Hydrology and sediment yield response to changes in land-use and climate in the Upper Blue Nile Basin, Ethiopia

Having a robust drought monitoring system for Ethiopia is crucial to mitigate the adverse impacts of droughts. Yet, such monitoring system still lacks in Ethiopia, and in the Upper Blue Nile (UBN) basin in particular. Several drought indices exist to monitor drought, however, these indices are unable, individually, to provide concise information on the occurrence of meteorological, agricultural and hydrological droughts. A combined drought index (CDI) using several meteorological, agricultural and hydrological drought indices can indicate the occurrence of all drought types, and can provide information that facilitates the drought management decision-making process. This thesis proposes an impact-based combined drought index (CDI) and a regression predictionmodel of crop yield anomalies for the UBN basin. The impact-based CDI is defined as a drought index that optimally combines the information embedded in other drought indices for monitoring a certain impact of drought, i.e. crop yield for the UBN. The developed CDI and the regression model have shown to be effective in indicating historic drought events in UBN basin. The impact-based CDI could potentially be used in the future development of drought monitoring in the UBN basin and support decision making in order to mitigate adverse drought impacts.

The aim of this study was to investigate the impacts of future climate change on discharge and evapotranspiration of the Upper Blue Nile (UBN) basin using multiple global circulation models (GCMs) projections and multiple hydrological models (HMs). The uncertainties of projections originating from HMs, GCMs, and representative concentration pathways (RCPs) were also analyzed. This study is part of the Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP) initiative (phase 2), which is a community driven modeling effort to assess global socio-economic impacts of climate change. The baseline period of 1981–2010 was used to identify climate change signals in two future periods: mid future (2036–2065) and far future (2070–2099). Our analyses showed that two out of four GCMs indicated a statistically significant increase in projected precipitation in the far future period. The projected change in mean annual precipitation varied between 4 and 10% relative to the baseline period. The HMs did not agree on the direction of climate change impacts on mean annual discharge. Furthermore, simulated changes in mean annual discharge by all HMs, except SWIM which simulated up to 6.6% increase for the far future period, were not statistically significant. All the HMs generally simulated a statistically significant increase in annual mean actual evapotranspiration (AET) in both periods. The HMs simulated changes in AET ranging from 1.9 to 4.4% for the far future period. In the UBN basin GCM structure was the main contributor of uncertainty in mean annual discharge projection followed by HM structure and RCPs, respectively. The results from this research suggest to use multiple impact models as well as multiple GCMs to provide a more robust assessment of climate change impacts in the UBN basin.

In the Upper Blue Nile (UBN) basin, there is very sparse and uneven distribution of ground-based meteorological stations which constrain assessments on rainfall distributions and representation. To assess the diurnal cycle of rainfall across the UBN basin, satellite observations from Tropical Rainfall Measuring Mission (TRMM) were used in this study. Data of 7 years (2002–2008) of Precipitation Radar (PR) and TRMM Microwave Imager (TMI) were processed, with analyses based on geographic information system (GIS) operations, statistical techniques, and harmonic analysis. Diurnal cycle patterns of rainfall occurrence and rain rate from three in-situ weather stations are well represented by the satellite observations. Harmonic analysis depicts large differences in the mean of the diurnal cycle, amplitude, and time of the amplitude across the study area. Diurnal cycle of rainfall occurrence has a single peak in Lake Tana, Gilgel Abbay, and Jemma subbasins and double peaks in Belles, Dabus, and Muger subbasins. Maximum rain rate occurs in the morning (Gilgel Abbay, Dabus, and Jemma), afternoon (Belles, Beshilo, and Muger), and evening (Lake Tana and along the river gorges). Results of this study indicate that satellite observations provide an alternative source of data to characterize diurnal cycle of rainfall in data-scarce regions. We noticed, however, that there are a number of constraints to the use of satellite observations. For more accurate assessments, satellite products require validation by a network of well-distributed ground stations. Also, we advocate bias correction.

