Water

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20 pages, 12360 KiB

Open AccessArticle

Effects of Hydrological Drought Periods on Thermal Stability of Brazilian Reservoirs

by Jucimara Andreza Rigotti, João Marcos Carvalho, Laura M. V. Soares, Carolina C. Barbosa, Alice R. Pereira, Barbara P. S. Duarte, Michael Mannich, Sergio Koide, Tobias Bleninger and José R. S. Martins

Water 2023, 15(16), 2877; https://doi.org/10.3390/w15162877 - 9 Aug 2023

Cited by 1 | Viewed by 1109

Abstract

Droughts can impact ecosystem services provided by reservoirs. Quantifying the intensity of droughts and evaluating their potential effects on the thermal stability of reservoirs are subjects that demand greater attention, due to both the importance of temperature on aquatic metabolism and the climate [...] Read more.

Droughts can impact ecosystem services provided by reservoirs. Quantifying the intensity of droughts and evaluating their potential effects on the thermal stability of reservoirs are subjects that demand greater attention, due to both the importance of temperature on aquatic metabolism and the climate change scenarios that predict an increase in the frequency of extreme weather events. This study aimed to investigate drought periods in ten Brazilian reservoirs and to discuss their effects on each reservoir’s thermal stability. The Standardized Precipitation Index at a twelve month timescale (SPI-12) was applied to identify the hydrological drought periods. One-dimensional vertical hydrodynamic modeling was used to simulate the water balance and the thermal dynamics in the reservoirs. Schmidt Stability Index (St) was calculated to assess the thermal stability of the reservoirs. The drought periods identified by the SPI-12 are related to decreasing water levels of the reservoirs, but the dam operating strategies and the upstream influence of cascading reservoirs are important drivers of fluctuations. A significant difference in St between wet and dry conditions was found only during summer for all reservoirs. Thus, this study identified alterations in thermal regime during drought periods according to the seasons and the reservoirs characteristics. Full article

(This article belongs to the Special Issue Challenges of Hydrological Drought Monitoring and Prediction)

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14 pages, 8931 KiB

Open AccessArticle

Spatiotemporal Evolution and Nowcasting of the 2022 Yangtze River Mega-Flash Drought

by Miaoling Liang, Xing Yuan, Shiyu Zhou and Zhanshan Ma

Water 2023, 15(15), 2744; https://doi.org/10.3390/w15152744 - 29 Jul 2023

Cited by 6 | Viewed by 1247

Abstract

Flash droughts challenge early warnings due to their rapid onset, which requires a proper drought index and skillful nowcasting system. A few studies have assessed the nowcast skill for flash droughts using a one-dimensional index, but whether the models can capture their spatiotemporal [...] Read more.

Flash droughts challenge early warnings due to their rapid onset, which requires a proper drought index and skillful nowcasting system. A few studies have assessed the nowcast skill for flash droughts using a one-dimensional index, but whether the models can capture their spatiotemporal evolution remains unclear. In this study, a three-dimensional meteorological flash drought index based on the percentile of 15-day moving average precipitation minus evapotranspiration (P-ET) is developed. The index is then used to investigate the spatiotemporal evolution of a mega-flash drought that occurred in the Yangtze River basin during the summer of 2022. The results show that the mega-flash drought started at the beginning of July in the upper reaches of the river and expanded to the middle and lower reaches at the beginning of August due to the spread of the high-pressure system. The evolution is well captured by the proposed three-dimensional index. The spatial correlations between the China Meteorological Administration global medium-range ensemble forecast system (CMA-GFS)’s nowcast and reanalysis ranged from 0.58 to 0.85, and the hit rate and equitable threat score are 0.54 and 0.26, respectively. This study shows that the CMA-GFS nowcast of the P-ET index roughly captured the drought’s evolution, which can be used for flash drought early warnings and water resource management. Full article

(This article belongs to the Special Issue Challenges of Hydrological Drought Monitoring and Prediction)

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21 pages, 5442 KiB

Open AccessArticle

Construction of an Agricultural Drought Monitoring Model for Karst with Coupled Climate and Substratum Factors—A Case Study of Guizhou Province, China

by Lihui Chen, Zhonghua He, Xiaolin Gu, Mingjin Xu, Shan Pan, Hongmei Tan and Shuping Yang

Water 2023, 15(9), 1795; https://doi.org/10.3390/w15091795 - 7 May 2023

Cited by 1 | Viewed by 1810

Abstract

Droughts are becoming more frequent in the karst region of southwest China due to climate change, and accurate monitoring of karst agricultural droughts is crucial. To this end, in this study, based on random forest (RF) and support vector regression (SVR) algorithms, the [...] Read more.

