Using reanalysis‐driven regional climate model outputs for hydrology modelling

Bias correction methods are usually applied to climate model outputs before using these outputs for hydrological climate change impact studies. However, the use of a bias correction procedure is debatable, due to the lack of physical basis and the bias nonstationarity of climate model outputs between future and historical periods. The direct use of climate model outputs for impact studies has therefore been recommended in a few studies. This study investigates the possibility of using reanalysis‐driven regional climate model (RCM) outputs directly for hydrological modelling by comparing the performance of bias‐corrected and nonbias‐corrected climate simulations in hydrological simulations over 246 watersheds in the Province of Québec, Canada. When using RCM outputs directly, the hydrological model is specifically calibrated using RCM simulations. Two evaluation metrics (Nash–Sutcliffe efficiency [NSE] and transformed root mean square error [TRMSE]) and three hydrological indicators (mean, high, and low flows) are used as criteria for this comparison. Two reanalysis‐driven RCMs with resolutions of 45 km and 15 km are used to investigate the scale effect of climate model simulations and bias correction approaches on hydrology modelling. The results show that nonbias‐corrected simulations perform better than bias‐corrected simulations for the reproduction of the observed streamflows when using NSE and TRMSE as criteria. The nonbias‐corrected simulations are also better than or comparable with the bias‐corrected simulations in terms of reproducing the three hydrological indicators. These results imply that the raw RCM outputs driven by reanalysis can be used directly for hydrological modelling with a specific calibration of hydrological models using these datasets when gauged observations are scarce or unavailable. The nonbias‐corrected simulations (at a minimum) should be provided to end users, along with the bias‐corrected ones, especially for studying the uncertainty of hydrological climate change impacts. This is especially true when using an RCM with a high resolution, since the scale effect is observed when the RCM resolution increases from a 45‐km to a 15‐km scale.     

Importance of Spatial Resolution in Global Groundwater Modeling

Global-scale gradient-based groundwater models are a new endeavor for hydrologists who wish to improve global hydrological models (GHMs). In particular, the integration of such groundwater models into GHMs improves the simulation of water flows between surface water and groundwater and of capillary rise and thus evapotranspiration. Currently, these models are not able to simulate water table depth adequately over the entire globe. Unsatisfactory model performance compared to well observations suggests that a higher spatial resolution is required to better represent the high spatial variability of land surface and groundwater elevations. In this study, we use New Zealand as a testbed and analyze the impacts of spatial resolution on the results of global groundwater models. Steady-state hydraulic heads simulated by two versions of the global groundwater model G³M, at spatial resolutions of 5 arc-minutes (9 km) and 30 arc-seconds (900 m), are compared with observations from the Canterbury region. The output of three other groundwater models with different spatial resolutions is analyzed as well. Considering the spatial distribution of residuals, general patterns of unsatisfactory model performance remain at the higher resolutions, suggesting that an increase in model resolution alone does not fix problems such as the systematic overestimation of hydraulic head. We conclude that (1) a new understanding of how low-resolution global groundwater models can be evaluated is required, and (2) merely increasing the spatial resolution of global-scale groundwater models will not improve the simulation of the global freshwater system.     

Embedding complex hydrology in the regional climate system – Dynamic coupling across different modelling domains

To improve our understanding of the impacts of feedback between the atmosphere and the terrestrial water cycle including groundwater and to improve the integration of water resource management modelling for climate adaption we have developed a dynamically coupled climate–hydrological modelling system. The OpenMI modelling interface is used to couple a comprehensive hydrological modelling system, MIKE SHE running on personal computers, and a regional climate modelling system, HIRHAM running on a high performance computing platform. The coupled model enables two-way interaction between the atmosphere and the groundwater via the land surface and can represent the lateral movement of water in both the surface and subsurface and their interactions, not normally accounted for in climate models. Meso-scale processes are important for climate in general and rainfall in particular. Hydrological impacts are assessed at the catchment scale, the most important scale for water management. Feedback between groundwater, the land surface and the atmosphere occurs across a range of scales. Recognising this, the coupling was developed to allow dynamic exchange of water and energy at the catchment scale embedded within a larger meso-scale modelling domain. We present the coupling methodology used and describe the challenges in representing the exchanges between models and across scales. The coupled model is applied to one-way and two-way coupled simulations for a managed groundwater-dominated catchment, the Skjern River, Denmark. These coupled model simulations are evaluated against field observations and then compared with uncoupled climate and hydrological model simulations. Exploratory simulations show significant differences, particularly in the summer for precipitation and evapotranspiration the coupled model including groundwater and the RCM where groundwater is neglected. However, the resulting differences in the net precipitation and the catchment runoff in this groundwater dominated catchment were small. The need for further decadal scale simulations to understand the differences and insensitivity is highlighted.     

