Hydrological Drought Monitoring Research Articles

Long-term hydrological drought monitoring and trend analysis in Blue Nile River basin.

This research aims to monitor the hydrological drought trends within the geographical confines of Ethiopia, Sudan, and Egypt in the Blue Nile River Basin. Historical drought circumstances in the basin were analyzed through the utilization of the stream flow drought index (SDI). The long-term historical drought trend was investigated via the application of the Mann - Kendall Sen (MK) test. Streamflow data were collected at the border (GERD) (Ethiopia) and Khartoum and Dongola (Sudan) spanning the period from 1900 to 2001. Four distinct temporal scales were examined, including monthly (SDI1), seasonal (SDI3), bi-annual (SDI6), and annual (SDI12) frequency. Notably, SDI1, SDI3, and SDI6 exhibited a higher frequency of drought occurrences, whereas SDI12 demonstrated lower frequencies, accompanied by the longest duration of drought in all gauged stations. For the preceding 102-year period, two extreme drought events were identified across all stations: 1912/1913 and 1913/1914 in the Border and Dongola stations, and 1912/1913 and 1986/1987 in the Khartoum station. Moreover, the SDI12 results revealed that severe drought events manifested three, six, and four times, in the Border, Khartoum, and Dongola stations, respectively. Furthermore, an investigation of historical extreme and severe drought patterns led to the conclusion that extreme hydrological drought does not pose an imminent threat to downstream nations, including Egypt and Sudan. However, the trend analysis revealed that an increasing drought trend was observed in the Autumn season across all stations, while a positive trend characteristic of a wet condition was observed in the remaining seasons. Annual trend analysis did not show any statistically significant findings. Nevertheless, the study highlighted the imperative role of soil and water conservation measures in upstream countries, such as the Ethiopian highlands, in mitigating the prolonged effects of meteorological drought which gradually propagates into severe hydrological drought. Consequently, downstream nations must engage in cooperative efforts with upstream countries to address this issue collectively, rather than bestowing sole responsibility on the latter.

Building a framework for a synoptic overview of drought.

Drought is a complex phenomenon with multifactorial impacts, requiring a multiscale approach for effective understanding and management. This study presents an innovative operational framework, "Drought Scan," designed to deepen drought understanding, improve monitoring, and streamline climate services to support effective adaptation and mitigation against drought impacts. At the core of the framework is a methodology that integrates two standardized indices: the standardized precipitation and streamflow indices (SPI and SQI, respectively). These indices are calculated over 1 to 36 (1:36) month-scales and synthesized into a single Standardized Integrated Drought Index ({SPI} and {SQI}). Additionally, the Cumulative Deviation from Normal (CDN) of SPI1 captures the system's memory of past wet and dry phases. We tested the framework in the Po River basin, where {SPI} improves the explained variability of SQI by up to 10% compared to the most effective SPI. Notably, when {SPI} falls below -1, it captures all severe droughts documented. Multi-year precipitation fluctuations drive the system toward relatively wet or dry phases, and contextualizing {SPI} and {SQI} within the CDN shows that severe droughts coincide with notable CDN drops, highlighting the role of preconditions in drought mitigation. The framework also reveals that severe hydrological droughts have intensified over the past two decades, likely influenced by human activities beyond precipitation patterns. Given the strong correlation between {SPI} and {SQI}, {SPI} may serve as a reliable proxy for hydrological drought monitoring in the absence of streamflow data, providing valuable insights to support climate services and informed water management.

