CLIMATE CHANGE/VARIABILITY AND HYDROLOGICAL IMPACT STUDIES IN ZIMBABWE: A REVIEW OF PROGRESS ...

CLIMATE CHANGE/VARIABILITY AND

 HYDROLOGICAL IMPACT STUDIES IN

 ZIMBABWE: A REVIEW OF PROGRESS

 AND KNOWLEDGE GAPS

Authors:

[1]Auther Maviza^{a, b}

Fethi Ahmed ^b 

Author affiliation

^a Department of Environmental Science and Health, Faculty of Applied Sciences, National University of Science and Technology, Corner Cecil Avenue and Gwanda Road, Ascot, Bulawayo, Zimbabwe

^b Geography and Environmental Studies Division, School of Geography,

Archaeology and Environmental Studies, University of Witwatersrand, 1 Jan Smuts Avenue

Braamfontein, Johannesburg, South Africa

ABSTRACT

  This paper reviews developments in climate science and hydrological modelling studies in Zimbabwe over the past three decades in an effort to expose the knowledge gaps within this domain of research. The review highlights the main themes, findings and conclusions arising from these studies and examines their implications for future climate and hydrological modelling research in Zimbabwe. Past climate change and variability studies are assessed to summarise their findings in as far the state of the past climatic conditions is concerned. The scope of coverage of these studies is also assessed and the thematic areas of focus of the various climate and hydrological studies presented in a descriptive manner. The state and progress towards integrated advanced systems research is assessed, tracking benchmarks in climate and hydrological systems research methodologies covering Geographic Information Systems (GIS) and Remote Sensing (RS) in Zimbabwe. Challenges associated with climate and hydrological research in Zimbabwe are also briefly discussed.  Gaps in knowledge in as far the research focus, scope and methodologies employed by various studies in this context are also exposed, we conclude by presenting plausible way forward shedding light on potential areas of advancing scientific knowledge to better under understand the climate-landuse-hydrology nexus in Zimbabwe and are also presented. While this paper is primary relevant for researchers, the general findings are relevant also for research policymakers as it exposes potential areas of policy intervention or agenda setting in as far as climate and hydrology science research is concerned so as to effectively address pertinent questions in this area.

Keywords: Zimbabwe, Climate change, Climate variability, Climate modelling, hydrological modelling, GIS, Remote sensing

1 Introduction

   A review of global climate changes since 1700 has revealed that over the centuries, twenty climatic events covering continental-scale temperature dips, hydroclimatic anomalies, stratospheric perturbations and general atmospheric composition changes have occurred impacting millions of people in many ways (Bronnimann, 2015). As such, understanding and predicting these inter-annual, inter- and multi-decadal variations in climate and the resultant impacts has become a critical area of research globally over the decades. Several studies have been done to quantify the extent of impacts and the dynamics (in space and time) of climate change on water resources (Arnell, 1999, Arnell, 2004, Gurdak et al., 2009, Hanson and Dettinger, 2005, Maina et al., 2012), food security (Pielke, 2013, Reddy, 2014, Stigter and Ofori, 2014), ecosystems (National Research, 2002, Thompson et al., 2017, Urban, 2015), energy, and human health (Pedersen et al., 2014, Sande et al., 2016). All these studies have revealed that climate change is a significant factor to consider in holistic planning for community resilience and adaption for sustainable development.

   In developing countries in Africa where the impacts of climate system change are predicted to be more manifest in more uncertain terms (Hulme et al., 2001, Scholes et al., 2015), expanding knowledge in this domain has become more pertinent hence the steady developments in research therein. In Southern Africa, studies also indicate a continued high climate variability marked by recurrent droughts in the future (Moriarty and Lovell, 2000, Van Wyk, 1998) notwithstanding the uncertainties in these studies.  The scope of these studies has been diverse covering various focus areas such climate modelling e.g. (Randall et al., 2007, Sévellec et al., 2016, Stouffer, 2006), hydrological impacts e.g. (Abtew and Melesse, 2016, Farjad et al., 2017, Mimikou et al., 1991) and other general impact studies e.g. (Cooper et al., 2008, Maina et al., 2013, Wanders and Wada, 2014).

    Despite all the advances made in these studies, gaps in knowledge are well acknowledged particularly considering the inherent uncertainties in and the new developments in climate science/ modelling and climate impact assessment techniques (Creese et al., 2016). Tools and approaches are now available and/ are being developed that allow for a better understanding, characterisation of the implications of climate change and variability to assist in better climate risk management strategy development (Cooper et al., 2008) in developing countries such as Zimbabwe.  As such, the scientific community within and outside of Zimbabwe has over the past decades been able to exploit various tools and techniques to generate new knowledge pertaining the local climate dynamics and impacts to better guide decision making specifically tailored to the local needs. One key area of focus has been the implications of climate change on water resources/ hydrological systems considering that a significant part of the Zimbabwe is generally semi-arid in nature.  

