Journal of Hydrologic Engineering

Flood mapping is a crucial tool for assisting urban planning and emergency response plans and, consequently, preventing or reducing the risks associated with flood disasters. However, in developing countries that often lack or have limited data, to produce such maps is a challenging task. When topographic data are lacking, digital elevation models (DEMs) derived from the Shuttle Radar Topography Mission (SRTM) are frequently used as a freely available surrogate, albeit with additional uncertainty. This work presents an integrated framework to investigate flood inundation areas using a Bayesian approach, while including steps for calibrating SRTM data and determining the river bathymetry below the WSE. A flood event in the Itaqui municipality, in the state of Rio Grande do Sul, southern Brazil is used to demonstrate the proposed framework. Findings suggest benefits in using calibrated SRTM DEMs for flood mapping regardless of whether flood inundation areas were derived directly from projections of WSEs on the terrain or based on hydraulic simulations. Results further highlight the potential of using a Bayesian approach to improve quality and reliability of flood hazards maps, especially in regions that lack topographic data.

Abstract

Infiltration-based sustainable urban drainage systems (i-SUDS) often turn out to be simple and effective solutions for on-site runoff and pollution control. Their ability to limit the discharge to sewer networks or receiving waters can be broadly assessed in terms of (pluri)annual stormwater volume reduction. Although accepted as a relevant efficiency metric, this long-term volume reduction does not integrate well in design practices that have traditionally relied on event-based approaches. This article introduces a modeling framework, involving a hydrological model and machine-learning emulation, from which a web app was developed to allow practitioners to investigate the relation between i-SUDS design and pluriannual volume reduction efficiencies. The theoretical basis for modeling and a description of the web app are first provided. A diagnosis of the hydrological model is then conducted. The uncertainty caused by model parameters that do not directly relate to i-SUDS design is evaluated through a sensitivity analysis performed over multiple design scenarios. The latter is found to be highly variable and potentially significant, thereby justifying its explicit consideration in the web app. As part of this diagnosis, the impact of a shallow groundwater or a low-permeability layer on simulated volume reduction efficiencies is later evaluated to clarify the validity domain of the model. Practical recommendations on the minimum distance to shallow groundwater or low permeability layer, for the rainfall conditions considered in the web app, are given as a function of project size and the permeability of the soil media. The applicability of the web app is later illustrated from a selection of outputs. Its outcomes are finally compared to those of a simple design rule based on the combination permanent storage (as rainfall depths) and drawdown duration targets. Results confirm the inability of such simple design rules to fully capture pluriannual volume reduction efficiency and point out the risk of oversizing i-SUDS.

Practical Applications

Stormwater infiltration in small vegetated systems can effectively reduce runoff and pollutant discharge to surface waters. A well-accepted performance objective for such systems is to achieve a significant reduction of the rainfall volume at the annual scale. However, integrating (pluri)annual volume reduction targets in design practices remains difficult as they do not accommodate well with the back-of-the-napkin, event-based calculations traditionally used by the stormwater profession. This paper introduces a web app that allows practitioners to easily investigate the relation between the design characteristics of infiltration-based solutions and pluriannual volume reduction efficiencies. The approach shows how machine learning can be used to replicate at low computational cost the outputs of specialized hydrological models and be incorporated in larger-audience tools. Through an analysis of the validity domain of the app, the study also points out the potential reduction of efficiency that may result from the presence of a shallow groundwater or a low-permeability layer, an aspect often overlooked in the design of infiltration-based systems. The applicability of the app is illustrated from different usage situations. The relevance of the proposed approach is finally demonstrated through a comparison to a simpler, event-based design method, which proves unable to adequately capture pluriannual volume reduction efficiency.