Assessing hydrological model predictive uncertainty using stochastically generated geological models

In distributed and coupled surface water–groundwater modelling, the uncertainty from the geological structure is unaccounted for if only one deterministic geological model is used. In the present study, the geological structural uncertainty is represented by multiple, stochastically generated geological models, which are used to develop hydrological model ensembles for the Norsminde catchment in Denmark. The geological models have been constructed using two types of field data, airborne geophysical data and borehole well log data. The use of airborne geophysical data in constructing stochastic geological models and followed by the application of such models to assess hydrological simulation uncertainty for both surface water and groundwater have not been previously studied. The results show that the hydrological ensemble based on geophysical data has a lower level of simulation uncertainty, but the ensemble based on borehole data is able to encapsulate more observation points for stream discharge simulation. The groundwater simulations are in general more sensitive to the changes in the geological structure than the stream discharge simulations, and in the deeper groundwater layers, there are larger variations between simulations within an ensemble than in the upper layers. The relationship between hydrological prediction uncertainties measured as the spread within the hydrological ensembles and the spatial aggregation scale of simulation results has been analysed using a representative elementary scale concept. The results show a clear increase of prediction uncertainty as the spatial scale decreases. Copyright © 2015 John Wiley & Sons, Ltd.     

Estimation of uncertainty sources in the projections of Lithuanian river runoff

Particular attention is given to the reliability of hydrological modelling results. The accuracy of river runoff projection depends on the selected set of hydrological model parameters, emission scenario and global climate model. The aim of this article is to estimate the uncertainty of hydrological model parameters, to perform sensitivity analysis of the runoff projections, as well as the contribution analysis of uncertainty sources (model parameters, emission scenarios and global climate models) in forecasting Lithuanian river runoff. The impact of model parameters on the runoff modelling results was estimated using a sensitivity analysis for the selected hydrological periods (spring flood, winter and autumn flash floods, and low water). During spring flood the results of runoff modelling depended on the calibration parameters that describe snowmelt and soil moisture storage, while during the low water period—the parameter that determines river underground feeding was the most important. The estimation of climate change impact on hydrological processes in the Merkys and Neris river basins was accomplished through the combination of results from A1B, A2 and B1 emission scenarios and global climate models (ECHAM5 and HadCM3). The runoff projections of the thirty-year periods (2011–2040, 2041–2070, 2071–2100) were conducted applying the HBV software. The uncertainties introduced by hydrological model parameters, emission scenarios and global climate models were presented according to the magnitude of the expected changes in Lithuanian rivers runoff. The emission scenarios had much greater influence on the runoff projection than the global climate models. The hydrological model parameters had less impact on the reliability of the modelling results.     

Quantifying and Reducing Uncertainty in the Large-Scale Response of the Water Cycle

Despite their obvious environmental, societal and economic importance, our understanding of the causes and magnitude of the variations in the global water cycle is still unsatisfactory. Uncertainties in hydrological predictions from the current generation of models pose a serious challenge to the reliability of forecasts and projections across time and space scales. This paper provides an overview of the current issues and challenges in modelling various aspects of the Earth’s hydrological cycle. These include: the global water budget and water conservation, the role of model resolution and parametrisation of precipitation-generating processes on the representation of the global and regional hydrological cycle, representation of clouds and microphysical processes, rainfall variability, the influence of land–atmosphere coupling on rainfall patterns and their variability, monsoon processes and teleconnections, and ocean and cryosphere modelling. We conclude that continued collaborative activity in the areas of model development across timescales, process studies and climate change studies will provide better understanding of how and why the hydrological cycle may change, and better estimation of uncertainty in model projections of changes in the global water cycle.     

