Hydrological Variables Research Articles

Assessing the effectiveness of reservoir operation and identifying reallocation opportunities under climate change

Reservoirs are designed and operated to mitigate hydroclimatic variability and extremes to fulfill various beneficial purposes. Existing reservoir infrastructure capacity and operation policies derived from historical records are challenged by hydrologic regime change and storage reduction from sedimentation. Furthermore, climate change could amplify the water footprint of reservoir operation (i.e. non-beneficial evaporative loss), further influencing the complex interactions among hydrologic variability, reservoir characteristics, and operation decisions. Disentangling and quantifying these impacts is essential to assess the effectiveness of reservoir operation under future climate and identify the opportunities for adaptive reservoir management (e.g. storage reallocation). Using reservoirs in Texas as a testing case, this study develops data-driven models to represent the current reservoir operation policies and assesses the challenges and opportunities in flood control and water supply under dynamically downscaled climate projections from the Coupled Model Intercomparison Project Phase 6. We find that current policies are robust in reducing future flood risks by eliminating small floods, reducing peak magnitude, and extending the duration for large floods. Current operation strategies can effectively reduce the risk of storage shortage for many reservoirs investigated, but reservoir evaporation and sedimentation pose urgent needs for revisions in the current guidelines to enhance system resilience. We also identify the opportunities for reservoir storage reallocation through seasonal-varying conservation pool levels to improve water supply reliability with negligible flood risk increase. This study provides a framework for stakeholders to evaluate the effectiveness of the current reservoir operation policy under future climate through the interactions among hydroclimatology, reservoir infrastructure, and operation policy.

Characterizing the multisectoral impacts of future global hydrologic variability

There is significant uncertainty in how global water supply will evolve in the future, due to uncertain climate, socioeconomic, and land use change drivers and variability of hydrologic processes. It is critical to characterize the potential impacts of uncertainty in future water supply given its importance for food and energy production. In this work, we introduce a framework that integrates stochastic hydrology and human-environmental systems to characterize uncertainty in future water supply and its multisector impacts. We develop a global stochastic watershed model and demonstrate that this model can generate a large ensemble of realizations of basin-scale runoff with global coverage that preserves the mean, variance, and spatial correlation of a historical benchmark. We couple this model with a well-known human-environmental systems model to explore the impacts of runoff variability on the water and agricultural sectors across spatial scales. We find that the impacts of future hydrologic variability vary across sectors and regions. Impacts are felt most strongly in the water and agricultural sectors for basins that are expected to have unsustainable water use in the future, such as the Indus River basin. For this basin, we find that the variability in future irrigation water withdrawals and irrigated cropland increase over time due to uncertainty in renewable water supply. We also use the Indus basin to show how our stochastic ensemble can be leveraged to explore the global multisector consequences of local extreme runoff conditions. This work introduces a novel technique to explore the propagation of future hydrologic variability across human and natural systems and spatial scales.

Flood Pulse Effects on the Growth of Pseudoplatystoma fasciatum in the Amazon Basin

Fish growth is a fundamental biological process driven by a multitude of intrinsic (within-individual) and extrinsic (environmental) factors that underpin individual fitness and population dynamics. Interannual variability in river hydrology regarding the intensity and duration of floods and droughts can induce interannual variations in the biotic and abiotic variables that regulate fish growth. However, the understanding of how interannual variability in river hydrology affects fish growth remains limited for most species and ecosystems. We evaluated how inter-annual hydrological variations within the Amazon River basin influence the growth of the catfish Pseudoplaystoma fasciatum. Our research questions were as follows: Do floods lead to the faster growth of P. fasciatum and droughts lead to the slower growth? And do floods and droughts affect all age classes in the same manner? We sampled 364 specimens of P. fasciatum from five sites in the Amazon basin, estimated their growth rates, and related the growth rates to indices of the intensity of floods and droughts. We fitted linear mixed-effects models to test the relationship between growth increments and hydrological indices (with F and D quantifying the intensities of floods and droughts, respectively), age as fixed effects, and basins and Fish ID as random effects. We found an inverse relationship between the increment width in the fish hard parts and hydrological indices. That is, intense floods and droughts negatively affected the growth rates. We also found that the growth of P. fasciatum was no different in years with intense and mild floods across age classes 1–5, although was different for age class 6. However, the growth of P. fasciatum was faster in years of mild droughts for all age classes. Our results showing that the growth of P. fasciatum was slower in years of intense droughts are supported by those of previous studies in the Amazon basin and elsewhere. However, our results showing for the first time that the growth of P. fasciatum is slower in years of intense flooding is the opposite of patterns found in other studies. These results thus suggest that the growth of P. fasciatum is maximized within an optimum range of hydrological conditions, where neither floods nor droughts are intense.

