Antecedent Precipitation Research Articles

Evaluation of the Effects of Rainwater Infiltration on Slope Instability Mechanisms

Mass movements can be caused by factors from different categories, such as geological factors and climate change. From a geological point of view, the soil profile and the geotechnical properties of the materials are crucial in influencing slope instability. From a climate change perspective, rainfall intensity is one of the main triggers of mass movements. Studies related to rainfall infiltration focus on saturated slope zones; therefore, areas of slope stability with infiltration in the unsaturated zone present large gaps. The Brazilian government environmental diagnostics company, the Mineral Resources Research Company (CPRM), identified the municipality of Areia/PB as a danger zone. The region has landslides that occur mostly during the rainy season. Such events lead to the presumption that rainwater infiltration is responsible for the failure of the municipality’s slopes. Thus, the studies proposed in this research aim to determine the influence of precipitation on the stability of the slopes present in the region. The results show that antecedent precipitation has a greater influence on stability, indicating that daily precipitation alone cannot be used as a determinant for landslides. It was concluded that the role of precipitation in slope stability will vary for different locations, with varying surface conditions, variable tropical rainfall, or different microclimatic conditions.

Quantitation of Rainfall Retention Capacity for Small Reservoirs Considering Spatial Soil Moisture

To realize the estimation of rainfall retention capacity for small reservoirs considering spatial soil moisture, a rainfall retention capacity model and its parameter schemes have been developed in this study. An iterative trial solution method considering potential rainfall and soil moisture for the model constructed was proposed for efficient computation. The rainfall retention capacity of 32 pilot small reservoirs located in ungauged basins of Hunan province was calculated starting from 21 August 2023. In addition, a continuous calculation was carried out from 1 August to 30 September 2023 using the proposed method for Heping reservoir. The results show that the Pearson’s correlation coefficients between rainfall retention capacity and available reservoir capacity and soil moisture, are 0.36 and −0.64, respectively. Using Heping reservoir as an example, this study effectively characterized the dynamic change in its rainfall retention capacity, which ranged from 123.6 mm to 68 mm in August 2023. The analysis indicates the rainfall retention capacity of the pilot small reservoirs calculated is reasonably related to the soil moisture, supporting risk visualization for small reservoirs within rain-affected regions. Furthermore, the impact of the antecedent precipitation on rainfall retention capacity can also be dynamically quantified in real time, which provides reference for the continuous management of small reservoirs.

Assessment of Long-Term Streamflow Response to Flash Drought in the São Francisco River Basin over the Last Three Decades (1991–2020)

Flash droughts, characterized by a rapid onset and severe intensity, pose a serious threat to water resource management. Extensive research has indicated that drought has lagged impacts on streamflow. Nevertheless, the hydrometeorological conditions by which streamflow dynamics respond to drought within the São Francisco River Basin (SFRB) remain ambiguous. To bridge this gap, we conducted a study on long-term streamflow responses to flash drought in the SFRB from 1991 to 2020, combining the Standardized Antecedent Precipitation Evapotranspiration Index (SAPEI) and quantile streamflow with a trend analysis. This study employed the SAPEI, a daily drought index, to identify flash droughts and assess the response of streamflow to the identified events across the SFRB. Our findings reveal insights into the direct response of streamflow to flash drought conditions, stimulated by the application of the SAPEI for analysis of flash drought events. The interannual flash droughts fluctuated over the years, with the middle part of the SFRB experiencing frequent, longer flash droughts, while the south part encountered shorter but less frequent events. About 55% of the study area is trended towards drying conditions. A comparative analysis of the SAPEI and streamflow identified a synchronized response to the onset of flash drought events, but the recovery timescale for the SAPEI and streamflow varied among the events. This study enhances understanding of the flash-drought–streamflow relationship in the SFRB and provides theoretical support for addressing drought risks under climate change.

