Base Flow Values Research Articles

Hydrological Modeling to Unravel the Spatiotemporal Heterogeneity and Attribution of Baseflow in the Yangtze River Source Area, China

Revealing the spatiotemporal variation in baseflow and its underlying mechanisms is critical for preserving the health and ecological functions of alpine rivers, but this has rarely been conducted in the source region of the Yangtze River (SRYR). Our study employed the Soil and Water Assessment Tool (SWAT) model coupled with two-parameter digital filtering and geostatistical approaches to obtain a visual representation of the spatiotemporal heterogeneity characteristics of the baseflow and baseflow index (BFI) in the SRYR. The SWAT model and multiple linear regression model (MLR) were used to quantitatively estimate the contribution of climate change and human activities to baseflow and BFI changes. The results underscore the robust applicability of the SWAT model within the SRYR. Temporally, the precipitation, temperature, and baseflow exhibited significant upward trends, and the baseflow and BFI showed contrasting intra-annual distribution patterns, which were unimodal and bimodal distribution, respectively. Spatially, the baseflow increased from northwest to southeast, and from the watershed perspective, the Tongtian River exhibited higher baseflow values compared to other regions of the SRYR. The baseflow and BFI values of the Dangqu River were greater than those of other tributaries. More than 50% of the entire basin had an annual BFI value greater than 0.7, which indicates that baseflow was the major contributor to runoff generation. Moreover, the contributions of climate change and human activities to baseflow variability were 122% and −22%, and to BFI variability, 60% and 40%. Specifically, precipitation contributed 116% and 60% to the baseflow and BFI variations, while the temperature exhibited contributions of 6% and 8%, respectively. Overall, it was concluded that the spatiotemporal distributions of baseflow and the BFI are controlled by various factors, and climate change is the main factor of baseflow variation. Our study offers valuable insights for the management and quantitative assessment of groundwater resources within the SRYR amidst climate change.

Calibrated Eckhardt’s filter versus alternative baseflow separation methods: A silica-based approach in a Brazilian catchment

The Eckhardt’s filter, a recursive digital filter widely used in hydrology to separate baseflow, relies on two parameters: the recession constant (a) and the maximum allowable long-term ratio of baseflow to streamflow (BFImax). While a can be determined through regression analyses, BFImax is often assigned an arbitrary value because of limited catchment information for a more objective determination. This study aimed to assess and calibrate BFImax for a 1.2 km2 rural catchment in southern Brazil using dissolved silica data. Calibration involved minimizing differences between filter-generated baseflow hydrographs and those derived from dissolved silica measurements and mass balance across 13 monitored rainfall-runoff events. The calibrated model was compared to three established models: a modified graphical method based on local minima, a backward filter model, and a one-parameter filter. The results yielded an optimal BFImax of 0.653 with high model performance (PBias ≈ 0, NSE = 0.85, KGE = 0.74, and NRMSD = 13 %) against silica-derived baseflow. Applying the calibrated filter to a six-year streamflow dataset yielded a long-term baseflow index (BFI) value of 54 %, aligned with regional and global averages. Comparisons with other models showed that the backward filter model produced unrealistic baseflow values nearly identical to streamflow values; the local minima method overestimated baseflow by +19 % and failed to accurately represent the smoothness and peaks of the baseflow hydrographs; and the one-parameter filter represented the shape and timing of the baseflow hydrographs well, but underestimated magnitude by −19 %. These findings validate the effectiveness of the Eckhardt’s filter in generating accurate baseflow hydrographs and confirm the importance of calibrating BFImax for optimal performance. The research also endorses the use of dissolved silica measurements and the mass balance method as a reliable approach for calibrating BFImax for long-term assessment of baseflows.