Fluvial Dynamics and Hydrological Variability in the Chiriquí Viejo River Basin, Panama: An Assessment of Hydro-Social Sustainability through Advanced Hydrometric Indexes

The objective of this study was to conduct a detailed analysis of the available flow series in the Chiriquí Viejo River basin in Panama. This paper examines the patterns of variation within these series and calculates various hydrological indexes indicative of the region’s hydrology. Utilizing advanced hydrological indexes within the Chiriquí Viejo River basin in Panama, which spans an area of 1376 km2 and supports an estimated population of 100,000 inhabitants, analytical methods were employed to compute indexes such as the Daily Flow Variation Index (QVAR), the Slope of the Flow Duration Curve (R2FDC), the Hydrological Regulation Index (IRH), and the average duration of low (DLQ75) and high (DHQ25) flow pulses. The results indicate moderate flow variability (QVAR of 0.72) and a Hydrological Regulation Index (IRH) of 2.32, signifying a moderate capacity for flow regulation. Notably, low flow events (DLQ75) lasted approximately 3.73 days, while high flow events (DHQ25) lasted around 4.08 days. The study highlights a significant capacity to respond to extreme events, with maximum annual flows reaching 80.25 m3/s and minimum flows dropping to 3.01 m3/s. Despite the significant contribution of the basin to hydroelectric power generation and other economic activities, there is an observed need for sustainable management that accommodates hydrological fluctuations and promotes resource conservation. The conclusions indicate that these findings are critical for future planning and conservation strategies in the region, emphasizing the importance of integrating multidisciplinary approaches for Hydro-Social Sustainability. This novel and holistic approach underscores the interdependence between hydrological dynamics, socio-economic activities, and environmental sustainability, aiming to ensure the long-term resilience of the Chiriquí Viejo basin and its communities.

Seasonal Groundwater Level Dynamics in Unconfined Aquifers across the United States

AbstractGroundwater hydrographs contain a rich set of information on the dynamics of aquifer systems and the processes and properties that influence them. While the importance of seasonal cycles in hydrologic and environmental state variables is widely recognized there has yet to be a comprehensive analysis of the seasonal dynamics of groundwater across the United States. Here we use time series of groundwater level measurements from 997 wells from the National Groundwater Monitoring Network to identify and describe groundwater seasonal cycles in unconfined aquifers across the United States. We use functional data analysis to obtain a functional form fit for each site and apply an unsupervised clustering algorithm to identify a set of five distinct seasonal cycles regimes. Each seasonal cycle regime has a distinctive shape and distinct timing of its annual minimum and maximum water level. There are clear spatial patterns in the occurrence of each seasonal cycle regime, with the relative occurrence of each regime strongly influenced by the geologic setting (aquifer system), climate, and topography. Our findings provide a comprehensive characterization of groundwater seasonal cycles across much of the United States and present both a methodology and results useful for assessing and understanding unconfined groundwater systems.

