Drought analysis of African rivers: a study of non-stationary low-flow trends in the Volta and Nile

Proceedings of the Seventh International Conference on Hydroscience and Engineering, Philadelphia, PA, September 2006. http://hdl.handle.net/1860/732The Volta basin is a significant river system in West Africa. It drains approximately 400,000 km2. Droughts continue to pose a threat to the economic development of six riparian states in the basin. In a region where food production and hydro-power generation are vulnerable to climatic risk, knowledge of drought patterns can prove indispensable in the effort towards the realization of the millennium Development Goals. The paper conducts a low-flow study in the Volta basin. Discharge proxies for drought conditions, consisting of annual minimum n-day flows obtained from gauging stations located in the Volta basin, are used to estimate drought quantiles. Weibull distribution is used for initial low-flow estimation. Simulated annealing is utilized as the optimization algorithm for Weibull parameter estimation towards an exploration of the maximum likelihood. Analysis of distribution fitting is then performed with L-Moment ratios which suggest the best fitted distribution function. The study is intended as a step toward the development of methodologies to evaluate impacts of extreme low-flow events on the design of reservoirs to suffice irrigation and hydropower in the Volta

Adaptation and application of the large LAERTES-EU regional climate model ensemble for modeling hydrological extremes: a pilot study for the Rhine basin

Enduring and extensive heavy precipitation events associated with widespread river floods are among the main natural hazards affecting central Europe. Since such events are characterized by long return periods, it is difficult to adequately quantify their frequency and intensity solely based on the available observations of precipitation. Furthermore, long-term observations are rare, not homogeneous in space and time, and thus not suitable to running hydrological models (HMs) with respect to extremes. To overcome this issue, we make use of the recently introduced LAERTES-EU (LArge Ensemble of Regional climaTe modEl Simulations for EUrope) data set, which is an ensemble of regional climate model simulations providing over 12 000 simulated years. LAERTES-EU is adapted for use in an HM to calculate discharges for large river basins by applying quantile mapping with a parameterized gamma distribution to correct the mainly positive bias in model precipitation. The Rhine basin serves as a pilot area for calibration and validation. The results show clear improvements in the representation of both precipitation (e.g., annual cycle and intensity distributions) and simulated discharges by the HM after the bias correction. Furthermore, the large size of LAERTES-EU also improves the statistical representativeness for high return values above 100 years of discharges. We conclude that the bias-corrected LAERTES-EU data set is generally suitable for hydrological applications and posterior risk analyses. The results of this pilot study will soon be applied to several large river basins in central Europe

Adaptation and application of the large LAERTES-EU RCM ensemble for modeling hydrological extremes: A pilot study for the Rhine basin

Enduring and extensive heavy precipitation associated with widespread river floods are among the main natural hazards affecting Central Europe. Since such events are characterized by long return periods, it is difficult to adequately quantify their frequency and intensity solely based on the available observations of precipitation. Furthermore, long-term observations are rare, not homogeneous in space and time, and thus not suitable to run hydrological models (HMs) with respect to extremes. To overcome this issue, we make use of the recently introduced LAERTES-EU (LArge Ensemble of Regional climaTe modEl Simulations for EUrope) data set, which is an ensemble of regional climate model simulations providing over 12.000 simulated years. LAERTES-EU is adapted for the use in an HM to calculate discharges for large river basins by applying a quantile mapping with a fixed density function to correct the mainly positive bias in model precipitation. The Rhine basin serves as a pilot area for calibration and validation. The results show clear improvements in the representation of both precipitation (e.g., annual cycle and intensity distributions) and simulated discharges by the HM after the bias correction. Furthermore, the large size of LAERTES-EU improves the statistical representativeness also for high return values above 100 years of discharges. We conclude that the bias-corrected LAERTES-EU data set is generally suitable for hydrological applications and posterior risk analyses. The results of this pilot study will soon be applied to several large river basins in Central Europe

Quantifying risks avoided by limiting global warming to 1.5 or 2 °C above pre-industrial levels

