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Trends and Variations in Hydroclimatic Variables

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A special issue of Hydrology (ISSN 2306-5338). This special issue belongs to the section "Hydrology–Climate Interactions".

Deadline for manuscript submissions: 26 September 2024 | Viewed by 13761

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Special Issue Editors

Dr. Kleoniki Demertzi

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Greek Biotope/Wetland Centre, The Goulandris Natural History Museum, 57001 Thessaloniki, Greece
Interests: hydroclimatic

Dr. Vassilis Aschonitis

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Soil and Water Resources Insitute, Hellenic Agricultural Organization—Demeter, 57001 Thessaloniki, Greece
Interests: hydrology; agro-meteorology; soil-water chemistry; nitrate vulnerability; ecological indicators; enviromental modelling; ecosystem services
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Dr. Mavromatis Theodoros

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Department of Meteorology and Climatology, School of Geology, Aristotle University of Thessaloniki, Thessaloniki, Greece
Interests: agricultural climatology; crop–climate relationships; crop simulation models; reanalysis datasets; drought indices; statistical climatology; climate change scenarios; statistical downscaling
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Dr. Ioannis Pytharoulis

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Department of Meteorology and Climatology, School of Geology, Aristotle University of Thessaloniki (AUTh), 54124 Thessaloniki, Greece
Interests: synoptic and dynamic meteorology; numerical weather prediction; operational weather forecasting; land/sea–air interaction; extreme weather events; pyro-meteorology
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Special Issue Information

Dear Colleagues,

This special issue seeks for studies that investigate state-of-the-art methods for analyzing trends and variations in hydroclimatic variables in space and time. As hydroclimatic variables are considered rainfall and all climate variables that affect evapotranspiration and drought/aridity but also other components of the water cycle such as streamflow/floods, level of inland waters (lakes, reservoirs) and groundwater, which are indirect measures of hydroclimatic disturbance. The methods used for analyzing trends and variations in hydroclimatic variables in space and time are grouped in three categories: a) statistical, b) machine learning, and c) deterministic/physical-based. The purpose of this special issue is to provide a collection of papers that will present a variety of such methods in different climatic environments and case studies, which can highlight the impact of hydroclimatic disturbance on the environment and on the socio-economic sector.

The topics covered by this Special Issue will include but not limited to the following:

Methods for analyzing temporal trends and variations in time-series of hydroclimatic variables;
Analysis of extreme events;
Detecting spatial variations and trends in hydroclimatic variables;
Climate change predictions and uncertainty assessment using global and regional circulation models;
Geostatistical methods in remote sensing and GIS for climate change impact assessment;
Climate change impact assessment using modelling of surface and groundwater hydrology at watershed scale;
Climate change/disturbance impact on agricultural water use and agrometeorological attributes;
Climate change/disturbance impact on natural ecosystems and ecosystem services;

Dr. Kleoniki Demertzi
Dr. Vassilis Aschonitis
Dr. Mavromatis Theodoros
Dr. Ioannis Pytharoulis
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Hydrology is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

time series analysis of hydroclimatic variables
analysis of extreme events
climate change assessment and uncertainty
disturbance in hydroclimatic and agrometeorological attributes
statistical, machine learning and deterministic modelling approaches
climate disturbance effects on agriculture
climate disturbance effects on ecosystem services

Published Papers (7 papers)

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Research

22 pages, 5413 KiB

Open AccessArticle

Reference Evapotranspiration in Climate Change Scenarios in Mato Grosso, Brazil

by Marlus Sabino, Andréa Carvalho da Silva, Frederico Terra de Almeida and Adilson Pacheco de Souza

Hydrology 2024, 11(7), 91; https://doi.org/10.3390/hydrology11070091 - 26 Jun 2024

