scholarly journals Regional Response to Global Warming: Water Temperature Trends in Semi-Natural Mountain River Systems

Water ◽  
2020 ◽  
Vol 12 (1) ◽  
pp. 283 ◽  
Author(s):  
Mariola Kędra
Keyword(s):  
Water Temperature ◽  
Water Resource Management ◽  
River Flow ◽  
Heat Waves ◽  
Management Strategies ◽  
Thermal Conditions ◽  
Kendall Test ◽  
Mountain River ◽  
River Systems ◽  
Temperature Trends

River water temperature (TW) is a key environmental factor that determines the quality of the fluvial environment and its suitability for aquatic organisms. Atmospheric warming, accompanied by more frequent extreme weather phenomena, especially heat waves and prolonged drought, may pose a serious threat to the river environment and native river ecosystems. Therefore, reliable and up-to-date information on current and anticipated changes in river flow and thermal conditions is necessary for adaptive water resource management and planning. This study focuses on semi-natural mountain river systems to reliably assess the magnitude of water temperature change in the Polish Carpathians in response to climatic warming. The Mann–Kendall test was used to detect trends in water temperature series covering the last 35 years (1984–2018). Significant, rising trends in annual water temperature were found for all studied sites, with differences in intensity (0.33–0.92 °C per decade). Trends in TW were strongest in summer and autumn (0.75–1.17 and 0.51–1.08 °C per decade), strong trends were found in spring (0.82–0.95 °C per decade), and weaker in winter (0.25–0.29 °C per decade). Simultaneous air temperature trends were broadly consistent with water temperature trends. This indicates the urgent need for adaptive management strategies to counteract thermal degradation of the fluvial environment under study.

scholarly journals Analysis of Non-Parametric Trend and Climatic Parameter Homogeneity Tests in a Data-scarce Region: A Spatio-Temporal Perspective in the Tawang River Basin, Eastern Himalayas

2021 ◽  
Author(s):  
Juna Probha Devi ◽  
Chandan Mahanta ◽  
Anamika Barua
Keyword(s):  
Water Resource Management ◽  
Management Strategies ◽  
Kendall Test ◽  
Climate Impact ◽  
Mean Annual Precipitation ◽  
Homogeneity Test ◽  
Eastern Region ◽  
Positive Trend ◽  
Temporal Perspective ◽  
Spatio Temporal

Abstract This study is aimed at studying long–term historical and future (1950-2099) trends for the RCP 4.5 and RCP 8.5 on approximately 30-year timescale at annual and seasonal for precipitation and at annual, seasonal, monthly, and diurnal temperature range (DTR) for temperature maximum (T_max), temperature minimum (T_min) variations using statistical trend analysis techniques– Mann–Kendall test (MK) and Sen's slope estimator (S) and the homogeneity test using Pettitt’s test. The study is carried out in three spatial points across the Tawang Chu in the district of Tawang, Arunachal Pradesh. The summer mean precipitation for RCP 4.5 (2006-2065) shows a positive trend with a rise in precipitation between 1.56 mm to 9.94 mm in all the study points. The mean annual precipitation statistics for all the points show an increase of RCP 4.5 in 2006-2052 and 2053-2099 timescale. Both RCP 4.5 and 8.5 scenarios exhibit a uniform rise in T_min and T_max during investigation. For all the points, the results likewise reveal a rising trend in mean annual T_min and T_max. Still, the inter-decadal temperature statistical analysis shows that the increase in mean annual T_min is greater than the increase in T_max, indicating a decreasing trend in DTR. It is anticipated that this study's outcomes will contribute to a better understanding of the relationship between change in climate and the regional hydrological behaviour and will be benefitting the society to develop a regional strategy for water resource management, can serve as a resource for climate impact research scope- assessments, adaptation, mitigation, and disaster management strategies for India's north-eastern region.

scholarly journals Perception welfare assessment of water reuse in competitive categories

