Fig 5 - uploaded by Leonard M. Lye
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(Color) Time series of mean-monthly land surface temperature from the VIC model and MODIS area-average basin estimates from 2000 to 2005
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Understanding the persistence in land surface processes, such as that in the deep subsurface moisture storage, has great implications for seasonal weather prediction over a drainage basin. The Canadian Prairies is a region of intense and recurrent drought outbreaks with myriad negative impacts on the regional ecosystem as well as on all sectors of...
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... basin mean monthly LST from the hydrological model from 2000 to 2005 were intercompared with the estimates of the LST from MODIS over the catchment and presented as shown in Fig. 5. VIC model simulation of the LST reveals similar annual oscillation as that in the MODIS estimates of the same var- iable. There is a good agreement between the hydrological model simulation and satellite estimates of the LST over this catchment in the summer, however, the LST estimates from MODIS appears much colder in the winter ...
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Soil moisture is often considered a direct way of quantifying agricultural drought since it is a measure of the availability of water to support crop growth. Measurements of soil moisture at regional scales have traditionally been sparse, but advances in land surface modelling and the development of satellite technology to indirectly measure surfac...
Citations
... More DA methods should be developed that include the time of concentration to address the issue of time lag, thereby increasing the accuracy of flood forecasts. It is also important to understand the strength of DA through an analysis comparing batch calibration of parameters with DA updated parameters (Grassi and Santucci de Magistris 2014, Agboma and Lye 2015). The recent advancements in remote sensing have enabled new opportunities to test various pseudo-observations in DA to improve streamflow forecasts (Y Liu et al. 2012). ...
In the present study a total of 201 research articles around the globe over the review period from 2001 to 2021 are identified and investigated. The study has identified some of the grand challenges associated with Ensemble Flood Forecasting (EFF). In addition, future opportunities include development of quality-controlled datasets for longer duration, new techniques to weigh the ensembles, inclusion of more physical based datasets in data assimilation techniques, simulation of more observable states by hydrological model to facilitate data assimilation, application of artificial intelligence and machine learning, optimization of computational efficiency to issue timely flood warnings. Moreover, it is worth mentioning that along with the technical aspects the effectiveness of the operational flood forecasting system depends on the non-technical aspects.
... Thus, there is no unique way to quantify hydrological memory. For example, catchment memory has been assessed based on soil moisture and groundwater dynamics (Agboma and Lye, 2015;Peters et al., 2006), on streamflow recession curves (Rodríguez-Iturbe and Valdes, 1979), on lag correlations between soil moisture and other fluxes within the catchment (Orth and Seneviratne, 2013), and on recovery times from droughts (Yang et al., 2017). ...
A decade-long (2010–2020) period with precipitation deficits in central–south Chile (30–41∘ S), the so-called megadrought (MD), has led to streamflow depletions of larger amplitude than expected from precipitation anomalies, indicating an intensification in drought propagation. We analysed the catchment characteristics and runoff mechanisms modulating such intensification by using the CAMELS-CL dataset and simulations from the HBV hydrological model. We compared annual precipitation–runoff (P–R) relationships before and during the MD across 106 basins with varying snow-/rainfall regimes and identified those catchments where drought propagation was intensified. Our results show that catchments' hydrological memory – modulated by snow and groundwater – is a key control of drought propagation. Snow-dominated catchments (30–35∘ S) feature larger groundwater contribution to streamflow than pluvial basins, which we relate to the infiltration of snowmelt over the Western Andean Front. This leads to longer memory in these basins, represented by a significative correlation between autumn streamflow (when snow has already melted) and the precipitation from the preceding year. Hence, under persistent drought conditions, snow-dominated catchments accumulate the effects of precipitation deficits and progressively generate less water, compared with their historical behaviour, notably affecting central Chile, a region with limited water supply and which concentrates most of the country's population and water demands. Finally, we addressed a general question: what is worse – an extreme single-year drought or a persistent moderate drought? In snow-dominated basins, where water provision strongly depends on both the current and previous precipitation seasons, an extreme drought induces larger absolute streamflow deficits; however persistent deficits induce a more intensified propagation of the meteorological drought. Hence, the worst scenario would be an extreme meteorological drought following consecutive years of precipitation below average, as occurred in 2019. In pluvial basins of southern Chile (35–41∘ S), hydrologic memory is still an important factor, but water supply is more strongly dependant on the meteorological conditions of the current year, and therefore an extreme drought would have a higher impact on water supply than a persistent but moderate drought.