Floods are among the most frequent and devastating climate-related hazards, causing significant environmental and socioeconomic impacts. This study integrates synthetic aperture radar (SAR)-based flood mapping via the Google Earth Engine (GEE) with hydraulic modeling in HEC-RAS to analyze flood dynamics downstream of the Gumara watershed, Upper Blue Nile (UBN) Basin, Ethiopia. A change detection approach using Sentinel-1 imagery was employed to generate flood inundation maps from 2017–2021. Among these events, flood events on 22 July, 3 August, and 27 August 2019 were used to calibrate the HEC-RAS model, achieving an F-score of 0.57, an overall accuracy (OA) of 86.92%, and a kappa coefficient (K) of 0.62 across the three events. Further validation using ground control points (GCPs) resulted in an OA of 86.33% and a K of 0.72. Using the calibrated HEC-RAS model, hydraulic simulations were performed to map flood inundation for return periods of 5, 10, 25, 50, and 100 years. Additionally, flood mapping was conducted for historical (1981–2005), near-future (2031–2055), and far-future (2056–2080) periods under extreme climate scenarios. The results indicate increases of 16.48% and 27.23% in the flood inundation area in the near-future and far-future periods, respectively, under the SSP5-8.5 scenario compared with the historical period. These increases are attributed primarily to deforestation, agricultural expansion, and intensified extreme rainfall events in the upstream watershed. The comparison between SAR-based flood maps and HEC-RAS simulations highlights the advantages of integrating remote sensing and hydraulic modeling for enhanced flood risk assessment. This study provides critical insights for flood mitigation and sustainable watershed management, emphasizing the importance of incorporating current and future flood risk analyses in policy and planning efforts.

Diurnal rainfall variability over the Upper Blue Nile Basin: A remote sensing based approach

Reliable estimates of regional evapotranspiration are necessary to improve water resources management and planning. However, direct measurements of evaporation are expensive and difficult to obtain. Some of the difficulties are illustrated in a comparison of several satellite‐based estimates of evapotranspiration for the Upper Blue Nile (UBN) basin in Ethiopia. These estimates disagree both temporally and spatially. All the available data products underestimate evapotranspiration leading to basin‐scale mass balance errors on the order of 35 percent of the mean annual rainfall. This paper presents a methodology that combines satellite observations of rainfall, terrestrial water storage as well as river‐flow gauge measurements to estimate actual evapotranspiration over the UBN basin. The estimates derived from these inputs are constrained using a one‐layer soil water balance and routing model. Our results describe physically consistent long‐term spatial and temporal distributions of key hydrologic variables, including rainfall, evapotranspiration, and river‐flow. We estimate an annual evapotranspiration over the UBN basin of about 2.55 mm per day. Spatial and temporal evapotranspiration trends are revealed by dividing the basin into smaller subbasins. The methodology described here is applicable to other basins with limited observational coverage that are facing similar future challenges of water scarcity and climate change.

Net radiation is an important factor in studies of land–atmosphere processes, water resource management, and global climate change. This is particularly true for the Upper Blue Nile (UBN) basin, where significant parts of the basin are dry and evapotranspiration (ET) is a major mechanism for water loss. However, net radiation has not yet been appropriately parameterized in the basin. In this study, we estimated the instantaneous distribution of the net radiation flux in the basin using data from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor onboard the Terra satellite and Automatic Weather Station (AWS) data. Downward shortwave radiation and air temperature usually vary with topography, so we applied residual kriging spatial interpolation techniques to convert AWS data for point locations into gridded surface data. Simulated net radiation outputs were validated through comparison with independent field measurements. Validation results show that our method successfully reproduced the downward shortwave, upward shortwave, and net radiation fluxes. Using AWS data and residual kriging spatial interpolation techniques makes our results robust and comparable to previous works that used satellite data at a finer spatial resolution than MODIS. The estimated net shortwave, longwave, and total radiation fluxes were in close agreement with ground truth measurements, with mean bias (MB) values of − 14.84, 5.7, and 20.53 W m−2 and root mean square error (RMSE) values 83.43, 32.54, and 78.07 W m−2, respectively. The method presented here has potential applications in research focused on energy balance, ET estimation, and weather prediction for regions with similar physiographic features to those of the Nile basin.

A significant fraction of the inter-annual variability in the Nile River flow is shaped by El Niño Southern Oscillation (ENSO). Here, we investigate a similar role for the Indian Ocean (IO) sea surface temperature (SST) in shaping the inter-annual variability of the Nile River flow. Using observations of global SST distribution and river flow in addition to atmospheric general circulation model sensitivity experiments, we show that North and Middle IO SSTs play a significant intermediate role in the teleconnection between ENSO and the Nile flow. Applying partial coherency analyses, we demonstrate that the connection between North and Middle IO SSTs and Nile flow is strongly coupled to ENSO. During El Niño events, SST in the North and Middle IO increases in response to the warming in the Tropical Eastern Pacific Ocean and forces a Gill-type circulation with enhanced westerly low-level flow over East Africa and the Western IO. This anomalous low-level flow enhances the low-level flux of air and moisture away from the Upper Blue Nile (UBN) basin resulting in reduction of rainfall and river flow. SSTs in the South IO also play a significant role in shaping the variability of the Nile flow that is independent from ENSO. A warming over the South IO, generates a cyclonic flow in the boundary layer, which reduces the cross-equatorial meridional transport of air and moisture towards the UBN basin, favoring a reduction in rainfall and river flow. This independence between the roles of ENSO and South IO SSTs allows for development of new combined indices of SSTs to explain the inter-annual variability of the Nile flow. The proposed teleconnections have important implications regarding mechanisms that shape the regional impacts of climate change over the Nile basin.