2019 ◽  
Vol 19 (5) ◽  
pp. 1525-1532 ◽  
Author(s):  
José A. Zabala ◽  
Mª Dolores de Miguel ◽  
José M. Martínez-Paz ◽  
Francisco Alcon
Keyword(s):  
Water Reuse ◽  
Water Resource Management ◽  
River Flow ◽  
Management Strategies ◽  
Reclaimed Water ◽  
Heterogeneous Preferences ◽  
Welfare Impact ◽  
Welfare Impacts ◽  
Degree Of Satisfaction ◽  
Reuse Options

Abstract The supply of reclaimed water to ecosystems increases their ecosystem service flows, which is directly translated into terms of social welfare. This study explores the factors that determine the different perceptions of the welfare impact of supplying reclaimed water to different, and competitive, ecosystems in the Segura River Basin (southern Spain): specifically, an agroecosystem (agricultural irrigation) and a river (higher river flow). The results of a contingent valuation exercise with the population of the Murcia Region show four different groups of respondents, depending on their willingness to pay (WTP) preferences. The factors that identify differences among welfare impacts are the age, the gender, the education level, the monthly income, the nearness of the household to the river, and, above all, the degree of satisfaction with funding of water reclamation. This study broadens our knowledge of individuals' heterogeneous preferences in water reuse options, which is crucial for policy makers in the development of socially accepted and sustainable water resource management strategies.

scholarly journals Stream temperature and discharge evolution in Switzerland over the last 50 years: annual and seasonal behaviour

2020 ◽  
Vol 24 (1) ◽  
pp. 115-142 ◽  
Author(s):  
Adrien Michel ◽  
Tristan Brauchli ◽  
Michael Lehning ◽  
Bettina Schaefli ◽  
Hendrik Huwald
Keyword(s):  
Water Temperature ◽  
Air Temperature ◽  
Water Resource Management ◽  
Temporal Trends ◽  
Stream Temperature ◽  
Mathematical Framework ◽  
Strengthening Effect ◽  
Temperature Trends ◽  
Glacier Melt ◽  
Wide Range

Abstract. Stream temperature and discharge are key hydrological variables for ecosystem and water resource management and are particularly sensitive to climate warming. Despite the wealth of meteorological and hydrological data, few studies have quantified observed stream temperature trends in the Alps. This study presents a detailed analysis of stream temperature and discharge in 52 catchments in Switzerland, a country covering a wide range of alpine and lowland hydrological regimes. The influence of discharge, precipitation, air temperature, and upstream lakes on stream temperatures and their temporal trends is analysed from multi-decadal to seasonal timescales. Stream temperature has significantly increased over the past 5 decades, with positive trends for all four seasons. The mean trends for the last 20 years are +0.37±0.11 ∘C per decade for water temperature, resulting from the joint effects of trends in air temperature (+0.39±0.14 ∘C per decade), discharge (-10.1±4.6 % per decade), and precipitation (-9.3±3.4 % per decade). For a longer time period (1979–2018), the trends are +0.33±0.03 ∘C per decade for water temperature, +0.46±0.03°C per decade for air temperature, -3.0±0.5 % per decade for discharge, and -1.3±0.5 % per decade for precipitation. Furthermore, we show that snow and glacier melt compensates for air temperature warming trends in a transient way in alpine streams. Lakes, on the contrary, have a strengthening effect on downstream water temperature trends at all elevations. Moreover, the identified stream temperature trends are shown to have critical impacts on ecological and economical temperature thresholds (the spread of fish diseases and the usage of water for industrial cooling), especially in lowland rivers, suggesting that these waterways are becoming more vulnerable to the increasing air temperature forcing. Resilient alpine rivers are expected to become more vulnerable to warming in the near future due to the expected reductions in snow- and glacier-melt inputs. A detailed mathematical framework along with the necessary source code are provided with this paper.

scholarly journals Estimation of the Dependence of Ice Phenomena Trends on Air and Water Temperature in River