Fish beta diversity associated with hydrologic and anthropogenic disturbance gradients in contrasting stream flow regimes

Understanding the role of hydrologic variation in structuring aquatic communities is crucial for successful conservation and sustainable management of native freshwater biodiversity. Partitioning beta diversity into the additive components of spatial turnover and nestedness can provide insight into the forces driving variability in fish assemblages across stream flow regimes. We examined stream fish beta diversity across hydrologic and anthropogenic disturbance gradients using long-term (1916–2016) site occurrence records (n = 17,375) encompassing 252 species. We assessed total beta diversity (Sørensen dissimilarity), spatial turnover, and nestedness of fish assemblages in contrasting stream flow regimes across a gradient of decreasing flow stability: groundwater stable (n = 77), groundwater (n = 67), groundwater flashy (n = 175), perennial runoff (n = 141), runoff flashy (n = 255), and intermittent (n = 63) streams. Differences in total beta diversity among the stream flow regimes were driven predominantly (>86 %) by spatial turnover (i.e. species replacement) as opposed to nestedness (i.e. species loss or gain). Total fish beta diversity and spatial turnover were highest in streams with intermediate flow stability (groundwater flashy), while more flow-stable streams (groundwater stable and groundwater) had lower turnover and higher nestedness. Species turnover was also strongly associated with seasonal variation in hydrology across all flow regimes, but these relationships were most evident for assemblages in intermittent streams. Distance-based statistical comparisons showed significant correlations between beta diversity and anthropogenic disturbance variables, including dam density, dam storage volume and water withdrawals in catchments of groundwater stable streams, while hydrologic variables were more strongly correlated with beta diversity in streams with runoff-dominated and flashy flow regimes. The high spatial turnover of species implies that fish conservation actions would benefit from watershed-focused approaches targeting multiple streams with wide spatial distribution, as opposed to simply focusing on preserving sites with the greatest number of species.

Water Flow Prediction Based on Improved Spatiotemporal Attention Mechanism of Long Short-Term Memory Network

The prediction of water plant flow should establish relationships between upstream and downstream hydrological stations, which is crucial for the early detection of flow anomalies. Long Short-Term Memory Networks (LSTMs) have been widely applied in hydrological time series forecasting. However, due to the highly nonlinear and dynamic nature of hydrological time series, as well as the intertwined coupling of data between multiple hydrological stations, the original LSTM models fail to simultaneously consider the spatiotemporal correlations among input sequences for flow prediction. To address this issue, we propose a novel flow prediction method based on the Spatiotemporal Attention LSTM (STA-LSTM) model. This model, based on an encoder–decoder architecture, integrates spatial attention mechanisms in the encoder to adaptively capture hydrological variables relevant to prediction. The decoder combines temporal attention mechanisms to better propagate gradient information and dynamically discover key encoder hidden states from all time steps within a window. Additionally, we construct an extended dataset, which preprocesses meteorological data with forward filling and rainfall encoding, and combines hydrological data from multiple neighboring pumping stations with external meteorological data to enhance the modeling capability of spatiotemporal relationships. In this paper, the actual production data of pumping stations and water plants along the East-to-West Water Diversion Project are taken as examples to verify the effectiveness of the model. Experimental results demonstrate that our STA-LSTM model can better capture spatiotemporal relationships, yielding improved prediction performance with a mean absolute error (MAE) of 3.57, a root mean square error (RMSE) of 4.61, and a mean absolute percentage error (MAPE) of 0.001. Additionally, our model achieved a 3.96% increase in R2 compared to the baseline model.

On the Challenges of Simulating Streamflow in Glacierized Catchments of the Himalayas Using Satellite and Reanalysis Forcing Data