  Furthermore, considering the  acknowledgement of spatio-temporal landuse-landcover changes (LULCC) as an important factor (with both direct and indirect implications) on hydrological systems (Mishra et al., 2014, Khare et al., 2015, Nie et al., 2011), attempts have also been made to explore the climate-LULCC-hydrology interlinkages using coupled systems approaches in various studies globally (Liu  et al., 2007, Werner and McNamara, 2007). All these studies indicate a wide scope of themes covered over the years as earlier mentioned and as such, it becomes important to explore and characterise these studies in a more systematic manner so as to better appreciate the advances and identify the knowledge gaps therein. To date, only one study by Bhatasara (2017) has attempted to review climate change research in Zimbabwe albeit from social science perspective. This paper thus explores the same aspects however by presenting key research developments in climate science and hydrological modelling in Zimbabwe over the past three decades and thus ultimately exposes areas for further research in this domain in Zimbabwe.

2 Climate change/variability and modelling

2.1 Climate change/variability

  Climate change refers to a statistically significant variation in either the mean state of the climate or in its variability, persisting for an extended period (typically decades or longer) due to natural internal processes or external forcings, or persistent anthropogenic changes in the composition of the atmosphere or in landuse (IPCC, 2007). Most scientists have however settled to use the term climate change to refer primarily to observed and predicted changes mainly as a result of human activities (Letcher, 2009, Maina et al., 2013) though others suggest that climate changes are a result of natural cycles (Easterbrook, 2016). More recently, the debate has evolved to include what has been termed the ‘climate change hiatus’ where scientists are beginning to re-interrogate the temporary slowdown in the global average surface temperature warming trend observed between 1998 and 2013 as a genuine slow down or a redistribution of energy in the earth system (Ferraro et al., 2015, Sévellec et al., 2016, Yan et al., 2016).

  Climate variability on the other hand has been defined as variations in the mean state and other statistics (such as standard deviations, the occurrence of extremes, etc.) of the climate on all temporal and spatial scales beyond that of individual weather events due to natural internal processes within the climate system (i.e. internal variability), or to variations in natural or anthropogenic external forcing (i.e. external variability) (IPCC, 2007).

2.1.1 Global and Regional Climatic conditions studies: Overview

     Studies by the Inter-governmental Panel on Climate Change (IPCC) experts and other scientists such as Scholes et al. (2015), Zachos et al. (2001) and Stocker et al. (2013) indicate that the earth's climate has experienced complex evolution marked by periodic and anomalous variability both at global and regional scales with diverse impacts on populations throughout-time. Such changing climatic patterns have been linked with various extreme events or phenomena such as droughts and floods (Reddy, 2014, Wanders and Wada, 2014, WaterNet, 2003). This notion is also buttressed in a review of observed (1900–2000) and possible future (2000–2100) climatic conditions across Africa by Hulme et al. (2001) which concluded that the climate of Africa is warmer than it was 100 year ago with some regions experiencing substantial inter-annual and multi-decadal rainfall variations with dramatic impacts on both the environment and some economies. Impacts of anthropogenic processes on the global carbon cycle and the result greenhouse effect have been acknowledged as directly linked to global and regional climatic systems perturbations with the same devastating effects on numerous vulnerable communities around the world (Kondratʹev et al., 2003, Randall et al., 2007, IPCC, 2001, IPCC, 2007).

    Numerous climate impacts studies covering vulnerabilities and adaptation e.g. (Parry et al., 2007, Berrang-Ford et al., 2011, Pielke, 2013, SADC, 2011, Spalding-Fecher et al., 2016) have been done and the general agreement is that climate variability presents serious vulnerability challenges for semi-arid regions including those in Southern Africa that depend on rainfall for their primary production for example (O’Brien and Vogel 2003). In this regard, researchers such as Berrang-Ford et al. (2011) have attempted to characterise human climate adaptation actions and while others e.g. (Smithers and Smit, 1997, Anwar et al., 2013, Reddy, 2014) have advanced knowledge by exploring modalities of developing frameworks for characterising and understanding community adaptation capacities to climatic variability and change vis-à-vis the spatio-temporal dynamics of climatic events such as the El Niño-Southern Oscillation (ENSO).

2.1.2 Climatic change and variability studies in Zimbabwe

      Climate in Zimbabwe is highly variable (Brown et al., 2013) and thus the country (with its limited coping capacity) is considered highly vulnerable to climate change and variability impacts like most developing countries in Africa (Jack et al., 2016, Magadza, 1994, IPCC, 2001). Despite this fact, not much research has been done expand knowledge on the evolution climatic conditions in Zimbabwe. One prominent known study on climate conditions Zimbabwe by Unganai (1996) revealed that over a 93 year period from the 1900, daytime temperatures in Zimbabwe rose by about 0.8°C, translating to a 0.1°C rise per decade while precipitation was observed to have declined by up to 10% on average over the same period, which is about 1% per decade. These findings however have been rebutted by (Mazvimavi, 2010) in his study covering 40 rainfall stations across all the rainfall regions for periods 1892–1940 and ending in 2000. He concluded that the purported climate change effects were not statistically significant within the time-series of total seasonal and annual rainfall in Zimbabwe, arguing that the findings of declining rainfall by Unganai (1996) are likely due to the presence of multi-decadal variability characterized by combining years with above and below average rainfall. On long-term predictability of rainfall trends, studies indicate that approximately 70% of the total summer rainfall variance in Zimbabwe is potentially predictable at long range (Unganai and Mason, 2002).