Water ◽  
2020 ◽  
Vol 12 (12) ◽  
pp. 3494
Author(s):  
Renata Graf
Keyword(s):  
Water Temperature ◽  
Negative Binomial ◽  
Hydrological Regime ◽  
Thermal Conditions ◽  
Kendall Test ◽  
Temperate Climate ◽  
Anthropogenic Pressure ◽  
Frazil Ice ◽  
The Impact ◽  
Ice Phenomena

The identification of changes in the ice phenomena (IP) in rivers is a significant element of analyses of hydrological regime features, of the risk of occurrence of ice jam floods, and of the ecological effects of river icing (RI). The research here conducted aimed to estimate the temporal and spatial changes in the IP in a lowland river in the temperate climate (the Noteć River, Poland, Central Europe), depending on air temperature (TA) and water temperature (TW) during the multi-annual period of 1987–2013. Analyses were performed of IP change trends in three RI phases: freezing, when there appears stranded ice (SI), frazil ice (FI), or stranded ice with frazil ice (SI–FI); the phase of stable ice cover (IC) and floating ice (FoI); and the phase of stranded ice with floating ice (SI–FoI), frazil ice with floating ice (FI–FoI), and ice jams (IJs). Estimation of changes in IP in connection with TA and TW made use of the regression model for count data with a negative binomial distribution and of the zero-inflated negative binomial model. The analysis of the multi-annual change tendency of TA and TW utilized a non-parametric Mann–Kendall test for detecting monotonic trends with Yue–Pilon correction (MK–YP). Between two and seven types of IP were registered at individual water gauges, while differences were simultaneously demonstrated in their change trends over the researched period. The use of the Vuong test confirmed the greater effectiveness of estimates for the zero-inflated model than for the temporal trend model, thanks to which an increase in the probability of occurrence of the SI phenomenon in the immediate future was determined; this, together with FI, was found to be the most frequently occurring IP in rivers in the temperate climate. The models confirmed that TA is the best estimator for the evaluation of trends of the occurrence of IC. It was shown that the predictive strength of models increases when thermal conditions are taken into consideration, but it is not always statistically significant. In all probability, this points to the impact of local factors (changes in bed and valley morphology and anthropogenic pressure) that are active regardless of thermal conditions and modify the features of the thermal-ice regime of rivers at specific spatial locations. The results of research confirm the effectiveness of compilating a few models for the estimation of the dependence of IP trends on air and water temperature in a river.

scholarly journals Lagged recovery of fish spatial distributions following a cold-water perturbation

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
M. D. Robertson ◽  
J. Gao ◽  
P. M. Regular ◽  
M. J. Morgan ◽  
F. Zhang
Keyword(s):  
Water Temperature ◽  
Species Distribution ◽  
Environmental Conditions ◽  
Cold Water ◽  
Heat Waves ◽  
Rapid Change ◽  
Nonlinear Effects ◽  
Spatial Distributions ◽  
Spatiotemporal Model ◽  
Yellowtail Flounder

AbstractAnomalous local temperature and extreme events (e.g. heat-waves) can cause rapid change and gradual recovery of local environmental conditions. However, few studies have tested whether species distribution can recover following returning environmental conditions. Here, we tested for change and recovery of the spatial distributions of two flatfish populations, American plaice (Hippoglossoides platessoides) and yellowtail flounder (Limanda ferruginea), in response to consecutive decreasing and increasing water temperature on the Grand Bank off Newfoundland, Canada from 1985 to 2018. Using a Vector Autoregressive Spatiotemporal model, we found the distributions of both species shifted southwards following a period when anomalous cold water covered the northern sections of the Grand Bank. After accounting for density-dependent effects, we observed that yellowtail flounder re-distributed northwards when water temperature returned and exceeded levels recorded before the cold period, while the spatial distribution of American plaice has not recovered. Our study demonstrates nonlinear effects of an environmental factor on species distribution, implying the possibility of irreversible (or hard-to-reverse) changes of species distribution following a rapid change and gradual recovery of environmental conditions.

