Spatial Relationship between Precipitation and Runoff in Africa

Abstract. Lack of sufficient and reliable hydrological information is a key hindrance to water resource planning and management in Africa. Hence, the objective of this research is to examine the relationship between precipitation and runoff at three spatial scales, including the whole continent, 25 major basins and 55 countries. For this purpose, the long-term monthly runoff coefficient (Rc) was estimated using the long-term monthly runoff data (R) calculated from the Global Runoff Data Centre (GRDC) streamflow records and Global Precipitation Climatology Centre (GPCC) precipitation datasets for the period of time spanning from 1901 to 2017. Subsequently, the observed Rc data were interpolated in order to estimate Rc over the ungauged basins under guidance of key runoff controlling factors, including the land-surface temperature (T), precipitation (P) and potential runoff coefficient (Co) inferred from the land use and land cover, slope and soil texture information. The results show that 16 % of the annual mean precipitation (672.52 mm) becomes runoff (105.72 mm), with a runoff coefficient of 0.16, and the remaining 84 % (566.80 mm) evapotranspirates over the continent during 1901–2017. Spatial analysis reveals that the precipitation–runoff relationship varies significantly among different basins and countries, mainly dependent on climatic conditions and its inter-annual variability. Generally, high runoff depths and runoff coefficients are observed over humid tropical basins and countries with high precipitation intensity compared to those located in subtropical and temperate drylands.

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Influence of radiative forcing factors on ground–air temperature coupling during the last millennium: implications for borehole climatology

Climate of the Past ◽

10.5194/cp-14-1583-2018 ◽

2018 ◽

Vol 14 (11) ◽

pp. 1583-1606 ◽

Cited By ~ 7

Author(s):

Camilo Melo-Aguilar ◽

J. Fidel González-Rouco ◽

Elena García-Bustamante ◽

Jorge Navarro-Montesinos ◽

Norman Steinert

Keyword(s):

Air Temperature ◽

Land Surface ◽

Radiative Forcing ◽

Spatial Scales ◽

Ground Surface ◽

Surface Air Temperature ◽

Small Variation ◽

Last Millennium ◽

Reconstruction Methods

Abstract. Past climate variations may be uncovered via reconstruction methods that use proxy data as predictors. Among them, borehole reconstruction is a well-established technique to recover the long-term past surface air temperature (SAT) evolution. It is based on the assumption that SAT changes are strongly coupled to ground surface temperature (GST) changes and transferred to the subsurface by thermal conduction. We evaluate the SAT–GST coupling during the last millennium (LM) using simulations from the Community Earth System Model LM Ensemble (CESM-LME). The validity of such a premise is explored by analyzing the structure of the SAT–GST covariance during the LM and also by investigating the evolution of the long-term SAT–GST relationship. The multiple and single-forcing simulations in the CESM-LME are used to analyze the SAT–GST relationship within different regions and spatial scales and to derive the influence of the different forcing factors on producing feedback mechanisms that alter the energy balance at the surface. The results indicate that SAT–GST coupling is strong at global and above multi-decadal timescales in CESM-LME, although a relatively small variation in the long-term SAT–GST relationship is also represented. However, at a global scale such variation does not significantly impact the SAT–GST coupling, at local to regional scales this relationship experiences considerable long-term changes mostly after the end of the 19th century. Land use land cover changes are the main driver for locally and regionally decoupling SAT and GST, as they modify the land surface properties such as albedo, surface roughness and hydrology, which in turn modifies the energy fluxes at the surface. Snow cover feedbacks due to the influence of other external forcing are also important for corrupting the long-term SAT–GST coupling. Our findings suggest that such local and regional SAT–GST decoupling processes may represent a source of bias for SAT reconstructions from borehole measurement, since the thermal signature imprinted in the subsurface over the affected regions is not fully representative of the long-term SAT variations.

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The new portfolio of global precipitation data products of the Global Precipitation Climatology Centre suitable to assess and quantify the global water cycle and resources

Proceedings of the International Association of Hydrological Sciences ◽

10.5194/piahs-374-29-2016 ◽

2016 ◽

Vol 374 ◽

pp. 29-34 ◽

Cited By ~ 27

Author(s):

Udo Schneider ◽

Markus Ziese ◽

Anja Meyer-Christoffer ◽

Peter Finger ◽

Elke Rustemeier ◽

...

