Wetland-based solutions against extreme flood and severe drought: Efficiency evaluation of risk mitigation

<p>In China, historical documents record a large quantity of information related to climate change and grain harvest. This information can help to explore the impacts of extreme drought or flood on crop production, which can provide implications for the adaptation of agriculture to higher-probability extreme climate in the context of global warming. In this paper, reported extreme drought/flood chronologies and reconstructed grain harvest series derived from historical documents were adopted in order to investigate the association between the reported frequency of extreme drought/flood in eastern China and reconstructed poor harvests during 801–1910. The results show that extreme droughts were reported more often in 801–870, 1031–1230, 1481–1530, and 1581–1650 over the whole of eastern China. On a regional scale, extreme droughts were reported more often in 1031–1100, 1441–1490, 1601–1650, and 1831–1880 in the North China Plain, 801–870, 1031–1120, 1161–1220, and 1471–1530 in Jianghuai, and 991–1040, 1091–1150, 1171–1230, 1411–1470, and 1481–1530 in Jiangnan. The grain harvest was reconstructed to be generally poor in 801–940, 1251–1650, and 1841–1910, but the reconstructed harvests were bumper in 951–1250 and 1651–1840, approximately. During the entire period from 801 to 1910, the frequency of reporting of extreme droughts in any subregion of eastern China was significantly associated over the long term with lower reconstructed harvests. The association between reported frequency of extreme floods and reconstructed low harvests appeared to be much weaker, while reconstructed harvest was much worse when extreme drought and extreme flood in different subregions were reported in the same year. The association between reconstructed poor harvests and reported frequency of regional extreme droughts was weak during the warm epoch of 920–1300 but strong during the cold epoch of 1310–1880, which could imply that a warm climate could weaken the impact of extreme drought on poor harvests; yet other historical factors may also contribute to these different patterns extracted from the two datasets.</p>

Mountainous areas on the Tibetan Plateau (TP) are of particular hydrological concern as topography and atmospheric conditions can result in large and sudden floods, and pose critical threats to water-related safety and sustainability in neighboring countries. The Yarlung Zangbo (YZ) river basin is the largest river basin on the southern TP, but the ways in which flood discharges in this basin will evolve under 21st-century climate change and the effect of precipitation extremes and glacier melt remain unclear. Here, we comprehensively quantify the future evolution of extreme flood frequency and intensity under 21st-century climate change, and determine the predominant drivers of flood changes in the YZ basin. We show that total runoff is projected to increase owing to continued wetting throughout the 21st century under two different shared socioeconomic pathways (SSPs) in the YZ basin. Both the frequency and intensity of flood extremes are projected to increase under both SSPs, primarily driven by enhanced total days and magnitude when daily precipitation estimates > 95th percentile. Glacier melt is projected to enhance the intensity and frequency of extreme floods by 12%–23% under both SSPs. This study aims to close the knowledge gap regarding future flood risks in the TP’s rainfall- or meltwater-impacted basins.

Abstract. In China, historical documents record a large quantity of information related to climate change and grain harvest. This information can help to explore the impacts of extreme drought or flood on crop production, which can provide implications for the adaptation of agriculture to higher-probability extreme climate in the context of global warming. In this paper, reported extreme drought/flood chronologies and reconstructed grain harvest series derived from historical documents were adopted in order to investigate the association between the reported frequency of extreme drought/flood in eastern China and reconstructed poor harvests during 801–1910. The results show that extreme droughts were reported more often in 801–870, 1031–1230, 1481–1530, and 1581–1650 over the whole of eastern China. On a regional scale, extreme droughts were reported more often in 1031–1100, 1441–1490, 1601–1650, and 1831–1880 in the North China Plain, 801–870, 1031–1120, 1161–1220, and 1471–1530 in Jianghuai, and 991–1040, 1091–1150, 1171–1230, 1411–1470, and 1481–1530 in Jiangnan. The grain harvest was reconstructed to be generally poor in 801–940, 1251–1650, and 1841–1910, but the reconstructed harvests were bumper in 951–1250 and 1651–1840, approximately. During the entire period from 801 to 1910, the frequency of reporting of extreme droughts in any subregion of eastern China was significantly associated over the long term with lower reconstructed harvests. The association between reported frequency of extreme floods and reconstructed low harvests appeared to be much weaker, while reconstructed harvest was much worse when extreme drought and extreme flood in different subregions were reported in the same year. The association between reconstructed poor harvests and reported frequency of regional extreme droughts was weak during the warm epoch of 920–1300 but strong during the cold epoch of 1310–1880, which could imply that a warm climate could weaken the impact of extreme drought on poor harvests; yet other historical factors may also contribute to these different patterns extracted from the two datasets.

