Model HBV-light Research Articles

Climate change impacts on the hydrological behaviour of watershed in northeastern Tunisia (Oued El Abid watershed)

Climate change significantly impacts watershed hydrology, altering water supplies, natural disasters, and eco-hydrologic processes. These effects vary geographically, with some regions facing severe droughts while others experience increased precipitation and flooding. Climate change influences atmospheric evaporation demand, precipitation patterns, vegetation composition, streamflow characteristics, and groundwater dynamics. The temperature increases and rainfall changes in some watersheds can lead to increased potential evapotranspiration and decreased streamflow and soil water. Flood frequency analyses indicate an overall increasing trend in the latter half of the 21st century. The Oued El Abid catchment area, as like all catchments in northern Tunisia, is likely to be subject to major climatic and hydrological variations as a result of climate change. Advanced hydrological modelling using the HBV-Light model was set up to simulate current conditions and future scenarios based on Afric-Cordex climate models. The model demonstrates robust performance, achieving Nash-Sutcliffe Efficiency (NSE) values of 0.71 in calibration (1972-1992), and 0.66 in validation from 1993-2001. This climate change has a direct effect on the hydrological projections, which reveal significant changes, with annual flow reductions ranging from 35% to 43%, with more severe impacts under the RCP8.5 scenario for 2069-2099. Spring and summer seasons are particularly vulnerable, showing flow decreases of up to 68% and over 40%, respectively, extreme monthly variability is projected, reaching 88% reduction in July under RCP8.5. This research provides important results for water resource managers, highlighting the necessity of long-term planning and sustainable water management practices in the face of climate change.

Characteristics of glacier ice melt runoff in three sub-basins in Urumqi River basin, eastern Tien Shan

Study regionThe Urumqi River basin located in eastern Tien Shan in Central Aisa Study focusGlacier runoff plays a pivotal role in water resources and stabilizing streamflow in mountainous regions. To assess the characteristics of glacier ice melt runoff in sub-basins within a single basin, three sub-basins with glacier ratios varying from 4% to 46% in the Urumqi River basin are investigated. Through the simulation by HBV light model on the basis of the observed meteorological and hydrological data. The characteristics and behaviour of glacier ice melt runoff in the three sub-basins are analysed. New hydrological insights for the regionIt was found that both the contribution ratios of ice melt runoff and glacier runoff increase linearly with the increasing glacier ratio for the three catchments, rather than logarithmically or exponentially as observed in previous studies. This is due to the relatively high contributions of ice melt and glacier runoff to river flow in a catchment characterized by high elevation and extensive glacier coverage (Catchment 1), resulting from the coincidence of summer precipitation maxima with snow and ice melt in this region. The coefficient of variations (CV) of river flow tends to decrease with the decreasing glacier ratio in sub-basins in the Urumqi River basin, indicating that river flow becomes more stable as it flows farther from the headwater in the Urumqi River basin. The lowest glacierized Catchment 3 exhibited the minimum CV value, demonstrating a stable outflow.

Estimating groundwater recharge rates in the Upper Awash Basin, Ethiopia under different combinations of model complexity and objective functions

Abstract This study addresses the critical need for reliable groundwater recharge quantification by investigating the uncertainty associated with recharge estimation based on various combinations of model complexity and objective functions. Focusing on the Hombele catchment in the upper Awash Basin, Ethiopia, the research aims to analyze parameter sensitivity under different model complexities and objectives while estimating groundwater recharge for the period 1986–2013. Employing a Monte-Carlo-based calibration scheme, the study fine-tunes model parameters using objective functions like KGE, NSE, LogNSE, R2, and VE across 10 combinations of model complexity and objective functions. Results identify FC, LP, and BETA as highly sensitive parameters, while UZL, K0, and MAXBAS show limited influence in all model complexity and objective function scenarios. The semi-distributed HBV-light model achieves calibration, validation, and overall period KGE (NSE) values of 0.89 (0.80), 0.80 (0.73), and 0.87 (0.77), respectively. Sensitivity analyses reveal significant impacts on model parameters and recharge estimation based on the chosen objective function and model complexity levels. Average annual recharge rates range from 185.9–280.5 mm when the HBV-light model is semi-distributed, contrasting with 185.3–321.7 mm under lumped model conditions, emphasizing the importance of considering these factors in groundwater resource assessments.

