Standardized Streamflow Index Research Articles

Numerical comparison of streamflow drought index (SDI) and standardized streamflow index (SSI) for evaluation of Isfahan drought status

ABSTRACT Hydrological droughts are the most serious and alarming variation of droughts due to their numerous long-term negative impacts on natural ecosystems, agricultural capacities, and the water regime of a specific region. Isfahan province, in Iran, is highly prone to this type of drought, and its arid climate has already made the efficient water management in this area a challenging task. Due to the lack of a uniform method for drought evaluation, various indices have been suggested by different researchers over the last few decades. The streamflow drought index (SDI) and standardized streamflow index (SSI) are the two primary hydrological drought indices that have been used in many regions with similar climatic conditions to Isfahan province. Their reliability in previous case studies and the easy computational process have made them suitable drought measurement tools for Isfahan. In this study, these two indices are calculated based on the obtained data from 10 hydrometric stations in Isfahan, for the timeframe (1988–2018). The analysis of SSI and SDI in this region shows consistent and station-specific drought patterns, with moderate drought conditions prevailing. These findings offer valuable insights for regional water resource management and climate adaptation, guiding policies to mitigate the impact of droughts.

Exploring cumulative probability functions for streamflow drought magnitude: Global scale analysis and parametric vs. non-parametric comparisons

Streamflow droughts pose significant challenges to water resource management and environmental sustainability. The accurate characterization of streamflow drought magnitude is essential for effective mitigation, assessment and adaptation strategies. In this study, we explore the implications of choosing different cumulative probability distribution functions (cdfs) on the determination of streamflow drought magnitude, as assessed by the z-score values of the standardized streamflow index (SSI) and return periods. Our investigation encompasses a comprehensive global-scale analysis, with the aim of identifying the most suitable cdfs for characterizing streamflow droughts. To accomplish this objective, we incorporate global simulated streamflow values from the model WaterGAP 2.2d and observed streamflow values from 280 Global Runoff Data Centre (GRDC) stations for the period 1981 – 2010. We evaluate the performance of 118 parametric cdfs for each of the 12 calendar monthly streamflow values at each model grid cell and GRDC station to determine the best fitting parametric cdf. Moreover, we go beyond the conventional scope of such studies by further assessing the best-fitting cdfs alongside non-parametric cdfs such as the Empirical cumulative distribution function (ecdf) and the cumulative distribution function derived from the Kernel density estimation function (kcdf). Among our findings, we establish a subset of 23 parametric cdfs that exhibit the best fit for the global streamflow values concerning all 12 calendar monthly streamflow values at individual GRDC stations or grid cells within the specified period. Furthermore, we observe that the accuracy of streamflow simulation and the length of the reference period directly influence the selection of the most suitable parametric cdf. Our comparative analysis reaffirms the existing understanding that opting for the best-fitting parametric cdf, rather than a non-parametric alternative, mitigates the risk of overestimating drought magnitudes. However, we emphasize the crucial importance of considering the intended purpose, approach-specific sensitivities, and uncertainties when choosing the most suitable parametric cdf for any calendar monthly streamflow values.

Expanding the applications of the standardized streamflow index through regionalization

Expanding the applications of the standardized streamflow index through regionalization

The Impact of Climate Change on Hydro-Meteorological Droughts in the Chao Phraya River Basin, Thailand

This study evaluated the impacts of climate change on hydro-meteorological droughts in the Chao Phraya River Basin (CPRB), Thailand under two Representative Concentration Pathway (RCP) scenarios (RCP4.5 and RCP8.5). We used three Reginal Climate Models (RCMs) of the Southeast Asia Regional Climate Downscaling/Coordinated Regional Climate Downscaling Experiment—Southeast Asia (SEACLID/CORDEX-SEA), which are bias corrected. The Soil and Water Assessment Tool (SWAT) was used to simulate streamflow for future periods. The Standardized Precipitation Index (SPI) and Standardized Streamflow Index (SSI) were estimated and used for drought characterization at three time scales (3, 6, and 12 months). The lag time between meteorological and hydrological droughts is approximately 1–3 months. The results suggest that the CPRB is likely to experience less frequent hydro-meteorological drought events in the future. The meteorological drought is projected to be longer, more severe, and intense. The severity of hydrological drought tends to decrease, but the intensity could increase. Climate change has been discovered to alter drought behaviors in the CPRB, posing a threat to drought monitoring and warning because droughts will be less predictable in future climate scenarios. The characterization of historical and future droughts over the CPRB is therefore valuable in developing an improved understanding of the risks of drought.

