Hydrological Drought Analysis Research Articles

Attribution of hydrological droughts in large river-connected lakes: Insights from an explainable machine learning model

Large lakes play an important role in water resource supply, regional climate regulation, and ecosystem support, but they face threats from frequent extreme drought events, necessitating an understanding of the mechanisms behind these events. In this study, we developed an explainable machine learning (ML) model that combines the Bayesian optimized (BO) long short-term memory (LSTM) model and the integrated gradients (IG) interpretation method to simulate and explain lake water level variations. In addition, the hydrological drought trends and extreme drought events in Poyang Lake from 1960 to 2022 were identified using the standardized water level index (SWI) and run theory. The analysis revealed that the frequency of hydrological droughts in Poyang Lake increased from 1960 to 2022, especially in the autumn after 2003. By selecting the flows of the catchment and the Yangtze River as the input features, the BO-LSTM model accurately predicted the water level of Poyang Lake. The IG method was then used to interpret the prediction results from three aspects: the importance ranking of the input features, their roles in the seasonal drought trends, and their roles in extreme drought events. The results indicate that (1) the most influential factor affecting the water level of Poyang Lake was the inflow of the Ganjiang River in the catchment. (2) The increase in the lake outflow caused by the Yangtze River's draining effect was the reason for the intensification of the autumn drought in Poyang Lake. (3) The extreme hydrological drought events were primarily caused by low catchment inflows. Overall, this research provides a new approach that balances prediction accuracy with interpretability for predicting and understanding the hydrological processes in large river-connected lakes. Moreover, this method was also applied to the attribution analysis of hydrological drought in Poyang Lake, providing theoretical support for regional water resource management.

Trend analysis of hydrological and meteorological drought in Apa Dam, Türkiye

Trend analysis of hydrological and meteorological drought in Apa Dam, Türkiye

Hydrological Drought and Trend Analysis in Kızılırmak, Yeşilırmak and Sakarya Basins

Hydrological Drought and Trend Analysis in Kızılırmak, Yeşilırmak and Sakarya Basins

Effects of multi-year droughts on the precipitation-runoff relationship: An integrated analysis of meteorological, hydrological, and compound droughts

Multi-year droughts cause severe water scarcity, ecosystem collapse, and significant socio-economic losses. Understanding the impact of these droughts on the precipitation-runoff (PR) relationship is crucial for managing water resources and mitigating drought-related damage. However, previous research focuses mainly on the influence of multi-year meteorological droughts on the PR relationship, neglecting multi-year hydrological droughts and their compound effects. Studies also generally overlook the speed and volume of precipitation-to-runoff conversion, considering only the quality of PR relationship. Furthermore, there is a lack of effective simulation methods using the intensity of multi-year droughts to understand the PR relationship. To address these gaps, this study defines multi-year compound droughts and employs three key parameters, namely, the determination of coefficient (R2), linear regression slope, and runoff coefficient, to assess the impact of multi-year droughts on changes in the PR relationship's quality, precipitation-to-runoff conversion speed, and runoff volume. A random forest model is then employed to simulate these parameters based on the intensity of multi-year droughts. Case studies conducted in three basins within the arid-semiarid region of northern China as well as in three basins in the humid region of southern China reveal that multi-year droughts result in water shortages and have a significant impact on the PR relationship. Among different types of multi-year droughts, hydrological droughts have the greatest impact on the PR relationship, followed by compound and meteorological droughts, respectively. During meteorological, hydrological, and compound drought periods, the average rainfall (runoff) is lower by 26 – 30% (33 – 58%), 18 – 28% (36 – 60%), and 18 – 26% (30 – 58%) respectively compared to non-drought periods. Furthermore, the PR relationship remains stable in the rivers of the humid region in southern China. In contrast, the Loess Plateau region in northern China exhibits a substantial decrease in both the speed and volume of precipitation-to-runoff conversion. Interestingly, this decline is not accompanied by a significant increase in the R2 of the PR relationship. The random forest approach effectively simulates key parameters of the PR relationship based on the intensity of multi-year droughts. These findings hold crucial implications for the long-term regional water resource planning.

