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Research of the Relationship between Climate Change and Runoff in Watershed

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A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Water and Climate Change".

Deadline for manuscript submissions: closed (1 June 2022) | Viewed by 22597

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Special Issue Editor

Dr. Jūratė Kriaučiūnienė

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Guest Editor

Laboratory of Hydrology, Lithuanian Energy Institute, Kaunas, Lithuania
Interests: hydrological modeling; climate change; environmental flow; uncertainty; water resources management
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report (AR5) states that in many regions changing precipitation or melting snow and ice are altering hydrological systems and affecting quantity and quality of water resources. The future projections of river runoff are mostly influenced by two main climate indices: precipitation and air temperature. From now on, representative concentration pathways (RCPs) scenarios, global climate models (GCM), and downscaling methods (SD) are being used for the determination of climate projections and future changes in the river hydrological regime. The different approaches to the preparation of climate input data and hydrological modeling of river runoff have affected the variability of runoff projections in the river catchments. The evaluation of uncertainties associated with selected sources (RCP, GCM, SD, parameters of hydrological models, etc.) is necessary for more accurate projecting of runoff changes in the future. Currently, there is a particular lack of research related to river runoff projection assessment in the ungauged river basins.

Potential topics include but are not limited to the following:

Relationship between climate and runoff projections in different river catchments
Variability of river runoff projections in time and space for different hydrological regions
Evaluation of uncertainty of runoff projections under a future climate
Assessment of river runoff projections in the ungauged river catchments.

Dr. Jūratė Kriaučiūnienė
Guest Editor

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Keywords

hydrological modeling
climate scenarios
global models
downscaling
runoff projections
uncertainty
ungauged rivers

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Published Papers (5 papers)

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Research

17 pages, 3649 KiB

Open AccessFeature PaperArticle

Assessment of Climate Change Impact on Discharge of the Lakhmass Catchment (Northwest Tunisia)

by Siwar Ben Nsir, Seifeddine Jomaa, Ümit Yıldırım, Xiangqian Zhou, Marco D’Oria, Michael Rode and Slaheddine Khlifi

Water 2022, 14(14), 2242; https://doi.org/10.3390/w14142242 - 17 Jul 2022

Cited by 5 | Viewed by 4115

Abstract

The Mediterranean region is increasingly recognized as a climate change hotspot but is highly underrepresented in hydrological climate change studies. This study aims to investigate the climate change effects on the hydrology of Lakhmass catchment in Tunisia. Lakhmass catchment is a part of the Medium Valley of Medjerda in northwestern Tunisia that drains an area of 126 km². First, the Hydrologiska Byråns Vattenbalansavdelning light (HBV-light) model was calibrated and validated successfully at a daily time step to simulate discharge during the 1981–1986 period. The Nash Sutcliffe Efficiency and Percent bias (NSE, PBIAS) were (0.80, +2.0%) and (0.53, −9.5%) for calibration (September 1982–August 1984) and validation (September 1984–August 1986) periods, respectively. Second, HBV-light model was considered as a predictive tool to simulate discharge in a baseline period (1981–2009) and future projections using data (precipitation and temperature) from thirteen combinations of General Circulation Models (GCMs) and Regional Climatic Models (RCMs). We used two trajectories of Representative Concentration Pathways, RCP4.5 and RCP8.5, suggested by the Intergovernmental Panel on Climate Change (IPCC). Each RCP is divided into three projection periods: near-term (2010–2039), mid-term (2040–2069) and long-term (2070–2099). For both scenarios, a decrease in precipitation and discharge will be expected with an increase in air temperature and a reduction in precipitation with almost 5% for every +1 °C of global warming. By long-term (2070–2099) projection period, results suggested an increase in temperature with about 2.7 °C and 4 °C, and a decrease in precipitation of approximately 7.5% and 15% under RCP4.5 and RCP8.5, respectively. This will likely result in a reduction of discharge of 12.5% and 36.6% under RCP4.5 and RCP8.5, respectively. This situation calls for early climate change adaptation measures under a participatory approach, including multiple stakeholders and water users. Full article

(This article belongs to the Special Issue Research of the Relationship between Climate Change and Runoff in Watershed)

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26 pages, 15657 KiB

Open AccessEditor’s ChoiceArticle

Wavelet Analysis of Dam Injection and Discharge in Three Gorges Dam and Reservoir with Precipitation and River Discharge

by Lirong Yin, Lei Wang, Barry D. Keim, Kory Konsoer and Wenfeng Zheng

Water 2022, 14(4), 567; https://doi.org/10.3390/w14040567 - 13 Feb 2022

Cited by 75 | Viewed by 4660

Abstract

The Yangtze River has been the primary support of the resources and transportation of China. The Three Gorges Dam and Reservoir on the Yangtze River is one of the world’s largest dams. The influence caused by the dam and reservoir on the river system has been overwhelming and destructive. For better water resource use and flood-prevention planning, more understanding is needed regarding the dam’s impact on river discharge, regional precipitation, and frequency of extreme rainfall events. This study aims to analyze the changes in river discharge and regional precipitation records before and after the construction of the Three Gorges Dam. This research examines temporal correlations among these data by collecting daily dam injection and dam discharge records, the precipitation from ground stations, and river discharge. The time series are analyzed with the wavelet analysis. The precipitation datasets decrease in wavelet magnitude after 1998 when the dam was built in the wavelet analysis. The annual cycle, shown as a bright year line through the time range, still exists in the analysis result after 1998, but the magnitude of the annual cycle has reduced. The river discharge shows a decrease of wavelet magnitude at the three downstream locations. The possible explanation of this pattern could be the human-controlled dam discharge. The constant water level maintained in the reservoir by human control would slow down the flow speed and stabilize it. Full article