Моделирование стока горных рек Илейского Алатау с применением модели HBV-light

The research aimed to evaluate the possibility of applying the HBV model for assessing the flow of the Ile Alatau Mountain Range rivers. The main part of the corresponding water resources forms on the northern slopes of the Ile Alatau making them a significant water balance and water supply factor for the major cities of Almaty, Kaskelen, Talgar, and Yesik. The article includes a brief description of the HBV model, as well as hydrometeorologicaland topographical inputs, and continues with the description of the flow simulation outputs for the rivers with the catchment area varying between 71-601 km2. For the periods of 2000-2016, the model parameters were calibrated using the GAP optimization algorithm. The model’s performance was evaluated based on several criteria: Nash-Sutcliffe Efficiency (NSE), Percent bias (PBIAS), and Root Mean Standard Deviation Ratio (RSR). The selection of optimal parameters allowed obtaining the following model efficiency values: 0.80-0.93 (as per NSE), -0.78 to -15.33% (as per PBIAS), and 0.27–0.80 (as per RSR). The calculated model efficiencies point to the sufficient correlation between the dynamics of the observed and simulated runoffs during the calibration period. The study likewise included the assessment of the HBV model applicability as a forecasting technique based on the ratio. The outcomes of that exercise confirmed the facility of using the model for predicting the runoff of the Kaskelen, Talgar, Ulken Almaty, and Kishi Almaty Rivers. Due to the fact that the observation data were available only for 2020, the HBV model parameters of all the studied rivers, except the Talgar River, underwent validation for the periods of 2017-2020. The calibrated and validated parameters of the obtained Ile Alatau Mountain River Model can be recommended for runoff modeling based on the HBV-light model as well as for runoff forecasting, namely for short- and medium-term water flow forecasts, with the exception of the Turgen, Prokhodnaya and Uzyn Kargaly River Basins.

Watershed groundwater level multistep ahead forecasts by fusing convolutional-based autoencoder and LSTM models

The development of deep learning-based groundwater level forecast models can tackle the challenge of high dimensional groundwater dynamics, predict groundwater variation trends accurately, and manage groundwater resources effectively, thereby contributing to sustainable water resources management. This study proposed a novel ConvAE-LSTM model, which fused a Convolutional-based Autoencoder model (ConvAE) and a Long Short-Term Memory Neural Network model (LSTM), to provide accurate spatiotemporal groundwater level forecasts over the next three months. The HBV-light and LSTM models are chosen as benchmarks. An ensemble of point data and the corresponding derived images concerning the past (observations) and the future (forecasts from a conceptual model) of groundwater levels at 33 groundwater wells in Jhuoshuei River basin of Taiwan between 2000 and 2019 constituted the case study. The findings showcase the effectiveness of the ConvAE-LSTM model in extracting crucial features from both point and imagery datasets. This model successfully establishes spatiotemporal dependencies between regional images and groundwater level data over diverse time frames, leading to accurate multi-step-ahead forecasts of groundwater levels. Notably, the ConvAE-LSTM model exhibits a substantial improvement, with the R-squared values showing an increase of more than 18%, 22%, and 49% for the R1, R2, and R3 regions, respectively, compared to the HBV-light model. Additionally, it outperforms the LSTM model in this regard. This study represents a noteworthy milestone in environmental modeling, offering key insights for designing sustainable groundwater management strategies to ensure the long-term availability of this vital resource.