The phenological soil water balance: a proposed model for estimating water resources for an entire watershed using crop coefficients

The aim of the research was to develop a model that would allow the application, at a watershed scale, of the hydrological soil water balance model proposed by the FAO for the dosing of irrigation water in agriculture, which uses crop coefficients (Kc) for the calculation of potential crop evapotranspiration (ETc). To be able to assess the water resources of a territory in which there are land uses other than agricultural ones, the application of the proposed model has made it necessary to determine the crop coefficients of the latter. Since crop coefficients vary according to phenological stage, this model was termed ‘phenological soil water balance’. A correction factor for precipitation and potential evapotranspiration, using an acclivity coefficient (i.e., the ratio between the actual area and the projected area), has also been proposed to obtain accurate results even in non-flat areas, which allowed us to consider the actual area of the territory instead of the projected one. The model was applied daily for 7 consecutive years (from 2013 to 2019) in the Santa Maria degli Angeli watershed (Urbino, central Italy) whose area is about 14 km2. The calibration and validation of the model were conducted by comparing the deep percolation computed by the model with baseflow values of the Santa Maria degli Angeli stream obtained by flow measurements made at the closing section of the sample watershed. The results of the model showed that the total values of deep percolation and measured baseflow only differed by 3% in the whole period considered; thus the phenological soil water balance model can be used to accurately estimate water resources and can be applied at different time intervals (daily, monthly, annual, etc.). The structure of the model makes it suitable for application in both small and large watersheds and territories.

Baseflow from Snow and Rain in Mountain Watersheds

After peak snowmelt, baseflow is the primary contributor to streamflow in snow-dominated watersheds. These low flows provide important water for municipal, agricultural, and recreational purposes once peak flows have been allocated. This study examines the correlation between peak snow water equivalent (SWE), post-peak SWE precipitation, and baseflow characteristics, including any yearly lag in baseflow. To reflect the hydrologic processes that are occurring in snow-dominated watersheds, we propose using a melt year (MY) beginning with the onset of snowmelt contributions (the first deviation from baseflow) and ending with the onset of the following year’s snowmelt contributions. We identified the beginning of an MY and extracted the subsequent baseflow values using flow duration curves (FDCs) for 12 watersheds of varying sizes across Colorado, USA. Based on the findings, peak SWE and summer rain both dictate baseflow, especially for the larger watersheds evaluated, as identified by higher correlations with the MY-derived baseflow. Lags in the correlation between baseflow and peak SWE are best identified when low-snow years are investigated separately from high-snow years. The MY is a different and more effective approach to calculating baseflow using FDCs in snow-dominated watersheds in Colorado.

Water Balance Components Estimation using WetSpass Model: A Case study of Mekelle Area, Tigray, Ethiopia

This research was aimed at estimating the spatially distributed Water balance components of the catchments in Mekelle area by using WetSpass hydrological model and verifying the model outputs with ground truth. Long term mean metrological data and physical characteristics of the catchments were used as an input to the model. The input data were manipulated using Arc- GIS tools. Results of this model depicted that about 73.13% and 19.96% of the precipitation in the study area was lost through evapotranspiration and surface runoff respectively. However, 7% of it replenished the groundwater. The annual runoff and groundwater recharge estimated in WetSpass model accumulated using Arc-GIS were verified using the annual runoff gauged records and base flow measurements. Accordingly the accumulated runoff and base flow derived from the WetSpass model using Arc-GIS software results were quite close to the observed runoff and base flow values measured in the gauging points. Therefore, WetSpass model is appropriate model in estimating water balance components in the study area.

Base flow separation method in conditions of the karst catchment

AbstractGeological conditions are an important parameter on hydrological modeling that strongly affects runoff generation processes. They are not usually taken into account in the parameters of hydrological models, especially karst catchments, where water transfers through fissures, cavities, caves, and phreatic channels are regularly discarded in studies because of their nonlinear behavior and the variability of their typology. The study compared using of base flow separation methods in the conditions of karst catchments, where the reference base flow values measured are not available. The base flow separation was realized by various methods and the models available, including BFI+ and WHAT

Baseflow Persistence and Magnitude in Oil Palm, Logged and Primary Tropical Rainforest Catchments in Malaysian Borneo: Implications for Water Management under Climate Change