Abstract Hydrologic assessment of climate change impacts on complex terrains and data-sparse regions like High Mountain Asia is a major challenge. Combining hydrological models with satellite and reanalysis data for evaluating changes in hydrological variables is often the only available approach. However, uncertainties associated with the forcing dataset, coupled with model parameter uncertainties, can have significant impacts on hydrologic simulations. This work aims to understand and quantify how the uncertainty in precipitation and its interaction with the model uncertainty affect streamflow estimation in glacierized catchments. Simulations for four precipitation datasets [Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG), Climate Hazards Group Infrared Precipitation with Station (CHIRPS), ERA5-Land, and Asian Precipitation–Highly Resolved Observational Data Integration Toward Evaluation (APHRODITE)] and two glacio-hydrological models [Glacio-Hydrological Degree-Day Model (GDM) and Hydrological Model for Distributed Systems (HYMOD_DS)] are evaluated for the Marsyangdi and Budhigandaki River basins in Nepal. Temperature sensitivity of streamflow simulations is also investigated. Relative to APHRODITE, which compared well with ground stations, ERA5-Land overestimates the catchment average precipitation for both basins by more than 70%; IMERG and CHIRPS overestimate by ∼20%. Precipitation uncertainty propagation to streamflow exhibits strong dependencies to model structure and streamflow components (snowmelt, ice melt, and rainfall-runoff), but overall uncertainty dampens through precipitation-to-streamflow transformation. Temperature exerts a significant additional source of uncertainty in hydrologic simulations of such environments. GDM was found to be more sensitive to temperature variations, with >50% increase in total flow for 20% increase in actual temperature, emphasizing that models that rely on lapse rates for the spatial distribution of temperature have much higher sensitivity. Results from this study provide critical insight into the challenges of utilizing satellite and reanalysis products for simulating streamflow in glacierized catchments. Significance Statement This work investigates the uncertainty of streamflow simulations due to climate forcing and model parameter/structure uncertainty and quantifies the relative importance of each source of uncertainty and its impact on simulating different streamflow components in glacierized catchments of High Mountain Asia. Results highlight that in high mountain regions, temperature uncertainty exerts a major control on hydrologic simulations and models that do not adequately represent the spatial variability of temperature are more sensitive to bias in the forcing data. These findings provide guidance on important aspects to be considered when modeling glacio-hydrological response of catchments in such areas and are thus expected to impact both research and operation practice related to hydrologic modeling of glacierized catchments.

Influence of climate variability and land cover dynamics on the spatio-temporal NDVI patterns in western hydrological regions of Bangladesh

Analyzing vegetation greenness considering climate and land cover changes is crucial for Bangladesh given the historically drier North-West and South-West regions of Bangladesh have shown prominent climatic and hydrological variations. Therefore, this study assessed the spatial and temporal variation of NDVI and its relationship with climate and land cover changes from 2000 to 2022 for these regions. In this study, Moran's I and Getis Ord Gi* were employed for spatial autocorrelation and Mann-Kendall, Sen's slope test along with Innovative Trend Analysis were deployed to identify temporal trends of NDVI. RMSE, MAE and R-squared values were assessed between computed and observed PET. Correlation of NDVI with climate variables were assessed through multivariate correlation analysis and correlation mapping. Additionally, Pearson product moment correlation was applied between different types of land cover and NDVI. Spatial autocorrelation outcomes showed that NDVI values have been clustered in distinct hotspots and cold-spots over the years. Temporal trend detection results indicate that NDVI values are rising significantly all over the regions. Multivariate correlation analysis identified no climate variable to be the limiting factor for NDVI changes. Similarly, the precipitation-NDVI correlation map displayed no significant correlation. Nonetheless, temperature-NDVI correlation map illustrated varying degrees of mostly moderate and strong positive correlations with distinct negative correlation results in the Sundarbans of South-West region. Land cover pattern analysis with NDVI showed a positive correlation to forest, cropland and vegetation area increasing and negative correlation to grassland and barren area decreasing. In this regard, Rangpur division exhibited stronger correlations than Rajshahi division in North-West. The findings indicate that NDVI changes in the regions are largely dependent on land cover changes in comparison to climate trends. This can instigate further research in other hydrological regions to explore the natural and man-made factors that can affect the greenery and vegetation density in specific regions.

