scholarly journals Modelling flow changes in potential climate change conditions – an example of the Kaczawa basin

2012 ◽  
Vol 34 (2) ◽  
pp. 51-61
Author(s):  
Leszek Kuchar ◽  
IWAŃSKI SŁAWOMIR ◽  
Leszek Jelonek ◽  
Wiwiana Szalińska
Keyword(s):  
Climate Change ◽  
Water Resources ◽  
Social Impact ◽  
Meteorological Data ◽  
Annual Runoff ◽  
Climate Change Scenarios ◽  
Rainfall Runoff ◽  
Data Generator ◽  
Flow Changes ◽  
Potential Climate Change

Abstract Climate change, regardless of the causes shaping its rate and direction, can have far-reaching environmental, economic and social impact. A major aspect that might be transformed as a result of climate change are water resources of a catchment. The article presents a possible method of predicting water resource changes by using a meteorological data generator and classical hydrological models. The assessment of water resources in a catchment for a time horizon of 30-50 years is based on an analysis of changes in annual runoff that might occur in changing meteorological conditions. The model used for runoff analysis was the hydrological rainfall-runoff NAM model. Daily meteorological data essential for running the hydrological model were generated by means of SWGEN model. Meteorological data generated for selected climate change scenarios (GISS, CCCM and GFDL) for the years 2030 and 2050 enabled analysing different variants of climate change and their potential effects. The presented results refer to potential changes in water resources of the Kaczawa catchment. It should be emphasized that the obtained results do not say which of the climate change scenarios is more likely, but they present the consequences of climate change described by these scenarios.

scholarly journals Model analysis of forest thinning impacts on the water resources during hydrological drought periods

2021 ◽  
Author(s):  
Hiroki Momiyama ◽  
Tomo'omi Kumagai ◽  
Tomohiro Egusa
Keyword(s):  
Water Resources ◽  
Meteorological Data ◽  
Rainfall Runoff ◽  
Forest Thinning ◽  
Flow Duration ◽  
Mountain Catchment ◽  
Data Generator ◽  
Flow Duration Curves ◽  
Rainfall Runoff Model ◽  
Runoff Model

In Japan, there has recently been an increasing call for forest thinning to conserve water resources from forested mountain catchments in terms of runoff during prolonged drought periods of the year. How their water balance and the resultant runoff are altered by forest thinning is examined using a combination of 8-year hydrological observations, 100-year meteorological data generator output, and a semi-process-based rainfall-runoff model. The rainfall-runoff model is developed based on TOPMODEL assuming that forest thinning has an impact on runoff primarily through an alteration in canopy interception. The main novelty in this analysis is that the availability of the generated 100-year meteorological data allows the investigations of the forest thinning impacts on mountain catchment water resources under the most severer drought conditions. The model is validated against runoff observations conducted at a forested mountain catchment in the Kanto region of Japan for the period 2010–2017. It is demonstrated that the model reproduces temporal variations in runoff and evapotranspiration at inter- and intra-annual time scales, resulting in well reproducing the observed flow duration curves. On the basis of projected flow duration curves for the 100-year, despite the large increase in an annual total runoff with ordinary intensifying thinning, low flow rates, i.e., water resources from the catchment in the drought period in the year, in both normal and drought years were impacted by the forest thinning to a lesser extent. Higher catchment water retention capacity appreciably enhanced the forest thinning effect on increasing available water resources.

scholarly journals Coupling Tank Model and Lars-Weather Generator in Assessments of the Impacts of Climate Change on Water Resources

2019 ◽  
Vol 27 (1) ◽  
pp. 14-24 ◽  
Author(s):  
Naser Mohammadzadeh ◽  
Bahman Jabbarian Amiri ◽  
Leila Eslami Endergoli ◽  
Shirin Karimi
Keyword(s):  
Climate Change ◽  
Water Resources ◽  
Weather Generator ◽  
Climate Change Scenarios ◽  
Rainfall Runoff ◽  
The Caspian Sea ◽  
Tank Model ◽  
Rainfall Runoff Model ◽  
The Impact ◽  
Runoff Model