Remote Sensing-Based Drought Monitoring in Iran’s Sistan and Balouchestan Province

Drought is a natural phenomenon that has adverse effects on agriculture, the economy, and human well-being. The primary objective of this research was to comprehensively understand the drought conditions in Sistan and Balouchestan Province from 2002 to 2017 from two perspectives: vegetation cover and hydrology. To achieve this goal, the study utilized MODIS satellite data in the first part to monitor vegetation cover as an indicator of agricultural drought. In the second part, GRACE satellite data were employed to analyze changes in groundwater resources as an indicator of hydrological drought. To assess vegetation drought, four indices were used: Vegetation Health Index (VHI), Vegetation Drought Index (VDI), Visible Infrared Drought Index (VSDI), and Temperature Vegetation Drought Index (TVDI). To validate vegetation drought indices, they were compared with Global Land Data Assimilation System (GLDAS) precipitation data. The vegetation indices showed a strong, statistically significant correlation with GLDAS precipitation data in most regions of the province. Among all indices, the VHI showed the highest correlation with precipitation (moderate (0.3–0.7) in 51.7% and strong (≥0.7) in 45.82% of lands). The output of vegetation indices revealed that the study province has experienced widespread drought in recent years. The results showed that the southern and central regions of the province have faced more severe drought classes. In the second part of this research, hydrological drought monitoring was conducted in fifty third-order sub-basins located within the study province using the Total Water Storage (TWS) deficit, Drought Severity, and Total Storage Deficit Index (TSDI Index). Annual average calculations of the TWS deficit over the period from April 2012 to 2016 indicated a substantial depletion of groundwater reserves in the province, amounting to a cumulative loss of 12.2 km3 Analysis results indicate that drought severity continuously increased in all study basins until the end of the study period. Studies have shown that all the studied basins are facing severe and prolonged water scarcity. Among the 50 studied basins, the Rahmatabad basin, located in the semi-arid northern regions of the province, has experienced the most severe drought. This basin has experienced five drought events, particularly one lasting 89 consecutive months and causing a reduction of more than 665.99 km3. of water in month 1, placing it in a critical condition. On the other hand, the Niskoofan Chabahar basin, located in the tropical southern part of the province near the Sea of Oman, has experienced the lowest reduction in water volume with 10 drought events and a decrease of approximately 111.214 km3. in month 1. However, even this basin has not been spared from prolonged droughts. Analysis of drought index graphs across different severity classes confirmed that all watersheds experienced drought conditions, particularly in the later years of this period. Data analysis revealed a severe water crisis in the province. Urgent and coordinated actions are needed to address this challenge. Transitioning to drought-resistant crops, enhancing irrigation efficiency, and securing water rights are essential steps towards a sustainable future.

Identification of propagation characteristics from meteorological drought to hydrological drought using daily drought indices and lagged correlations analysis

Study regionThe Juam Dam area in Suncheon, Jeollanam-do, South Korea Study focusThis study aims to analyze drought propagation characteristics using time-lagged correlation analysis based on daily drought index, determining the lag time from meteorological drought to propagation into hydrological drought. The target period for correlation analysis is the onset date of the dry spell preceding the occurrence of hydrological drought and the termination date of the dry spell following the termination of the drought for each drought event. The Standardized Precipitation Index (SPI) at various time-scales for meteorological drought and the Standardized Reservoir Supply Index (SRSI) for hydrological drought were applied. New hydrological insights for the regionThis study presents the objectivity and accuracy of drought onset and termination date for drought propagation analysis through daily lagged correlation analysis. Through ROC analysis, SPI90, SPI180, and SPI365 are shown to increase by an average of 16.0 %, 8.8 %, and 6.0 %, respectively. From 1993–2023, long-term hydrological droughts lasting 2 years occurred 5 times, with a maximum duration of 408 days, magnitude −629, and severity −1.76. The daily lag between the multi-scale SPIs and SRSI of individual drought events presents the possibility of predicting hydrological drought through multiple regression analysis. This research provides insights for improving hydrological drought monitoring, prediction, and response strategies through results of individual propagation characteristics of drought events.