Evaluating the future total water storage change and hydrological drought under climate change over lake basins, East Africa

Climate change led an increase in the frequency of severe droughts, the effects of which are exacerbated by a lack of water storage. Despite increasing research on global-scale total water storage (TWS) change and drought prediction, basin-scale long-term hydrological drought, TWS change, and El Nino–Southern Oscillation (ENSO) influence in East Africa (EA) remain unexplored. In this paper, we used multiple approaches, of ensemble machine learning (ML) and weighted averaging, to conduct a basin-scale exploratory analysis of future hydrological drought and TWS change for the period 2025–2099. Climate change will affect future hydrological conditions in the basins, with maximum severity values of −90.3, −726.6, and −2567.7 Gt in the Tana, Turkana, and Victoria basins, respectively, under RCP6.0. Similarly, we found maximum severities of −187.5 and −277.7 Gt in the Abaya-Chamo and Tanganyika basins, respectively, under RCP 2.6. The most severe El Nino drought and exceptional La Nina conditions in the basins were found under both RCP scenarios. To ensure future water security in EA, sustainable water resource management strategies that can mitigate the impacts of extreme climate events in the future are urgently needed.

Quantifying Uncertainty in Hydrological Drought Index Using Calibrated SWAT Model

The hydrological model requires precise parameter calibration to reflect the basin characteristicsproperly. The optimum parameter values depend highly on the runoff characteristics; thus, the simulated runoff data affects the uncertainty in hydrological drought assessment using hydrological models. This study quantified the hydrological drought uncertainty based on all available calibrated parameters for 12 different observation periods. The Soil and water assessment Tool was used for hydrological modelling, and the uncertainty was analyzed using Bayesian model averaging (BMA). The hydrological drought index using the simulated runoff was analyzed to vary significantly with the optimized hydrological parameter cases. The drought index calculation using the estimated runoff showed that the number of droughts occurred frequently as the duration increased, But, it was observed that severe droughts of 21-month duration occur most frequently. The uncertainty analysis conducted with BMA revealed that for durations shorter than 18 months, the uncertainties in drought index calculations were higher than those in runoff simulations. As the duration increased, uncertainties decreased, with values of 57%, 54%, 50%, 23%, 23%, 19%, 16% and 16% for 3, 6, 9, 12, 15, 18, 21 and 24-month durations, respectively. The average uncertainty was larger for runoff estimation than for drought calculation. Still, the uncertainty variation by each PC was larger for drought calculation, ranging from 0 to 94% as the duration increased to 24 months. Therefore,the results of this study confirm that hydrological drought analysis using hydrological models contains uncertainties depending on the hydrological model parameter cases.

Assessment and Prediction of Hydrometeorological Drought in Corong River Basin, Indonesia

Hydrological drought analysis in a river basin is crucial because it impacts water resource management, agriculture, drinking water supply, industry, ecology, and disaster risk mitigation. It plays a key role in water usage planning, safeguarding agricultural yields, and ensuring a stable drinking water supply. In the context of this research, the Corong River basin is used as a case study. This study aims to determine the level of hydrological drought (deficit) using the Threshold Level Method (TLM) and predict hydrological drought using the Seasonal Autoregressive Integrated Moving Average (SARIMA) model. This model understands the characteristics of drought and predicts drought in the Corong River basin for early mitigation and anticipation of drought in the region. The results of this research indicate a strong relationship between the Hydrological Drought Index (HDI) and streamflow and Precipitation (PRCP), which can be used to forecast future droughts. This model is highly robust and accurate in observing the level of hydrological drought (deficit, duration, and sharpness) and predicting drought events in the Corong River basin. These findings have significant practical implications for water management and disaster risk mitigation in this river basin while also advancing the scientific understanding of hydrological drought.

