Yiwen Mei
Sun Yat-Sen University, School of Geography and Urban Planning, Faculty Member
- Yiwen Mei is the Associate Professor of School of Geography and Planning at Sun Yat-sen University. His primary resea... moreYiwen Mei is the Associate Professor of School of Geography and Planning at Sun Yat-sen University. His primary research focus is hydrological modeling and catchment hydrology. He has authored 31 publications in leading journals of the field. He is the associate editor of Journal of Hydrology.edit
Optimizing empirical baseflow filters using environmental tracers (e.g., specific electrical conductance (SEC), turbidity) is an effective and efficient way to quantify the contribution of baseflow to total flow. To execute this baseflow... more
Optimizing empirical baseflow filters using environmental tracers (e.g., specific electrical conductance (SEC), turbidity) is an effective and efficient way to quantify the contribution of baseflow to total flow. To execute this baseflow separation, three key components are needed: The tracer, the method to estimate tracer concentration in different flow components, and the empirical baseflow filter. However, a comprehensive evaluation of the various combinations of these components, especially with a large sample of catchments, is currently lacking in the literature. Therefore, our study assembles 16 hybrid baseflow filters from two tracers, two concentration estimation methods, and four empirical baseflow filters, and evaluated their performance in baseflow separation and producing two long-term baseflow signatures for 1,100 catchments in the Contiguous United States. Our results suggest that SEC is a superior tracer to turbidity for baseflow separation. Additionally, using monthly maximum and minimum values to represent tracer concentration in flow components produces better separation than using a power function relationship between flow rate and concentration. The four empirical baseflow filters offer a similar level of performance, regardless of the other options used. Yet, some of these filters produce inconsistent results in calculating the baseflow signatures for the catchments. Our analysis shed light on the optimization of hybrid baseflow filters for the accurate quantification of baseflow contribution. Plain Language Summary River flow can be broken down into two components: fast flow and slow flow. The latter is usually known as baseflow, and it represents the stable portion of river flow that comes from stored water sources, such as groundwater or snowpack. It is crucial to understand the proportion of baseflow in river flow for effective water resource management. A commonly used method to separate baseflow from river flow is by filtering streamflow data with empirical baseflow filters. These filters contain some parameters that are often optimized using geochemical data, such as specific electrical conductance (SEC) and turbidity, to ensure reasonable performance of baseflow separation. This study examined how SEC and turbidity can be used to optimize four empirical baseflow filters for quantitative assessment of baseflow contribution to streamflow. Our analysis of 1,100 catchments across the Contiguous United States revealed that SEC is a more reliable indicator of baseflow than turbidity. Interestingly, the choice of empirical baseflow filter had minimal impact, though some filters produced inconsistent results for the quantification of baseflow contribution. This research enhances our ability to accurately estimate baseflow, aiding in water resource planning and management.
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Spatiotemporal variation in rainfall erosivity resulting from changes in rainfall characteristics due to climate change has implications for soil erosion in developing countries. To promote soil and water conservation planning, it is... more
Spatiotemporal variation in rainfall erosivity resulting from changes in rainfall characteristics due to climate change has implications for soil erosion in developing countries. To promote soil and water conservation planning, it is essential to understand past and future changes in rainfall erosivity and their implications on a national scale. In this study, we present an approach that uses a Bayesian model averaging (BMA) method to merge multiple regional climate models (RCMs), thereby improving the reliability of climate-induced rainfall erosivity projections. Our multi-climate model and multi-emission scenario approach utilize five RCMs and two Representative Concentration Pathways (RCP4.5 and RCP8.5) scenarios for the baseline period (1986–2005) and future periods (2071–2090) to characterize the spatiotemporal projection of rainfall erosivity and assess variations in China. Our results indicate that the two models outperform other models in reproducing the spatial distribution and annual cycle of rainfall erosivity in China. Moreover, we found an increasing trend in the annual rainfall erosivity from the baseline climate up to the RCMs for all models, with an average change in erosivity of approximately 10.9% and 14.6% under RCP4.5 and RCP8.5, respectively. Our BMA results showed an increase in the absolute value of rainfall erosivity by 463.3 and 677.0 MJ·mm·hm−2·h−1, respectively, in the South China red soil region and the Southwest China karst region under the RCP8.5 scenario. This increase indicates that climate warming will significantly enhance the potential erosion capacity of rainfall in these regions. Additionally, our study revealed that the Southwest China karst region and the Northwest China Loess Plateau region are more sensitive to radiation forcing. To mitigate the risk of soil erosion caused by climate change, it is necessary to consider changes in rainfall erosivity, local soil conditions, vegetation coverage, and other factors in different regions and take appropriate soil and water conservation measures.
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The land surface model (LSM) is extensively utilized to simulate terrestrial processes between land surface and atmosphere in the Earth system. Hydrology simulation is the key component of the model, which can directly reflect the... more
The land surface model (LSM) is extensively utilized to simulate terrestrial processes between land surface and atmosphere in the Earth system. Hydrology simulation is the key component of the model, which can directly reflect the capability of LSM. In this study, three offline LSM simulations were conducted over China using the Community Land Model version 5.0 (CLM5) driven by different meteorological forcing datasets, namely China Meteorological Forcing Dataset (CMFD), Global Soil Wetness Project Phase 3 (GSWP3), and bias-adjusted ERA5 reanalysis (WFDE5), respectively. Both gridded and in situ reference data, including evapotranspiration (ET), soil moisture (SM), and runoff, were employed to evaluate the performance levels of three CLM5-based simulations across China and its ten basins. In general, all simulations realistically replicate the magnitudes, spatial patterns, and seasonal cycles of ET over China when compared with remote-sensing-based ET observations. Among ten basins, Yellow River Basin (YRB) is the basin where simulations are the best, supported by the higher KGE value of 0.79. However, substantial biases occur in Northwest Rivers Basin (NWRB) with significant overestimation for CMFD and WFDE5 and underestimation for GSWP3. In addition, both grid-based or site-based evaluations of SM indicate that systematic wet biases exist in all three CLM5 simulations for shallower soil layer over nine basins of China. Comparatively, the performance levels in simulating SM for deeper soil layer are slightly better. Moreover, all three types of CLM5 simulate reasonable runoff spatial patterns, among which CMFD can capture more detailed information, but GSWP3 presents more comparable change trends of runoff when compared to the reference data. In summary, this study explored the capacity of CLM5 driven by different meteorological forcing data, and the assessment results may provide important insights for the future developments and applications of LSM.
