Publications by Year: 2019

Borga M, Comiti F, Ruin I, Marra F. Forensic analysis of flash flood response. Wiley Interdisciplinary Reviews: Water [Internet]. 2019;6 (2). Publisher's VersionAbstract
The last decade has witnessed the development of methodologies for the post‐flood documentation of both hydrogeomorphological and social response to extreme precipitation. These investigations are particularly interesting for the case of flash floods, whose space–time scales make their observations by conventional hydrometeorological monitoring networks particularly challenging. Effective flash flood documentation requires post‐flood survey strategies encompassing accurate radar estimation of rainfall, field and remote‐sensing observations of the geomorphic processes, indirect reconstruction of peak discharges—as well eyewitness interviews. These latter can give valuable information on both flood dynamics and the related individual and collective responses. This study describes methods for post‐flood surveys based on interdisciplinary collaborations between natural and social scientists. These surveys may help to better understand the links between hydrometeorological dynamics and geomorphic processes as well as the relationship between flood dynamics and behavioral response in the context of fast space–time changes of flooding conditions. This article is categorized under: Science of Water > Methods Science of Water > Hydrological Processes A flash flood and its forensic analysis.
McGraw D, Nikolopoulos EI, Marra F, Anagnostou EN. Precipitation frequency analyses based on radar estimates: An evaluation over the contiguous United States. Journal of Hydrology [Internet]. 2019. Publisher's VersionAbstract
The lack of knowledge on precipitation frequency over ungauged areas introduces a significant source of uncertainty in relevant engineering designs and risk estimation procedures. Radar-based observations offer precipitation information over ungauged areas and thus have gained increasing attention as a potential solution to this problem. However, due to their relative short data records and inherent uncertainty sources, their ability to provide accurate estimates on the frequency of precipitation extremes requires evaluation. This study involves the evaluation of at-site precipitation frequency estimates from NEXRAD Stage IV radar precipitation dataset. We derive precipitation annual maxima series from the 16yrs record (2002-2017) of NEXRAD and we compare against 539 long-term (50yrs) hourly gauge records. In addition, Intensity-Duration-Frequency (IDF) curves are estimated from both radar and gauge dataset and compared. IDF estimation is based on fitting the Generalize Extreme Value distribution to annual precipitation maxima. Evaluation is carried out over the contiguous United States and results are grouped and presented for five dominant climate classes and for a range of return period and precipitation durations. NEXRAD was shown to overestimate intensities at shorter durations (1- and 3-hr) and low quantiles, while it tends to underestimate higher quantiles at longer durations (24hr). In addition, evaluation of the IDF curves estimated from NEXRAD revealed a distinct geographic dependence with certain regions exhibiting a tendency to overestimation (e.g. east of the Rocky Mountains) or underestimation (Midwest). Overall, this analysis suggests that, while significant discrepancies may exist, there are several cases where NEXRAD provide estimates within the uncertainty bounds of the reference rain gauge dataset. The climate/geographic region and the temporal duration are important aspects to consider. Findings provided in this work on these aspects will hopefully serve as a general guideline for those interested in using NEXRAD estimates for further research or applications on precipitation extremes.
Miller O, Helman D, Svoray T, Morin E, Bonfil DJ. Explicit wheat production model adjusted for semi-arid environments. Field Crops Research [Internet]. 2019;231 (July 2018) :93–104. Publisher's VersionAbstract
Current literature suggests that wheat production models are limited either to wide-scale or plot-based predictions ignoring pattern of habitat conditions and surficial hydrological processes. We present here a high-spatial resolution (50 m) non-calibrated GIS-based wheat production model for predictions of aboveground wheat biomass (AGB) and grain yield (GY). The model is an integration of three sub-models, each simulating elemental processes relevant for wheat growth dynamics in water-limited environments: (1) HYDRUS-1D, a finite element model that simulates one-dimensional movement of water in the soil profile; (2) a two-dimensional GIS-based surface runoff model; and (3) a one-dimensional process-driven mechanistic wheat growth model. By integrating the three sub-models, we aimed to achieve a more accurate spatially continuous water balance simulation with a better representation of root zone soil water content (SWC) impacts on plant development. High-resolution grid-based rainfall data from a meteorological radar system were used as input to HYDRUS-1D. Twenty-two commercial wheat fields in Israel were used to validate the model in two seasons (2010/11 and 2011/12). Results show that root zone SWC was accurately simulated by HYDRUS-1D in both seasons, particularly at the top 10-cm soil layer. Observed vs simulated AGB and GY were highly correlated with R2 = 0.93 and 0.72 (RMSE = 171 g m−2 and 70 g m−2) having low biases of -41 g m−2 (8%) and 52 g m−2 (10%), respectively. Model sensitivity test showed that HYDRUS-1D was mainly driven by spatial variability in the input soil characteristics while the integrated wheat production model was mostly affected by rainfall spatial variability indicating the importance of using accurate high-resolution rainfall data as model input. Using the integrated model, we predict decreases in AGB and GY of c. 10.5% and c. 12%, respectively, for 1 °C of warming and c. 7.7% and c. 7.3% for 5% reduction in rainfall amount in our study sites. The suggested model could be used by scientists to better understand the causes of spatial and temporal variability in wheat production and the consequences of future scenarios such as climate change.