Attribution analysis of non-stationary hydrological drought using the GAMLSS framework and an improved SWAT model

Climate change and human activities are two key factors that modify the hydrological cycle and drought characteristics of a watershed. Quantifying the effects of climate change and human activities on hydrological drought is of great significance for water resources management. The traditional method of hydrological drought assessment is based on the assumption of stationarity, which is no longer applicable. In this study, using the Hanjiang River basin (HRB) as a study area, the Pettitt test and heuristic segmentation algorithm are first used to identify the change points in the runoff time series. Then, a distributed hydrological model (SWAT) is used to reconstruct the natural runoff series during the impacted period. Finally, the impacts of climate change and human activities on hydrological drought are quantitatively assessed using the non-stationary Standardized Runoff Index (NSRI), developed based on the GAMLSS model. Results show that runoff processes in the basin are non-stationary, and the year 1991 is identified as the change point of the runoff series. Both climate change and human activities exacerbate the hydrological drought by increasing its duration, severity, and peak, with climate change playing a dominant role in the 1990s. Human activities, however, have gradually surpassed the impact of climate change on hydrological drought since the 21st century. In addition, reservoir regulation extends the drought duration and severity but mitigates the peak. These findings provide valuable information for drought management and water resource regulation under changing environments.

Meteorological and hydrological drought analysis of Sinop, Kastamonu, Bartın provinces in the Western Black Sea

In recent years, a significant change has been observed in the climate characteristics of Turkey. These changes, which are compatible with the general trend of global climate change, are also felt in the Western Black Sea Basin. This paper reported on the calculation of drought indices, with emphasis on two recently developed indices, the Reconnaissance Drought Index (RDI) and the Stream flow Drought Index (SDI) using specialized software package, named Drin C (Drought Indices Calculator). Additionally, Drin C includes a module for the estimation of potential evapo transpiration (PET) through temperature-based methods (Hargreaves, Blaney - Criddle, and Thornthwaite) that can be used for the calculation of RDI. Therefore, in this study carried out in the Western Black Sea region, meteorological drought analyzes for1-, 3-, 6- ve 12 months were made for 3 precipitation observation stations in the provinces of Sinop, Kastamonu, Bartın, and monthly total precipitation and monthly average temperature data were used to determine the meteorological drought.

Multidecadal trend analysis of hydrological drought along River Niger using the Streamflow Drought Index

Droughts affect human well‐being and the economy of countries across the world. Understanding the long‐term evolution of droughts within a particular region will help in drought mitigation and adaptation plans, thereby reducing drought impact on the environment. This study examined the multidecadal trends in hydrological droughts at two stations along River Niger using 3‐month, 6‐month, and annual time scales. Hydrological drought events were identified using the streamflow drought index (SDI) between 1915 and 1990 based on the Theil‐Sen slope and Mann‐Kendall approaches. Across the timescales, extreme and severe droughts occurred in 1982/84 and 1983/84 with ‐2 ≤ Sd < ‐1.5. On an annual scale, the results from the annual SDI further showed that the 7th and 8th decades (1971−1990) recorded more drought events of varying degrees ranging from mild to extreme drought in both stations than in other decades. The last two decades (7th and 8th) further revealed the most extended hydrological drought duration from 1974/75 to 1988/89 for Baro and from 1979/80 to 1988/89 for Lokoja. The highest severity recorded at Baro was ‐15.56 and ‐14.26 at Lokoja. The prolonged duration of drought events across the stations and their associated yearly intensities suggests that more proactive measures are needed to ameliorate the hydrological drought impact in the study area.

Bivariate Frequency Analysis of Hydrological Drought Using Copula: A Case Study of Northern Iraq

In this research work, copula-based methodology is adopted to analyze the hydrological drought frequency. Standardized Runoff Index SRI was calculated using monthly-streamflow data for 50 years of two gauging stations in the northern region of Iraq. The drought duration and severity were extracted using run theory. Three Archimedean family and Gaussian copulas were used and compared to select the most appropriate copula model for bivariate frequency analysis of hydrological-drought characteristics. The dependence between drought duration and drought severity was estimated by Pearson's, Spearman's rho and Kendall's tau correlations. Various probability distributions were utilized to determine the best fit marginal distributions for drought characteristic variables based on the Kolmogorov-Smirnov and Chi-squared statistics. Uni-variate and joint return periods were estimated and compared. Generally, the results indicate that Archimedean copulas performed better than the Gaussian copulas. Exponential and Weibull distributions are the best fit for drought duration and severity, respectively, except for drought severity in case of the 9-month time scale at Lesser Zab region, where lognormal distribution was chosen. The current study can give helpful information for drought-risk assessment and water-resource management under climate change. KEYWORDS: Hydrological drought, Copula, SRI, Greater Zab river, Lesser Zab river, Bivariate return periods.