While timber harvesting has plateaued, repeat-logging and conversion into plantations (especially oil palm) are still active in the tropics. The associated hydrological impacts especially pertaining to enhanced runoff, flood, and erosion have been well-studied, but little attention has been given to water resource availability in the humid tropics. In the light of the increasing climate extremes, this paper compared baseflow values and baseflow recession constants (K) between headwater catchments of five differing land-uses in Sabah, Malaysian Borneo, namely primary forest (PF), old growth/virgin jungle reserve (VJR), twice-logged forest with 22 years regeneration (LF2), multiple-logged forest with 8 years regeneration (LF3), and oil palm plantation (OP). Hydrological and meteorological sensors and dataloggers were established in each catchment. Daily discharge was used for computing K via four estimation methods. Catchment ranks in terms of decreasing K were VJR (0.97841), LF3 (0.96692), LF2 (0.90347), PF (0.83886), and OP (0.86756). Catchment ranks in terms of decreasing annual baseflow were PF (1877 mm), LF3 (1265 mm), LF2 (812 mm), VJR (753 mm), and OP (367 mm), corresponding to 68%, 55%, 51%, 42%, and 38% of annual streamflow, respectively. Despite the low K, PF had the highest baseflow magnitude. OP had the fastest baseflow recession and lowest baseflow magnitude. Baseflow persistence decreased with increasing degree of disturbance. K showed strong association to catchment stem density instead of basal area. For dynamic catchments in this study, the Kb3 estimator is recommended based on its lowest combination of coefficient of variation (CoV) and root mean squared error (RMSE) of prediction. For wetter catchments with even shorter recession events, the Kb4 estimator may be considered. Regarding climate change, logging and oil palm agriculture should only be conducted after considering water resource availability. Forests (even degraded ones) should be conserved as much as possible in the headwaters for sustainable water resource.

Baseflow index on Miu watershed based on a digital graphical method

Baseflow is essential for water resource management in the dry season at the watershed scale. Management strategy water resources play a crucial role, especially those related to estimation efforts water supply, water quality management, water balance calculation, and strategy development of other water resources. This study aims to determine the value of baseflow using the Fixed Interval Method. This study used Hydro Office software with input daily discharge data from 2004 to 2013. The average baseflow value obtained was 5.53 m3/s, the process of calibrating by taking discharge data in the dry season, with no rainfall input. The BFI value obtained an average of 0. 9. This value indicates that the Miu watershed has high storage permeability characteristics. It indicates that the Miu watershed has a relatively stable flow during dry periods. The greater the BFI value, the better the water supply in a watershed.

Adaptive ecological baseflow computation based on the ecological requirements: a case study in Weihe Basin, China

Abstract A certain flow plays a key role in preventing the shrinkage and drying of the river while maintaining good functionality. In recent years, ecological baseflow has become an important research issue in China with the emergence of ecological protection. Various ecological baseflow methods and their improvement methods were proposed and applied, but the reliability and applicability of the results are yet to be verified. This paper proposes an advanced ecological baseflow computation-dynamic thematic evaluation service model by considering the ecological protection objectives. The method was applied to five key cross-sections of the Weihe River, and a set of recommended ecological baseflow values was obtained considering the connectivity, water environment, and water-sediment balance, which is in line with the actual requirements. The analysis shows that the method has reliability and operability, which can provide support for management decisions.

Quantifying interaction uncertainty between subwatersheds and base-flow partitions on hydrological processes.