Assessing cotton flooding disasters accompanied by high temperatures: A case study in the middle and lower reaches of the Yangtze River, China

Due to climate change, flooding disasters accompanied by high temperatures (FL-HT) are becoming increasingly common during cotton growing seasons, severely restricting cotton production. However, the characteristics and impacts of cotton FL-HT have rarely been investigated at the regional scale. In this work, based on existing field FL-HT research, we employed daily air temperatures and an agrometeorological indicator called the standardized antecedent precipitation evapotranspiration index to establish a novel approach to characterize FL-HT events during different cotton growth stages in the middle and lower reaches of the Yangtze River (MLRYR) during 1961–2020. Additionally, the influence of accompanying high temperatures (AHT) on the yield-reducing effect of cotton flooding was examined. The results demonstrated that more than half of the cotton FL-HT events were submonthly events, which were efficiently captured by our approach. Over the past six decades, the temporal trends of the cotton FL-HT frequency, the AHT intensity, and the conversion rate (from flooding to FL-HT) were upward at 83.0 % of the sites within the MLRYR, and up to 36 sites exhibited significant (p<0.05) increasing trends. In the most recent decade (the 2010s), the historic highs of the indicators were detected, as demonstrated by the most frequent FL-HT events (780 counts), the most intensive AHT, and the highest conversion rate (20.9 %). During the flowering and boll-filling stage, all the indicators were much greater than those during the other stages; the conversion rate reached 42.2 %, and the FL-HT frequency accounted for 63.0 % of the total FL-HT frequency over the whole growth period. Spatially, 43 % of cotton FL-HT events occurred in the southeastern MLRYR, which was identified as the region most affected by cotton FL-HT. The gravity centers of the cotton FL-HT indicators in different decades exhibited a northward-moving trend. Finally, according to the correlations between flooding intensity and cotton climatic yield, the yield-reducing effect of flooding was much stronger in the years with greater AHT (6 districts had significant correlations) than in the years with less AHT (only 2 districts had significant correlations), verifying the strengthening effect of AHT in cotton in response to flooding. In summary, this work provides new insights into monitoring cotton flooding-HT disasters and reducing flooding risk under climate change.

Icings as sentinels and modifiers of water flow through winter landscapes: An exploration of physico‐chemical processes on the lake‐dominated, discontinuous permafrost Taiga Shield

AbstractThe winter hydrological period is in transition across the Canadian subarctic, as climate warming is shifting precipitation regimes, thawing permafrost, and altering active layer dynamics, and thus increasing the overall amount, and variability, of winter streamflow. Effects of these changes are poorly understood on the Taiga Shield, which comprises ~20% of North America's permafrost‐covered area, and is characterized by a unique ‘fill‐and‐spill’ hydrology whereby runoff generation requires the exceedance of lake basin storage thresholds. Here, we assessed lake hydrostatic levels and used trail camera images of icings, which are sheet‐like masses of layered ice that are common manifestations of wintertime flow on the Taiga Shield, to understand landscape controls on winter water movement in this region. We further used paired geochemical measurements to explore how source water characteristics affect icing chemistry, and the degree to which icings may modify the chemical composition of active winter flow. We undertake this work over 2 years, and across watersheds of different sizes and lake basin characteristics. We show that icing growth is driven by hydroclimatic controls that include fill‐and‐spill hydrologic constraints and winter air temperatures, and that pre‐freshet pulses of water flow are common within this landscape. Across winters with variable antecedent precipitation levels, a larger catchment was able to support icing growth via continued runoff generation, while small catchments were not. Icings were often chemically dilute compared with source waters, indicating that solute exclusion may actively enrich geochemical concentrations in flowing water. Across icings, chemical variation appeared related to source water type (groundwater versus lake; lake size) and apparent redox conditions. These results highlight that streamwater hydrology and biogeochemistry can be dynamic during the understudied winter period, and illustrate that icings may alter the composition of wintertime flow as it moves through fluvial networks.

