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This paper addresses the question of how catchment-scale water and energy balances have responded to climatic and land surface changes over the last 70 years in the federal state of Saxony in eastern Germany. Therefore, observational data of hydrological and meteorological monitoring sites from 1951 to 2020 across 71 catchments are examined in a relative water- and energy-partitioning framework to put the recent drought-induced changes into a historical perspective. A comprehensive visualization method is used to analyze the observed time series. The study focuses on changes on a decadal timescale and finds the largest decline in observed runoff in the last decade (2011–2020). The observed decline can be explained by the significant increase in aridity, caused by the reduction in annual mean rainfall and a simultaneous increase in potential evaporation. In a few mainly forested headwater catchments, the observed decline in runoff was even stronger than predicted by climate conditions alone. These catchments are still recovering from past widespread forest damages sustained in the 1970s to 1980s, resulting in a continuous increase in actual evapotranspiration due to forest regrowth. On the contrary, runoff stayed almost constant in other catchments despite an increase in aridity. These results highlight that water budgets in Saxony are in an unstable, non-stationary regime due to significant climatic changes and the regional impacts of land surface changes such as forest health. The recent decreases in the mean annual runoff are substantial and must be taken into account by the authorities for freshwater management. [ABSTRACT FROM AUTHOR]

Lakes are directly exposed to climate variations as their recharge processes are driven by precipitation and evapotranspiration, and they are also affected by groundwater trends, changing ecosystems and changing water use. In this study, we present a downward model development approach that uses models of increasing complexity to identify and quantify the dependence of lake level variations on climatic and other factors. The presented methodology uses high-resolution gridded weather data inputs that were obtained from dynamically downscaled ERA5 reanalysis data. Previously missing fluxes and previously unknown turning points in the system behavior are identified via a water balance model. The detailed lake level response to weather events is analyzed by calibrating data-driven models over different segments of the data time series. Changes in lake level dynamics are then inferred from the parameters and simulations of these models. The methodology is developed and presented for the example of Groß Glienicker Lake, a groundwater-fed lake in eastern Germany that has been experiencing increasing water loss in the last half-century. We show that lake dynamics were mainly controlled by climatic variations in this period, with two systematically different phases in behavior. The increasing water loss during the last decade, however, cannot be accounted for by climate change. Our analysis suggests that this alteration is caused by the combination of regional groundwater decline and vegetation growth in the catchment area, with some additional impact from changes in the local rainwater infrastructure. [ABSTRACT FROM AUTHOR]

Long-term trends in flood losses are regulated by multiple factors including climate variation, demographic dynamics, economic growth, land-use transitions, reservoir construction and flood risk reduction measures. The attribution of those drivers through the use of counterfactual scenarios of hazard, exposure or vulnerability first requires a good representation of historical events, including their location, their intensity and the factual circumstances in which they occurred. Here, we develop a chain of models that is capable of recreating riverine, coastal and compound floods in Europe between 1950 and 2020 that had a potential to cause significant socioeconomic impacts. This factual catalogue of almost 15 000 such events was scrutinized with historical records of flood impacts. We found that at least 10 % of them led to significant socioeconomic impacts (including fatalities) according to available sources. The model chain was able to capture events responsible for 96 % of known impacts contained in the Historical Analysis of Natural Hazards in Europe (HANZE) flood impact database in terms of persons affected and economic losses and for 81 % of fatalities. The dataset enables the study of the drivers of vulnerability and flood adaptation due to a large sample of events with historical impact data. The model chain can be further used to generate counterfactual events, especially those related to climate change and human influence on catchments. [ABSTRACT FROM AUTHOR]