   Research has advanced to explore the teleconnectivity between summer rainfall patterns in Zimbabwe and sea-surface temperatures (SST), the Southern Oscillation index (SOI), the Quasi-biennial Oscillation (QBO), Outgoing Longwave Radiation (OLR) and wind (Mamombe et al., 2017). Similarly, Nangombe et al. (2016) concluded strong correlations in this regard, relating severe droughts and circulation patterns and weather systems in the Indian Ocean and Equatorial Pacific Ocean i.e. the ENSO SOI, the QBO and the Luni-solar tide at 20, 12.5, 3.2, and 2.7 year cycles. This revealed the possibility of predicting drought occurrences using these established relationships though this knowledge has not been fully utilised in the country.

    From these examples of studies, the predominant focus area has been the rainfall phenomenon e.g. (Matarira and Jury, 1992, Nangombe et al., 2016, Mamombe et al., 2017, Makarau and Jury, 1997, Unganai and Mason, 2002) with less attention on temperature and other climatic parameters such as evaporation and solar radiation. Studies that gave some attention to other climatic parameters e.g. (Rocha and Simmonds, 1997, Lyon and Mason, 2006, Hulme et al., 2001, Unganai, 1996, Brown et al., 2013) had limited detail to give a comprehensive picture on these parameters. Some have presented the climate in Zimbabwe in a general sense considering that they had a regional scope of coverage i.e. covered Southern Africa e.g. (Rocha and Simmonds, 1997, Lyon and Mason, 2006, Scholes et al., 2015, Moyo and Nangombe, 2015) i.e. did not explore the spatio-temporal dynamics of the climatic parameters in detail. Furthermore, some of these studies such as Matarira and Jury (1992) had were limited by temporal resolution of their assessments i.e. they were cross-sectional and thus missed on the multi-temporal aspects of the climatic conditions in Zimbabwe.

  While advances in climate research globally have seen the move towards use of Geographic information Systems (GIS) and Remote Sensing (RS) to augment climate data series, and assist in better and advanced analyses climate dynamics in space and time in various regions of the world e.g. (Beniston and Verstraete, 2001, Zinnert et al., 2011, Turner et al., 2004, Tong et al., 2014, Latifovic and Pouliot, 2007, Bolch, 2007), very few known climatic studies in Zimbabwe over the past decades have attempted to directly apply these tools and/ techniques at a national scale on a multi-temporal scale. This gap is worsened by the limited availability of in-situ climatic data such as rainfall, temperature measurements especially in Zimbabwe where most such data has gaps or is incomplete due to poor distribution and investment in necessary infrastructure/ facilities to observe important climatic phenomena. The data if available is not easily accessible, not consistent and has poor spatial coverage thus potentially limiting climate research in the country e.g. historic temperature and rainfall data for Zimbabwe for example is incomplete and often costly to purchase (Gumindoga et al., 2016) which has become a limiting factor in climate research. In light of this limitation, some researchers such as Chikodzi (2013),  Dlamini et al. (2016) and Mpala et al. (2016) have exploited remotely sensed data to overcome this challenge. Most climate studies in this review revealed that they predominantly focused on climate impacts assessment, for example in agriculture and hydrological modelling at varying spatial scales.  

2.2 Climate modelling

2.2.1 An overview of climate models

    Two main types of models i.e. Global Climate Models (GCMs) and Regional Climate Models (RCMs) are used in climate modelling studies. GCMs are numerical tools/models representing physical processes in the atmosphere, ocean, cryosphere and land surface used for simulating the response of the global climate system to increasing greenhouse gas concentrations (IPCC, 2001). Examples of GCMs include the Hadley Centre Coupled Model, version 3 (HadCM3), the Geophysical Fluid Dynamics Laboratory Climate Model version 2.5 (GFDL CM2.5) (Delworth et al., 2012) and the Model for Interdisciplinary Research on Climate – Earth Systems Model (MIROC-ESM) (Watanabe et al., 2011). Over the years, the GCMs have been used to improve understanding of the way the climate system works, to forecast the drivers of climate change, improve estimates of climate sensitivity and to predict future climate conditions and impacts (McCarroll, 2015). Advances in this domain, have seen the emergence of coupled GCMs allowing for more reliable projections of climate at various spatial and temporal scales (IPCC, 2013, Pitman et al., 2011). This has been realised albeit the inherent uncertainties and weaknesses associated with use of such models. For example, Motesharrei et al (2016)  argued that two-way feedbacks are missing from most climate models and other critical socio-economic variables such as inequality, consumption, and population are often inadequately-modelled hence increasing uncertainty in outputs. Fowler et al. (2007) further highlights that GCMs have a relatively coarse resolution hence are unable to resolve significant sub-grid scale features such as landuse-landcover (LULC) and topography thus limiting their accuracy and application at a local scale. To resolve these shortcoming of GCMs, downscaling techniques (Bouwer et al., 2004, Hewitson et al., 2004, Hidalgo et al., 2008) have been used to come up with finer resolution Regional Climatic Models with varying levels of accuracies at sub-grid scale. Examples of such RCMs include the Consortium for Small-Scale Modelling and Regional Climate Model (COSMO-CLM), Regional Climate Model version 4 (RegCM4) and the Providing Regional Climates for Impacts Studies (PRECIS) model. Because of their higher resolution (compared to GCMs), RCM data have been widely used in numerous studies as input in hydrological models in an attempt to assess the variability of hydrological responses due to climate change e.g. (Dosio et al., 2014, Kalognomou et al., 2013, Landman et al., 2006, Moyo and Nangombe, 2015).