On the timescale of drought indices for monitoring streamflow drought considering catchment hydrological regimes

Abstract. There is a wide variety of drought indices, yet a consensus on suitable indices and temporal scales for monitoring streamflow drought remains elusive across diverse hydrological settings. Considering the growing interest in spatially distributed indices for ungauged areas, this study addresses the following questions: (i) What temporal scales of precipitation-based indices are most suitable to assess streamflow drought in catchments with different hydrological regimes? (ii) Do soil moisture indices outperform meteorological indices as proxies for streamflow drought? (iii) Are snow indices more effective than meteorological indices for assessing streamflow drought in snow-influenced catchments? To answer these questions, we examined 100 near-natural catchments in Chile with four hydrological regimes, using the standardised precipitation index (SPI), standardised precipitation evapotranspiration index (SPEI), empirical standardised soil moisture index (ESSMI), and standardised snow water equivalent index (SWEI), aggregated across various temporal scales. Cross-correlation and event coincidence analysis were applied between these indices and the standardised streamflow index at a temporal scale of 1 month (SSI-1), as representative of streamflow drought events. Our results underscore that there is not a single drought index and temporal scale best suited to characterise all streamflow droughts in Chile, and their suitability largely depends on catchment memory. Specifically, in snowmelt-driven catchments characterised by a slow streamflow response to precipitation, the SPI at accumulation periods of 12–24 months serves as the best proxy for characterising streamflow droughts, with median correlation and coincidence rates of approximately 0.70–0.75 and 0.58–0.75, respectively. In contrast, the SPI at a 3-month accumulation period is the best proxy over faster-response rainfall-driven catchments, with median coincidence rates of around 0.55. Despite soil moisture and snowpack being key variables that modulate the propagation of meteorological deficits into hydrological ones, meteorological indices are better proxies for streamflow drought. Finally, to exclude the influence of non-drought periods, we recommend using the event coincidence analysis, a method that helps assessing the suitability of meteorological, soil moisture, and/or snow drought indices as proxies for streamflow drought events.

Streamflow response to catastrophic windthrow and forest recovery in subalpine spruce forest

Windstorms account for about half of forest disturbances in Europe and are considered as drivers of natural regeneration of plant species, accompanied by significant changes in hydrological and soil moisture conditions. Our study aims to identify the effects of deforestation caused by catastrophic windthrow on streamflow characteristics in conjunction with meteorological and soil moisture data. The research was conducted in the headwater catchment (34.4 km2), in the Western Tatra Mountains, Poland. Streamflow characteristics such as average, high, and low flows, coefficient of variability, as well as flashiness of the streamflow (Richard-Baker Flashiness Index) and drought frequency (Standardised Streamflow Index) were calculated in the pre-event (2007–2013) and post-event (2013–2020) periods. The analysis was carried out for calendar months, seasons of the year, as well as for the growing and non-growing seasons. Wavelet Analysis was applied to track the changes in streamflow time series in relation to meteorological conditions (precipitation, air temperature) and remotely sensed soil wetness in the root zone. Principal Components Analysis was applied in order to identify the major drivers of the observed variability. The results revealed increasing low (+14%), average (+8%), and maximum monthly flows (16%) in the post-event period. The most significant increase in low flows (+47%, p<0.1) occurred during snowmelt. The increase in average (+31%, p<0.1) and high flows (+90%, p<0.05) occurred during mid-winter thaws. The frequency of hydrological droughts decreased by up to 36% in the post-event period, despite similar climatic conditions. The flashiness of streamflow increased during mid-winter thaws and rainfall events. The short-scale periodicity in streamflow increased in the post-event period. The results revealed the presence of 3-month periodicity in the root zone soil wetness associated with long-term soil water storage, which decreased after the catastrophic windthrow. Streamflow in the post-event period became strongly coupled with precipitation and root zone soil wetness at short period bands (∼3–16 days) in hydrologically active season. Short-scale correlation between precipitation and streamflow was decreasing with the gradual succession of vegetation.