Viewed by 686

Abstract

Our understanding of spatiotemporal variability in reference evapotranspiration (ETo) and its long-term trends is of paramount importance for water cycle studies, modeling, and water resource management, especially in the context of climate change. Therefore, the primary aim of this study is to critically evaluate the performance of various CMIP5 global climate models in simulating the Penman–Monteith reference evapotranspiration and its associated climate variables (maximum and minimum air temperature, incident solar radiation, relative humidity, and wind speed). This evaluation is based on data from nine climate models and 33 automatic meteorological stations (AWSs) in the state of Mato Grosso, spanning the period 2007–2020, within the areas of the biomes Amazon and Cerrado and around the Pantanal biome. The statistical metrics used for evaluation include bias, root mean square error, and Pearson and Spearman correlation coefficients. The projections of the most accurate model were then used to analyze the spatial and temporal changes and trends in ETo under the Representative Concentration Pathways (RCPs) of 2.6, 4.5, and 8.5 scenarios from 2007 to 2100. The HadGEM2-ES model projections indicate static averages similar to current conditions until the end of the century in the RCP 2.6 scenario. However, in the RCP 4.5 and 8.5 scenarios, there is a continuous increase in ETo, with the most significant increase occurring during the dry period (May to September). The areas of the Amazon biome in the north of Mato Grosso exhibit the largest increases in ETo when comparing the observed (2007–2020) and projected (2020–2100) averages. The trend analysis reveals significant changes in ETo and its variables across the state of Mato Grosso in the RCP 4.5 and 8.5 scenarios. In the RCP 2.6 scenario, significant trends in ETo are observed only in the northern Amazon areas. Despite not being observed in all AWSs, the trend analysis of the observed data demonstrates more intense changes in ETo and the existence of the evapotranspiration paradox, with an increase in the Cerrado areas and reductions in the Pantanal and southern Amazon areas. Full article

(This article belongs to the Special Issue Trends and Variations in Hydroclimatic Variables)

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18 pages, 6222 KiB

Open AccessArticle

Anthropogenic Activity in the Topo-Climatic Interaction of the Tapajós River Basin, in the Brazilian Amazon

by Vânia dos Santos Franco, Aline Maria Meiguins de Lima, Rodrigo Rafael Souza de Oliveira, Everaldo Barreiros de Souza, Giordani Rafael Conceição Sodré, Diogo Correa Santos, Marcos Adami, Edivaldo Afonso de Oliveira Serrão and Thaiane Soeiro da Silva Dias

Hydrology 2024, 11(6), 82; https://doi.org/10.3390/hydrology11060082 - 13 Jun 2024

Viewed by 570

Abstract

This research aimed to analyze the relationship between deforestation (DFT) and climatic variables during the rainy (CHU+) and less-rainy (CHU−) seasons in the Tapajós River basin. Data were sourced from multiple institutions, including the Climatic Research Unit (CRU), Center for Weather Forecasts and Climate Studies (CPTEC), PRODES Program (Monitoring of Brazilian Amazon Deforestation Project), National Water Agency (ANA) and National Centers for Environmental Prediction/National Oceanic and Atmospheric Administration (NCEP/NOAA). The study assessed anomalies (ANOM) in maximum temperature (TMAX), minimum temperature (TMIN) and precipitation (PREC) over three years without the occurrence of the El Niño–Southern Oscillation (ENSO) atmospheric–oceanic phenomenon. It also examined areas with higher DFT density using the Kernel methodology and analyzed the correlation between DFT and climatic variables. Additionally, it assessed trends using the Mann–Kendall technique for both climatic and environmental data. The results revealed significant ANOM in TEMP and PREC. In PREC, the highest values of ANOM were negative in CHU+. Regarding temperature, the most significant values were positive ANOM in the south, southwest and northwestern regions of the basin. Concerning DFT density, data showed that the highest concentration was of medium density, primarily along the highways. The most significant correlations were found between DFT and TEMP during the CHU− season in the Middle and Lower Tapajós sub-basins, regions where the forest still exhibits more preserved characteristics. Furthermore, the study identified a positive trend in TEMP and a negative trend in PREC. Full article

(This article belongs to the Special Issue Trends and Variations in Hydroclimatic Variables)

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25 pages, 6650 KiB

Open AccessArticle

Climate and Land Use/Land Cover Changes within the Sota Catchment (Benin, West Africa)

by Kevin S. Sambieni, Fabien C. C. Hountondji, Luc O. Sintondji, Nicola Fohrer, Séverin Biaou and Coffi Leonce Geoffroy Sossa

Hydrology 2024, 11(3), 30; https://doi.org/10.3390/hydrology11030030 - 23 Feb 2024