Keyword(s):

Land Surface ◽

Water Cycle ◽

Spatial Scales ◽

Research Programme ◽

Special Importance ◽

Product Portfolio ◽

Precipitation Climatology ◽

Global Precipitation Climatology Centre ◽

The World ◽

Global Precipitation

Abstract. Precipitation plays an important role in the global energy and water cycle. Accurate knowledge of precipitation amounts reaching the land surface is of special importance for fresh water assessment and management related to land use, agriculture and hydrology, incl. risk reduction of flood and drought. High interest in long-term precipitation analyses arises from the needs to assess climate change and its impacts on all spatial scales. In this framework, the Global Precipitation Climatology Centre (GPCC) has been established in 1989 on request of the World Meteorological Organization (WMO). It is operated by Deutscher Wetterdienst (DWD, National Meteorological Service of Germany) as a German contribution to the World Climate Research Programme (WCRP). This paper provides information on the most recent update of GPCC's gridded data product portfolio including example use cases.

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Terrestrial Processes and Their Roles in Climate Change

Oxford Research Encyclopedia of Climate Science ◽

10.1093/acrefore/9780190228620.013.825 ◽

2021 ◽

Author(s):

Nathalie de Noblet-Ducoudré ◽

Andrew J. Pitman

Keyword(s):

Climate Change ◽

Greenhouse Gases ◽

Land Surface ◽

Seasonal Cycle ◽

Spatial Scales ◽

Climatic Conditions ◽

Surface Processes ◽

Climate Modelling ◽

Climate Simulations

The land surface is where humans live and where they source their water and food. The land surface plays an important role in climate and anthropogenic climate change both as a driver of change and as a system that responds to change. Soils and vegetation influence the exchanges of water, energy and carbon between the land and the overlying atmosphere and thus contribute to the variability and the evolution of climate. But the role of the land in climate is scale dependent which means different processes matter on different timescales and over different spatial scales. Climate change alters the functioning of the land with changes in the seasonal cycle of ecosystem growth, in the extent of forests, the melt of permafrost, the magnitude and frequency of disturbances such as fire, drought, … Those changes feedback into climate at both the global and the regional scales. In addition, humans perturb the land conditions via deforestation, irrigation, urbanization, … and this directly affects climatic conditions at the local to regional scales with also sometimes global consequences via the release of greenhouse gases. Not accounting for land surface processes in climate modelling, whatever the spatial scale, will result in biases in the climate simulations.

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Exploring the 36Cl/Cl input in arid environments: New insights gained by 81Kr groundwater dating in the Negev Desert, Israel

10.5194/egusphere-egu21-4143 ◽

2021 ◽

Author(s):

Roi Ram ◽

Roland Purtschert ◽

Christof Vockenhuber ◽

Reika Yokochi ◽

Eilon M. Adar ◽

...

Keyword(s):

Land Surface ◽

Noble Gas ◽

Climatic Conditions ◽

Arid Environments ◽

Near Surface ◽

Spatial Differences ◽

Practical Tool ◽

Groundwater Dating ◽

Long Timescales

&#160;&#160; 36Cl and 81Kr (half-lives of 301 and 229 kyr, respectively) are among a very few age tracers with dating capabilities in the 104&#8211;106 yr timescale. Although widely applied since the 1980s in various hydrological studies, the 36Cl/Cl system has been found complex as an effective dating tool. In contrast, 81Kr has become a practical tool only recently and is considered to be an ideal dating tool due to the inert properties of the noble gas. In the present study, simultaneous measurements of both radioisotopes were used to assess the 36Cl/Cl input ratios and the Cl- content for paleorecharge into the deep, transboundary Nubian Sandstone Aquifer (NSA) which stretches below the hyperarid deserts of the Sinai Peninsula (Egypt) and the Negev (Israel).&#160;&#160; By means of 81Kr data, reconstructed Cl- content of recharge that occurred during the late Pleistocene was found to be 300&#8211;400 mg/L with an initial 36Cl/Cl ratio of 50 &#215; 10-15. This latter value is in agreement with the 36Cl/Cl ratio in recent local rainwater, indicating constancy over prolonged periods with possible variable climatic conditions. This similarity in values suggests a process that is rather insensitive to atmospheric 36Cl fallout rates. Erosion and weathering of near-surface materials in the desert environment could dominate the hydrochemistry of rains, floods, and the consequent groundwater recharge. This near-surface Cl- reservoir integrates various sources and processes, including marine and terrestrial Cl-, cosmogenic 36Cl fallout, and cosmogenic 36Cl production in the shallow unsaturated zone, all of which are active over long timescales and accumulate on the land surface and in the epigene zone.&#160; Spatial differences in the reconstructed initial 36Cl/Cl ratio are attributed to differences in the mineral aerosol sources for specific recharge areas of the NSA. The results of this study highlight the potential of integrating 81Kr age information in evaluating the initial 36Cl/Cl and Cl- input, which is essential for the calibration of 36Cl radioisotope as a long-term dating tool for a given basin.

