Hydrological Events Research Articles

Hydrological drought life-cycle: Faster onset and recovery in humid than semi-arid basins in China

The hydrological drought life-cycle begins at the onset and includes intensification and recovery stages. Previous studies have mainly considered each stage separately, so a comprehensive life-cycle pattern analysis is lacking. Moreover, differences in hydrological drought life-cycle patterns across different climatic basins still need in-depth research. This study proposed an integrated framework for analyzing the life-cycle patterns of hydrological droughts and used it to compare semi-arid and humid basins of China. The empirical analysis involved long-term (≥30 years) monthly runoff and precipitation data from six basins—three in semi-arid and three in humid zones of China. We identified the onset thresholds of hydrological drought events from the response relationship of hydrological drought to meteorological drought, considering both duration and severity thresholds. Then, using the “time–speed” process relationship, the intensification and recovery stages of hydrological drought events were quantified. Finally, the life-cycle patterns of hydrological drought in humid and semi-arid basins of China were compared. The results indicated that: (1) the proposed integrated framework, utilizing life-cycle analysis techniques to investigate the onset thresholds, intensification, and recovery of hydrological drought, provided valuable insights into their differences across different climatic basins; (2) compared to the semi-arid basins of northern China, the humid basins exhibited lower onset thresholds, slower intensification, and quicker recovery. This indicated that, in the humid basins of southern China, the onset thresholds for hydrological drought are reached rapidly after meteorological drought, followed by more gradual intensification and faster recovery stages. This underscored the importance of recognizing the distinctive life-cycle patterns of hydrological drought in different climatic zones so that tailored strategies for effective drought prevention and mitigation can be devised

Runoff simulation of the Kaidu River Basin based on the GR4J-6 and GR4J-6-LSTM models

Study regionThe Kaidu River Basin originates from the southern slope of the Tienshan Mountains in the Xinjiang Uygur Autonomous Region, China. Study focusAccurate runoff simulation and prediction significantly affect flood control, drought resilience, and water resource allocation decisions. This study establishes the GR4J-6 model (modèle du Génie Rural à 4 paramètres Journalier-6, including a snowmelt module) and integrates it with the LSTM (Long Short-Term Memory) model to construct the hybrid GR4J-6-LSTM model and enhance the simulation accuracy of snowmelt runoff. A case study is conducted in the Kaidu River Basin to demonstrate the applicability of these models in cold and arid regions. The accuracy of the GR4J-6, LSTM, and GR4J-6-LSTM models is evaluated using Nash-Sutcliffe efficiency (NSE), Kling-Gupta Efficiency (KGE), and Root Mean Squared Error (RMSE) metrics. In addition, the contributions of each feature variable in the models are analyzed using the SHapley Additive exPlanations (SHAP) method to enhance the reliability of the results. New hydrological insights for the regionThe GR4J-6 model demonstrated good applicability in the Kaidu River Basin, with NSE, KGE, and RMSE values of 0.69, 0.79, and 39.39 m3/s during the validation period, respectively. The hybrid model GR4J-6-LSTM exhibited the highest comprehensive accuracy among all the models, with NSE, KGE, and RMSE values of 0.84, 0.87, and 28.79 m3/s, respectively. In the LSTM model, temperature and precipitation were found to significantly influence the simulated runoff, indicating that higher temperature and precipitation lead to increased runoff. In the GR4J-6-LSTM model, Tmin (minimum temperature) and the hydrological feature variable Qsim exhibited a strong positive correlation with simulated runoff, as Tmin and Qsim increased, they promoted stronger flow production. This study provides a framework for runoff simulation in snowmelt river basins, offering a reference for projecting extreme hydrological events under climate change.