Droughts are becoming more frequent in the karst region of southwest China due to climate change, and accurate monitoring of karst agricultural droughts is crucial. To this end, in this study, based on random forest (RF) and support vector regression (SVR) algorithms, the monthly precipitation, monthly potential evapotranspiration, monthly normalised difference vegetation Index (NDVI), elevation, and karst development intensity from January to December 2001–2020 were used as independent variables, and the standardised soil moisture index (SSI) calculated by GLDAS soil moisture was used as the dependent variable to construct karst agricultural drought monitoring models at different timescales, using Guizhou Province as an example. The performance of the models constructed by the two algorithms was also evaluated using root mean square error (RMSE), coefficient of determination (R²), and correlation analysis, and the spatial and temporal evolution trends of karst agricultural drought at different timescales were analysed based on the model with better performance. The prediction of karst agricultural drought from January to December 2021–2025 was based on the seasonal difference autoregressive moving average (SARIMA) model and the analysis of change trends was performed using the Bayesian estimator of abrupt change, seasonal change, and trend (RBEAST). The results showed that (1) the drought model constructed by the RF regression algorithm performed better than the SVR algorithm at 1-, 3-, 6-, 9-, and 12-month timescales and was superior for monitoring karst agricultural drought. (2) The model showed that the overall trend of agricultural drought at different timescales was alleviated; 2010, 2011, and 2012 were typical drought years. At the same time, most regions showed a trend of drought mitigation, whereas a few regions (Bijie City, Liupanshui City, and Qianxinan Prefecture) showed a trend of aggravation. (3) The study predicted an overall high west–east distribution of drought intensity by 2021–2025. The 1- and 3-month timescales showed a trend of agricultural drought mitigation, and the 6-, 9-, and 12-month timescales showed a trend of aggravation; in 2021, 2022, and 2024, the abrupt change rates of autumn and winter droughts were higher. The results can provide a reference basis for the monitoring of agricultural drought in karst agriculture and the formulation of drought prevention and anti-drought measures. Full article

(This article belongs to the Special Issue Challenges of Hydrological Drought Monitoring and Prediction)

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22 pages, 21128 KiB

Open AccessArticle

A Screening Procedure for Identifying Drought Hot-Spots in a Changing Climate

by Andrea Galletti, Giuseppe Formetta and Bruno Majone

Water 2023, 15(9), 1731; https://doi.org/10.3390/w15091731 - 29 Apr 2023

Cited by 1 | Viewed by 1292

Abstract

Droughts are complex natural phenomena with multifaceted impacts, and a thorough drought impact assessment should entail a suite of adequate modelling tools and also include observational data, thus hindering the feasibility of such studies at large scales. In this work we present a [...] Read more.

Droughts are complex natural phenomena with multifaceted impacts, and a thorough drought impact assessment should entail a suite of adequate modelling tools and also include observational data, thus hindering the feasibility of such studies at large scales. In this work we present a methodology that tackles this obstacle by narrowing down the study area to a smaller subset of potential drought hot-spots (i.e., areas where drought conditions are expected to be exacerbated, based on future climate projections). We achieve this by exploring a novel interpretation of a well-established meteorological drought index that we link to the hydrological drought status of a catchment by calibrating its use on the basis of streamflow observational data. We exemplify this methodology over 25 sub-catchments pertaining to the Adige catchment. At the regional level, our findings highlight how the response to meteorological drought in Alpine catchments is complex and influenced by both the hydrological properties of each catchment and the presence of water storage infrastructures. The proposed methodology provides an interpretation of the hydrologic behavior of the analyzed sub-catchments in line with other studies, suggesting that it can serve as a reliable tool for identifying potential drought hot-spots in large river basins. Full article

