A comparison between generalized least squares regression and top-kriging for homogeneous cross-correlated flood regions

Persiano Simone; Jose Luis Salinas; Jery Russell Stedinger; William H. Farmer; David Lun; Alberto Viglione; Gunter Bloschl; Attilio Castellarin

doi:10.1080/02626667.2021.1879389

USGS · 70241892

A comparison between generalized least squares regression and top-kriging for homogeneous cross-correlated flood regions

Persiano Simone·Jose Luis Salinas·Jery Russell Stedinger·William H. Farmer·David Lun·Alberto Viglione·Gunter Bloschl·Attilio Castellarin

View original source ↗Export metadata ↓

Abstract

Spatial cross-correlation among flood sequences impacts the accuracy of regional predictors. Our study investigates this impact for two regionalization procedures, generalized least squares (GLS) regression and top-kriging (TK), which deal with cross-correlation in two fundamentally different ways and therefore might be associated with different accuracy and uncertainty of predicted flood quantiles. We perform a Monte Carlo experiment based on a dataset of annual maximum flood series for 20 catchments in a hydrologically homogeneous region. Based on a log-Pearson type III parent distribution, we generate 3000 realizations of the region with different degrees of cross-correlation. For each realization, GLS and TK are applied in leave-one-out cross-validation to predict at-site flood quantiles. Our study shows that (a) TK outperforms GLS when catchment area is the only catchment descriptor used for predicting “true” population (theoretical) flood quantiles, regardless of the level of cross-correlation, and (b) GLS and TK perform similarly when multiple catchment descriptors are used.

Explore related subjects

Hydrological Sciences Journal Article

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

Persiano Simone, Jose Luis Salinas, Jery Russell Stedinger, William H. Farmer, David Lun, Alberto Viglione, Gunter Bloschl, Attilio Castellarin. 2021-03-18. A comparison between generalized least squares regression and top-kriging for homogeneous cross-correlated flood regions. https://doi.org/10.1080/02626667.2021.1879389

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related USGS reports

USGS2026

Characterizing the influence of remotely sensed wetland and lake water storage on discharge using LSTM models

Globally, many wetlands and lakes are at risk for further loss, which can amplify downstream consequences of flood and drought events. We derived remotely sensed based time series of surface water storage (SW storage ) to determine when and where accounting for SW storage dynamics improves predictions of river discharge. We trained four long short-term memory (LSTM) models, that differed in their inclusion of storage data and catchment characteristics, to simulate daily river discharge (2016–2023) for select watersheds across the conterminous United States. Adding SW storage to a meteorology-only or meteorology-and-catchment characteristics model improved upon model Nash-Sutcliffe efficiency (NSE) in 80.6% of the watersheds. Residuals during low-flow (Q70) events decreased by 47.6% when adding storage to meteorological data. Improvements were most consistent in ecoregions with a greater abundance of non-floodplain lakes and wetlands. This effort represents the first exploration to train a multi-watershed LSTM on landscape-scale remotely sensed time series of SW storage .

conterminous United States↗

USGS2024

New insights on the origin of the Richardson-Richards equation

The Richardson-Richards equation (RRE), despite known shortcomings especially in regard to preferential flow, provides the basis of the vast majority of unsaturated flow models in use today. L.F. Richardson published this equation in 1922, nine years before L.A. Richards. Whereas Richards approached this problem directly from the groundbreaking developments of Edgar Buckingham, Richardson, surprisingly, cited as his starting point only the earlier work of L.J. Briggs. Collectively, these four scientists’ published and unpublished work reveals that: (1) Briggs’ work, though qualitative, captured the essential physical principles needed for quantifying unsaturated flow; (2) Buckingham came very close to deriving the RRE and explained why he stopped short of doing so; (3) derivation of the RRE from the work of either Briggs or Buckingham required only modest developmental work; and (4) besides deriving the RRE, Richards carried through much of the experimental agenda that Buckingham considered a necessary precursor to mathematical treatment.

Hydrological Sciences Journal↗

USGS2024

Streamflow timing and magnitude during snow drought depend on snow drought type and regional hydroclimate

Communities around the world rely on snowmelt to meet water demands, and periods of lower than normal snow accumulation, snow droughts, can decrease water supplies. Leveraging 172 minimally disturbed and seasonally snow-covered watersheds, we developed an approach to examine the effects of cool & dry, warm & dry, and warm & wet snow droughts on streamflow timing and magnitude by hydrologic region. Our results showed all types of snow droughts in all regions correlate with lower annual streamflow, lower maximum and minimum flows, and lower runoff ratios, with more numerous low flow days and earlier streamflow timing. However, departures from non-snow drought conditions differed substantially between drought types and regions. Consecutive snow droughts further reduced runoff ratios and increased low flow days, likely due to additional subsurface storage depletion. With warm snow drought occurrence expected to increase, we discuss impacts for water management systems whose design specifications may not reflect the changing hydroclimate.

Hydrological Sciences Journal↗