Response time of fast flowing hydrologic pathways controls sediment hysteresis in a low-gradient watershed, as evidenced from tracer results and machine learning models

Hydrologic controls on the timing of sediment transport and sediment hysteresis patterns remain an open area of investigation in hydrology, especially for low-gradient watersheds with substantial instream sediment deposition. Sediment hysteresis, which describes the mismatch between hydrograph peak and sedigraph peak, aids with elucidation of the mechanisms of sediment transport in watersheds. Most frequently, the controls of hysteresis are attributed to the proximity of sediment sources to monitoring locations in a watershed. However, this assumption, while widely applied, is infrequently verified. We investigated the controls of sediment hysteresis in a low gradient system located in the Bluegrass Region of central Kentucky, USA. Turbidity and conductivity sensors installed at the basin outlet provided data to quantify sediment hysteresis and separate hydrologic flow pathways (i.e., by describing the source of water delivered to the watershed’s outlet) using a tracer-based approach. Predictive hydrologic parameters, including hydrologic pathways, event magnitude, and antecedent conditions, were estimated and grouped based on hydrologic similitude. Thereafter, we identified parameters required to predict sediment hysteresis using a tailored ensemble feature selection approach coupled with three machine learning algorithms—Random Forest, K-Nearest Neighbors, and Gradient Boosted Trees. Results from the analysis of 68 storm events occurring over a two-year period showed that clockwise events accounted for 85 % of the total sediment yield despite comprising only 53 % of the events. The hysteresis index (HI) can be predicted (r = 0.8, RMSE = 0.12) using three, out of the thirty-nine hydrologic parameters considered. The most important predictors of HI reflect the volume of event rainfall and the relative proportions of new water (i.e., water derived from precipitation during the storm event) and old water (i.e., water previously stored in the watershed) comprising the hydrograph. Further analyses reveal that new water timing—which changes with the rainfall volume—and sediment timing are closely linked, suggesting that variations in the hysteresis patterns are controlled by changes in the response time of fast flowing water pathways. This implies that hydrologic pathways, as opposed to sediment proximity to the watershed outlet, control sediment hysteresis in this watershed. These results have important implications for better understanding the mechanisms controlling sediment transport at the watershed scale.

A new Multivariate Drought Severity Index to identify short-term hydrological signals: case study of the Amazon River basin

The Earth's climate is changing rapidly and unexpectedly, causing more frequent, longer and more severe droughts, with lasting impacts on plants, ecosystems, communities and people. Consequently, this is leading to an increased importance of monitoring the climate and water storage trends in different regions. This information on a global scale is already commonly derived using satellite-based geodetic techniques such as the Global Positioning System (GPS) and the Gravity Recovery and Climate Experiment (GRACE). The use of both techniques has significant advantages, especially in regions where changes in the hydrosphere are notable, such as the Amazon basin, where 25 GPS stations were lately classified as benchmarks for hydrogeodesy. We show that the vertical displacements obtained from GPS and GRACE have good spatio-temporal agreement with the Standardized Precipitation and Standardized Precipitation Evapotranspiration indices, abbreviated respectively as SPI and SPEI, for all these stations. Drought severity index (DSI) estimated separately from GPS-observed and GRACE-derived vertical displacements on a station-by-station basis is capable to identify dry and wet events previously reported for the Amazon basin. However, due to the weaknesses of both techniques, such as technique-related systematic errors or coarse spatial resolution, a few extreme hydrological events may not be properly captured by GPS-DSI and/or GRACE-DSI. To take full advantage of both techniques and overcome their weaknesses, we introduce a completely new methodology to combine individual GPS-DSI and GRACE-DSI indices. As a novelty, both indices are estimated using short-term changes (<9 months) of monthly vertical displacements observed by GPS permanent stations and those derived by GRACE for GPS locations. Then, to capture and detect drought events that either both geodetic techniques metrics missed or incorrectly depicted, the Multivariate Drought Severity Index (MDSI) is estimated through the concept of Frank copulas. We demonstrate that the MDSI captures more hydroclimatic events reported in previous studies, which are not identified by individual series of GPS-DSI or GRACE-DSI indices, and is temporally consistent with Standardized Streamflow Index (SSI) based on the in-situ river discharge changes.