Spatiotemporal heterogeneity in meteorological and hydrological drought patterns and propagations influenced by climatic variability, LULC change, and human regulations

This study aims to quantify meteorological–hydrological drought propagations and examine the potential impacts by climatic variability, LULC change (LULC), and human regulations. An integrated observation-modeling framework quantifies drought propagation intervals and assesses mechanisms influencing hydrological droughts. Meteorological droughts are characterized using the Standardized Precipitation Evapotranspiration Index (SPEI), and hydrological droughts are assessed through the Standardized Streamflow Index (SSI) across diverse climatic zones. Cross-correlation analysis between SPEI and SSI time series identifies the lag time associated with the highest correlation as the drought propagation interval. Mechanisms are investigated via a coupled empirical-process modeling framework incorporating the Soil and Water Assessment Tool (SWAT). Discrepancies between simulated and observed SSI time series help quantify the extent of human regulation impacts on hydrological drought characteristics and propagation. The Yellow River Basin (YRB), divided into six subzones based on climate characteristics, is selected as the case study. Key findings include: (1) Meteorological droughts were extremely severe across most YRB during the 1990s, while the 2000s showed some mitigation primarily due to precipitation increases. (2) Hydrological droughts and propagation times from meteorology to hydrology demonstrated substantial spatiotemporal variability. In general, summer propagation times were shorter than other seasons. (3) Propagation times were shorter in arid regions with cropland or built-up land cover versus grassland and woodland, while the reverse held for humid regions. (4) Human regulations prolonged propagation times, likely due to reservoir regulations designed to overcome water deficits. While the YRB is the focus of this paper, the methodologies and findings are applicable to other regions worldwide to enhance drought forecasting and water resource management. In various hydrological and climatic contexts worldwide.

Current State of Advances in Quantification and Modeling of Hydrological Droughts

Hydrological droughts may be referred to as sustained and regionally extensive water shortages as reflected in streamflows that are noticeable and gauged worldwide. Hydrological droughts are largely analyzed using the truncation level approach to represent the desired flow condition such as the median, mean, or any other flow quantile of an annual, monthly, or weekly flow sequence. The quantification of hydrologic droughts is accomplished through indices, such as the standardized streamflow index (SSI) in tandem with the standardized precipitation index (SPI) commonly used in meteorological droughts. The runs of deficits in the SSI sequence below the truncation level are treated as drought episodes, and thus, the theory of runs forms an essential tool for analysis. The parameters of significance from the modeling perspective of hydrological droughts (or tantamount to streamflow droughts in this paper) are the longest duration and the largest magnitude over a desired return period of T-year (or month or week) of the streamflow sequences. It is to be stressed that the magnitude component of the hydrological drought is of paramount importance for the design and operation of water resource storage systems such as reservoirs. The time scales chosen for the hydrologic drought analysis range from daily to annual, but for most applications, a monthly scale is deemed appropriate. For modeling the aforesaid parameters, several methodologies are in vogue, i.e., the empirical fitting of the historical drought sequences through a known probability density function (pdf), extreme number theorem, Markov chain analysis, log-linear, copulas, entropy-based analyses, and machine learning (ML)-based methods such as artificial neural networks (ANN), wavelet transform (WT), support vector machines (SVM), adaptive neuro-fuzzy inference systems (ANFIS), and hybrid methods involving entropy, copulas, and machine learning-based methods. The forecasting of the hydrologic drought is rigorously conducted through machine learning-based methodologies. However, the traditional stochastic methods such as autoregressive integrated moving average (ARIMA), seasonal autoregressive integrated moving average (SARIMA), copulas, and entropy-based methods are still popular. New techniques for flow simulation are based on copula and entropy-based concepts and machine learning methodologies such as ANN, WT, SVM, etc. The simulated flows could be used for deriving drought parameters in consonance with traditional Monte Carlo methods of data generation. Efforts are underway to use hydrologic drought models for reservoir sizing across rivers. The ML methods whilst combined in the hybrid form hold promise in drought forecasting for better management of existing water resources during the drought periods. Data mining and pre-processing techniques are expected to play a significant role in hydrologic drought modeling and forecasting in future.

