Human activities both aggravate and alleviate streamflow drought. Here we show that aggravation is dominant in contrasting cases around the world analysed with a consistent methodology. Our 28 cases included different combinations of human-water interactions. We found that water abstraction aggravated all drought characteristics, with increases of 20%–305% in total time in drought found across the case studies, and increases in total deficit of up to almost 3000%. Water transfers reduced drought time and deficit by up to 97%. In cases with both abstraction and water transfers into the catchment or augmenting streamflow from groundwater, the water inputs could not compensate for the aggravation of droughts due to abstraction and only shift the effects in space or time. Reservoir releases for downstream water use alleviated droughts in the dry season, but also led to deficits in the wet season by changing flow seasonality. This led to minor changes in average drought duration (−26 to +38%) and moderate changes in average drought deficit (−86 to +369%). Land use showed a smaller impact on streamflow drought, also with both increases and decreases observed (−48 to +98%). Sewage return flows and pipe leakage possibly counteracted the effects of increased imperviousness in urban areas; however, untangling the effects of land use change on streamflow drought is challenging. This synthesis of diverse global cases highlights the complexity of the human influence on streamflow drought and the added value of empirical comparative studies. Results indicate both intended and unintended consequences of water management and infrastructure on downstream society and ecosystems.

Small reservoirs play an important role in providing water to rural communities. Increased construction of small reservoirs to mitigate the effects of droughts leads to a High-density Reservoirs Network (HdRN) of small reservoirs, which can potentially modify the streamflows both in dry and wet periods. However, there is a lack of understanding of the interannual behavior of flow retention and the impact of future increases in the number of small reservoirs, mainly for HdRN in dryland catchments. This research aims to determine the possible impact of the increase in the number of small reservoirs on dry hydrological networks, evaluating the annual flows generated at the outlet of a dryland watershed for scenarios with different densities of small reservoirs (number of reservoirs per area). The study area was the Conceição river catchment (3347 km²) in the semiarid of Brazil. The hydrological model of the study area was developed in SWAT. The model obtained appropriate results for daily streamflows, with values of 0.63, 0.81, and 0.53% for NSE, KGE, and PBIAS, respectively. The current density of small reservoirs in the region was estimated at 0.068 reservoirs per square kilometer (res/km²). Eight expansion scenarios were defined for densities between 0.1 res/km² and 3.0 res/km². The results showed that the influence of the HdRN on runoff reduction mostly occurs for a probability of exceedance between 1% and 10% of month flows and is very small for months with very high peaks of flow. The reduction in the outlet flow due to the increase in the number of small reservoirs was stronger during dry years (up to 30%) than during wet years (up to 8%), and it tended to increase in years with a consecutive lack of rain (from about 7% in the first year to about 20% in the last year and in the worst scenario), which may intensify the period of extended droughts. This research provides insights about the impact of the increase in the number of small reservoirs on the interannual variability of flow retention, and the understanding of the influence of small reservoirs on runoff reduction may help water resources agencies better prepare for hydrologic extremes (droughts and floods).

Building reservoirs is a response to cope with drought in drylands. However, this human modification to the landscape may trigger both positive and negative effects. Here, we investigate how a network of reservoirs influences the propagation of meteorological drought (MD) into hydrological drought (HD) in a large semi-arid catchment in Brazil. We applied 12-month standardized indices to classify droughts. Then, the drought series were compared to each other. The downstreamness concept was used to describe the spatial water distribution. The onset of reservoir drought (RD) was much later than MD, but the delay between MD and RD peaks was even longer. Streamflow drought started with a small delay after MD, but it transformed into a similar RD. RD was initially more severe downstream. When the drought severity was at its highest, upstream and downstream RD equalized. The perceptual model of drought propagation may help in developing reservoir management systems in drylands.

The semiarid northeast of Brazil is one of the most densely populated dryland regions in the world and recurrently affected by severe droughts. Thus, reliable seasonal forecasts of streamflow and reservoir storage are of high value for water managers. Such forecasts can be generated by applying either hydrological models representing underlying processes or statistical relationships exploiting correlations among meteorological and hydrological variables. This work evaluates and compares the performances of seasonal reservoir storage forecasts derived by a process-based hydrological model and a statistical approach. Driven by observations, both models achieve similar simulation accuracies. In a hindcast experiment, however, the accuracy of estimating regional reservoir storages was considerably lower using the process-based hydrological model, whereas the resolution and reliability of drought event predictions were similar by both approaches. Further investigations regarding the deficiencies of the process-based model revealed a significant influence of antecedent wetness conditions and a higher sensitivity of model prediction performance to rainfall forecast quality. Within the scope of this study, the statistical model proved to be the more straightforward approach for predictions of reservoir level and drought events at regionally and monthly aggregated scales. However, for forecasts at finer scales of space and time or for the investigation of underlying processes, the costly initialisation and application of a process-based model can be worthwhile. Furthermore, the application of innovative data products, such as remote sensing data, and operational model correction methods, like data assimilation, may allow for an enhanced exploitation of the advanced capabilities of process-based hydrological models.