​​​​​​​Severe floods with extreme return periods of 100 years and beyond have been observed in several large rivers in Bavaria in the last 3 decades. Flood protection structures are typically designed based on a 100-year event, relying on statistical extrapolations of relatively short observation time series while ignoring potential temporal non-stationarity. However, future precipitation projections indicate an increase in the frequency and intensity of extreme rainfall events, as well as a shift in seasonality. This study aims to examine the impact of climate change on the 100-year flood (HF 100) events of 98 hydrometric gauges within hydrological Bavaria. A hydrological climate change impact (CCI) modeling chain consisting of a regional Single Model Initial-condition Large Ensemble (SMILE) and a single hydrological model was created. The 50 equally probable members of the Canadian Regional Climate Model version 5 large ensemble (CRCM5-LE) were used to drive the hydrological model WaSiM (Water balance Simulation Model) to create a hydro-SMILE. As a result, a database of 1500 model years (50 members × 30 years) per investigated time period was established for extreme value analysis (EVA) to illustrate the benefit of the hydro-SMILE approach for a robust estimation of HF 100 based on annual maxima (AM) and to examine the CCI on the frequency and magnitude of HF 100 in different discharge regimes under a strong-emission scenario (RCP8.5). The results demonstrate that the hydro-SMILE approach provides a clear advantage for a robust estimation of HF 100 using the empirical probability of 1500 AM compared to its estimation using the generalized extreme value (GEV) distribution of 1000 samples of typically available time series sizes of 30, 100, and 200 years. Thereby, by applying the hydro-SMILE framework, the uncertainty from statistical estimation can be reduced. The study highlights the added value of using hydrological SMILEs to project future flood return levels. The CCI of HF 100 varies for different flow regimes, with snowmelt-driven catchments experiencing severe increases in frequency and magnitude, leading to unseen extremes that impact the distribution. Pluvial regimes show a lower intensification or even decline. The dynamics of HF 100 driving mechanisms depict a decline in snowmelt-driven events in favor of rainfall-driven events, an increase in events driven by convective rainfall, and almost no change in the ratio between single-driver and compound events towards the end of the century. [ABSTRACT FROM AUTHOR]

Extremely large floods that far exceed previously observed records are often considered virtually "impossible", yet they are an ever-present threat similar to the sword suspended over the head of Damocles in the classical Greek anecdote. Neglecting such floods may lead to emergency situations where society is unprepared and to disastrous consequences. Four reasons why extremely large floods are often considered next to impossible are explored here, including physical (e.g. climate change), psychological, socio-economic and combined reasons. It is argued that the risk associated with an "impossible" flood may often be larger than expected and that a bottom-up approach should be adopted that starts from the people affected and explores possibilities of risk management, giving high priority to social in addition to economic risks. Suggestions are given for managing this risk of a flood considered impossible by addressing the diverse causes of the presumed impossibility. [ABSTRACT FROM AUTHOR]

An event-based approach has been used to explore the potential effects of climate change on the spatial and temporal coherence of widespread flood events in Great Britain. Time series of daily mean river flow were generated using a gridded national-scale hydrological model (Grid-to-Grid) driven by a 12-member ensemble of regional climate projections from UK Climate Projections 2018 (UKCP18), for 30-year baseline (1980–2010) and future (2050–2080) time slices. From these, sets of widespread extreme events were extracted. The question of what defines a "widespread flood event" is discussed; here it was defined as an event exceeding an at-site 99.5th percentile (equivalent to 2 d per year) simultaneously over an area of at least 20 km 2 , with a maximum duration of 14 d. This resulted in a set of 14 400 widespread events: approximately 20 events per year, per ensemble member, per time slice. Overall, results have shown that events are more temporally concentrated in winter in the future time slice compared to the baseline. Distributions of event area were similar in both time slices, but the distribution of at-site return periods showed some heavier tails in the future time slice. Such information could be useful for adaptation planning and risk management for floods under climate change, but the potential future changes have to be interpreted in the context of some differences in event characteristics between the baseline climate-projection-driven model runs and an observation-driven model run. While the focus here is Great Britain, the methods and analyses described could be applied to other regions with hydrological models and climate projections of appropriate resolution. [ABSTRACT FROM AUTHOR]