Vulnerability of water resources under a changing climate and human activity in the lower Great Lakes region

Globally, the number of people experiencing water stress is expected to increase by millions by the end of the century. The Great Lakes region, representing 20% of the world's surface freshwater, is not immune to stresses on water supply due to uncertainties on the impacts of climate and land use change. It is imperative for researchers and policy makers to assess the changing state of water resources, even if the region is water rich. This research developed the integrated surface water-groundwater GSFLOW model and investigated the effects of climate change and anthropogenic activities on water resources in the lower Great Lakes region of Western New York. To capture a range of scenarios, two climate emission pathways and three land development projections were used, specifically RCP 4.5, RCP 8.5, increased urbanization by 50%, decreased urbanization by 50%, and current land cover, respectively. Model outputs of surface water and groundwater discharge into the Great Lakes and groundwater storage for mid- and late century were compared to historical to determine the direction and amplitude of changes. Both surface water and groundwater systems show no statistically significant changes under RCP 4.5 but substantial and worrisome losses with RCP 8.5 by mid-century and end of century. Under RCP 8.5, streamflow decreased by 22% for mid-century and 42% for late century. Adjusting impervious surfaces revealed complex land use effects, resulting in spatially varying groundwater head fluctuations. For instance, increasing impervious surfaces lowered groundwater levels from 0.5 to 3.8 m under Buffalo, the largest city in the model domain, due to reduced recharge in surrounding suburban areas. Ultimately, results of this study highlight the necessity of integrated modelling in assessing temporal changes to water resources. This research has implications for other water-rich areas, which may not be immune to effects of climate change and human activities.     

An inverse-modelling approach to assess the impacts of climate change in the Seyhan River basin,Turkey / Une approche de modélisation inverse pour évaluer les impacts du changement climatique dans le bassin versant de la Rivière Seyhan,Turquie

Abstract

One of the most significant anticipated consequences of global climate change is the change in frequency of hydrological extremes. Predictions of climate change impacts on the regime of hydrological extremes have traditionally been conducted by a top-down approach that involves a high degree of uncertainty associated with the temporal and spatial characteristics of general circulation model (GCM) outputs and the choice of downscaling technique. This study uses the inverse approach to model hydrological risk and vulnerability to changing climate conditions in the Seyhan River basin, Turkey. With close collaboration with the end users, the approach first identifies critical hydrological exposures that may lead to local failures in the Seyhan River basin. The Hydro-BEAM hydrological model is used to inversely transform the main hydrological exposures, such as floods and droughts, into corresponding meteorological conditions. The frequency of critical meteorological conditions is investigated under present and future climate scenarios by means of a weather generator based on the improved K-nearest neighbour algorithm. The weather generator, linked with the output of GCMs in the last step of the proposed methodology, allows for the creation of an ensemble of scenarios and easy updating when improved GCM outputs become available. Two main conclusions were drawn from the application of the inverse approach to the Seyhan River basin. First, floods of 100-, 200- and 300-year return periods under present conditions will have 102-, 293- and 1370-year return periods under the future conditions; that is, critical flood events will occur much less frequently under the changing climate conditions. Second, the drought return period will change from 5.3 years under present conditions to 2.0 years under the future conditions; that is, critical drought events will occur much more frequently under the changing climate conditions.     