Compound flood disaster risks are increasing due to multiple river floods within a single storm event. Many studies targeted dependence structure based on multivariate extreme value theory; however, fewer studies focused on the sum of subset joint probabilities (SSJP), defined as the probability that any combination of rivers are flooded over the target area as an integral index of compound flooding risks. Modeling multivariate extremes at high dimensions faces two challenges: model complexity and sample size. In this study, as a classical asymptotic dependence model, the Hüsler–Reiss (HR) model was explored to resolve the former issue owing to its simplicity. From the multivariate HR model, any subset joint probability is explicitly obtained without numerical integration of angular measure, and dependence parameters are constructed from only pairwise parameters. The latter challenge was addressed using a large ensemble (50 members of 60-year simulation) of the database for policy decision-making for future climate change (d4PDF), which is consequently regarded as the annual maximum flow data of 3,000 years. This study analytically derived SSJP based on the HR model and tested its applicability using annual maximum flow data simulated from d4PDF for 20 rivers in Kyushu Island, Japan. The simulated SSJP based on the HR model and empirical SSJP were compared as the probability plot of the number of river basins where peak discharge exceeds the design level. As a result, under the constraint of HR that the partial correlation must range from $-1$ to 1, the estimated SSJP by the HR model was in agreement with the empirical SSJP. The case study presents promising results of the proposed HR model–based approach.

The design of optimal precipitation station configuration (network) is pivotal for obtaining accurate spatiotemporal data in a cost-efficient manner in terms of high operation, management and maintenance costs of stations, and missing data completion. In the present study, historical spatiotemporal precipitation data of 18 stations located in the Upper Euphrates watershed basin are initially exposed to empirical orthogonal function (EOF) analysis to exploit the general intrinsic low dimensionality of the precipitation phenomenon. Along with the basic EOF analysis, robust and mean-centered versions are also developed to improve the prediction accuracy of spatiotemporal precipitation data and optimize the number of stations in the watershed basin. Importantly, for the first time, robust EOF (R-EOF) analysis has been carried out in a hydrological predictive study. The matrix that contains the obtained modes (EOFs) is fed into the QR factorization with a column pivoting algorithm and sparse precipitation gauge locations are identified. The assessment of the model using the Nash–Sutcliffe coefficient of efficiency (CE), root mean square error (RMSE), and mean absolute error (MAE) metrics reveals that the complete dimensional state space can be reconstructed effectively, and its future evolution can be predicted accurately even with a small number of observation stations. Remarkably, the spatiotemporal precipitation data for the entire field can be reconstructed using only four, five, 10, or 12 stations, utilizing robust mean-centered (R-MC-EOF), robust (R-EOF), mean-centered (MC-EOF), and standalone EOF models. These models demonstrate high performance with CE values of 0.96, 0.94, 0.84, and 0.81 and RMSE values of 2.2, 3.8, 5.7, and 6.8 mm, respectively. Notably, both the R-EOF and MC-EOF models outperformed their standalone counterparts in terms of model performance. When a sufficient amount of spatiotemporal data is available, the optimal number and locations of precipitation gauges can be easily determined using the QR with a column pivoting algorithm. This algorithm is user friendly and can be implemented in popular programming environments such as Python, MATLAB, and R. Due to the limited budgets and/or low accessibility conditions, challenging basin topography, and bad weather conditions, not many areas are extensively equipped with instruments to measure the precipitation; thus, high-resolution data is not always available. Acquiring reliable and accurate data is critical for water resources management, flood and drought warning, irrigation networks, hydrological (e.g., watershed, rain-runoff) modeling, and urban and environmental planning. This renders the proposed methodology very crucial in obtaining high-fidelity spatiotemporal data.

