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At least 37 records · Page 2Linked to original sources
USGS2015

Calculating crop water requirement satisfaction in the West Africa Sahel with remotely sensed soil moisture

The Soil Moisture Active Passive (SMAP) mission will provide soil moisture data with unprecedented accuracy, resolution, and coverage, enabling models to better track agricultural drought and estimate yields. In turn, this information can be used to shape policy related to food and water from commodity markets to humanitarian relief efforts. New data alone, however, do not translate to improvements in drought and yield forecasts. New tools will be needed to transform SMAP data into agriculturally meaningful products. The objective of this study is to evaluate the possibility and efficiency of replacing the rainfall-derived soil moisture component of a crop water stress index with SMAP data. The approach is demonstrated with 0.1°-resolution, ~10-day microwave soil moisture from the European Space Agency and simulated soil moisture from the Famine Early Warning Systems Network Land Data Assimilation System. Over a West Africa domain, the approach is evaluated by comparing the different soil moisture estimates and their resulting Water Requirement Satisfaction Index values from 2000 to 2010. This study highlights how the ensemble of indices performs during wet versus dry years, over different land-cover types, and the correlation with national-level millet yields. The new approach is a feasible and useful way to quantitatively assess how satellite-derived rainfall and soil moisture track agricultural water deficits. Given the importance of soil moisture in many applications, ranging from agriculture to public health to fire, this study should inspire other modeling communities to reformulate existing tools to take advantage of SMAP data.

Journal of Hydrometeorology↗
USGS2015

Climatology of extreme daily precipitation in Colorado and its diverse spatial and seasonal variability

The climatology of Colorado’s historical extreme precipitation events shows a remarkable degree of seasonal and regional variability. Analysis of the largest historical daily precipitation totals at COOP stations across Colorado by season indicates that the largest recorded daily precipitation totals have ranged from less than 60 mm day −1 in some areas to more than 250 mm day −1 in others. East of the Continental Divide, winter events are rarely among the top 10 events at a given site, but spring events dominate in and near the foothills; summer events are most common across the lower-elevation eastern plains, while fall events are most typical for the lower elevations west of the Divide. The seasonal signal in Colorado’s central mountains is complex; high-elevation intense precipitation events have occurred in all months of the year, including summer, when precipitation is more likely to be liquid (as opposed to snow), which poses more of an instantaneous flood risk. Notably, the historic Colorado Front Range daily rainfall totals that contributed to the damaging floods in September 2013 occurred outside of that region’s typical season for most extreme precipitation (spring–summer). That event and many others highlight the fact that extreme precipitation in Colorado has occurred historically during all seasons and at all elevations, emphasizing a year-round statewide risk.

Journal of Hydrometeorology↗
USGS2015

Evaluation of the Global Land Data Assimilation System (GLDAS) air temperature data products

There is a high demand for agrohydrologic models to use gridded near-surface air temperature data as the model input for estimating regional and global water budgets and cycles. The Global Land Data Assimilation System (GLDAS) developed by combining simulation models with observations provides a long-term gridded meteorological dataset at the global scale. However, the GLDAS air temperature products have not been comprehensively evaluated, although the accuracy of the products was assessed in limited areas. In this study, the daily 0.25° resolution GLDAS air temperature data are compared with two reference datasets: 1) 1-km-resolution gridded Daymet data (2002 and 2010) for the conterminous United States and 2) global meteorological observations (2000–11) archived from the Global Historical Climatology Network (GHCN). The comparison of the GLDAS datasets with the GHCN datasets, including 13 511 weather stations, indicates a fairly high accuracy of the GLDAS data for daily temperature. The quality of the GLDAS air temperature data, however, is not always consistent in different regions of the world; for example, some areas in Africa and South America show relatively low accuracy. Spatial and temporal analyses reveal a high agreement between GLDAS and Daymet daily air temperature datasets, although spatial details in high mountainous areas are not sufficiently estimated by the GLDAS data. The evaluation of the GLDAS data demonstrates that the air temperature estimates are generally accurate, but caution should be taken when the data are used in mountainous areas or places with sparse weather stations.