A novel coupled algorithm-based method, and the characteristics and driving mechanism of water shortage in the upper Yellow River

ABSTRACT Gaining a clear understanding of the spatiotemporal characteristics of water shortage and their driving mechanism in the upper Yellow River (UYR) is crucial for the assessment and effective management of water resources in the region. The standardized streamflow index (SSI) and the improved Hilbert-Huang transform (IHHT) algorithm are combined to form a coupled SSI-IHHT method (CSIHM). The streamflow above Lanzhou Station over 1969–2015 is analysed. Results show that summer and autumn are high-risk periods of continuous water shortage. Annual streamflow has inter-annual change cycles of 3.5–9.0 a and inter-decadal change cycles between 16.4 and 28.2 a. The annual streamflow change is mainly affected by climate change. But the seasonal streamflow change is mainly influenced by human activities resulting from the operation of reservoirs. The CSIHM has advantages of mutual verification of the results and wide applicability to the field of water shortage.

Deciphering the influence of climate change and human activities on the drought propagation

Study regionThe Yiluo River Basin (YLRB), China Study focusUnderstanding the alterations in drought propagation under evolving environmental conditions is crucial for the efficient management of water resources. In this study, the 'simulation-observation' comparison method was employed to analyze the drought propagation in distinct periods: baseline, simulated, and disturbed periods. This facilitated the quantification of the impact of climate change and human activities on drought propagation characteristics, such as the response time of the Standardized Streamflow Index (SSI) to the Standardized Precipitation Index (SPI) and the propagation threshold. New hydrological insights for the regionThe response times of the SSI to the SPI has been prolonged at monthly and seasonal scales due to climate change, while it has been reduced at the annual scale. The impact of human activities on the lengthening of response time is only evident at longer temporal scales. Human activities have contributed to higher thresholds for severe and extreme droughts on monthly and annual scales, increasing the probability of extreme droughts. Meanwhile, climate change has partially offset the negative impacts of human activities. However, at the seasonal scale, climate change is the main cause of the increasing propagation thresholds. The conclusions drawn from this research provide valuable insights for the development of policies aimed at managing drought in a changing environment.

Monitoring and characterization of meteorological and hydrological drought in the high Ziz watershed

The understanding of the characteristics and the effects of drought is essential for an effective drought assessment and management in all regions of the globe. The south-east of Morocco is characterized by an arid and semi-arid climate, and the High Ziz watershed, which is part of this region of Morocco, is the area in that this study was conducted, consisting of the evaluation and characterization of the meteorological and the hydrological drought, respectively, through the application of the Standardized Precipitation Index (SPI), and the standardized Streamflow Index (SSI) indices on the time scales of 3, 6 and 12 months. The analysis of the indices shows that the study area has experienced several drought events with different characteristics depending on the time scale of calculation. In addition, this analysis has enabled the behavior of meteorological and hydrological drought.

A new non-stationary standardised streamflow index using the climate indices and the optimal anthropogenic indices as covariates in the Wei River Basin, China

Study regionCatchment area above the Huaxian station along the Wei River Basin, China. Study focusThis study attempts to construct a new Non-stationary Standardized Streamflow Index (NSSI) applicable to the variable streamflow sequence of the Wei River Basin based on the climate index and the optimal anthropogenic index, and analyse the drought characteristics of the basin. The climate index is used to quantify climate change factors and three anthropogenic indices are used to quantify the factor of human activities, including the reservoir index, the human-induced index calculated based on the Variable Infiltration Capacity (VIC) hydrological model and the Long Short-Term Memory (LSTM) model machine learning approach, respectively. New hydrological insights for the regionThe human-induced index based on the LSTM model is more suitable for quantifying anthropogenic factors in the Wei River Basin. The NSSI performs better than the SSI in drought identification. The NSSI based on the LSTM model can capture more frequent severe drought and extreme drought events. The frequency of severe drought and extreme drought is higher in summer and autumn than in the others. The NSSI can better characterize the hydrological drought processes under a non-stationary condition, thus it can provide a more effective reference for regional drought assessment and related policy-making from the perspective of a changing environment.

