Drought events have affected many regions of the world, having negative economic, environmental and social impacts. When accompanied by increasing water demands, these events can lead to water scarcity. Since droughts can significantly vary in each geographic area, several indices have been developed around the world. Hazard indexes are commonly used to predict meteorological, agricultural and hydrological droughts. These indexes intend to predict hazards, but they do not provide information on when and where deficits can have negative consequences. This study presents a new planning and decision-support tool for monitoring water scarcity situations in a given region. This tool, called the Water Scarcity Risk Index (W-ScaRI), is formed by two subindices, which are proposed to describe a hazard and its consequences. Each subindex was constructed using a group of indicators and indices selected from the technical literature or originally proposed in this work. The W-ScaRI was applied to the Rio de Janeiro Metropolitan Region (RJMR), supplied with water by the Guandu/Lajes/Acari system. The RJMR is one of the most densely populated regions in Brazil, located in an area that has no natural water bodies capable of meeting its supply needs. Therefore, the Guandu River, which, in fact, is formed by two discharge transpositions from the Paraíba do Sul River, is the main drinking water supply source for this region. The RJMR suffered the consequences of unexpected, prolonged droughts in the Southeast region in 2003 and 2014–2015, leading the local authorities to implement temporary emergency measures in the management system of Paraíba do Sul and Guandu Basins, avoiding water shortage but showing the urgent need for planning and management support tools to anticipate possible future problems. The results of the study show that the formulation of the W-ScaRI can represent the water scarcity risk in a relatively simple way and, at the same time, with adequate conceptual and methodological consistency. Full article

Understanding the patterns of streamflow drought frequency and intensity is critical in defining potential environmental and societal impacts on processes associated with surface water resources; however, analysis of these processes is often limited to the availability of data. The objective of this study is to quantify the annual and monthly variability of low flow river conditions over the Southeastern United States (US) using National Water Model (NWM) retrospective simulations (v2.1), which provide streamflow estimates at a high spatial density. The data were used to calculate sums of outflow deficit volumes at annual and monthly scales, from which the autocorrelation functions (ACF), partial autocorrelation functions (PACF) and the Hurst exponent (H) were calculated to quantify low flow patterns. The ACF/PACF approach is used for examining the seasonal and multiannual variation of extreme events, while the Hurst exponent in turn allows for classification of “process memory”, distinguishing multi-seasonal processes from white noise processes. The results showed diverse spatial and temporal patterns of low flow occurrence across the Southeast US study area, with some locations indicating a strong seasonal dependence. These locations are characterized by a longer temporal cycle, whereby low flows were arranged in series of several to dozens of years, after which they did not occur for a period of similar length. In these rivers, H was in the range 0.8 (+/−0.15), which implies a stronger relation with groundwater during dry periods. In other river segments within the study region the probability of low flows appeared random, determined by H oscillating around the values for white noise (0.5 +/−0.15). The initial assessment of spatial clusters of the low flow parameters suggests no strict relationships, although a link to geologic characteristics and aquifer depth was noticed. At monthly scales, low flow occurrence followed precipitation patterns, with streamflow droughts first occurring in the Carolinas and along the Gulf Coast around May and then progressing upstream, reaching maxima around October for central parts of Mississippi, Alabama and Georgia. The relations for both annual and monthly scales are better represented with PACF, for which statistically significant lags were found in around 75% of stream nodes, while ACF explains on average only 20% of cases, indicating that streamflow droughts in the region occur in regular patterns (e.g., seasonal). This repeatability is of greater importance to defining patterns of extreme hydrologic events than the occurrence of high magnitude random events. The results of the research provide useful information about the spatial and temporal patterns of low flow occurrence across the Southeast US, and verify that the NWM retrospective data are able to differentiate the time processes for the occurrence of low flows. Full article

As water resources enter the era of the Anthropocene, the process of anthropogenic droughts arises as the interplay between climate cycles and human-centered water management in rivers. In their natural conditions, rivers exhibit a natural hydrologic variability, wet and dry cycles, that are a vital property for promoting ecological resilience. Human activities alter the temporal variability of streamflow, a resilience property of river systems. We argue that anthropogenic droughts in river basins can lead to changes in the resilience properties of the system depicted in stability landscapes. This study aims to analyze anthropogenic droughts and the changes provoked to the stability landscapes of the streamflow system of a river basin. We use 110 years of regulated and naturalized streamflow data to analyze the hydrologic variability (wet periods and droughts) of a river system. First, we determined the streamflow drought index (SDI), and the results were assessed using probability distribution functions to construct stability landscapes and explore the resilience properties of the system. The transboundary basin of the Rio Grande/Rio Bravo (RGB) is used as a case study. Our main findings include evidence of resilience erosion and alterations to the properties of the stability landscape by the human-induced megadrought in the RGB, which resulted from extensive anthropogenic alteration and fragmentation of the river system. The novelty of this research is to provide a baseline and move forward into quantifying ecological resilience attributes of river basins in water resources planning and management. Full article