Abstract With the aim of assessing the impact of climate change on surface water resources, a conceptual rainfall-runoff model (the tank model) was coupled with LARS-WG as a weather generator model. The downscaled daily rainfall, temperature, and evaporation from LARS-WG under various IPCC climate change scenarios were used to simulate the runoff through the calibrated Tank model. A catchment (4648 ha) located in the southern basin of the Caspian Sea was chosen for this research study. The results showed that this model has a reasonable predictive capability in simulating minimum and maximum temperatures at a level of 99%, rainfall at a level of 93%, and radiation at a level of 97% under various scenarios in agreement with the observed data. Moreover, the results of the rainfall-runoff model indicated an increase in the flow rate of about 108% under the A1B scenario, 101% under the A2 scenario, and 93% under the B1 scenario over the 30-year time period of the discharge prediction.

Climate Change Impact on the Water Resources of the Limpopo Basin

2018 ◽  
pp. 177-200
Author(s):  
Berhanu F. Alemaw ◽  
Thebeyame Ronald Chaoka
Keyword(s):  
Climate Change ◽  
Soil Moisture ◽  
Water Resources ◽  
Potential Evapotranspiration ◽  
Annual Runoff ◽  
Climate Change Scenarios ◽  
Atmospheric Processes ◽  
Hydrologic Regimes ◽  
Change Impact ◽  
High Runoff

This chapter aims to evaluate the impacts of climate change on both hydrologic regimes and water resources of the Limpopo River Basin in southern Africa. Water resources availability in the basin, in terms of, seasonal and annual runoff (R), soil moisture (S) and actual evapotranspiration (Ea) is simulated and evaluated using the hydrological model, HATWAB. These water balances were computed from precipitation (P), potential evapotranspiration (Ep) and other variables that govern the soil-water-vegetation-atmospheric processes at 9.2km latitude/ longitude gird cells covering the basin. The 1961-90 simulated mean annual runoff reveals mixed patterns of high and low runoff across the region. Although relatively small changes in runoff simulations are prevalent among the three climate change scenarios, generally the OSU simulated relatively high runoff compared to the UKTR and HADCM2 GCMs.

Climate Change Impact on the Water Resources of the Limpopo Basin

2018 ◽  
pp. 656-680
Author(s):  
Berhanu F. Alemaw ◽  
Thebeyame Ronald Chaoka
Keyword(s):  
Climate Change ◽  
Soil Moisture ◽  
Water Resources ◽  
Potential Evapotranspiration ◽  
Annual Runoff ◽  
Climate Change Scenarios ◽  
Atmospheric Processes ◽  
Hydrologic Regimes ◽  
Change Impact ◽  
High Runoff

This chapter aims to evaluate the impacts of climate change on both hydrologic regimes and water resources of the Limpopo River Basin in southern Africa. Water resources availability in the basin, in terms of, seasonal and annual runoff (R), soil moisture (S) and actual evapotranspiration (Ea) is simulated and evaluated using the hydrological model, HATWAB. These water balances were computed from precipitation (P), potential evapotranspiration (Ep) and other variables that govern the soil-water-vegetation-atmospheric processes at 9.2km latitude/ longitude gird cells covering the basin. The 1961-90 simulated mean annual runoff reveals mixed patterns of high and low runoff across the region. Although relatively small changes in runoff simulations are prevalent among the three climate change scenarios, generally the OSU simulated relatively high runoff compared to the UKTR and HADCM2 GCMs.

scholarly journals Assessing the Impacts of Climate Change on Surface Water Resources Using WEAP Model in Narok County, Kenya

2022 ◽  
Vol 3 ◽  
Author(s):  
A. O. Opere ◽  
Ruth Waswa ◽  
F. M. Mutua
Keyword(s):  
Climate Change ◽  
Surface Water ◽  
Water Resources ◽  
Goodness Of Fit ◽  
Meteorological Data ◽  
Coefficient Of Determination ◽  
Climate Projections ◽  
Climate Change Scenarios ◽  
Time Step ◽  
Surface Water Resources