HYDROLOGICAL MODELING AND WATERSHED ANALYSIS OF SWAT RIVER BASIN BY USING HBV LIGHT MODEL AND ARC GIS

Climate change is a pivotal global phenomenon, particularly impactful in nations like Pakistan. This study delves into the myriad factors driving climate change, specifically focusing on its profound effects on economically vulnerable countries heavily reliant on natural resources for water. Utilizing historical climatic and discharge data from the Swat River, located within the Khyber Pakhtunkhwa province, the research employs temperature, precipitation, and discharge data to scrutinize changes in the KALAM basin at a regional level. Situated between 34° and 36° N latitude and 71° to 72° E longitude, the Swat River Basin relies heavily on glacier snowmelt. The HBV model and GIS techniques are harnessed to dissect climate variations within the KALAM Basin. GIS aids in mapping the study area and visualizing temperature fluctuations in the basin. The basic HBV rainfall-runoff model undergoes automatic calibration using a straightforward yet effective method, providing satisfactory results during the 1981-2000 calibration and 2001-2010 validation periods. Noteworthy findings include the HBV-light model's adeptness at simulating stream flow and snowmelt in the snow-fed basin. However, the study emphasizes the substantial impact of parameter set values on the model's performance, highlighting the challenge of selecting an ideal parameter configuration. Sensitivity tests underscore the significant influence of climate change, specifically temperature increases, on stream flow across yearly, seasonal, and snowmelt contributions. Employing geographic information systems (GIS) alongside a digital elevation model proves instrumental in managing and analyzing water resources, aiding in watershed delineation—a crucial step in hydrological modeling. GIS, particularly the Arc Hydro tool, is valuable for determining grid directions in catchment areas, drainage lines, and flow accumulation. The research underscores the need for comprehensive scientific exploration to address knowledge gaps and enhance understanding of the applications of the HBV light model in the context of climate variations, particularly concerning water resources.

Inferring reservoir filling strategies under limited-data-availability conditions using hydrological modeling and Earth observations: the case of the Grand Ethiopian Renaissance Dam (GERD)

Abstract. The filling of the Grand Ethiopian Renaissance Dam (GERD) started in 2020, posing additional challenges for downstream water management in the Blue Nile River in the Republic of the Sudan, which is already struggling to cope with the effects of climate change. This is also the case for many transboundary rivers that are affected by a lack of cooperation and transparency during the filling and operation of new dams. Without information about water supply from neighboring countries, it is risky to manage downstream dams as usual, but operational information is needed to apply modifications. This study aims to develop a novel approach/framework that utilizes hydrological modeling in conjunction with remote-sensing data to retrieve reservoir filling strategies under limited-data-availability conditions. Firstly, five rainfall products (i.e., ARC2, CHIRPS, ERA5, GPCC, and PERSIANN-CDR; see Sect. 2.3 for more information) were evaluated against historical measured rainfall at 10 stations. Secondly, to account for input uncertainty, the three best-performing rainfall products were forced in the conceptual hydrological model HBV-light with potential evapotranspiration and temperature data from ERA5. The model was calibrated during the period from 2006 to 2019 and validated during the period from 1991 to 1996. Thirdly, the parameter sets that obtained very good performance (Nash–Sutcliffe efficiency, NSE, greater than 0.75) were utilized to predict the inflow of GERD during the operation period (2020–2022). Then, from the water balance of GERD, the daily storage was estimated and compared with the storage derived from Landsat and Sentinel imageries to evaluate the performance of the selected rainfall products and the reliability of the framework. Finally, 3 years of GERD filling strategies was retrieved using the best-performing simulation of CHIRPS with an RMSE of 1.7 ×109 and 1.52 ×109m3 and an NSE of 0.77 and 0.86 when compared with Landsat- and Sentinel-derived reservoir storage, respectively. It was found that GERD stored 14 % of the monthly inflow of July 2020; 41 % of July 2021; and 37 % and 32 % of July and August 2022, respectively. Annually, GERD retained 5.2 % and 7.4 % of the annual inflow in the first two filling phases and between 12.9 % and 13.7 % in the third phase. The results also revealed that the retrieval of filling strategies is more influenced by input uncertainty than parameter uncertainty. The retrieved daily change in GERD storage with the measured outflow to the Republic of the Sudan allowed further interpretation of the downstream impacts of GERD. The findings of this study provide systematic steps to retrieve filling strategies, which can serve as a base for future development in the field, especially for data-scarce regions. Locally, the analysis contributes significantly to the future water management of the Roseires and Sennar dams in the Republic of the Sudan.