Cited by 2 | Viewed by 1919

Abstract

Climate and land cover changes are key factors in river basins’ management. This study investigates on the one hand 60-year (1960 to 2019) rainfall and temperature variability using station data combined with gridded data, and on the other hand land cover changes for the years 1990, 2005, and 2020 in the Sota catchment (13,410 km², North Benin, West Africa). The climate period is different from the chosen land use change period due to the unavailability of satellite images. Standardized anomaly index, break points, trend analysis, and Thiessen’s polygon were applied. Satellite images were processed and ground truthing was carried out to assess land cover changes. The analyses revealed a wet period from 1960 to 1972, a dry period from 1973 to 1987, and another wet period from 1988 to 2019. The annual rainfall decreases from the south to the north of the catchment. In addition, rainfall showed a non-significant trend over the study period, and no significant changes were identified between the two normals (1960–1989 and 1990–2019) at catchment scale, although some individual stations exhibited significant trends. Temperatures, in contrast, showed a significant increasing trend over the study period at catchment scale, with significant break points in 1978, 1990, and 2004 for Tmax, and 1989 for Tmin. An increase of 0.4 °C and 1.2 °C is noted, respectively, for Tmax and Tmin between the two normals. The study also revealed increases in agricultural areas (212.1%), settlements (76.6%), waterbodies (2.9%), and baresoil (52%) against decreases in woodland (49.6%), dense forest (42.2%), gallery forest (21.2%), and savanna (31.9%) from 1990 to 2020. These changes in climate and land cover will have implications for the region. Appropriate adaptation measures, including Integrated Water Resources Management and afforestation, are required. Full article

(This article belongs to the Special Issue Trends and Variations in Hydroclimatic Variables)

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15 pages, 1852 KiB

Open AccessArticle

Evaluating Non-Stationarity in Precipitation Intensity-Duration-Frequency Curves for the Dallas–Fort Worth Metroplex, Texas, USA

by Binita Ghimire, Gehendra Kharel, Esayas Gebremichael and Linyin Cheng

Hydrology 2023, 10(12), 229; https://doi.org/10.3390/hydrology10120229 - 2 Dec 2023

Viewed by 2263

Abstract

Extreme precipitation has become more frequent and intense with time and space. Infrastructure design tools such as Intensity-Duration-Frequency (IDF) curves still rely on historical precipitation and stationary assumptions, risking current and future urban infrastructure. This study developed IDF curves by incorporating non-stationarity trends in precipitation annual maximum series (AMS) for Dallas–Fort Worth, the fourth-largest metropolitan region in the United States. A Pro-NEVA tool was used to develop non-stationary IDF curves, taking historical precipitation AMS for seven stations that showed a non-stationary trend with time as a covariate. Four statistical indices—the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), Root Mean Square Error (RMSE), and Nash–Sutcliffe Efficiency (NSE)—were used as the model goodness of fit evaluation. The lower AIC, BIC, and RMSE values and higher NSE values for non-stationary models indicated a better performance compared to the stationary models. Compared to the traditional stationary assumption, the non-stationary IDF curves showed an increase (up to 75%) in the 24 h precipitation intensity for the 100-year return period. Using the climate change adaptive non-stationary IDF tool for the DFW metroplex and similar urban regions could enable decision makers to make climate-informed choices about infrastructure investments, emergency preparedness measures, and long-term urban development and water resource management planning. Full article

(This article belongs to the Special Issue Trends and Variations in Hydroclimatic Variables)

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20 pages, 4918 KiB

Open AccessArticle

Characterization of Extreme Rainfall and River Discharge over the Senegal River Basin from 1982 to 2021

by Assane Ndiaye, Mamadou Lamine Mbaye, Joël Arnault, Moctar Camara and Agnidé Emmanuel Lawin

Hydrology 2023, 10(10), 204; https://doi.org/10.3390/hydrology10100204 - 21 Oct 2023

Viewed by 2967

Abstract

Extreme hydroclimate events usually have harmful impacts of human activities and ecosystems. This study aims to assess trends and significant changes in rainfall and river flow over the Senegal River Basin (SRB) and its upper basin during the 1982–2021 period. Eight hydroclimate indices, namely maximum river discharge (QMAX), standardized flow index, mean daily rainfall intensity index (SDII), maximum 5-day consecutive rainfall (RX5DAY), annual rainfall exceeding the 95th percentile (R95P), annual rainfall exceeding the 99th percentile (R99P), annual flows exceeding the 95th percentile (Q95P), and annual flows exceeding the 99th percentile (Q95P), were considered. The modified Mann–Kendall test (MMK) and Innovative Trend Analysis (ITA) were used to analyze trends, while standard normal homogeneity and Pettit’s tests were used to detect potential breakpoints in these trends. The results indicate an irregular precipitation pattern, with high values of extreme precipitation indices (R95p, R99p, SDII, and RX5DAY) reaching 25 mm, 50 mm, 20 mm/day, and 70 mm, respectively, in the southern part, whereas the northern part recorded low values varying around 5 mm, 10 mm, 5 mm/day, and 10 mm, respectively, for R95P, R99P, SDII, and RX5DAY. The interannual analysis revealed a significant increase (p-value < 5%) in the occurrences of heavy precipitation between 1982 and 2021, as manifested by a positive slope; a notable breakpoint emerged around the years 2006 and 2007, indicating a transition to a significantly wetter period starting from 2008. Concerning extreme flows, a significant increase was observed between 1982 and 2021 with Sen’s slopes for extreme flows (29.33 for Q95P, 37.49 for Q99P, and 38.55 for QMAX). This study provides a better understanding of and insights into past hydroclimate extremes and can serve as a foundation for future research in the field. Full article