An Electrical Parameter Characterizing Solute Heterogeneity: The Mixing Factor M

AbstractQuantitative estimates of hydrological state variables using electrical or electromagnetic geophysical methods are systematically biased by overlooked heterogeneity below the spatial scale resolved by the method. We generalize the high‐salinity asymptotic limit of electrical conduction in porous media at the continuous (e.g., Darcy) scale, by introducing a new petrophysical parameter, the mixing factor M, which accounts for the effect of fluid conductivity heterogeneity on the equivalent electrical conductivity tensor; it is expressed in terms of the volume‐average of the product of mean‐removed fluid conductivity and electric fields. We investigate the behavior of M for static and evolving fluid conductivity scenarios. Considering 2‐D ergodic log‐normal random fields of fluid conductivity, we demonstrate, in absence of surface conductivity, that observing the components of the M‐tensor allows univocally determining the variance and anisotropy of the field. Further, time‐series of the M‐tensor under diffusion‐limited mixing allows distinguishing between different characteristic temporal scales of diffusion, which are directly related to the initial integral scales of the salinity field. Under advective‐diffusive transport and for a pulse injection, the time‐series of M have a strong dependence on the Péclet number. Since M is defined in the absence of surface conductivity, we investigate how to correct measurements for surface conductivity effects. The parameter M provides conceptual understanding about the impact of saline heterogeneity on electrical measurements. Further work will investigate how it can be incorporated into hydrogeophysical inverse formulations and interpretative frameworks.

Chuvas intensas no estado do Acre, Amazônia ocidental

ABSTRACT Preventing the occurrence of extreme hydrological events exceeds the current abilities of humanity. However, using adequate tools to estimate the probability associated with different values of hydrological variables, such as heavy rainfall, can help mitigate social and environmental damage. This study aimed to verify the hypothesis that the records of maximum rainfall, at different intervals of duration, in the three main rainfall stations in the state of Acre fit the log-normal distribution of two parameters (LN2) and, therefore, to evaluate the events of heavy rainfalls, based on the association between the duration of the rain and the return period. The equations conformed to the classic model of the LN2 model using the method of maximum likelihood once the baseline assumption of stationarity was met using the Mann-Kendall test. Adherence to the model was verified using the Anderson-Darling test. The adjusted distributions allowed for estimating the rainfall intensity at different durations and return periods for the cities of Cruzeiro do Sul, Tarauacá, and Rio Branco and are relevant elements for planning actions aimed at strengthening the components of water safety in the state of Acre.

Hydrological Extremes Heighten Vulnerability to Schistosomiasis

AbstractClimate change is altering precipitation regimes that amplify hydrological extremes and increase vulnerability to schistosomiasis, a water‐related parasitic disease with hundreds of millions at risk globally. Explicit recognition of the elements of hydrological variability and linkages with extreme precipitation is necessary to combat the spread of schistosomiasis. We call for the use of a vulnerability framework to account holistically for the dynamic interplay among environmental and human factors that influence susceptibility to schistosomiasis infection across spatial scales.

Comparative Study of Low Flow Frequency Analysis Using Bivariate Copula Model at Soyanggang Dam and Chungju Dam

A univariate analysis that relies solely on precipitation data in low flow frequency analysis is a technique to express meteorological drought, so it is limited to analyzing the characteristics of hydrological drought related to available water resources. In addition, if the data for the model calibration are insufficient, the uncertainty of a single variable limits the construction of a reliable model. To improve this problem, a frequency analysis was performed by constructing a bivariate copula model as a multivariate model with a high correlation between variables targeting reservoir inflows. The methodology utilizes the theory of runs to identify low flow events, establishing a threshold based on the mandatory regional water supply plan, and determining the low flow duration and cumulative water deficit. The Gumbel copula function, effective in capturing correlations between hydrological variables, was applied to derive a joint bivariate probability distribution, facilitating the calculation of combined low flow event return periods. This study compared low flow frequencies at Soyanggang dam (’74–’22) and Chungju dam (’86–‘22), which are in the same Han River basin but have different capacities and water demands, using a bivariate copula model. The top four extreme low flow events for the two adjacent dam basins did not occur in the same year and, in the years of the extreme low flow events at one of the two dam basins, there was an insignificant magnitude at the remaining dam basin. This result is noteworthy because it shows that the possibility of extreme low flow events appearing simultaneously in both watersheds is not as high as expected. The operational efficiency can be improved by setting the coordinated operation rules of the two reservoirs using the copula dependency structure.

