Analysis of the Hydrological Behavior of Watersheds in the Context of Climate Change (Northwestern Algeria)

The aim of this work is to study the temporal evolution of the rainfall-runoff relations of four basins in northwestern Algeria: the Tafna Maritime, Isser Sikkak, downstream Mouilah and Upper Tafna basins. The adopted approach consists of analyzing hydroclimatic variables using statistical methods and testing the nonstationarity of the rainfall-runoff relation by the cross-simulation method using the GR2M model. The results of the different statistical methods applied to the series of rainfall and hydrometric variables show a decrease due to a break in stationarity detected since the mid-1970s and the beginning of the 1980s. The annual rainfall deficits reached average values of 34.6% during the period of 1941–2006 and 29.1% during the period of 1970–2010. The average annual wadi flows showed average deficits of 61.1% between 1912 and 2000 and 53.1% between 1973 and 2009. The GR2M conceptual model simulated the observed hydrographs in an acceptable manner by providing calculated runoff values in the calibration and validation periods greater or less than the observed runoff values. The application of the cross-simulation method highlighted the nonstationarity of the rainfall-runoff relations in three of the four studied basins, indicating downward trends of monthly runoff.

Revealing alarming changes in spatial coverage of joint hot and wet extremes across India

Compared to any single hydroclimatic variable, joint extremes of multiple variables impact more heavily on the society and ecosystem. In this study, we developed new joint extreme indices (JEIs) using temperature and precipitation, and investigated its spatio-temporal variation with observed records across Indian mainland. Analysis shows an alarming rate of change in the spatial extent of some of the joint extreme phenomena, tending to remain above normal. For example, above normal hot nights and wet days events expands at a rate of 0.61% per year considering entire Indian mainland. If the historical trend continues at the same rate, consecutive cold and wet day events will drop below the threshold of mean value observed in the base line period (1981–2010) everywhere in the country by the end of the twenty-first century. In contrast, the entire country will be covered by hot nights and wet days events only (frequency of occurrence will cross the threshold of mean value observed in the base line period). This observation is also supported by the CMIP6 climate model outputs. It is further revealed that extremes of any single variable, i.e. either precipitation or temperature (e.g., Extreme Wet Days, Consecutive Wet Days, Hot Nights, and Cold Spell Duration Index), do not manifest such an alarming spatial expansion/contraction. This indicates that the consideration of the joint indices of hydroclimatic variables is more informative for the climate change impact analysis.

Monitoring Drought through the Lens of Landsat: Drying of Rivers during the California Droughts

Water scarcity during severe droughts has profound hydrological and ecological impacts on rivers. However, the drying dynamics of river surface extent during droughts remains largely understudied. Satellite remote sensing enables surveys and analyses of rivers at fine spatial resolution by providing an alternative to in-situ observations. This study investigates the seasonal drying dynamics of river extent in California where severe droughts have been occurring more frequently in recent decades. Our methods combine the use of Landsat-based Global Surface Water (GSW) and global river bankful width databases. As an indirect comparison, we examine the monthly fractional river extent (FrcSA) in 2071 river reaches and its correlation with streamflow at co-located USGS gauges. We place the extreme 2012–2015 drought into a broader context of multi-decadal river extent history and illustrate the extraordinary change between during- and post-drought periods. In addition to river extent dynamics, we perform statistical analyses to relate FrcSA with the hydroclimatic variables obtained from the National Land Data Assimilation System (NLDAS) model simulation. Results show that Landsat provides consistent observation over 90% of area in rivers from March to October and is suitable for monitoring seasonal river drying in California. FrcSA reaches fair (>0.5) correlation with streamflow except for dry and mountainous areas. During the 2012–2015 drought, 332 river reaches experienced their lowest annual mean FrcSA in the 34 years of Landsat history. At a monthly scale, FrcSA is better correlated with soil water in more humid areas. At a yearly scale, summer mean FrcSA is increasingly sensitive to winter precipitation in a drier climate; and the elasticity is also reduced with deeper ground water table. Overall, our study demonstrates the detectability of Landsat on the river surface extent in an arid region with complex terrain. River extent in catchments of deficient water storage is likely subject to higher percent drop in a future climate with longer, more frequent droughts.