As global temperature rises, the frequency of extreme climate events, e.g., severe droughts and floods, has increased significantly and caused severe damage over the past years. To this regard, precipitation efficiency, a crucial meteorological parameter, could provide valuable insights for a better understanding of the patterns and characteristics of these extreme events. In this study, taking Guangdong province as an exemplary region, we first obtained long-term and high-resolution historical records of precipitation efficiency by integrating the observations from a dense network of Global Navigation Satellite System (GNSS) stations with precipitation data, and then characterized the extreme drought and wetness through climate indices. We found a distinct seasonal trend in precipitation efficiency in Guangdong, with annual fluctuations ranging from 10 to 25%. Notably, precipitation efficiency is higher in proximity to the Pearl River Delta Plain and gradually decreases towards the east and west. The occurrence of anomalous peaks and valleys in precipitation efficiency generally corresponds to dry and wet conditions, respectively. A total of 9 extreme wet events and 6 dry events occurred from January 2007 to May 2022, with durations from 3 to 6 months. Our results also demonstrated that both wet and dry frequencies exhibit an increasing trend with the expansion of the time scale, and the frequency of extreme events near the Pearl River Delta Plain surpasses that of other regions. Furthermore, the propagation time from meteorological anomalies to agricultural and hydrological anomalies is about 3 months. The periodic characteristics of meteorological anomalies are identified as the primary driver for other anomalous periodic patterns. Our work unveils the long-term dynamic behavior of precipitation efficiency, as well as the characteristics of extreme drought and wetness events in the regions characterized by intricate land–atmosphere interactions.

The severity and frequency of droughts are projected to increase globally due to climate change, but the effects of this on water quality are uncertain. The Murray-Darling Basin (MDB) is the largest river system in Australia and has been impacted by droughts of varying severity within recent decades. In this study, we assessed the influence of hydrological droughts and their characteristics (severity and duration) on water quality, utilising a long-term (1980–2017) dataset from two monitoring sites. The main drought periods, and their duration and severity, were identified using the calculated Standardised Drought Index values (SDI) from averaged monthly streamflow data. While several hydrological drought periods were identified, the longest duration and greatest severity were during the Millennium Drought (1998–2010). Nutrient loads and concentrations of Total Nitrogen and Total Phosphorus of drought and post-drought periods were significantly different. The drought period showed the lowest median and interquartile range of nutrient (total nitrogen, TN; oxidised nitrogen, NOX; total phosphorus, TP; and soluble reactive phosphorus, SRP) concentrations and loads for both sites, whereas the highest nutrient loads and concentrations were reported during the post-drought period (approx. 1 × 103 to 1 × 105 kg day−1 increase in nutrient loads). Our analysis found significant relationships between nutrient loads and SDI during droughts. The load of N and P in the initial flush post-drought increased with drought at both sites. This suggests that nutrients were retained in the landscape during the drought and released in higher loads post-drought when the catchment became wetter, the hydrology was activated, and nutrients were mobilised. Hydrology is a key driver controlling the water quality within the inter-drought period and the peak nutrient loads post-drought. The duration and the severity of droughts had a significant (p = 0.01) influence on peak TN and TP monthly loads but not cumulative loads over a 12-month period. Hydrological droughts are important factors in controlling the water quality of the MDB. Therefore, management efforts should be focused on reducing the occurrence and duration of these events, along with the implementation of catchment nutrient control measures.