There is high confidence that global warming intensifies all components of the global water cycle. This work investigates the possible effects of global warming on river flows worldwide in the coming decades. We conducted 18 global hydrological simulations to assess how river flows are projected to change in the near future (2015–2050) compared to the recent past (1950–2014). The simulations are forced by runoff from the High Resolution Model Intercomparison Project (HighResMIP) CMIP6 global climate models (GCMs), which assume a high-emission scenario for the projections. The assessment includes estimating the signal-to-noise (S/N) ratio and the time of emergence (ToE) of all the rivers in the world. Consistently with the water cycle intensification, the hydrological simulations project a clear positive global river discharge trend from ∼2000 that emerges beyond the levels of natural variability and becomes "unfamiliar" by 2017 and "unusual" by 2033. Simulations agree that the climate change signal is dominated by strong increases in the flows of rivers originating in central Africa and South Asia and those discharging into the Arctic Ocean, partially compensated for by the reduced flow projected for Patagonian rivers. The potential implications of such changes may include more frequent floods in central African and South Asian rivers, driven by the projected magnification of the annual cycles with unprecedented peaks, a freshening of the Arctic Ocean from extra freshwater release, and limited water availability in Patagonia given the projected drier conditions of its rivers. This underscores the critical need for a paradigm shift in prioritizing water-related concerns amidst the challenges of global warming. [ABSTRACT FROM AUTHOR]

Widespread afforestation has been proposed internationally to reduce atmospheric carbon dioxide; however, the specific hydrological consequences and benefits of such large-scale afforestation (e.g. natural flood management) are poorly understood. We use a high-resolution land surface model, the Joint UK Land Environment Simulator (JULES), with realistic potential afforestation scenarios to quantify possible hydrological change across Great Britain in both present and projected climate. We assess whether proposed afforestation produces significantly different regional responses across regions; whether hydrological fluxes, stores and events are significantly altered by afforestation relative to climate; and how future hydrological processes may be altered up to 2050. Additionally, this enables determination of the relative sensitivity of land surface process representation in JULES compared to climate changes. For these three aims we run simulations using (i) past climate with proposed land cover changes and known floods and drought events; (ii) past climate with independent changes in precipitation, temperature, and CO 2 ; and (iii) a potential future climate (2020–2050). We find the proposed scale of afforestation is unlikely to significantly alter regional hydrology; however, it can noticeably decrease low flows whilst not reducing high flows. The afforestation levels minimally impact hydrological processes compared to changes in precipitation, temperature, and CO 2. Warming average temperatures (+3 °C) decreases streamflow, while rising precipitation (130 %) and CO 2 (600 ppm) increase streamflow. Changes in high flow are generated because of evaporative parameterizations, whereas low flows are controlled by runoff model parameterizations. In this study, land surface parameters within a land surface model do not substantially alter hydrological processes when compared to climate. [ABSTRACT FROM AUTHOR]

This article provides high-resolution information on the projected changes in annual extreme rainfall and high- and low-streamflow events over Southeast Asia under extreme climate change. The analysis was performed using the bias-corrected result of the High-Resolution Model Intercomparison Project (HighResMIP) multi-model experiment for the period 1971–2050. Eleven rainfall indices were calculated, along with streamflow simulation using the PCR-GLOBWB hydrological model. The historical period 1981–2010 and the near-future period 2021–2050 were considered for this analysis. Results indicate that, over former mainland Southeast Asia, Myanmar will face more challenges in the near future. The east coast of Myanmar will experience more extreme high-rainfall conditions, while northern Myanmar will have longer dry spells. Over the Indonesian maritime continent, Sumatra and Java will suffer from an increase in dry-spell length of up to 40 %, while the increase in extreme high rainfall will occur over Borneo and mountainous areas in Papua. Based on the streamflow analysis, the impact of climate change is more prominent in a low-flow event than in a high-flow event. The majority of rivers in the central Mekong catchment, Sumatra, Peninsular Malaysia, Borneo, and Java will experience more extreme low-flow events. More extreme dry conditions in the near future are also seen from the increasing probability of future low-flow occurrences, which reaches 101 % and 90 %, on average, over Sumatra and Java, respectively. In addition, based on our results over Java and Sumatra, we found that the changes in extreme high- and low-streamflow events are more pronounced in rivers with steep hydrographs (rivers where flash floods are easily triggered), while rivers with flat hydrographs have a higher risk in terms of the probability of low-flow change. [ABSTRACT FROM AUTHOR]