A spatial downscaling method for SMAP soil moisture considering vegetation memory and spatiotemporal fusion

ABSTRACT Soil moisture (SM) plays an essential role in the hydrological cycle, drought monitoring, and water resources management. However, passive microwave remote sensing products offer coarse spatial resolutions (approximately 25–40 km), greatly limiting their applications. In this study, we considered vegetation memory and increased the amount of vegetation data by spatiotemporal fusion, integrated lagged vegetation index and fusion data as model inputs, and downscaled the spatial resolution of SMAP SM using the Random Forest (RF) model as well as multi-source remote sensing data. The downscaled SM was validated with 30 in situ SM measurements and daily precipitation data and explored the contributions of NDVIlagged and NDVIfused in downscaling. The results show that the downscaled SM is highly consistent with the in situ SM measurements. The correlation ranges from 0.628 to 0.849 with an average of 0.689. The root mean square error (RMSE) ranges from 0.024 to 0.094 m3/m3 with an average of 0.057 m3/m3. The proposed method leverages the strengths of the RF model, fusion vegetation data, and lagged vegetation data to improve high-resolution SM accuracy.

Spatial downscaling analysis of GPM IMERG precipitation dataset based on multiscale geographically weighted regression model: a case study of the Inner Mongolia Reach of the Yellow River basin

The Inner Mongolia Reach of the Yellow River Basin is characterized by a relative scarcity of meteorological stations. While satellite precipitation products can complement observations from meteorological stations, their limited spatial resolution restricts their efficacy in regional studies. This study utilizes the GPM IMERG precipitation dataset, considering various factors that influence the spatial distribution of precipitation, such as the Normalized Difference Vegetation Index (NDVI), elevation, slope, aspect, and topographical relief, to construct a multiscale geographically weighted regression (MGWR) model. A spatial downscaling method for the GPM IMERG precipitation dataset is proposed, and its reliability is validated through an accuracy assessment. Moreover, the scale differences in the impact of different factors on the spatial pattern of precipitation in the Inner Mongolia Reach of the Yellow River Basin are scrutinized. The results indicate that: 1) The downscaled GPM IMERG precipitation data (1 km × 1 km) exhibit enhanced accuracy compared to the pre-downscaled data (approximately 11 km × 11 km). The correlation coefficient, Bias, and RMSE of the annual precipitation data after downscaling of GPM IMERG are 0.865, 6.05%, and 68.50 mm/year, respectively. For the monthly downscaled precipitation data, the correlation coefficient, Bias, and RMSE are 0.895, 6.09%, and 16.25 mm/month, respectively. The downscaled GPM IMERG precipitation dataset exhibit high accuracy on both annual and monthly temporal scales. 2) Different factors demonstrate localized effects on precipitation in both dry and wet years. Elevation is the dominant factor influencing the spatial heterogeneity of annual precipitation. The findings from this study can provide technical support for hydrological modeling, drought monitoring, and water resource management in data-scarce areas of the Inner Mongolia Reach of the Yellow River Basin.

Global groundwater droughts are more severe than they appear in hydrological models: An investigation through a Bayesian merging of GRACE and GRACE-FO data with a water balance model

Realistic representation of hydrological drought events is increasingly important in world facing decreased freshwater availability. Index-based drought monitoring systems are often adopted to represent the evolution and distribution of hydrological droughts, which mainly rely on hydrological model simulations to compute these indices. Recent studies, however, indicate that model derived water storage estimates might have difficulties in adequately representing reality. Here, a novel Markov Chain Monte Carlo - Data Assimilation (MCMC-DA) approach is implemented to merge global Terrestrial Water Storage (TWS) changes from the Gravity Recovery And Climate Experiment (GRACE) and its Follow On mission (GRACE-FO) with the water storage estimations derived from the W3RA water balance model. The modified MCMC-DA derived summation of deep-rooted soil and groundwater storage estimates is then used to compute 0.5∘ standardized groundwater drought indices globally to show the impact of GRACE/GRACE-FO DA on a global index-based hydrological drought monitoring system. Our numerical assessment covers the period of 2003–2021, and shows that integrating GRACE/GRACE-FO data modifies the seasonality and inter-annual trends of water storage estimations. Considerable increases in the length and severity of extreme droughts are found in basins that exhibited multi-year water storage fluctuations and those affected by climate teleconnections.

