Downstream Streamflow Research Articles

Effects of Cascading Dams on Streamflow within the Downstream Areas of the Rufiji River Basin in Tanzania

Despite their popularity, the construction and operation of hydropower reservoirs pose challenges to water resources. This study investigated the impacts of cascading dams on streamflow in Tanzania’s Rufiji River Basin. The SWAT model was developed to represent the entire Rufiji River Basin. The model simulated the streamflow for 41 years, from 1982 to 2022, and developed two main scenarios: with-dam and without-dam. To capture the influence of all dams, the results were emphasized from 2000 to 2022, when all three dams were operating. Calibration and validation were applied at the Rufiji-Stiegler and Kilombero-Swero stations with good performance. The results show that cascading dams annually decrease the streamflow by 1% at Rufiji-Stiegler station. In contrast, individually, the Mtera Dam displayed a 5% decrease while the Kidatu and Kihansi Dams exerted a 1% increase on the annual streamflow downstream at Rufiji-Stiegler. During 2000–2022, the Rufiji River Basin showed an annual reduction in streamflow contribution of 104.97 m3/s. Therefore, the reservoir’s operation significantly impacts the downstream streamflow. The findings are expected to guide policymakers, water resource managers, and environmentalists in mitigating potential adverse effects while optimizing the benefits of hydropower generation and water regulation within the region.

Reservoir operation impacts on streamflow and sediment dynamics in the transboundary river basin, Vietnam

AbstractHuman interventions activities around the world, particularly reservoir operation, have dramatically altered hydrological and sediment regimes in most of the major river basins. In the Mekong River, specifically the Upper Srepok River Basin (USRB) which is a main tributary of the river basin connected to the Mekong Delta's rice bowl and the Tonle Sap Lake's top inland fisheries, there are increasing concerns about the impacts of cascade reservoir operations on downstream streamflow and sediment budgets. Previous studies estimating impacts either relied solely on observed data or did not verify simulations of regulated streamflow. Using a process‐based hydrological model calibrated and validated for both natural and regulated streamflow in the USRB, it was found that the monthly hydrological changes were up to ±20% compared to pre‐dam periods at the most downstream station bordering between Vietnam and Cambodia (i.e., Ban Don station). The basin also experienced a slight decrease (less than 2%) in annual streamflow. Additionally, average and peak suspended sediment concentration decreased significantly in both of the annual and seasonal periods. At the Ban Don station, sediment loads were reduced 140 thousand tons/year (i.e., 15%) compared to pre‐dam period. Most of the changes in streamflow and sediment budgets in the basin were driven by the Buon Tua Srah reservoir, which had the highest degree of regulation in the basin. Therefore, integrated and transboundary water and sediment management, particularly at Buon Tua Srah reservoir, needs to be developed for the sustainability of the river basin.

Hydrological modelling of largest braided river of India using MIKE Hydro River software with rainfall runoff, hydrodynamic and snowmelt modules

Abstract The Brahmaputra River is one of the world's largest river systems, India's largest braided river, and its springtime runoff and downstream streamflow are mostly due to snowmelt processes. This study analysed and used Tropical Rainfall Measuring Mission (TRMM)/Global Forecast System (GFS) rainfall, PET, and snowmelt data as inputs to a Rainfall–Runoff (RR) model, which is based on the MIKE Hydro River NAM software package. The mathematical model was calibrated against the available observed discharge data for the sub-catchments. The model performed reasonably well and simulated discharge in good agreement with observed discharge in terms of timing, rate, volume, and shape of the hydrograph. During the calibration procedure, the optimum values of the nine RR-NAM parameters are obtained. The performance of each model has been checked against measured discharge using a coefficient of determination (R2). It is observed that the value of R2 varies from 0.6 to 0.86. This is deemed acceptable for the purposes of this study. In addition to R2, the overall Water Balance error is also checked. The WBL error is less than 6%. Despite the inherent uncertainties in hydrological modelling, it is determined that the calibrated RR-NAM model can be utilized for the Brahmaputra basin's Flood Forecasting and Early Warning System design, as well as water resource management and planning.