Lakes experience shifts in the timing of physical and biogeochemical events as a result of climate warming, and relative changes in the timing of events may have important ecological consequences. Spring, in particular, is a period in which many key processes that regulate the ecology and biogeochemistry of lakes occur and also a time that may experience significant changes under the influence of global warming. In this study, we used a coupled catchment–lake model forced by future climate projections to evaluate changes in the timing of spring discharge, ice-off, the spring phytoplankton peak, and the onset of stratification in a temperate mesotrophic lake. Although the model explained only part of the variation in these events, the overall patterns were simulated with little bias. All four events showed a clear trend towards earlier occurrence under climate warming, with ice cover tending to disappear at the end of the century in the most extreme climate scenario. Moreover, relative shifts in the timing of these springtime events also occurred, with the onset of stratification tending to advance more slowly than the other events and the spring phytoplankton peak and ice-off advancing faster in the most extreme climate scenario. The outcomes of this study stress the impact of climate change on the phenology of events in lakes and especially the relative shifts in timing during spring. This can have profound effects on food web dynamics as well as other regulatory processes and influence the lake for the remainder of the growing season. [ABSTRACT FROM AUTHOR]

In drought-prone Australia, multi-year droughts have detrimental impacts on both the natural environment and human societies. For responsible water management, we need a thorough understanding of the full range of variability in multi-year droughts and how this might change in a warming world. But research into the long-term frequency, persistence, and severity of Australian droughts is limited. This is partly due to the length of the observational record, which is short relative to the timescales of hydroclimatic variability and hence may not capture the range of possible variability. Using simulations of Australian precipitation over the full past millennium (850–2000), we characterise the nature of multi-year meteorological droughts across Australia and include a particular focus on the Murray–Darling Basin (MDB), the largest agricultural region in Australia. We find that simulated Australian droughts in the 20th century (1900–2000) are within the bounds of pre-industrial natural variability in terms of drought intensity, severity, and frequency. A tendency for droughts to last longer in southwestern and eastern Australia (including the MDB) in the 20th century, compared with the pre-industrial period, suggests an emerging anthropogenic influence, consistent with projected rainfall changes in these regions. Large volcanic eruptions tend to promote drought-free intervals in the MDB. Model simulations of droughts over the last millennium suggest that future droughts across Australia could be much longer than what was experienced in the 20th century, even without any human influence. With the addition of anthropogenic climate change, which favours drought conditions across much of southern Australia due to reduced cool-season rainfall, it is likely that future droughts in Australia will exceed recent historical experience. [ABSTRACT FROM AUTHOR]

In response to flood risk, design flood estimation is a cornerstone of planning, infrastructure design, setting of insurance premiums, and emergency response planning. Under stationary assumptions, flood guidance and the methods used in design flood estimation are firmly established in practice and mature in their theoretical foundations, but under climate change, guidance is still in its infancy. Human-caused climate change is influencing factors that contribute to flood risk such as rainfall extremes and soil moisture, and there is a need for updated flood guidance. However, a barrier to updating flood guidance is the translation of the science into practical application. For example, most science pertaining to historical changes to flood risk focuses on examining trends in annual maximum flood events or the application of non-stationary flood frequency analysis. Although this science is valuable, in practice, design flood estimation focuses on exceedance probabilities much rarer than annual maximum events, such as the 1 % annual exceedance probability event or even rarer, using rainfall-based procedures, at locations where there are few to no observations of streamflow. Here, we perform a systematic review to summarize the state-of-the-art understanding of the impact of climate change on design flood estimation in the Australian context, while also drawing on international literature. In addition, a meta-analysis, whereby results from multiple studies are combined, is conducted for extreme rainfall to provide quantitative estimates of possible future changes. This information is described in the context of contemporary design flood estimation practice to facilitate the inclusion of climate science into design flood estimation practice. [ABSTRACT FROM AUTHOR]