Stochastic Modeling of Hydroclimatic Processes Using Vine Copulas

The generation of synthetic time series is important in contemporary water sciences for their wide applicability and ability to model environmental uncertainty. Hydroclimatic variables often exhibit highly skewed distributions, intermittency (that is, alternating dry and wet intervals), and spatial and temporal dependencies that pose a particular challenge to their study. Vine copula models offer an appealing approach to generate synthetic time series because of their ability to preserve any marginal distribution while modeling a variety of probabilistic dependence structures. In this work, we focus on the stochastic modeling of hydroclimatic processes using vine copula models. We provide an approach to model intermittency by coupling Markov chains with vine copula models. Our approach preserves first-order auto- and cross-dependencies (correlation). Moreover, we present a novel framework that is able to model multiple processes simultaneously. This method is based on the coupling of temporal and spatial dependence models through repetitive sampling. The result is a parsimonious and flexible method that can adequately account for temporal and spatial dependencies. Our method is illustrated within the context of a recent reliability assessment of a historical hydraulic structure in central Mexico. Our results show that by ignoring important characteristics of probabilistic dependence that are well captured by our approach, the reliability of the structure could be severely underestimated.

Modeling of leptospirosis outbreaks in relation to hydroclimatic variables in the northeast of Argentina

The transmission of leptospirosis is conditioned by climatic variables. In northeastern Argentina leptospirosis outbreaks occur mainly in coincidence with periods of abundant precipitation and high hydrometric level. A Susceptible Infectious Recovered Epidemiological Model (SIR) is proposed, which incorporates hydroclimatic variables for the three most populated cities in the area (Santa Fe, Parana and Rosario), between 2009 and 2018. Results obtained by solving the proposed SIR model for the 2010 outbreaks are in good agreement with the actual data, capturing the dynamics of the leptospirosis outbreak wave. However, the model does not perform very well when isolated cases appear outside the outbreak periods, probably due to non-climatic factors not explicitly considered in the present version of the model. Nevertheless, the dynamic modeling of infectious diseases considering hydroclimatic variables constitutes a climatic service for the public health system, not yet available in Argentina.

A call for consistency with the terms ‘wetter’ and ‘drier’ in climate change studies

Ongoing and future hydroclimatic changes have large environmental and societal impacts. In terrestrial ecosystems, these changes are usually described with the terms ‘wetter’ and ‘drier’, which refer to the change in the quantity and/or presence of water, either as water fluxes or stocks. We conducted a literature review of almost 500 recent climate change studies to quantitatively investigate the consistency of the use of these terms across disciplines, regarding the hydroclimatic variables they are related to. We found that although precipitation is prevalently used to describe ‘wetter’ and ‘drier’ conditions, many other variables are also used to refer to changes in water availability between research fields, pointing to a varied perspective on the use of these terms. Some studies do not define the terms at all. In order to facilitate meta-analyses across disciplines, we therefore highlight the need to explicitly state which hydroclimatic variables authors are referring to. In this way, we hope that the terms ‘wetter’ and ‘drier’ used in scientific studies are easier to relate to hydroclimatic processes, which should facilitate the application by authorities and policy makers.

The hydroclimate-vegetation relationship in the Amazon basin during the last 20 years: an analysis focused on the southwestern region

<p>The relationship between multiple hydroclimatic variables and vegetation conditions in the upper Madeira Basin (southwestern Amazon) has been analyzed. Vegetative dynamics are characterized using NDVI dataset as an indicator of the photosynthetic capacities of vegetation. Hydroclimatic variability is analyzed using satellite-based precipitation datasets, observed river discharge, and satellite measurements of terrestrial water storage (TWS). Our results show that the vegetation in the Basin varies from energy- to water-limited. During the peak of the wet season (January-February), rainfall, discharge, and TWS are negatively correlated with NDVI (r=-0.48 to -0.65), suggesting that during this period the vegetation is mainly energy-dependent. Outside this period, these correlations are positive (r=0.55 to 0.9), suggesting that vegetation depends mainly on water availability. This higher water dependence is more noticeable during the vegetation dry season (VDS; June-October). Considering the predominant land cover types, differences in the hydroclimate-NDVI relationship are observed. Evergreen forests remain energy-limited during the beginning of the VDS, but they become water-dependent almost at the end. Savannas show a different behavior, where water dependence occurs months before the onset of the VDS. On the other hand, unlike the other variables, the TWS better explains the NDVI in evergreen forests during the VDS (r=0.7 to 0.85). This is probably because evergreen forests are more dependent on deep soil water. A spatial analysis between hydroclimatic variables and the NDVI shows the predominance of positive correlations in most of the basin. However, specific areas do not show significant correlations. The weak relationship in these areas is explained by two factors i) very wet conditions during most of the year in the "rainfall hotspot" regions, where the vegetation is not water-limited, and ii) recent land-use changes (deforestation) that break the natural response in the hydroclimate-vegetation system. These findings provide new evidence on the impacts of the land cover changes on the natural relationship between vegetation and hydroclimatic variability, which is particularly relevant given the increasing rates of deforestation in this region during recent years.</p>

