Historic Climatic Variability and Change: The Importance of Managing Holocene and Late Pleistocene Groundwater in the Limpopo River Basin, Southern Africa

2021 ◽  
pp. 131-143
Author(s):  
Tamiru A. Abiye ◽  
Khahliso C. Leketa
Keyword(s):  
River Basin ◽  
Late Pleistocene ◽  
Southern Africa ◽  
Climatic Variability

scholarly journals The potential of global reanalysis datasets in identifying flood events in Southern Africa

2018 ◽  
Vol 22 (9) ◽  
pp. 4667-4683 ◽  
Author(s):  
Gaby J. Gründemann ◽  
Micha Werner ◽  
Ted I. E. Veldkamp
Keyword(s):  
Water Resources ◽  
River Basin ◽  
Southern Africa ◽  
Large Scale ◽  
Meteorological Data ◽  
Climatic Variability ◽  
Added Value ◽  
Flood Events ◽  
Global Models ◽  
Global Reanalysis

Abstract. Sufficient and accurate hydro-meteorological data are essential to manage water resources. Recently developed global reanalysis datasets have significant potential in providing these data, especially in regions such as Southern Africa that are both vulnerable and data poor. These global reanalysis datasets have, however, not yet been exhaustively validated and it is thus unclear to what extent these are able to adequately capture the climatic variability of water resources, in particular for extreme events such as floods. This article critically assesses the potential of a recently developed global Water Resources Reanalysis (WRR) dataset developed in the European Union's Seventh Framework Programme (EU-FP7) eartH2Observe (E2O) project for identifying floods, focussing on the occurrence of floods in the Limpopo River basin in Southern Africa. The discharge outputs of seven global models and ensemble mean of those models as available in the WRR dataset are analysed and compared against two benchmarks of flood events in the Limpopo River basin. The first benchmark is based on observations from the available stations, while the second is developed based on flood events that have led to damages as reported in global databases of damaging flood events. Results show that, while the WRR dataset provides useful data for detecting the occurrence of flood events in the Limpopo River basin, variation exists amongst the global models regarding their capability to identify the magnitude of those events. The study also reveals that the models are better able to capture flood events at stations with a large upstream catchment area. Improved performance for most models is found for the 0.25° resolution global model, when compared to the lower-resolution 0.5° models, thus underlining the added value of increased-resolution global models. The skill of the global hydrological models (GHMs) in identifying the severity of flood events in poorly gauged basins such as the Limpopo can be used to estimate the impacts of those events using the benchmark of reported damaging flood events developed at the basin level, though this could be improved if further details on location and impacts are included in disaster databases. Large-scale models such as those included in the WRR dataset are used by both global and continental forecasting systems, and this study sheds light on the potential these have in providing information useful for local-scale flood risk management. In conclusion, this study offers valuable insights in the applicability of global reanalysis data for identifying impacting flood events in data-sparse regions.

scholarly journals The potential of global re-analysis datasets in identifying flood events in Southern Africa

2018 ◽  
Author(s):  
Gaby J. Gründemann ◽  
Micha Werner ◽  
Ted I. E. Veldkamp
Keyword(s):  
Water Resources ◽  
River Basin ◽  
Southern Africa ◽  
Large Scale ◽  
Meteorological Data ◽  
Climatic Variability ◽  
Analysis Data ◽  
Added Value ◽  
Flood Events ◽  
Global Models

