Journal of Hydrometeorology

Walraven, Bas; Overeem, Aart; Coenders-Gerrits, Miriam; Hut, Rolf; van der Valk, Luuk; Uijlenhoet, Remko

doi: 10.1175/jhm-d-24-0023.1pmid: N/A

AbstractPotentially, the greatest benefit of commercial microwave links (CMLs) as opportunistic rainfall sensors lies in regions that lack dedicated rainfall sensors, most notably low- and middle-income countries. However, current CML rainfall retrieval algorithms are predominantly tuned and applied to (European) CML networks in temperate or Mediterranean climates. This study investigates whether local quantitative precipitation estimates from CMLs in a tropical region, specifically Sri Lanka, can be improved by optimizing two dominant parameters in the rainfall retrieval algorithm RAINLINK, namely, the wet antenna attenuation correction factor Aa and the relative contribution of minimum and maximum received signal levels α. Using a grid search, based on 10 months of CML data from 22 link–gauge clusters consisting of 105 sublinks that lie within 1 km of a daily rain gauge, the optimal values of Aa and α are first derived for the entire country and compared to the default RAINLINK values. Subsequently, the CMLs are grouped by link length, frequency, climate zone, and daily rainfall depth classes, and Aa and α are derived for each of these classes. Calibrating parameters on all clusters across the country only leads to minor improvements. The actual optimal Aa and α values depend on the performance metric favored. Calibrating on network properties, particularly short link length and high-frequency classes, does significantly improve rainfall estimates. By relating the optimal Aa and α values to known network metadata, the results from this study are potentially applicable to other tropical CML networks that lack nearby reference rainfall data.Significance StatementThe purpose of this study is to improve rainfall estimates from commercial microwave links in Sri Lanka by optimizing two important rainfall retrieval algorithm parameters. Our results show that relating the optimal parameter values to operating frequency and pathlength improves rainfall estimates more than applying a single optimal parameter set to the entire network. By relating the optimal parameter values to readily known network properties, we aim to make these results applicable to other tropical countries, particularly low- and middle-income countries, that lack adequate reference rainfall data to calibrate rainfall estimates from commercial microwave links on.

Cho, Eunsaem; Ahmadisharaf, Ebrahim; Ahmadisharaf, Amin; Nematirad, Reza; AghaKouchak, Amir

doi: 10.1175/jhm-d-23-0217.1pmid: N/A

AbstractThis paper explored temporal changes in magnitude and seasonality of low, median, and high inflows of 51 dams across the western and southwestern United States over the 1993–2022 period. Changes in precipitation, air temperature (an indicator of snowpack and evaporation), soil moisture, and vegetation were also examined to identify potential reasons for the temporal trends in dam inflows. Using monotonic and nonmonotonic tests, we found a general downward trend in dam inflows, particularly across the Upper Colorado and California regions. More than 30% of the dams showed a downward trend in their annual median inflows, high inflows during spring, and median inflows during fall. The downward trend of dam inflows was associated with decreasing precipitation and soil moisture and increasing temperatures. While vegetation exhibited positive associations with inflows, it did not seem to be a primary factor for explaining the inflow trends. We also observed shifts in the seasonality of low and high inflows; there was an increase in the proportion of inflows occurring during summer and fall and a decrease in winter proportions for low inflows. Similarly, high inflows exhibited an increase in spring proportions and a decrease in fall proportions. Our changepoint analyses detected nonmonotonic trends between 2002 and 2012 in ∼13% of the dams; the majority were located in the Upper Colorado and California regions. More than half of these changepoints were in 2011, likely due to widespread droughts then. Our study has implications for reservoir managers to identify changes that dams experience over time and assist them in proposing actions that maintain the dams’ functionality.