Base flow, as an important component of runoff, is the main recharge source of runoff during the dry period, especially in the Yellow River Basin located in a semiarid area. However, the process of obtaining base flow has great uncertainty when considering hydrological simulations. Thus, in this study, a three-step framework is proposed, i.e., the particle swarm optimization (PSO) algorithm is used to calibrate model parameters under different subbasin partitioning schemes; then, the hydrograph separation (HYSEP), Improved United Kingdom Institute of Hydrology (IUKIH) and Lyne and Hollick filter (Lyne-Hollick) methods are used to separate the baseflow from the total runoff process, thereby exploring the uncertainty impacts of baseflow segmentation methods on the hydrological simulation process. The subsample-variance-decomposition method is used to quantify the independent and interactive uncertainty in the hydrological simulation process. The results show that the Topmodel model can be better applied to the source area of the Yellow River (the KGE values in the Sub5, Sub13, Sub21, Sub29, Sub37 and Sub13 scenarios were 0.91 and 0.65, 0.94 and 0.86, 0.94 and 0.88, 0.92 and 0.82, 0.95 and 0.89, and 0.92 and 0.83, respectively). The subbasin division uncertainty had less impact on simulated streamflow during the dry season and had a significant impact in the wet season, such as, the subbasin division uncertainty caused the difference between the median of the simulated streamflow to be as high as 213.09 m3/s in August but only 107.19 m3/s in January; Meanwhile, the baseflow segmentation method uncertainty has a significant impact on the annual mean streamflow values under different subbasin segmentation schemes. In addition, the baseflow values estimated by the Lyne-Hollick and HYSEP methods were obviously higher than those estimated by the IUKIH method during the wet season. The uncertainty influence of subbasin partitioning schemes and baseflow segmentation methods had significant differences on hydrological processes in different periods. The uncertainty influence of subbasin partitioning schemes was dominant in the dry season, accounting for 86%, and the baseflow segmentation methods took second place, accounting for approximately 12%. In the wet season, the uncertainty influence of the baseflow segmentation methods was gradually weakened, which may have been due to the uncertainty influence of the hydrological model. These results provide a reference for the calibration and validation of hydrological model parameters using baseflow components.

Performance evaluation of an automated method for hydrograph separation in Mellah catchment, Northeastern Algeria

This paper explains a procedure based on the master recession curve (MRC) for hydrograph separation into individual flow components, using the recession constant K. To evaluate the performance of this computational technique, the results of the baseflow component were compared to the baseflow component obtained from the recursive digital filter (RDF) method. The study has revealed that the results of baseflow achieved by both methods show similarity to a great extent, and the baseflow values agree better with (R2 > 0.77). Moreover, automated baseflow separation underestimates the simulated baseflow from RDF method by as much as 2.35%, or overestimates it by up to 23.55%, during rainfall. These methods were applied to a set of hydrometric measures in the Mellah catchment, Algeria. The results proved that the proposed method gives a better estimate of flow components.

Flowpath influence on stream acid events in tropical urban streams in Singapore

AbstractWe investigated processes contributing to periodic acidification events in headwater streams of Nee Soon Forest Catchment (NSFC) in Singapore by monitoring hydrochemical changes in response to rainfall inputs. Stream chemistry response to most rainfall events was characterized by decreases in pH from means ranging from 5.1 to 5.3 to below 4.8–5.0 and corresponding increases in specific electrical conductivity from baseflow values of 15–30 to 50–80 μS cm−1, indicative of low‐total dissolved solids in stormflow. The decreases in pH in the streams, which are typically acidic year‐round, were related to occasional highly acidic rainfall inputs (pH ~4.05) and likely the flushing of organic acids into the stream by shallow subsurface flow interacting with surface litter and/or organic‐rich soil horizons. The interaction of rainwater runoff with organic matter in the soil matrix possibly alters the chemical composition of stormflow, influencing pH. Decomposition of instream organic matter also reduces stream water pH. Leaching experiments revealed that the overland flow passing through organic matter and A horizon material has the potential to lower stream water pH by approximately a half unit or more, in part, by flushing nitrates that were produced by microbial decomposition of organic matter and/or precipitated sulphur that enterred the forest by wet or dry atmospheric deposition. The observed periodic acid events are a natural phenomenon in the stream system in this urban environment because of naturally acidic rainfall and granitic soils with low buffering potential. However, acid events are likely amplified in frequency and magnitude by anthropogenic pollution emissions of sulphur and nitrogen species (e.g., SO2and NOx) from local and regional sources that lower rainfall pH. Although, acid runoff events are typically short‐lived (<12–24 h), further longitudinal monitoring and experimental studies are needed to investigate the long‐term implications on sensitive taxa in the NSFC streams. Finally, understanding the flow pathways of stormflow water in the nested system was critical for deciphering the mechanisms driving stream acid events at the site.