(This article belongs to the Special Issue Trends and Variations in Hydroclimatic Variables)

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15 pages, 7210 KiB

Open AccessArticle

Observed Changes in Rainfall and Characteristics of Extreme Events in Côte d’Ivoire (West Africa)

by Daouda Konate, Sacre Regis Didi, Kouakou Bernard Dje, Arona Diedhiou, Kouakou Lazare Kouassi, Bamory Kamagate, Jean-Emmanuel Paturel, Houebagnon Saint Jean-Patrick Coulibaly, Claude Alain Koffi Kouadio and Talnan Jean Honoré Coulibaly

Hydrology 2023, 10(5), 104; https://doi.org/10.3390/hydrology10050104 - 30 Apr 2023

Cited by 2 | Viewed by 2229

Abstract

This study evaluates how the characteristics of daily rainfall and extreme events in Côte d’Ivoire changed during 1961–2015 using the rain gauge observation network of the National Meteorological Service (SODEXAM). The results indicate that the northern and southern parts of Cote d’Ivoire experienced a change from a wet to a dry period, with cut-offs in 1982 and 1983, respectively. In the northern part, this dry period was marked by a decrease in rainfall intensity, the length of wet spells, and the contribution of heavy and extreme rainfall, as well as an increase in the number of rainy days and a decrease in the length of dry spells. Over the southern part, this dry period was marked by an increase in the maximum length of dry spells associated with an increase in the maximum 1-day and 5-day precipitation events. The western part of Côte d’Ivoire experienced a late cut-off from the wet to dry period in 2000; the dry period was associated with a decrease in the number of rainy days, rainfall intensities, and maximum length of wet spells. Changes in the central part of Cote d’Ivoire presented high variability, and trends were less marked, even though a cut-off from a wet to dry period was detected in 1991. This study shows that Côte d’Ivoire, which is located in a subhumid and humid region and has an economy dependent on agriculture (especially cash crops, which comprise 60% of the GDP), is experiencing dry spells that are increasing in frequency and length. Combined with deforestation to increase production, this situation could lead to desertification and compromise the sustainable development goals of the country. The contribution of heavy rainfall was found to increase during the last 15 years, increasing the overall risk of floods, especially in urban areas where city authorities and populations are not prepared, thereby threatening infrastructure and human security. Full article

(This article belongs to the Special Issue Trends and Variations in Hydroclimatic Variables)

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21 pages, 8575 KiB

Open AccessArticle

Integrating Satellite Imagery and Ground-Based Measurements with a Machine Learning Model for Monitoring Lake Dynamics over a Semi-Arid Region

by Kenneth Ekpetere, Mohamed Abdelkader, Sunday Ishaya, Edith Makwe and Peter Ekpetere

Hydrology 2023, 10(4), 78; https://doi.org/10.3390/hydrology10040078 - 31 Mar 2023

Cited by 4 | Viewed by 2248

Abstract

The long-term variability of lacustrine dynamics is influenced by hydro-climatological factors that affect the depth and spatial extent of water bodies. The primary objective of this study is to delineate lake area extent, utilizing a machine learning approach, and to examine the impact of these hydro-climatological factors on lake dynamics. In situ and remote sensing observations were employed to identify the predominant explanatory pathways for assessing the fluctuations in lake area. The Great Salt Lake (GSL) and Lake Chad (LC) were chosen as study sites due to their semi-arid regional settings, enabling the testing of the proposed approach. The random forest (RF) supervised classification algorithm was applied to estimate the lake area extent using Landsat imagery that was acquired between 1999 and 2021. The long-term lake dynamics were evaluated using remotely sensed evapotranspiration data that were derived from MODIS, precipitation data that were sourced from CHIRPS, and in situ water level measurements. The findings revealed a marked decline in the GSL area extent, exceeding 50% between 1999 and 2021, whereas LC exhibited greater fluctuations with a comparatively lower decrease in its area extent, which was approximately 30% during the same period. The framework that is presented in this study demonstrates the reliability of remote sensing data and machine learning methodologies for monitoring lacustrine dynamics. Furthermore, it provides valuable insights for decision makers and water resource managers in assessing the temporal variability of lake dynamics. Full article

(This article belongs to the Special Issue Trends and Variations in Hydroclimatic Variables)

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