2.2.2 Global and regional climate modelling studies

     Numerous climate modelling studies have been done globally and regionally in an effort to better understand the past, present and future climate dynamics in space and time. While significant progress has been realised in global climate modelling science e.g. (Hamududu and Killingtveit, 2012, Kondratʹev et al., 2003, Zachos et al., 2001, Pitman et al., 2011, Delworth, 2006, Arnell, 1999), there has been relatively less work published for Africa in the same regard (Hulme et al., 2001) and let alone at local scale (country) level. GCMs such as the Canadian Climate Centre Model (CCCM) and GFDL-3 have simulated changes of plus 2 to 4°C increases in mean surface air temperature of across Southern Africa under doubled atmospheric carbon dioxide scenarios showing over and underestimations when validated with observed data over local areas (Unganai, 1996). Others models that have applied in Africa include the HadCM3,

   Parallel Climate Model (PCM) and the Coupled Global Climate Model (CGCM3), (Faramarzi et al., 2012).

On the contrary, downscaled RCMs have demonstrated more competence in simulating local climatic conditions compared to GCMs e.g. (Shongwe et al., 2015, Meque and Abiodun, 2015, Hewitson et al., 2004) though the contradictions and parameter over and underestimation e.g. of  rainfall and temperature scenarios still persists when model outputs are compared (World Meteorological Organization, 1975, Refsgaard and Knudsen, 1996, IPCC, 2013). The general consensus however among climate scientists is that projections of future climate change are restricted to assumptions of climate forcing, limited by shortcomings of the climate models used and inherently also subject to internal variability when considering specific periods (Henderson-Sellers and McGuffie, 2012).

2.2.3 Climate modelling studies in Zimbabwe

     Despite the advances in climate modelling science globally and regionally, the scope of application of climate models in Zimbabwe has been rather limited. In this review, it was noted that about 5% of climate studies done in Zimbabwe over the past three decades directly focused or indirectly involved climate modelling at some level. The studies covered modelling in relation to aspects such as disease vector distribution, climate impacts on hydrological systems, agricultural productivity and natural ecosystems and general prediction of future climatic conditions. Unganai (1996) used two GCMs (the GFDL and the CCCM) to simulate future climate conditions for Zimbabwe using a doubled carbon dioxide concentration forcing and concluded that the models were inefficient in predicting the magnitude of precipitation change for example. Similarly, Makadho (1996) used the same two GCMs to assess potential impacts of climate change on maize production while (Matarira and Mwamuka, 1996) used the Goddard Institute of Space Studies (GISS) model to assess forest vulnerability to climate change. In their stable malaria transmission study, Ebi et al. (2005) used four GCMs i.e. (the CCCM, the United Kingdom Meteorological Office (UKMO) model, the Henderson-Sellers and the GISS model) to simulate future climatic scenarios in this impact study. The general indication (in terms of model usage) is that majority of these studies have been using GCMs without downscaling them which has inherent implications in terms of accuracy of findings. Other studies have of course attempted to use RCMs e.g. (Pedersen et al., 2014) while others have used downscaling techniques to generate more accurate climate simulations from GCMs and RCMs for their studies. For example, Makuvaro (2014) used Statistical and Regional dynamic Downscaling of Extremes for European regions (STARDEX) to come up with downscaled local scenarios for his study. Overall, what is emerging in this review if that most of these studies have used GCMs and RCMs (downscaled or directly) with limited regard to inherent limitations of these different models implying possible inaccuracies in some of the finding and conclusions of these studies For example, some models are developed to be more region specific and transferring them to or applying them in other regions of the world will give inaccurate results by the inherent design of he model. Furthermore, considering the advances in revision and/ improvements on old climate models and the development of new, better and region specific models e.g. under the Coordinated Regional Climate Downscaling Experiment (CORDEX) programme (Dosio et al., 2014, Kalognomou et al., 2013), it is apparent that there is a need to advance climate modelling science in Zimbabwe to catch up with the current developments in this area.