Lee, Sanghyun; Nourani, Vahid; Danandeh Mehr, Ali; Moriasi, Daniel; Mirchi, Ali

doi: 10.1175/jhm-d-24-0041.1pmid: N/A

AbstractDroughts may exhibit spatiotemporal heterogeneity at a regional scale. Effective drought assessment and management necessitates identifying homogeneous areas. However, previous studies often simplified clustering analysis by focusing only on a single variable. In this study, we present a novel drought clustering map for the southern plains (SP) region of the United States by integrating the wavelet-entropy approach with k-means clustering algorithm to capture spatiotemporal patterns of drought-related variables across various resolutions while eliminating redundant information. We considered multiple drought indicators and indices including gridded precipitation (P), potential evapotranspiration (PET), normalized difference vegetation index (NDVI), and standardized precipitation evapotranspiration index (SPEI) as well as geographical coordinates and topography map. Through evaluating five different combinations of input datasets, we selected the one demonstrating optimal results based on the Davies–Bouldin and Calinski–Harabasz criteria. In addition to P, PET, and NDVI, including the coordinates and elevation as secondary variables significantly enhanced the clustering performance. Using these variables, the region was subdivided into 21 clusters. Pearson’s correlation coefficients for the SPEI between centroid members and corresponding cells within clusters averaged between 0.84 and 0.94. Comparison with an existing cluster map [Drought Risk Atlas (DRA)] for the region revealed that our proposed cluster map showed higher variability between clusters for P, PET, and NDVI, confirming the robustness of the clustering results for drought conditions in the SP. The new clustering framework is expected to provide valuable insights for understanding and addressing drought dynamics in the SP region.

Mengouna, François Xavier; Philippon, N.; Vondou, Derbetini A.; Moron, Vincent; Maranan, Marlon; Fink, Andreas H.

doi: 10.1175/jhm-d-23-0067.1pmid: N/A

AbstractUsing half-hourly rainfall data from 14 automated weather stations over central Africa, rainfall events [≥0.1 mm (30 min)−1] characteristics are explored. A total of 10 096 wet events (WEs) were identified and classified into six storm types (STs), mostly discretized by their duration and intensity. ST 1 is very short (<1.5 h) with low rainfall intensities over a small area and contributes the least to the total rainfall (7%) but is by far the most frequent (70% of the WEs). ST 2 is short (∼1.5 h) and sudden with very intense rains and of medium spatial scale (<200 km). ST 3 is short and of medium scale too but with low intensities and rainfall amounts. ST 4 and ST 5 are of large scale and long (∼3–4 h), with high rainfall amounts so that they contribute the most to the total rainfall (29% and 20%). Last, ST 6 is the largest, longest, and rainiest, although moderately intense. A complementary classification is performed on lagged gridded rainfall fields from IMERG to document the possible space–time evolution of the rainfall field during the life cycle of WEs. Four spatial types are identified. Spatial type 1 gathers the most frequent, less intense ones. Spatial type 2 is far less frequent but pictures westward-moving rainfall patterns, probably associated with mesoscale convective systems. Two spatial types (3 and 4) are related to high-intensity near-stationary rainfall events, respectively, located to the southwest and northwest of the stations. ST 4 is mostly present at stations close to the Atlantic Ocean.

Cocking, Katherine; Singer, Michael Bliss; MacLeod, David; Cuthbert, Mark O.; Rosolem, Rafael; Muthusi, Flavian; Paron, Paolo; Kimutai, Joyce; Omondi, Phillip; Hassan, Ahmed Mohamed; Teshome, Asaminew; Michaelides, Katerina

doi: 10.1175/jhm-d-23-0228.1pmid: N/A

AbstractSeasonal rainfall is critical to lives and livelihoods within the Horn of Africa drylands (HAD), but it is highly variable in space and time. The main HAD rainfall seasons are typically defined as March–May (MAM) and October–December (OND). However, these 3-month periods are only generalized definitions of seasonality across the HAD, and local experience of rainfall may depart from these substantially. Here, we use daily rain gauge data with a duration of at least 10 years from 69 stations across the drylands of Kenya, Somalia, and Ethiopia to locally delineate key rainfall seasons. By calculating local seasonal rainfall timings, totals, and extremes, we obtain more accurate estimates of the spatial variability in rainfall delivery across the HAD, as well as climatological patterns. Results show high spatial variability in season onset, cessation, and length across the region, indicating that a homogenous classification of rainfall seasons across the HAD (e.g., MAM and OND) is inadequate for representing local rainfall characteristics. Our results show that the “long rains” season is not significantly longer than the “short rains” season over the period of study. This could be related to the previously documented decline of the “long rains” seasonal totals over recent decades. Several rainfall metrics also vary spatially between seasons, and the rainfall on the most extreme days can accumulate to double the local mean seasonal total. The locally defined rainfall seasons better capture the bulk of the rainfall during the season, giving improved characterization of rainfall metrics, consistent with the aim of a better understanding of rainfall impacts on local communities.