Quantifying the impacts of hydraulic infrastructure on tropical streamflows

AbstractHydraulic infrastructures, such as reservoirs and water diversion channels, cause altered streamflow worldwide. Therefore, this study aimed to assess the coupled impacts of reservoir operations and water transfer on downstream streamflow over 42 years (1979–2020) for a tropical river in Vietnam, the Vu Gia Thu Bon (VGTB). We also quantified variations in the multi‐sub‐basin contributions to the water budget associated with hydraulic structure development. Therefore, a semi‐distributed hydrological model, SWAT (Soil and Water Assessment Tool), was developed for the entire VGTB basin considering two plausible scenarios: with a dam and without a dam. In this study, reservoirs substantially affected the streamflow during the 2011–2020 period when 12 cascading hydropower dams were constructed in the Vu Gia sub‐basins. The cascading reservoirs across the Vu Gia River reduced the annual average streamflow by 28.1% during this period, whereas their influence was augmented by 13.9% at reaches further downstream. In contrast, the local reservoir and flow diversions created on the Thu Bon River resulted in a 6.5% increase in streamflow. The upstream reservoir operation significantly increased streamflow at the midstream stations by 27.8% compared to the no‐dam period. The streamflow decreased in the dry season by 5.6% in the Vu Gia sub‐basins and increased by 61.7% in the Thu Bon sub‐basins. However, the impacts decreased in the wet season by 41.3% due to the operation of reservoirs, in which Dak Mi 4 had the most significant influence. It was found that the water diverted to the Thu Bon River was governed and reduced by the cascading hydropower dams. Therefore, the operation of 11 reservoirs has partially compensated for the lost water in the Vu Gia sub‐basins, to which the Dak Mi 4 plant has transferred 19.7 m3/s (14%). Our findings classify the impact of cascading dams and diversion structures and their interaction with climate change.

Temporal and Spatial Variation Characteristics of Water Quality in the Middle and Lower Reaches of the Lijiang River, China and Their Responses to Environmental Factors.

As the climate and the external environment have changed, the environmental factors of the Lijiang River Basin (LRB) have changed, posing new threats to the environmental quality, ecosystem balance, and management and protection of the water environment of the Lijiang River. Water quality indicators and environmental factors vary spatially along the Lijiang River, which runs through urban areas, farmland, and karst areas. However, research on the response of water quality to water environmental factors is still lacking. Within this context, this study considered statistical methods and hydrological, meteorological, and water quality data of the middle and lower reaches of the Lijiang River from 2012 to 2018, expounded on the temporal and spatial change characteristics and evolution trends of water quality indicators; we analyzed the correlation between water quality indicators and environmental factors; we quantitatively assessed the sensitivity and contribution rate of water quality indicators to environmental factors. The results demonstrated that rainfall feedback on the river streamflow was lagging, and upstream precipitation often affected downstream streamflow. The water quality in the upper reaches of Guilin has improved year by year, and pollution levels have increased slightly when flowing through the urban area of Guilin. In spite of this, it still falls within the range of self-purification. River characteristics heavily influence the impact of environmental factors on water quality indicators; in contrast, the effects of different locations along the same river are more similar. Four water quality indicators are negatively correlated with water temperature, pH, and dissolved oxygen (DO). The sensitivities of ammonia nitrogen (NH4-N) and chemical oxygen demand (CODMn) to streamflow increase with the flow direction. The contribution rates of DO-to-total phosphorus (TP) and pH-to-TP are over −6%. Water temperature is the major contributing factor in the Lijiang River, while DO has a higher contribution in tributaries. The external sources affect the concentration of various water quality indicators and the sensitivity of water quality indicators to the external environment. There should be a series of measures implemented to reduce pollution, such as using oxygenation or chemical means to increase pH in Dahe and Yangshuo to control water pollutants. Tourism and particular karst topography make LRB’s calculations unique, but the research method can be applied to other watersheds as well.

Effects of the Three Gorges Dam on the downstream streamflow based on a large-scale hydrological and hydrodynamics coupled model

Study regionThe Yangtze River basin, China. Study focusIn this study, a large-scale coupled model of hydrological and hydrodynamic processes was developed via the Distributed Time Variant Gain Model and the hydrodynamics-operation coupled model. Two scenarios including operated by the Three Gorges Dam (TGD) and natural condition hypothesis without the reservoir construction, were analyzed based on the coupled model to investigate the effects of TGD operation on the streamflow from 1970 to 2018. New hydrological insightsResults exhibited that the annual average relative deviation of flow operated by the TGD was approximately 24% compared with the natural state at Yichang, while the value of Chenglingji, Hankou, Hukou and Datong were 8.5~12.8%. During water supply period, the TGD operation increase the streamflow by 33% at Yichang, and the streamflow in downstream can gain about 6–17%. For impounding process, except Hukou, the others showed a decreasing trend, and the reduction ranged from about 7–20%. For flood season, the dam operation mainly intercepts high flood peaks while the average streamflow changes a little. The high flood peaks at Yichang can be reduced by 20%, and the changes of high flood peaks in downstream reduce to no more than 10% due to the influence of interval inflow. The deviations during drawdown period are almost below 10%.