2.3 Climate change/ variability impact studies

2.3.1 An overview of the global perspectives

   Many researchers have explored the impacts of climate change and/or variability on various natural and human systems (Henderson-Sellers and McGuffie, 2012, IPCC, 2001, IPCC, 2007, Wanders and Wada, 2014) and the resultant heightened community vulnerabilities (Pielke, 2013, Stigter and Ofori, 2014, Ninan and Inoue, 2017, Yanda and Mubaya, 2011) at a global, regional and local scale. Amongst other noted impacts, most of these studies have shown that climate change has an overall negative impact on hydrological systems in the world (Vicuna and Dracup, 2007, Arnell, 1999, Knowles and Cayan, 2002). For example, it has reduced runoff in the Mediterranean, Central and Southern America, and Southern Africa (Arnell, 2004) and increased evaporation in some areas (Miralles et al., 2014, Dolman and de Jeu, 2010). In Southern Africa, climate change has also been linked to the El Niño–Southern Oscillation (ENSO) induced droughts (Christensen et al., 2013, Meque and Abiodun, 2015, Rocha and Simmonds, 1997, Kalognomou et al., 2013) with devastating effects on communities and the environment in general.

    Several studies have also been done to quantify the extent of impacts and the dynamics (in space and time) of climate change on water resources (Arnell, 1999, Arnell, 2004, Gurdak et al., 2009, Hanson and Dettinger, 2005, Maina et al., 2012), food security (Pielke, 2013, Reddy, 2014, Stigter and Ofori, 2014), ecosystems (National Research, 2002, Scholes et al., 2015, Caspary, 1990), energy (Clastres, 2011, Bollen et al., 2010, Bang, 2010, Spalding-Fecher et al., 2016, Hamududu and Killingtveit, 2012), and human health (Pedersen et al., 2014, Sande et al., 2016, Rogers et al., 2010). All these studies have revealed that climate change is a very significant factor to consider in holistic planning for community resilience and adaption for sustainable development, and more importantly in African developing countries such as Zimbabwe. Considering the intricate coupling of the human and natural systems, most of these studies have used diverse advanced methods in an attempt to understand the climate change dynamics vis-à-vis all the earlier mentioned factors. Of note has been the widespread embrace of climate models (global and regional) to predict future climatic scenarios (IPCC, 2013, Arnell, 2004) and the related likely impacts on various sectors such as agriculture, health, biodiversity and water security.

  While these impacts are well acknowledged to be more devastating in vulnerable communities in developing countries with weak institutional arrangements and policies for resilience and adaptation (SADC, 2011), climate science research still lags behind in most of these countries (India and Bonillo, 2013, Letcher, 2009) thus heightening future vulnerability due to limited scientific knowledge to guide pragmatic policy development and strategies for adaptation and resilience. This presents a greater need for the expansion of climate impacts research to close any knowledge gaps in this regard. In Zimbabwe, for example, various studies e.g. (Matarira and Mwamuka, 1996, Brown et al., 2012) have been undertaken in the past decades to explore climate change and variability impacts within the socio-economic and environmental management domains at varying scales. These studies have demonstrated some progress in testing and comparing tools and techniques, generating valuable new knowledge to assist the country in many ways in relation climate adaptation and resilience efforts.

2.1.4 Climatic change and variability impact studies in Zimbabwe

     Climate change and variability impact studies were noted (in this review) to be the most dominant theme in climate research in Zimbabwe for the past two decades (years 1996 to 2017). The main study themes covered by these studies ranged from climate and agricultural impacts e.g. (Gwimbi, 2009), climate changes response and policy e.g. (Brown et al., 2012), climate change adaptation and resilience e.g. (Mutekwa, 2009),  climate change and forestry/ ecosystem impacts e.g. (Matarira and Mwamuka, 1996), and climate and hydrological impacts (Ncube, 2010, Nyoni, 2012). Just over 50% of the reviewed impact studies focused on climate – agricultural impacts followed by the hydrological impacts of climate change theme. The scope of coverage of these studies ranged from national through district, ward to catchment level.

The climate-agricultural impact studies revealed among other issues that climate change and/ variability has compounded Zimbabwean peasant farmers’ vulnerability to drought hence food insecurity and poverty (Gukurume, 2013). This has necessitated pragmatic adaptive management of agro-biodiversity to ensure equitable sharing of resources in the face of climate change and uncertainties (Dunja et al., 2017). In light of the fact that small-holder and subsistence farmers are highly vulnerable to climate change (Mutekwa, 2009), there is need for investment in climate adaptation strategies and clear policies on irrigation and early warning systems (Gwimbi, 2009, Chikozho, 2010) in line with the Southern African Development Community (SADC) Climate Change Adaptation Strategy (SADC, 2011). Crop production has also been related to climatic conditions in some studies e.g. cotton production levels were noted to have declined as precipitation decreased and temperatures increased in Gokwe district of Zimbabwe (Gwimbi, 2009), while maize productivity has been projected to decrease in response to various global climate change scenarios (Makadho, 1996) and the ENSO successfully linked to rainfall, drought and maize yield (Zinyengere et al., 2011, Nkomozepi and Chung, 2012, Unganai et al., 2013, Makaudze, 2016) and livestock productivity (Senda et al., 2016).  Within agro-ecology, Mugandani et al. (2012) and (Unganai et al., 2013) agree that that the main food productions natural co-regions of Zimbabwe (regions 1 and 2) are likely to decrease in size due to climate change and variability pointing to possible reduction in food production and food insecurity.  