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Coastal cities are affected by the compound effects of flood hazard systems, including the ocean, river, and coastal land, within which the disaster-induced factors interact complexly. To explore the combined effects of multiple... more
Coastal cities are affected by the compound effects of flood hazard systems, including the ocean, river, and coastal land, within which the disaster-induced factors interact complexly. To explore the combined effects of multiple disaster-causing factors under changing environment, this study first constructed univariate distribution by maximum daily precipitation of the year at Zhuhai Station from 1962 to 2020 and maximum daily average river discharge of the year at Makou Station from 1951 to 2005. Based on the integrated model of the validated Delft3D-FLOW and HEC-RAS hydrodynamic models, the river level process of compound floods was simulated for 24 h, and the effects of extreme compound flood events involving storm surge, rainfall and river flood under future scenarios is analyzed. In this study, the Modaomen waterway in Zhuhai City of China is used as the study area. The results show: (1) The Lognormal distribution is selected as the optimal distribution, and the design values of precipitation and discharge in different recurrence periods are calculated based on Lognormal distribution. The precipitation and discharge for the 100-year return period were 450.3 mm and 51000 m 3 , respectively, which could be used in the compound flood simulation; (2) The application of Delft3D-FLOW model ensures the accurate simulation of the compound flood process. In the different future scenarios of rising sea levels and increasing wind intensity (WI), the effect of multiple disaster-causing factors results to a more serious inundation than that of a single disaster-causing factor, with the increase of maximum storm surge level from 8.09 % to 20.31 %; and (3) Fluvial flooding and storm surge became the major causes of compound flood when the city coastal protection is vulnerable, while in the city with robust coastal protection, precipitation serves as a main disaster-causing factor. Taking the inundation under scenario S1 as an example, when the seawall elevation is 2 m, the composite flooding causes significant inundation in the riparian area (the total inundation area is 15.29 km 2), whereas when the seawall elevation is raised to 2.5 m, the inundation area in the riparian area caused by the compound flood decreases significantly (the total inundation area is 0.03 km 2). This study is expected to provide scientific references on disaster prevention planning, compound disaster risk management and urban resilience construction along the coastline, as well as have a significant impact on modern economic growth, social stability, and the security in coastal cities.
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Understanding the drivers of flooding is essential for flood disaster prevention. However, conventional flood prediction methods are hindered by their reliance on local discharge data, which can be constrained by limited spatial... more
Understanding the drivers of flooding is essential for flood disaster prevention. However, conventional flood prediction methods are hindered by their reliance on local discharge data, which can be constrained by limited spatial resolution. To address this limitation, we present a machine learning model that can categorize floods without requiring discharge data during inference. We first use circular statistics to calculate the relative importance of three candidate flood-generating mechanisms. Global land areas are classified into three primary categories and eight sub-categories based on the proportion of relative importance. A random forest model is then applied to identify the flood types by assuming that the discharge data is unavailable. The findings from circular statistics highlight that globally, soil moisture excess is the most influential driver of floods followed by extreme precipitation and snowmelt, with an average relative importance of 0.535, 0.387, and 0.078, respectively. The RF model performs well in resembling the three primary flood categories with an accuracy of 0.701 and a F1-score of 0.692 in 10-fold cross-validation. The trained gridded-based model provides a swift and efficient approach for analyzing flood mechanisms, even in limited discharge scenarios, allowing for rapid insights.
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Hydrological models are usually calibrated to in-situ streamflow observations with reasonably long and uninterrupted records. This is challenging for poorly gage or ungaged basins where such information is not available. Even for gaged... more
Hydrological models are usually calibrated to in-situ streamflow observations with reasonably long and uninterrupted records. This is challenging for poorly gage or ungaged basins where such information is not available. Even for gaged basins, the single-objective calibration to gaged streamflow cannot guarantee reliable forecasts because, as has been documented elsewhere, the inverse problem is mathematically ill-posed. Therefore, inclusion of other observations, and reproduction of other hydrological variables beyond streamflow, become critical components of accurate hydrological forecasting. In this study, six single- and multi-objective model calibration schemes based on different combinations of gaged streamflow, global-scale gridded soil moisture, actual evapotranspiration (ET), and runoff products are used for the calibration of a process-based hydrological model for 20 catchments located within the Lake Michigan watershed, of the Laurentian Great Lakes. Results show that the addition of gridded soil moisture to gaged streamflow in model calibration improves the ET simulation performance for most of the catchments, leading to the overall best performing models. The monthly streamflow simulation performance for the experiments using gridded runoff products to inform the model is outperformed by those using the gaged streamflow, but the discrepancy is mitigating with increasing catchment scale. A new visualization method that effectively synthesizes model performance for the simulations of streamflow, soil moisture, and ET was also proposed. Based on the method, it is revealed that the streamflow simulation performance is relatively weak for baseflow-dominated catchments; overall, the 20 catchment models simulate streamflow and ET better than soil moisture.