Salinity in estuaries varies naturally due to tides, weather, geomorphology, freshwater flow, climate, and sea level. Before the 1950s, water management in southern Florida focused on diverting freshwater to the ocean to make historic wetlands more amenable to development and to protect human life. However, current water management activities aim to restore wetlands and estuaries while maintaining flood control and drinking water for the human population. Due to anthropogenic alteration, the spatiotemporal variability in salinity within Biscayne Bay, Florida, is a significant concern for ecosystem restoration under the Comprehensive Everglades Restoration Plan (CERP). This study aims to analyze daily seasonal salinity trends within the Bay and quantify the change in salinity per year (salinity slope). Salinity data, collected at 30 stations within the central and southern regions of Biscayne Bay over 16 years (2005–2020), were examined for trends. The nonparametric seasonal Kendall trend test, at a 0.05 significance level, was used for the analysis. Results of the trend analysis show that salinity slopes were consistently positive (indicating increasing salinity over time) in the southern portion of the study area and negative (indicating decreasing salinity over time) in the northern portion of the study area. Throughout the study region, most salinity slopes were positive in the wet season and negative in the dry season. The study results show trends in seasonal salinity, which helps in understanding changes in this region. This study will aid future management efforts within Biscayne Bay.

The state of Iowa in the Central United States has experienced increasing flooding, with major events occurring most recently in 1993, 2008, 2011, and 2019. These floods caused over $23B in damage despite Iowa’s three flood control reservoirs and expansive levee systems, suggesting the need for additional solutions. Iowa is home to over 4,000 small dams whose cumulative capacity more than double the state’s current flood storage. These locations are operated passively, i.e., without the use of gated outlets to control basin storage utilization, thus limiting their flood mitigating potential. Here, the authors simulate gated outlets at 130 small dams within a ${660\mathrm{km}}^2$ watershed to (1) evaluate how effectively these storages can be activated across a watershed using gated outlets; and (2) quantify the utilization capacity of an activated distributed storage system for flow reduction. The authors used stochastic storm transposition to generate thousands of spatially variable rainfall events using Stage IV rainfall data within the Iowa domain at durations of 6, 12, 24, and 48 h and annual exceedance probabilities (AEPs) of 0.2, 0.1, 0.02, and 0.01. This expands the effective period of record, providing storms of various durations, intensities, and spatiotemporal distributions. An active management scheme was defined within the reservoir module of the hillslope link model designed to store water within the ponding locations. The study calculated the flow reductions that were achieved through this active scheme and found that flows were reduced for every rainfall duration and probability regardless of basin spatial scale. Reductions reached as high as 70% for a 6 h, 0.2 AEP event at a ${93\mathrm{km}}^2$ drainage area, while flows were reduced by roughly 12% for a 48 h, 0.01 AEP event at the basin outlet. This work establishes activated distributed storage as a meaningful flood reduction measure under realistic rainfall conditions at a variety of spatial scales.

This study utilized hybrid artificial intelligence (AI) models, created by integrating the extreme gradient boosting model with particle swarm optimization (XGBoost-PSO) and differential evolution (XGBoost-DE) algorithms. These models are applied to predict scour depths for two conditions, i.e., static (jet is turned off) and dynamic (jet is operational), that are formed due to submerged circular vertical jets. To assess the model’s effectiveness, a comparative analysis is conducted among individual AI models, including support vector regression, adaptive neuro-fuzzy inference system, and traditional regression methods, such as multiple linear regression and multiple nonlinear regression. The results from the cross-validation analysis reveal that the XGBoost-DE and XGBoost-PSO models showcase the highest performance metrics. In the testing dataset, both models achieve a high coefficient of determination (${\mathrm{R}}^2$) of 0.93 for static scour depth prediction. For dynamic scour depth, the XGBoost-DE model achieves an ${\mathrm{R}}^2$ value of 0.88, while the XGBoost-PSO model achieves an ${\mathrm{R}}^2$ value of 0.91, confirming their predictive capabilities. The XGBoost-DE model exhibited low values of root mean square error (RMSE) and mean absolute error (MAE) at 0.199 and 0.09, respectively, in static scour depth prediction. Similarly, the XGBoost-PSO model posted RMSE and MAE values of 0.227 and 0.159 for dynamic scour depths. In a nutshell, the significance of the study includes: (1) the utilization of first-hand hybridized AI models to predict the scour depths under submerged circular vertical jets and (2) the demonstrated superiority of these hybrid models over existing methods. These advancements provide valuable support to civil engineers in accurately estimating scour depths under submerged circular vertical jets.