Journal of Hydrometeorology↗
USGS2015

Hydroclimatic conditions preceding the March 2014 Oso landslide

The 22 March 2014 Oso landslide was one of the deadliest in U.S. history, resulting in 43 fatalities and the destruction of more than 40 structures. We examine synoptic conditions, precipitation records and soil moisture reconstructions in the days, months, and years preceding the landslide. Atmospheric reanalysis shows a period of enhanced moisture transport to the Pacific Northwest beginning on 11 February 2014. The 21- to 42-day periods prior to the landslide had anomalously high precipitation; we estimate that 300-400 mm of precipitation fell at Oso in the 21 days prior to the landslide. Relative only to historical periods ending on 22 March, the return periods of these precipitation accumulations are large (25-88 years). However, relative to the largest accumulations from any time of the year (annual maxima), return periods are more modest (2-6 years). In addition to the 21-42 days prior to the landslide, there is a secondary maximum in the precipitation return periods for the 4 years preceding the landslide. Reconstructed soil moisture was anomalously high prior to the landslide, with a return period that exceeded 40 years about a week before the event.

Washington↗
USGS2018

Hydroclimatology of the Missouri River basin

Despite the importance of the Missouri River for navigation, recreation, habitat, hydroelectric power, and agriculture, relatively little is known about the basic hydroclimatology of the Missouri River basin (MRB). This is of particular concern given the droughts and floods that have occurred over the past several decades and the potential future exacerbation of these extremes by climate change. Here, observed and modeled hydroclimatic data and estimated natural flow records in the MRB are used to 1) assess the major source regions of MRB flow, 2) describe the climatic controls on streamflow in the upper and lower basins , and 3) investigate trends over the instrumental period. Analyses indicate that 72% of MRB runoff is generated by the headwaters in the upper basin and by the lowest portion of the basin near the mouth. Spring precipitation and temperature and winter precipitation impacted by changes in zonal versus meridional flow from the Pacific Ocean play key roles in surface water supply variability in the upper basin. Lower basin flow is significantly correlated with precipitation in late spring and early summer, indicative of Atlantic-influenced circulation variability affecting the flow of moisture from the Gulf of Mexico. Although increases in precipitation in the lower basin are currently overriding the effects of warming temperatures on total MRB flow, the upper basin’s long-term trend toward decreasing flows, reduction in snow versus rain fraction, and warming spring temperatures suggest that the upper basin may less often provide important flow supplements to the lower basin in the future.

Missouri River basin↗
USGS2018

Warming is driving decreases in snow fractions while runoff efficiency remains mostly unchanged in snow-covered areas of the western United States

Winter snowfall and accumulation is an important component of the surface water supply in the western United States. In these areas, increasing winter temperatures T associated with global warming can influence the amount of winter precipitation P that falls as snow S . In this study we examine long-term trends in the fraction of winter P that falls as S (Sfrac) for 175 hydrologic units (HUs) in snow-covered areas of the western United States for the period 1951–2014. Because S is a substantial contributor to runoff R across most of the western United States, we also examine long-term trends in water-year runoff efficiency [computed as water-year R /water-year P (Reff)] for the same 175 HUs. In that most S records are short in length, we use model-simulated S and R from a monthly water balance model. Results for Sfrac indicate long-term negative trends for most of the 175 HUs, with negative trends for 139 (~79%) of the HUs being statistically significant at a 95% confidence level ( p = 0.05). Additionally, results indicate that the long-term negative trends in Sfrac have been largely driven by increases in T . In contrast, time series of Reff for the 175 HUs indicate a mix of positive and negative long-term trends, with few trends being statistically significant (at p = 0.05). Although there has been a notable shift in the timing of R to earlier in the year for most HUs, there have not been substantial decreases in water-year R for the 175 HUs.

Arizona, California, Colorado, Idaho, Montana, Nev↗
USGS2018

100-year lower Mississippi floods in a global climate model: Characteristics and future changes