Review of Green Water Systems for Urban Flood Resilience: Literature and Codes

Achieving Urban Flood Resilience (UFR) is essential for modern societies, requiring the implementation of effective practices in different countries to mitigate hydrological events. Green Water Systems (GWSs) emerge as a promising alternative to achieve UFR, but they are still poorly explored and present varied definitions. This article aims to define GWSs within the framework of sustainable practices and propose a regulation that promotes UFR. Through a systematic review of existing definitions and an analysis of international regulations on sustainable urban drainage systems (SuDSs), this study uncovers the varied perceptions and applications of GWSs and their role in Blue–Green Infrastructure (BGI). Furthermore, the research puts forth a standardized definition of GWSs and emphasizes the implementation of SuDSs in Peru. This approach aims to address the existing knowledge gap and contribute to the advancement of sustainable urban infrastructure.

Safety Assessment of Gas Pipelines Crossing River through Hydrodynamic Analysis

Gas pipelines are buried and installed across rivers to supply the gas necessary for daily life. When crossing rivers, gas pipelines are typically installed on bridges; however, when installation on bridges is not feasible, the pipelines are buried in riverbeds. This study utilized both a one-dimensional model (HEC-RAS) and two-dimensional models (SMS) to simulate river flow and estimate the potential for scour and deposition around buried pipelines. The hydrodynamic simulations considered critical factors, including sediment transport, river discharge, and geological characteristics, to derive the maximum scour depth and assess the risk of pipeline exposure. The findings from the long-term and short-term simulations confirmed that riverbed changes due to natural hydrological events do not exceed the minimum burial depth standards, thereby ensuring pipeline safety. In addition, the study proposed specific reinforcement measures tailored to local site conditions, addressing concerns of continuous subsidence and ensuring long-term structural stability. This research offers important insights into pipeline risk management and contributes to the development of more effective regulatory standards for gas pipelines buried in riverbeds, enhancing both their safety and environmental sustainability

A dataset of floating debris accumulation at bridges after July 2021 flood in Germany and Belgium

This paper presents a dataset documenting 71 floating debris accumulations at bridges following an extreme hydrological event that hit Belgium and Germany in July 2021. Data were collected from various sources including public authorities’ documents, public online databases, post event pictures and field visits. The dataset covers bridges geometry, flood conditions and debris accumulation. In particular, it systematically details accumulations dimensions and quantifies accumulations components, which contain a significant portion of anthropogenic objects, in addition to driftwood. This dataset constitutes a unique set of invaluable data to better understand debris accumulation at bridges but also to analyze their impact on both the affected structures and flood conditions.

A multi-scenario ensemble approach incorporating stepwise cluster analysis to reduce uncertainty in large-scale watershed precipitation projections: a case study of Pearl River Basin, South China.

Assessing and selecting climate models with lower uncertainty is necessary to predict future climate and hydrological risks at the watershed scale. In this study, we integrated stepwise cluster analysis (SCA) to propose a multi-model ensemble downscaling framework aimed at reducing the uncertainty of GCM-based precipitation projections in large-scale watersheds. The Pearl River Basin (PRB) in southern China was selected as the study area to validate the reliability of this framework. Spatially, we investigated the features of terrain-related spatial heterogeneity in precipitation simulation of different climate models using a stepwise cluster zoning approach. The spatial performance of most CMIP6 models was effective in capturing the annual mean precipitation from the source region to the downstream of the PRB. To further evaluate the model's skill in simulating precipitation patterns, we conducted a seasonal analysis for different periods throughout the year. However, the seasonal precipitation cycle exhibited a wet bias during cold seasons, and the most significant deviation of precipitation percentage intervals occurred during winter. The TSS ranking of CMIP6 models was used to select the top-performing models to construct an improved multi-model ensemble mean (MEM5), resulting in a more accurate precipitation simulation for PRB. Results showed consistent precipitation increases (p < 0.05) for all scenarios in the PRB, with the middle and lower reaches being the most sensitive to changes in precipitation. The improved MEM5 can serve as a valuable reference for accurately simulating hydrological regimes and extreme weather events in the PRB. The proposed multi-model ensemble downscaling framework, which incorporates SCA, offers a new approach for high-resolution and low-uncertainty climate simulations in other large-scale watersheds.