A preliminary investigation of the small rockfall triggering conditions along a road network in Slovenia

AbstractRockfalls are among the natural hazards that endanger infrastructure, cause major economic disruptions, and threaten human lives. These phenomena result from long-term geological processes such as tectonic rock deformation or weathering, but the actual rockfall itself occurs suddenly, usually without warning. The triggering mechanisms are complex and difficult to trace. In this study, we investigated the triggering mechanisms of more than 2100 small rockfalls that occurred in 2021 in Slovenia, Europe, along the 51,000 km long national road network. We analyzed their spatial and temporal characteristics, as well as the triggering mechanisms of their occurrence, based on different weather scenarios. Multiple data mining methods were used to investigate triggering conditions, and scenario analyses were used to understand the triggering mechanisms. Most small rockfalls in 2021 were recorded in winter and spring at a density of 1 rockfall per 10 km2. The results show that winter weather conditions have the greatest potential for triggering small rockfalls in Slovenia, both in terms of spatial extent and frequency of triggering weather conditions. The analysis showed that summer storms are an important but indeterminant factor for the occurrence of small rockfalls. Among the scenarios tested, the winter scenario, which includes a combination of antecedent precipitation, snowmelt, and freeze‒thaw cycles a few days before the event, was able to predict 72% of the events in the colder season.

Precipitation and time effects on vegetation change in Southern Arizona abandoned farmland

Farmland abandonment represents a significant issue globally, and in arid environments reclaiming ecosystems is challenging. In the Sonoran Desert, much irrigated farmland has been abandoned since 1950. Our objective was to compare changes in vegetative (canopy) cover and species over a 26-year period on abandoned farmland and unfarmed controls in southcentral Arizona (precipitation <300 mm/year). We evaluated time since abandonment and precipitation effects on these variables between 1976 and 2003 with point frame methods on eight farmland sites using stepwise multiple regression. Results suggest antecedent precipitation (AP) is more important than time since abandonment in explaining cover changes. On 26 abandoned sites, we used non-parametric ANOVA to show that vegetative cover (point frame and line intercept methods) declined by about 43% from 1977 to 2023, which was greater than on non-farmed controls. Few farmlands had cover and species composition like reference communities in 2003. Paired farmland-control sites exhibited similar vegetation in 1977 and 2003 suggesting local factors (e.g., soil chemistry, roadways) may have overwhelmed that of farming. Uninterrupted (natural) surface water flow may predict where revegetation was most successful. Our research indicates that farmland may exist in alternative states, and passive revegetation will not reestablish native Sonoran Desert communities on most abandoned farmland sites.

Quantification of enrichment processes in throughfall and stemflow in a mixed temperate forest

AbstractForest ecosystems depend on throughfall and stemflow fluxes for both water and nutrient input. Spatial and temporal variability of throughfall and stemflow fluxes are large and differ between tree species. The nutrient fluxes that accompany throughfall and stemflow are affected by climate, precipitation intensity, the seasonality of dry deposition, and canopy exchange processes. The interdependence of these factors makes it challenging to quantify changes in throughfall and stemflow amounts as well as their nutrient content. Here, we provide observation‐based evidence from 3.5 years of data collection with 222 rainfall events, of the seasonal variability of throughfall and stemflow magnitude and ion concentrations under a beech (Fagus silvatica) and spruce (Picea abies) tree. Interception and canopy cover were seasonally variable, average annual interception was 53% below beech, 61% below spruce and 68% below young spruce canopies. Further, we assess seasonality of ionic nutrients such as NH4 and NO3 as well as Mg, Ca and K and their dependence on both dry deposition and canopy exchange. Throughfall and stemflow were enriched compared to precipitation, with large differences between ions and different months. Antecedent precipitation was a main control on throughfall and stemflow enrichment. We developed a conceptual model of the potential drivers of throughfall and stemflow enrichment based on our observations. While NH4 and NO3 enrichment are likely dominated by dry deposition and dew and fog accumulation, Mg, Ca and K were additionally affected by canopy exchange. Observation based studies such as this one are needed to understand precipitation and nutrient partitioning across forests, which enables the prediction of how changes in climate and forest composition will affect local hydrology and nutrient inputs into forest ecosystems.