Abstract. Sufficient and accurate hydro-meteorological data are essential to manage water resources. Recently developed global re-analysis datasets have significant potential in providing these data, especially in regions such as Southern Africa that are both vulnerable and data poor. These global re-analysis datasets have, however, not yet been exhaustively validated and it is thus unclear to what extent these are able to adequately capture the climatic variability of water resources, in particular for extreme events such as floods. This article critically assesses the potential of a recently developed global Water Resource Re-analysis (WRR) dataset developed in the EU FP7 eartH2Observe project for identifying floods, focussing on the occurrence of floods in the Limpopo River basin in Southern Africa. The discharge outputs of seven global models and ensemble mean of those models as available in the WRR dataset are analysed and compared against two benchmarks of flood events in the Limpopo River basin. The first benchmark is based on observations from the available stations, while the second is developed based on flood events that have led to damages as reported in global databases of damaging flood events. Results show that while the WRR dataset provides useful data for detecting the occurrence of flood events in the Limpopo River basin, variation exists amongst the global models regarding their capability to identify the magnitude of those events. The study also reveals that the models are better able to capture flood events at stations with a large upstream catchment area. Improved performance for most models is found for the 0.25 degrees resolution global model, when compared to the lower resolution 0.5 degrees models, thus underlining the added value of increased resolution global models. The skill of the global hydrological models in identifying the severity of flood events in poorly gauged basins such as the Limpopo can be used to estimate the impacts of those events using the benchmark of reported damaging flood events developed at the basin level, though could be improved if further detail on location and impacts are included in disaster databases. Large-scale models such as those included in the WRR dataset are used by both global and continental forecasting systems, and this study sheds light on the potential these have in providing information useful for local scale flood risk management. In conclusion, this study offers valuable insights in the applicability of global re-analysis data for identifying impacting flood events in data sparse regions.

A multi-proxy reconstruction of climate during the late-Pleistocene to early Holocene transition in the northeastern, USA

2021 ◽  
pp. 1-17
Author(s):  
Laurie D. Grigg ◽  
Kevin J. Engle ◽  
Alison J. Smith ◽  
Bryan N. Shuman ◽  
Maximilian B. Mandl
Keyword(s):  
Late Pleistocene ◽  
Climatic Variability ◽  
Warming Trend ◽  
Early Holocene ◽  
Individual Species ◽  
Environmental Preferences ◽  
The North ◽  
Show Evidence ◽  
Dry Conditions ◽  
Components Analysis

Abstract A multiproxy record from Twin Ponds, VT, is used to reconstruct climatic variability during the late Pleistocene to early Holocene transition. Pollen, ostracodes, δ18O, and lithologic records from 13.5 to 9.0 cal ka BP are presented. Pollen- and ostracode-inferred climatic reconstructions are based on individual species’ environmental preferences and the modern analog technique. Principal components analysis of all proxies highlights the overall warming trend and centennial-scale climatic variability. During the Younger Dryas cooling event (YD), multiple proxies show evidence for cold winter conditions and increasing seasonality after 12.5 cal ka BP. The early Holocene shows an initial phase of rapid warming with a brief cold interval at 11.5 cal ka BP, followed by a more gradual warming; a cool, wet period from 11.2 to 10.8 cal ka BP; and cool, dry conditions from 10.8 to 10.2 cal ka BP. The record ends with steady warming and increasing moisture. Post-YD climatic variability has been observed at other sites in the northeastern United States and points to continued instability in the North Atlantic during the final phases of deglaciation.

scholarly journals The bias in GRACE estimates of continental water storage variations

2006 ◽  
Vol 3 (6) ◽  
pp. 3557-3594 ◽  
Author(s):  
R. Klees ◽  
E. A. Zapreeva ◽  
H. C. Winsemius ◽  
H. H. G. Savenije
Keyword(s):  
River Basin ◽  
Southern Africa ◽  
A Priori ◽  
Water Storage ◽  
Target Area ◽  
Hydrological Models ◽  
A Priori Information ◽  
Basin Scale ◽  
Priori Information ◽  
Gravity Field Models