Flood hazards in large river basins are shaped by multiple interacting factors, including how long a flood lasts, how high the peak flow becomes, and how much water passes through the river system. Traditional approaches that focus only on the flood peak often miss important aspects of flood behaviour and can underestimate risks, especially for long, slowly building floods or short, intense flash events. This study provides a basin-wide assessment of flood characteristics in the Narmada River Basin, India, using 50 years of daily streamflow data from 13 monitoring stations. Instead of analysing peak flows alone, we consider three dimensions of flood behaviour—peak discharge, volume, and duration—and combine them into two complementary metrics. The Short-Duration Flood Index (SDFI) highlights quick, intense floods typically driven by burst rainfall and rapid runoff, while the Long-Duration Flood Index (LDFI) represents slower, persistent floods that build up over days as catchments saturate. These indices allow floods to be compared across the entire basin despite large differences in catchment size and hydrologic setting. The analysis reveals clear spatial contrasts in flood behaviour. Upstream mountainous and plateau regions experience more sustained, long-duration floods, reflecting greater storage and slower runoff processes. Mid-basin tributaries show pronounced flashiness and frequent short-duration floods driven by intense rainfall and limited buffering capacity. Downstream areas receive the highest overall flows as water converges from upstream, but exhibit mixed characteristics depending on event type and rainfall distribution. Importantly, the index-based approach identifies severe flood events that traditional peak-only assessments tend to overlook. By capturing both flash floods and slow-building high-volume floods within a single framework, the method provides a more complete picture of basin-wide flood hazard. Although demonstrated in the Narmada Basin, the approach is applicable to other major river systems and can support flood mitigation planning, early-warning design, and water-resource management—especially as climate change continues to alter rainfall patterns and flood regimes.Keywords: Multivariate flood analysis; Flood indices; Flood frequency analysis; Hydrological extremes; Narmada River Basin

Understanding streamflow changes under climate warming is critical for water resource management and disaster prevention in mid- and high-latitude regions. However, the factors driving streamflow changes remain unclear. This study investigated long-term streamflow trends, influencing factors, responses to climate change, and future flood risks in the Nenjiang River Basin, a typical mid-latitude region in Northeast China that is highly sensitive to droughts and floods. Using data from 1960 to 2016, we applied the Mann–Kendall trend and mutation tests and random forest models, and modeled streamflow responses to climate change to explore streamflow–climate interactions. Results showed significant warming (0.24–0.41 °C/decade, p < 0.05) and increased winter precipitation (0.77 mm/decade, p < 0.05), accompanied by declining annual and autumn streamflow (−6.03 and −3.62 mm/decade, p < 0.05), with abrupt shifts in 1963, 1996, and 2013. Climate change dominated streamflow changes, although human activities contributed 65% during 2013–2016. Streamflow responded more to precipitation than temperature, except in spring. Extreme floods occurred in 1960, 1998, and 2013. Scenario analysis (2021–2100) showed the highest flood frequency under the high-emission pathway (SSP5–8.5). These findings have crucial implications for water resource management, flood risk control, farming systems, and food security.

Spatiotemporal dynamics of meteorological and agricultural drought, part of Manbhum-Singhbhum Plateau (India): Four decades study using NASA POWER and MODIS data