In the context of climate change, irrigation has become a must for ensuring crop production because in some regions, the drought episodes became more frequent. The decision to efficiently allocate water resources should be made quickly, based on tools that provide correct information with a low computational effort. Therefore, we propose a new user-friendly tool—IrrigTool—for assessing the irrigation rate considering the precipitation, temperature, evapotranspiration, soil type, and crop. IrrigTool implements the Thornthwaite equations and can be used to identify weakness due to drought stress and as an educational tool. Apart from the computation, it provides a graphical representation of the results and possible comparisons of the output for two locations. The application is built in Microsoft Excel for graphics and Visual Basic VBA. The user does not have programming knowledge to use it. Data on monthly precipitation and temperature data must be introduced in the specified fields, and after pressing the run button, the results are automatically displayed. The article exemplifies the functioning on data series from Romania’s Dobrogea region. Full article

Low flow events (a.k.a. streamflow drought) are described as episodes where stream flows are lower or equal to a specified minimum threshold level. This threshold is usually predefined at the methodological stage of a study and is generally applied as a chosen flow percentile, determined from a flow duration curve (FDC). Unfortunately, many available methods for choosing both the percentile and FDCs result in a large range of potential thresholds, which reduces the ability to statistically compare the results from the different methods while also losing the natural character of the phenomenon. The aim of this work is to introduce a new approach for low flow threshold calculation through the application of an objective approach using breakpoint analysis. This method allows for the identification of an environmental moment of river transition, from atmospheric feed flows to base flow, which characterizes the moment at the beginning of the hydrological drought. The method allows for not only the capture of the genesis of a low flow event but, above all, unifies the approach toward threshold levels and completely excludes the impact of the subjective researcher’s decisions, which occur at the methodological stage when selecting the threshold criteria or when choosing a respective percentile. In addition, the method can be successfully used in datasets characterized by a high level of discretization, such as numerical model data, where the subsurface runoff component is not described in sufficient detail. Results of this work show that the objective identification method is better able to capture the occurrence of a low flow event, improving the ability to identify hydrologic drought conditions. The proposed method is published together with the Python module objective_thresholds for broad use in other studies. Full article

Water body feature extraction using a remote sensing technique represents an important tool in the investigation of water resources and hydrological drought assessment. Nuntasi-Tuzla Lake, a component of the Danube Delta Natural Reserve, is located on the Romanian Black Sea littoral. On account of an event in summer 2020, when the lake surface water decreased significantly, this study aims to identify the variation of the Nuntasi-Tuzla Lake surface water over a long-term period in correlation with human intervention and climate change. To this end, it provides an analysis in the period 1965–2021 via hydrological drought indices and data mining classification. The latter approach is based on several water indices derived from Landsat TM/ETM+/OLI and MODIS full-time series datasets: Normalized Difference Vegetation Index (NDVI), Normalized Difference Vegetation Index (NDVI), Modified NDWI (MNDWI), Weighted Normalized Difference Water Index (WNDWI), and Water Ratio Index (WRI). The experimental results indicate that the proposed classification methods can extract relevant features from waterbodies using remote sensing imagery with a high accuracy. Moreover, the study shows a similarity in the evolution of surface water cover identified with the data mining classification and the drought periods detected in the flow data series for the Nuntasi and Sacele Rivers that supply the Nuntasi-Tuzla Lake. Overall, the results of our investigation show that human intervention and hydrological drought had an extensive impact on the long-term changes in surface water of the Nuntasi-Tuzla Lake. Full article

Global impacts of drought conditions pose a major challenge towards the achievement of the 2030 Sustainable Development Goals. As a result, a clarion call for nations to take actions aimed at mitigating the adverse negative effects, managing key natural resources and strengthening socioeconomic development can never be overemphasized. The present study evaluated hydrological drought conditions in three Cape provinces (Eastern, Western and Northern Cape) of South Africa, based on the Standardized Streamflow Index (SSI) calculated at 3- and 6-month accumulation periods from streamflow data spanning over the 3.5 decades. The SSI features were quantified by assessing the corresponding annual trends computed by using the Modified Mann–Kendall test. Drought conditions were also characterized in terms of the duration and severity across the three Cape provinces. The return levels of drought duration (DD) and drought severity (DS) associated with 2-, 5-, 10-, 20- and 50-year periods were estimated based on the generalized extreme value (GEV) distribution. The results indicate that hydrological drought conditions have become more frequent and yet exhibit spatial contrasts throughout the study region during the analyzed period. To this end, there is compelling evidence that DD and DS have increased over time in the three Cape provinces. Return levels analysis across the studied periods also indicate that DD and DS are expected to be predominant across the three Cape provinces, becoming more prolonged and severe during the extended periods (e.g., 20- and 50-year). The results of the present study (a) contribute to the scientific discourse of drought monitoring, forecasting and prediction and (b) provide practical insights on the nature of drought occurrences in the region. Consequently, the study provides the basis for policy- and decision-making in support of preparedness for and adaptation to the drought risks in the water-linked sectors and robust water resource management. Based on the results reported in this study, it is recommended that water agencies and the government should be more proactive in searching for better strategies to improve water resources management and drought mitigation in the region. Full article