Narok County in Kenya is the home to the Maasai Mara Game Reserve, which offers important habitats for a great variety of wild animals, hence, a hub for tourist attraction, earning the county and country an extra income through revenue collection. The Mau Forest Complex in the north is a source of major rivers including the Mara River and a water catchment tower that supports other regions as well. Many rivers present in the region support several activities and livelihood to the people in the area. The study examined how the quantity of surface water resources varied under the different climate change scenarios, and the sensitivity of the region to a changing climate. Several datasets used in this study were collated from different sources and included hydro–meteorological data, Digital Elevation Model (DEM), and Coordinated Regional Downscaling Experiment (CORDEX) climate projections. The WEAP (Water Evaluation and Planning) model was applied using the rainfall–runoff (soil moisture method) approach to compute runoff generated with climate data as input. All the calculations were done on a monthly time step from the current year account to the last year of the scenario. Calibration of the model proceeded using the PEST tool within the WEAP interface. The goodness of fit was evaluated using the coefficient of determination (R2), percentage bias (PBIAS), and Nash–Sutcliffe efficiency (NSE) criterion. From the tests, it was clear that WEAP performed well in simulating stream flows. The coefficient of determination (R2) was greater than the threshold R2 > 0.5 in both periods, i.e., 0.83 and 0.97 for calibration and validation periods, respectively, for the monthly flows. A 25-year mean monthly average was chosen with two time slices (2006–2030 and 2031–2055), which were compared against the baseline (1981–2000). There will be a general decrease in water quantity in the region in both scenarios: −30% by 2030 and −23.45% by 2055. In comparison, RCP4.5 and Scenario3 (+2.5°C, +10% P) were higher than RCP8.5 and Scenario 2 respectively. There was also a clear indication that the region was highly sensitive to a perturbation in climate from the synthetic scenarios. A change in either rainfall or temperature (or both) could lead to an impact on the amount of surface water yields.

scholarly journals Water resources of Ukraine in the XXI century under climate change scenarios (RCP4.5 AND RCP8.5)

2017 ◽  
pp. 114-122
Author(s):  
N.S. Loboda ◽  
Y.V. Bozhok
Keyword(s):  
Climate Change ◽  
Water Resources ◽  
Meteorological Data ◽  
Climate Change Scenarios ◽  
Intergovernmental Panel ◽  
The Past ◽  
Temperature And Precipitation ◽  
Calculation Results ◽  
Grid Nodes

Data of climate change scenarios RCP8.5 and RCP4.5 (Representative Concentration Pathways) were used. They were proposed for consideration in the Fifth Report of Intergovernmental Panel on Climate Change. Average long-term annual flow values using meteorological data (air temperature and precipitation) from the scenarios for the period 2011-2050 were calculated. 84 points (grid nodes) uniformly distributed on the territory of Ukraine were studied. The calculations were made based on the model "climate-runoff", developed in Odessa State Environmental University. Projection of changes in water resources was given by comparing the calculation results in the past (before 1989) and in the future (2011-2050). The major trends in water resources of Ukraine were established. It is shown that by the middle of the XXI century reducing of water resources is expected on the plain territory of Ukraine (70% in the southeast). In the geographical zone of the Ukrainian Carpathians, especially in the Tisa river basin, its stability or growth is possible. Analysis of changes in the ratio of moisture and heat resources showed that climate aridity will be intensify and the insufficient moisture zone and the semiarid zone will be widen.