Abstract

In recent years, rising demands for water resources have led to extensive groundwater exploitation, particularly in arid and semiarid regions, triggering land subsidence. The Arak Plain, located in Iran’s Markazi Province, a vital area for agriculture and industry, has recently faced significant subsidence due to excessive groundwater extraction. This study investigates the correlation between land subsidence and declining groundwater levels in this plain. To assess land subsidence, the differential interferometry synthetic aperture radar (DInSAR) method was employed, utilizing high-accuracy Sentinel 1 IW SLC C-band products. The analysis was conducted using the SNAP open-source software from 2016 to 2021. Groundwater level declines from 2005 to 2021 were determined using universal kriging with an exponential semivariogram. Subsequently, a comparison between subsidence rates and piezometric levels was conducted. The analysis revealed continuous increase in the rate and extent of subsidence over time, with the highest annual subsidence occurring in 2020 ($9\mathrm{cm}/\mathrm{year}$) and extensive subsidence at a rate of approximately $5.5\mathrm{cm}/\mathrm{year}$ in most areas of the plain in 2021. Additionally, the southern and southwestern regions of the Mighan Playa exhibit remarkable subsidence due to groundwater level declines. This distinctive subsidence pattern results from geological structure and specific environmental conditions. The findings of this research demonstrate that, while excessive groundwater extraction is not the sole cause of subsidence, it remains one of the primary contributing factors. In light of these results, urgent implementation of effective measures is crucial for the long-term sustainable management of groundwater resources in the Arak Plain.

Practical Applications

In arid and semiarid regions, the growing demand for water resources has resulted in excessive groundwater extraction, leading to land subsidence. This poses significant risks to above-ground and underground infrastructure, such as buildings, railways, and utility lines. The Arak Plain, in Iran’s Markazi Province, a key area for agriculture and industry, has experienced notable subsidence due to excessive groundwater extraction. This study employs remote sensing, a technical method for assessing subsidence, using open-source software from 2016 to 2021. The decline in groundwater levels from 2006 to 2021 was determined, and a comparison of subsidence rates and groundwater levels was conducted. The analysis revealed a continuous increase in the rate and extent of subsidence over time. The highest annual subsidence occurred in 2020 ($9\mathrm{cm}/\mathrm{year}$), with most areas of the plain experiencing significant subsidence at a rate of approximately $5.5\mathrm{cm}/\mathrm{year}$ in 2021. This research highlights that, while excessive groundwater extraction is not the sole driver of subsidence, it is a significant contributing factor. These findings emphasize the importance of implementing effective measures for sustainable management of groundwater resources in the Arak Plain.

Abstract

Civil infrastructure systems have traditionally been designed assuming stationarity in precipitation. However, climate change is making this assumption invalid, affecting both existing infrastructure designed assuming stationarity and the design of new infrastructure. Although many studies have analyzed potential increases in precipitation due to climate change, fewer have attempted to translate these changes into the impact of stream discharge in a way that could be incorporated into infrastructure design. Therefore, this study aimed to assess the potential impact of climate change on both rainfall and peak discharge to aid in bridge and road infrastructure design. Results showed that the median increase in model-derived rainfall intensity across the selected rainfall stations in Virginia was 10%–30% for the midcentury (centered on 2045) and 10%–40% for the end of the century (centered on 2085), with the higher increase for the Representative Concentration Pathway 8.5 (RCP8.5) scenario compared with the RCP4.5 scenario. A regression analysis was performed to relate peak discharge to watershed size for midcentury and end-of-century periods for the study area. In terms of peak discharge, smaller watersheds ($<25{\mathrm{km}}^2$) had a percent increase for a given return period that was independent of the watershed size. Considering both RCP4.5 and RCP8.5 scenarios, for a 100-year return period, the increase was 39% and 49%, respectively, for the midcentury periods and 36% and 52%, respectively, for the end-of-century periods. For larger watersheds ($>25{\mathrm{km}}^2$), the increase in peak discharge decreased as the watershed size increased, suggesting a dampening effect for larger watersheds in this coastal plain region of Virginia. For a watershed size of $\mathrm{1,700}{\mathrm{km}}^2$, the largest watershed included in the analysis, the percent increase in peak discharge for a 100-year return period was 14% and 39% during the midcentury, and 16% and 40% at the end of the century, for the two emission scenarios. These findings and the general methodology used in the study can aid transportation and water resources engineers in incorporating changing rainfall impacts into assessing current infrastructure and designing future infrastructure. They can also help to prioritize resources for more costly hydraulic analyses of potentially vulnerable infrastructure.