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The rise of global surface temperature due to warming climate is expected to increase the intensity and occurrence of extreme precipitation events. Previous studies in Southeast Asia revealed complex variations in changes of precipitation... more
The rise of global surface temperature due to warming climate is expected to increase the intensity and occurrence of extreme precipitation events. Previous studies in Southeast Asia revealed complex variations in changes of precipitation extremes. This study presents a spatial-temporal analysis on changes of precipitation extremes in Peninsular Malaysia by utilizing long-term daily rainfall records at 64 observed stations during 1989-2018. The modified Mann-Kendall and Sen's slope tests were performed to detect the significance and magnitude of trends in eight extreme precipitation indices recommended by the Expert Team on Climate Change Detection and Indices. Statistically significant increasing trends are observed for four of these extreme indices in the annual assessment. Spatial analysis demonstrates an obvious contrast between
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This second paper of the two-part series focuses on demonstrating the impact of assimilating satellite-based snow cover and freeze/thaw observations into the hyper-resolution, offline terrestrial modeling system used for the High Mountain... more
This second paper of the two-part series focuses on demonstrating the impact of assimilating satellite-based snow cover and freeze/thaw observations into the hyper-resolution, offline terrestrial modeling system used for the High Mountain Asia (HMA) region from 2003 to 2016. To this end, this study systematically evaluates a total of six sets of 0.01° (∼1 km) model simulations forced by different precipitation forcings, with and without the dual assimilation scheme enabled, at point-scale, basin-scale, and domain-scale. The key variables of interest include surface net shortwave radiation, surface net longwave radiation, skin temperature, near-surface soil temperature, snow depth, snow water equivalent (SWE), and total runoff. First, the point-scale assessment is mainly conducted via evaluating against ground-based measurements. In general, the assimilation enabled estimates are better than no-assimilation counterparts. Second, the basin-scale runoff assessment demonstrates that across three snow-dominated basins, the assimilation enabled experiment yields systematic improvements in all goodness-of-fit statistics through mitigating the negative effects brought by the fixed long-term precipitation correction factors. For example, when forced by the bias-corrected precipitation, the assimilation-enabled experiment improves the bias by 69%, the root-mean-squared error by 30%, and the unbiased root-mean-squared error by 18% (relative to the no-assimilation counterpart). Finally, the domain-scale assessment is conducted via evaluating against satellite-based SWE and skin temperature products. Both sets of domain-scale analysis further corroborate the findings in the point-scale evaluations. Overall, this study suggests the benefits of the proposed multi-variate assimilation system in improving the cryospheric-hydrological process within a land surface model for use in HMA.
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The coronavirus disease 2019 (COVID-19) has been first reported in December 2019 and rapidly spread worldwide. As other severe acute respiratory syndromes, it is a widely discussed topic whether seasonality affects the COVID-19 infection... more
The coronavirus disease 2019 (COVID-19) has been first reported in December 2019 and rapidly spread worldwide. As other severe acute respiratory syndromes, it is a widely discussed topic whether seasonality affects the COVID-19 infection spreading. This study presents two different approaches to analyse the impact of social activity factors and weather variables on daily COVID-19 cases at county level over the Continental U.S. (CONUS). The first one is a traditional statistical method, i.e., Pearson correlation coefficient, whereas the second one is a machine learning algorithm, i.e., random forest regression model. The Pearson correlation is analysed to roughly test the relationship between COVID-19 cases and the weather variables or the social activity factor (i.e. social distance index). The random forest regression model investigates the feasibility of estimating the number of county-level daily confirmed COVID-19 cases by using different combinations of eight factors (county population, county population density, county social distance index, air temperature, specific humidity, shortwave radiation, precipitation, and wind speed). Results show that the number of daily confirmed COVID-19 cases is weakly correlated with the social distance index, air temperature and specific humidity through the Pearson correlation method. The random forest model shows that the estimation of COVID-19 cases is more accurate with adding weather variables as input data. Specifically, the most important factors for estimating daily COVID-19 cases are the population and population density, followed by the social distance index and the five weather variables, with temperature and specific humidity being more critical than shortwave radiation, wind speed, and precipitation. The validation process shows that the general values of correlation coefficients between the daily COVID-19 cases estimated by the random forest model and the observed ones are around 0.85.
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Abstract This first paper of the two-part series focuses on demonstrating the accuracy of a hyper-resolution, offline terrestrial modeling system used for the High Mountain Asia (HMA) region. To this end, this study systematically... more
Abstract This first paper of the two-part series focuses on demonstrating the accuracy of a hyper-resolution, offline terrestrial modeling system used for the High Mountain Asia (HMA) region. To this end, this study systematically evaluates four sets of model simulations at point scale, basin scale, and domain scale obtained from different spatial resolutions including 0.01° (?1-km) and 0.25° (?25-km). The assessment is conducted via comparisons against ground-based observations and satellite-derived reference products. The key variables of interest include surface net shortwave radiation, surface net longwave radiation, skin temperature, near-surface soil temperature, snow depth, snow water equivalent, and total runoff. In the evaluation against ground-based measurements, the superiority of the 0.01° estimates are mostly demonstrated across relatively complex terrain. Specifically, hyper-resolution modeling improves the skill in meteorological forcing estimates (except precipitation) by 9% relative to coarse-resolution estimates. The model forced by downscaled forcings in its entirety yields the highest skill in model output states as well as precipitation, which improves the skill obtained by coarse-resolution estimates by 7%. These findings, on one hand, corroborate the importance of employing the hyper-resolution versus coarse-resolution modeling in areas characterized by complex terrain. On the other hand, by evaluating four sets of model simulations forced with different precipitation products, this study emphasizes the importance of accurate hyper-resolution precipitation products to drive model simulations.
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The past decade was the wettest on record for much of central and eastern North America. Near the beginning of this period of regional water abundance, however, drought conditions reinforced concerns that high temperatures and... more
The past decade was the wettest on record for much of central and eastern North America. Near the beginning of this period of regional water abundance, however, drought conditions reinforced concerns that high temperatures and evapotranspiration foreshadowed a persistent imbalance in the hydrologic cycle characterized by water loss. These fluctuating hydrologic conditions were manifest by water level variability on the Laurentian Great Lakes, the largest system of lakes on Earth. We show that, during this period, the two dominant hydrologic forces acting directly on the vast surfaces of the lakes, overlake precipitation and overlake evaporation, have evolved differently. More specifically, we find that overlake precipitation has risen to extraordinary levels, while overlake evaporation diminished rapidly in 2014 (coinciding with a strong Arctic polar vortex deformation). Our findings offer a new perspective on the impacts of competing hydrologic forces on large freshwater systems in an era of climate change.