Climate impacts on river flow: projections for the Medway catchment,UK, with UKCP09 and CATCHMOD

The potential impact of climate change on areas of strategic importance for water resources remains a concern. Here, river flow projections for the River Medway, above Teston in southeast England are presented, which is just such an area of strategic importance. The river flow projections use climate inputs from the Hadley Centre Regional Climate Model (HadRM3) for the time period 1960–2080 (a subset of the early release UKCP09 projections). River flow predictions are calculated using CATCHMOD, the main river flow prediction tool of the Environment Agency (EA) of England and Wales. In order to use this tool in the best way for climate change predictions, model setup and performance are analysed using sensitivity and uncertainty analysis. The model's representation of hydrological processes is discussed and the direct percolation and first linear storage constant parameters are found to strongly affect model results in a complex way, with the former more important for low flows and the latter for high flows. The uncertainty in predictions resulting from the hydrological model parameters is demonstrated and the projections of river flow under future climate are analysed. A clear climate change impact signal is evident in the results with a persistent lowering of mean daily river flows for all months and for all projection time slices. Results indicate that a projection of lower flows under future climate is valid even taking into account the uncertainties considered in this modelling chain exercise. The model parameter uncertainty becomes more significant under future climate as the river flows become lower. This has significant implications for those making policy decisions based on such modelling results. Copyright © 2010 John Wiley & Sons, Ltd.     

Climate change impacts on groundwater hydrology – where are the main uncertainties and can they be reduced?

ABSTRACT

This paper assesses how various sources of uncertainty propagate through the uncertainty cascade from emission scenarios through climate models and hydrological models to impacts, with a particular focus on groundwater aspects from a number of coordinated studies in Denmark. Our results are similar to those from surface water studies showing that climate model uncertainty dominates the results for projections of climate change impacts on streamflow and groundwater heads. However, we found uncertainties related to geological conceptualization and hydrological model discretization to be dominant for projections of well field capture zones, while the climate model uncertainty here is of minor importance. How to reduce the uncertainties on climate change impact projections related to groundwater is discussed, with an emphasis on the potential for reducing climate model biases through the use of fully coupled climate–hydrology models.

Editor D. Koutsoyiannis; Associate editor not assigned     

The Sources of Uncertainty in Forecasting Caspian Sea Level and Estimating the Inundation Risk of Coastal Areas

Stochastic variations in the climate and hydrological regime, both natural and anthropogenic, are the main cause of uncertainty in long-term hydrological forecasts and hence increase the estimated risk of economic activity in the coastal zone of internal seas. Some sources of uncertainty, which appear during the hydrological analysis, are considered with the purpose to assess this risk. Digital relief models were used to determine the morphological characteristics (as functions of the sea level) and assess their contribution to variations in the level regime. To take into account the sample uncertainty in the parameter estimates of stochastic models of the “impellent” processes, it is proposed to use the existing methodology of probabilistic-deterministic prediction of water level variations in a closed water body in combination with the Bayesian approach.     

Assessing the results of scenarios of climate and land use changes on the hydrology of an Italian catchment: modelling study

Hydrological models are recognized as valid scientific tools to study water quantity and quality and provide support for the integrated management and planning of water resources at different scales. In common with many catchments in the Mediterranean, the study catchment has many problems such as the increasing gap between water demand and supply, water quality deterioration, scarcity of available data, lack of measurements and specific information. The application of hydrological models to investigate hydrological processes in this type of catchments is of particular relevance for water planning strategies to address the possible impact of climate and land use changes on water resources. The distributed catchment scale model (DiCaSM) was selected to study the impact of climate and land use changes on the hydrological cycle and the water balance components in the Apulia region, southern Italy, specifically in the Candelaro catchment (1780 km²). The results obtained from this investigation proved the ability of DiCaSM to quantify the different components of the catchment water balance and to successfully simulate the stream flows. In addition, the model was run with the climate change scenarios for southern Italy, i.e. reduced winter rainfall by 5–10%, reduced summer rainfall by 15–20%, winter temperature rise by 1·25–1·5 °C and summer temperature rise by 1·5–1·75 °C. The results indicated that by 2050, groundwater recharge in the Candelaro catchment would decrease by 21–31% and stream flows by 16–23%. The model results also showed that the projected durum wheat yield up to 2050 is likely to decrease between 2·2% and 10·4% due to the future reduction in rainfall and increase in temperature. In the current study, the reliability of the DiCaSM was assessed when applied to the Candelaro catchment; those parameters that may cause uncertainty in model output were investigated using a generalized likelihood uncertainty estimation (GLUE) methodology. The results showed that DiCaSM provided a small level of uncertainty and subsequently, a higher confidence level. Copyright © 2010 John Wiley & Sons, Ltd.     