A deep learning model designed for time series predictions, the long short-term memory (LSTM) architecture, is regularly producing reliable results in local and regional rainfall–runoff applications around the world. Recent large-sample hydrology studies in North America and Europe have shown the LSTM model to successfully match conceptual model performance at a daily time step over hundreds of catchments. Here we investigate how these models perform in producing monthly runoff predictions in the relatively dry and variable conditions of the Australian continent. The monthly time step matches historic data availability and is also important for future water resources planning; however, it provides significantly smaller training datasets than daily time series. In this study, a continental-scale comparison of monthly deep learning (LSTM) predictions to conceptual rainfall–runoff (WAPABA model) predictions is performed on almost 500 catchments across Australia with performance results aggregated over a variety of catchment sizes, flow conditions, and hydrological record lengths. The study period covers a wet phase followed by a prolonged drought, introducing challenges for making predictions outside of known conditions – challenges that will intensify as climate change progresses. The results show that LSTM models matched or exceeded WAPABA prediction performance for more than two-thirds of the study catchments, the largest performance gains of LSTM versus WAPABA occurred in large catchments, the LSTMs struggled less to generalise than the WAPABA models (e.g. making predictions under new conditions), and catchments with few training observations due to the monthly time step did not demonstrate a clear benefit with either WAPABA or LSTM. [ABSTRACT FROM AUTHOR]

The major rivers on the Tibetan Plateau supply important freshwater resources to riparian regions but have been undergoing significant climate change in recent decades. Understanding the sensitivities of hydrological processes to climate change is important for water resource management, but large divergences exist in previous studies because of the uncertainties of hydrological models and climate projection data. Meanwhile, the spatial pattern of local hydrological sensitivities was poorly explored despite the strong heterogeneity on the Tibetan Plateau. This study adopted the climate perturbation method to analyze the hydrological sensitivities of a typical large mountainous basin (Yarlung Tsangpo River, YTR) to climate change. We utilized the tracer-aided hydrological model Tsinghua Representative Elementary Watershed-Tracer-aided version (THREW-T) to simulate the hydrological and cryospheric processes in the YTR basin. Multiple datasets and internal stations were used to validate the model to provide confidence in the baseline simulation and the sensitivity analysis. Results indicated that (1) the THREW-T model performed well in simulating the streamflow, snow cover area (SCA), glacier mass balance (GMB) and stream water isotope, ensuring good representation of the key cryospheric processes and a reasonable estimation of the runoff components. The model performed acceptably in simulating the streamflow at eight internal stations located in the mainstream and two major tributaries, indicating that the spatial pattern of hydrological processes was reflected by the model. (2) Increasing temperature led to decreasing annual runoff, smaller inter-annual variation, more even intra-annual distribution and an earlier maximum runoff. It also influenced the runoff regime by increasing the contributions of rainfall and glacier melt overland runoff but decreasing the subsurface runoff and snowmelt overland runoff. Increasing precipitation had the opposite effect to increasing temperature. (3) The local runoff change in response to increasing temperature varied significantly, with a changing rate of -18.6 % to 54.3 % for 5 ∘ of warming. The glacier area ratio (GAR) was the dominant factor in the spatial pattern of hydrological sensitivities to both perturbed temperature and precipitation. Some regions had a non-monotonic runoff change rate in response to climate perturbation, which represented the most dynamic regions within the basin, as they kept shifting between energy- and water-limited stages. The GAR and mean annual precipitation (MAP) of the non-monotonic regions had a linear relation and formed the boundary of regions with different runoff trends in the GAR–MAP plot. [ABSTRACT FROM AUTHOR]