   Other emerging themes in the reviewed impacts studies related to ecosystem impacts (Pedersen et al., 2014) and  forestry. For example, Matarira and Mwamuka (1996) in their modelling study projected a 17 to 18% land area shift from subtropical thorn woodland and subtropical dry forest to tropical very dry forest under modelled climate scenario of reduced precipitation and an increase in ambient temperatures. With regards to public health, studies indicate downward trends e.g. results from a climate suitability for stable malaria transmission in Zimbabwe under different climate change scenarios by Ebi et al. (2005) suggest that changes in temperature and precipitation could alter the spatial distribution of malaria in Zimbabwe, with previously malaria unsuitable areas of dense human population such as Bulawayo becoming suitable for transmission. Others are indicating changes in abundance and distribution of tsetse flies, suggesting possible redistribution of African trypanosomiasis (sleeping sickness) incidence in Zimbabwe in the future due to climate change (Pilossof and at:, 2016). Table 1 shows some of the projected climate impacts by sector for year 2080. Of note is that projections indicate deterioration or worsening of negative impacts in almost all the sectors. For example, runoff is projected to decrease significantly within major catchments such as the Save and the Mzingwane with wide-ranging consequences for resident communities and in the face of high vulnerability and low resilience human.

Table 1 Projected climate changes impacts by sector in Zimbabwe (Adopted from (Brown et al., 2012) after Murwira et. al nd)

   Several studies in Zimbabwe such as (Richard and Rafik, 2014, Moriarty and Lovell, 2000) corroborate the acknowledged conclusion by the IPCC (2014) and Kundzewicz et al. (2008) that observational records and climate projections provide abundant evidence that freshwater resources are vulnerable and will be strongly impacted by climate change, with wide-ranging consequences for human societies and ecosystems globally. Studies on climate impacts on water resources all indicate negative trends i.e. reduction in water reservoir water levels, increased evaporation and surface and groundwater storage (Nyoni, 2012, Matarira and Jury, 1992, Ncube, 2010, Dalu et al., 2012) which are directly related to climate impacts on rainfall and temperature. Very few studies have focused modelling the hydrological impacts of climate change and/ variability in Zimbabwe indicating a gap in knowledge in this regard. These are covered more extensively under hydrological modelling studies section of this review.

   Of note is that in all the climate impact studies reviewed here, approximately 10% involved climate modelling and/ climate impacts simulation in their study methodologies e.g. Grothmann and Patt (2005) and  Matarira and Mwamuka (1996) showing the limited utilisation of these otherwise valuable techniques and tools to expand knowledge in climate impact studies in Zimbabwe.

3 Hydrological modelling in Zimbabwe: Developments

3.1 Hydrological modelling and modelling studies: An overview

   Hydrological models are representative simplifications of complicated hydrological processes using mathematical means to demonstrate the principal elements of the processes, their combination and function as a comprehensive hydrologic system (Xu, 2002). These hydrological models have been classified in various ways but Refsgaard and Knudsen (1996) classified them into three broad categories namely, (i) empirical black box models, (ii) lumped conceptual models, and (iii) distributed physically based system. Examples of these include the HEC-HMS (Oleyiblo and Li, 2010),TOPMODEL, Système Hydrologique Européen (SHE), SWAT (Ghoraba, 2015) and complex conceptual models such as MODHYDROLOG (Chiew et al., 1993). A review of the pros and cons of these models by Sivapalan et al (2013) revealed that distributed-physically based models have the advantage of accounting for spatial heterogeneities and provide detailed description of the hydrological processes in a catchment with limited demands of input data hence their widespread use in hydrological studies such as those by (Refsgaard, 1997, Uhlenbrook et al., 2004, Wooldridge et al., 2001). The same notion has been confirmed by the World Meteorological Organization (1975) in their inter-comparison of conceptual hydrological models for operational hydrological forecasting. Furthermore, considering that these models use parameters which are directly related to the physical characteristics of the river basins (e.g. topography, soil, LULC and geology) and account for spatial variability of meteorological conditions (Refsgaard and Knudsen, 1996), they have been very useful in studies advancing understanding of changes in hydrological process such as surface run-off e.g. (Karvonen et al., 1999, Gal et al., 2016, Chiew et al., 1993) and groundwater storage (Finch, 1990) over an area in space and time and simulating future hydrologic conditions.