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Modeling water balance using the Budyko framework over variable timescales under diverse climates

10.5194/hess-2017-441 ◽

2017 ◽

Cited By ~ 2

Author(s):

Chuanhao Wu ◽

Pat J.-F. Yeh ◽

Kai Xu ◽

Bill X. Hu ◽

Guoru Huang ◽

...

Keyword(s):

Water Balance ◽

Temporal Variability ◽

Land Surface ◽

Potential Evapotranspiration ◽

Climatic Conditions ◽

Climate Control ◽

Temporal Scales ◽

Control Factors ◽

Modeling Errors

Abstract. Understanding the effects of climate and catchment characteristics on overall water balance at different temporal scales remains a challenging task due to the large spatial heterogeneity and temporal variability. Based on a long-term (1960–2008) land surface hydrologic dataset over China, this study presented a systematic examination of the applicability of the Budyko model (BM) under various climatic conditions at long-term mean annual, annual, seasonal and monthly temporal scales. The roles of water storage change (WSC, dS/dt) in water balance modeling and the dominant climate control factors on modeling errors of BM are investigated. The results indicate that BM performs well at mean annual scale and the performance in arid climates is better than humid climates. At other smaller timescales, BM is generally accurate in arid climates, but fails to capture dominant controls on water balance in humid climates due to the effects of WSC not included in BM. The accuracy of BM can be ranked from high to low as: dry seasonal, annual, monthly, and wet seasonal timescales. When WSC is incorporated into BM by replacing precipitation (P) with effective precipitation (i.e., P minus WSC), significant improvements are found in arid climates, but to a lesser extent in humid climates. The ratio of the standard deviation of WSC to that of evapotranspiration (E), which increases from arid to humid climates, is found to be the key indicator of the BM simulation errors due to the omission of the effect of WSC. The modeling errors of BM are positively correlated with the temporal variability of WSC and hence larger in humid climates, and also found to be proportional to the ratio of potential evapotranspiration (PET) to E. More sophisticated models than the BM which explicitly incorporate the effect of WSC are required to improve water balance modeling in humid climates particularly at all the annual, seasonal, and monthly timescales.

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Large-Scale Runoff from Landmasses: A Global Assessment of the Closure of the Hydrological and Atmospheric Water Balances*

Journal of Hydrometeorology ◽

10.1175/jhm-d-13-0157.1 ◽

2014 ◽

Vol 15 (6) ◽

pp. 2111-2139 ◽

Cited By ~ 37

Author(s):

Christof Lorenz ◽

Harald Kunstmann ◽

Balaji Devaraju ◽

Mohammad J. Tourian ◽

Nico Sneeuw ◽

...

Keyword(s):

