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The interplay between climate and land use/cover significantly shapes streamflow characteristics within watersheds, with dominance varying based on geography and watershed attributes. This study quantifies the relative and combined impacts of land use/cover change (LULCC) and climate change (CC) on streamflow variability in the Baro River Basin (BRB) using the Soil and Water Assessment Tool Plus (SWAT+). The model was calibrated and validated with observed streamflow data from 1985 to 2014 and projected the future streamflow from 2041 to 2070 under two Shared Socio-Economic Pathway (i.e., SSP2-4.5 and SSP5-8.5) scenarios, based on the ensemble of four Coupled Model Intercomparison Project (CMIP6) models. The LULCC was analyzed through Google Earth Engine (GEE) and predicted for the future using the Land Change Modeler (LCM), revealing reductions in forest and wetlands, and increases in agriculture, grassland, and shrubland. Simulations show that the decrease in streamflow is attributed to LULCC, whereas an increase in flow is attributed to the impact of CC. The combined impact of LULCC and CC results in a net increase in streamflow by 9.6% and 19.9% under SSP2-4.5 and SSP5-8.5 scenarios, respectively, compared to the baseline period. Our findings indicate that climate change outweighs the impact of land use/cover (LULC) in the basin, emphasizing the importance of incorporating comprehensive water resources management and adaptation approaches to address the changing hydrological conditions.

Abstract Extreme hydrological events, like floods and droughts, exert considerable effects on both human and natural systems. The frequency, intensity, and duration of these events are expected to change due to climate change, posing challenges for water resource management and adaptation. In this study, the Soil and Water Assessment Tool plus (SWAT +) model was calibrated and validated to simulate flow under future shared socioeconomic pathway (SSP2-4.5 and SSP5-8.5) scenarios in the Baro River Basin with R2 values of 0.88 and 0.83, NSE of 0.83 and 0.74, and PBIAS of 0.39 and 8.87 during calibration and validation. Six bias-corrected CMIP6 Global Climate Models (GCM) were selected and utilized to investigate the effects of climate change on the magnitude and timing of hydrological extremes. All climate model simulation results suggest a general increase in streamflow magnitude for both emission scenarios (SSP2-4.5 and SSP5-8.5). The multi-model ensemble projections show yearly flow increases of 4.8% and 12.4% during the mid-term (MT) (2041–2070) and long-term (LT) (2071–2100) periods under SSP2-4.5, and 15.7% and 35.6% under SSP5-8.5, respectively. Additionally, the analysis revealed significant shifts in the projected annual 1 day, 3 day, 7 day, and 30 day maximum flows, whereas the annual 3 day and 7 day minimum flow fluctuations do not present a distinct trend in the future scenario compared to the baseline (1985–2014). The study also evaluated the timing of hydrological extremes, focusing on low and peak flow events, utilizing the annual 7 day maximum and minimum flow for this analysis. An earlier occurrence was noted for both peak and low flow in the SSP2-4.5 scenario, while a later occurrence was observed in the SSP5-8.5 scenario compared to the baseline. In conclusion, this study showed the significant effect of climate change on river hydrology and extreme flow events, highlighting their importance for informed water management and sustainable planning.

This study assessed the impacts of the land use/cover (LULC) and climate changes on the runoff and sediment flows in the Megech watershed. The Geospatial Water Erosion Prediction Project (GeoWEPP) was used to assess LULC and climate changes’ impact on runoff, soil loss, and sediment yield. The QGIS 2.16.3 plugin module for land use change evaluation (MOLUSCE) tool with the cellular automata artificial neural network (CA-ANN) was used for LULC prediction based on historical data and exploratory maps. Two commonly used representative concentration pathways (RCPs)—4.5 and 8.5—were used for climate projection in the 2030s, 2050s, and 2070s. The LULC prediction analysis showed an expansion of cropland and settlement areas, with the reduction in the forest and rangelands. The climate projections indicated an increase in maximum temperatures and altered precipitation patterns, particularly with increased wet months and reduced dry periods. The average annual soil loss and sediment yield rates were estimated to increase under both the RCP4.5 and RCP8.5 climate scenarios, with a more noticeable increase under RCP8.5. By integrating DEM, soil, land use, and climate data, we evaluated runoff, soil loss, and sediment yield changes on only land use/cover, only climate, and the combined impacts in the watershed. The results revealed that, under all combined scenarios, the sediment yield in the Megech Reservoir was projected to substantially increase by 23.28–41.01%, showing a potential loss of reservoir capacity. This study recommends strong climate adaptation and mitigation measures to alleviate the impact on land and water resources. It is possible to lessen the combined impacts of climate and LULC change through implementing best-management practices and adaptation strategies for the identified scenarios.