Drought Forecasting Using Standard Precipitation Index Based on Rainfall of Western Region

Drought has always been one of the most dangerous natural disasters for manhood. Due to the continuous global climate change, drought occurrences have become more frequent and severe, affecting human existence and long-term social progress. PIStandard values are a measure of the probability of a given precipitation event occurring. They are calculated using a statistical distribution of precipitation data. The three statistical distributions that are most commonly used to model precipitation data are the gamma distribution, the normal distribution, and the log-normal distribution. Therefore, utilising all three of the above-mentioned theoretical probability distributions, the drought index PIStandard has been computed. PIStandard range more than 2 (extremely wet) to less than -2 (extremely dry), with 0.99 to - 0.99 considered the near-normal range. PIStandard is calculated at different time scales which can be 1, 3, 6, 12 and 24 months, time scales. The temporal trends of SPI at the stations were identified using the Mann-Kendall test. PIStandard were computation at 1, 3, 6, 9, 12 and 24-month time scales. PIStandard provides a better analysis of meteorological drought at multiple different timescales for short- and long-term planning because it uses the running sum of rainfall values at 1 to 24 months and more parameters for the statistical distribution used. For short-term drought monitoring and agricultural crop planning, a 1- to 3-month PIStandard can be utilized; however, long-term hydrological drought monitoring and water management planning require PIStandards of 6 to 9 months and 12 to 24 months, respectively. Drought analysis using PIStandard results can be used to design rainwater harvesting and storage structures in drought-affected areas for appropriate crop planning.

Application of the Standardised Streamflow Index for Hydrological Drought Monitoring in the Western Cape Province, South Africa: A Case Study in the Berg River Catchment

In many regions around the world, drought has been recurrent, more frequent, and more intense over time. Hence, scientific research on drought monitoring has become more urgent in recent years. The aim of this study was to test the applicability of the Standardised Streamflow Index (SSI) for hydrological drought monitoring in the Berg River catchment (BRC), Western Cape (WC) province, South Africa (SA). Using various methods described in this study, the sensitivity of the SSI to the commonly used Gamma, Log-normal, Log-logistic, Pearson Type III, and Weibull Probability Distribution Functions (PDFs) was tested. This study has found that all the tested PDFs produced comparable results for mild to severe drought conditions. The SSI calculated using the Gamma, Log-Normal, and Weibull PDFs is recommended for the BRC because it consistently identified extreme drought conditions during the 1990–2022 study period and identified the 2015–2018 droughts as the worst during the study period. Although more studies are required to test other PDFs not considered, this study has shown that the SSI can be applicable in the BRC. This study has provided a foundation for more research on the application of the SSI in the BRC and other catchments in SA.

Meteorological and hydrological drought monitoring and trend analysis for selected gauged watersheds in the Lake Tana basin, Ethiopia: Under future climate change impact scenario

Since most of the countries in Africa rely on Agriculture supported by rainfall, they are especially vulnerable to dangers brought on by climate change, such as floods and droughts. Droughts in the Horn of Africa are immediately in danger of starvation, particularly in Ethiopia. This study uses ensemble GCM data at the Coupled Model Intercomparison Project 5 (CMIP5) to examine the future metrological and hydrological drafts of the chosen gauged watershed, specifically Gilgel Abay, Gumara, Megech, and Ribb. For the analysis, the Standardized Precipitation Index (SPI), Reconnaissance Drought Index (RDI), and Streamflow Drought Index (SDI) were used, along with data from the RCP4.5 and RCP8.5 scenarios. The SPI and RDI were used to analyze metrological drought, and the SDI was used to identify hydrological drought in the mid (2040–2079) to long-term (2050–2079) period. Overall meteorological drought results show that annual time scale drought occurrence is higher than biannual time scale in both indices. Under the biannual time scale (SPI6) and annual time scale (RDI12) for RCP8.5 scenarios of all time horizons, 20.69% of the maximum drought record was observed. While the (SPI12) indices of RCP4.5 and RCP8.5 for all time domains (mid and far) display a drought frequency of 17.24%. And according to the RCP8.5 scenario, RDI6 also predicts that droughts would occur 18.10% more frequently in the far future. Additionally, the RCP8.5 scenario has the greatest frequency record for hydrological droughts across all time horizons at 18.10%. Gumara and Ribb gauged watersheds are typically the most susceptible to drought in the mid periods. The study revealed that drought analysis is important to take planning activities for those who are working as researchers, water resource managers and other professions in the basin. As a result, the study will assist the government, policymakers, and decision-makers in putting drought adoptions and early warning actions in place.