Floods in the Mississippi basin can have large negative societal, natural, and economic impacts. Understanding the drivers of floods, now and in the future, is relevant for risk management and infrastructure-planning purposes. We investigate the drivers of 100-yr-return lower Mississippi River floods using a global coupled climate model with an integrated surface water module. The model provides 3400 years of physically consistent data from a static climate, in contrast to available observational data (relatively short records, incomplete land surface data, transient climate). In the months preceding the model’s 100-yr floods, as indicated by extreme monthly discharge, above-average rain and snowfall lead to moist subsurface conditions and the buildup of snowpack, making the river system prone to these major flooding events. The meltwater from snowpack in the northern Missouri and upper Mississippi catchments primes the river system, sensitizing it to subsequent above-average precipitation in the Ohio and Tennessee catchments. An ensemble of transient forcing experiments is used to investigate the impacts of past and projected anthropogenic climate change on extreme floods. There is no statistically significant projected trend in the occurrence of 100-yr floods in the model ensemble, despite significant increases in extreme precipitation, significant decreases in extreme snowmelt, and significant decreases in less extreme floods. The results emphasize the importance of considering the fully coupled land–atmosphere system for extreme floods. This initial analysis provides avenues for further investigation, including comparison to characteristics of less extreme floods, the sensitivity to model configuration, the role of human water management, and implications for future flood-risk management.

Journal of Hydrometeorology↗
USGS2019

Seasonal precipitation influences streamflow vulnerability to the 2015 drought in the western United States

Streamflow was exceptionally low in the spring and summer of 2015 across much of the western United States because of a regional drought that exploited the sensitivity of both snow- and rain-dominant rivers. Streamflow during 2015 was examined at 324 gauges in the region to assess its response to the amount, form, and seasonal timing of precipitation and the viability of using spatially aggregated, normative models to assess streamflow vulnerability to drought. Seasonal rain and spring snowmelt had the strongest effects on runoff during the same season, but their effects persisted into subsequent seasons as well. Below-normal runoff in the spring of 2015 was pervasive across the region, while distinct seasonal responses were evident in different hydroclimatic settings: January–March (winter) runoff was above normal in most snow-dominant rivers and runoff in all seasons was above normal for much of the desert Southwest. Summer precipitation contributed to summer runoff in both the Pacific Northwest and desert Southwest. A first-order model that presumes runoff is a constant fraction of precipitation (the precipitation elasticity of runoff, E = 1) could be used for assessing and forecasting runoff responses to precipitation deficits across the region, but runoff generally is more vulnerable to drought (E > 1) than predicted by a first-order model. Uncertainty in spring and summer precipitation forecasts remain critical issues for forecasting and predicting summer streamflow vulnerability to drought across much of the western United States.

Journal of Hydrometeorology↗
USGS2019

Uncertainty of reference pixel soil moisture averages sampled at SMAP core validation sites

Despite extensive efforts to maximize ground coverage and improve upscaling functions within core validation sites (CVS) of the NASA Soil Moisture Active/Passive (SMAP) mission, spatial averages of point-scale soil moisture observations often fail to accurately capture the true average of the reference pixels. Therefore, some level of pixel-scale sampling error from in situ observations must be considered during the validation of SMAP soil moisture retrievals. Here, uncertainties in the SMAP core site average soil moisture (CSASM) due to spatial sampling errors are examined and their impact on CSASM-based SMAP calibration and validation metrics is discussed. The estimated uncertainty (due to spatial sampling limitations) of mean CSASM over time is found to be large, translating into relatively large sampling uncertainty levels for SMAP retrieval bias when calculated against CSASM. As a result, CSASM-based SMAP bias estimates are statistically insignificant at nearly all SMAP CVS. In addition, observations from temporary networks suggest that these (already large) bias uncertainties may be underestimated due to under-sampled spatial variability. The unbiased root-mean-square error (ubRMSE) of CSASM is estimated via two approaches: classical sampling theory and triple collocation, both of which suggest that CSASM ubRMSE is generally within the range 0.01 to 0.02 m3/m3. Although limitations in both methods likely lead to underestimation of ubRMSE, the results suggest that CSASM captures the temporal dynamics of the footprint-scale soil moisture relatively well and is thus a reliable reference for SMAP ubRMSE calculations. Therefore, spatial sampling errors are revealed to have very different impacts on efforts to estimate SMAP bias and ubRMSE metrics using CVS data.