Drought is a recurrent hazard with significant ecological and socioeconomic impacts, requiring continuous monitoring and assessment. This study evaluates the occurrence and spatial-temporal distribution of drought in the Megech-Dirma Watershed, Ethiopia, over the period 2001-2022. Agricultural drought indices Vegetation Condition Index (VCI), Temperature Condition Index (TCI), Vegetation Health Index (VHI) and the meteorological Standardized Precipitation Index (SPI) at 3-month temporal scales were analyzed using the Google Earth Engine platform. The time-series analysis revealed minimum VCI values, indicating severe drought, in 2009 and 2010, while moderate to no drought conditions dominated during 2001 to 2009 and 2010 to 2021. Moderate droughts also showed a declining trend in 2009 and 2010. The TCI series identified frequent extreme drought events in 2002, 2003, 2004, 2009, 2013, 2016, and 2019, with severe drought recorded in 2006, 2007, 2010, 2014, 2015, 2017, 2021, and 2022. Mild to moderate droughts occurred in 2001, 2002, 2008, 2018, 2020, and 2022. Minimum VHI values, also indicating severe drought, were observed in 2002, 2003, 2004, 2009, 2012, 2013, and 2016, while 2001, 2006, 2007, 2010, 2014, 2015, and 2017 to 2022 were dominated by mild to moderate drought conditions. SPI revealed moderate drought conditions, showing weak correlations with the vegetation-based indices (correlation coefficients: -0.08 with TCI, -0.04 with VCI, and - 0.06 with VHI). Drought severity increased spatially in the watershed's northern regions. Of the total area, 9.63% experienced extreme drought, 20.95% severe drought, 29.12% moderate drought, 26.94% mild drought, and 13.36% no drought. Drought hazard maps revealed extreme and severe drought concentrated in the northern and southern parts, whereas moderate, mild, and no drought conditions were distributed across the southwest, east, and central regions. These findings highlight the spatial-temporal complexity of drought in the watershed and provide a robust basis for drought monitoring, water resource management, and ecological protection. The results emphasize the importance of continuous observation, proactive measures, and adaptive strategies to mitigate the impacts of drought.

In this paper standardized precipitation index (SPI) is used to assess meteorological drought for all 30 districts covering 10 agro-climatic zones in an eastern Indian state, Odisha. Monthly rainfall data of 115 years (1901-2015) for all 30 districts of Odisha are analyzed using SPI on 1, 3, 6, 9 and 12-month timescale. These timescales reflect the impact of drought on the availability of different water resources. Results indicate that in all the agro-climatic zones of Odisha, mild drought events have the highest frequencies of occurrence followed by moderate drought events for different timescales. Severe and extreme drought frequencies are comparatively lesser than mild and moderate drought frequencies. SPI analysis shows that 32-46 years are affected by mild drought, 4-16 years affected by moderate drought, 1-9 years are affected by severe drought and 1-5 years are affected by extreme drought during study period of 115 years in different agro-climatic zones of Odisha. It is observed 50.3% areas in the state are affected by drought in June out of which chances of occurrence of mild drought is maximum (28.7%). In the months of July, August and September, 51.7, 48.5 and 46.1% areas are affected by droughts. On average 49.15% areas of the entire state is affected by drought of various intensities out of which the share of mild, moderate, severe and extreme drought is 28.38, 13.28, 5.06 and 2.43%, respectively.

The East African region is highly susceptible to severe floods and persistent droughts, which greatly impact the livelihood of millions of people. Early warnings, at least a few seasons in advance, would help implement mitigation measures. However, most prediction systems using dynamical models perform poorly at long lead times. In this study, we propose a statistical deep learning approach based on a convolutional neural network (CNN) to predict extreme floods and droughts during the short rains season (October–December). The proposed CNN model captures the phase of extreme floods and droughts two to three seasons ahead, except for a few cases. By diagnosing the model’s skills using heatmaps, we find that predicted extreme floods and droughts are linked with the sea surface temperature anomalies of the Indian Ocean Dipole at shorter leads and with western and southern Indian Ocean, equatorial Pacific, and southern Atlantic Ocean at longer leads. Although there were a few poorly predicted exceptions, the superior skill of our CNN-based predictions at longer leads provides a significant advantage in developing mitigation measures.

Due to a continuous increase in global temperature, the climate has been changing without sign of alleviation. An increase in the air temperature has caused changes in the hydrologic cycle, which have been followed by several emergencies of natural extreme events around the world. Thailand is one of the countries that has incurred a huge loss in assets and lives from the extreme flood and drought events, especially in the northern part. Therefore, the purpose of this study was to assess the hydrological regime in the Yom and Nan River basins, affected by climate change as well as the possibility of extreme floods and droughts. The hydrological processes of the study areas were generated via the physically-based hydrological model, namely the Soil and Water Assessment Tool (SWAT) model. The projected climate conditions were dependent on the outputs of the Global Climate Models (GCMs) as the Representative Concentration Pathways (RCPs) 2.6 and 8.5 between 2021 and 2095. Results show that the average air temperature, annual rainfall, and annual runoff will be significantly increased in the intermediate future (2046–2070) onwards, especially under RCP 8.5. According to the Flow Duration Curve and return period of peak discharge, there are fluctuating trends in the occurrence of extreme floods and drought events under RCP 2.6 from the future (2021–2045) to the far future (2071–2095). However, under RCP 8.5, the extreme flood and drought events seem to be more severe. The probability of extreme flood remains constant from the reference period to the near future, then rises dramatically in the intermediate and the far future. The intensity of extreme droughts will be increased in the near future and decreased in the intermediate future due to high annual rainfall, then tending to have an upward trend in the far future.