3.1.1 GIS and Remote Sensing in hydrological modelling

    Over the years, GIS and Remote sensing (RS) techniques have become indispensable in most state-of-the-art hydrological models premising on the extensive spatio-temporal data capture and analysis capabilities of these technologies. Schultz (1988) presented three main applications of RS in hydrological modelling studies as, (i) model parameter estimation with the aid of multi/ hyper-spectral satellite data; (ii) computation of historic monthly runoff using satellite data as input; and (iii) real-time flood forecasting using radar rainfall measurements as input. In this regard, many researchers have used GIS and RS in hydrological modelling studies aimed at optimisation of catchment management in the Mediterranean regions (Makhamreh, 2011), water resources management in India (Bhavsar, 1984, Long et al., 2016), forest hydrology (Thanapakpawin et al., 2007, Stewart and Finch, 1993, Worku et al., 2017), assessing water quality vis-à-vis human activities in Korea (Lim and Choi, 2015), monitoring small dams in semi-arid regions (Deus et al., 2013, Finch, 1997) and general parameterisation of hydrological models (Gangodagamage et al., 2001, Uhlenbrook et al., 2004, Cohen et al., 2012, Fang et al., 2017). GIS and RS have been noted to have a major advantage of accurately sizing and characterising catchments in rainfall-runoff modelling over and above the fact that analysis can be performed much faster, especially when there are complex mixtures of land use classes and different soil types (Shadeed and Almasri, 2010). In Africa, numerous researches have also exploited the same tools and techniques in similar studies to advance knowledge in this domain. This has been enhanced by improved and free access to valuable satellite earth observation data from various systems such as Meteorological satellites (Haggard, 1972), and Tropical Rainfall Measuring Mission (TRMM) (Simpson et al., 1988). All these studies indicate that GIS and remote sensing have become an almost indispensable part of hydrological modelling studies over the past decades globally.

3.2 Hydrological modelling studies in Zimbabwe: Progress and gaps

   Hydrological modelling studies in Zimbabwe backdate to 1991 where (Vörösmarty and Moore (1991) used a simple catchment-scale model to simulate seasonal variation in discharge in the Zambezi river and how it might respond to climate and land use change. Though developments have been slow in the past three decades, advances have seen shifts from use of simple statistical models to empirical-black box models, lumped conceptual models to coupled distributed-physically-based hydrological models more recently. Love et al. (2001) used an empirical model (the Hydrologiska Byråns Vattenbalansavdelning (HBV) model) to simulate hydrological processes in the Northern Limpopo basin while the HEC-HMS model has been successfully used in simulating run-off in the gauged and ungauged Upper Manyame sub-catchments of Zimbabwe (Gumindoga et al., 2015). The same model has been applied by Gumindoga et al. (2016) in modelling the water balance of the Lower Middle Zambezi Basin, successfully estimating the total inflows into the Cahora Bassa Dam and recommending ways of management artificial floods in this basin. Other models that have used in Zimbabwe include the Surface Energy Balance System (SEBS) Water Balance Model to determine actual evapotranspiration in the Upper Manyame catchment (Rwasoka et al., 2011) and the TOPMODEL to simulate streamflow of Upper Save River catchment (Gumindoga et al., 2011). From these studies, it is apparent that distributed physically-based models have been the predominant type of model being used in hydrological studies in the past years in Zimbabwe.

     A review of the scope of coverage of these studies revealed that most modelling studies have been done in the Zambezi basin catchments i.e. the Mazoe, the Manyame and part of the Sanyati while little has been done in the other strategic catchments such as the Save and the Mzingwane and no known studies were found for the Gwayi and the Runde catchments showing a vast knowledge gap in this regard. However, other non-modelling hydrological studies have been done in almost all catchments in the country e.g. (Nyoni, 2012, Lorup et al., 1998, Mahere et al., 2014, Love et al., 2010, Love et al., 2001, Kibena et al., 2014, Finch, 1990, Dalu et al., 2012, Sawunyama et al., 2006, Munamati and Nyagumbo, 2010). In as far as the integration of landuse-landcover change, and climate modelling in hydrological modelling studies, this review found no known/ published study to date that has attempted to advance knowledge in this direction i.e. employ advanced, coupled distributed-physically based hydrological modelling techniques to expand the scope of understanding of the climate-landuse-hydrology nexus in Zimbabwe thus showing a huge knowledge gap in this regard as well.

  Notwithstanding the inherent shortcomings of these tools, the advances in integration/ streamlining of GIS and RS in hydrological modelling research globally have been tremendous over the past decades as earlier discussed (Sui and Maggio, 1999, Bhatt et al., 2014). In Zimbabwe however, very few studies have applied these tools and techniques in this regard e.g. (Dalu et al., 2012, Finch, 1990, Gumindoga et al., 2016, Gumindoga et al., 2015, Lorup et al., 1998) showing a need to expand knowledge in this area. This could be attributed to limited expertise and GIS and Remote sensing infrastructure/equipment and software to fully streamline use of the techniques in hydrological modelling studies, notwithstanding the already highlighted challenge of limited accessibility, accuracy, availability, consistency and coverage of in-situ climatic /meteorological data such as rainfall, temperature measurements.