Assessing different roles of baseflow and surface runoff for long-term streamflow forecasting in southeastern China

ABSTRACT Accurate long-term streamflow forecast is essential to alleviate and solve the water security problems related to flood and drought disaster warnings. In this study, a new strategy for forecasting monthly streamflow is proposed and four scenarios are designed for the evaluation of different roles of baseflow and surface runoff on performances of long-term streamflow forecasting. The developed models are evaluated at multiple streamflow sites located in the Zhejiang Province of China. The results show that artificial intelligence (AI)-based models with two predictor variables (i.e. baseflow and surface runoff) performed better than that with a single predictor (streamflow) for all the months in a year, and the prediction accuracy of annual peak and monthly streamflow values is improved. Based on the comprehensive evaluations of all the models, the baseflow and surface runoff values are recommended as inputs to AI-based models for an improved prediction accuracy of streamflows.

Lake water quality observed after extreme rainfall events: implications for water quality affected by stormy runoff

To investigate the effects of extreme rainfall events (EREs) on lake water quality, we analyzed the lake monthly monitoring data at the stations close to inflow rivers. We tested the hypothesis that the effects depend on rainfall magnitude, season, distance from the rivers, etc. The highest correlations with many water-quality indicators were obtained for the rainfall amount for the preceding 10 days before the water sampling days; i.e., negatively with Na+, Cl−, chlorophyll a (Chla), and COD, and positively with ortho-phosphate. We used the threshold of rainfall amount ≥ 140 mm to choose EREs and evaluate the water quality on such occasions because quite low values of Na+, Cl−, and Chla thresholds (≤ 25th percentile) were observed in some of the EREs. The event samples (group 1; G1) meeting the above thresholds probably represented the inflow waters during EREs (generally occurred in Oct–May), and the others (group 2; G2) indicated significant changes in the lakes (Jun to Sep), probably depending on the lake’s state of primary production. In the events of lower rainfall events (50 mm to 140 mm for 10 days), fast recoveries of algal production usually occurred even in Oct to May. The values of COD, total phosphorus, and ortho-phosphate in G1 were higher than the values of river water quality in the baseflow conditions, whereas both the G1 and baseflow values of nitrogen were nearly the same. The effects of ERFs on lake water quality from the viewpoints of tempo-longitudinal distributions, future monitoring, and climate change were discussed.

Studi Pemisahan Aliran Dasar Metode Local Minimum Method Daerah Aliran Sungai Miu

The two main elements of a river flow hydrograph are direct runoff and baseflow. Base flow is a flow that comes from groundwater and it is available during the rainy and dry seasons. Information of baseflow value of a watershed has an important meaning in efforts to develop and manage water resources, including the provision of clean water, irrigation systems, flood management and others. Miu watershed located in Sigi Regency often experiences natural disasters such as floods and droughts that occur almost every year, which is the research location with an area of 65,452.01 Ha. This study aims to determine the value of baseflow using the Local Minimum Method and to find out whether this method can be used for baseflow modeling in research watersheds. This study was conducted using Hydro Office software with input data in the form of daily discharge data from 2004 to 2013. The average baseflow value obtained was 5.43 m3/s. The process of calibrating the baseflow value by taking flow data in the dry season where there is no rainfall input. It is obtained that BFI have value an average of 0.89. This value indicates that the Miu watershed has high storage permeability characteristics, The Miu watershed has quite stable flow during dry periods, because the greater the BFI value, the better the water supply in a watershed. In the statistical test, the observed discharge is the discharge data from the measurement results for the period 2004-2013, while the calculated discharge is the result of the estimated base flow value obtained using the graphical method. From the results of the statistical test of RMSE and R2, the RMSE value was 0.147 and the R2 value was 0.751. Both statistical tests show that the local minimum method has a fairly good performance in modeling the base flow in the Miu watershed.