Improving the quality of atmospheric precipitation measurements is crucial in the view of minimizing the uncertainty in weather forecasting, climate change impact assessment, water resource assessment and management, and drought and flood prediction. Remote sensing technology has considerably improved the spatio-temporal assessment of precipitation. Despite the advancement in the remote sensing technology, there is a need to investigate the robust approach towards integrating ground-based-measured and satellite-product precipitation to better understand the hydrologic process of any basin. Several data-merging methods have been proposed; however, the application of merged precipitation products for hydrological simulation has rarely been investigated. Thus, in this review, technical characteristics including basic assumptions, along with their procedures, are discussed. Moreover, the limitations of eight commonly used merging approaches, (1) Multiple Linear Regression, (2) Residual Inverse Distance Weighting, (3) Linearized Weighting, (4) Inverse Root-Mean-Square Error Weighting, (5) Optimal Interpolation, (6) Random-Forest-Based Merging Procedure, (7) Bayesian Model Averaging, and (8) the Kriging Method, and their advances with respect to hydrological simulation are discussed. Finally, future research directions towards improving data merging approaches are recommended.

Land use and land cover (LULC) change detection and prediction studies are crucial for supporting sustainable watershed planning and management. Hence, this study aimed to detect historical LULC changes from 1985 to 2019 and predict future changes for 2035 (near future) and 2065 (far future) in the Gumara watershed, Upper Blue Nile (UBN) Basin, Ethiopia. LULC classification for the years 1985, 2000, 2010, and 2019 was performed using Landsat images along with vegetation indices and topographic factors. The random forest (RF) machine learning algorithm built into the cloud-based platform Google Earth Engine (GEE) was used for classification. The results of the classification accuracy assessment indicated perfect agreement between the classified maps and the validation dataset, with kappa coefficients (K) of 0.92, 0.94, 0.90, and 0.88 for the LULC maps of 1985, 2000, 2010, and 2019, respectively. Based on the classified maps, cultivated land and settlement increased from 58.60 to 83.08% and 0.06 to 0.18%, respectively, from 1985 to 2019 at the expense of decreasing forest, shrubland and grassland. Future LULC prediction was performed using the cellular automata–Markov (CA–Markov) model under (1) the business-as-usual (BAU) scenario, which is based on the current trend of socioeconomic development, and (2) the governance (GOV) scenario, which is based on the Green Legacy Initiative (GLI) program of Ethiopia. Under the BAU scenario, significant expansions of cultivated land and settlement were predicted from 83.08 to 89.01% and 0.18 to 0.83%, respectively, from 2019 to 2065. Conversely, under the GOV scenario, the increase in forest area was predicted to increase from 2.59% (2019) to 4.71% (2065). For this reason, this study recommends following the GOV scenario to prevent flooding and soil degradation in the Gumara watershed. Finally, the results of this study provide information for government policymakers, land use planners, and watershed managers to develop sustainable land use management plans and policies.

Lack of consistent streamflow data has been an increasing challenge reported by many studies in developing countries. This study aims to understand the current challenges in streamflow monitoring in Ethiopia to prioritize research topics that can support sustained streamflow monitoring in the country and elsewhere. A workshop-based expert consultation, followed by a systematic literature review, was conducted to build a collective understanding of the challenges and opportunities of streamflow monitoring in Ethiopia. The experts’ consultation identified the top ten research priorities to improve streamflow monitoring through research, education, remote sensing applications, and institutions. The experts’ views were supported by a systematic review of more than 300 published articles. The review indicated scientific investigation in Ethiopian basins was constrained by streamflow data gaps to provide recent and relevant hydrological insights. However, there is inadequate research that seeks solutions, while some researchers use experimental methods to generate recent streamflow data, which is an expensive approach. Articles that attempted to fill data gaps make up less than 20% of the reviewed articles. This study identified research priorities that can benefit streamflow data providers and the research community in alleviating many of the challenges associated with streamflow monitoring in countries such as Ethiopia.