Impacts of the Three Gorges Dam on the streamflow fluctuations in the downstream region

The impacts of dams on the downstream streamflow is an important issue for the downstream flood control and water resources management. Most of the existing researches only focus on the impacts of dams on the first-order behavior of streamflow, i.e. its magnitude, but ignore the impacts on the second-order behavior of streamflow, i.e. its fluctuation. This study is aimed to study the impacts of the Three Gorges Dam (TGD) on the downstream flow in terms of not only their magnitudes but also fluctuations. The study method includes two steps, i.e. first to use the ARMAX-GARCH model to simulate the conditional mean and conditional variance of the single-site downstream streamflow series, and then to use the DCC-GARCH model to simulate the conditional covariance between streamflow series at different downstream sites. The method is applied to the downstream data series of the TGD for both the pre-dam period and post-dam period. By comparing the results of the two time periods, the conclusions are made as follows: (1) The TGD alleviates the extreme downstream streamflow, and this effect gradually weakens along the river; (2) the memory of single-site streamflow fluctuations in the mainstream of the middle reaches of the Yangtze River has been weakened by the TGD, the memory of streamflow fluctuations in the mainstream of the lower reaches has been enhanced; (3) the single-site streamflow fluctuations of Yichang and Datong have been enhanced by the TGD, and the streamflow fluctuations of the four sites between Yichang and Datong have been weakened; (4) the joint fluctuations (i.e. the covariance) and correlation between the streamflow series at different sites in the middle reaches of the Yangtze River have been weakened by the TGD, the joint fluctuations and correlation at different sites in the lower reaches have been enhanced. The results of this study are helpful for an improved understanding of the impacts of the dam on the downstream streamflow.

The compensating effect of glaciers: Characterizing the relation between interannual streamflow variability and glacier cover

AbstractMeltwater from glaciers is not only a stable source of water but also affects downstream streamflow dynamics. One of these dynamics is the interannual variability of streamflow. Glaciers can moderate streamflow variability because the runoff in the glacierized part, driven by temperature, correlates negatively with the runoff in the non‐glacierized part of a catchment, driven by precipitation, thereby counterbalancing each other. This is also called the glacier compensation effect (GCE), and the effect is assumed to depend on relative glacier cover. Previous studies found a convex relationship between streamflow variability and glacier cover of different glacierized catchments, with lowest streamflow variability at a certain optimum glacier cover. In this study, we aim to revisit these previously found curves to find out if a universal relationship between interannual streamflow variability and glacier cover exists, which could potentially be used in a space‐for‐time substitution analysis. Moreover, we test the hypothesis that the dominant climate drivers (here precipitation and temperature) switch around the suggested optimum of the curve. First, a set of virtual nested catchments, with the same absolute glacier area but varying non‐glacierized area, were modelled to isolate the effect of glacier cover on streamflow variability. The modelled relationship was then compared with a multicatchment data set of gauged glacierized catchments in the European Alps. In the third step, changes of the GCE curve over time were analysed. Model results showed a convex relationship and the optimum in the simulated curve aligned with a switch in the dominant climate driver. However, the multicatchment data and the time change analyses did not suggest the existence of a universal convex relationship. Overall, we conclude that GCE is complex due to entangled controls and changes over time in glacierized catchments. Therefore, care should be taken to use a GCE curve for estimating and/or predicting interannual streamflow variability in glacierized catchments.