Hydrological drought analysis of Yeşilırmak Basin of Turkey by streamflow drought index (SDI) and innovative trend analysis (ITA)

Hydrological drought analysis of Yeşilırmak Basin of Turkey by streamflow drought index (SDI) and innovative trend analysis (ITA)

Meteorological and Hydrological Drought Analysis of The Kızılırmak Basin

Global climate variation revealing its effects intensely after the Industrial Revolution, have increased extremely with the rise in the number and variation of flood and drought problems worldwide within the years from1990 to 2000. Our country is strongly suffering from this climate variation and its undesired consequences increasing every year. Extreme temperature values, serious flood, and drought problems occuring in some regions have begun to produce considerable damages on daily human life. As a result of the decrease in the discharge of streams, the lack of freshwater resources have become a serious problem which have been considered to be solved with the derivation of fresh water resources for natural life and for other human purposes. Drought conditions of the basin were analyzed using hydrological and meteorological data of Kızılırmak Basin. Standardized precipitation index (SPI) expressing meteorological drought with rainfall parameter and streamflow drought index (SDI) expressing hydrological drought with current parameter were calculated. Droughts were observed in the basin and it was understood that these two indices give harmonious results.

Harmonic oscillator seasonal trend (HOST) model for hydrological drought pattern identification and analysis

Droughts, due to their complex nature, are difficult to predict; however, research is showing that there are often clearly defined patterns to their occurrence. The temporal aspects are usually more apparent when comparing regions with defined climatic characteristics, such as those with recognized wet or dry seasons, but recognizing these patterns can help in defining and understanding the overall processes influencing drought occurrence. Especially in the context of climate change, it is crucial to recognize temporal patterns and quantify them to help diagnose current and future event probabilities. The objective of this work is to introduce a new tool for assessing temporal patterns of streamflow drought, based on the harmonic oscillator theorem called the harmonic oscillator seasonal trend (HOST) model. The testing dataset consists of monthly minimal flows and drought occurrence information calculated based on daily flows from the National Water Model (NWM) retrospective v.2.1 dataset for the period 1979–2020. The model framework allows for trend and seasonality signal decomposition as well as the extraction of the first five harmonics by means of a Fast Fourier Transform (FFT)-based machine learning algorithm. The model framework enables the extraction and evaluation of five different harmonic models based on short and long-range components as well as linear regression sloped harmonics. The results show that the best-fit models are based on two superimposed sinusoidal functions with an accuracy of around 70–90 % for streamflow drought occurrence, with different spatial parameters reflecting varied environmental conditions. The models were able to capture two patterns: (1) annual precipitation variation and (2) longer-term inertia, the latter of which is likely driven by large-scale circulation with a roughly-eight year period.

An Investigation of Hydrological Drought Characteristics in Kızılırmak Basin, Türkiye: Impacts and Trends

Drought is a natural disaster that is generally defined as precipitations which are significantly below the precipitation recorded in normal times. Drought has been classified as meteorological, hydrological, agricultural, and socio-economic by many researchers. Hydrological drought takes place when deficiencies in the surface and ground waters occur as a result of the long-term lack of precipitation. In this study, a hydrological drought analysis has been performed for Kızılırmak Basin which is the second biggest basin of Turkey by using streamflow drought index (SDI) and innovative trend analysis (ITA) for the time scales of 1, 3, 6, 9, 12-month. Monthly mean streamflow records of 7 stations are obtained from the General Directorate of State Hydraulic Works (DSİ). Drought severity and drought duration which is two important drought characteristics have been calculated for each time scale with their occurrence term. Results have shown that Mild Drought and Wet (SDI≥0) have the highest percentage of occurrences. Using Run Theory, the longest lasted and highest drought has been noted in the SDI-12-time scale of E15A017 station with 149.72 and 103 months as severity and duration, respectively. From the highest severity and longest lasted droughts, it is seen that starting with 2000, the basin is exposed to the highest occurrence of droughts. ITA results have shown that in most of the SDI series of any time scale a trend is existent and these trends are mostly decreasing trends. Therefore, the results of this have shown that the basin needs to be kept from the potential effects of droughts with effective water resources management plan.