Journal of Hydrometeorology↗
USGS2020

Patterns and drivers of atmospheric river precipitation and hydrologic impacts across the western United States

Atmospheric rivers (ARs) significantly influence precipitation and hydrologic variability in many areas of the world, including the western United States. As ARs are increasingly recognized by the research community and the public, there is a need to more precisely quantify and communicate their hydrologic impacts, which can vary from hazardous to beneficial depending on location and on the atmospheric and land surface conditions prior to and during the AR. This study leverages 33 years of atmospheric and hydrologic data for the western United States to 1) identify how water vapor amount, wind direction and speed, temperature, and antecedent soil moisture conditions influence precipitation and hydrologic responses (runoff, recharge, and snowpack) using quantile regression and 2) identify differences in hydrologic response types and magnitudes across the study region. Results indicate that water vapor amount serves as a primary control on precipitation amounts. Holding water vapor constant, precipitation amounts vary with wind direction, depending on location, and are consistently greater at colder temperatures. Runoff efficiencies further covary with temperature and antecedent soil moisture, with precipitation falling as snow and greater available water storage in the soil column mitigating flood impacts of large AR events. This study identifies the coastal and maritime mountain ranges as areas with the greatest potential for hazardous flooding and snowfall impacts. This spatially explicit information can lead to better understanding of the conditions under which ARs of different precipitation amounts are likely to be hazardous at a given location.

Journal of Hydrometeorology↗
USGS2020

Spatial variability in seasonal snowpack trends across the Rio Grande headwaters (1984 - 2017)

This study evaluated the spatial variability of trends in simulated snowpack properties across the Rio Grande headwaters of Colorado using the SnowModel snow evolution modeling system. SnowModel simulations were performed using a grid resolution of 100 m and 3-hourly time step over a 34-yr period (1984–2017). Atmospheric forcing was provided by phase 2 of the North American Land Data Assimilation System, and the simulations accounted for temporal changes in forest canopy from bark beetle and wildfire disturbances. Annual summary values of simulated snowpack properties [snow metrics; e.g., peak snow water equivalent (SWE), snowmelt rate and timing, and snow sublimation] were used to compute trends across the domain. Trends in simulated snow metrics varied depending on elevation, aspect, and land cover. Statistically significant trends did not occur evenly within the basin, and some areas were more sensitive than others. In addition, there were distinct trend differences between the different snow metrics. Upward trends in mean winter air temperature were 0.3°C decade −1 , and downward trends in winter precipitation were −52 mm decade −1 . Middle elevation zones, coincident with the greatest volumetric snow water storage, exhibited the greatest sensitivity to changes in peak SWE and snowmelt rate. Across the Rio Grande headwaters, snowmelt rates decreased by 20% decade −1 , peak SWE decreased by 14% decade −1 , and total snowmelt quantity decreased by 13% decade −1 . These snow trends are in general agreement with widespread snow declines that have been reported for this region. This study further quantifies these snow declines and provides trend information for additional snow variables across a greater spatial coverage at finer spatial resolution.

Colorado↗
USGS2022

Selecting the optimal fine-scale historical climate data for assessing current and future hydrological conditions

High-resolution historical climate grids are readily available and frequently used as inputs for a wide range of regional management and risk assessments, including water supply, ecological processes, and as baseline for climate change impact studies that compare them to future projected conditions. Because historical gridded climates are produced using various methods, their portrayal of landscape conditions differ, which becomes a source of uncertainty when they are applied to subsequent analyses. Here we tested the range of values from five gridded climate datasets. We compared their values to observations from 1231 weather stations, first using each dataset’s native scale, and then after each was rescaled to 270-m resolution. We inputted the downscaled grids to a mechanistic hydrology model and assessed the spatial results of six hydrological variables across California, in 10 ecoregions and 11 large watersheds in the Sierra Nevada. PRISM was most accurate for precipitation, ClimateNA for maximum temperature, and TopoWx for minimum temperature. The single most accurate dataset overall was PRISM due to the best performance for precipitation and low air temperature errors. Hydrological differences ranged up to 70% of the average monthly streamflow with an average of 35% disagreement for all months derived from different historical climate maps. Large differences in minimum air temperature data produced differences in modeled actual evapotranspiration, snowpack, and streamflow. Areas with the highest variability in climate data, including the Sierra Nevada and Klamath Mountains ecoregions, also had the largest spread for snow water equivalent, recharge, and runoff.

