Finding sustainable water futures in data-sparse regions under climate change: Insights from the Turkwel River basin, Kenya

Study region the Turkwel river basin, Kenya experiences a high level of water scarcity due to its arid climate, high rainfall variability and rapidly growing water demand. Study focus Climate change, variability and rapid growth in water demand pose significant challenges to current and future water resources planning and allocation worldwide. In this paper a novel decision-scaling approach was applied to model the response of the Turkwel river basin’s water resources system to growing demand and climate stressors. A climate response surface was constructed by combining a water resource system model, climate data, and a range of water demand scenarios. New hydrological insights The results show that climate variability and increased water demand are each important drivers of water scarcity in the basin. Increases in water demand due to expanded irrigation strongly influences on the resilience of the basin’s water resource system to droughts caused by the global climate variability. The climate response surface offers a visual and flexible tool for decision-makers to understand the ways in which the system responds to climate variability and development scenarios. Policy decisions to accelerate water-dependent development and poverty reduction in arid and semi-arid lands that are characterised by rapid demographic, political and economic change in the short- to medium term have to promote low-regrets approaches that incorporate longer-term climate uncertainty

Development and evaluation of a framework for global flood hazard mapping

Nowadays, the development of high-resolution flood hazard models have become feasible at continental and global scale, and their application in developing countries and data-scarce regions can be extremely helpful to increase preparedness of population and reduce catastrophic impacts.The present work describes the development of a novel procedure for global flood hazard mapping, based on the most recent advances in large scale flood modelling. We derive a long-term dataset of daily river discharges from the hydrological simulations of the Global Flood Awareness System (GloFAS). Streamflow data is downscaled on a high resolution river network and processed to provide the input for local flood inundation simulations, performed with a two-dimensional hydrodynamic model. All flood-prone areas identified along the river network are then merged to create continental flood hazard maps for different return periods at 30'' resolution. We evaluate the performance of our methodology in several river basins across the globe by comparing simulated flood maps with both official hazard maps and a mosaic of flooded areas detected from satellite images. The evaluation procedure also includes comparisons with the results of other large scale flood models. We further investigate the sensitivity of the flood modelling framework to several parameters and modelling approaches and identify strengths, limitations and possible improvements of the methodology

The impact of lake and reservoir parameterization on global streamflow simulation

Lakes and reservoirs affect the timing and magnitude of streamflow, and are therefore essential hydrological model components, especially in the context of global flood forecasting. However, the parameterization of lake and reservoir routines on a global scale is subject to considerable uncertainty due to lack of information on lake hydrographic characteristics and reservoir operating rules. In this study we estimated the effect of lakes and reservoirs on global daily streamflow simulations of a spatially-distributed LISFLOOD hydrological model. We applied state-of-the-art global sensitivity and uncertainty analyses for selected catchments to examine the effect of uncertain lake and reservoir parameterization on model performance. Streamflow observations from 390 catchments around the globe and multiple performance measures were used to assess model performance. Results indicate a considerable geographical variability in the lake and reservoir effects on the streamflow simulation. Nash-Sutcliffe Efficiency (NSE) and Kling-Gupta Efficiency (KGE) metrics improved for 65% and 38% of catchments respectively, with median skill score values of 0.16 and 0.2 while scores deteriorated for 28% and 52% of the catchments, with median values −0.09 and −0.16, respectively. The effect of reservoirs on extreme high flows was substantial and widespread in the global domain, while the effect of lakes was spatially limited to a few catchments. As indicated by global sensitivity analysis, parameter uncertainty substantially affected uncertainty of model performance. Reservoir parameters often contributed to this uncertainty, although the effect varied widely among catchments. The effect of reservoir parameters on model performance diminished with distance downstream of reservoirs in favor of other parameters, notably groundwater-related parameters and channel Manning’s roughness coefficient. This study underscores the importance of accounting for lakes and, especially, reservoirs and using appropriate parameterization in large-scale hydrological simulations.</p

The effect of reference climatology on global flood forecasting

The Global Flood Awareness System (GloFAS) is a preoperational suite performing daily streamflow simulations to detect severe floods in large river basins. GloFAS defines the severity of a flood event with respect to thresholds estimated based on model-simulated streamflow climatology. Hence, determining accurate and consistent critical thresholds is important for its skillful flood forecasting. In this work, streamflow climatologies derived from two global meteorological inputs were compared, and their impacts on global flood forecasting were assessed. The first climatology is based on precipitation-corrected reanalysis data (ERA-Interim), which is currently used in the operational GloFAS forecast, while the second is derived from reforecasts that are routinely produced using the latest weather model. The results of the comparison indicate that 1) flood thresholds derived from the two datasets have substantial dissimilarities with varying characteristics across different regions of the globe; 2) the differences in the thresholds have a spatially variable impact on the severity classification of a flood; and 3) ERA-Interim produced lower flood threshold exceedance probabilities (and flood detection rates) than the reforecast for several large rivers at short forecast lead times, where the uncertainty in the meteorological forecast is smaller. Overall, it was found that the use of reforecasts, instead of ERA-Interim, marginally improved the flood detection skill of GloFAS forecasts