<title>Abstract</title> Context Globally, climate change is increasing the frequency and severity of droughts, significantly impacting forest ecosystems. While previous studies suggest taller forests are more vulnerable during drought, their capacity to recover following drought remains poorly understood, creating uncertainty for forest management. Objectives This study aimed to quantify the role of canopy height in post-drought forest recovery and determine how its importance changes with drought severity at a regional scale. Methods We focused on evergreen broad-leaved forests in southwest China following a severe drought in 2010. We used MODIS EVI to define a forest recovery index, SPEI to classify drought severity, and a LiDAR-derived canopy height map. We then used linear regression and Random Forest models to analyze the relationships and the relative importance of contributing factors. Results Forest recovery was positively correlated with the intensity of the preceding drought. Under severe and extreme drought conditions, forest recovery increased significantly with canopy height (p &lt; 0.001). The relative importance of canopy height as a predictor of recovery also grew with drought intensity, increasing from 18.03% under normal conditions to 20.08% under extreme drought. Conclusions Our findings reveal that while tall forests may suffer more during severe droughts, they exhibit heightened recovery and resilience afterward. This size-dependent recovery dynamic is a critical and previously overlooked factor that should be incorporated into forest dynamic models and climate-adaptive resource management strategies.

Drought presents a major challenge to the management of rocky desertification and ecological restoration in the delicate karst ecosystems of Guangxi. In this study, the normalized difference vegetation index (NDVI), fractional vegetation cover (FVC) and net primary productivity (NPP) were selected as vegetation remote sensing parameters, and the spatial response characteristics of different types of vegetation in karst areas of Guangxi Province to light, moderate, severe and extreme drought were analyzed to provide scientific basis for the evaluation of the impact of drought on vegetation in karst areas. The results are as follows: (1) NDVI, FVC and NPP showed a fluctuating increasing trend from 2000 to 2022, and the increasing rates were 0.058, 6.90%, and 43.3gC.m-2 per decade respectively. During this period, the number of light, moderate and severe drought days showed a decreasing trend, but the number of extreme drought days tended to increase. (2) The negative correlation of NDVI, FVC and NPP and drought increased from moderate to extreme drought, and from light to extreme drought, the negative correlation between NDVI and FVC and drought decreased, while that of NPP increased. (3) Light and moderate droughts had obvious negative impact on Chinese fir and broad-leaved forest, whereas severe and extreme droughts had obvious negative effect on eucalyptus and bamboo forest.

The present investigation emphasizes on the meteorological drought characteristics of the Marathwada region using the Standardized Precipitation Index (SPI) method. Rainfall data of eight districts in the region from 1901 to 2021 have been investigated. The analysis revealed that the region experienced a significant 49 per cent drought with some districts, such as Hingoli and Nanded, facing more frequent droughts than precipitation events. The longest drought, spanning from 1918 to 1927, and the most severe drought years, including 1918, 1920, 1929, 1972 and 2015, underscore the severity of the situation. Notably, extreme drought (ED) and severe drought (SD) events were experienced between 2 per cent and 5 per cent, with mild drought (MD) and mild wet (MW) classes being the dominant classes. Spatial analysis using the Kriging method revealed that the southern and eastern parts of the region are at higher risk of drought. Trend analysis using Mann‐Kendall (MK) and Spearman Rho (SR) tests showed an increasing drought trend. In contrast, there is a significant increase in the extreme drought class using the Innovative Trend Analysis (ITA) method. The spatial occurrence rates of extreme and severe droughts have increased since 2010, highlighting the urgent need for improved water resource management and climate adaptation strategies to reduce future drought risks.