Drought Monitoring and Forecasting across Turkey: A Contemporary Review

One of the critical consequences of climate change at both local and regional scales is a change in the patterns of extreme climate events such as droughts. Focusing on the different types of droughts, their quantifying indices, associated indicators, and sources of data (remote sensing (RS)/in situ measurements), this article reviewed the recent studies (from 2010 to 2022) that have explored drought features in Turkey. To this end, a total of 71 articles were selected from the Web of Science (WoS) and Scopus databases. The selected papers were clustered into two categories: (i) drought monitoring studies and (ii) drought forecasting articles. Then, the representative papers were reviewed in detail regarding the implemented indices, models (techniques), case study area, and source of the indicators used to derive drought indices. The review results showed that most of the studies aimed at meteorological drought monitoring and forecasting. An increasing trend was also observed in the use of machine learning for short-term meteorological and hydrological drought prediction. On the other hand, the emerging RS technology and satellite-driven indicators were rarely used in the country. The review showed that there is room for more research on agricultural and hydrological drought monitoring, forecasting, and pattern detection in Turkey.

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.

Hydrological and Meteorological Drought Monitoring and Trend Analysis in Abbay River Basin, Ethiopia

The definition of drought is very controversial due to its multi-dimensional impact and slow propagation in onset and end. Predicting the accurate occurrence of drought remains a challenging task for researchers. The study focused on hydrological and meteorological drought monitoring and trend analysis in the Abbay river basin, using the streamflow drought index (SDI), standardized precipitation index (SPI), and reconnaissance drought index (RDI), respectively, to fill this research gap. The study also looked into the interrelationships between the two drought indicators. The SDI, SPI, and RDI were calculated using long-term streamflow, precipitation, and temperature data collected from 1973 to 2014. The data were collected from eight streamflow stations and fifteen meteorological gauge stations. DrinC software (Drought Indices Calculator) was used to calculate the SDI, SPI, and RDI values. The result from meteorological drought using SPI12 and RDI12 shows that 1975, 1981, 1984, 1986, 1991, 1994, and 2010 were extreme drought years, whereas 1983, 1984, 2001, and 2010 were the most extreme hydrological drought years based on the SDI12 result. Except for Bahir Dar and Gondar, a severe drought occurs at least once a decade in all stations considered in this study. In general, the SPI, RDI, and SDI results indicated that the study area was exposed to the most prolonged severe and extreme drought from 1981 to 1991. The findings of this study also demonstrated that the occurrence of hydrometeorological droughts in the Abbay river basin has a positive correlation at long time scales of 6 and 12 months. The trend analysis using the Mann–Kendall test implied that there was a significant meteorological drought trend in two stations (Debre Berhan and Fiche) at SPI12 and RDI12 time scale, but for the remaining thirteen stations, there is no trend in all time scales. The hydrological drought trend analysis in the basin on a seasonal (SDI3) and yearly (SDI12) time scale also revealed that three streamflow stations have a positive trend (Kessie, Gummera, and Border). This implies that water resource management is still a vital tool for the sustainable development of the Abbay river basin in the future.