Daily streamflow prediction using optimally pruned extreme learning machine

Daily streamflow prediction is important for flood warning, navigation, sediment control, reservoir operations and environmental protection. The current paper examines the prediction and estimation capability of a new heuristic method, optimally pruned extreme learning machine (OP-ELM) model, for daily streamflows of Fujiangqiao and Shehang stations at Fujiang River. Prediction accuracy of OP-ELM method is compared with other soft computing models, i.e. adaptive neuro-fuzzy inference system-particle swarm optimization (ANFIS-PSO), multivariate adaptive regression splines (MARS) and M5 model tree (M5Tree) using cross validation technique. Prediction results of the both stations reported that the OP-ELM and ANFIS-PSO are the best in modeling daily streamflows of upstream and downstream, respectively. For improving prediction accuracy of the OP-ELM method, various kernel types are tried and the linear, linear + sigmoid + Gaussian and linear + sigmoid provide the best results for both stations. The OP-ELM outperforms the other methods during estimation of downstream streamflow using hydro climatic data as input. The OP-ELM reduces the prediction error of ANFIS-PSO by 12% in estimation of daily streamflow. It is also found that including local data considerably improves the prediction accuracy in estimation of downstream streamflows. The overall results indicate that the OP-ELM method could be successfully used in predicting and estimating daily streamflow by using hydro climatic data as inputs.

Impact of the spatial variability of daily precipitation on hydrological projections: A comparison of GCM‐ and RCM‐driven cases in the Han River basin, Korea

AbstractIn this study, we investigate the impact of the spatial variability of daily precipitation on hydrological projections based on a comparative assessment of streamflow simulations driven by a global climate model (GCM) and two regional climate models (RCMs). A total of 12 different climate input datasets, that is, the raw and bias‐corrected GCM and raw and bias‐corrected two RCMs for the reference and future periods, are fed to a semidistributed hydrological model to assess whether the bias correction using quantile mapping and dynamical downscaling using RCMs can improve streamflow simulation in the Han River basin, Korea. A statistical analysis of the daily precipitation demonstrates that the precipitation simulated by the GCM fails to capture the large variability of the observed daily precipitation, in which the spatial autocorrelation decreases sharply within a relatively short distance. However, the spatial variability of precipitation simulated by the two RCMs shows better agreement with the observations. After applying bias correction to the raw GCM and raw RCMs outputs, only a slight change is observed in the spatial variability, whereas an improvement is observed in the precipitation intensity. Intensified precipitation but with the same spatial variability of the raw output from the bias‐corrected GCM does not improve the heterogeneous runoff distributions, which in turn regulate unrealistically high peak downstream streamflow. GCM‐simulated precipitation with a large bias correction that is necessary to compensate for the poor performance in present climate simulation appears to distort streamflow patterns in the future projection, which leads to misleading projections of climate change impacts on hydrological extremes.

Spatial propagation of streamflow data in ungauged watersheds using a lumped conceptual model

Abstract Streamflow data are required for the effective management of flood damage; however, streamflow is rarely measured in small watersheds. In this study, a lumped conceptual model was adopted to generate streamflow values at small, ungauged watersheds using the spatial propagation concept. During the process of spatial propagation, parameters related to physical properties were fixed, while event-based initial conditions were optimized to ensure that error rates on the simulated streamflow data were comparable to those for measured data. Then the concept was validated using data from 21 flood events in the ChungJu Dam (CJD) watershed, Korea, which was divided into 22 small, ungauged watersheds. The propagated peak streamflow data at ungauged cross-validation points have average Nash-Sutcliffe efficiency (NSE) values of 0.91–0.99. Averages of NSE values for volume and time to peak streamflow are over 0.85 which satisfies the model criterion (NSE > 0.5). It is concluded that streamflow data for small, ungauged watersheds located upstream can be generated by one gauged downstream streamflow without an extensive amount of gauged streamflow data from other locations using the proposed concept. For accurate simulation, availability of rainfall data is essential for accurately modeling the spatial propagation of streamflow using a lumped conceptual model.

Quantifying human impacts on hydrological drought using a combined modelling approach in a tropical river basin in central Vietnam