Quantification of hydrologic model prediction uncertainty for various flow quantiles is of great importance for water resource planning and management. Thus, this study is designed to assess the effect of subbasin spatial scale on the hydrological model prediction uncertainty for different flow quantiles. The Soil Water Assessment Tool (SWAT), a geographic information system (GIS) interfaced hydrological model, was used in this study. Here, the spatial variations within the sub-basins of the Omo Gibe River basin in Ethiopia’s Abelti, Wabi, and Gecha watersheds from 1989 to 2020 were examined. The results revealed that (1) for the Abelti, Wabi, and Gecha watersheds, SWAT was able to reproduce the observed hydrograph with more than 85%, 82%, and 73% accuracy in terms of the Nash-Sutcliffe efficiency coefficient (NSE), respectively; (2) the variation in the spatial size of the subbasin had no effect on the overall flow simulations. However, the reproduction of the flow quantiles was considerably influenced by the subbasin spatial scales; (3) the coarser subbasin spatial scale resulted in the coverage of most of the observations. However, the finer subbasin spatial scale provided the best simulation closer to the observed stream flow pattern; (4) the SWAT model performed much better in recreating moist, high, and very-high flows than it did in replicating dry, low, and very-low flows in the studied watersheds; (5) a smaller subbasin spatial scale (towards to distributed model) may better replicate low flows, while a larger subbasin spatial scale (towards to lumped model) enhances high flow replication precision. Thus, it is crucial to investigate the subbasin spatial scale to reproduce the peak and low flows; (6) in this study, the best subbasin spatial scales for peak and low flows were found to be 79–98% and 29–42%, respectively. Hence, it is worthwhile to investigate the proper subbasin spatial scales in reproducing various flow quantiles toward sustainable management of floods and drought.

Modeling soil erosion, sediment yield, and runoff are crucial for managing reservoir capacity, water quality, and watershed soil productivity. However, the monitoring and modeling of soil erosion and sedimentation rates in developing countries such as Ethiopia is not well practiced; thus, the reservoir capacity is diminishing at faster rates. In this study, the soil erosion, sediment yield, and runoff in the Megech watershed, Upper Blue Nile Basin, Ethiopia were modeled using the physically-based geospatial interface, the Water Erosion Prediction Project (GeoWEPP). The GoWEPP model was calibrated and validated at the Angereb sub-watershed and simulated to representative sites to capture the spatiotemporal variability of soil erosion and sediment yield of the Megech watershed. The model parameter sensitivity analysis showed that the hydraulic conductivity (Ke) for all soil types was found to be the dominant parameter for runoff simulation, while rill erodibility (Kr), hydraulic conductivity (Ke), critical shear stress (τc), and inter rill erodibility (Ki) were found to be sensitive for sediment yield and soil loss simulation. The model calibration (2000–2002) and validation (2003–2004) results showed the capability of the GeoWEPP model; with R2 and NSE values, respectively, of 0.94 and 0.94 for calibration; and 0.75 and 0.65 for validation. In general, the results show that the sediment yield in the study watershed varied between 10.3 t/ha/year to 54.8 t/ha/year, with a weighted mean value of 28.57 t/ha/year. The GeoWEPP model resulted in higher sediment value over that of the design sediment yield in the study basin, suggesting the implementation of the best watershed management practices to reduce the rates of watershed sediment yield. Moreover, the mean soil loss rate for the Angerb sub-watershed was found to be 32.69 t/ha/year.

Study region: The Lake Tana Basin (15,114Â km2) in Ethiopia, which is a source of the Blue Nile River Basin. Study focus: We assessed daily streamflow predictions by applying two simple conceptual models and one complex model for four major gauged watersheds of the study area and compared these modelâs capabilities in reproducing observed streamflow in the time and quantile domains. New hydrological insights for the region: The multi-criteria based model comparison shows that the simple conceptual models performed best in smaller watersheds for reproducing observed streamflow in the time domain, whereas the complex model performed best for the largest watershed. For reproducing observed streamflow in the quantile domain, the simple conceptual models performed best for simulation of high, moist, mid-range, and dry-flows in the Gilgelabay watershed; of dry and low-flows in the Gummera and Megech watersheds; and of high flows in the Ribb watershed. For the remaining flow ranges of each watershed, the complex model performed better. This study also addressed the sensitivity of the complex model for the number of partitioned subbasins. In the largest watershed, the performance of the complex model improved when the number of partitioned subbasins was increased. This finding indicates that the distributed models are especially applicable for the complex watershed because of its physical heterogeneity. In general, integrating these three models may be suitable for water resources assessment. Keywords: Simple conceptual model, Semi-distributed model, Model comparison, Lake Tana Basin, Ethiopia