Drier streams despite a wetter climate in woody-encroached grasslands

Grasslands, covering 40% of ice-free Earth surface, are experiencing woody encroachment globally. The hydrological impacts of woody encroachment are highly uncertain because they are compounded by the concurrent influence of climate change. Here we ask the questions 1) How water balance (evapotranspiration versus streamflow) and streamflow partitioning (into surface runoff and subsurface flows) evolve over time in woody-encroached grasslands? 2) What is the relative influence of climate change and woody encroachment? We used the hydrology model HBV-light and decades of hydrometeorological and streamflow data from two intermittent streams draining catchments with different degrees of woody encroachment at the Konza Prairie, Kansas, US. Results indicate that both streams have become drier and have experienced more hydrological droughts over time, more so in the substantially encroached site, with increasing evapotranspiration despite a wetter climate. In contrast, a modelled hypothetical “Climate Only” scenario without woody encroachment suggests streamflow would have increased under climate change alone. Moreover, results suggest that flow paths have deepened with increasing fractions of deeper groundwater flow in the substantially encroached site. These findings raise questions about mechanisms of these changes and commonality of drier streams in a wetter climate in woody-encroached areas and beyond. Answers to these questions can have far-reaching implications for the occurrence of droughts, water availability, water quality, and ecosystem health.

Projections of drought characteristics based on combined drought index under CMIP6 models

Abstract This study assesses climate change's impact on drought in Iran's Dez Basin. It introduces the Hydro-Meteorological Drought Index (HMDI), integrating the Standardized Precipitation Evapotranspiration Index (SPEI) and Standardized Runoff Index (SRI). Using Climatic Research Unit Time Series (CRU TS) data (1980-2012) and downscaling forecasted data from three CMIP6 models (2020-2052) for SSP1-2.6 and SSP5-8.5 scenarios, we employ the rainfall-runoff Hydrologiska Byråns Vattenbalansavdelning Hydrological Bureau's Water Balance Model (HBV)-Light model to predict future streamflow. Drought characteristics are analyzed. Under SSP5-8.5, CanEsm5 shows substantial temperature and runoff increases, notably in Bakhtiari and Borujerd sub-basins (63% and 56%). Future droughts are expected to intensify, particularly under SSP5-8.5. The most severe HMDI-derived drought (HMDI 12) in Borujerd station is projected to increase from -43.44 to -44.05. SSP5-8.5 is likelier to cause severe and prolonged HMDI-derived droughts than SSP1-2.6 or the historical period. The analysis suggests that normal drought levels will persist, while mild and severe drought levels will rise in the future.

Application of hydrological model to simulate streamflow contribution on water balance in Himalaya river basin, Nepal

Hydrological models are widely used and often regarded as reliable tools for accurately estimating various components of the water balance. In a remote Himalayan catchment, such as Tamakoshi basin, where limited hydrometric dataset is available, such models often provide essential insights that are crucial to water researchers and planners. In this regard, we employed the semi-distributed HBV-light (version 4.0.0.25) hydrological model for glacierized Tamakoshi river basin and attempted to quantify various water balance components. For our model tests, using the daily streamflow records, we selected two distinct periods, i.e., 2004–2008 as a calibration period whilst 2011–2012 for model validation. Based on our findings, the model was able to reasonably predict the streamflow (validation efficiency: Nash-Sutcliffe Efficiency of 0.82 and percent bias −21%). At our site, HBV-light model predicted that the change in streamflow was mostly governed by monsoonal rain (62%) followed by baseflow (20%), glacier melt (13%) and snowmelt (5%). As expected, the streamflow peaked during the month of August where monsoon-induced rain and melting of glaciers significantly contributed to river flow. As a result, monsoon period showcased largest fluctuation in water storage while negligible change was observed during post-monsoon season. Nonetheless, our findings revealed that the baseflow contribution to streamflow was maximum during the month of October and lowest during February. Our findings indicated that the water balance of the Tamakoshi basin is largely influenced by monsoonal rain during June–September window as well as baseflow and glacier melt during the dry season. Runoff components contribution to streamflow was increasing but water storage changes was decreasing in recent decade (2011–2020). We believe our findings are crucial for future initiatives involving water resources, water-induced disaster management, and studies of climate change may benefit from the findings of this study, especially in a region with limited hydrometric data availability.