Land Surface ◽

Large Scale ◽

Global Precipitation Climatology Project ◽

Research Unit ◽

Climatic Conditions ◽

Climatic Research Unit ◽

Precipitation Climatology ◽

Global Precipitation Climatology Centre ◽

Global Land ◽

Global Precipitation

Abstract The performance of hydrological and hydrometeorological water-balance-based methods to estimate monthly runoff is analyzed. Such an analysis also allows for the examination of the closure of water budgets at different spatial (continental and catchment) and temporal (monthly, seasonal, and annual) scales. For this analysis, different combinations of gridded observations [Global Precipitation Climatology Centre (GPCC), Global Precipitation Climatology Project (GPCP), Climate Prediction Center (CPC), Climatic Research Unit (CRU), and University of Delaware (DEL)], atmospheric reanalysis models [Interim ECMWF Re-Analysis (ERA-Interim), Climate Forecast System Reanalysis (CFSR), and Modern-Era Retrospective Analysis for Research and Applications (MERRA)], partially model-based datasets [Global Land Surface Evaporation: The Amsterdam Methodology (GLEAM), Moderate Resolution Imaging Spectroradiometer (MODIS) Global Evapotranspiration Project (MOD16), and FLUXNET Multi-Tree Ensemble (FLUXNET MTE)], and Gravity Recovery and Climate Experiment (GRACE) satellite-derived water storage changes are employed. The derived ensemble of hydrological and hydrometeorological budget–based runoff estimates, together with results from different land surface hydrological models [Global Land Data Assimilation System (GLDAS) and the land-only version of MERRA (MERRA-Land)] and a simple predictor based on the precipitation–runoff ratio, is compared with observed monthly in situ runoff for 96 catchments of different sizes and climatic conditions worldwide. Despite significant shortcomings of the budget-based methods over many catchments, the evaluation allows for the demarcation of areas with consistently reasonable runoff estimates. Good agreement was particularly observed when runoff followed a dominant annual cycle like the Amazon. This holds true also for catchments with an area far below the spatial resolution of GRACE, like the Rhine. Over catchments with low or nearly constant runoff, the budget-based approaches do not provide realistic runoff estimates because of significant biases in the input datasets. In general, no specific data combination could be identified that consistently performed over all catchments. Thus, the performance over a specific single catchment cannot be extrapolated to other regions. Only in few cases do specific dataset combinations provide reasonable water budget closure; in most cases, significant imbalances remain for all the applied datasets.

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Regularities of soil temperature changes during the period with snow cover in modern climatic conditions of the Eurasian Subarctic

Journal of the Belarusian State University. Geography and Geology ◽

10.33581/2521-6740-2021-1-13-22 ◽

2021 ◽

pp. 13-22

Author(s):

Lev M. Kitaev

Keyword(s):

Standard Deviation ◽

Soil Temperature ◽

Snow Cover ◽

Air Temperature ◽

Land Surface ◽

Environmental Changes ◽

Surface Air Temperature ◽

Climatic Conditions ◽

Winter Period

The influence of snow cover on the dynamics of soil temperature in the modern climatic conditions of the Eurasian Subarctic was investigated through a quantitative assessment of the features of the seasonal and long-term variation of parameters. Seasonal and long-term values of soil temperature for stable snow period decrease from west to east: a decrease of snow thickness and air temperature from west to east of Eurasia leads to a weakening of the heat-insulating properties of the snow cover with a significant decrease in regional air temperatures. With the emergence of a stable snow cover, the soil temperature seasonal and long-term standard deviation sharply decreases compared to the autumn and spring periods. With the appearance of snow cover, the soil temperature standard deviation drops sharply compared to the autumn and spring periods. An exception is the northeast of Siberia: here, a relatively small thickness of snow determines a noticeable dependence of the course of soil temperature on the dynamics of surface air temperature. There are no significant long-term trends in soil temperature due to its low variability during winter period. Analysis of the course of the studied characteristics anomalies showed an insignificant and non-systematic number of their coincidences. Currently, we have not found similar research results for large regions. The revealed patterns can be used in the analysis of the results of monitoring the state of the land surface, in the development of remote sensing algorithms, in the refinement of predictive scenarios of environmental changes.

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Human-caused long-term changes in global aridity

npj Climate and Atmospheric Science ◽

10.1038/s41612-021-00223-5 ◽

2021 ◽

Vol 4 (1) ◽

Author(s):

Rongfan Chai ◽

Jiafu Mao ◽

Haishan Chen ◽

Yaoping Wang ◽

Xiaoying Shi ◽

...