The largest freshwater lake in Ethiopia, Lake Tana, has faced ecological disaster due to water hyacinth (Eichhornia crassipes) infestation. The water hyacinth is a threat not only to the ecology but also to the socioeconomic development of the region and cultural value of the lake, which is registered as a UNESCO reserve. This study aims to map the spatiotemporal dynamics of the water hyacinth using high-resolution PlanetScope satellite images and assesses the major environmental variables that relate to the weed spatial coverage dynamics for the period August 2017 to July 2018. The plausible environmental factors studied affecting the weed dynamics include lake level, water and air temperature, and turbidity. Water temperature and turbidity were estimated from the moderate resolution imaging spectroradiometer (MODIS) satellite image and the water level was estimated using Jason-1 altimetry data while the air temperature was obtained from the nearby meteorological station at Bahir Dar station. The results indicated that water hyacinth coverage was increasing at a rate of 14 ha/day from August to November of 2017. On the other hand, the coverage reduced at a rate of 6 ha/day from December 2017 to June 2018. However, the length of shoreline infestation increased significantly from 4.3 km in August 2017 to 23.4 km in April 2018. Lake level and night-time water temperatures were strongly correlated with water hyacinth spatial coverage (p < 0.05). A drop in the lake water level resulted in a considerable reduction of the infested area, which is also related to decreasing nutrient levels in the water. The water hyacinth expansion dynamics could be altered by treating the nutrient-rich runoff with best management practices along the wetland and in the lake watershed landscape.

The frequency and intensity of flood quantiles and its attendant damage in agricultural establishments have generated a lot of issues in Ethiopia. Moreover, precise estimates of flood quantiles are needed for efficient design of hydraulic structures; however, quantification of these quantiles in data-scarce regions has been a continuing challenge in hydrologic design. Flood frequency analysis is thus essential to reduce possible flood damage by investigating the most suitable flood prediction model. The annual maximum discharges from six representative stations in the Upper Blue Nile River Basin were fitted to the commonly used nine statistical distributions. This study also assessed the performance evolution of the probability distributions with varying spatial scales, such that three different spatial scales of small-, medium-, and large-scale basins in the Blue Nile River Basin were considered. The performances of the candidate probability distributions were assessed using three goodness-of-fit test statistics, root mean square error, and graphical interpretation approaches to investigate the robust probability distribution for flood frequency analysis over different basin spatial scales. Based on the overall analyses, the generalized extreme value distribution was proven to be a robust model for flood frequency analysis in the study region. The generalized extreme value distribution significantly improved the performance of the flood prediction over different spatial scales. The generalized extreme value flood prediction performance improvement measured in root mean square error varied between 5.84 and 67.91% over other commonly used probability distribution models. Thus, the flood frequency analysis using the generalized extreme value distribution could be essential for the efficient planning and design of hydraulic structures in the Blue Nile River Basin. Furthermore, this study suggests that, in the future, significant efforts should be put to conduct similar flood frequency analyses over the other major river basins of Ethiopia.

In this paper, we prove that the uniform radius of spatial analyticity σ(t) of the solution at time t to the two-dimensional quintic fourth-order nonlinear Schrödinger equation i∂tu − Δ2u = |u|4u is bounded from below by c|t|−1/2 as |t|→∞, given the initial data that is analytic with fixed radius σ0. The proof of our main theorems is based on a method of almost conservation law in the modified Gevrey spaces, space–time estimates and Sobolev embedding. [ABSTRACT FROM AUTHOR]

We prove that the uniform radius of spatial analyticity σ(t)$$ \sigma (t) $$ of solution at time t$$ t $$ to the one‐dimensional fourth‐order nonlinear Schrödinger equation i∂tu−∂x4u=|u|2u$$ i{\partial}_tu-{\partial}_x&amp;#x0005E;4u&amp;#x0003D;{\left&amp;#x0007C;u\right&amp;#x0007C;}&amp;#x0005E;2u $$cannot decay faster than 1/t$$ 1/\sqrt{t} $$ for large t$$ t $$, given that the initial data are analytic with fixed radius σ0$$ {\sigma}_0 $$. The main ingredients in the proof are a modified Gevrey space, a method of approximate conservation law, and a Strichartz estimate for free wave associated with the equation. [ABSTRACT FROM AUTHOR]