Evaluation of Extreme Scenarios of Climate Change on Discharge of the Shahid Rajaei Dam Catchment under HBV-light Model

Evaluation of Extreme Scenarios of Climate Change on Discharge of the Shahid Rajaei Dam Catchment under HBV-light Model

Evaluation of three gridded potential evapotranspiration datasets for streamflow simulation in three inland river basins in the arid Hexi Corridor, Northwest China

Study regionThis study focuses on three data-limited inland river basins in the arid Hexi Corridor, Northwest China. Study focusThe lack of ground-based observation data for estimating potential evapotranspiration (PET) usually limits the streamflow simulation. This study explored the feasibility of using three gridded PET datasets (GLDAS, GLEAM, and ERA5) to force the hydrologic model. The hydrologic performance of these PET datasets was investigated using the HBV-light model. New hydrological insights for the regionThe results show that GLEAM performs best in estimating PET on both a daily and monthly scale. GLDAS generally captures the seasonal variations and patterns well but shows a higher estimation in magnitude. For the hydrological simulation, all three PET datasets can be used as forcing data to drive the HBV-light model, as the Nash-Sutcliffe efficiency (NSE) values are greater than 0.64 during the validation period for the three PET inputs in the three basins. The magnitude and temporal signal of PET forcing data do not significantly affect streamflow simulations of the HBV-light. The model mainly adjusts the water content of the soil box (WCS) to eliminate the efficiency loss caused by the PET magnitude difference to achieve the best simulation results.

Impact of Climate Change on the Glacier and Runoff of a Glacierized Basin in Harlik Mountain, Eastern Tianshan Mountains

The impact of climate change on glaciers and the hydrological processes in the easternmost end of the eastern Tianshan Mountains has yet to be understood. This study investigated the glacier change (area, surface elevation and volume change) and analyzed the variation of the observed runoff series over the past four decades in the Yushugou Basin, Eastern Tianshan Mountains. The hydrological processes were also simulated through the HBV-light model to quantify the impact of climate change on the glacier and runoff. The results showed that the glacier area has decreased by 13% and the total volume has decreased by 0.018 km3 over the past four decades. A significant increasing trend (p < 0.01) was detected for the annual runoff and monthly runoff (May to September; p < 0.01). The simulation results revealed that the Yushugou River was highly recharged by glacial runoff and a negative tendency was found for the glacier mass balance on the basin scale over the past 38 years. As a region with an extremely dry climate and the lowest precipitation in the Tianshan Mountains, the observation and simulation of glaciers is critical to the security assessment of local water resources.

Assessment of Climate Change Impact on Discharge of the Lakhmass Catchment (Northwest Tunisia)

The Mediterranean region is increasingly recognized as a climate change hotspot but is highly underrepresented in hydrological climate change studies. This study aims to investigate the climate change effects on the hydrology of Lakhmass catchment in Tunisia. Lakhmass catchment is a part of the Medium Valley of Medjerda in northwestern Tunisia that drains an area of 126 km². First, the Hydrologiska Byråns Vattenbalansavdelning light (HBV-light) model was calibrated and validated successfully at a daily time step to simulate discharge during the 1981–1986 period. The Nash Sutcliffe Efficiency and Percent bias (NSE, PBIAS) were (0.80, +2.0%) and (0.53, −9.5%) for calibration (September 1982–August 1984) and validation (September 1984–August 1986) periods, respectively. Second, HBV-light model was considered as a predictive tool to simulate discharge in a baseline period (1981–2009) and future projections using data (precipitation and temperature) from thirteen combinations of General Circulation Models (GCMs) and Regional Climatic Models (RCMs). We used two trajectories of Representative Concentration Pathways, RCP4.5 and RCP8.5, suggested by the Intergovernmental Panel on Climate Change (IPCC). Each RCP is divided into three projection periods: near-term (2010–2039), mid-term (2040–2069) and long-term (2070–2099). For both scenarios, a decrease in precipitation and discharge will be expected with an increase in air temperature and a reduction in precipitation with almost 5% for every +1 °C of global warming. By long-term (2070–2099) projection period, results suggested an increase in temperature with about 2.7 °C and 4 °C, and a decrease in precipitation of approximately 7.5% and 15% under RCP4.5 and RCP8.5, respectively. This will likely result in a reduction of discharge of 12.5% and 36.6% under RCP4.5 and RCP8.5, respectively. This situation calls for early climate change adaptation measures under a participatory approach, including multiple stakeholders and water users.