Using raw regional climate model outputs for quantifying climate change impacts on hydrology

General circulation model outputs are rarely used directly for quantifying climate change impacts on hydrology, due to their coarse resolution and inherent bias. Bias correction methods are usually applied to correct the statistical deviations of climate model outputs from the observed data. However, the use of bias correction methods for impact studies is often disputable, due to the lack of physical basis and the bias nonstationarity of climate model outputs. With the improvement in model resolution and reliability, it is now possible to investigate the direct use of regional climate model (RCM) outputs for impact studies. This study proposes an approach to use RCM simulations directly for quantifying the hydrological impacts of climate change over North America. With this method, a hydrological model (HSAMI) is specifically calibrated using the RCM simulations at the recent past period. The change in hydrological regimes for a future period (2041–2065) over the reference (1971–1995), simulated using bias‐corrected and nonbias‐corrected simulations, is compared using mean flow, spring high flow, and summer–autumn low flow as indicators. Three RCMs driven by three different general circulation models are used to investigate the uncertainty of hydrological simulations associated with the choice of a bias‐corrected or nonbias‐corrected RCM simulation. The results indicate that the uncertainty envelope is generally watershed and indicator dependent. It is difficult to draw a firm conclusion about whether one method is better than the other. In other words, the bias correction method could bring further uncertainty to future hydrological simulations, in addition to uncertainty related to the choice of a bias correction method. This implies that the nonbias‐corrected results should be provided to end users along with the bias‐corrected ones, along with a detailed explanation of the bias correction procedure. This information would be especially helpful to assist end users in making the most informed decisions.     

Seasonal Water Storage Variations as Impacted by Water Abstractions: Comparing the Output of a Global Hydrological Model with GRACE and GPS Observations

Better quantification of continental water storage variations is expected to improve our understanding of water flows, including evapotranspiration, runoff and river discharge as well as human water abstractions. For the first time, total water storage (TWS) on the land area of the globe as computed by the global water model WaterGAP (Water Global Assessment and Prognosis) was compared to both gravity recovery and climate experiment (GRACE) and global positioning system (GPS) observations. The GRACE satellites sense the effect of TWS on the dynamic gravity field of the Earth. GPS reference points are displaced due to crustal deformation caused by time-varying TWS. Unfortunately, the worldwide coverage of the GPS tracking network is irregular, while GRACE provides global coverage albeit with low spatial resolution. Detrended TWS time series were analyzed by determining scaling factors for mean annual amplitude (f _GRACE) and time series of monthly TWS (f _GPS). Both GRACE and GPS indicate that WaterGAP underestimates seasonal variations of TWS on most of the land area of the globe. In addition, seasonal maximum TWS occurs 1 month earlier according to WaterGAP than according to GRACE on most land areas. While WaterGAP TWS is sensitive to the applied climate input data, none of the two data sets result in a clearly better fit to the observations. Due to the low number of GPS sites, GPS observations are less useful for validating global hydrological models than GRACE observations, but they serve to support the validity of GRACE TWS as observational target for hydrological modeling. For unknown reasons, WaterGAP appears to fit better to GPS than to GRACE. Both GPS and GRACE data, however, are rather uncertain due to a number of reasons, in particular in dry regions. It is not possible to benefit from either GPS or GRACE observations to monitor and quantify human water abstractions if only detrended (seasonal) TWS variations are considered. Regarding GRACE, this is mainly caused by the attenuation of the TWS differences between water abstraction variants due to the filtering required for GRACE TWS. Regarding GPS, station density is too low. Only if water abstractions lead to long-term changes in TWS by depletion or restoration of water storage in groundwater or large surface water bodies, GRACE may be used to support the quantification of human water abstractions.     