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

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

Maintenance of High Phytoplankton Diversity in the Danubian Floodplain Lake over the Past Half-Century.

Riverine floodplains are recognized as centers of biodiversity, but due to intense anthropogenic pressures, many active floodplains have disappeared during the last century. This research focuses on the long-term changes in phytoplankton diversity in the floodplain lake situated in the Kopački Rit (Croatia), one of the largest conserved floodplains in the Middle Danube. The recent dataset from 2003 to 2016 and historical data from the 1970s and 1980s indicate high phytoplankton diversity, summarising 680 taxa for nearly half a century. The variability of species richness is driven by specific in-lake variables, particularly water temperature, water depth, total nitrogen, pH, and transparency, determined by a redundancy analysis of the current data. The high phytoplankton diversity levels are sustained regardless of intense pressures on the lake environment, including exposure to strong anthropogenic pollution in the past and extreme hydrological events, both droughts and floods, which have increasingly affected this part of the Danube in the last decades. The conserved hydrological connection between various biotopes along the river-floodplain gradient seems crucial in maintaining high phytoplankton diversity. Accordingly, conserving natural flooding is mandatory to maintain high biodiversity in complex and dynamic river-floodplain systems.

Large-sample detection of reservoir impacts on flow regime alteration through improved paired-catchment approach

The 20th century witnessed a substantial surge in global reservoir construction, representing a significant milestone and resulting in a profound anthropogenic alteration of flow regimes. Despite this surge, a comprehensive large-sample quantification of reservoir impacts on flow regime alteration has been impeded by limitations in statistical and modeling methodologies. Here we present an improved paired-catchment method tailored to discern the impact of reservoirs on flow regime within the context of climate change. Employing this method, we identified and analyzed 177 paired catchments from a selection of over 12,000 global catchments. Our findings highlight the effectiveness of the improved method, demonstrating that reservoirs predominantly alleviate high flows while enhancing low flows. This serves to moderate the annual periodicity of streamflow signals while amplifying their low-frequency components. Furthermore, predetermined water releases play a significant role in diminishing the seasonality of low flows and reducing the sensitivity of runoff to precipitation. Additionally, the extent of flow regime alteration caused by reservoirs is closely linked to their functionalities and regulatory capacity. For example, reservoirs designed for irrigation and water supply show minimal enhancement of low flow values, while the magnitude of reservoir-induced changes rises with higher regulatory capacity. The improved method can be further employed in the future to investigate the impact of reservoirs on extreme hydrological events such as droughts and floods. It emerges as a crucial scientific tool for effective water resource management and the mitigation of water-related disasters in the Anthropocene era.

Intensified carbonate weathering during storm events in a highly-erosion river catchment

Carbonate weathering is sensitive to tectonic and climatic settings due to its rapid dissolution kinetics, playing an important role in the carbon cycle at short time scales (102 to 104 years). Under global warming, extreme weather and hydrological events have become more frequent worldwide. How carbonate weathering and its associated carbon budget respond to these extreme events remains unclear. Here, high-temporal-resolution (weekly) riverine chemistry during 2010 was investigated in the upper Min Jiang, a highly-erosion river catchment on the eastern Tibetan Plateau. River water chemistry during the monsoon season fluctuated with six storm events (9 days in total) that were characterized by 7–14 times higher water discharge and 10–40 times higher erosion than those of the non-monsoon seasons. Based on a forward model, riverine chemistry was dominated by carbonate dissolution in the upper Min Jiang. Compared to the non-monsoon seasons, the storm events triggered 270% and 264% increases in the average fluxes of carbonate weathering and associated CO2 consumption, respectively. Furthermore, an incongruent carbonate dissolution (ICD) model as a possible mechanism was employed to reveal the preferential leaching of Mg2+ and Sr2+ relative to Ca2+ during carbonate weathering under extremely-high erosion at basin scale. Accompanying the ICD, the storm-derived carbonates were likely carried downstream as suspended particles for further weathering. This study demonstrates the intensification of carbonate weathering and a carbon sink by storm events, with incongruent carbonate dissolution under storm-induced high erosion.