The study examines the financial distress situation and its determinants in insurance sectors in Ethiopia. To achieve study objectives, revised Altman's 2000 is adopted to measure the financial distress situation. The study adopted an explanatory research design with an arrangement of secondary data analysis via document analysis, quantitative approach, and deductive method of inquiry. The study used panel data from ten insurance companies over the study period 2010/11-2020/21. Descriptive and regression analyses were performed to analyze the data using STATA 14. Econometric model estimation procedures and multiple regression assumptions were tested accordingly. The random effect regression result revealed that firm-specific factors (liquidity and profitability) have a significant positive association, whereas firm size significantly negatively impacts financial distress. While the random effect regression result also proposed inflation has a positive and significant association with financial distress. However, firm-specific factors (revenue growth and leverage) have positive and negative, respectively, and macroeconomic factors (Gross Domestic Product) have positive but statically insignificant to the financial distress situation of insurance sectors in Ethiopia.

Persistence of spatial analyticity is studied for solutions of the generalized Korteweg‐de Vries (KdV) equation with higher order dispersion ∂tu+(−1)j+1∂x2j+1u=∂xu2k+1,$$\begin{equation*} \partial _{t} u+(-1)^{j+1}\partial _{x}^{2j+1} u= \partial _x{\left(u^{2k+1} \right)}, \end{equation*}$$where j≥2$j\ge 2$, k≥1$k\ge 1$ are integers. For a class of analytic initial data with a fixed radius of analyticity σ0$\sigma _0$, we show that the uniform radius of spatial analyticity σ(t)$\sigma (t)$ of solutions at time t$t$ cannot decay faster than 1t$\frac{1}{\sqrt t}$ as t→∞$t\rightarrow \infty$. In particular, this improves a recent result due to Petronilho and Silva [Math. Nachr. 292 (2019), no. 9, 2032–2047] for the modified Kawahara equation (j=2$j=2$, k=1$k=1$), where they obtained a decay rate of order t−4+$ t^{-4 +}$. Our proof relies on an approximate conservation law in a modified Gevrey spaces, local smoothing, and maximal function estimates. [ABSTRACT FROM AUTHOR]

Insurance company managers and shareholders should be conscious of the effect of intellectual capital efficiency and its components on financial performance. The purpose of this study is to examine the role of intellectual capital efficiency and its components on the financial performance of insurance companies. To achieve study objectives Modified value-added intellectual coefficient is adopted to measure the effect of intellectual capital efficiency. The study adopted an explanatory research design with an arrangement of secondary data analysis via document analysis, quantitative approach, and deductive method of inquiry. Panel data was used with a sample of 14 insurance companies from 2012-2022. Descriptive and regression analyses were performed to analyze the data using STATA version 15.0. Econometric model estimation procedures and multiple regression assumptions were tested accordingly. The random effect regression result revealed that the value-added intellectual capital and its component human capital and capital employed efficiency have a positive significance association with financial performance. Whereas, relational capital efficiency and structural capital efficiency do not have a significant contribution to the financial performance of insurance sectors in Ethiopia. The findings of this study contribute to the theoretical and practical understanding of the relationship between intellectual capital efficiency and financial performance in the context of insurance companies in Ethiopia.

This study examines outward-looking foreign direct investment (FDI) and the determinants of four highly indebted low-income countries in East Africa. To achieve the stated objective, the study utilizes the pooled mean group (PMG) approach for panel data encompassing the period from 1990 to 2022. Additionally, bound testing and the autoregressive distributed lag (ARDL) model are applied to analyze time series data from individual countries within the sample. The panel PMG/ARDL estimation suggests that both market size and exchange rate have a significant positive impact on FDI inflows, both in the short run and the long run. Specifically, the time series analysis using ARDL estimation reveals that market size has a positive and significant impact on FDI inflows for both Rwanda and Tanzania, both in the long run and the short run. Furthermore, the association between the labor force and FDI inflows is positive only in the long run for Rwanda, while for Tanzania, it shows a positive association in both the short run and the long run. In terms of the availability of natural resources, the analysis indicates a positive impact in the short run but a negative association with FDI inflows in the long run, with the exception of a positive association with Rwanda in the long run. Additionally, external debt has a positive and significant impact on FDI inflows for Kenya, both in the short run and the long run. Based on the findings, the study recommends that policymakers should focus on policies and strategies that promote market expansion and create a larger consumer base. This can be achieved through initiatives such as market development programs, trade agreements, and regional cooperation.