The Paris Agreement aims to constrain global warming to ‘well below 2 °C’ and to ‘pursue efforts’ to limit it to 1.5 °C above pre-industrial levels. We quantify global and regional risk-related metrics associated with these levels of warming that capture climate change–related changes in exposure to water scarcity and heat stress, vector-borne disease, coastal and fluvial flooding and projected impacts on agriculture and the economy, allowing for uncertainties in regional climate projection. Risk-related metrics associated with 2 °C warming, depending on sector, are reduced by 10–44% globally if warming is further reduced to 1.5 °C. Comparing with a baseline in which warming of 3.66 °C occurs by 2100, constraining warming to 1.5 °C reduces these risk indicators globally by 32–85%, and constraining warming to 2 °C reduces them by 26–74%. In percentage terms, avoided risk is highest for fluvial flooding, drought, and heat stress, but in absolute terms risk reduction is greatest for drought. Although water stress decreases in some regions, it is often accompanied by additional exposure to flooding. The magnitude of the percentage of damage avoided is similar to that calculated for avoided global economic risk associated with these same climate change scenarios. We also identify West Africa, India and North America as hotspots of climate change risk in the future

Application of a fuzzy logic tool for linking hydro-ecological simulation output to decision support

Proceedings of the Seventh International Conference on Hydroscience and Engineering, Philadelphia, PA, September 2006. http://hdl.handle.net/1860/732Riverine ecosystems are critical habitats for a variety of threatened species. They are under continuous threat of destruction and are bedevilled with complex hydro-environmental problems. Mathematical models can serve as powerful tools in solving water resources problems. Most of the models available for water management are crisp, deterministic and precise in character. However, most water related problems are neither crisp nor deterministic. Solutions to such problems require a cocktail of models along with expert knowledge, which is formulated with words. This paper is expected to help bridge the gap between simulation output and policy formulation by proposing a framework for the integration of linguistic guidelines and indicators, developed a priori, into a purely numerical hydro-ecological system. The paper also discusses limitations in the use of traditional numerical models to aid decision support and initiate policy. Principles of the LiNK algorithm concept are illustrated with an example of a hydropower project on the habitat of endangered hippopotamus in a protected park

Quantification of impacts between 1.5°C and 4°C of global warming on flooding risks in six countries

We project climate change induced changes in fluvial flood risks for six global warming levels between 1.5 and 4°C by 2100, focusing on the major river basins of six countries. Daily time series of precipitation, temperature and monthly potential evapotranspiration were generated by combining monthly observations, daily reanalysis data, and projected changes in the five CMIP5 GCMs also selected in the ISI-MIP fast track project. These series were then used to drive the HBV hydrological model and the CaMa-Flood hydrodynamic model to simulate river discharge and flood inundation. Our results indicate that return periods of 1 in 100-year floods in the late 20th century (Q100-20C) are likely to decrease with warming. At 1.5°C warming, 47%, 66%, 27%, 65%, 62% and 92% of the major basin areas in Brazil, China, Egypt, Ethiopia, Ghana and India respectively experience a decrease in the return period of Q100-20C, increasing to 54%, 81%, 28%, 82%, 86% and 96% with 4°C warming. The decrease in return periods leads to increased number of people exposed to flood risks, particularly with 4°C warming, where exposure in the major river basin areas in the six countries increase significantly, ranging from a doubling (China) to more than 50-fold (Egypt). Limiting warming to 1.5°C would avoid much of these increased risks, resulting in increases ranging from 12% to 1266% for the 6 countries

Quantifying risks avoided by limiting global warming to 1.5 or 2 °C above pre-industrial levels

Funder: Department for Business, Energy and Industrial Strategy; doi: http://dx.doi.org/10.13039/100011693Abstract The Paris Agreement aims to constrain global warming to ‘well below 2 °C’ and to ‘pursue efforts’ to limit it to 1.5 °C above pre-industrial levels. We quantify global and regional risk-related metrics associated with these levels of warming that capture climate change–related changes in exposure to water scarcity and heat stress, vector-borne disease, coastal and fluvial flooding and projected impacts on agriculture and the economy, allowing for uncertainties in regional climate projection. Risk-related metrics associated with 2 °C warming, depending on sector, are reduced by 10–44% globally if warming is further reduced to 1.5 °C. Comparing with a baseline in which warming of 3.66 °C occurs by 2100, constraining warming to 1.5 °C reduces these risk indicators globally by 32–85%, and constraining warming to 2 °C reduces them by 26–74%. In percentage terms, avoided risk is highest for fluvial flooding, drought, and heat stress, but in absolute terms risk reduction is greatest for drought. Although water stress decreases in some regions, it is often accompanied by additional exposure to flooding. The magnitude of the percentage of damage avoided is similar to that calculated for avoided global economic risk associated with these same climate change scenarios. We also identify West Africa, India and North America as hotspots of climate change risk in the future.</jats:p