Rainfall-Runoff Simulation Using HEC-HMS Model in the Benanain Watershed, Timor Island

Floods in a watershed area are caused by reduced water recharge due to changes in land use, increasing their discharge volume. Benanain watershed is an extensive area with many tributaries. Watershed morphometrics provides initial information about the hydrological behavior and the hydrograph shape of flooding in these areas. Furthermore, rainfall-runoff modeling uses as a unit to approach the hydrological values of the flooding process. This study determines the physical characteristics of the Benanain watershed based on curve number (CN) values, land cover, peak discharge, and peak time. It was conducted on the Benanain watershed with 29 sub-watersheds covering 3,181.521 km2. Data were collected on the rainfall experienced for 13 years from 1996 to 2008 and analyzed using the Log Pearson Type III method, while the HEC HMS model was used for flood discharge analysis. HEC-HMS model must calibrate by adjusting the model parameter values until the model results match historical data such as initial abstraction, lag time, recession, baseflow values, and curve number. The results show that the curve number values range from 56.55 - 73.90, comprising secondary dryland forest and shrubs. Moreover, the rock lithology in the Benanain watershed is dominated by scaly clay and other rock blocks. This means the area has low to very low permeability, which affects the volume of runoff. The return period of a 1000-year flood discharge obtained a peak of 5,794.50 m3/s, with a peak time of ± 14 hours. Morphometry of the Temef watershed with large catchment, radial shape pattern, an average of steep slope river, and meandering affects the peak of flood discharge hydrograph and the peak time of the flood.

Impact of climate change and human activities on the baseflow in a typical karst basin, Southwest China

Baseflow is an important component of river runoff and an important ecological indicator of environmental health. A quantitative analysis of the impact of climate change and human activities on baseflow based on a scientific and reasonable baseflow separation scheme is helpful for the maintenance of river ecological health and the rational allocation of water resources, especially in the karst basins of Southwest China. The major difficulties and challenges lie in how to select the best baseflow separation scheme for karst basins and how to quantitatively analyse the impacts of climate change and human activity on the baseflow value and the baseflow intra-annual distribution characteristics. Therefore, the original achievement and objective of this study were to further contribute to the literature to fill the gap in the knowledge of how to analyse the impacts of climate change and human activities on baseflow more reasonably and comprehensively in a karst basin. The proposed framework consists of a trend analysis of the precipitation, mean temperature and potential evapotranspiration, an ensemble method for selecting the optimal baseflow separation scheme, the climate elasticity method and the slope change ratio of the cumulative quantity method to quantitatively estimate the contributions from climate change and human activities to changes in baseflow values and baseflow intra-annual distribution characteristics. The methodology was successfully implemented using a 55-year time series (1963–2017) of meteorological data and flow data recorded by the stations in the Chengbi River Karst Basin (Southwest China). The results show that annual rainfall and annual potential evapotranspiration display an upward and downward trend, respectively, while the average annual temperature is relatively stable. The Chapman-Maxwell method with a parameter of 0.970 outperforms the other methods and is the best baseflow separation scheme. Human activities are estimated to be the main factors influencing the variation of baseflow values and the variation of the baseflow annual distribution characteristics, with a contribution rate of 65.89%–149.83%, and the contribution rate of climate change is −49.83%–34.11%.