Multivariate regression trees as an “explainable machine learning” approach to explore relationships between hydroclimatic characteristics and agricultural and hydrological drought severity: case of study Cesar River basin

Abstract. The typical drivers of drought events are lower than normal precipitation and/or higher than normal evaporation. The region's characteristics may enhance or alleviate the severity of these events. Evaluating the combined effect of the multiple factors influencing droughts requires innovative approaches. This study applies hydrological modelling and a machine learning tool to assess the relationship between hydroclimatic characteristics and the severity of agricultural and hydrological droughts. The Soil Water Assessment Tool (SWAT) is used for hydrological modelling. Model outputs, soil moisture and streamflow, are used to calculate two drought indices, namely the Soil Moisture Deficit Index and the Standardized Streamflow Index. Then, drought indices are utilised to identify the agricultural and hydrological drought events during the analysis period, and the index categories are employed to describe their severity. Finally, the multivariate regression tree technique is applied to assess the relationship between hydroclimatic characteristics and the severity of agricultural and hydrological droughts. Our research indicates that multiple parameters influence the severity of agricultural and hydrological droughts in the Cesar River basin. The upper part of the river valley is very susceptible to agricultural and hydrological drought. Precipitation shortfalls and high potential evapotranspiration drive severe agricultural drought, whereas limited precipitation influences severe hydrological drought. In the middle part of the river, inadequate rainfall partitioning and an unbalanced water cycle that favours water loss through evapotranspiration and limits percolation cause severe agricultural and hydrological drought conditions. Finally, droughts are moderate in the basin's southern part (Zapatosa marsh and the Serranía del Perijá foothills). Moderate sensitivity to agricultural and hydrological droughts is related to the capacity of the subbasins to retain water, which lowers evapotranspiration losses and promotes percolation. Results show that the presented methodology, combining hydrological modelling and a machine learning tool, provides valuable information about the interplay between the hydroclimatic factors that influence drought severity in the Cesar River basin.

River network-based index to clarify transmission of hydrological drought in reservoir-regulated basins

Study regionHanjiang River basin (HRB), China. Study focusUnderstanding the transmission of hydrological drought along river networks is of extreme importance for monitoring, forewarning, and mitigation of drought. However, it is extremely difficult to do so under the situation of complex river networks, particularly in the presence of spatially distributed reservoirs. Taking advantage of hydrological reasoning information within a hierarchical river network structure, this study proposed a nonstationary standardized streamflow index (NSSIrn) to clarify the transmission of hydrological drought in reservoir-regulated basins. Then, the generalized regression model based on the normal distribution was used to clarify the transmission of hydrological drought along reservoir-regulated river networks. New hydrological insights for the regionThe NSSIrn aided with river network information enables reservoir-induced alteration in drought transmission to be better clarified under nonstationary conditions. The NSSIrn-based drought characteristics determined throughout the HRB showed that droughts that occurred in the downstream region were more severe and longer lasting than those that occurred in the upstream region during 1957–2019. Moreover, regulation of the Danjiangkou reservoir weakens the correlation between upstream and downstream hydrological droughts, i.e., roughly one quarter of upstream drought events did not develop into drought events downstream of the reservoir. This study offers a valuable reference for evaluating the effect of other interventions on drought transmission in human-modified basins.

Significant relationships between drought indicators and impacts for the 2018–2019 drought in Germany

Despite the scientific progress in drought detection and forecasting, it remains challenging to accurately predict the corresponding impact of a drought event. This is due to the complex relationships between (multiple) drought indicators and adverse impacts across different places/hydroclimatic conditions, sectors, and spatiotemporal scales. In this study, we explored these relationships by analyzing the impacts of the severe 2018–2019 central European drought event in Germany. We first computed the standardized precipitation index (SPI), the standardized precipitation evaporation index (SPEI), the standardized soil moisture index (SSMI) and the standardized streamflow index (SSFI) over various accumulation periods, and then related these indicators to sectorial losses from the European drought impact report inventory (EDII) and media sources. To cope with the uncertainty associated with both drought indicators and impact data, we developed a fuzzy method to categorize them. Lastly, we applied the method at the region level (EU NUTS1) by correlating monthly time series. Our findings revealed strong and significant relationships between drought indicators and impacts over different accumulation periods, albeit in some cases region-specific and time-variant. Furthermore, our analysis established the interconnectedness between various sectors, which displayed systematically co-occurring impacts. As such, our work provides a new framework to explore drought indicators-impacts dependencies across space, time, sectors, and scales. In addition, it emphasizes the need to leverage available impact data to better forecast drought impacts.