Severe wildfire increased variability in the response of gross primary production to climate in an Eucalyptus dominated semi-arid woodland in South Australia

Frequency and severity of wildfires have intensified in many regions in the world. High severity fires often initiate vegetation regrowth and succession, and thus alter ecosystem processes and functioning. Understanding how high severity fire-induced variations in gross primary production (GPP) respond to climate variability is critically important. This study aimed to quantify fire-induced changes in the response of GPP to concurrent and antecedent precipitation and air temperature in a semi-arid woodland in South Australia, using MODIS derived near-infrared reflectance of vegetation (NIRv) time series data (2000–2021) and associated climate data. Additionally, the magnitude and time course of climate legacies on GPP of unburnt and severely burnt sites were determined. The results showed that fire severity increased the variability in post-fire vegetation recovery. Prior to fire disturbance, NIRv as a proxy of GPP, was predominately affected by both concurrent and antecedent precipitation. Pre-fire precipitation legacy was about 9 months. Post-fire precipitation legacy duration was shorter in the unburnt patches within fire scar (ca. 9 months) than outside the fire scar (ca. 15 months), suggesting that the unburnt patches remaining within fire scar were more vulnerable to climate change than the adjacent intact woodland. In addition, the temperature legacies varied over time, likely mediated by water availability in this woodland. Post-fire regrowth from lignotubers was not significantly affected by concurrent and antecedent climate variables, but dependent upon endogenous growth which depends on previous growth conditions. This may be due to the smaller root system which reduced the capacity for taking up rain water after the fire event when annual precipitation was relatively low. We suggest that higher temperature together with reduced and more variable precipitation in the future could induce greater uncertainty in recovery of Eucalyptus trees post-fire.

Precipitation-induced landslide risk escalation in China’s urbanization with high-resolution soil moisture and multi-source precipitation product

Landslides pose a formidable natural hazard. Accurate risk assessment of landslides triggered by precipitation heavily relies on hydrometeorological factors, specifically precipitation and soil moisture. However, the insufficient ground-based observations and the coarse spatio-temporal resolution hinder the performance of landslide prediction. It is not clear what hydrometeorological thresholds and triggering mechanisms are more likely to trigger landslides in China, particularly in the context of rapid urbanization. To address these questions, this study investigated 1504 shallow landslide events in Chinese urban and non-urban areas from 2007 to 2019. It utilized daily 1 km soil moisture at various depths (20–100 cm) and multi-source precipitation datasets, including gauge-based gridded dataset, in conjunction with three multi-source fusion precipitation products (Multi-Source Weighted-Ensemble Precipitation − MSWEP, the Climate Hazards Group InfraRed Precipitation with Station dataset − CHIRPS, and the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement − GPM-IMERG), along with dynamic urban impervious area datasets. It aims to determine the optimal multi-source precipitation predictor, the critical soil moisture depth that triggers landslides, and to establish the hydrometeorological thresholds for landslides. Additionally, the impact of urbanization on landslide occurrences was estimated by comparing antecedent precipitation accumulation, soil moisture, and impervious surface ratio dynamics between urban and non-urban areas. The results indicated that a combination of 2-day cumulative CHIRPS precipitation and 100 cm soil moisture provided the most accurate predictions for landslides in urban regions of China (accuracy = 88.5 %), outperforming interpolated ground-based observations and other fusion products. Specifically, landslides become more prone when antecedent cumulative rainfall surpasses 97.42 mm in 2 days and soil moisture exceeds 39.6 % m3/m3 saturation in China. Urban areas experienced high antecedent precipitation levels, lower precipitation (64.40 mm) threshold and soil moisture threshold (38.9 %), and shorter durations at landslide sites compared to non-urban areas (71.90 mm, 41.4 %, and 7 days, respectively). The process of urbanization is observed to decrease soil moisture levels while concurrently elevating rainfall amounts. This phenomenon, combined with anthropogenic activities, including distance from roads and urban impervious surface expansion, contributes to 44.6 % of the causes of landslides by reducing slope stability and increasing the presence of loose material. These findings have implications for landslide warnings in urban areas with limited measurements and contribute to understanding urbanization’s impact on landslide risks in developing nations.