A new composite index for global soil plant atmosphere continuum drought monitoring combing remote-sensing based terrestrial water storage and vapor pressure deficit anomalies

Drought is known as a complex natural phenomenon because of its multifaceted effect on the environment. Composite drought indices have been considered a useful tool to capture the compound drought impacts, however, few of them are capable of operating at multi-timescales. Moreover, the vast of composite drought indices fail to depict the whole water deficit on terrestrial because of the ignoring of groundwater. In this study, we linearly combined two satellite observed variables, TWS (Terrestrial Water Storage) and VPD (Vapor Pressure Deficit), to develop a new index WDDI (Water Deficit Drought Index). It can provide the global composite drought conditions at multi-timescales by different weight combinations. The global multiscalar SPEI (Standardized Precipitation Evapotranspiration Index) from 2002 to 2011 and 2012 to 2017 were selected to train and assess the weights respectively. Weights assessment results showed that SPEI and WDDI reached moderate correlations (r > 0.4) over the globe, especially stronger in the summer season (0.5 < r < 0.88). Good agreements between WDDI and scPDSI (self-calibrated Palmer Drought Severity Index), SPEI were found in the global historical drought area statistics, especially in area statistics of severe or worse drought conditions. WDDI at short and long timescales were found to be sensitive to drought signals in the meteorological and hydrological system respectively as it produced moderate or stronger correlations with VsmCI (Volumetric soil moisture Condition Index) and RCI (Runoff Condition Index) in most parts of the world. The application of WDDI in the specific drought events (the 2010 Russian drought and the 2012–2016 California drought) further reinforced the above inferences and revealed the unique superiority of considering groundwater deficit in hydrological drought monitoring. The new proposed multiscalar WDDI could help practitioners better identify the drought conditions and understand the drought propagation mechanism from the meteorological to the hydrological system.

Novel Index for Hydrological Drought Monitoring Using Remote Sensing Approach: Standardized Water Surface Index (SWSI)

Most of the drought indices designed for hydrological drought monitoring use location-specific data, while there are only a handful of indices designed for hydrological drought monitoring using remote sensing data. This study revealed a novel drought index, Standardized Water Surface Index (SWSI), developed for hydrological drought monitoring. The water surface areas required to calculate the SWSI can be extracted from remote sensing data entirely using both the optical (Landsat 5, 7, and 8) and SAR (Sentinel-1). Furthermore, the developed index was applied to five major reservoirs/tanks; Iranamadu, Mahavilachchiya, Kantale, Senanayaka Samudhraya, and Udawalawa, located in Sri Lanka to monitor respective hydrological drought status for the period from 2000 to 2020. Cloud computing platform such as Google Earth Engine (GEE) provides a good basement to use this index effectively, as it can extract long-term water surface area covering a large geographical area efficiently and accurately. The surface water area extraction from satellite data of those tanks shows an accuracy of more than 95%, and in the event of a severe hydrological drought, the water surface area of the tanks is less than 25% of the total and lasts for more than three to four months. It was also determined that in some years, the surface water area of tanks dropped to as low as 7%. The strong correlation observed between the Standardized Precipitation Index (SPI) and SWSI is indicated by the Pearson correlation coefficient ranging from 0.58 to 0.67, while the correlation between the Vegetation Condition Index (VCI) and SWSI ranges from 0.75 to 0.81. Timely drought monitoring over large geographical areas can be more accurately performed with the SWSI index compared to existing hydrological drought monitoring indices. The SWSI could be more useful for areas that do not have measurable field data.

Assessment of drought trends in the Senegal River Basin by a terrestrial water storage index (GRACE)

Droughts lead to significant environmental and economic consequences, especially in arid and semi-arid areas like the Sahel. While site-level assessments of drought in the Sahel are abundant, assessments at the scale of entire hydrological basins are less common. Here, we use a new drought index called the terrestrial water storage index (TWSI) to assess trends in drought throughout the Senegal River Basin. This area covers parts of Guinea, Mali, Senegal, and Mauritania, the study period is between 2003 and 2020. Over the entire period, water storage in the Senegal River Basin is increasing by 0.87 km3 y-1 on the total area of the basin. However, we observed two distinct phases within the time period: an overall water deficit between 2003 and 2012 and a surplus between 2013 and 2020. We also found variations in terrestrial water storage from highly negative at the end of the dry season (-12.47 cm in May 2003) to strongly positive at the end of the rainy season (15.30 cm in September 2020). Our study suggests that the TWSI can be a useful index for regional hydrological drought monitoring, especially for areas where meteo-hydrological observations are insufficient.