Investigating Possible Effects of Changing Hydroclimatic Persistence on Lake Cyanobacterial Harmful Algal Blooms

<p>Harmful Algal Blooms (HABs) are a major environmental problem worldwide. Apart from their adverse effects on aquatic habitat, and possible economic losses, they also pose a serious threat to public health. Future climatic uncertainties that include possible shifts in patterns of climatic variables are points of concern in terms of how such changes would affect the development, growth, and duration of HABs. Weather whiplash, abrupt dry-to-wet or wet-to-dry condition transitions, is one of these shifts in climatic patterns and despite its potential environmental impacts, few studies have examined the implications of such changes on lake water quality. Lake Champlain, located on the US-Canada border, has repeatedly faced cyanobacterial HABs predominately in its shallow bays. The aim of the current work is to (i) investigate potential changes in the persistence of hydroclimatic variables (precipitation and temperature) and (ii) examine their effects on cyanobacterial HABs in the lake’s shallow Missisquoi Bay. Our approach focuses on short-term persistence (STP) shifts over different timescales (daily, monthly, seasonal, and annual). STP scenarios that capture these plausible shifts are constructed using projected climate scenarios for the period 2000-2040. An Integrated Assessment Model that simulates the Missisquoi Basin’s physical processes, including watershed hydrology, management, and the Missisquoi Bay’s water quality dynamics, is utilized to run the modeled STP scenarios for each timescale. The determination of changes in STP through a scenario-based approach offers a framework to rigorously investigate the effects of persistence at different timescales on lake cyanobacterial HABs.</p>

Flash Drought identification – a comparison of definitions across different datasets.

<p>Flash droughts recently started to draw a larger curiosity to its occurrence and, therefore, its features. Differently from the slow development of droughts (months to years), flash droughts evolve over a short time (weeks) of a rapid intensification. Over the last few years, multiple methods for flash drought identification were proposed. Those methods, although sharing some characteristics, as tracking of soil water content and/or evapotranspiration (actual and potential), end up not flagging the same periods under flash drought events. We compared six well-known flash drought identification methods from the literature and used two different datasets. The datasets are: (1) the FluxNET15 dataset (Pastorello et al, 2020), a collection of worldwide, quality-controlled measurements of several hydroclimatic variables, such as soil water content, precipitation, temperature, and wind speed; and (2) the ECMWF Reanalysis 5 (ERA5 – Hersbach et al., 2019) provides over three hundred different data including soil water content in multiple levels, evapotranspiration, precipitation, and temperature. Ten stations from FluxNET15 were selected and the data from the ERA5 on the respective pixels was acquired. The aim of this work is to compare the event identification of different methods using different datasets as input (direct measures and reanalysis based).</p>

Towards benefitting from diverse inferred features and foreseen behaviours of hydroclimatic variables in predictive modelling contexts

<p>We discuss possible pathways towards reducing uncertainty in predictive modelling contexts in hydrology. Such pathways may require big datasets and multiple models, and may include (but are not limited to) large-scale benchmark experiments, forecast combinations, and predictive modelling frameworks with hydroclimatic time series analysis and clustering inputs. Emphasis is placed on the newest concepts and the most recent methodological advancements for benefitting from diverse inferred features and foreseen behaviours of hydroclimatic variables, derived by collectively exploiting diverse essentials of studying and modelling hydroclimatic variability and change (from both the descriptive and predictive perspectives). Our discussions are supported by big data (including global-scale) investigations, which are conducted for several hydroclimatic variables at several temporal scales.</p>