Abstract. The estimation of terrestrial water storage variations at river basin scale is among the best documented applications of the GRACE (Gravity and Climate Experiment) satellite gravity mission. In particular, it is expected that GRACE closes the water balance at river basin scale and allows the verification, improvement and modeling of the related hydrological processes by combining GRACE amplitude estimates with hydrological models' output and in-situ data. When computing monthly mean storage variations from GRACE gravity field models, spatial filtering is mandatory to reduce GRACE errors, but at the same time yields biased amplitude estimates. The objective of this paper is three-fold. Firstly, we want to compute and analyze amplitude and time behaviour of the bias in GRACE estimates of monthly mean water storage variations for several target areas in Southern Africa. In particular, we want to know the relation between bias and the choice of the filter correlation length, the size of the target area, and the amplitude of mass variations inside and outside the target area. Secondly, we want to know to what extent the bias can be corrected for using a priori information about mass variations. Thirdly, we want to quantify errors in the estimated bias due to uncertainties in the a priori information about mass variations that are used to compute the bias. The target areas are located in Southern Africa around the Zambezi river basin. The latest release of monthly GRACE gravity field models have been used for the period from January 2003 until March 2006. An accurate and properly calibrated regional hydrological model has been developed for this area and its surroundings and provides the necessary a priori information about mass variations inside and outside the target areas. The main conclusion of the study is that spatial smoothing significantly biases GRACE estimates of the amplitude of annual and monthly mean water storage variations. For most of the practical applications, the bias will be positive, which implies that GRACE underestimates the amplitudes. The bias is mainly determined by the filter correlation length; in the case of 1000 km smoothing, which is shown to be an appropriate choice for the target areas, the annual bias attains values up to 50% of the annual storage; the monthly bias is even larger with a maximum value of 75% of the monthly storage. A priori information about mass variations can provide reasonably accurate estimates of the bias, which significantly improves the quality of GRACE water storage amplitudes. For the target areas in Southern Africa, we show that after bias correction, GRACE annual amplitudes differ between 0 and 30 mm from the output of a regional hydrological model, which is between 0% and 25% of the storage. Annual phase shifts are small, not exceeding 0.25 months, i.e. 7.5 deg. Our analysis suggests that bias correction of GRACE water storage amplitudes is indispensable if GRACE is used to calibrate hydrological models.

scholarly journals Climate change impacts on hydrology and water resources of Indian River basin

2018 ◽  
Vol 13 (1) ◽  
pp. 32-43 ◽  
Author(s):  
Umesh Kumar Singh ◽  
Balwant Kumar
Keyword(s):  
Climate Change ◽  
Groundwater Recharge ◽  
River Basin ◽  
Greenhouse Gas Emission ◽  
Hydrological Cycle ◽  
Indian Subcontinent ◽  
Climatic Variability ◽  
River Basins ◽  
Extreme Weather Events ◽  
Runoff Generation

Anthropogenic greenhouse gas emission is altering the global hydrological cycle due to change in rainfall pattern and rising temperature which is responsible for alteration in the physical characteristics of river basin, melting of ice, drought, flood, extreme weather events and alteration in groundwater recharge. In India, water demand for domestic, industrial and agriculture purposes have already increased many folds which are also influencing the water resource system. In addition, climate change has induced the surface temperature of the Indian subcontinent by 0.48 ºC in just last century. However, Ganges–Brahmaputra–Meghna (GBM) river basins have great importance for their exceptional hydro-geological settings and deltaic floodplain wetland ecosystems which support 700 million people in Asia. The climatic variability like alterations in precipitation and temperature over GBM river basins has been observed which signifies the GBM as one of the most vulnerable areas in the world under the potential impact of climate change. Consequently, alteration in river discharge, higher runoff generation, low groundwater recharge and melting of glaciers over GBM river basin could be observed in near future. The consequence of these changes due to climate change over GBM basin may create serious water problem for Indian sub-continents. This paper reviews the literature on the historical climate variations and how climate change affects the hydrological characteristics of different river basins.

A Late Pleistocene plant-bearing deposit in Currie Township, near Matheson, Ontario

1979 ◽  
Vol 16 (5) ◽  
pp. 1130-1136 ◽  
Author(s):  
W. E. Brereton ◽  
J. A. Elson
Keyword(s):  
River Basin ◽  
Late Pleistocene ◽  
Glacial Lake ◽  
Fossil Plants ◽  
Fossil Plant ◽  
James Bay ◽  
Plant Detritus ◽  
Lake Ojibway ◽  
Moose River ◽  
James Bay Lowlands

Two overburden test holes drilled to bedrock in Currie Township, southwest of Matheson, Ontario, penetrated stratified beds containing fossil plant detritus resting on an oxidized substrate, which are between two till sheets underlying glacial Lake Ojibway-Barlow varved clays. The fossil plants, chiefly mosses, represent an environment that is common in the region today, and are radiocarbon dated (GSC-2148) as older than 37000 years. The interglacial deposit is tentatively correlated with the Missinaibi Formation in the Moose River basin of the James Bay lowlands, probably of Sangamon age.

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