The effect of reference climatology on global flood forecasting

The Global Flood Awareness System (GloFAS) is a preoperational suite performing daily streamflow simulations to detect severe floods in large river basins. GloFAS defines the severity of a flood event with respect to thresholds estimated based on model-simulated streamflow climatology. Hence, determining accurate and consistent critical thresholds is important for its skillful flood forecasting. In this work, streamflow climatologies derived from two global meteorological inputs were compared, and their impacts on global flood forecasting were assessed. The first climatology is based on precipitation-corrected reanalysis data (ERA-Interim), which is currently used in the operational GloFAS forecast, while the second is derived from reforecasts that are routinely produced using the latest weather model. The results of the comparison indicate that 1) flood thresholds derived from the two datasets have substantial dissimilarities with varying characteristics across different regions of the globe; 2) the differences in the thresholds have a spatially variable impact on the severity classification of a flood; and 3) ERA-Interim produced lower flood threshold exceedance probabilities (and flood detection rates) than the reforecast for several large rivers at short forecast lead times, where the uncertainty in the meteorological forecast is smaller. Overall, it was found that the use of reforecasts, instead of ERA-Interim, marginally improved the flood detection skill of GloFAS forecasts

Calibration of the Global Flood Awareness System (GloFAS) using daily streamflow data

This paper presents the calibration and evaluation of the Global Flood Awareness System 33 (GloFAS), an operational system that produces ensemble streamflow forecasts and threshold 34 exceedance probabilities for large rivers worldwide. The system generates daily streamflow 35 forecasts using a coupled H-TESSEL land surface scheme and the LISFLOOD model forced by 36 ECMWF IFS meteorological forecasts. The hydrology model currently uses a priori parameter 37 estimates with uniform values globally, which may limit the streamflow forecast skill. Here, the 38 LISFLOOD routing and groundwater model parameters are calibrated with ECMWF reforecasts 39 from 1995 to 2015 as forcing using daily streamflow data from 1287 stations worldwide. The 40 calibration of LISFLOOD parameters is performed using an evolutionary optimization algorithm 41 with the Kling-Gupta Efficiency (KGE) as objective function. The skill improvements are 42 quantified by computing the skill scores as the change in KGE relative to the baseline simulation 43 using a priori parameters. The results show that simulation skill has improved after calibration 44 (KGE skill score > 0.08) for the large majority of stations during the calibration (67% globally 45 and 77% outside of North America) and validation (60% globally and 69% outside of North 46 America) periods compared to the baseline simulation. However, the skill gain was impacted by 47 the bias in the baseline simulation (the lowest skill score was obtained in basins with negative 48 bias) due to the limitation of the model in correcting the negative bias in streamflow. Hence, 49 further skill improvements could be achieved by reducing the bias in the streamflow by 50 improving the precipitation forecasts and the land surface model. The results of this work will 51 have implications on improving the operational GloFAS flood forecasting 52 (www.globalfloods.eu)

Impacts of climate change and population growth on river nutrient loads in a data scarce region: the upper Awash River (Ethiopia)

Assessing the impact of climate change and population growth on river water quality is a key issue for many developing countries, where multiple and often conflicting river water uses (water supply, irrigation, wastewater disposal) are placing increasing pressure on limited water resources. However, comprehensive water quality datasets are often lacking, thus impeding a full-scale data-based river water quality assessment. Here we propose a model-based approach, using both global datasets and local data to build an evaluation of the potential impact of climate changes and population growth, as well as to verify the efficiency of mitigation measures to curb river water pollution. The upper Awash River catchment in Ethiopia, which drains the city of Addis Ababa as well as many agricultural areas, is used as a case-study. The results show that while decreases in runoff and increases in temperature due to climate change are expected to result in slightly decreased nutrient concentrations, the largest threat to the water quality of the Awash River is population growth, which is expected to increase nutrient loads by 15 to 20% (nitrate) and 30 to 40% (phosphorus) in the river by the second half of the 21st century. Even larger increases are to be expected downstream of large urban areas, such as Addis Ababa. However, improved wastewater treatment options are shown to be efficient in counteracting the negative impact of population growth and returning water pollution to acceptable levels