Appraisal of long-term hydrological variability in the Luni River Basin: a quantitative statistical approach

Appraisal of long-term hydrological variability in the Luni River Basin: a quantitative statistical approach

Wetland restoration enhances habitat for an endangered bat, Eumops floridanus

Restoring lost or degraded wetlands is a major challenge in contemporary conservation. Understanding how wetland restoration and changes in hydrology affect wildlife is increasingly urgent for endangered species conservation. This is especially pertinent for the endangered Florida bonneted bat (Eumops floridanus), whose range is almost entirely contained within one of the world's most iconic wetland systems, the Greater Everglades Ecosystem. We investigated how E. floridanus may respond to future hydrological and vegetation changes associated with current and planned Everglades hydrologic restoration efforts. We conducted acoustic surveys at 194 random points stratified across a restoration gradient (no hydrologic restoration, partial hydrologic restoration, full hydrologic restoration, and reference). Using generalized linear mixed models, we determined the most important predictors explaining variation in bat activity and foraging likelihood. Positive associations between bat activity and several hydrologic variables expected to increase with restoration (hydroperiod, water depth, distance to canals, and extent of freshwater forested wetlands, ecologically intact reference areas, and zones with full hydrological restoration) suggest that foraging habitat for this species will likely benefit from hydrologic restoration both in the near term (immediate increases in hydroperiod and water depth) and in the longer term (as freshwater forested wetlands expand). Our results inform immediate management decisions for this species and suggest the benefits of restoration for wildlife adapted to historically longer hydroperiods and greater water depths, which are anticipated to increase with the gradual return of natural hydrological regimes.

Evaluating Hydrogeomorphic Condition Across Ecosystem States in a Non-tidal, Brackish Peat Marsh of the Florida Coastal Everglades, USA

Emergent marsh and open water have been identified as alternate stable states in tidal marshes with large, relative differences in hydrogeomorphic conditions. In the Florida coastal Everglades, concern has been raised regarding the loss of non-tidal, coastal peat marsh via dieback of emergent vegetation and peat collapse. To aid in the identification of alternate stable states, our objective was to characterize the variability of hydrogeomorphic and biologic conditions using a field survey and long-term monitoring of hydrologic and geomorphic conditions across a range of vegetated (emergent, submerged) and unvegetated (open water) communities, which we refer to as “ecosystem states,” in a non-tidal, brackish peat marsh of the coastal Everglades. Results show (1) linear relationships among field-surveyed geomorphic, hydrologic, and biologic variables, with a 35-cm mean difference in soil surface elevation between emergent and open water states, (2) an overall decline in soil elevation in the submerged state that was related to cumulative dry days, and (3) a 2× increase in porewater salinity during the dry season in the emergent state that was also related to the number of dry days. Coupled with findings from previous experiments, we propose a conceptual model that describes how seasonal hydrologic variability may lead to ecosystem state transitions between emergent and open water alternate states. Since vegetative states are only moderately salt tolerant, as sea-level rise pushes the saltwater front inland, the importance of continued progress on Everglades restoration projects, with an aim to increase the volume of freshwater being delivered to coastal wetlands, is the primary management intervention available to mitigate salinization and slow ecosystem state shifts in non-tidal, brackish peat marshes.

Big infrastructure and/as systemic flexibility: The Sites Reservoir story

If constructed as planned, the Sites Reservoir Project will add roughly 1.85 billion m3 (1.5 million acre-feet) of storage capacity to California's water system. Per project proponents, however, the reservoir complex should be understood as infrastructure that would not only increase available water supply but also provide the state's water network with an infusion of a vital systemic quality: flexibility. This article considers such discourses of flexibility through a case study of the Sites project, grounded in analysis of planning documents, government, think tank, and NGO reports, and media coverage from outlets across California. The resulting account shows how the ideal of the flexible system orients efforts to adapt the California water network to the heightened forms of hydrological variability associated with climate change. The analysis traces the emergence of the notion of a "flexibility crisis" plaguing the state's water network and elaborates the temporalities and spatialities characteristic of the systemic flexibility that Sites is understood to enhance. The project of increasing the water network's flexibility through the construction of new large-scale storage infrastructures like Sites is shown to be an undertaking that serves to sustain of many of the system's established (and notably inflexible) consumptive arrangements. The case thus demonstrates the importance of attending to how material properties like flexibility – commonly glossed as desirable system-scale qualities – come to be unevenly spatialized within infrastructural networks and across landscapes.