SEBARAN KEKERINGAN HIDROLOGI BERDASARKAN DEBIT ALIRAN DI KABUPATEN BIMA

A phenomenon that often occurs in Indonesia which is also an annual disaster that hit several areas is drought. Drought is a condition in which an area, land, or community experiences a shortage of water so that it cannot meet their needs. Drought can be caused because an area does not experience rain or is dry for a long period of time or the rainfall is below normal, so that the water content in the soil is reduced or even non-existent. The areas to be analyzed are the Hidirasa watershed, the Rontu watershed, and the Jangka watershed in Bima Regency, West Nusa Tenggara. The analysis drought aims to determine the deficit, drought duration, drought criteria based on deficits and surpluses that occur by analogy to Oldeman's criteria, hydrological drought index analysis, and hydrological drought distribution maps. In this study, data were used for the Tawali watershed in the Hidirasa watershed, the Sari watershed for the Jangka and the Kumbe water for the Rontu watershed to determine the Q50 and Q80 thresholds using the method Flow Duration Curve (FDC) to obtain hydrological drought characteristics. In the analysis of hydrological drought in several watersheds in Bima Regency, the results show that the maximum deficit in the Hidirasa, Term and Rontu watersheds was 1.09 m³/s, 0.14 m³/s, 0.49 m³/s which occurred in 2001, 1995, and 2001. From the deficit, it was found that the average drought period occurred 8-9 months from May to December and even some until January which indicated that the drought criteria were in zone 2 according to Oldeman. The largest hydrological drought index is the Hidirasa watershed, the Jangka Watershed and the Rontu watershed, respectively, -0.45, -1.00, -1.00, with a very strong drought category.

Hydrological drought forecasting and monitoring system development using artificial neural network (ANN) in Ethiopia

The objective of this study is to investigate and perform long–term forecasting of both streamflow and hydrological drought over Ethiopia. Observed streamflow and precipitation data are collected from 17 streamflow stations and 34 rainfall gauge stations to forecast future streamflow and hydrological drought from 2026 to 2099. Streamflow forecasting is performed using an artificial neural network (ANN) in conjunction with python software. Observed precipitation and streamflow data from 1973 to 2014 are used to train and test the ANN model by 70 and 30% ratios, respectively. After training the model, future downscaled precipitation data from regional climate models (RCM) have been used as input data to forecast future streamflow. Three RCM models were used to downscale historical and future climate data. RACMO is found a good downscaling model for all selected stations. The linear scaling bias correction technique results in less than 2% error compared to other alternative techniques. The result indicates that ANN is a good tool to forecast streamflow in areas having a good correlation between precipitation and streamflow such as Abbay, Awash, Baro, Omo Gibe, and Tekeze river basins. But in arid areas for example Genale Dawa, Wabishebele, and Rift Valley basins, the model is not suitable because the input data (precipitation) have high variation than the output variable (streamflow). In such areas, meteorological drought analysis and forecasting are better than hydrological drought analysis. Finally, future hydrological drought is analyzed using forecasted streamflow data as input to the streamflow drought index (SDI). The result indicates that 2028, 2036, 2042, 2044, 2062, and 2063 are the expected extreme drought years in most river basins of Ethiopia in the future. This shows that at least one extreme drought is expected in each decade in the future. Therefore, extensive research in drought analysis and forecasting is needed to develop an effective drought early warning system, and water resource management policy.