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This study proposes a physically based downscaling approach for a set of atmospheric variables that relies on correlations with landscape information, such as topography, surface roughness, and vegetation. A proof-of-concept has been... more
This study proposes a physically based downscaling approach for a set of atmospheric variables that relies on correlations with landscape information, such as topography, surface roughness, and vegetation. A proof-of-concept has been implemented over Oklahoma, where high-resolution, high-quality observations are available for validation purposes. Hourly North America Land Data Assimilation System version 2 (NLDAS-2) meteorological data (i.e., near-surface air temperature, pressure, humidity, wind speed, and incident long-wave and shortwave radiation) have been spatially downscaled from their original 1/88 resolution to a 500-m grid over the study area during 2015. Results show that correlation coefficients between the downscaled products and ground observations are consistently higher than the ones between the native resolution NLDAS-2 data and ground observations. Furthermore, the downscaled variables present smaller biases than the original ones with respect to ground observations. Results are therefore encouraging toward the use of the 500-m dataset for land surface and hydrological modeling. This would be especially useful in regions where ground-based observations are sparse or not available altogether, and where downscaled global reanalysis products may be the only option for model inputs at scales that are useful for decision-making.
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The goal of this work is to estimate surface and root zone soil moisture at resolutions that are useful for decision making and water resources management. A 500-m atmospheric forcing dataset is developed from the 12.5-km NLDAS-2 (North... more
The goal of this work is to estimate surface and root zone soil moisture at resolutions that are useful for decision making and water resources management. A 500-m atmospheric forcing dataset is developed from the 12.5-km NLDAS-2 (North America Land Data Assimilation System) products across Oklahoma, where high-quality observations are available for validation purposes. A land surface model is then forced with three combinations of input variables to simulate surface and root zone soil moisture: 1) NLDAS-2 atmospheric forcings at their original resolution; 2) downscaled NLDAS-2 atmospheric variables (i.e., near-surface air temperature and humidity, wind speed and direction, incident longwave and shortwave radiation, pressure) and original resolution NLDAS-2 precipitation; and 3) downscaled NLDAS-2 atmospheric variables and precipitation. Results show that the third simulation is able to bring modeled standard-normal deviates of both surface and root zone soil moisture closer to in-situ observations, whereas the second simulation only shows slight improvements with respect to one forced with original resolution NLDAS-2 data. This is particularly evident for negative values of standard-normal deviates, which correspond to drier than usual cases, due to the improved ability of the downscaled precipitation to detect missed events and no-rain cases. In summary, finer resolution forcings have the potential to improve simulations of soil moisture and the resolution of precipitation plays a critical role in improving time series of soil moisture standard-normal deviates.
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The accurate representation of the local-scale variability of precipitation plays an important role in understanding the hydrological cycle and land-atmosphere interactions in the High Mountain Asia region. Therefore, the development of... more
The accurate representation of the local-scale variability of precipitation plays an important role in understanding the hydrological cycle and land-atmosphere interactions in the High Mountain Asia region. Therefore, the development of hyper-resolution precipitation data is of urgent need. In this study, we propose a statistical framework to downscale the Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2) precipitation product using the random forest classification and regression algorithm. A set of variables representing atmospheric, geographic, and vegetation cover information are selected as model predictors, based on a recursive feature elimination method. The downscaled precipitation product is validated in terms of magnitude and variability against a set of ground-and satellite-based observations. Results suggest improvements with respect to the original resolution MERRA-2 precipitation product and comparable performance with gauge-adjusted satellite precipitation products.
Despite increasing evidence of intensification of extreme precipitation events associated with a warming climate, the magnitude of peak river flows is decreasing in many parts of the world. To better understand the range of relationships... more
Despite increasing evidence of intensification of extreme precipitation events associated with a warming climate, the magnitude of peak river flows is decreasing in many parts of the world. To better understand the range of relationships between precipitation extremes and floods, we analyzed annual precipitation extremes and flood events over the contiguous United States from 1980 to 2014. A low correlation (less than 0.2) between changes in precipitation extremes and changes in floods was found, attributable to a small fraction of co-occurrence. The covariation between precipitation extremes and floods is also substantially low, with a majority of catchments having a coefficient of determination of less than 0.5, even among the catchments with a relatively high fraction of annual maxima precipitation that can be linked to floods. The findings indicate a need for more investigations into causal mechanisms driving a nonlinear response of floods to intensified precipitation extremes in a warming climate.
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We have used the following convention to highlight our changes in response to the referees' comments: red text indicates modification, blue text indicates addition, and margin parameters indicate responses to specific comment labeled as... more
We have used the following convention to highlight our changes in response to the referees' comments: red text indicates modification, blue text indicates addition, and margin parameters indicate responses to specific comment labeled as RxCy (Reviewer #x Comment #y) and RxMCy (Reviewer #x Minor Comment #y) With more satellite and model precipitation data becoming available, new analytical methods are needed that can take advantage of emerging data patterns to make well informed predictions in many hydrological applications. We propose a new strategy where we extract precipitation variability patterns and use correlation map to build the resulting density map that serves as an input to centroidal Voronoi tessellation construction that optimizes placement of precipitation gauges. We provide results of numerical experiments based on the data from the Alto-Adige region in Northern Italy and Oklahoma and compare them against actual gauge locations. This method provides an automated way for choosing new gauge locations and can be generalized to include physical constraints and to tackle other types of resource allocation problems.
This study investigates the efficiency of correcting radar rainfall estimates using a stochastic error model in the upper Iguaçu river basin in Southern Brazil for improving streamflow simulations. The 2-Dimensional Satellite Rainfall... more
This study investigates the efficiency of correcting radar rainfall estimates using a stochastic error model in the upper Iguaçu river basin in Southern Brazil for improving streamflow simulations. The 2-Dimensional Satellite Rainfall Error Model (SREM2D) is adopted here and modified to account for topographic complexity, season-ality, and distance from the radar. SREM2D was used to correct the radar rainfall estimates and produce an ensemble of equally probable rainfall fields, that were then used to force a distributed hydrological model. Systematic and random errors in simulated streamflow were evaluated for a cascade of sub-basins of the Iguaçu catchment, with drainage area ranging from 1,808 to 21,536 km 2). Results showed an improvement in the statistical metrics when the SREM2D ensemble was used as input to the hydrological model in place of the radar rainfall estimates in most sub-basins. Specifically, SREM2D was able to remove the relative bias (up to 50%) in the radar rainfall dataset regardless of the basin dimension, whereas the random error was reduced more prominently in the larger basins (up to 100 m 3 s −1). An event scale evaluation was also performed for nine selected flood events in three sub-basins. SREM2D reduced the overestimation in the cumulative rainfall and streamflow volumes during these events.