Deep learning versus hybrid regularized extreme learning machine for multi-month drought forecasting: A comparative study and trend analysis in tropical region

Drought is a hazardous natural disaster that can negatively affect the environment, water resources, agriculture, and the economy. Precise drought forecasting and trend assessment are essential for water management to reduce the detrimental effects of drought. However, some existing drought modeling techniques have limitations that hinder precise forecasting, necessitating the exploration of suitable approaches. This study examines two forecasting models, Long Short-Term Memory (LSTM) and a hybrid model integrating regularized extreme learning machine and Snake algorithm, to forecast hydrological droughts for one to six months in advance. Using the Multivariate Standardized Streamflow Index (MSSI) computed from 58 years of streamflow data for two drier Malaysian stations, the models forecast droughts and were compared to classical models such as gradient boosting regression and K-nearest model for validation purposes. The RELM-SO model outperformed other models for forecasting one month ahead at station S1, with lower root mean square error (RMSE = 0.1453), mean absolute error (MAE = 0.1164), and a higher Nash-Sutcliffe efficiency index (NSE = 0.9012) and Willmott index (WI = 0.9966). Similarly, at station S2, the hybrid model had lower (RMSE = 0.1211 and MAE = 0.0909), and higher (NSE = 0.8941 and WI = 0.9960), indicating improved accuracy compared to comparable models. Due to significant autocorrelation in the drought data, traditional statistical metrics may be inadequate for selecting the optimal model. Therefore, this study introduced a novel parameter to evaluate the model's effectiveness in accurately capturing the turning points in the data. Accordingly, the hybrid model significantly improved forecast accuracy from 19.32 % to 21.52 % when compared with LSTM. Besides, the reliability analysis showed that the hybrid model was the most accurate for providing long-term forecasts. Additionally, innovative trend analysis, an effective method, was used to analyze hydrological drought trends. The study revealed that October, November, and December experienced higher occurrences of drought than other months. This research advances accurate drought forecasting and trend assessment, providing valuable insights for water management and decision-making in drought-prone regions.

Challenges of typical inter-basin water transfer projects in China: Anticipated impacts of climate change on streamflow and hydrological drought under CMIP6

Inter-basin water transfer projects are critical for economic development in water scarcity regions. However, the streamflow reduction and the frequent dry extremes in a warming environment raise significant concerns regarding the sustainability of such projects. Based on the latest CMIP6 data, hydrological model, Standardized Streamflow Index (SSI), and run theory, this study systemically examined the future streamflow and hydrological drought changes in the Hanjiang to Weihe River Water Diversion Project (HWWDP) area, China. Significantly, as the most challenging project management scenario, the response of spatially compound hydrological drought (i.e., concurrent hydrological drought in both the water intake and receiving area) was further revealed. This analysis offers the potential to yield novel insights into the prospective management strategies for the HWWDP. The investigation covers the baseline period (1971–2014) and future periods (2021–2100) under SSP245 and SSP585 scenarios. The outcomes demonstrate a decline in annual average streamflow of 5.0–6.4 (SSP245) hundred million cubic meters in the water receiving area and 4.6–7.5 (SSP245) and 3.4–8.1 (SSP585) hundred million cubic meters in the water intake area, respectively, compared to the baseline period. Furthermore, hydrological drought frequency may increase in both the water receiving and source areas, particularly in the summer under SSP585. The duration, severity, and peak intensity of hydrological drought events in the receiving area are expected to exacerbate due to climate change, while those impacts on water intake area should be relatively minor. The spatially compound hydrological drought is expected to occur more frequently in different months except April. The conditional probabilities of spatially compound hydrological drought occurrence may rise by 5.2%-18.7% (SSP245) and 5.9%-20.4% (SSP585) in different months when drought occurs in the water intake area. Moreover, higher probabilities are expected to be observed from April to October when the water receiving area experiences drought. These findings underscore the necessity for adaptive climate change management of the HWWDP to address the future reduction in streamflow and more severe hydrological drought.