4 Conclusion

4.1 Climate studies

   Despite the developments in climate and hydrological research, and the already confirmed climate impacts on human livelihoods, economies and general well-being (Sango and Godwell, 2014) and water resources in Zimbabwe (Richard and Rafik, 2014), the scope of understanding of the climate-landuse-hydrology interlink is still limited/poor i.e. it has gaps as earlier discussed. With regards to climatic conditions in Zimbabwe, studies covered in this review present varying and in some instances contradictory conclusions though most agree that the climate has been changing or varying considerably in space and time with an estimated 0.1°C rise and a 10% decrease per decade for temperature and rainfall respectively since 1900 to 1993. Follow up studies in this regard basically indicate the same temperature and rainfall trends though magnitudes of change have been varying and in some instances contradictory owing to the different methodologies used in these studies. It was noted that use of different methodologies in analysis of data in these studies further compounds the problem of comparability of findings e.g. other studies used simple parametric inferential statistics to test for significance of climatic trends e.g. (Love et al., 2010, Lorup et al., 1998) while other used non-parametric techniques e.g. (Makuvaro, 2014). This basically shows the need for care in interpreting and/ comparing study findings in this regard.

   Climate change and variability impact studies were found to be the dominant type of climate studies in Zimbabwe with greater bias toward agricultural impacts e.g. climate impacts on crop productivity and yield, crop vulnerability and drought. Other impact themes that emerged from the review included impacts on forestry and ecosystems, surface and groundwater resources, and community livelihoods in general. Other related themes that emerged in this review related to climate change vulnerability, resilience and adaptation. Findings in this regard converged on this general conclusion by IPCC (2007) that Zimbabwe is a highly climate vulnerable country  with limited resilience and poor adaptation policies and strategies in place to avert the inherent impacts of climate change and variability. Furthermore, considering that the global and regional climate forecasts indicate worsening of conditions (Henderson-Sellers and McGuffie, 2012), it is thus very important that climate science in Zimbabwe is updated to generate new and contextual knowledge in this regard rather than rely on outdated conclusions from past studies to inform climate policy formulation and strategy development for the country.  

In this review, it has been revealed that climate modelling research is still a largely grey area in Zimbabwe e.g. past studies have used GCMs mostly and only a few have used RCMs without downscaling implying limited local applicability of some of their findings. This thus necessitates further expansion of knowledge on the same by leveraging on the potential presented by new and advanced GCMs and RCMs which have the ability to generate accurate climate perturbations at regional and local scale (Gleick, 1986, Shongwe et al., 2015, Landman et al., 2006, Dosio et al., 2014) through advanced downscaling techniques. New studies could expand knowledge by modelling impact scenarios within agriculture, biodiversity and hydrology such as surface run-off which determines overall water availability and security in Zimbabwe. Advancing knowledge in this regard will be vital especially for identifying for example the hydrologic consequences of changes in important climatic variables such as temperature, precipitation, and other landscape variables such as landcover thus contributing to holistic policy development and effective planning of current and future water management and security interventions.

4.2 Hydrological modelling studies

   Within hydrology studies, hydrological modelling is a relatively grey area of research in Zimbabwe with less than 3% of the studies reviewed herein covering this area. Of the seven water catchments in Zimbabwe, the Zambezi basin catchments i.e. the Manyame and the Mazoe catchment have received most attention in terms hydrological modelling studies whiles others have been researched though not extensively. No published research or knowledge exists for the Gwayi and the Runde while studies on the Mzingwane catchment which involved hydrological modelling are rather outdate considering the advances (new models developed) that have been made in this domain. With such knowledge gaps, vis-à-vis the already acknowledged climate vulnerability of Zimbabwe and the predicated worsening climatic conditions in the future, it thus become very critical that deliberate efforts cascading from policy level prioritise climate-hydrology modelling research in Zimbabwe as all these aspects speak to present and future water security and livelihoods.

   With regards to the types of models, there is generally a need to test or apply new/ advanced hydrological models to better understand interlinkages between climate-hydrology and landuse as well. In order to achieve this, researchers such as Werner and McNamara (2007) and Xu (1999) have advocated for the coupling of distributed hydrological models and properly downscaled GCMs/RCMs. Such models have proved to better enhance understanding of feedback mechanisms and interrelations between key natural-human systems influencing community livelihoods hence it is an imperative that climate-hydrological modelling studies in Zimbabwe advance knowledge in this direction as it has been noted in this review that no known studies have attempted to explore this specialised area of research.

   Overall, climate science and hydrology research in Zimbabwe needs to be update with a more holistic outlook using among other methodologies, a coupled systems-based approaches (integrating climate-landuse-hydrological modelling) to have a better understanding of past, present and future climatic conditions and their impacts without negation the significance social dynamics of climate change (presented by Bhatasara  (2017)) in the development of sustainable and inclusive climate adaptation policies, strategies and pathways suggested for the country.

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[1] Corresponding author contacts:

Department of Environmental Science and Health, Faculty of Applied Sciences, National University of Science and Technology, Corner Cecil Avenue and Gwanda Road, Ascot, Bulawayo, Zimbabwe

(Email: auther.maviza@nust.ac.zw)