Retrieval of All-Weather 1 km Land Surface Temperature from Combined MODIS and AMSR2 Data over the Tibetan Plateau

Land surface temperature (LST) is one of the most valuable variables for applications relating to hydrological processes, drought monitoring and climate change. LST from satellite data provides consistent estimates over large scales but is only available for cloud-free pixels, greatly limiting applications over frequently cloud-covered regions. With this study, we propose a method for estimating all-weather 1 km LST by combining passive microwave and thermal infrared data. The product is based on clear-sky LST retrieved from Moderate-resolution Imaging Spectroradiometer (MODIS) thermal infrared measurements complemented by LST estimated from the Advanced Microwave Scanning Radiometer Version 2 (AMSR2) brightness temperature to fill gaps caused by clouds. Terrain, vegetation conditions, and AMSR2 multiband information were selected as the auxiliary variables. The random forest algorithm was used to establish the non-linear relationship between the auxiliary variables and LST over the Tibetan Plateau. To assess the error of this method, we performed a validation experiment using clear-sky MODIS LST and in situ measurements. The estimated all-weather LST approximated MODIS LST with an acceptable error, with a coefficient of correlation (r) between 0.87 and 0.99 and a root mean square error (RMSE) between 2.24 K and 5.35 K during the day. At night-time, r was between 0.89 and 0.99 and the RMSE was between 1.02 K and 3.39 K. The error between the estimated LST and in situ LST was also found to be acceptable, with the RMSE for cloudy pixels between 5.15 K and 6.99 K. This method reveals a significant potential to derive all-weather 1 km LST using AMSR2 and MODIS data at a regional and global scale, which will be explored in the future.

Reduction of Errors in Hydrological Drought Monitoring – A Novel Statistical Framework for Spatio-Temporal Assessment of Drought

Reduction of Errors in Hydrological Drought Monitoring – A Novel Statistical Framework for Spatio-Temporal Assessment of Drought

Editorial: Challenges of Hydrological Drought Monitoring and Prediction

Editorial: Challenges of Hydrological Drought Monitoring and Prediction

Propagation from Meteorological to Hydrological Drought and Its Influencing Factors in the Huaihe River Basin

Understanding the propagation from meteorological to hydrological drought is crucial for hydrological drought monitoring and forecasting. In this study, daily precipitation and streamflow data of 16 sub-catchments in the Huaihe River Basin from 1980 to 2014 are used to establish a framework to quantitatively reveal the propagation relationship between meteorological and hydrological drought and explore the impact of climate, catchment properties, and human activities on drought propagation. The propagation from meteorological to hydrological drought is divided into three types. Type-1 propagation indicates that one or several meteorological droughts trigger a hydrological drought. The occurrence probability of Type-1 calculated by the conditional probability on SPI and SRI series varies from 0.25 to 0.48 among all catchments. Features of Type-1 propagation can be concluded as lengthening of duration, amplification of severity, lag of onset time, and reduction of speed. Type-2 propagation indicates that a meteorological drought occurs but no hydrological drought occurs, which accounts for 63–77% of the total meteorological drought events in all catchments. Type-3 indicates that a hydrological drought occurs without a proceeding meteorological drought, which is caused mostly by human activities. The occurrence probability of Type-3 ranges from 0.31 to 0.58. Climate factors have significant effects on hydrological drought duration, while catchment properties represented by topographic index and base flow index significantly relate to hydrological drought severity, propagation time, and occurrence probability of Type-1 propagation. The ratio of crop land reflecting irrigation on hydrological drought is far less than that of topographic index, denoting that the impact of irrigation on hydrological drought is less than that of catchment properties. Reservoirs have significant effects on alleviating the duration and severity of extreme hydrological droughts, but little effects on the average duration and severity of hydrological droughts.