the estimation of the MATRF. The value of the MATRF showed a declining pattern from the lower to higher elevation areas of the watershed. [ABSTRACT FROM AUTHOR]

Total suspended solids (TSS) is an important environmental parameter to monitor in the Chesapeake Bay due to its effects on submerged aquatic vegetation, pathogen abundance, and habitat damage for other aquatic life. Chesapeake Bay is home to an extensive and continuous network of in situ water quality monitoring stations that include TSS measurements. Satellite remote sensing can address the limited spatial and temporal extent of in situ sampling and has proven to be a valuable tool for monitoring water quality in estuarine systems. Most algorithms that derive TSS concentration in estuarine environments from satellite ocean color sensors utilize only the red and near-infrared bands due to the observed correlation with TSS concentration. In this study, we investigate whether utilizing additional wavelengths from the Moderate Resolution Imaging Spectroradiometer (MODIS) as inputs to various statistical and machine learning models can improve satellite-derived TSS estimates in the Chesapeake Bay. After optimizing the best performing multispectral model, a Random Forest regression, we compare its results to those from a widely used single-band algorithm for the Chesapeake Bay. We find that the Random Forest model modestly outperforms the single-band algorithm on a holdout cross-validation dataset and offers particular advantages under high TSS conditions. We also find that both methods are similarly generalizable throughout various partitions of space and time. The multispectral Random Forest model is, however, more data intensive than the single band algorithm, so the objectives of the application will ultimately determine which method is more appropriate. [ABSTRACT FROM AUTHOR]

Diarrheal disease remains a major cause of childhood mortality and morbidity causing poor health and economic outcomes. In low-resource settings, young children are exposed to numerous risk factors for enteric pathogen transmission within their dwellings, though the relative importance of different transmission pathways varies by pathogen species. The objective of this analysis was to model associations between five household-level risk factors-water, sanitation, flooring, caregiver education, and crowding-and infection status for endemic enteric pathogens in children in five surveillance studies. Data were combined from 22 sites in which a total of 58,000 stool samples were tested for 16 specific enteropathogens using qPCR. Risk ratios for pathogen- and taxon-specific infection status were modeled using generalized linear models along with hazard ratios for all-cause diarrhea in proportional hazard models, with the five household-level variables as primary exposures adjusting for covariates. Improved drinking water sources conferred a 17% reduction in diarrhea risk; however, the direction of its association with particular pathogens was inconsistent. Improved sanitation was associated with a 9% reduction in diarrhea risk with protective effects across pathogen species and taxa of around 10-20% risk reduction. A 9% reduction in diarrhea risk was observed in subjects with covered floors, which were also associated with decreases in risk for zoonotic enteropathogens. Caregiver education and household crowding showed more modest, inconclusive results. Combining data from diverse sites, this analysis quantified associations between five household-level exposures on risk of specific enteric infections, effects which differed by pathogen species but were broadly consistent with hypothesized transmission mechanisms. Such estimates may be used within expanded water, sanitation, and hygiene (WASH) programs to target interventions to the particular pathogen profiles of individual communities and prioritize resources.

Spending time outdoors is associated with increased physical activity; however, high ambient temperature/humidity, together with built environment features in urban versus rural environments, may influence physical activity. We conducted an intervention trial with 89 urban and 88 rural participants performing normal activities on Days 1-2 (baseline) and spending an additional 30 min outdoors on Days 3-7 (intervention) in the summer. Participants wore a pedometer with real-time visual feedback to track daily steps taken and a thermometer clipped to their shoe to track temperatures experienced individually. Hygrometer-thermometers were deployed in participants' neighborhoods to collect finer resolution ambient heat indexes in addition to regional weather station measurements. Using linear mixed effects models and adjusting for ambient conditions and individual-level factors, participants on average walked 637 (95%CI (83, 1192)) more steps and had a 0.59 °C (95%CI (0.30, 0.88)) lower daily mean individually experienced heat index during intervention days compared to baseline days. The intervention benefit of increased physical activity was greater in rural residents who were less active at baseline, compared to urban residents. Our results suggest adding a small amount of additional time outdoors may improve physical activity without increasing participants' heat exposure, even during summer in a humid subtropical climate.