... As a consequence, there is no unique way to quantify hydrological memory. For example, catchment memory has been assessed based on soil moisture and groundwater dynamics (Agboma and Lye, 2015;Peters et al., 2006), on streamflow recession curves (Rodríguez-Iturbe and Valdes, 1979), on lagcorrelations between soil moisture and other fluxes within the catchment (Orth and Seneviratne, 2013), and based on recovery times from droughts (Yang et al., 2017). 180 ...
A decade-long (2010–2019) period with precipitation deficits in central-south Chile (30–41º S), the so-called megadrought (MD), has led to larger than expected hydrological response and water deficits, indicating an intensification in drought propagation. We used the CAMELS-CL dataset and simulations from the HBV hydrological model to explore the causes of such intensification. Across 124 basins with varying snow/rainfall regimes, we compared annual rainfall-runoff (R-R) relationships and runoff generation mechanisms before and during the MD, and identified those catchments where drought propagation was intensified. We show that catchments’ hydrological memory -mediated by groundwater flows- is a key control of drought propagation intensity, and that baseflow contribution to runoff is positively correlated with snow accumulation preceding the year affected by a drought. Hence, under persistent drought conditions, snow-dominated catchments progressively generate less water, compared with their historical behaviour, notably affecting the semi-arid regions in central Chile. Finally, we addressed a general question: what is worse, an extreme single year drought or a persistent moderate drought? In semi-arid regions, where water provision strongly depends on both the current and previous precipitation seasons, the worst scenario would be an extreme meteorological drought following consecutive years of precipitation below average. In temperate regions of southern Chile, where catchments have more pluvial regimes, hydrologic memory is still an important factor, but water supply is more strongly dependant on the meteorological conditions of the current year, and therefore an extreme drought would have a higher impact on water supply than a persistent but moderate drought.
... Nevertheless, such an operation may help to extend the 'efficient period', considering that the short influence period is one of the obstacles that limits the application of Kalman filter DA in an operational hydrological forecast (Muluye 2011, Agboma and Lye 2014, Grassi and de Magistris 2014. ...
There is great potential in Data Assimilation (DA) for the purposes of uncertainty identification, reduction and
real time correction of the hydrological models. This paper reviews the latest development of Kalman filters,
particularly the Extended Kalman Filter (EKF) and the Ensemble Kalman Filter (EnKF) in hydrological DA. The
hydrological DA targets, methodologies and their applicability are examined. The recent applications of EKF
and EnKF in hydrological DA are summarized and assessed critically. Furthermore, this review highlights the
existing challenges in the implementation of EKF and EnKF, especially error determination and joint
parameter estimation. A detailed review of these issues would not only benefit the Kalman type DA but also
provide an important reference to other type hydrological DA.
Anomalous atmospheric conditions which, for the most part, manifest in the form of heavy precipitation events leading to the generation of pluvial or fluvial flooding in some years, or as extended extreme dry spells resulting in the development of severe drought conditions in other years has significant implications for subsurface moisture budgets in regional and local catchments in the Canadian Prairies. The myriad implications of these frequent extended dry spells over this Alberta’s Agricultural Region can be far reaching economically, socially and otherwise, thereby exerting substantial negative toll on agricultural productivity (crop cultivation, livestock husbandry, and pasture management) within the Province. Soil moisture dynamics both in spatial and temporal scales has critical implications for agricultural water management especially under semi-arid climate conditions that are typical of the local- and regional-scale catchments in southern Alberta. This study is focused on the assessment of the observations of the soil water storage, soil temperature and meteorological variables (precipitation and air temperature) acquired from 39 monitoring stations spanning a 12-year period with the principal objective of examining the spatial and temporal dynamics in the shallow and deep soil water storage processes across the study domain. This study revealed that the moisture anomalies in the deep soil moisture store exhibit the longest hydrologic memory relative to those in the shallow and intermediate soil moisture storages as well as those in the meteorological variables across the study domain. The anomalies computed for the soil moisture measurements undertaken in the deep soil moisture store revealed frequency features that are dominant at seasonal cycle (9-month) and at a higher periodicity of 32-month cycle. This low-frequency feature (32-month cycle) is evidently non-existent in the soil water storage measured at the shallower soil depths nor in the meteorological variables evaluated in this study. This implies that the low-frequency feature of the soil water storage wavelet spectra increases with increasing soil depth across the Agricultural Region; indicating that at deeper soil depths, the largest variances are shifted to lower frequencies. This observed dominant long-memory feature in the deep soil moisture store could have important implications for characterizing the development of temperature extremes as well as for the evaluation of the severity of the recurrent drought outbreaks over regional watersheds in Alberta and potentially for other Canadian Prairie catchments.