Evolution of megadrought and pluvial events in the Qaidam Basin and Hexi Corridor, Northwest China, during the period 455–2100 CE

AbstractMillennium‐long hydroclimate reconstructions in the Qaidam Basin (QB) and Hexi Corridor (HXC) suggest markedly differing moisture change trends between the two regions in the 20th century; however, it remains unclear whether these current moisture states are exceptional in a long‐term context, and how megadrought and pluvial events have evolved in these regions. Here, we used previously published historical hydroclimate reconstructions combined with model‐based future moisture simulations to assess past, current and future hydroclimate anomalies in a long‐term context (i.e., 455–2100 CE), and investigate the evolution of megadrought and pluvial events. Compared with the QB, moisture variability in the HXC was higher and more prone to the occurrence of severe and long‐lasting megadrought and pluvial events. Megadroughts in the QB mainly occurred in the Little Ice Age (1200–1800 CE) accompanied by lower temperatures, whereas in the HXC, megadroughts mostly occurred during the Medieval Climate Anomaly (800–1200 CE) accompanied by higher temperatures. The Significant Zero crossing of derivatives (SiZer) and Time of Emergence (TOE) analyses were used to reveal the initiation of recent humidity changes and the duration above the natural variability threshold. We found that the QB has experienced a significant wetting trend since the middle of the 20th century, with this trend exceeding the range of natural hydroclimate variability in 1975 CE. The HXC became drier from the early 20th century, but has become wetter since the late 20th century; this trend may exceed the natural hydroclimate variability range by 2032 CE. We also found that the duration and severity of megadrought and pluvial events are positively correlated in each region. Given the higher past hydrological variability in the HXC compared with the QB, our study implies that future extreme hydrological events are more likely to occur in the former of these two regions.

A Probabilistic Approach to Characterizing Drought Using Satellite Gravimetry

AbstractIn the recent past, the Gravity Recovery and Climate Experiment (GRACE) satellite mission and its successor GRACE Follow‐On (GRACE‐FO), have become invaluable tools for characterizing drought through measurements of Total Water Storage Anomaly (TWSA). However, the existing approaches have often overlooked the uncertainties in TWSA that stem from GRACE orbit configuration, background models, and intrinsic data errors. Here we introduce a fresh view on this problem which incorporates the uncertainties in the data: the Probabilistic Storage‐based Drought Index (PSDI). Our method leverages Monte Carlo simulations to yield realistic realizations for the stochastic process of the TWSA time series. These realizations depict a range of plausible drought scenarios that later on are used to characterize drought. This approach provides probability for each drought category instead of selecting a single final category at each epoch. We have compared PSDI with the deterministic approach (Storage‐based Drought Index, SDI) over major global basins. Our results show that the deterministic approach often leans toward an overestimation of storage‐based drought severity. Furthermore, we scrutinize the performance of PSDI across diverse hydrologic events, spanning continents from the United States to Europe, the Middle East, Southern Africa, South America, and Australia. In each case, PSDI emerges as a reliable indicator for characterizing drought conditions, providing a more comprehensive perspective than conventional deterministic indices. In contrast to the common deterministic view, our probabilistic approach provides a more realistic characterization of the TWS drought, making it more suited for adaptive strategies and realistic risk management.

Application of RiTiCE in understanding hydro-meteorological controls on ice break-up patterns in River Tornionjoki

The Arctic region experiences significant annual hydrologic events, with the spring flood and ice break-up being the most prominent. River ice break-up, in particular, poses high socioeconomic and ecological expenses, including morphological changes and damage to riverine structures. This study aims to investigate the spatiotemporal patterns of river ice in the River Tornionjoki, including the timing of ice break-up at different latitudes. We utilized observation data and remote sensing techniques to track changes in ice patterns overtime on the River Tornionjoki. The study indicates that the ice break-up in the River Tornionjoki basin typically occurs during Apr-Jun based on the reach location in different latitudes; therefore, different stations behave according to their latitudinal location. We observed significant spatial variations in ice break-up timing across the basin, with an earlier break-up in the lower latitudes compared to the upper latitudes. The average ice break-up day in lower latitude stations ranges between 200–205, while in higher latitude stations the average ice break-up day ranges between 215–228.