Changes in land use and land cover (LULC) and climate increasingly influence flood occurrences in the Gumara watershed, located in the Upper Blue Nile (UBN) basin of Ethiopia. This study assesses how these factors impact return period-based peak floods, flood source areas, and future high-flow extremes. Merged rainfall data (1981–2019) and ensemble means of four CMIP5 and four CMIP6 models were used for historical (1981–2005), near-future (2031–2055), and far-future (2056–2080) periods under representative concentration pathways (RCP4.5 and RCP8.5) and shared socioeconomic pathways (SSP2-4.5 and SSP5-8.5). Historical LULC data for the years 1985, 2000, 2010, and 2019 and projected LULC data under business-as-usual (BAU) and governance (GOV) scenarios for the years 2035 and 2065 were used along with rainfall data to analyze flood peaks. Flood simulation was performed using a calibrated Hydrologic Engineering Center–Hydrologic Modeling System (HEC-HMS) model. The unit flood response (UFR) approach ranked eight subwatersheds (W1–W8) by their contribution to peak flood magnitude at the main outlet, while flow duration curves (FDCs) of annual maximum (AM) flow series were used to analyze changes in high-flow extremes. For the observation period, maximum peak flood values of 211.7, 278.5, 359.5, 416.7, and 452.7 m3/s were estimated for 5-, 10-, 25-, 50-, and 100-year return periods, respectively, under the 2019 LULC condition. During this period, subwatersheds W4 and W6 were identified as major flood contributors with high flood index values. These findings highlight the need to prioritize these subwatersheds for targeted interventions to mitigate downstream flooding. In the future period, the highest flow is expected under the SSP5-8.5 (2056–2080) climate scenario combined with the BAU-2065 land use scenario. These findings underscore the importance of strategic land management and climate adaptation measures to reduce future flood risks. The methodology developed in this study, particularly the application of RF-MERGE data in flood studies, offers valuable insights into the existing knowledge base on flood modeling.

Water resources development and research significantly suffered from lack of stream low data. Regionalization of model parameters was found veryuseful in filling such data gaps. We therefore regionalized the parameters of the HBV model so that the model could be used in ungauged catchments ofthe Upper Blue Nile (UBN) basin. Although we collected stream flow data for 76 stations from the Ministry of Water, Irrigation and Electricity, our dataquality assessment indicated that only 20 stations were suitable for calibrating the HBV model. We calibrated the model using hydro-climate data of 6 years and validated the calibrated model for independent data of 4 years. The calibrated model reproduced the overall pattern and base flow of the catchments. However, it noticeably missed several peak flows. The values of the calibrated parameters varied with the characteristics of the catchments. We therefore developed a multiple regression relationship between the parameters and catchment characteristics for the basin. The relationship was statistically significant and therefore could be used to apply the HBV model for un-gauged catchments in UBN for water balance studies, climate change impact assessment, and recharge estimation. However, additional work specifically improved data sets were needed to improve the regionalization results for peak flows.

The present study aimed to analyze the long-term trends and variability of rainfall in the Upper Blue Nile (UBN) basin in Ethiopia using Climate Hazards Group Infrared Precipitation with Stations (CHIRPS-v2) rainfall product from 1981 to 2018. A pixel-based rainfall variability and trend analysis was made at the annual, seasonal, and monthly temporal scales. The coefficient of variation (CV) was applied to compute the rainfall variability. The trends for each pixel were analyzed using Mann–Kendall (MK) trend test, while the Theil-Sen slope was used to estimate the magnitude of the trend. Results indicated that monthly and seasonal rainfall variabilities are high with the CV up to 140% and 70%, respectively. Annual rainfall variability was found to be low with CV ranging from 6 to 18%. The annual rainfall anomaly analysis, on the other hand, indicated that there are variations in the amount of rainfall received at different years and parts of the basin. It captured the spatial distributions of below- and above-average rainfall periods and its associated drought and flood events, respectively, observed in the basin. In general, the MK monotonic trend analysis for the annual, summer, spring, and autumn rainfall over the entire basin showed an average increasing trend at a rate of 2.48 mm year−1,1.16 mm year−1, 0.92 mm year−1, and 0.67 mm year−1, respectively. In contrast, the rainfall average trend during the winter season (− 0.15 mm year−1) indicated a decreasing trend. The consistency of per-pixel trends with previous studies, based on rain gauge observations, demonstrates the robustness of the trends. Furthermore, the completely raster-based analysis made in this study has provided essential information regarding the spatial variability and trends of rainfall in the basin, which was not satisfactorily captured by the conventional systems due to the larger inter-station distance between rain gauges. Thus, this could be a valuable addition to the existing knowledge of rainfall characteristics in the UBN basin.