scholarly journals Long-chain diols in rivers: distribution and potential biological sources

2018 ◽  
Author(s):  
Julie Lattaud ◽  
Frédérique Kirkels ◽  
Francien Peterse ◽  
Chantal V. Freymond ◽  
Timothy I. Eglinton ◽  
...  
Keyword(s):  
River Flow ◽  
18S Rrna Gene ◽  
Rrna Gene ◽  
River Systems ◽  
Eukaryotic Diversity ◽  
Suspended Particulate ◽  
Fractional Abundance ◽  
Biological Sources ◽  
Rrna Gene Sequencing ◽  
Long Chain

Abstract. Long chain diols (LCDs) occur widespread in marine environments and also in lakes and rivers. Transport of LCDs from rivers may impact the distribution of LCDs in coastal environments, however relatively little is known about the distribution and biological sources of LCDs in river systems. In this study, we investigated the distribution of LCDs in suspended particulate matter (SPM) of three river systems (Godavari, Danube, and Rhine) in relation with season, precipitation, temperature, and source catchments. The dominant long-chain diol is the C32 1,15-diol followed by the C30 1,15-diol in all studied river systems. In regions influenced by marine waters, such as delta systems, the fractional abundance of the C30 1,15-diol is substantially higher than in the river itself, suggesting different LCD producers in marine and freshwater environments. A change in the LCD distribution along the downstream transects of the rivers studied was not observed. However, an effect of river flow is observed, i.e. the concentration of the C32 1,15-diol is higher in stagnant waters, such as reservoirs and during seasons with river low stands. A seasonal change in the LCD distribution was observed in the Rhine, likely due to a change in the producers. Eukaryotic diversity analysis by 18S rRNA gene sequencing of SPM from the Rhine showed extremely low abundances of sequences (i.e.

scholarly journals Streamflow regionalization for the Mortes River Basin upstream from the Funil Hydropower Plant, MG

2020 ◽  
Vol 15 (3) ◽  
pp. 1 ◽  
Author(s):  
Jhones Da Silva Amorim ◽  
Rubens Junqueira ◽  
Vanessa Alves Mantovani ◽  
Marcelo Ribeiro Viola ◽  
Carlos Rogério de Mello ◽  
...  
Keyword(s):  
Resource Management ◽  
River Basin ◽  
Water Resource ◽  
Water Resource Management ◽  
Extreme Values ◽  
Kendall Test ◽  
Hydropower Plant ◽  
Coefficient Of Determination ◽  
Historical Series ◽  
Anderson Darling

 Maximum and minimum streamflow are fundamental for water resource management, especially for water rights. However, lack of monitoring and scarce streamflow data limit such studies. Streamflow regionalization is a useful tool to overcome these limitations. The study developed models for regionalization of the maximum and minimum reference streamflows for the Mortes River Basin (MRB) (Water Resources Planning and Management Unit - GD2), Southern Minas Gerais State. The study used long-term streamflow historical series provided by the Brazilian National Water Agency (ANA). Previous exploratory analysis was performed, and it was observed that the streamflow series are stationary according to the Mann-Kendall test. The estimation of the streamflow for different return periods (RP) was performed by fitting Probability Density Functions (PDFs) that were tested by the Anderson-Darling (AD) test. The Generalized Extreme Values (GEV) and Wakeby were the most appropriate PDFs for maximum and minimum streamflows, respectively. The streamflow models were fitted using a power regression procedure, considering the drainage area of the watersheds as inputs. The fittings reached the coefficient of determination (R2) greater than 0.90. Thus, the streamflow regionalization models demonstrated good performance and are a potential tool to be used for water resource management in the studied basin.