(This article belongs to the Special Issue Research of the Relationship between Climate Change and Runoff in Watershed)

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33 pages, 9919 KiB

Open AccessFeature PaperArticle

Patterns of Past and Future Droughts in Permanent Lowland Rivers

by Serhii Nazarenko, Jūratė Kriaučiūnienė, Diana Šarauskienė and Darius Jakimavičius

Water 2022, 14(1), 71; https://doi.org/10.3390/w14010071 - 1 Jan 2022

Cited by 5 | Viewed by 2514

Abstract

The problem of droughts is acute due to climate change. The study aims to assess the temporal and spatial drought patterns in Lithuanian lowland rivers in the past and to project these phenomena according to climate scenarios and models. Drought analysis was based on Standardized Precipitation Index (SPI), Reconnaissance Drought Index (RDI) and Streamflow Drought Index (SDI). To evaluate the past patterns, the hydrometeorological data of 17 rivers were used from 1961–2020. Future drought changes were analyzed in 2021–2100 according to the selected RCPs (Representative Concentration Pathways) using the hydrological model HBV. There were different patterns of droughts in three hydrological regions of Lithuania (Western, Central and Southeastern). The Southeastern region was more prone to extreme summer hydrological droughts, and they had a shorter accumulation period compared to the other two regions. SPI and RDI indices showed that the number of dry months and the minimum value of the index increased, extending the accumulation period. The highest correlation was recorded between RDI-12/SPI-12 and SDI-12. The amplitude between extremely wet and dry values of river runoff will increase according to RCP8.5. The projections indicated that hydrological drought intensity in the Central region is expected to increase under both analyzed RCPs. Full article

(This article belongs to the Special Issue Research of the Relationship between Climate Change and Runoff in Watershed)

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16 pages, 3191 KiB

Open AccessEditor’s ChoiceArticle

Deep Learning Framework with Time Series Analysis Methods for Runoff Prediction

by Zhenghe Li, Ling Kang, Liwei Zhou and Modi Zhu

Water 2021, 13(4), 575; https://doi.org/10.3390/w13040575 - 23 Feb 2021

Cited by 23 | Viewed by 5420

Abstract

Recent advances in deep learning, especially the long short-term memory (LSTM) networks, provide some useful insights on how to tackle time series prediction problems, not to mention the development of a time series model itself for prediction. Runoff forecasting is a time series prediction problem with a series of past runoff data (water level and discharge series data) as inputs and a fixed-length series of future runoff as output. Most previous work paid attention to the sufficiency of input data and the structural complexity of deep learning, while less effort has been put into the consideration of data quantity or the processing of original input data—such as time series decomposition, which can better capture the trend of runoff—or unleashing the effective potential of deep learning. Mutual information and seasonal trend decomposition are two useful time series methods in handling data quantity analysis and original data processing. Based on a former study, we proposed a deep learning model combined with time series analysis methods for daily runoff prediction in the middle Yangtze River and analyzed its feasibility and usability with frequently used counterpart models. Furthermore, this research also explored the data quality that affect the performance of the deep learning model. With the application of the time series method, we can effectively get some information about the data quality and data amount that we adopted in the deep learning model. The comparison experiment resulted in two different sites, implying that the proposed model improved the precision of runoff prediction and is much easier and more effective for practical application. In short, time series analysis methods can exert great potential of deep learning in daily runoff prediction and may unleash great potential of artificial intelligence in hydrology research. Full article

(This article belongs to the Special Issue Research of the Relationship between Climate Change and Runoff in Watershed)

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26 pages, 5654 KiB

Open AccessFeature PaperEditor’s ChoiceArticle

Modelling of the Discharge Response to Climate Change under RCP8.5 Scenario in the Alata River Basin (Mersin, SE Turkey)

by Ümit Yıldırım, Cüneyt Güler, Barış Önol, Michael Rode and Seifeddine Jomaa

Water 2021, 13(4), 483; https://doi.org/10.3390/w13040483 - 13 Feb 2021

Cited by 14 | Viewed by 4766

Abstract

This study investigates the impacts of climate change on the hydrological response of a Mediterranean mesoscale catchment using a hydrological model. The effect of climate change on the discharge of the Alata River Basin in Mersin province (Turkey) was assessed under the worst-case climate change scenario (i.e., RCP8.5), using the semi-distributed, process-based hydrological model Hydrological Predictions for the Environment (HYPE). First, the model was evaluated temporally and spatially and has been shown to reproduce the measured discharge consistently. Second, the discharge was predicted under climate projections in three distinct future periods (i.e., 2021–2040, 2046–2065 and 2081–2100, reflecting the beginning, middle and end of the century, respectively). Climate change projections showed that the annual mean temperature in the Alata River Basin rises for the beginning, middle and end of the century, with about 1.35, 2.13 and 4.11 °C, respectively. Besides, the highest discharge timing seems to occur one month earlier (February instead of March) compared to the baseline period (2000–2011) in the beginning and middle of the century. The results show a decrease in precipitation and an increase in temperature in all future projections, resulting in more snowmelt and higher discharge generation in the beginning and middle of the century scenarios. However, at the end of the century, the discharge significantly decreased due to increased evapotranspiration and reduced snow depth in the upstream area. The findings of this study can help develop efficient climate change adaptation options in the Levant’s coastal areas. Full article

(This article belongs to the Special Issue Research of the Relationship between Climate Change and Runoff in Watershed)

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