Interpretation and implications of high-resolution hydroclimatic records of the past 7,000 years based on Gaho paleolake sediments in South Korea

In this study, we investigated the past hydroclimate and its controlling factors over East Asia by reconstructing hydroclimate variability during the middle-to-late Holocene using rainfall-driven sedimentary features in the Gaho paleolake, Hapcheon County on the southern Korean Peninsula. Based on radiocarbon dates, median grain sizes, and elemental ratios of strontium (Sr), titanium (Ti), and zirconium (Zr) measured by high-resolution X-ray fluorescence core scanning, we tested potential indicators of past heavy rainfall. During the past 7000 years, temporal changes in median grain size and elemental ratios (Sr/Ti and Zr/Ti) were found to be similar to those in the Asian monsoonal precipitation index and sea surface temperature (SST) in the western North Pacific region, suggesting that periods with increased elemental ratios may have been influenced by intensified regional rainfall events and higher SSTs. During the past 2000 years, time series of Sr/Ti and Zr/Ti ratios in the paleolake sediments appeared to covary with a megadrought period (AD 1593–1698; 357-252 cal BP), flooding events in the 1500s, and the collapse of ancient nations in Korea and other parts of East Asia. This similarity between sedimentary records and historical events suggests considerable potential for the dating of elemental ratios in lake sediments as high-resolution analogs of past hydrological events to support historical records.

Towards a Canadian National River Water Quality-Modelling System: State of Science and Future Prospects

Water quality is of significant concern and ultimately critical to every Canadian’s quality of life and security. Canada has diverse and vast landscapes and stressors that impact various waterbodies differentially, with influencing factors including contaminant and nutrient loads from human activity (mining effluent, wastewater, agricultural runoff, plastics), landscape change (wetland drainage, urbanization) and climate change (warming water temperatures, longer open water seasons, extreme hydrological events, intensifying wildfires). Canadian rivers are especially important to the overall biogeochemistry, hydrology, biodiversity, and ultimate health of aquatic and terrestrial ecosystems. While each of Canada’s provinces and territories has extensive river water quality (physical, chemical, biotic) data and monitoring programs, Environment and Climate Change Canada coordinates various national programs that contribute to the collection and consolidation of these data and conducts extensive research into the study and modelling of key river water quality processes. Despite program-specific efforts, there remains poor capacity to predict current and future conditions in monitored and unmonitored Canadian rivers, particularly remote or northern rivers, due to a myriad of factors including lack of coordination amongst groups and examination of areas in which modelling efforts might be integrated. Herein, we review and analyse the current state of data availability, process studies, and modelling systems for Canadian river water quality. Our synthesis reveals that specific physical processes (water temperature, ice formation, permafrost thaw, sediment dynamics), biogeochemical processes (dissolved oxygen, dissolved organic matter, nutrient cycling, metals/contaminants) and ecological/biological features (biota mass, functional indicators) are well understood, though complex, and are amenable to empirical or mechanistic modelling. Review of this information assists us in identifying opportunities and challenges for developing a national water quality modelling system (NWQMS), that would eventually include similar modelling activities for parallel processes in lakes and integrated watersheds. We identify needs for stronger coordination of monitoring programs in remote areas, recommend use of novel remote sensing technologies, and development of a flexible, iterative ‘process’ for integrated modelling to which stakeholders beyond government can contribute. Such a platform would support short and long-term predictive models of Canadian water quality and ecosystem health, inform effluent concentration limits, and be an early warning system for source waters.