Practical Applications

The study aims to understand better how climate change will impact future precipitation and how this change in precipitation will impact peak discharge for watersheds of various sizes. Using Virginia as a study region, the median increase in precipitation intensity was found to be between 10% and 30% for the midcentury and 10%–40% for the end of the century across the state. Smaller watersheds ($<25{\mathrm{km}}^2$) had an increase in peak discharge for a given return period that was independent of the watershed size. In comparison, larger watersheds ($>25{\mathrm{km}}^2$) had an increase in peak discharge, but the percent increase in peak discharge decreased as the size of the watershed increased, especially for the RCP8.5 emission scenario. Regression equations were developed to provide a first approximation of peak discharge based on the watershed area, the emission scenario, and the return period to aid decision-makers in the region when including climate change impacts when assessing existing or designing new hydraulic structures.

Drought forecasting is a critical aspect of water resource management, where droughts have substantial economic and environmental impacts. This study employs a Monte Carlo–based approach, complemented by statistical distribution fitting and trend analysis, to forecast future streamflows on long-narrow type drainage basins. Therefore, the Kızılırmak River Basin in Turkey, which is a long and narrow type, has been selected to test the suggested method. Historical data are used to determine the best-fitting distributions, ensuring reliability in the selection of future streamflow scenarios also using trend analysis. The study reveals valuable insights into potential drought occurrences over the next 25 years, aiding decision makers in implementing water management strategies. Based on the analysis results, it is expected that the drought frequency is increased up to 64.7%. Drought severity is classified into different categories, offering an understanding of drought characteristics. The findings contribute to effective water resource planning by aiming the assessment of future hydrological droughts.

US probable maximum precipitation (PMP) estimation guidance fundamentally relies on the assumption that any change in precipitable water is consistent with the change in precipitation. While this assumption is theoretically sound in extreme storms that are convective in nature with lifting so vigorous as to convert all available atmospheric water vapor into precipitation, this type of storm rarely occurs in the Pacific Northwest of the United States; the assumption may be invalid. This study investigates the relationship between changes in precipitable water and changes in precipitation using high-resolution model-based precipitation maximization for a large number of atmospheric river (AR) events impacting the Columbia River Basin (CRB) in the Pacific Northwest. Analysis indicates that the relationship between changes in precipitable water and changes in precipitation cannot be simply approximated as either $1:1$ (i.e., consistent) or a linear relationship ($R=0.13$; $p=0.17$). Our analysis on the precipitation maximization results showed that the horizontal wind speed at 10m plays an important role in determining the relationship between these changes. The relationship between integrated water vapor transport (IVT) change and precipitation change was found to be stronger ($R=0.47$) and statistically significant ($p<0.01$) for storms impacting the CRB, which was approximated as precipitation change = 2.0 × IVT change $-0.6$. Our finding underscores the importance of considering not only atmospheric water vapor amounts but also the accompanying flows transporting atmospheric water vapor, in maximizing precipitation depths over a target region.