Spatial and temporal variabilities in baseflow characteristics across the continental USA

Trends in baseflows based on observed daily streamflow data are evaluated in this study at several sites in the least anthropogenically affected watersheds in the USA. Trends were determined for annual maximum, annual mean, and annual median baseflow. Baseflow values derived at 574 stations in the USA for the 44 years from 1970 through 2013 are analyzed using two nonparametric trend tests (Spearman’s rho (SR) test and Mann-Kendall (MK)). Results from the trend tests are compiled for 18 major regions to understand the spatial variability of changes in baseflows across the USA. Results from SR tests indicate that almost half of the stations show statistically significant trends in annual maximum baseflows. Trends in annual median baseflows show that 32.06% of the gauging stations have downward trends, and a total of 56.45% of sites show significant trends for annual mean baseflows. The Souris-Red-Rainy, Missouri, and California watershed regions have a larger number of sites with higher upward trends compared with those from other regions in the USA. The results from the SR test indicate that 262 sites have statistically significant trends in annual maximum baseflow compared with the 254 sites with similar trends noted from the MK test. Based on limited data, it can be concluded that baseflow and precipitation values accumulated for the same month are correlated in some regions. In general, the number of sites with decreasing trends for annual maximum, mean, and median baseflows is larger than the number of sites with increasing trends. Decreasing trends in baseflows are cause for concern and have serious implications on future planning for low flow management strategies for several streams in the USA.

Impact of Coastal Wetland Restoration Plan on the Water Balance Components of Heeia Watershed, Hawaii

Optimal restoration and management of coastal wetland are contingent on reliable assessment of hydrological processes. In this study, we used the Soil and Water Assessment Tool (SWAT) model to assess the impacts of a proposed coastal wetland restoration plan on the water balance components of the Heeia watershed (Hawaii). There is a need to optimize between water needs for taro cultivation and accompanying cultural practices, wetland ecosystem services, and streamflow that feeds downstream coastal fishponds and reefs of the Heeia watershed. For this, we completed two land use change scenarios (conversion of an existing California grassland to a proposed taro field and mangroves to a pond in the wetland area) with several irrigation water diversion scenarios at different percent of minimum streamflow values in the reach. The irrigation water diversion scenarios aimed at achieving sustainable growth of the taro crop without compromising streamflow value, which plays a vital role in the health of a downstream fishpond and coastal environment of the watershed. Findings generally suggest that the conversion of a California grassland to a patched taro field is expected to decrease the baseflow value, which was a major source of streamflow for the study area, due to soil layer compaction, and thus decrease in groundwater recharge from the taro field. However, various taro irrigation water application and management scenarios suggested that diverting 50% of the minimum streamflow value for taro field would provide sustainable growth of taro crop without compromising streamflow value and environmental health of the coastal wetland and downstream fishponds.

Modelling the impacts of climatic variables on the hydrology of the Upper Crocodile River Basin, Johannesburg, South Africa

The impacts of climatic variables on the hydrology of the Upper Crocodile River Basin (UCRB) were modelled using the Precipitation-Runoff Modelling System (PRMS) for the period between 1996 and 2017. The values for the coefficient of determination (R2) and the Nash–Sutcliffe model efficiency coefficient (EN) gave a satisfactory correlation between the measured and simulated streamflow values, while a higher root mean square error (RMSE) was obtained for the validation period compared to the calibration period. PRMS was confirmed suitable for baseflow simulations because its mean annual recharge estimate of 5.3% was close to that of 7.5%, which was obtained using a separate filter parameter technique, both calculated from baseflow values. The climate scenario simulations revealed that the streamflow components are sensitive to temperature and rainfall. Higher decline in baseflow is observed when temperature increases than when rainfall decreases. Also, a smaller decline is observed in baseflow as compared to total streamflow, indicating the resistance of baseflow to drought. The scenario where temperatures were increased by 1.5 °C and rainfall decreased by 20% resulted in a decrease of 39% in total streamflow and 28% in baseflow. When the natural streamflow decreases by 39%, the wastewater discharge (5 m3/s) exceeds natural streamflow 80% of the simulation period. As urbanisation increases in the UCRB, wastewater will also increase. It is concluded that, while higher amounts of streamflow currently dilute wastewater, with changing climate variables and increasing urbanisation, less natural streamflow will be available to dilute wastewater, leading to highly polluted surface water. Therefore, better treatment and constraints on wastewater discharge are recommended.