Climate impacts on high-intensity sapling mortality: Comparative study and future warnings for Larix kaempferi, Abies sachalinensis, and Picea glehnii

Climate change profoundly affects plant viability and forest dynamics, creating uncertainty in future forest management. Post-planting, high-intensity sapling mortality may require costly replanting, disrupting plans, and risking forest management goals. Therefore, this study focused on high-intensity sapling mortality (mortality rates exceeding 30 %) in Larix kaempferi, Abies sachalinensis, and Picea glehnii planted in northern Japan attributed to summer drought. Utilizing a dataset spanning approximately three decades, the correlation between high-intensity mortality occurrence and climatic stress was explored employing a Bayesian approach. Additionally, the probability of high-intensity mortality occurrence by the end of this century was predicted based on two representative climate scenarios (RCP2.6 and RCP8.5). The predictive accuracy of high-intensity mortality occurrence probabilities exceeded 70 % after comprehensive consideration of climatic stress factors, thereby accentuating the robustness of the proposed model. These encompass the antecedent precipitation index (API), which serves as a soil drought indicator; 30-day mean daily maximum temperature (MaxTMP̅); 30-day mean daily total solar radiation (TSR̅); and day of year (DOY). All sapling species manifested a pronounced negative correlation between API and the probability of high-intensity mortality occurrence, whereas TSR̅ exhibited a positive impact on this probability. Additionally, MaxTMP̅ positively influenced this probability for L. kaempferi and A. sachalinensis. Delving into interspecific differentials, API exhibited the most pronounced impact on P. glehnii in comparison to the other two tree species, whereas TSR̅ exerted a substantial influence on A. sachalinensis and P. glehnii. The susceptibility to MaxTMP̅ was hierarchically ordered as L. kaempferi >A. sachalinensis >P. glehnii. DOY, used to assess phenological effects like leaf morphology, negatively affected the probability of high-intensity mortality occurrence exclusively for L. kaempferi. The median probability of high-intensity mortality occurrence in Hokkaido, calculated from the estimated probabilities across all regions of Hokkaido (encompassing non-forested areas but excluding remote islands), at a 1 km resolution, was 0.37 for L. kaempferi, 0.57 for A. sachalinensis, and 0.41 for P. glehnii under RCP2.6 by the century’s end. These probabilities have decreased in comparison to those observed in 2018–2022. In stark contrast, under RCP8.5, the probabilities soared to 0.91, 0.87, and 0.56, representing an increase of 2.12 times for L. kaempferi, 1.36 times for A. sachalinensis, and 1.14 times for P. glehnii in comparison to 2018–2022. Even the least affected species, P. glehnii, witnessed a increase in mortality probability. This study underscores the importance of forest management and adaptation to climate change.

From threat to opportunity: Hydrologic uncertainty regionalization across large domains

Study regionThe study was carried out in Northern Canada (90,000 km2) and Southern Canada (18,000 km2). These basins represent case studies with a larger application target of high latitude regions. Study focusAs model domains become large, ungauged basins are inevitably encountered, and basin heterogeneity increases. This study aims to develop the computationally frugal Model Agnostic Uncertainty Transfer (MAUT) method to regionalize an uncertainty analysis from a set of donor basins to a receiver. The goal is an evaluation of the MAUT method for regionalizing over sufficiently heterogeneous domains to be applicable to high latitude data sparse regions. New hydrological insights for the regionWe propose the Model Agnostic Uncertainty Transfer (MAUT) method, a surrogate method to perform uncertainty analysis in large-domain modelling with a focus on high-latitude regions where data scarcity is especially severe. With the MAUT method, antecedent precipitation and simulated flow are related. Deviation from this relationship is assumed to represent modelling uncertainty and is quantified by quantile regression lines. These lines act as transfer functions requiring only antecedent precipitation to generate uncertainty bounds. The MAUT method requires uncertainty donors and a target receiver model. Key results suggest the method is relatively insensitive to precipitation dataset differences. Simulation length was the most sensitive input, with 15–20 years being ideal for reasonable results. Overall, the MAUT method is viable for high latitude domains.