Comparing surface water supply index and streamflow drought index for hydrological drought analysis in Ethiopia

Recently, floods and drought have become common natural hydroclimatic hazards in several countries. Consequently, the identification of an appropriate drought index is now a challenging task for researchers. It is obvious that there is not a single best drought index; rather a comparison of indices will give a relative option. The objective of this study was to compare two hydrological drought indices; the modified surface water supply index (M1SWSI) and streamflow drought index (SDI) over eight river basins, in Ethiopia. The M1SWSI and SDI value was computed from 1973 to 2014 using 34 streamflow stations, 42 rainfall gauge stations, and 3 lake-level data. The two indices results showed that the 1980s were the most severe drought years for all river basins. But for the case of Genale Dawa and Wabishebele basins, the drought severity increased from 2000 to 2014. Hydrological drought analysis using SDI has more drought occurrence frequency than M1SWSI. In all river basins from 1973 to 2014, there were a total of 18 severe drought events when using M1SWSI, but there were a total of 39 severe and 12 extreme drought events when using SDI. This implied that M1SWSI reduced the occurrence probability of severe drought by 53.85% and extreme drought by 100%. It is known that Ethiopia is stricken by extreme droughts in the last few decades. But M1SWSI doesn't detect those invidious drought events. In this study, SDI is found to be a better hydrological drought index. Therefore, policy and strategic planners, master plan developers, and decision-makers can use SDI to analyze historical and future hydrological drought trends to develop effective drought mitigation measures.

Uncertainty Analysis of Hydrological Drought Due to Record Length, Time Scale, and Probability Distribution Functions Using Monte Carlo Simulation Method

Standardized Runoff Index (SRI), as one of the well-known hydrological drought indices, may contain uncertainties caused by the employment of the distribution function, time scale, and record length of statistical data. In this study, the uncertainty in the SRI estimation of monthly discharge data of 30- and 49-year lengths from the Minab Dam watershed, south of Iran, was investigated. Four probability distribution functions (Gamma, Weibull, Lognormal, and Normal) were used to fit the cumulative discharge data at 3-, 6-. 9-, 12-, 24-, and 48-month time scales, with their goodness-of-fit and normality evaluated by K-S and normality tests, respectively. Using Monte Carlo sampling, 50,000 statistical data were generated for each event and each time scale, followed by 95% confidence interval. The width of the confidence interval was used as uncertainty, and sources of uncertainty were investigated using miscellaneous factors. It was found that the maximum uncertainty was related to Normal and Lognormal distributions and the minimum uncertainty to Gamma and Weibull distributions. Furthermore, the increase in both time scale and record length led to the decrease in uncertainty.

LONG-TERM HYDROLOGICAL DROUGHT ANALYSIS IN AGRICULTURAL IRRIGATION AREA: THE CASE OF DÖRTYOL-ERZIN PLAIN, TURKEY

In recent years, with the effect of the climate change, drought is accepted as one of the most important natural disasters. In the planning, development and management processes of water resources, studies on the analysis of past droughts and the decreasing of possible negative effects in the future, have become even more substantial. The best adaptation to drought risk can only be achieved by adopting holistic approaches. In this study, Dörtyol-Erzin Plain, which is located in the south of Turkey and covers the fertile agricultural lands of the Asi River Basin with a drainage area of approximately 7800 km2, was preferred as the case study for hydrological drought analysis. In the literature, it is stated that there is a slow drought progress for the Asi River Basin. It is highlighted that decreasing trend in groundwater and increasing trend in evaporation and temperature parameters are remarked. Since the agricultural irrigation of Dörtyol-Erzin Plain is dependent on groundwater and surface resources, hydrological drought analysis over the long period will be beneficial for the future studies. Accordingly, Streamflow Drought Index (SDI) method was used for the hydrological drought analysis by using 35 years of flow data between the years of 1986-2020. The open source “SPI_SL_6.exe” program via National Drought Mitigation Center (NDMC) was operated in the calculations. Drought results were analyzed at different time scales of 3, 6, 12, 24 and 48 months, afterwards relevant graphs and tables were created. Consequently, the longest dry period has been determined between 2008 and 2012 water years, while the wet period has been evaluated between 2003 and 2007 ones. Furthermore, it is concluded that SDI values decreased as the monthly time periods increased, while the maximum indice values were obtained with SDI-3 in all drought periods. When all graphs are examined detailed, it can be expressed long-term droughts for certain water years are notable.