Abstract. Hydrological droughts are one of the most damaging disasters in terms of economic loss in central Vietnam and other regions of South-east Asia, severely affecting agricultural production and drinking water supply. Their increasing frequency and severity can be attributed to extended dry spells and increasing water abstractions for e.g. irrigation and hydropower development to meet the demand of dynamic socioeconomic development. Based on hydro-climatic data for the period from 1980 to 2013 and reservoir operation data, the impacts of recent hydropower development and other alterations of the hydrological network on downstream streamflow and drought risk were assessed for a mesoscale basin of steep topography in central Vietnam, the Vu Gia Thu Bon (VGTB) River basin. The Just Another Modelling System (JAMS)/J2000 was calibrated for the VGTB River basin to simulate reservoir inflow and the naturalized discharge time series for the downstream gauging stations. The HEC-ResSim reservoir operation model simulated reservoir outflow from eight major hydropower stations as well as the reconstructed streamflow for the main river branches Vu Gia and Thu Bon. Drought duration, severity, and frequency were analysed for different timescales for the naturalized and reconstructed streamflow by applying the daily varying threshold method. Efficiency statistics for both models show good results. A strong impact of reservoir operation on downstream discharge at the daily, monthly, seasonal, and annual scales was detected for four discharge stations relevant for downstream water allocation. We found a stronger hydrological drought risk for the Vu Gia river supplying water to the city of Da Nang and large irrigation systems especially in the dry season. We conclude that the calibrated model set-up provides a valuable tool to quantify the different origins of drought to support cross-sectorial water management and planning in a suitable way to be transferred to similar river basins.

Configurational entropy theory for streamflow forecasting

This study develops configurational entropy theory (CET) for monthly streamflow forecasting. The theory is comprised of three main parts: (1) determination of spectral density (2) determination of parameters by cepstrum analysis, and (3) extension of autocorrelation function. Comparison with the Burg entropy theory (BET) shows that CET yields higher resolution spectral density with more accurate location of spectral peaks. Cepstrum analysis yields more accurate parameters than the Levinson algorithm in the autoregressive (AR) method and the Levinson–Burg algorithm in BET. CET is tested using monthly streamflow data from 19 river basins covering a broad range of physiographic characteristics. Testing shows that CET captures streamflow seasonality and satisfactorily forecasts both high and low flows. High flows are satisfactorily forecasted with the coefficient of determination (r2) higher than 0.92 for one year ahead of time, with r2 higher than 0.85 for two years ahead of time, and up to 60 months ahead with r2 higher than 0.80. However, low flows are forecasted with r2 higher than 0.50 for one year ahead time. When relative drainage area is considered for analyzing streamflow characteristics and spectral patterns, it is found that upstream streamflow is forecasted more accurately (r2 = 0.84) than downstream streamflow (r2 = 0.75). Residuals of forecasted values relative to observed values are found to follow normal distribution.

Human water consumption intensifies hydrological drought worldwide

Over the past 50 years, human water use has more than doubled and affected streamflow over various regions of the world. However, it remains unclear to what degree human water consumption intensifies hydrological drought (the occurrence of anomalously low streamflow). Here, we quantify over the period 1960–2010 the impact of human water consumption on the intensity and frequency of hydrological drought worldwide. The results show that human water consumption substantially reduced local and downstream streamflow over Europe, North America and Asia, and subsequently intensified the magnitude of hydrological droughts by 10–500%, occurring during nation- and continent-wide drought events. Also, human water consumption alone increased global drought frequency by 27 (±6)%. The intensification of drought frequency is most severe over Asia (35 ± 7%), but also substantial over North America (25 ± 6%) and Europe (20 ± 5%). Importantly, the severe drought conditions are driven primarily by human water consumption over many parts of these regions. Irrigation is responsible for the intensification of hydrological droughts over the western and central US, southern Europe and Asia, whereas the impact of industrial and households’ consumption on the intensification is considerably larger over the eastern US and western and central Europe. Our findings reveal that human water consumption is one of the more important mechanisms intensifying hydrological drought, and is likely to remain as a major factor affecting drought intensity and frequency in the coming decades.