Flash floods induced by high-intensity and short-duration monsoon rainfall can cause severe damage in arid regions. To properly size in-stream infrastructures, such as levees, bridges, and culverts, it is crucial to accurately calculate the peak runoff and sediment load from these flash floods. This case study utilized the Hydrologic Engineering Center Hydrologic Modeling System (HEC-HMS) model to simulate flash floods and sediment transport in the Lucky Hills watershed located in the Walnut Gulch Experimental Watershed in southern Arizona. The Lucky Hills watershed has two rain gauges and three flumes to measure runoff and sediment load. The HEC-HMS model was used to simulate the three largest precipitation events observed in 2007, 2009, and 2010 with precipitation volumes of 41.66, 46.36, and 37.85 mm, and durations of 126, 99, and 101 min, respectively. The study discussed various methods for simulating rainfall loss, surface and channel flow routing, and soil erosion. Watershed delineations were adopted to evaluate the accuracy of the simulated runoff and sediment concentration. Results showed that the HEC-HMS model can accurately predict surface runoff and sediment concentration, but the threshold value for subbasin size is critical for the model to converge to accurate results.

Hydrological extremes and land use are intimately connected, as land use alterations frequently exacerbate or mitigate the consequences of extreme events. Therefore, assessment and prediction of land use dynamics retain great significance in projected scenarios of hydrological extremes. This study aimed to quantify the impact of land use changes on future landslide-prone sites, flash flood vulnerable zones (FFVZs), and monsoon runoff. In this context, a Markov chain–cellular automata (MC-CA) model integrated with a multilayer perceptron–neural network (MLP-NN) model was designed to predict near-future land use/land cover changes (LULCC) using high-resolution existing land use/land cover (LULC) information. Uncorrected finer-scale Coupled Model Intercomparison Project Phase 6 (CMIP6) meteorological data sets and predicted LULC data were used to simulate daily runoff with the help of the Soil and Water Assessment Tool (SWAT). Statistical evaluation matrices were used to assess the efficacy of using uncorrected CMIP6 data sets for hydrological modeling. The methodology was evaluated for suitability in the ungauged high-altitude Ranikhola watershed. The prediction results showed that the watershed can be expected to be more susceptible to landslides and flash floods (9%). The most challenging discovery from this analysis was an anticipated 32.50% increase in average monsoon runoff from 2021 to 2032 compared to the observed period, 2015–2020. In addition, the runoff peak magnitude is projected to rise by 26.00%. Consequently, it is imperative to develop strategies and approaches for sustainable watershed management to address future challenges.

An increase in soil erosion affects the storage capacity of reservoirs, navigation, floodplain inundation, water quality, and quantity in different parts of the world. Therefore, identifying vulnerable soil erosion zones within the watersheds and developing a catchment treatment plan can minimize soil erosion losses and improve reservoir sedimentation management. In this study, we calculated the average annual soil loss using the revised universal soil loss equation (RUSLE) method for the Hirakud catchment of the Mahanadi River Basin, India. The Hirakud dam, located on the river Mahanadi is considered the longest dam in Asia, witnessing major sedimentation problems over the past years. Our results suggest that the average annual soil loss is approximately 29 tons per hectare per year. At the same time, 0.7% of the land area contributes to very high erosion. The average annual soil loss is $0.8\mathrm{t}/\mathrm{ha}$. Using remote sensing and GIS tools, suitable soil conservation methods are recommended based on the watershed’s Prioritization. Utilizing Saaty’s AHP (Analytic Hierarchy Process) techniques, each erosion hazard parameter (EHP) is designated by weight, and the vulnerable regions are prioritized by overlaying the weights over the study area. Overall, nearly 17% of the area falls under the high priority, and 1% falls under the category of extremely high priority. In the extremely high-priority watershed, it is recommended to construct 38 farm ponds, 14 check dams, nine gully plugs, 27 percolation ponds, and two surface dikes, depending on the ground and stream characteristics.