Toward qualifying hydrologic changes in the High Mountain Asia (HMA) region, this study explores the use of a hyper-resolution (1 km) land data assimilation (DA) framework developed within the NASA Land Information System using the Noah... more
Toward qualifying hydrologic changes in the High Mountain Asia (HMA) region, this study explores the use of a hyper-resolution (1 km) land data assimilation (DA) framework developed within the NASA Land Information System using the Noah Multi-parameterization Land Surface Model (Noah-MP) forced by the meteorological boundary conditions from Modern-Era Retrospective analysis for Research and Applications, Version 2 data. Two different sets of DA experiments are conducted: (1) the assimilation of a satellite-derived snow cover map (MOD10A1) and (2) the assimilation of the NASA MEaSUREs landscape freeze/thaw product from 2007 to 2008. The performance of the snow cover assimilation is evaluated via comparisons with available remote sensing-based snow water equivalent product and ground-based snow depth measurements. For example, in the comparison against ground-based snow depth measurements, the majority of the stations (13 of 14) show slightly improved goodness-of-fit statistics as a result of the snow DA, but only four are statistically significant. In addition, comparisons to the satellite-based land surface temperature products (MOD11A1 and MYD11A1) show that freeze/thaw DA yields improvements (at certain grid cells) of up to 0.58 K in the root-mean-square error (RMSE) and 0.77 K in the absolute bias (relative to model-only simulations). In the comparison against three ground-based soil temperature measurements along the Himalayas, the bias and the RMSE in the 0–10 cm soil temperature are reduced (on average) by 10 and 7%, respectively. The improvements in the top layer of soil estimates also propagate through the deeper soil layers, where the bias and the RMSE in the 10–40 cm soil temperature are reduced (on average) by 9 and 6%, respectively. However, no statistically significant skill differences are observed for the freeze/thaw DA system in the comparisons against ground-based surface temperature measurements at mid-to-low altitude. Therefore, the two proposed DA schemes show the potential of improving the predictability of snow mass, surface temperature, and soil temperature states across HMA, but more ground-based measurements are still required, especially at high-altitudes, in order to document a more statistically significant improvement as a result of the two DA schemes.
This study uses an analytical hydrological framework to investigate the error propagation from satellite precipitation products to hydrological simulations. Specifically, the analytical formulation of the framework allows linking the... more
This study uses an analytical hydrological framework to investigate the error propagation from satellite precipitation products to hydrological simulations. Specifically, the analytical formulation of the framework allows linking the error in hydrograph properties (i.e., cumulative volume, centroid, and dispersion) to the space-time characteristics of error in satellite-precipitation, runoff generation, and routing. Main finding from this study are that (i) the error in spatial and temporal covariance between rainfall and runoff generation is not contributing significantly to the error in cumulative volume of flood events; (ii) errors in runoff generation and routing time are of equal importance in terms of the overall error in the arrival of flood event centroid; and (iii) errors in the variability of runoff generation time is the main contributor to the error in dispersion of flood event hydrograph. Furthermore, sensitivity tests show that errors in hydrograph properties are strongly correlated with errors in the space-time characteristics of precipitation, runoff generation and routing parameters estimated by the analytical framework.
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Notwithstanding the rich record of hydrometric observations compiled by the U.S. Geological Survey (USGS) across the contiguous United States (CONUS), flood event catalogs are sparse and incomplete. Available databases or inventories are... more
Notwithstanding the rich record of hydrometric observations compiled by the U.S. Geological Survey (USGS) across the contiguous United States (CONUS), flood event catalogs are sparse and incomplete. Available databases or inventories are mostly survey- or report-based, impact oriented, or limited to flash floods. These data do not represent the full range of flood events occurring in CONUS in terms of geographical locations, severity, triggering weather, or basin morphometry. This study describes a comprehensive dataset consisting of more than half a million flood events extracted from 6,301 USGS flow records and radar-rainfall fields from 2002 to 2013, using the characteristic point method. The database features event duration; first- (mass center) and second- (spreading) order moments of both precipitation and flow, flow peak and percentile, event runoff coefficient, base flow, and information on the basin geomorphology. It can support flood modeling, geomorphological and geophysical impact studies, and instantaneous unit hydrograph and risk analyses, among other investigations. Preliminary data analysis conducted in this study shows that the spatial pattern of flood events affected by snowmelt correlates well with the mean annual snowfall accumulation pattern across CONUS, the basin morphometry affects the number of flood events and peak flows, and the concentration time and spreadness of the flood events can be related to the precipitation first- and second-order moments.
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Intensity–duration–frequency (IDF) curves are widely used to quantify the probability of occurrence of rainfall extremes. The usual rain gauge-based approach provides accurate curves for a specific location, but uncertainties arise when... more
Intensity–duration–frequency (IDF) curves are widely used to quantify the probability of occurrence of rainfall extremes. The usual rain gauge-based approach provides accurate curves for a specific location, but uncertainties arise when ungauged regions are examined or catchment-scale information is required. Remote sensing rainfall records, e.g. from weather radars and satellites, are recently becoming available, providing high-resolution estimates at regional or even global scales; their uncertainty and implications on water resources applications urge to be investigated. This study compares IDF curves from radar and satellite (CMORPH) estimates over the eastern Mediterranean (covering Mediterranean, semiarid, and arid climates) and quantifies the uncertainty related to their limited record on varying climates. We show that radar identifies thicker-tailed distributions than satellite, in particular for short durations, and that the tail of the distributions depends on the spatial and temporal aggregation scales. The spatial correlation between radar IDF and satellite IDF is as high as 0.7 for 2–5-year return period and decreases with longer return periods, especially for short durations. The uncertainty related to the use of short records is important when the record length is comparable to the return period (∼50,∼100, and∼150% for Mediterranean, semiarid, and arid climates, respectively). The agreement between IDF curves derived from different sensors on Mediterranean and, to a good extent, semiarid climates, demonstrates the potential of remote sensing datasets and instils confidence on their quantitative use for ungauged areas of the Earth.