Deep learning (DL) rainfall–runoff models outperform conceptual, process-based models in a range of applications. However, it remains unclear whether DL models can produce physically plausible projections of streamflow under climate change. We investigate this question through a sensitivity analysis of modeled responses to increases in temperature and potential evapotranspiration (PET), with other meteorological variables left unchanged. Previous research has shown that temperature-based PET methods overestimate evaporative water loss under warming compared with energy budget-based PET methods. We therefore assume that reliable streamflow responses to warming should exhibit less evaporative water loss when forced with smaller, energy-budget-based PET compared with temperature-based PET. We conduct this assessment using three conceptual, process-based rainfall–runoff models and three DL models, trained and tested across 212 watersheds in the Great Lakes basin. The DL models include a Long Short-Term Memory network (LSTM), a mass-conserving LSTM (MC-LSTM), and a novel variant of the MC-LSTM that also respects the relationship between PET and evaporative water loss (MC-LSTM-PET). After validating models against historical streamflow and actual evapotranspiration, we force all models with scenarios of warming, historical precipitation, and both temperature-based (Hamon) and energy-budget-based (Priestley–Taylor) PET, and compare their responses in long-term mean daily flow, low flows, high flows, and seasonal streamflow timing. We also explore similar responses using a national LSTM fit to 531 watersheds across the United States to assess how the inclusion of a larger and more diverse set of basins influences signals of hydrological response under warming. The main results of this study are as follows: The three Great Lakes DL models substantially outperform all process-based models in streamflow estimation. The MC-LSTM-PET also matches the best process-based models and outperforms the MC-LSTM in estimating actual evapotranspiration. All process-based models show a downward shift in long-term mean daily flows under warming, but median shifts are considerably larger under temperature-based PET (- 17 % to - 25 %) than energy-budget-based PET (- 6 % to - 9 %). The MC-LSTM-PET model exhibits similar differences in water loss across the different PET forcings. Conversely, the LSTM exhibits unrealistically large water losses under warming using Priestley–Taylor PET (- 20 %), while the MC-LSTM is relatively insensitive to the PET method. DL models exhibit smaller changes in high flows and seasonal timing of flows as compared with the process-based models, while DL estimates of low flows are within the range estimated by the process-based models. Like the Great Lakes LSTM, the national LSTM also shows unrealistically large water losses under warming (- 25 %), but it is more stable when many inputs are changed under warming and better aligns with process-based model responses for seasonal timing of flows. Ultimately, the results of this sensitivity analysis suggest that physical considerations regarding model architecture and input variables may be necessary to promote the physical realism of deep-learning-based hydrological projections under climate change. [ABSTRACT FROM AUTHOR]

The interaction between forest and climate exhibits regional differences due to a variety of biophysical mechanisms. Observational and modeling studies have investigated the impacts of forested and non-forested areas on a single climate variable, but the influences of forest cover change on a combination of temperature and precipitation (e.g., drought) have not been explored, owing to the complex relationship between drought conditions and forests. In this study, we use historical forest and climate datasets to explore the relationship between forest cover fraction and drought from 1992–2018. A set of linear models and an analysis of variance approach are utilized to investigate the effect of forest cover change, precipitation and temperature on droughts across different timescales and climate zones. Our findings reveal that precipitation is the dominant factor (among the three factors) leading to drought in the equatorial, temperate and snow regions, while temperature controls drought in the arid region. The impact of forest cover changes on droughts varies under different precipitation and temperature quantiles. Precipitation modulates forest cover's impact on long-term drought in the arid region, while temperature modulates the impact of forest cover changes on both short- and long-term drought in the arid region as well as only on long-term drought in the temperate region. Forest cover can also modulate the impacts of precipitation and temperature on drought. High forest cover leads to a combined effect of precipitation and temperature on long-term drought in arid and snow regions, while precipitation is the only dominant factor in low forest cover conditions. In contrast, low forest cover triggers a strong combined effect of precipitation and temperature on drought in the temperate region. Our findings improve the understanding of the interaction between land cover change and the climate system and further assist decision-makers to modulate land management strategies in different regions in light of climate change mitigation and adaptation. [ABSTRACT FROM AUTHOR]