Wavelet and Gaussian Approaches for Estimation of Groundwater Variations Using GRACE Data

In this study, a scheme is presented to estimate groundwater storage variations in Iran. The variations are estimated using 11 years of Gravity Recovery and Climate Experiments (GRACE) observations from period of 2003 to April 2014 in combination with the outputs of Global Land Data Assimilation Systems (GLDAS) model including soil moisture, snow water equivalent, and total canopy water storage. To do so, the sums of GLDAS outputs are subtracted from terrestrial water storage variations determined by GRACE observations. Because of stripping errors in the GRACE data, two methodologies based on wavelet analysis and Gaussian filtering are applied to refine the GRACE data. It is shown that the wavelet approach could better localize the desired signal and increase the signal‐to‐noise ratio and thus results in more accurate estimation of groundwater storage variations. To validate the results of our procedure in estimation of ground water storage variations, they are compared with the measurements of pisometric wells data near the Urmia Lake which shows favorable agreements with our results.     

Evaluation of basin storage–discharge sensitivity in Taiwan using low‐flow recession analysis

Sensitivity analysis of the hydrological behaviour of basins has mainly focused on the correlation between streamflow and climate, ignoring the uncertainty of future climate and not utilizing complex hydrological models. However, groundwater storage is affected by climatic change and human activities. The streamflow of many basins is primarily sourced from the natural discharge of aquifers in upstream regions. The correlation between streamflow and groundwater storage has not been thoroughly discussed. In this study, the storage–discharge sensitivity of 22 basins in Taiwan was investigated by means of daily streamflow and rainfall data obtained over more than 30 years. The relationship between storage and discharge variance was evaluated using low‐flow recession analysis and a water balance equation that ignores the influence of rainfall and evapotranspiration. Based on the obtained storage–discharge sensitivity, this study explored whether the water storage and discharge behaviour of the studied basins is susceptible to climate change or human activities and discusses the regional differences in storage–discharge sensitivity. The results showed that the average storage–discharge sensitivities were 0.056 and 0.162 mm^?1 in the northern and southern regions of Taiwan, respectively. In the central and eastern regions, the values were both 0.020 mm^?1. The storage–discharge sensitivity was very high in the southern region. The regional differences in storage–discharge sensitivity with similar climate conditions are primarily due to differences in aquifer properties. Based on the recession curve, other factors responsible for these differences include land utilization, land coverage, and rainfall patterns during dry and wet seasons. These factors lead to differences in groundwater recharge and thus to regional differences in storage–discharge sensitivity.     

Incorporating uncertainty in hydrological predictions for gauged and ungauged basins in southern Africa

Abstract

The increasing demand for water in southern Africa necessitates adequate quantification of current freshwater resources. Watershed models are the standard tool used to generate continuous estimates of streamflow and other hydrological variables. However, the accuracy of the results is often not quantified, and model assessment is hindered by a scarcity of historical observations. Quantifying the uncertainty in hydrological estimates would increase the value and credibility of predictions. A model-independent framework aimed at achieving consistency in incorporating and analysing uncertainty within water resources estimation tools in gauged and ungauged basins is presented. Uncertainty estimation in ungauged basins is achieved via two strategies: a local approach for a priori model parameter estimation from physical catchment characteristics, and a regional approach to regionalize signatures of catchment behaviour that can be used to constrain model outputs. We compare these two sources of information in the data-scarce region of South Africa. The results show that both approaches are capable of uncertainty reduction, but that their relative values vary.

Editor D. Koutsoyiannis

Citation Kapangaziwiri, E., Hughes, D.A., and Wagener, T., 2012. Incorporating uncertainty in hydrological predictions for gauged and ungauged basins in southern Africa. Hydrological Sciences Journal, 57 (5), 1000–1019.     