Self-organising map rainfall-runoff multivariate modelling for runoff reconstruction in inadequately gauged basins

2012 ◽  
Vol 43 (5) ◽  
pp. 603-617 ◽  
Author(s):  
Adebayo J. Adeloye ◽  
Rabee Rustum
Keyword(s):  
Water Resources ◽  
Meteorological Data ◽  
Rainfall Runoff ◽  
Main River ◽  
Multivariate Modelling ◽  
Southwest Nigeria ◽  
Management Schemes ◽  
Viable Approach ◽  
Self Organising Map ◽  
Gauged Basins

Water resources assessment activities in inadequately gauged basins are often significantly constrained due to the insufficiency or total lack of hydro-meteorological data, resulting in huge uncertainties and ineffectual performance of water management schemes. In this study, a new methodology of rainfall-runoff modelling using the powerful clustering capability of the self-organising map (SOM), unsupervised artificial neural networks, is proposed as a viable approach for harnessing the multivariate correlation between the typically long record rainfall and short record runoff in such basins. The methodology was applied to the inadequately gauged Osun basin in southwest Nigeria for the sole purpose of extending the available runoff records and, through that, reducing water resources planning uncertainty associated with the use of short runoff data records. The extended runoff records were then analysed to determine possible abstractions from the main river source at different exceedance probabilities. This study demonstrates the successful use of emerging tools to overcome practical problems in sparsely gauged basins.

scholarly journals Evaluating quantile-based bias adjustment methods for climate change scenarios

2021 ◽  
Author(s):  
Fabian Lehner ◽  
Imran Nadeem ◽  
Herbert Formayer
Keyword(s):  
Climate Change ◽  
Climate Models ◽  
Climate Model ◽  
Meteorological Data ◽  
Climate Change Signal ◽  
Historical Period ◽  
Climate Change Scenarios ◽  
Statistical Bias ◽  
Bias Adjustment ◽  
Direct Model

Abstract. Daily meteorological data such as temperature or precipitation from climate models is needed for many climate impact studies, e.g. in hydrology or agriculture but direct model output can contain large systematic errors. Thus, statistical bias adjustment is applied to correct climate model outputs. Here we review existing statistical bias adjustment methods and their shortcomings, and present a method which we call EQA (Empirical Quantile Adjustment), a development of the methods EDCDFm and PresRAT. We then test it in comparison to two existing methods using real and artificially created daily temperature and precipitation data for Austria. We compare the performance of the three methods in terms of the following demands: (1): The model data should match the climatological means of the observational data in the historical period. (2): The long-term climatological trends of means (climate change signal), either defined as difference or as ratio, should not be altered during bias adjustment, and (3): Even models with too few wet days (precipitation above 0.1 mm) should be corrected accurately, so that the wet day frequency is conserved. EQA fulfills (1) almost exactly and (2) at least for temperature. For precipitation, an additional correction included in EQA assures that the climate change signal is conserved, and for (3), we apply another additional algorithm to add precipitation days.

scholarly journals Soil salinization under different climate change scenarios: A global scale analysis

2021 ◽  
Author(s):  
Nima Shokri ◽  
Amirhossein Hassani ◽  
Adisa Azapagic
Keyword(s):  
Climate Change ◽  
Water Resources ◽  
Soil Salinity ◽  
Global Scale ◽  
Soil Salinization ◽  
Water Evaporation ◽  
Climate Change Scenarios ◽  
Spatio Temporal ◽  
Salt Affected Soils