Divergent patterns of landslide activity and triggering factors at a local scale of a single mountain massif (Island Beskid Mts., Western Carpathians, Poland)

Landslide protection and mitigation are critical issues in many regions worldwide, where lives and livelihoods depend on correctly determining landslide hazards. However, local differences in the response of landslide slopes to meteorological or seismic triggering factors can decrease the performance of landslide models and forecasts. Thus, recognising and understanding spatial patterns of landslide activity at a local scale should be one of the priorities of landslide research. In this study, we analyse the activity and triggering factors of two landslides in one mountain massif, <2 km apart, in the Western Carpathians, Poland. We analysed 61-year-long (1959–2019) records of the activity of study landslides, i.e. dendrochronological reconstruction based on ring eccentricity and compression wood dated in 65 specimens of European silver fir (Abies alba). We compared the dendrochronological proxy of landslide activity at two study slopes with precipitation and earthquakes in the study area. Based on statistical correlations with 560 precipitation indicators, we found that the similarities between the two study landslides include only the direct triggers in June–July and March. The Wiśnia landslide, unlike Hajdowska, can also be triggered by precipitation in October. However, the main difference between the study landslides lies in the preparatory precipitation. Hajdowska shows significant dependence from precipitation of the previous December and current February, while at the Wiśnia study site, the preparatory precipitation is less important and covers the previous October–November and current January. Our results show that although study landslides could be reactivated at one time by similar direct triggers of spring or summer, such simultaneous reactivation would require preconditions of antecedent precipitation of the previous winter half-year (October–March) different for each landslide. Suppose, the requirements of antecedent precipitation are fulfilled only for one of the landslides during the winter half-year. In that case, the other will remain inactive despite the later occurrence of a shared direct trigger in spring or summer. The example of Hajdowska and Wiśnia shows that significant differences in landslide activity, hazard and triggering factors can exist even at the local scale. We argue that omitting even such subtle, unevident differences in the conditions of landslide activity leads to errors in landslide modelling and forecasting. For example, linking Hajdowska and Wiśnia landslides with one precipitation threshold or model of slope instability based on their adjacent location would not be effective in predicting reactivations. The precision of landslide modelling and forecasting depends on the accuracy of preceding analyses of landslide activity and triggering factors. We argue that in the case of the two adjacent landslides studied the divergent patterns of their activity and triggers would not be revealed without analysing long-term (> 60 years long) data sets.

Evaluating the impacts of flooding on crop yields by different meteorological indices: A regional case study in the middle-lower reach of the Yangtze River, China

Flooding is a worldwide destructive disaster that severely restricts agricultural production. Meteorological indices are common tools for regionally assessing the impact of flooding on crop yields, but their performances are rarely compared. In this work, six convenient meteorological indices, namely, precipitation (P), standardized precipitation anomaly (PA), China Z index (CZI), standardized precipitation index (SPI), standardized precipitation and evapotranspiration index (SPEI), and standardized antecedent precipitation and evapotranspiration index (SAPEI), were employed to establish correlations between flooding intensity and crop meteorological yield (FL-Ym correlation). Four major crops in the middle-lower reach of the Yangtze River (cotton, oilseed rape, wheat, and maize) were selected as the study crops. The results indicated that the flooding intensities quantified by the SPI, SPEI, and CZI had strong interconnections, whereas those quantified by the P and SAPEI were less related to the others. In the cases with stronger negative FL-Ym correlations, different indices were more likely to yield consistent identifications. In terms of the districts witnessing significant FL-Ym correlations, in only 26%, 41%, 44%, and 75% of them (respectively corresponding to cotton, wheat, maize, and oilseed rape), the correlations were consistently identified as significant by the majority of the indices, demonstrating the nonnegligible influence of index selection. The relative performances of the examined indices varied with the employed calculation periods (whole growth period and single critical stage), whereas the SPEI was generally the best performer. The SAPEI performed best in assessing the flooding impact during crop critical growth stages, in sharp contrast with its mediocre performance over the whole crop growth period. According to the results of multiple indices, flooding during the middle stages of the study crops exerted the greatest negative impact. Additionally, oilseed rape and Anhui Province were identified as the crop and the region most affected by flooding. This work can provide support for flooding disaster assessment and agricultural water management.