scholarly journals An improved framework to predict river flow time series data

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e7183 ◽  
Author(s):  
Hafiza Mamona Nazir ◽  
Ijaz Hussain ◽  
Ishfaq Ahmad ◽  
Muhammad Faisal ◽  
Ibrahim M. Almanjahie
Keyword(s):  
Time Series ◽  
Water Resource Management ◽  
River Flow ◽  
Time Series Data ◽  
Flow Time ◽  
Indus Basin ◽  
Series Data ◽  
Percentage Error ◽  
Empirical Bayesian ◽  
Noise Characteristics

Due to non-stationary and noise characteristics of river flow time series data, some pre-processing methods are adopted to address the multi-scale and noise complexity. In this paper, we proposed an improved framework comprising Complete Ensemble Empirical Mode Decomposition with Adaptive Noise-Empirical Bayesian Threshold (CEEMDAN-EBT). The CEEMDAN-EBT is employed to decompose non-stationary river flow time series data into Intrinsic Mode Functions (IMFs). The derived IMFs are divided into two parts; noise-dominant IMFs and noise-free IMFs. Firstly, the noise-dominant IMFs are denoised using empirical Bayesian threshold to integrate the noises and sparsities of IMFs. Secondly, the denoised IMF’s and noise free IMF’s are further used as inputs in data-driven and simple stochastic models respectively to predict the river flow time series data. Finally, the predicted IMF’s are aggregated to get the final prediction. The proposed framework is illustrated by using four rivers of the Indus Basin System. The prediction performance is compared with Mean Square Error, Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE). Our proposed method, CEEMDAN-EBT-MM, produced the smallest MAPE for all four case studies as compared with other methods. This suggests that our proposed hybrid model can be used as an efficient tool for providing the reliable prediction of non-stationary and noisy time series data to policymakers such as for planning power generation and water resource management.

Why is large sample hydrology important in hydrological forecasting? 

2021 ◽  
Author(s):  
Ilias Pechlivanidis ◽  
Louise Crochemore ◽  
Marc Girons Lopez
Keyword(s):  
River Flow ◽  
Forecast Skill ◽  
Machine Learning Techniques ◽  
Precipitation Forecast ◽  
River Systems ◽  
Local Conditions ◽  
Large Sample ◽  
Streamflow Forecasts ◽  
Seasonal Streamflow

<p>The scientific community has made significant progress towards improving the skill of hydrological forecasts; however, most investigations have normally been conducted at single or in a limited number of catchments. Such an approach is indeed valuable for detailed process investigation and therefore to understand the local conditions that affect forecast skill, but it is limited when it comes to scaling up the underlying hydrometeorological hypotheses. To advance knowledge on the drivers that control the quality and skill of hydrological forecasts, much can be gained by comparative analyses and from the availability of statistically significant samples. Large-scale modelling (at national, continental or global scales) can complement the in-depth knowledge from single catchment modelling by encompassing many river systems that represent a breadth of physiographic and climatic conditions. In addition to large sample sizes which cover a gradient in terms of climatology, scale and hydrological regime, the use of machine learning techniques can contribute to the identification of emerging spatiotemporal patterns leading to forecast skill attribution to different regional physiographic characteristics.</p><p>Here, we draw on two seasonal hydrological forecast skill investigations that were conducted at the national and continental scales, providing results for more than 36,000 basins in Sweden and Europe. Due to the large generated samples, we are capable of demonstrating that the quality of seasonal streamflow forecasts can be clustered and regionalized, based on a priori knowledge of the local hydroclimatic conditions. We show that the quality of seasonal streamflow forecasts is linked to physiographic and hydroclimatic descriptors, and that the relative importance of these descriptors varies with initialization month and lead time. In our samples, hydrological similarity, temperature, precipitation, evaporative index, and precipitation forecast biases are strongly linked to the quality of streamflow forecasts. This way, while seasonal river flow can generally be well predicted in river systems with slow hydrological responses, predictability tends to be poor in cold and semiarid climates in which river systems respond immediately to precipitation signals.</p>

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