The water level of the Urmia Lake Basin (ULB), located in the northwest of Iran, started to decline dramatically about two decades ago. As a result, the area has become the focus of increasing scientific research. In order to improve understanding of the connections between declining lake level and changing local drought conditions, three common drought indices are employed to analyze the period 1981–2018: The Standard Precipitation Index (SPI), the Standard Precipitation-Evaporation Index (SPEI), and the Standardized Snow Melt and Rain Index (SMRI). Although rainfall is a significant indicator of water availability, temperature is also a key factor since it determines rates of evapotranspiration and snowmelt. These different processes are captured by the three drought indices mentioned above to describe drought in the catchment. Therefore, the main objective of this paper is to provide a comparative analysis of drought over the ULB by incorporating different drought indices. Since there is not enough long-term observational data of sufficiently high density for the ULB region, ECMWF Reanalysis data version 5(ERA5) has been used to estimate SPI, SPEI, and SMRI drought indicators. These are shown to work well, with AUC-ROC > 0.9, in capturing different classes of basin drought characteristics. The results show a downward trend for SPEI and SMRI (but not for SPI), suggesting that both evaporation and lack of snowmelt exacerbate droughts. Owing to the increasing temperatures in the basin and the decrease in snowfall, drought events have become particularly pronounced in the SPEI and SMRI time series since 1995. No significant SMRI drought was detected prior to 1995, thus indicating that sufficient snowfall was available at the beginning of the study period. The study results also reveal that the decrease in lake water level from 2010 to 2018 was not only caused by changes in the water balance components, but also by unsustainable water management. Full article

The standardized precipitation index (SPI) is used for characterizing and predicting meteorological droughts on a range of time scales. However, in forecasting applications, when SPI is computed on the entire available dataset, prior to model-validation, significant biases are introduced, especially under changing climatic conditions. In this paper, we investigate the theoretical and numerical implications that arise when SPI is computed under stationary and non-stationary probability distributions. We demonstrate that both the stationary SPI and non-stationary SPI (NSPI) lead to increased information leakage to the training set with increased scales, which significantly affects the characterization of drought severity. The analysis is performed across about 36,500 basins in Sweden, and indicates that the stationary SPI is unable to capture the increased rainfall trend during the last decades and leads to systematic underestimation of wet events in the training set, affecting up to 22% of the drought events. NSPI captures the non-stationary characteristics of accumulated rainfall; however, it introduces biases to the training data affecting 19% of the drought events. The variability of NSPI bias has also been observed along the country’s climatic gradient with regions in snow climates strongly being affected. The findings propose that drought assessments under changing climatic conditions can be significantly influenced by the potential misuse of both SPI and NSPI, inducing bias in the characterization of drought events in the training data. Full article

Extreme hydro-meteorological phenomena have become more frequent in recent years compared to the year 2000 in Europe, including Romania. Flooding occurs from heavy rainfalls favored by natural and anthropogenic factors such as the valley’s flat slope or settlements situated near the river. Ţigăneşti and Brânceni villages (from southern Romania) are no exception and have been affected by floods many times. One of these events is that from 2005, when the flow reached 676 m³/s (a value 80 times higher than the normal flow of the Vedea River) in Brȃnceni. This paper aims to present a simulation of the flood that occurred during 3–6 July 2005 and its impact on the settlements, roads, and land, using field observation (including some from 2005), GIS software (ArcGIS), software for flood simulations (HEC-RAS—Hydrologic Engineering Center River Analysis System), and flow data from the Romanian National Institute of Hydrology. Simulations were run in HEC-RAS. The obtained flooded areas imported back into GIS (Geographic Information System) were used to determine the area covered by water and the length of affected roads. The surface and number of flooded buildings were calculated using different tools from ArcMap. Results were interpreted, commented on, and compared with data and maps provided by the Romanian Water National Administration. The simulation shows that the villages would be protected from the flood by building a levee along the Vedea River. Significant losses can be prevented, and money can be saved. Full article