Correlation of drought indices with climatic and socio-economic factors in San Diego, USA

Droughts have become more severe and prolonged in recent years, causing significant social and environmental impacts. Analysing drought conditions with various climatic and socio-economic factors is critical for effective drought monitoring. This study investigated the correlation between drought severity and climatic and socio-economic changes in a study area – San Diego County, CA, USA. The drought severity was quantified using three drought indices: standardised precipitation index, standardised precipitation evapotranspiration index and standardised streamflow index. These indices were also used to describe the variation of the drought severity in the study area. The study employed Spearman’s correlation coefficient analysis to evaluate the correlations between the drought indices and climatic and socio-economic factors. The results showed that the drought indices indicated similar variations of the droughts in the study area. However, the severity, duration and frequency of the droughts varied with the timescales in the drought index analysis. Additionally, this study found that the drought conditions based on the three indices had strong negative correlations with precipitation, soil moisture, agricultural area and forest area, which were more pronounced as the timescale increased. The findings will provide insights into effective drought monitoring and management, particularly targeting severe droughts.

Estimation of possible extreme droughts for a dam catchment in Korea using a regional-scale weather model and long short-term memory network

Abstract To prepare measures to respond to climate-induced extreme droughts, consideration of various weather conditions is necessary. This study tried to generate extreme drought weather data using the Weather Research and Forecasting (WRF) model and apply it to the Long Short-Term Memory (LSTM), a deep learning artificial intelligence model, to produce the runoff instead of using conventional rainfall–runoff models. Finally, the standardized streamflow index (SSFI), the hydrological drought index, was calculated using the generated runoff to predict extreme droughts. As a result, the sensitivity test of meteorological data to runoff showed that using similar types of meteorological data could not improve runoff simulations with a maximum difference of 0.02 in Nash–Sutcliffe efficiency. During the drought year of 2015, the runoff generated by WRF and LSTM exhibited reduced monthly runoffs and more severe SSFI values below −2 compared to the observed data. This shows the significance of WRF-generated meteorological data in simulating potential extreme droughts based on possible physical atmospheric conditions using numerical representations. Furthermore, LSTM can simulate runoff without requiring specific physical data of the target catchment; therefore, it can simulate runoff in any catchment, including those in developing countries with limited data.

Drought intensity–duration–frequency curves based on deficit in precipitation and streamflow for water resources management

Abstract. Drought estimates in terms of physically measurable variables such as precipitation deficit or streamflow deficit are key knowledge for an effective water management. How these deficits vary with the drought event severity indicated by commonly used standardized indices is often unclear. Drought severity calculated from the drought index does not necessarily correspond to the same amount of deficit in precipitation or streamflow at different regions, and it is different for each month in the same region. We investigate drought to remove this disadvantage of the index-based drought intensity–duration–frequency (IDF) curves and develop IDF curves in terms of the associated deficit. In order to study the variation of deficits, we use the link between precipitation and streamflow and the associated indices, the Standardized Precipitation Index (SPI) and the Standardized Streamflow Index (SSI). More specifically, the analysis relies on frequency analysis combined with the total probability theorem applied to the critical drought severity. The critical drought has varying durations, and it is extracted from dry periods. IDF curves in terms of precipitation and streamflow deficits for the most severe drought of each drought duration in each year are then subject to comparison of statistical characteristics of droughts for different return periods. Precipitation and streamflow data from two catchments, the Seyhan River (Türkiye) and the Kocher River (Germany), provide examples for two climatically and hydrologically different cases. A comparison of the two cases allows a similar method to be tested in different hydrological conditions. We found that precipitation and streamflow deficits vary systematically, reflecting seasonality and the magnitude of precipitation and streamflow characteristics of the catchments. Deficits change from one month to another at a given station. Higher precipitation deficits were observed in winter months compared to summer months. Additionally, we assessed observed past major droughts experienced in both catchments on the IDF curves, which show that the major droughts have return periods at the order of 100 years at short durations. This coincides with the observation in the catchments and shows the applicability of the IDF curves. The IDF curves can be considered a tool for using in a range of specific activities of agriculture, ecology, industry, energy and water supply, etc. This is particularly important to end users and decision-makers to act against the drought quickly and precisely in a more physically understandable manner.