Debris Flows Resulting From Glacial-Lake Outburst Floods in Tibet, China

During the last 70 years of general climatic amelioration, 18 glacial-lake outburst floods (GLOFs) and related debris flows have occurred from 15 moraine-dammed lakes in Tibet, China. Catastrophic loss of life and property has occurred because of the following factors: the large volumes of water discharged, the steep gradients of the U-shaped channels, and the amount and texture of the downstream channel bed and bank material. The peak discharge of each GLOF exceeded 1000 m3/s. These flood discharges transformed to non-cohesive debris flows if the channels contained sufficient loose sediment for entrainment (bulking) and if their gradients were >1%. We focus on this key element, transformation, and suggest that it be included in evaluating future GLOF-related risk, the probability of transformation to debris flow and hyperconcentrated flow. The general, sequential evolution of the flows can be described as from proximal GLOFs, to sedimentladen streamflow, to hyperconcentrated flow, to non-cohesive debris flow (viscous or cohesive debris flow only if sufficient fine sediment is present), and then, distally, back to hyperconcentrated flow and sediment-laden streamflow as sediment is progressively deposited. Most of the Tibet examples transformed only to non-cohesive debris flows. The important lesson for future hazard assessment and mitigation planning is that, as a GLOF entrains (bulks) enough sediment to become a debris flow, the flow volume must increase by at least three times (the "bulking factor"). In fact, the transforming flow waves overrun and mix with downstream streamflow, in addition to adding the entrained sediment (and thus enabling addition of yet more sediment and a bulking factor in excess of three times). To effectively reduce the risk of GLOF debris flows, reducing the level of a potentially dangerous lake with a siphon or excavated spillway or installing gabions in combination with a downstream debris dam are the primary approaches.

Assessment of Climate Change Impact on the Inflow and Outflow of Two Agricultural Reservoirs in Korea

This study assessed the impact of potential future climate change on the inflow to an agricultural reservoir and its downstream streamflow from reservoir operation for paddy irrigation using SLURP (semi-distributed land use-based runoff process), a physically based hydrological model. For a 366.5 km2 watershed that includes two agricultural reservoirs located in the upper middle part of South Korea, the SLURP model was calibrated using six years (1998-2003) of daily streamflow records and validated using three years (2004-2006) of streamflow data, before the future analysis. The CCCma CGCM2 data of the SRES (special report on emissions scenarios) A2 and B2 scenarios of the IPCC (Intergovernmental Panel on Climate Change) was adopted for the future climate condition, and then the change factor method was used to downscale the future weather data through bias-correction. In addition, the future land covers were predicted with a modified CA (cellular automata)-Markov technique using a time series of Landsat land cover data, and the future vegetation information was predicted by linear regression between the monthly NDVI (normalized difference vegetation index) from NOAA AVHRR (advanced very high resolution radiometer) images and the monthly mean temperature data. The results for the future impact on reservoir storage showed that it would not have recovered to 100% by the end of the year (December) because of a decrease in precipitation and an increase in evaporation from July to September. Therefore, this reduced reservoir storage could bring a spring drought that causes an insufficient guarantee of water for rice paddy transplanting at the end of May. The future impact on the monthly streamflow of the watershed showed that the future streamflow for the three months of April, May, and June would increase because of a future increase in precipitation, and that the future streamflow for the next three months (July, August, and September) would decrease because of a future decrease in precipitation and increase in temperature. Considering the future decrease in the summer and autumn streamflows, the reservoir operation has to be more conservative when supplying water for paddy irrigation during this period and in the decision making process for reservoir releases during storm events.

Reviewing SEBAL input parameters for assessing evapotranspiration and water productivity for the Low-Middle São Francisco River basin, Brazil: Part B: Application to the regional scale

The intensification of irrigated agriculture in the semi-arid region of Brazilian North-east results in a change of natural vegetation by irrigated fruit crops. New applications of remote sensing technologies are presented in this paper to estimate the impact of this land use change on regional water consumption – and ultimately the water balance – in Low-Middle São Francisco River basin. Ten Landsat images for a period from 2001 to 2007 were used, together with the locally calibrated Surface Energy Balance Algorithm for Land (SEBAL) and agro-meteorological data to derive information on regional actual evapotranspiration (ET), biomass production (BIO), and crop water productivity (CWP). The Landsat-based results revealed that regional mean ET for irrigated crops was 3.6 mm d −1 being higher than for natural vegetation (1.4 mm d −1). Similar incremental ET values between natural and irrigated ecosystems were found from micro-meteorological field experiments. The consequence of this land use change on São Francisco River's downstream stream flow was assessed by estimating volumetric incremental evapotranspiration at the regional scale. The bio-physical crop water productivity per unit of actual evapotranspiration (CWP ET) varied between 0.4 and 1.7 l of wine per m 3 of water for wine grapes; 1.7 and 4.0 kg of fruits per m 3 of water for table grapes; and 2.2 and 5.0 kg of fruits per m 3 of water for mangos. The accompanying paper (Part A) describes the calibration and validation of SEBAL steps witnessed under the actual field conditions in this study area.