Journal of Hydrometeorology↗
USGS2025

Wet antecedent soil moisture increases atmospheric river streamflow magnitudes non-linearly

Atmospheric rivers (ARs) drive most riverine floods on the United States (U.S.) West Coast. However, estimating flood risk based solely on AR intensity and duration is challenging because precipitation phase, antecedent conditions, and physical watershed characteristics (e.g., slope and soil depth) can influence the magnitude of floods. Here, we analyze how antecedent soil moisture (ASM) conditions contribute to variability in streamflow during AR events and how that changes across climatic regimes and physiography in 122 U.S. West Coast watersheds. We identify a robust non-linear relationship between streamflow and ASM during ARs in 89% of watersheds. The inflection point in this relationship represents a watershed-specific critical ASM threshold, above which event maximum streamflow is, on average, two to four and a half times larger. Wet ASM conditions amplify the hydrologic impacts of more frequent but weaker, lower moisture transport AR events, while dry ASM conditions attenuate the hydrologic impacts that stronger, higher moisture transport AR events could otherwise cause. Our research shows that watersheds prone to ASM-amplified streamflows have higher evaporation ratios, lower cold-season precipitation, lower snow-to-rain ratios, and shallower, clay-rich soils. Higher evaporation and lower precipitation lead to greater ASM variability during the cold season, increasing streamflow during wet periods and buffering streamflow during dry periods. Lower snow fraction and shallower soils limit the antecedent water storage capacity of a watershed, contributing to greater sensitivity of streamflow peaks to ASM variability. Incorporating ASM thresholds into hydrologic models in these regions prone to AR-amplified streamflow could improve forecasts and decrease uncertainty.

California, Nevada, Oregon, Washington↗
USGS2025

Alaskan hydrology in transition: Changing precipitation and evapotranspiration patterns are projected to reshape seasonal streamflow and water temperature by midcentury (2035-2064)

High spatial and temporal resolution models are essential for understanding future climate impacts and developing effective climate resilience plans. However, existing regional and global river models often lack the resolution needed to accurately capture local conditions. This study uses a series of high-resolution models, including the Regional Arctic System Model, mizuRoute, and the river basin model, to analyze Arctic and sub-Arctic Alaskan hydrology. We compare a historical baseline (1991–2020) with six midcentury (2035–64) futures: two pseudo–global warming scenarios based on historical meteorology and four direct dynamically downscaled global climate models. The six futures reveal significant uncertainty in future annual discharge and peak flows, although a widespread increase in discharge during April (+63%) and October (+31%) is consistently shown across models. Projected increases in rain and shifting weather patterns lead to a transition from snow to rain in spring and autumn, reducing the fraction of snowmelt contributing to river discharge. Rising evapotranspiration moderates discharge changes, particularly in autumn, by offsetting precipitation increases. Average summer river temperatures are projected to increase by approximately 1.5°C, doubling the number of river segments that experience 18°C days, a critical threshold for salmon survival, and intensifying the heat flux to the ocean adding an average of 3.3 × 10 12 MJ yr −1 . These changes in the hydrologic cycle could profoundly impact riverine and oceanic ecosystems, posing substantial challenges to communities reliant on these environments.

Alaska, Yukon↗
USGS2026

Trends in subdaily to daily rainfall in Florida, 1990–2022

Changing rainfall patterns and intensifying rainfall extremes affect urban infrastructure and can increase flash-flood risk. Understanding how climate change has altered rainfall can support state and local agencies as they adapt and build resiliency. In this study, rainfall data from 23 weather stations in Florida were used to examine temporal and spatial trends over the period 1990–2022. Subdaily to daily rainfall events of durations 1, 2, 3, 6, 12, and 24 h were examined. A variety of statistical methods were applied to examine annual and seasonal trends, including quantile regression, extreme value analysis, run theory using the Mann–Kendall test, Sen–Theil slope, and Poisson and negative binomial tests, and threshold exceedance rates using generalized additive models. Using subdaily rainfall data posed challenges, including equipment failures, limited documentation of the quality assurance and control process, and potential measurement interferences. Results indicated that over 1990–2022, there was a decrease in hourly rainfall extremes but an increase at moderate quantiles. Overall, the number of rainfall events increased, particularly at shorter durations, but the mean total rainfall per event decreased. Additionally, the annual number of daily rainfall extremes showed more decreases than increases.