Threshold-based flood early warning in an urbanizing catchment through multi-source data integration: Satellite and citizen science contribution

An effective flood early warning system is vital to take action to save lives and protect properties in urban areas which are increasingly prone to flooding. Despite substantial progress in flood early warning systems, limited available and accessible data often impede their advancement and reliability. Engaging communities affected by flooding can help address data and information gaps in flood early warning systems, facilitated by appropriate methods. This study developed and evaluated a flood threshold combination method to support a community-based flood early warning system in the Akaki catchment, home to Addis Ababa, the capital city of Ethiopia. Various flood threshold combinations were formulated, calibrated and validated by integrating multiple sources of data: rainfall, antecedent precipitation index estimates, Sentinel-1 Synthetic Aperture Radar satellite time series of flood extent, long-term simulated streamflow, citizen science data, river water level and three days lead-time numerical weather prediction rainfall forecast. During validation, the rainfall and river water level threshold combination outperformed other threshold combinations with probability of detection, false alarm ratio, and critical success index estimates of 0.74, 0.18 and 0.63, respectively. The flood threshold combination showed high detection performance for most flooding conditions. Flood forecasts with a 1-day lead-time exhibited a high likelihood in detecting historical severe flood events. The study provides a tested methodology for selecting suitable flood threshold-combinations, enhance the engagement of citizen scientists in a community–based flood early warning system in urban communities.

PrISM at Operational Scale: Monitoring Irrigation District Water Use during Droughts

Efficient water management strategies are of utmost importance in drought-prone regions, given the fundamental role irrigation plays in avoiding yield losses and food shortages. Traditional methodologies for estimating irrigation amounts face limitations in terms of overall precision and operational scalability. This study proposes to estimate irrigation amounts from soil moisture (SM) data by adapting the PrISM (Precipitation Inferred from Soil Moisture) methodology. The PrISM assimilates SM into a simple Antecedent Precipitation Index (API) model using a particle filter approach, which allows the creation and estimation of irrigation events. The methodology is applied in a semi-arid region in the Ebro basin, located in the north-east of Spain (Catalonia), from 2016 to 2023. Multi-year drought, which started in 2020, particularly affected the region starting from the spring of 2023, which led to significant reductions in irrigation district water allocations in some of the areas of the region. This study demonstrates that the PrISM approach can correctly identify areas where water restrictions were adopted in 2023, and monitor the water usage with good performances and reliable results. When compared with in situ data for 8 consecutive years, PrISM showed a significant person’s correlation between 0.58 and 0.76 and a cumulative weekly root mean squared error (rmse) between 7 and 11 mm. Additionally, PrISM was applied to three irrigation districts with different levels of modernization, due to the different predominant irrigation systems: flood, sprinkler, and drip. This analysis underlined the strengths and limitations of PrISM depending on the irrigation techniques monitored. PrISM has good performances in areas irrigated by sprinkler and flood systems, while difficulties are present over drip irrigated areas, where the very localized and limited irrigation amounts could not be detected from SM observations.

Application of the Flood Guidance Model (FG) in the Yi River basin in Uruguay

This study focuses on the application/adaptation of the Flash Flood Guidance (FFG) model developed by the National Weather Service (NWS). The FFG is defined as the total amount of rainfall of a given duration sufficient to cause bankfull flow at the outlet of the watershed. Since flash floods do not occur in Uruguay due to the slower hydrologic response of the watershed, an adaptation of the above model is evaluated and renamed Flood Guidance (FG). Nine storm events that occurred between 2014 and 2020 in the Yi River basin in Uruguay were analyzed. The Natural Resources Conservation Service (NRCS) Curve Number model, a simple and widely used model, is used to determine effective precipitation. Two alternatives are considered to determine the antecedent soil moisture: the use of an antecedent precipitation index (API), and the use of SMOS satellite imagery. The FG is then calculated and compared to the precipitation forecast to determine if there is a risk of flooding at the watershed outlet. The results are compared to observations to evaluate the quality of the warning forecast. The application for forecasting flooded rural and agricultural areas using Sentinel-2 satellite imagery and the NDWI index is considered and analyzed.