Non-stationary models for hydrological extremes in the mountain rivers of the Argentinean Central Andes

ABSTRACT The assessment of extreme hydrological events relies on the assumption that time series are independent and identically distributed. However, empirical evidence contradicts this assumption, indicating the presence of nonstationarity, including trends, step changes, or both, in the river records of the Argentinean Central Andes. This study proposes the use of generalized additive models in location, scale and shape (GAMLSS) to model the annual maximum and minimum flow of seven rivers. Firstly, the presence of trends and step changes was assessed. Subsequently, the models were formulated considering time and various climatic indices as covariates. The findings reveal declining trends and negative jumps over the past 50 to 70 years. The location parameters of the models exhibit linear or smoothing dependence with time or climate indices covariates. Different modes of climate variability are associated with hydrological extremes. The nonstationary models provide a novel complementary framework for water resource planning in the region.

Fire impacts on the biology of stream ecosystems: A synthesis of current knowledge to guide future research and integrated fire management.

Freshwater ecosystems host disproportionately high biodiversity and provide unique ecosystem services, yet they are being degraded at an alarming rate. Fires, which are becoming increasingly frequent and intense due to global change, can affect these ecosystems in many ways, but this relationship is not fully understood. We conducted a systematic review to characterize the literature on the effects of fires on stream ecosystems and found that (1) abiotic indicators were more commonly investigated than biotic ones, (2) most previous research was conducted in North America and in the temperate evergreen forest biome, (3) following a control-impact (CI) or before-after (BA) design, (4) predominantly assessing wildfires as opposed to prescribed fires, (5) in small headwater streams, and (6) with a focus on structural and not functional biological indicators. After quantitatively analyzing previous research, we detected great variability in responses, with increases, decreases, and no changes being reported for most indicators (e.g., macroinvertebrate richness, fish density, algal biomass, and leaf decomposition). We shed light on these seemingly contradicting results by showing that the presence of extreme hydrological post-fire events, the time lag between fire and sampling, and whether the riparian forest burned or not influenced the outcome of previous research. Results suggest that although wildfires and the following hydrological events can have dramatic impacts in the short term, most biological endpoints recover within 5-10 years, and that detrimental effects are minimal in the case of prescribed fires. We also detected that no effects were more often reported by BACI studies than by CI or BA studies, raising the question of whether this research field may be biased by the inherent limitations of CI and BA designs. Finally, we make recommendations to help advance this field of research and guide future integrated fire management that includes the protection of freshwater ecosystems.

Comparative analysis of satellite and reanalysis data with ground‐based observations in Northern Ghana

AbstractAccurate predictions of streamflow and flood events are contingent upon the availability of reliable hydrometeorological data. In regions characterized by scarcity of ground‐based hydrometeorological observations, satellite and reanalysis data assume prominence as alternative predictors. Floods and droughts have emerged as a significant concern in Northern Ghana, yet the scarcity of ground‐based hydrometeorological data impedes effective prediction of these hydrological events. Consequently, the identification of suitable surrogate hydrometeorological data holds paramount importance in addressing these challenges. This study, therefore, assessed the accuracy of satellite and reanalysis data against ground‐based data in Northern Ghana. Rainfall and mean temperature spanning from 1998 to 2019 and soil moisture datasets from 2019 to 2022 were collected from GMet, ISMN (ground‐based), CHIRPS, PERSIANN‐CDR, ERA5, ARC2, MERRA‐2, TRMM and CFSR (satellite and reanalysis). Employing rigorous statistical measures, namely standard deviation, mean absolute error (MAE) and mean bias error (MBE), the accuracy of these datasets was thoroughly evaluated. The results revealed that CHIRPS and PERSIANN‐CDR exhibited superior accuracy in rainfall simulation, with CHIRPS demonstrating particularly consistent congruence with observed data. In terms of mean temperature prediction, ERA5 surpassed MERRA‐2 and CFSR. Regarding soil moisture assessments, both ERA5 and CFSR offered satisfactory simulations. Hence, our findings advocate for the preference of CHIRPS (for rainfall data), ERA5 (for temperature data) and a combination of CFSR/ERA5 (for soil moisture data) as dependable primary data sources for streamflow modelling, drought analysis, flood prediction and water resource management in the context of Northern Ghana.