(This article belongs to the Special Issue Challenges of Hydrological Drought Monitoring and Prediction)

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15 pages, 3541 KiB

Open AccessArticle

Comprehensive Effects of Atmosphere and Soil Drying on Stomatal Behavior of Different Plant Types

by Zhi Xu, Ye Tian, Zhiwu Liu and Xinran Xia

Water 2023, 15(9), 1675; https://doi.org/10.3390/w15091675 - 25 Apr 2023

Cited by 3 | Viewed by 2040

Abstract

The soil water supply and atmospheric humidity conditions are crucial in controlling plants’ stomatal behavior and water use efficiency. When there is water stress caused by an increase in saturated water vapor pressure (VPD) and a decrease in soil water content (SWC), plants [...] Read more.

The soil water supply and atmospheric humidity conditions are crucial in controlling plants’ stomatal behavior and water use efficiency. When there is water stress caused by an increase in saturated water vapor pressure (VPD) and a decrease in soil water content (SWC), plants tend to close stomata to reduce water loss. This affects the gross primary productivity (GPP) and evapotranspiration (ET), subsequently leading to changes in water use efficiency (WUE) and carbon use efficiency (CUE) in plants. However, land–atmosphere interactions mean that water vapor in the atmosphere and soil moisture content causing water stress for plants are closely related. This study aims to compare and estimate the effects of VPD and SWC on the carbon cycle and water cycle for different plant functional types. Based on the fluxnet2015 dataset from around the world, the WUE and CUE of five plant functional types (PFTs) were estimated under varying levels of VPD and SWC. The results showed that high VPD and low SWC limit the stomatal conductance (Gs) and gross primary productivity (GPP) of plants. However, certain types of vegetation (crops, broad-leaved forests) could partially offset the negative effects of high VPD with higher SWC. Notably, higher SWC could even alleviate limitations and partially promote the increase in GPP and net primary production (NPP) with increasing VPD. WUE and CUE were directly affected by Gs and productivity. In general, the increase in VPD in the five PFTs was the dominant factor in changing WUE and CUE. The impact of SWC limitations on CUE was minimal, with an overall impact of only −0.05μmol/μmol on the four PFTs. However, the CUE of savanna plants changed differently from the other four PFTs. The rise in VPD dominated the changes in CUE, and there was an upward trend as SWC declined, indicating that the increase in VPD and decrease in SWC promote the increase in the CUE of savanna plants to some extent. Full article

(This article belongs to the Special Issue Challenges of Hydrological Drought Monitoring and Prediction)

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13 pages, 3928 KiB

Open AccessFeature PaperArticle

Propagation Characteristics of Hydrological Drought Based on Variable and Fixed Threshold Methods in Snowmelt and Rainfall Driven Catchments

by Jiefeng Wu, Huaxia Yao and Guoqing Wang

Water 2022, 14(20), 3219; https://doi.org/10.3390/w14203219 - 13 Oct 2022

Cited by 2 | Viewed by 1475

Abstract

Based on long-term (>30 years) monthly streamflow data from two catchments with different hydrological features, i.e., snowmelt-driven in Harp Lake, south-central, Canada and rainfall-driven in Dongjiang river, south China, the differences in the hydrological drought (HD) propagation characteristics identified by fixed [...] Read more.

Based on long-term (>30 years) monthly streamflow data from two catchments with different hydrological features, i.e., snowmelt-driven in Harp Lake, south-central, Canada and rainfall-driven in Dongjiang river, south China, the differences in the hydrological drought (HD) propagation characteristics identified by fixed (FDT) and variable drought thresholds (VDT) were explored. The results showed that (i) despite both FDT and VDT methods being able to describe HD propagation patterns well (i.e., slow intensification but quick recovery), the onset time, peak intensity time, and termination time of HD within a year were significantly different between the two methods, due to the different drought conceptual backgrounds of the methods. (ii) The HD months identified by VDT were close to evenly distributed in each month of the year, while the HD months identified by FDT were mainly concentrated in the dry season. (iii) The onset, peak intensity, and termination time of HD identified by FDT were in good agreement with the dryness/wetness attributes of the two study basins and can be recommended in the study case. (iv) More methods for monitoring and predicting HD, and for revealing the driving mechanisms for HD propagation, are needed. Full article