This study analyzed the annual and low-flow trends of rivers located in Serbia, a part of southeast Europe. To achieve this, a comprehensive framework for flow trend estimation was established using both the conventional Mann–Kendall trend test and its modified version, which considers the serial correlation of flows, alongside the Theil-Sen trend estimator for modeling linear trends. The investigation was performed at a large spatial scale for sites of 80 hydrological stations with negligible human influence. The most important rivers are given as follows: the Velika Morava, Južna Morava, Kolubara, Nišava, Timok, and Toplica rivers. Selected hydrological stations at these rivers drain the watershed areas from $79{\mathrm{km}}^2$ to $995{\mathrm{km}}^2$ with the respective recording periods (average length of the recorded data is equal to 60 years). The results implied that decreasing annual and low-flow trends are occurring for southern and eastern Serbian river streams while increasing trends were estimated at only a few hydrological sites. Hence, the results assist in revealing the most vulnerable river basins with statistically significant decreasing trends and provide a solid basis for the estimation of the design annual and low-flow trends in Serbia.

Abstract

This study examines the application of the recently developed next-generation intensity-duration-frequency (NG-IDF) curves in hydrological design, focusing on a small snow-dominated basin in Washington state. Four methods are used to assess the NG-IDF performance: (1) basin outlet flood frequency analysis, (2) use of standard precipitation-based intensity-duration-frequency (PREC-IDF) curves following surface water design manuals typically used in the basin, (3) utilization of locally constructed PREC-IDF curves, and (4) use of locally constructed NG-IDF curves. The standard PREC-IDF design method assumes precipitation as rainfall and neglecting snowmelt or rain-on-snow (ROS) events that can lead to significant flood underestimations in the test basin. In contrast, the NG-IDF method incorporates snow processes and shows promising improvements in accurately estimating design floods. The validation analysis demonstrates the superiority of NG-IDF over standard and local PREC-IDF in this test basin. The study highlights the necessity for adjustments in current design manuals to ensure sufficient flood protection, particularly in light of the planned substantial investments in US infrastructure.

Practical Applications

Engineers use precipitation data to plan for floods, but current methods have limitations by assuming all precipitation is in the form of rain. The paper discusses how traditional methods often underestimate the risk of floods in snow areas. To address these issues, the study introduces the next-generation tool. The next-generation tool considers not only rainfall but also snowmelt events, which are common in mountainous areas. This approach is especially useful in regions like the Yakima River Basin (YRB) in Washington State, where snowmelt dominates the flow. The study tested the next-generation tool and compared it with the traditional rainfall-based method. It found that the next-generation tool better estimates flood risk in a small snow basin within YRB. These findings are crucial for engineers and planners because they suggest that the next-generation tool can improve flood risk assessments and better prepare for potential snowmelt-related floods. The next-generation tool could become a useful tool in flood management and infrastructure planning.

Climate change impacts hydrologic processes, compelling more regional water budget studies to understand spatiotemporal hydrological extremes. This study investigates the effects of climate change on water budgets, with a focus on soil moisture, in two Tennessee watersheds with different geoclimatic characteristics, the Obion and Nolichucky Rivers. Using the variable infiltration capacity (VIC) hydrological model, the study projects water budgets for these watersheds until 2099, analyzing annual and seasonal runoff, recharge, and soil moisture to identify trends and extremes. Results showed that an increase in temperature of 2.7°C–6.4°C and a change of precipitation by 1%–4% is predicted for Tennessee, which will impact seasonal patterns, water balances, and soil moisture regimes. The overlapping of such impacts can lower soil moisture below the wilting point during 40%–50% of the growing season, impacting crop yields. The study identifies field capacity, clay soil percentage, and organic matter as key factors impacting the spatial extremes of croplands’ irrigation requirements. These findings underscore the need to understand soil moisture variability and extreme soil water conditions to optimize soil water management scenarios and mitigate future water shortage risks.