Kumar, Anil; Saharia, Manabendra; Kirstetter, Pierre

doi: 10.1175/jhm-d-23-0196.1pmid: N/A

AbstractFlash floods are one of the most devastating natural disasters, yet many aspects of their severity and impact are poorly understood. The recession limb is related to postflood recovery and its impact on communities, yet it remains less documented than the rising limb of the hydrograph to predict the peak discharge and timing of floods. This work introduces a new metric called flash flood recovery or recoveriness, which is the potential for recovery of a watershed to preflood conditions. Using a comprehensive database of 78 years and supervised machine learning algorithms, flash flood recovery is mapped in the conterminous United States. A suite of geomorphological and climatological variables is used as predictors to provide probabilistic estimates of recoveriness. Slope index, river basin area, and river length are found to be the most significant predictors to predict recoveriness. Several new localized hotspots were identified, such as the western slopes of the Appalachians consisting of Kentucky, Tennessee, and West Virginia and the interlinked areas of western Montana and northern Idaho. This new metric can be useful for prioritizing relief and rehabilitation efforts as well as precautionary measures for disaster risk reduction.

Adhikari, Pramod; Geerts, Bart; Rahimi-Esfarjani, Stefan; Smith, Kaitlin; Shuman, Bryan N.; Schneider, Timothy L.

doi: 10.1175/jhm-d-24-0063.1pmid: N/A

AbstractThis study evaluates the ability of 15 CMIP6 global climate models (GCMs), dynamically downscaled to a 9-km grid, to effectively simulate the observed regional hydroclimate across the complex terrain of the western United States. The evaluation focuses on orographic precipitation, surface temperature, and snow water equivalent (SWE), evaluated over a 33-yr period (1981–2014) using gridded gauge- and station-based datasets and a snowpack reanalysis product. Additional comparisons are made against two ERA5-driven climate reconstructions: one at 9-km resolution, with the same physical choices, and one at 4-km resolution. The latter better captures the terrain and orographic processes and uses different physics. Model performance is evaluated in four geographic regions, and mountains are contrasted against the surrounding plains. The evaluation is challenged by the fact that gridded observational estimates of climate parameters in complex terrain have a poorly quantified uncertainty related to measurement and/or representativeness issues. The ensemble mean of the downscaled GCMs overestimates cold-season precipitation, its orographic enhancement, and peak SWE in the mountains. Its diurnal temperature cycle and its mountain–plain temperature contrast are suppressed, compared to observations: its temperature estimates err toward the mean on both sides of both distributions. But the same applies to the identically downscaled (9 km) ERA5, indicating that these biases are due to model physics, not a misrepresentation of the climate system. The 4-km ERA5-based reconstruction has smaller biases in terms of precipitation and temperature, especially over mountains, but underestimates SWE. Model performance differs between mountains and plains, and the differences vary by region.Significance StatementIn the western United States, much of the precipitation contributing to streamflow falls over mountains. In addition, the mountain snowpack seasonally stores water for consumptive use in the warm season. Amid growing concerns about changing water availability associated with the changing hydrometeorological patterns in a warming climate, it is essential to evaluate how well climate models capture precipitation and the snowpack across the headwater regions. Dynamically downscaled CMIP6 models, on average, capture the historical climate quite well, but uncertainty remains about how well they capture key climate parameters over the mountains. This uncertainty is due, in part, to the paucity of measurements in the mountains. Here, we demonstrate that dynamic downscaling to finer grid resolutions reduces this uncertainty.

Alshehri, Mohammed; Mascaro, Giuseppe; Kunkel, Kenneth E.

doi: 10.1175/jhm-d-24-0024.1pmid: N/A

AbstractA critical task to better quantify changes in precipitation (P) mean and extreme statistics due to global warming is to gain insights into the underlying physical generating mechanisms (GMs). Here, the dominant GMs associated with daily P recorded at 2861 gauges in the conterminous United States from 1980 to 2018 were identified from atmospheric reanalyses and publicly available datasets. The GMs include fronts (FRTs), extratropical cyclones (ETCs), atmospheric rivers (ARs), tropical cyclones (TCs), and North American monsoon (NAM). Climatologies of the GM occurrences were developed for the nonzero P (NZP) and annual P maxima (APM) samples, characterizing the marginal and extreme P distributions, respectively. FRT is everywhere the most frequent (45%–75%) GM of NZP followed by ETC (12%–33%). The FRT contribution declines for APM (19%–66%), which are dominated by AR (50%–65%) in western regions and affected by TC (10%–18%) in southern and eastern regions. The GM frequencies exhibit trends with the same signs over large regions, which are not statistically significant except for an increase in FRT (TC) frequency in the northeast (central region). Two-sample tests showed well-defined spatial patterns with regions where 1) both the marginal and extreme P distributions of the two dominant GMs likely belong to different statistical populations and 2) only the marginal or extreme distributions could be considered statistically different. These results were interpreted through L-moments and parametric distributions that adequately model NZP and APM frequency. This work provides useful insights to incorporate mixed populations and nonstationarity in P frequency analyses.