Adequacy of satellite derived data for streamflow simulation in three Hexi inland river basins, Northwest China

The limited gauge measurements usually constrain the accurate hydrological modeling of runoff, especially in the ungauged or gauge-sparsely areas with complex terrain conditions. Satellite derived data provide alternative forcing data for hydrological modeling in ungauged areas, while quantitative evaluation on their performance is still needed. This study explores the adequacy and accuracy of satellite derived data based on the gauge observations over three inland river basins in the arid Hexi Corridor, Northwest China, where the satellite-based products are adopted to drive the HBV-Light model, including six satellite precipitation datasets (CHIRPS, CMORPH, GPM, GSMaP, MSWEP, PERSIANN) and the widely used evaporation product (GLEAM) combing with MODIS land surface temperature product (MOD11A1). Seven statistical metrics (CC, ME, RMSE, BIAS, POD, FAR, CSI) are adopted to assess satellite-based precipitation estimates, and three indices (NSE, R2, PBIAS) are used to evaluate the hydrological performance. Results indicate that: (1) For precipitation evaluation at point-to-pixel scale, all products have a relatively poor agreement with gauged observations with the CC ranging from 0.16 to 0.56. Both MSWEP and GPM outperformed other products at daily scale with higher CC values, and showed the capability of detecting the precipitation events with higher POD and CSI. PERSIANN, GSMaP, GPM, and CMORPH tend to underestimate the precipitation amount in three river basins. (2) For the hydrological evaluation, the statistical assessment showed that the GPM presents the best results at daily scale, while underestimates streamflow amount in three watersheds. All the grided precipitation datasets show higher NSE and R2 in the relatively humid HHRB and XYRB than SLRB. (3) The combination of MOD11A1, GLEAM and satellite-based precipitation as forcing data of the HBV-Light can achieve good performance in the three inland river basins. This study aims to provide an additional reference for the hydrological application of satellite derived data in gauge-sparsely areas located in high altitude and arid regions.

Merging satellite rainfall estimates and daily rain gauge observations for improved flood simulation in MelkaKuntire catchment, upper Awash Basin, Ethiopia

Acquiring high-quality rainfall data is very important for studying catchment hydrology. This data can be obtained by merging Satellite Rainfall Estimates (SREs) and gauge observations as highlighted by several researchers. However, the application of these merged rainfall products for hydrological modeling has rarely been explored in a complex topographic region like Melkakuntire catchment, Ethiopia. Therefore, this study aims to assess the suitability of merged and unmerged SREs in simulating three flow conditions (daily streamflow, seasonal and high flows) using the HBV-light model. A total of nine SREs of which three from the unmerged (raw) SREs (IMERG v06B, TRMM 3B42v7, and TAMSAT v3), three from the downscaled-merged SREs (IMERG-D + Gauge, TRMM-D + Gauge, and TAMSAT-D + Gauge), and three from the undownscaled-merged SREs (IMERG + Gauge, TRMM + Gauge, and TAMSAT + Gauge) were used for the period 2003–2015. We used the Random Forest (RF) algorithm to merge SREs with gauges, with and without downscaling the spatial resolution of the selected SREs. For the RF algorithm, the gridded SREs and elevation values were used as the independent variables while daily gauge observations were considered as the dependent variable. Results show that the merged SREs outperform both the unmerged SREs and the gauge data in simulating all three flow conditions when the model is calibrated to each rainfall input independently. Similarly, the hydrologic model forced by merged SREs produces better performances while running the model with the baseline parameter values derived from gauges as well. Moreover, the suitability of SREs in simulating all three flow conditions is more improved if the model is calibrated to each SREs than to gauge observations. Overall, the downscaled-merged SREs produce the best result in closing the water budget of the study catchment, but further studies could be needed to validate these merged rainfall products, particularly the best performing IMERG-D + Gauge product, in other data-scare regions.