<p>Population growth and climate change is projected to increase the pressure on land and water resources, especially in arid and semi-arid regions. This pressure is expected to affect all driving mechanisms of soil salinization comprising alteration in soil hydrological balance, sea salt intrusion, wet/dry deposition of wind-born saline aerosols — leading to an increase in soil salinity. Soil salinity influences soil stability, bio-diversity, ecosystem functioning and soil water evaporation (1). It can be a long-term threat to agricultural activities and food security. To devise sustainable action plan investments and policy interventions, it is crucial to know when and where salt-affected soils occur. However, current estimates on spatio-temporal variability of salt-affected soils are majorly localized and future projections in response to climate change are rare. Using Machine Learning (ML) algorithms, we related the available measured soil salinity values (represented by electrical conductivity of the saturated paste soil extract, EC<sub>e</sub>) to some environmental information (or predictors including outputs of Global Circulation Models, soil, crop, topographic, climatic, vegetative, and landscape properties of the sampling locations) to develop a set of data-driven predictive tools to enable the spatio-temporal predictions of soil salinity. The outputs of these tools helped us to estimate the extent and severity of the soil salinity under current and future climatic patterns at different geographical levels and identify the salinization hotspots by the end of the 21<sup>st</sup> century in response to climate change. Our analysis suggests that a soil area of 11.73 Mkm<sup>2</sup> located in non-frigid zones has been salt-affected in at least three-fourths of the 1980 - 2018 period (2). At the country level, Brazil, Peru, Sudan, Colombia, and Namibia were estimated to have the highest rates of annual increase in the total area of soils with an EC<sub>e</sub> ≥ 4 dS m<sup>-1</sup>. Additionally, the results indicate that by the end of the 21<sup>st</sup> century, drylands of South America, southern and Western Australia, Mexico, southwest United States, and South Africa will be the salinization hotspots (compared to the 1961 - 1990 period). The results of this study could inform decision-making and contribute to attaining the United Nation’s Sustainable Development Goals for land and water resources management.</p><p>1. Shokri-Kuehni, S.M.S., Raaijmakers, B., Kurz, T., Or, D., Helmig, R., Shokri, N. (2020). Water Table Depth and Soil Salinization: From Pore-Scale Processes to Field-Scale Responses. Water Resour. Res., 56, e2019WR026707. https://doi.org/ 10.1029/2019WR026707</p><p>2. Hassani, A., Azapagic, A., Shokri, N. (2020). Predicting Long-term Dynamics of Soil Salinity and Sodicity on a Global Scale, Proc. Nat. Acad. Sci., 117, 52, 33017–33027. https://doi.org/10.1073/pnas.2013771117</p>

scholarly journals The potential habitat of desert locusts is contracting: predictions under climate change scenarios

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e12311
Author(s):  
Jingyun Guan ◽  
Moyan Li ◽  
Xifeng Ju ◽  
Jun Lin ◽  
Jianguo Wu ◽  
...  
Keyword(s):  
Climate Change ◽  
Food Security ◽  
Radiative Forcing ◽  
Social Impact ◽  
Regional Cooperation ◽  
Extreme Weather Events ◽  
Climate Change Scenarios ◽  
Desert Locust ◽  
Maxent Model ◽  
Potential Habitat

Desert locusts are notorious for their widespread distribution and strong destructive power. Their influence extends from the vast arid and semiarid regions of western Africa to northwestern India. Large-scale locust outbreaks can have devastating consequences for food security, and their social impact may be long-lasting. Climate change has increased the uncertainty of desert locust outbreaks, and predicting suitable habitats for this species under climate change scenarios will help humans deal with the potential threat of locust outbreaks. By comprehensively considering climate, soil, and terrain variables, the maximum entropy (MaxEnt) model was used to predict the potential habitats of solitary desert locusts in the 2050s and 2070s under the four shared socioeconomic pathways (SSP126, SSP245, SSP370, and SSP585) in the CMIP6 model. The modeling results show that the average area under the curve (AUC) and true skill statistic (TSS) reached 0.908 ± 0.002 and 0.701, respectively, indicating that the MaxEnt model performed extremely well and provided outstanding prediction results. The prediction results indicate that climate change will have an impact on the distribution of the potential habitat of solitary desert locusts. With the increase in radiative forcing overtime, the suitable areas for desert locusts will continue to contract, especially in the 2070s under the SSP585 scenario, and the moderately and highly suitable areas will decrease by 0.88 × 106 km2 and 1.55 × 106 km2, respectively. Although the potentially suitable area for desert locusts is contracting, the future threat posed by the desert locust to agricultural production and food security cannot be underestimated, given the combination of maintained breeding areas, frequent extreme weather events, pressure from population growth, and volatile sociopolitical environments. In conclusion, methods such as monitoring and early warning, financial support, regional cooperation, and scientific prevention and control of desert locust plagues should be further implemented.

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