This study presents an investigation of historical and future changes of annual maximum streamflow using a comprehensive streamflow archive and six global hydrological models (GHMs). Historical trends simulated by GHMs were evaluated across 3,666 locations over the period from 1971 to 2005, focusing on four aspects of trends: (i) mean, (ii) standard deviation, (iii) percentage of locations showing significant trends and (iv) spatial pattern. The capacity of models to simulate spatial patterns of historical trends is moderate, with 12-25% variance of observed trends explained by model simulations.

Under the RCP6.0 scenario, most discharge simulations project an increasing trend in flood magnitude over Siberia, South and South-East Asia while southern Australia, the Mediterranean, and eastern Europe are projected to exhibit a decreasing trend. High-risk regions (i.e. projected with an increasing trend by at least 11 models), however, are currently sparsely monitored, covered by less than 1% of all gauges available in the Global Streamflow Indices and Metadata archive.

Over the last two decades, water levels of the Laurentian Great Lakes have exhibited an accelerated transition from extreme lows to extreme highs, posing a challenge to regional water resources management. Improved understanding of the factors driving changes in water levels of the Great Lakes is essential to enable the modification and advancement of new water policies in the context of climate change. This study investigated the variation of water budget components and their contributions to changes in water levels across the Great Lakes, with a focus on understanding changes in the peak water level season. To identify drivers of recent water level changes, we developed a new seventy-year long record of monthly water budget data using the Large Lakes Statistical Water Balance Model, a recently developed model encoded in a Bayesian Markov chain Monte Carlo framework. The recent surge in water levels was observed consistently over the Great Lakes system, but the key mechanism driving this pattern varies substantially across individual lakes. The new water budget estimate, with the capacity to reconcile the water balance, highlights the benefit of moving to a probabilistic framework in water resources accounting for the Great Lakes.

Lake Chilwa is the second-largest lake in Malawi, and services as a critical resource to the region for drinking water supply, the fishing industry, and agricultural production. However, Lake Chilwa has recently experienced episodic extreme drops in water level, raising concerns about its ability to continue supporting these needs. There are multiple historical studies of Lake Chilwa, however they tend to focus on fish productivity and ecosystem habitat. Here, we provide one of the first comprehensive assessments of the Lake Chilwa water balance using a combination of historical, sparse, in situ data records, and a novel statistical model that has previously been applied to the Laurentian Great Lakes (in North America). More specifically, we employ this statistical model to develop historical estimates of Lake Chilwa precipitation, evaporation, and tributary inflow that close the water balance over consecutive historical periods. This new data record not only helps citizens and practitioners understand drivers of Lake Chilwa water level variability over time, it also provides a platform for a new seasonal water balance and water level forecasting system that can help practitioners better plan for water level variability in the future.

Numerous on-line interactive tools and data sets have been developed to better understand climate and hydrological variability across Earth’s large lakes, including the Laurentian Great Lakes of North America. Many of these tools were developed through an interactive process involving broad teams of scientists, water management practitioners, and representatives from the public at-large. Here, we present the most recent evolution of this work involving the customization of an on-line tool for viewing, understanding, and accessing hydro-climate data for lakes in Eastern Africa. Specifically, we highlight ongoing work with the Malawi government officials and research scientists to synthesize historical water level and water balance data for both Lake Malawi and Lake Chilwa (Malawi’s two largest lakes). This work strive to generate a historical record that reflects the impacts of a changing climate while allowing managers to better plan for future water level variability as well.