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This study evaluates the feasibility of using satellite precipitation datasets in flood frequency analysis based on the accuracy of different return period flows derived using a hydrological model driven with satellite and ground-based... more
This study evaluates the feasibility of using satellite precipitation datasets in flood frequency analysis based on the accuracy of different return period flows derived using a hydrological model driven with satellite and ground-based reference rainfall fields over the Connecticut River Basin. Four quasi-global satellite products (TRMM-3B42V7, TRMM-3B42RT, CMORPH, and PERSIANN) at 3-h/0.25 resolution and the National Weather Service (Stage IV) gauge-adjusted radar rainfall dataset (representing the reference rainfall) are integrated in this study, with the Coupled Routing and Excess Storage distributed hydrological model to simulate annual peak flows during warm season (May–November) months. The log-Pearson type III frequency distribution applied to an 11-year record of annual peak flow data is used to derive different return period flows. Evaluation against the Stage IV-driven simulations shows that the TRMM-3B42V7 product has the highest correlation and lowest bias in terms of the derived annual maxima flows compared to the other satellite products. In terms of the different return period flood frequency curves, the various satellite product-based results well-represent the variability across the different basins depicted in the reference precipitation-driven simulations. With the increasing record length of high-resolution satellite products, results from this paper can motivate future studies over basins lacking adequate ground-based records to support flood frequency analyses.
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Basin morphometry is vital information for relating storms to hydrologic hazards, such as landslides and floods. In this paper we present the first comprehensive global dataset of distributed basin morphometry at 30 arc seconds... more
Basin morphometry is vital information for relating storms to hydrologic hazards, such as landslides and floods. In this paper we present the first comprehensive global dataset of distributed basin morphometry at 30 arc seconds resolution. The dataset includes nine prime morphometric variables; in addition we present formulas for generating twenty-one additional morphometric variables based on combination of the prime variables. The dataset can aid different applications including studies of land-atmosphere interaction, and modelling of floods and droughts for sustainable water management. The validity of the dataset has been consolidated by successfully repeating the Hack's law.
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Catchment flood response consists of multiple components of flow generated by the heterogeneity of catchment surface. This study proposes an analytical framework built upon the Viglione et al. (2010a) to assess the dependence of catchment... more
Catchment flood response consists of multiple components of flow generated by the heterogeneity of catchment surface. This study proposes an analytical framework built upon the Viglione et al. (2010a) to assess the dependence of catchment flood response on different flow components. The analytical framework is compared to simulations from a distributed hydrologic model. A large number of rainfall-runoff events from three catchments 5 of Tar River basin in North Carolina are used to illustrate the analytical framework. Specifically, the framework is used to estimate three flood events characteristics (cumulative runoff volume, centroid and spreadness of hydrograph) through three corresponding framework parameters: the rainfall excess and the mean and variance of catchment response time. Results show that under the smooth topographic setups of the study area, the spatial and/or temporal correlation between rainfall and runoff generation are insignificant to flood response; delay in 10 flood response due to runoff generation and routing are of equal importance; the shape of flood is mainly controlled by the variability in runoff generation stage but with non-negligible contribution from the runoff routing stage. Sensitivity tests show that the framework's main error source is the systematic underestimation of flood event's centroid and spreadness, while the random error is relatively low.
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The error in satellite precipitation-driven complex terrain flood simulations is characterized in this study for eight different global satellite products and 128 flood events over the Eastern Italian Alps. The flood events are grouped... more
The error in satellite precipitation-driven complex terrain flood simulations is characterized in this study for eight different global satellite products and 128 flood events over the Eastern Italian Alps. The flood events are grouped according to two flood types: rain floods and flash floods. The satellite precipitation products and runoff simulations are evaluated based on systematic and random error metrics applied on the matched event pairs and basin-scale event properties (i.e., rainfall and runoff cumulative depth and time series shape). Overall, error characteristics exhibit dependency on the flood type. Generally, timing of the event precipitation mass center and dispersion of the time series derived from satellite precipitation exhibits good agreement with the reference; the cumulative depth is mostly underestimated. The study shows a dampening effect in both systematic and random error components of the satellite-driven hydrograph relative to the satellite-retrieved hyetograph. The systematic error in shape of the time series shows a significant dampening effect. The random error dampening effect is less pronounced for the flash flood events and the rain flood events with a high runoff coefficient. This event-based analysis of the satellite precipitation error propagation in flood modeling sheds light on the application of satellite precipitation in mountain flood hydrology.
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This study investigates the error characteristics of six quasi-global satellite precipitation products and their error propagation in flow simulations for a range of mountainous basin scales (255 to 6967 km2) and two different... more
This study investigates the error characteristics of six quasi-global satellite precipitation products and their error propagation in flow simulations for a range of mountainous basin scales (255 to 6967 km2) and two different periods (May-Aug & Sep-Nov) in northeast Italy. Statistics describing the systematic and random error, the temporal similarity and error ratios between precipitation and runoff are presented. Overall, we show strong over/under-estimation associated with the near-real-time 3B42/CMORPH products. Results suggest positive correlation between the systematic error and basin elevation. Performance evaluation off low simulations yields8higher degree of consistency for the moderate to large basin scales and May-Aug period. Gauge-adjustment for the different satellite products is shown to moderate their error magnitude and increase their correlation with reference precipitation and streamflow simulations. Moreover, ratios of precipitation to streamflow simulation error metrics show dependencies in terms of magnitude and variability. Random error and temporal dissimilarity are shown to reduce from basin-average rainfall to the streamflow simulations, while the systematic error exhibits no clear pattern in the rainfall-runoff transformation.