Cyclical ‘wetting’ phenomenon in the source region of Yellow River under long-term trends from 1956 to 2022

Study regionthe source region of the Yellow River (SRYB) Study focusDue to environmental changes, the drying and wetting processes have become increasingly intricate, and it is uncertain whether the wetting phenomenon in the SRYB indicates a long-term trend or a temporary cyclic event. This study utilized monthly streamflow and precipitation records from 1956 to 2022 to calculate hydrological and meteorological-dryness/wetness, represented by the standardized streamflow and precipitation indices, respectively. This study used detrended, wavelet, and cross-wavelet methods to investigate dry-wet variability and identified the atmospheric circulation phenomena driving the dryness/wetness and cyclical changes in the region. New hydro-meteorological insights for the SRYB(i) The SRYB showed an overall fluctuating wetting trend, which intensified after 2000. (ii) There were notable oscillatory cycles at 3–5 years, 10–12 years, and approximately 20–25 years in both hydrological and meteorological dry-wet variability. (iii) Transitions from dry to wet conditions had no significant link to short-term cycles (e.g., 3–5 years), but were linked to decadal cycles (e.g., 20–25 years). (iv) Spring and winter precipitation and streamflow dominated the interannual dry-wet variability. (v) The ENSO, AMO, and PDO influenced short-term cyclic fluctuations, while the AO and PDO influenced decadal cyclic fluctuations. This indicates that the wetting trend follows a cyclic pattern, and the intensification since 2000 is consistent with a decadal cyclic signal.

Uncertainty estimation of hydrological modelling using gridded precipitation as model inputs in the Gandaki River Basin

Study RegionFour subbasins in the Gandaki River Basin in the southern Himalayan region. Study FocusIn the southern Himalayan region, gridded precipitation datasets are often used to complement sparse gauge network observations in hydrological modelling for water resource assessment. However, the applicability and uncertainty of using these datasets in hydrological modelling remain to be evaluated. In this study, nine high spatial resolution gridded precipitation datasets were used to force a widely used hydrological model to simulate the daily runoff at four hydrological stations in this region. The accuracy of these datasets was evaluated, and the model calibration results and modelling uncertainty were analyzed. New Hydrological Insights for the RegionOur analysis revealed large differences among the nine gridded precipitation datasets, and most datasets underestimated the annual precipitation in the low-elevation regions and overestimated that in the high Himalayas. Furthermore, through the adjustments of model parameters, the underestimations of precipitation by gridded datasets were compensated for by the overestimation of glacial melt. Therefore, the model performance was better, and the model uncertainty was lower in basins with higher glacial coverage. Notably, our findings indicated that the performances of gridded precipitation datasets were heterogeneous and that their direct use in hydrological modelling may result in unreasonable hydrological process variables; thus, these datasets should be evaluated and used with caution in water resource assessments in Himalayan regions.

Unstable State of Hydrologic Regime and Grain Yields in Northern Kazakhstan Estimated with Tree-Ring Proxies

Changes in the hydrologic regime impose great challenges for grain production. We investigated the impact of dry and wet extremes on the recent losses of crops in Severo-Kazakhstanskaya Oblast (SKO), where 25% of Kazakhstan’s wheat is produced. We reconstructed the Palmer Drought Severity Index (June–August PDSI) and average grain yields (with an explained variance of 48% and 44%, respectively) using five tree ring width chronologies. The extended history of the moisture variability and yields of spring wheat, oats, and barley shows the strong impact of hydrology, rather than the heat, on the grain production. We defined three distinctive hydrologic regimes in SKO: (1) 1886–1942, (2) 1943–1977, (3) 1978–2023. The early regime had fewer drought events, including some that covered a single year. Their duration increased up to 3 years in the second period. The latest regime is an extreme mode of hydrologic variability with events abruptly switching from extremely dry to extremely wet conditions (called “whiplash”). The 21st century regime signifies that the intensified and prolonged decade-long drought transitioned into pluvial condition. The new regime created sizable instability for grain producers. This crop yield reconstruction denotes the potential of the tree-ring proxy for understanding the impact of climate change on the agriculture and food security of Central Asia.