Regional regression models for hydro‐climate change impact assessment

Hydro‐climatic impacts in water resources systems are typically assessed by forcing a hydrologic model with outputs from general circulation models (GCMs) or regional climate models. The challenges of this approach include maintaining a consistent energy budget between climate and hydrologic models and also properly calibrating and verifying the hydrologic models. Subjective choices of loss, flow routing, snowmelt and evapotranspiration computation methods also increase watershed modelling uncertainty and thus complicate impact assessment. An alternative approach, particularly appealing for ungauged basins or locations where record lengths are short, is to predict selected streamflow quantiles directly from meteorological variable output from climate models using regional regression models that also include physical basin characteristics. In this study, regional regression models are developed for the western Great Lakes states using ordinary least squares and weighted least squares techniques applied to selected Great Lakes watersheds. Model inputs include readily available downscaled GCM outputs from the Coupled Model Intercomparison Project Phase 3. The model results provide insights to potential model weaknesses, including comparatively low runoff predictions from continuous simulation models that estimate potential evapotranspiration using temperature proxy information and comparatively high runoff projections from regression models that do not include temperature as an explanatory variable. Copyright © 2014 John Wiley & Sons, Ltd.     

Quantifying the impacts of climate change and land use on hydrological processes: A comparison between mountain and lowland agricultural watersheds

Mountain and lowland watersheds are two distinct geographical units with considerably different hydrological processes. Understanding their hydrological processes in the context of future climate change and land use scenarios is important for water resource management. This study investigated hydrological processes and their driving factors and eco-hydrological impacts for these two geographical units in the Xitiaoxi watershed, East China, and quantified their differences through hydrological modelling. Hydrological processes in 24 mountain watersheds and 143 lowland watersheds were simulated based on a raster-based Xin'anjiang model and a Nitrogen Dynamic Polder (NDP) model, respectively. These two models were calibrated and validated with an acceptable performance (Nash-Sutcliffe efficiency coefficients of 0.81 and 0.50, respectively) for simulating discharge for mountain watersheds and water level for lowland watersheds. Then, an Indicators of Hydrological Alteration (IHA) model was used to help quantify the alterations to the hydrological process and their resulting eco-hydrological impacts. Based on the validated models, scenario analysis was conducted to evaluate the impacts of climate and land use changes on the hydrological processes. The simulation results revealed that (a) climate change would cause a larger increase in annual runoff than that under land use scenario in the mountain watersheds, with variations of 19.9 and 10.5% for the 2050s, respectively. (b) Land use change was more responsible for the streamflow increment than climate change in the lowland watersheds, causing an annual runoff to increase by 27.4 and 16.2% for the 2050s, respectively. (c) Land use can enhance the response of streamflow to the climatic variation. (d) The above-mentioned hydrological variations were notable in flood and dry season in the mountain watersheds, and they were significant in rice season in the lowland watersheds. (e) Their resulting degradation of ecological diversity was more susceptible to future climate change in the two watersheds. This study demonstrated that mountain and lowland watersheds showed distinct differences in hydrological processes and their responses to climate and land use changes.     

Hydrological modelling for assessing spatio-temporal groundwater recharge variations in the water-stressed Amathole Water Supply System,Eastern Cape,South Africa

To increase the resilience of regional water supply systems in South Africa in the face of anticipated climatic changes and a constant increase in water demand, water supply sources require diversification. Many water-stressed metropolitan regions in South Africa depend largely on surface water to cover their water demand. While climatic and river discharge data is widely available in these regions, information on groundwater resources – which could support supply source diversification – is scarce. Groundwater recharge is a key parameter that is used to estimate groundwater amounts that can be sustainably exploited at a sub-watershed level. Therefore, the objective of this study was to develop a reliable hydrological modelling routine that enables the assessment of regional spatio-temporal variations of groundwater recharge to discern the most promising areas for groundwater development. Accordingly, we present a semi-distributed hydrological modelling approach that incorporates water balance routines coupled with baseflow modelling techniques to yield spatio-temporal variations of groundwater recharge on a regional level. The approach is demonstrated for the actively managed catchment areas of the Amathole Water Supply System situated in a semi-arid part of the Eastern Cape of South Africa. In the investigated study area, annual groundwater recharge exhibits a high spatio-temporal heterogeneity and is estimated to vary between ~0.5% and 8% of annual rainfall. Despite some uncertainties induced by limited data availability, calibration and validation of the model were found to be satisfactory and yielded model results similar to (point) data of annual groundwater recharge reported in earlier studies. Our approach is therefore found to derive crucial information for efficiently targeting more detailed groundwater exploration studies and could work as a blueprint for orientating groundwater potential exploration in similar environments.