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Hydrograph separation is considered as the first step to catchment-scale water balance analysis. A wide variety of hydrograph separation methods exists ranging from empirical to analytical and physical. This study discusses a... more
Hydrograph separation is considered as the first step to catchment-scale water balance analysis. A wide variety of hydrograph separation methods exists ranging from empirical to analytical and physical. This study discusses a physically-based approach that combines baseflow separation and event identification with minimal data requirement. The input datasets are basin-average rainfall and discharge time series. Outputs are baseflow time series, the timing of the runoff events, differentiated as single- or multi-peak, and the associated rainfall event time series. To assess the method’s feasibility, hydrograph properties are evaluated for both long-term (annual and monthly) and event-scale time series. Results show that the long-term derived baseflow indices are positive (negative) correlated with basin area (runoff coefficient). The event scale analysis shows that the timing-related parameters (i.e. durations of rainfall and flow events and time lag between rainfall to flow events) increase with basin area in terms of magnitude and variability. Similar dependence on basin scale is shown for the water balance-related parameters determined from this analysis, namely event rainfall and baseflow volumes and baseflow index. Water balance parameters are shown to be characterized with less degree of variability for single-peak events relative to multi-peak events.
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Accurate quantitative precipitation estimation over mountainous basins is of great importance because of their susceptibility to hazards such as flash floods, shallow landslides, and debris flows, triggered by heavy precipitation events... more
Accurate quantitative precipitation estimation over mountainous basins is of great importance because of their susceptibility to hazards such as flash floods, shallow landslides, and debris flows, triggered by heavy precipitation events (HPEs). In situ observations over mountainous areas are limited, but currently available satellite precipitation products can potentially provide the precipitation estimation needed for hydrological applications. In this study, four widely used satellite-based precipitation products [Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) 3B42, version 7 (3B42-V7), and in near?real time (3B42-RT); Climate Prediction Center (CPC) morphing technique (CMORPH); and Precipitation Estimation from Remotely Sensed Imagery Using Artificial Neural Networks (PERSIANN)] are evaluated with respect to their performance in capturing the properties of HPEs over different basin scales. Evaluation is carried out over the upper Adige River basin (eastern Italian Alps) for an 8-yr period (2003?10). Basin-averaged rainfall derived from a dense rain gauge network in the region is used as a reference. Satellite precipitation error analysis is performed for warm (May?August) and cold (September?December) season months as well as for different quantile ranges of basin-averaged precipitation accumulations. Three error metrics and a score system are introduced to quantify the performances of the various satellite products. Overall, no single precipitation product can be considered ideal for detecting and quantifying HPE. Results show better consistency between gauges and the two 3B42 products, particularly during warm season months that are associated with high-intensity convective events. All satellite products are shown to have a magnitude-dependent error ranging from overestimation at low precipitation regimes to underestimation at high precipitation accumulations; this effect is more pronounced in the CMORPH and PERSIANN products.
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This study uses a long-term (8 years) dataset of radar-rainfall and runoff observations for the Tar River Basin in North Carolina, to explore the rainfall space-time organization control on the flood response of mild-slope (max slope < 32... more
This study uses a long-term (8 years) dataset of radar-rainfall and runoff observations for the Tar River Basin in North Carolina, to explore the rainfall space-time organization control on the flood response of mild-slope (max slope < 32 degrees) basins. We employ the concepts of “spatial moments of catchment rainfall” and “catchment scale storm velocity” to quantify the effect of spatial rainfall variability and basin geomorphology on flood response. A calibrated distributed hydrologic model is employed to assess the relevance of these statistics in describing the degree of spatial rainfall organization, which is important for runoff modeling. Furthermore, the Tar River Basin is divided into four nested sub-basins ranging from 1106 km2 to 5654 km2, in order to investigate the scale dependence of results. The rainfall spatiotemporal distribution represented in the analytical framework is shown to describe well the differences in hydrograph timing (less so in terms of magnitude of the simulated hydrographs) determined from forcing the hydrologic model with lumped vs. distributed rainfall. Specifically, the first moment exhibits a linear relationship with the difference in timing between lumped and distributed rainfall forcing. The analysis shows that the catchment scale storm velocity is scale dependent in terms of variability and rainfall dependent in terms of its value, assuming typically small values. Accordingly, the error in dispersion of simulated hydrographs between lumped and distributed rainfall forcing is relatively insensitive to the catchment scale storm velocity, which is attributed to the spatial variability of routing and hillslope velocities that is not accounted by the conceptual framework used in this study.
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Hydrograph separation method reported in Mei & Anagnostou (2015) includes two aspects: baseflow separation and event identification. The baseflow separation method, namely filtered revised constant k (FRCK) method, is a hybrid-method of... more
Hydrograph separation method reported in Mei & Anagnostou (2015) includes two aspects: baseflow separation and event identification. The baseflow separation method, namely filtered revised constant k (FRCK) method, is a hybrid-method of the revised constant k (RCK) and recursive digital filter (RDF); it splits the streamflow record into baseflow and event flow component. The event identification method is called characteristic point method (CPM); it extracts rainfall-runoff events from long records of streamflow and basin- average rainfall time series, based on the time series characteristics. More information about this method can be found in the "CPM User Manual_4.3.2017.pdf" document.
The above hydrograph separation methods are contained in six Matlab codes: two for the baseflow separation section and four codes for the event identification. The manuscript provides detailed explanation on the uses, inputs and outputs of these Matlab codes. It is accompanied by a demo Matlab code, illustrating an example basin.
Matlab code can be downloaded from http://ucwater.engr.uconn.edu/models-data/ or https://www.researchgate.net/profile/Yiwen_Mei/publication/316148287_User_Manual_of_the_Characteristic_Point_Method_for_Automatic_Hydrograph_Separation/data/5936ba83458515969b900991/CPM-662017.zip.