Florida↗
USGS2006

One-way coupling of an atmospheric and a hydrologic model in Colorado

This paper examines the accuracy of high-resolution nested mesoscale model simulations of surface climate. The nesting capabilities of the atmospheric fifth-generation Pennsylvania State University (PSU)-National Center for Atmospheric Research (NCAR) Mesoscale Model (MM5) were used to create high-resolution, 5-yr climate simulations (from 1 October 1994 through 30 September 1999), starting with a coarse nest of 20 km for the western United States. During this 5-yr period, two finer-resolution nests (5 and 1.7 km) were run over the Yampa River basin in northwestern Colorado. Raw and bias-corrected daily precipitation and maximum and minimum temperature time series from the three MM5 nests were used as input to the U.S. Geological Survey's distributed hydrologic model [the Precipitation Runoff Modeling System (PRMS)] and were compared with PRMS results using measured climate station data. The distributed capabilities of PRMS were provided by partitioning the Yampa River basin into hydrologic response units (HRUs). In addition to the classic polygon method of HRU definition, HRUs for PRMS were defined based on the three MM5 nests. This resulted in 16 datasets being tested using PRMS. The input datasets were derived using measured station data and raw and bias-corrected MM5 20-, 5-, and 1.7-km output distributed to 1) polygon HRUs and 2) 20-, 5-, and 1.7-km-gridded HRUs, respectively. Each dataset was calibrated independently, using a multiobjective, stepwise automated procedure. Final results showed a general increase in the accuracy of simulated runoff with an increase in HRU resolution. In all steps of the calibration procedure, the station-based simulations of runoff showed higher accuracy than the MM5-based simulations, although the accuracy of MM5 simulations was close to station data for the high-resolution nests. Further work is warranted in identifying the causes of the biases in MM5 local climate simulations and developing methods to remove them. ?? 2006 American Meteorological Society.

Journal of Hydrometeorology↗
USGS2006

Onset of snowmelt and streamflow in 2004 in the Western Unites States: How shading may affect spring streamflow timing in a warmer world

Historic streamflow records show that the onset of snowfed streamflow in the western United States has shifted earlier over the past 50 yr, and March 2004 was one of the earliest onsets on record. Record high temperatures occurred throughout the western United States during the second week of March, and U.S. Geological Survey (USGS) stream gauges throughout the area recorded early onsets of streamflow at this time. However, a set of nested subbasins in Yosemite National Park, California, told a more complicated story. In spite of high air temperatures, many streams draining high-elevation basins did not start flowing until later in the spring. Temperatures during early March 2004 were as high as temperatures in late March 2002, when streams at all of the monitored Yosemite basins began flowing at the same time. However, the March 2004 onset occurred before the spring equinox, when the sun was lower in the sky. Thus, shading and solar radiation differences played a much more important role in 2004, leading to differences in streamflow timing. These results suggest that as temperatures warm and spring melt shifts earlier in the season, topographic effects will play an even more important role than at present in determining snowmelt timing. ?? 2006 American Meteorological Society.

Journal of Hydrometeorology↗
USGS2015

Groundwater recharge assessment at local and episodic scale in a soil mantled perched karst aquifer in southern Italy

Groundwater recharge assessment of karst aquifers, at various spatial and temporal scales, is a major scientific topic of current importance, since these aquifers play an essential role for both socio-economic development and fluvial ecosystems. In this study, groundwater recharge was estimated at local and episodic scales in a representative perched karst aquifer in a region of southern Italy with a Mediterranean climate. The research utilized measurements of precipitation, air temperature, soil water content, and water-table depth, obtained in 2008 at the Acqua della Madonna test area (Terminio Mount karst aquifer, Campania region). At this location the aquifer is overlain by ash-fall pyroclastic soils. The Episodic Master Recession (EMR) method, an improved version of the Water Table Fluctuation (WTF) method, was applied to estimate the amount of recharge generated episodically by individual rainfall events. The method also quantifies the amount of precipitation generating each recharge episode, thus permitting calculation of the Recharge to the Precipitation Ratio (RPR) on a storm-by-storm basis. Depending on the seasonally varying air temperature, evapotranspiration, and precipitation patterns, calculated values of RPR varied between 35% and 97% among the individual episodes. A multiple linear correlation of the RPR with both the average intensity of recharging rainfall events and the antecedent soil water content was calculated. Given the relatively easy measurability of precipitation and soil water content, such an empirical model would have great hydrogeological and practical utility. It would facilitate short-term forecasting of recharge in karst aquifers of the Mediterranean region and other aquifers with similar hydrogeological characteristics. By establishing relationships between the RPR and climate-dependent variables such as average storm intensity, it would facilitate prediction of climate-change effects on groundwater recharge. The EMR methodology could further be applied to other aquifers for evaluating the relationship of recharge to various hydrometeorological and hydrogeological processes.

Journal of Hydrology↗