(This article belongs to the Special Issue Challenges of Hydrological Drought Monitoring and Prediction)

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19 pages, 5878 KiB

Open AccessArticle

Calibrating a Hydrological Model in an Ungauged Mountain Basin with the Budyko Framework

by Zexing Yu, Xiaohong Chen and Jiefeng Wu

Water 2022, 14(19), 3112; https://doi.org/10.3390/w14193112 - 2 Oct 2022

Cited by 2 | Viewed by 1837

Abstract

Calibrating spatially distributed hydrological models in ungauged mountain basins is complicated due to the paucity of information and the uncertainty in representing the physical characteristics of a drainage area. In this study, an innovative method is proposed that incorporates the Budyko framework and [...] Read more.

Calibrating spatially distributed hydrological models in ungauged mountain basins is complicated due to the paucity of information and the uncertainty in representing the physical characteristics of a drainage area. In this study, an innovative method is proposed that incorporates the Budyko framework and water balance equation derived water yield (WYLD) in the calibration of the Soil and Water Assessment Tool (SWAT) with a monthly temporal resolution. The impact of vegetation dynamics (i.e., vegetation coverage) on Budyko curve shape parameter ω was considered to improve the Budyko calibration. The proposed approach is applied to the upstream Lancang-Mekong River (UL-MR), which is an ungauged mountain basin and among the world’s most important transboundary rivers. We compared the differences in SWAT model results between the different calibration approaches using percent bias (PBIAS), coefficient of determination (R²), and Nash–Sutcliffe efficiency (NSE) coefficient. The results demonstrated that the Budyko calibration approach exhibited a significant improvement against an unfitted priori parameter run (the non-calibration case) though it did not perform as good as fitting of the calibration by the observed streamflow. The NSE value increased by 44.59% (from 0.46 to 0.83), the R² value increased by 2.30% (from 0.87 to 0.89) and the PBIAS value decreased by 55.67% (from 39.7 to 17.6) during the validation period at the drainage outlet (Changdu) station. The outcomes of the analysis confirm the potential of the proposed Budyko calibration approach for runoff predictions in ungauged mountain basins. Full article

(This article belongs to the Special Issue Challenges of Hydrological Drought Monitoring and Prediction)

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15 pages, 3729 KiB

Open AccessArticle

A Simulation Study Using Machine Learning and Formula Methods to Assess the Soybean Groundwater Contribution in a Drought-Prone Region

by Yuliang Zhang, Yuliang Zhou, Shangming Jiang, Shaowei Ning, Juliang Jin, Yi Cui, Zhiyong Wu and Huihui Feng

Water 2022, 14(19), 3092; https://doi.org/10.3390/w14193092 - 1 Oct 2022

Viewed by 1305

Abstract

Groundwater contributes to the delivery of phreatic water to crop aeration zones via evapotranspiration, which is important for crop growth in drought-prone regions. Most studies on groundwater contribution have not considered the influence of crop growth stage or daily evapotranspiration. In this study, [...] Read more.

Groundwater contributes to the delivery of phreatic water to crop aeration zones via evapotranspiration, which is important for crop growth in drought-prone regions. Most studies on groundwater contribution have not considered the influence of crop growth stage or daily evapotranspiration. In this study, a neural network based on a genetic algorithm and the Levenberg–Marquardt backpropagation algorithm, as well as formula methods based on an accelerated genetic algorithm, were built to assess soybean groundwater contribution; in addition, a performance comparison was conducted. The results indicated that machine learning had the best performance for fitting errors, with values for relative mean error (RME), root mean square percentage error (RMSPE), and correlation coefficient of 1.088, 2.165, and 0.762, respectively; in addition, for validation errors, it had values for RME, RMSPE, and correlation coefficient of 1.069, 2.136, and 0.735, respectively. The machine learning method is recommended for readers seeking to calculate groundwater contribution. Full article

(This article belongs to the Special Issue Challenges of Hydrological Drought Monitoring and Prediction)

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