The above hydrograph separation methods are contained in six Matlab codes: two for the baseflow separation section and four codes for the event identification. The manuscript provides detailed explanation on the uses, inputs and outputs of these Matlab codes. It is accompanied by a demo Matlab code, illustrating an example basin.
Matlab code can be downloaded from http://ucwater.engr.uconn.edu/models-data/ or https://www.researchgate.net/profile/Yiwen_Mei/publication/316148287_User_Manual_of_the_Characteristic_Point_Method_for_Automatic_Hydrograph_Separation/data/5936ba83458515969b900991/CPM-662017.zip.
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The overarching goal of the research described in this dissertation is to understand the hydrologic implications of error propagation from satellite precipitation products to hydrologic simulations. The complex interaction between... more
The overarching goal of the research described in this dissertation is to understand the hydrologic implications of error propagation from satellite precipitation products to hydrologic simulations. The complex interaction between precipitation error and corresponding hydrologic response is examined following a numerical- and an analytical-based approach. The application of a hydrologic model forced by various satellite precipitation products is adopted as the numerical-based framework that is used to identify the properties of error propagation with respect to a number of factors (e.g. basin scale, seasonality, severity of rainfall and flow). The investigation is conducted in complex terrain basins of the Eastern Italian Alps. Results show better consistency between gauges for events occurred over larger scale basins during warm season months that are associated with moderate intensity of rain and flow rate. Furthermore, an event -based error analysis is conducted focusing on the evaluation of satellite-simulated flood event characteristics for different flood types. Results revealed that on average systematic rainfall error is reduced in simulated runoff, highlighting the dampening effect on error during the rainfall-runoff transformation. The dampening effect on random error was decreasing with increasing runoff coefficient. In addition to the numerical investigation, an analytical framework is developed to decompose the error propagation into space and time components. This essentially allows to assess the relative contribution of the different processes of catchment flood response on error propagation. Demonstration of the analytical framework is conducted based on 180 rainfall-runoff events that occurred over the Tar River basin in North Carolina. It is shown that error in timing of flood event is attributed equally to error in runoff generation and routing time. Error in hydrograph shape is mainly controlled by the error in the variability of runoff generation time while error in flood volume is predominantly controlled by the error in rainfall volume. Overall, these investigations provide important information for the hydrologic modelers to choose the appropriate precipitation products for the hydrologic-related practice. It also serves as guidance for the satellite precipitation-product developers on the designs of more advance retrieval algorithms.
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This study uses data from the Tar-River Basin in North Carolina to explore how space-time rainfall variability influences the hydrologic response from observational and modeling perspectives. For understanding the basin scale effect, the... more
This study uses data from the Tar-River Basin in North Carolina to explore how space-time rainfall variability influences the hydrologic response from observational and modeling perspectives. For understanding the basin scale effect, the Tar-River Basin is divided into four cascade sub-basins ranging from 1106 km2 up to 5654 km2. The study evaluates the catchments’ response to rainfall for a large number of storm events by computing the event runoff coefficient based on streamflow observations and through simulations from a semi-distributed hydrological model. Comparison of observed to simulated hydrographs from the hydrological model shows that distributed rainfall forcing gives improved performance evaluation metrics relative to basin-average rainfall forcing data. We employ the concepts of “Spatial Moments of Catchment Rainfall (defined as Δ1 and Δ2)” and “Catchment Scale Storm Velocity (defined as Vs)” reported in Zoccatelli et al. (2011) to quantify the effect of spatial rainfall organization and basin geomorphology on modeling the flood response. Our analysis using the above conceptual framework shows that the rainfall spatiotemporal variation plays a significant role on the timing and dispersion of the simulated hydrographs. Specifically, Δ1 increases linearly with the difference in timing between lumped and distributed rainfall forcing. Δ2 and the product between Vs and the variance of hydrograph arrival time exhibit an increasing trend with the difference in dispersion of simulated
hydrographs between lumped and distributed rainfall forcing.
hydrographs between lumped and distributed rainfall forcing.
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Floods are among the most devastating natural hazards in terms of both the number of people affected globally and the proportion of individual fatalities. The prediction of flood hazard requires accurate precipitation estimation produced... more
Floods are among the most devastating natural hazards in terms of both the number of people affected globally and the proportion of individual fatalities. The prediction of flood hazard requires accurate precipitation estimation produced at fine space–time scales. The high space–time variability, the scarcity of ground-based sensors, and the observational limitations associated with their operation in mountainous terrain make monitoring of flood-producing storms and their hydrologic response a particularly challenging task. Precipitation measurements from space-borne sensors offer unique advantages relative to ground-based sensors, since they are uninhibited by terrain or spatial inconsistencies and can provide quantitative precipitation estimates at quasi-global scale. The significance of these advantages in quantitative precipitation estimation has been recognized by the hydrologic community, where numerous studies in the past decades have demonstrated the use of satellite precipitation data for the prediction of floods over the globe. This chapter provides an overview of the satellite precipitation applications in flood modeling to highlight current challenges and opportunities in satellite-precipitation-driven flood prediction.
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Droughts are constantly threatening the global water availability and food securities worldwide. This study aims to evaluate the short-and long-term (1-, 6-and 12-month) drought conditions in Peninsular Malaysia during 1989-2018 using... more
Droughts are constantly threatening the global water availability and food securities worldwide. This study aims to evaluate the short-and long-term (1-, 6-and 12-month) drought conditions in Peninsular Malaysia during 1989-2018 using Standardized Precipitation Index and Evaporative Demand Drought Index. Historical trends of drought conditions were analyzed using modified Mann-Kendall test. Spearman's ρ approach was also applied to examine the spatial patterns of correlations between these drought indices. Based on the findings, Evaporative Demand Drought Index shows increasing tendency towards drier conditions in the northern half of Peninsular Malaysia, but opposite trends are observed for Standardized Precipitation Index. The time series of Evaporative Demand Drought Index are generally well-correlated to that of Standardized Precipitation Index at all three timescales for the whole study area, except for the northern region. The evidence presented suggests Evaporative Demand Drought Index is a great alternative for drought monitoring applications in Peninsular Malaysia.