Keyword(s):

Land Surface ◽

Spatial Scales ◽

Dominant Role ◽

Coupled Model ◽

Aridity Index ◽

Factors Affecting ◽

Statistical Confidence ◽

Detection And Attribution ◽

Aerosol Emissions

AbstractWidespread aridification of the land surface causes substantial environmental challenges and is generally well documented. However, the mechanisms underlying increased aridity remain relatively underexplored. Here, we investigated the anthropogenic and natural factors affecting long-term global aridity changes using multisource observation-based aridity index, factorial simulations from the Coupled Model Intercomparison Project phase 6 (CMIP6), and rigorous detection and attribution (D&A) methods. Our study found that anthropogenic forcings, mainly rising greenhouse gas emissions (GHGE) and aerosols, caused the increased aridification of the globe and each hemisphere with high statistical confidence for 1965–2014; the GHGE contributed to drying trends, whereas the aerosol emissions led to wetting tendencies; moreover, the bias-corrected CMIP6 future aridity index based on the scaling factors from optimal D&A demonstrated greater aridification than the original simulations. These findings highlight the dominant role of human effects on increasing aridification at broad spatial scales, implying future reductions in aridity will rely primarily on the GHGE mitigation.

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Drought Model DISS Based on the Fusion of Satellite and Meteorological Data under Variable Climatic Conditions

Remote Sensing ◽

10.3390/rs12182944 ◽

2020 ◽

Vol 12 (18) ◽

pp. 2944

Author(s):

Katarzyna Dabrowska-Zielinska ◽

Alicja Malinska ◽

Zbigniew Bochenek ◽

Maciej Bartold ◽

Radoslaw Gurdak ◽

...

Keyword(s):

Environmental Conditions ◽

Land Surface ◽

Drought Index ◽

Extreme Values ◽

Meteorological Data ◽

Climatic Conditions ◽

Agricultural Drought ◽

Drought Monitoring ◽

Large Area

The use of effective methods for large-area drought monitoring is an important issue; hence, there have been many attempts to solve this problem. In this study, the Drought Information Satellite System (DISS) index is presented, based on the synergistic use of meteorological data and information derived from satellite images. The index allows us to monitor drought phenomena in various climatic and environmental conditions. The approach utilizes two indices for constructing a drought index: (1) the hydrothermal coefficient (HTC), which characterizes meteorological conditions across the study area over a long-term period; and (2) the temperature condition index (TCI) derived from Moderate-resolution Imaging Spectroradiometer (MODIS) data, which refers instantaneous land surface temperature (LST) to long-term extreme values. The model for drought assessment based on the DISS index was applied for generating drought index maps for Poland for the 2001–2019 vegetation seasons. The performance of the index was verified through comparison of the extent of agricultural drought to the reduction in cereal and maize yield. Analysis of variance revealed a significant relationship between the area of drought determined by the drought index and the decrease in cereal yield due to unfavorable growth conditions. The presented study proves that the proposed drought index can be an effective tool for large-area drought monitoring under variable environmental conditions.

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A geostatistical framework for estimating flow indices by exploiting short records and long-term spatial averages – Application to annual and monthly runoff

10.5194/hess-2019-415 ◽

2019 ◽

Author(s):

Thea Roksvåg ◽

Ingelin Steinsland ◽

Kolbjørn Engeland

Keyword(s):

Missing Values ◽

Interpolation Method ◽

Gaussian Random Fields ◽

Climatic Conditions ◽

Kriging Interpolation ◽

Linear Regression Method ◽

Runoff Generation ◽

Kriging Interpolation Method ◽

Monthly Runoff

Abstract. In this article, we present a Bayesian geostatistical framework that is particularly suitable for interpolation of hydrological data when the available dataset is sparse and includes missing values and short records of data. A key feature of the proposed framework is that several years of runoff is modeled simultaneously with two Gaussian random fields (GRFs): One that is common for all years under study and represents the runoff generation due to long-term climatic conditions, and one that is year specific. The climatic GRF learns how short records of runoff from partially gauged catchments vary relatively to longer time series from other catchments, and transfers this information across years. Another property, is that the model takes the nested structure of catchments into account such that the water balance is preserved for any point in the landscape. The framework is demonstrated by interpolation of annual and monthly runoff from around 200 catchments in Norway, and we compare it to Top-Kriging (interpolation method) and simple linear regression (method for exploiting short records). The results show that if the correlation between neighboring catchments is high, a model that considers several years of runoff simultaneously is considerably better at capturing large spatial variability than a model that treats each year of data separately.

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