The Upper Blue Nile (UBN) basin is less-explored in terms of drought studies as compared to other parts of Ethiopia and lacks a basin-specific drought monitoring system. This study compares six drought indices: Standardized Precipitation Index (SPI), Standardized Precipitation Evaporation Index (SPEI), Evapotranspiration Deficit Index (ETDI), Soil Moisture Deficit Index (SMDI), Aggregate Drought Index (ADI), and Standardized Runoff-discharge Index (SRI), and evaluates their performance with respect to identifying historic drought events in the UBN basin. The indices were calculated using monthly time series of observed precipitation, average temperature, river discharge, and modeled evapotranspiration and soil moisture from 1970 to 2010. The Pearson’s correlation coefficients between the six drought indices were analyzed. SPI and SPEI at 3-month aggregate period showed high correlation with ETDI and SMDI (r > 0.62), while SPI and SPEI at 12-month aggregate period correlate better with SRI. The performance of the six drought indices in identifying historic droughts: 1973–1974, 1983–1984, 1994–1995, and 2003–2004 was analyzed using data obtained from Emergency Events Database (EM-DAT) and previous studies. When drought onset dates indicated by the six drought indices are compared with that in the EM-DAT. SPI, and SPEI showed early onsets of drought events, except 2003–2004 drought for which the onset date was unavailable in EM-DAT. Similarly, ETDI, SMDI and SRI-3 showed early onset for two drought events and late onsets in one-drought event. In contrast, ADI showed late onsets for two drought events and early onset for one drought event. None of the six drought indices could individually identify the onsets of all the selected historic drought events; however, they may identify the onsets when combined by considering several input variables at different aggregate periods.

The objective of this paper is to investigate the potential of sentinel-1 SAR sensor products and the contribution of soil roughness parameters to estimate volumetric residual soil moisture (RSM) in the Upper Blue Nile (UBN) basin, Ethiopia. The backscatter contribution of crop residue water content was estimated using Landsat sensor product and the water cloud model (WCM). The surface roughness parameters were estimated from the Oh and Baghdadi models. A feed-forward artificial neural network (ANN) method was tested for its potential to translate SAR backscattering and surface roughness input variables to RSM values. The model was trained for three inversion configurations: (i) SAR backscattering from vertical transmit and vertical receive (SAR VV) polarization only; (ii) using SAR VV and the standard deviation of surface heights (), and (iii) SAR VV, , and optimal surface correlation length (). Field-measured volumetric RSM data were used to train and validate the method. The results showed that the ANN soil moisture estimation model performed reasonably well for the estimation of RSM using the single input variable of SAR VV data only. The ANN prediction accuracy was slightly improved when SAR VV and the surface roughness parameters ( and ) were incorporated into the prediction model. Consequently, the ANN’s prediction accuracy with root mean square error (RMSE) = 0.035 cm3/cm3, mean absolute error (MAE) = 0.026 cm3/cm3, and r = 0.73 was achieved using the third inversion configuration. The result implies the potential of Sentinel-1 SAR data to accurately retrieve RSM content over an agricultural site covered by stubbles. The soil roughness parameters are also potentially an important variable to soil moisture estimation using SAR data although their contribution to the accuracy of RSM prediction is slight in this study. In addition, the result highlights the importance of combining Sentinel-1 SAR and Landsat images based on an ANN approach for improving RSM content estimations over crop residue areas.

ABSTRACTClimatic and hydrological changes will likely be intensified in the Upper Blue Nile (UBN) basin by the effects of global warming. The extent of such effects for representative concentration pathways (RCP) climate scenarios is unknown. We evaluated projected changes in rainfall and evapotranspiration and related impacts on water availability in the UBN under the RCP4.5 scenario. We used dynamically downscaled outputs from six global circulation models (GCMs) with unprecedented spatial resolution for the UBN. Systematic errors of these outputs were corrected and followed by runoff modelling by the HBV (Hydrologiska ByrånsVattenbalansavdelning) model, which was successfully validated for 17 catchments. Results show that the UBN annual rainfall amount will change by −2.8 to 2.7% with a likely increase in annual potential evapotranspiration (in 2041–2070) for the RCP4.5 scenario. These changes are season dependent and will result in a likely decline in streamflow and an increase in soil moisture deficit in the basin.

Evaluation of runoff estimation from GRACE coupled with different meteorological gridded products over the Upper Blue Nile Basin