Spatial-Temporal Variations of Drought-Flood Abrupt Alternation Events in Southeast China

Under climate change, the frequency of drought-flood abrupt alternation (DFAA) events is increasing in Southeast China. However, there is limited research on the evolution characteristics of DFAA in this region. This study evaluated the effectiveness of the drought and flood indexes including SPI (Standardized Precipitation Index), SPEI (Standardized Precipitation Evapotranspiration Index), and SWAP (Standardized Weighted Average Precipitation Index) in identifying DFAA events under varying days of antecedent precipitation. Additionally, the evolution characteristics of DFAA events in Fujian Province from 1961 to 2021 were explored. The results indicate that (1) SPI-12d had the advantages of high effectiveness, optimal generalization accuracy, and strong generalization ability of identification results, and it can be used as the optimal identification index of DFAA events in Southeast China. (2) There was an overall increase in DFAA events at a rate of 1.8 events/10a. The frequency of DFAA events showed a gradual increase from the northwest to the southeast. (3) DTF events were characterized by moderate drought to flood, particularly in February, July, and August, while FTD events were characterized by light/moderate flood to drought, with more events occurring from June to October. (4) DTF event intensity increased in the northern and western regions from 1961 to 2021. For FTD events, the intensity notably increased in the western region from 1961 to 2001, while a significant increase occurred in all regions except the central region from 2001 to 2021. These findings emphasize the need for precautionary measures to address the increasing frequency and severity of DFAA events in Southeast China.

The Value of Large‐Scale Climatic Indices for Monthly Forecasting Severity of Widespread Flooding Using Dilated Convolutional Neural Networks

AbstractSpatially co‐occurring floods pose a threat to the resilience and recovery of the communities. Their timely forecasting plays a crucial role for increasing flood preparedness and limiting associated losses. In this study we investigated the potential of a dilated Convolutional Neural Network (dCNN) model conditioned on large‐scale climatic indices and antecedent precipitation to forecast monthly severity of widespread flooding (i.e., spatially co‐occurring floods) in Germany with 1 month lead time. The severity was estimated from 63 years of daily streamflow series as the sum of concurrent exceedances of at‐site 2‐year return periods within a given month across 172 mesoscale catchments (median area 516 km2). The model was trained individually for the whole country and three diverse hydroclimatic regions to provide insights on heterogeneity of model performance and flood drivers. Our results showed a considerable potential for forecasting widespread flood severity using dCNN especially as the length of training series increases. However, event‐based evaluation of model skill indicates large underestimation for rainfall‐generated floods during dry conditions despite overall lower severity of these events compared to the rain‐on‐snow floods. Feature attribution and wavelet coherence analyses both indicated considerable difference in the major flood drivers in three regions. While the flooding in North‐Eastern region is strongly affected by the Baltic Sea, the North‐Western region is affected more by global patterns associated with the El‐Niño activity. In the Southern region in addition to global patterns we detected the effect of the Mediterranean Sea, while antecedent precipitation plays a less important role in this region.

Basin-Scale Daily Drought Prediction Using Convolutional Neural Networks in Fenhe River Basin, China

Drought is a natural disaster that occurs globally and can damage the environment, disrupt agricultural production and cause large economic losses. The accurate prediction of drought can effectively reduce the impacts of droughts. Deep learning methods have shown promise in drought prediction, with convolutional neural networks (CNNs) being particularly effective in handling spatial information. In this study, we employed a deep learning approach to predict drought in the Fenhe River (FHR) basin, taking into account the meteorological conditions of surrounding regions. We used the daily SAPEI (Standardized Antecedent Precipitation Evapotranspiration Index) as the drought evaluation index. Our results demonstrate the effectiveness of the CNN model in predicting drought events 1~10 days in advance. We evaluated the predictions made by the model; the average Nash–Sutcliffe efficiency (NSE) between the predicted and true values for the next 10 days was 0.71. While the prediction accuracy slightly decreased with longer prediction lengths, the model remained stable and effective in predicting heavy drought events that are typically difficult to predict. Additionally, key meteorological variables for drought predictions were identified, and we found that training the CNN model with these key variables led to higher prediction accuracy than training it with all variables. This study approves an effective deep learning approach for daily drought prediction, particularly when considering the meteorological conditions of surrounding regions.