Future changes in suspended sediment export from deglaciating high-alpine catchments affect downstream hydropower reservoirs, flood hazard, ecosystems and water quality. Yet, quantitative projections of future sediment export have so far been hindered by the lack of process-based models that can take into account all relevant processes within the complex systems determining sediment dynamics at the catchment scale. As a promising alternative, machine-learning (ML) approaches have recently been successfully applied to modeling suspended sediment yields (SSYs). This study is the first, to our knowledge, exploring a machine-learning approach to derive sediment export projections until the year 2100. We employ quantile regression forest (QRF), which proved to be a powerful method to model past SSYs in previous studies, for two nested glaciated high-alpine catchments in the Ötztal, Austria, above gauge Vent (98.1 km 2) and gauge Vernagt (11.4 km 2). As predictors, we use temperature and precipitation projections (EURO-CORDEX) and discharge projections (AMUNDSEN physically based hydroclimatological and snow model) for the two gauges. We address uncertainties associated with the known limitation of QRF that underestimates can be expected if values in the projection period exceed the range represented in the training data (out-of-observation-range days, OOOR). For this, we assess the frequency and extent of these exceedances and the sensitivity of the resulting mean annual suspended sediment concentration (SSC) estimates. We examine the resulting SSY projections for trends, the estimated timing of peak sediment and changes in the seasonal distribution. Our results show that the uncertainties associated with the OOOR data points are small before 2070 (max. 3 % change in estimated mean annual SSC). Results after 2070 have to be treated more cautiously as OOOR data points occur more frequently, and glaciers are projected to have (nearly) vanished by then in some projections, which likely substantially alters sediment dynamics in the area. The resulting projections suggest decreasing sediment export at both gauges in the coming decades, regardless of the emission scenario, which implies that peak sediment has already passed or is underway. This is linked to substantial decreases in discharge volumes, especially during the glacier melt phase in late summer, as a result of increasing temperatures and thus shrinking glaciers. Nevertheless, high(er) annual yields can occur in response to heavy summer precipitation, and both developments would need to be considered in managing sediments, as well as e.g., flood hazard. While we chose the predictors to act as proxies for sediment-relevant processes, future studies are encouraged to try and include geomorphological changes more explicitly, e.g., changes in connectivity, landsliding, rockfalls or vegetation colonization, as these could improve the reliability of the projections. [ABSTRACT FROM AUTHOR]

Africa depends on its water resources for hydroelectricity, inland fisheries and water supply for domestic, industrial and agricultural operations. Anthropogenic climate change (CC) has changed the state of these water resources. Land use and land cover have also undergone significant changes due to the need to provide resources to a growing population. Yet, the impact of the land-use and land cover change (LULCC) in addition to CC on the water resources of Africa is underexplored. Here we investigate how precipitation, evapotranspiration (ET) and river flow respond to both CC and LULCC scenarios across the entire African continent. We set up a Soil and Water Assessment Tool (SWAT +) model for Africa and calibrated it using the hydrological mass balance calibration (HMBC) methodology detailed in Chawanda et al. (2020a). The model was subsequently driven by an ensemble of bias-adjusted global climate models to simulate the hydrological cycle under a range of CC and LULCC scenarios. The results indicate that the Zambezi and the Congo River basins are likely to experience reduced river flows under CC with an up to 7 % decrease, while the Limpopo River will likely have higher river flows. The Niger River basin is likely to experience the largest decrease in river flows in all of Africa due to CC. The Congo River basin has the largest difference in river flows between scenarios with (over 18 % increase) and without LULCC (over 20 % decrease). The projected changes have implications for the agriculture and energy sectors and hence the livelihood of people on the continent. Our results highlight the need to adopt policies to halt global greenhouse gas emissions and to combat the current trend of deforestation to avoid the high combined impact of CC and LULCC on water resources in Africa. [ABSTRACT FROM AUTHOR]