How suitable are satellite rainfall estimates in simulating high flows and actual evapotranspiration in MelkaKunitre catchment, Upper Awash Basin, Ethiopia?

Understanding the suitability of Satellite Rainfall Estimates (SREs) in simulating high flows and Actual Evapotranspiration (AET) is crucial for developing flood monitoring systems. Therefore, this study aims to assess i) the suitability of SREs in simulating both high flows and AET for different levels of model complexity, and ii) the effect of streamflow calibration on simulating AET for different rainfall inputs in Melkakunitre catchment, Upper Awash Basin, Ethiopia. Three state-of-the-art SREs (TRMM 3B42v7, IMERG v06B, and TAMSAT v3) were used and their usefulness in simulating high flows (Q5), daily streamflow, and wet season flows (from June to September) was assessed using the HBV-light model for the period 2003–2015. The model was set up for two levels of complexity: with and without considering the effect of orography on rainfall and temperature. Moreover, the water balance derived AET was compared against three remotely sensed AET products, MOD 16A2, GLEAM v3, and SSEBob, so as to examine the effect of streamflow calibration on AET simulation. Results show that rainfall inputs and model complexity have a strong impact on simulating streamflow and AET. For all rainfall forcing datasets, the performance of the hydrological model improves when we consider the effects of orography on rainfall and temperature. The IMERG v06B and TAMSAT v3 products showed the highest and least performances in simulating all the three flow conditions, respectively. Moreover, the MODIS-AET is the best remotely sensed AET product in reproducing the water balance-derived AET for all rainfall inputs except TAMSAT v3. The HBV-light model parameters calibrated with streamflow provided better results for simulating AET as well. On average, the usefulness of the IMERG v06B product for simulating high flows and AET is outstanding and can be thus used for developing flood monitoring and management systems in the study catchment.

Modeling implications of climate induced streamflow changes on the fish species of the Soan River, Pakistan

Climate change has significantly impacted the hydrological cycle in the rivers of Pakistan and the streamflow regimes of different rivers have witnessed noticeable flow alteration. These changes in streamflow affect aquatic biodiversity (e.g., freshwater fish species) and the productivity of freshwater ecosystems. This study, therefore, evaluates the streamflow alterations and their impacts on the fish species at the upstream and downstream of the Soan River in Pakistan. The hydrological model HBV light was calibrated and validated for both up- (Chirah) and down- (Dhoke Pathan) stream gauged stations. The model was then forced with an ensemble of NEX-GDDP Global Climate Models (GCMs) to simulate historic and future streamflow. Afterwards, changes in the streamflow characteristics for two future periods against a historic one were assessed. Different ecologically relevant streamflow indices were used. The base flow at upstream and downstream was projected to increase under both the RCP 4.5 and RCP 8.5 emission scenarios for mid and end of century periods. Under the RCP 8.5, base flow was projected to increase with more inter-annual variability at upstream station, and changes depicted less variability for downstream under both emission scenarios. A number of high flow pulses were projected to decrease under both RCP 4.5 and RCP 8.5 in both future periods for upstream and downstream; however, high variability was depicted for upstream than downstream area. The duration of high flow pulses was also projected to increase under the RCP 8.5 for end of century period for both upstream and downstream. These projections of streamflow characteristics may have a marked (positive) influence on the habitat and ecological conditions for the fish species of the Soan River.

Estimating surface runoff and groundwater recharge in an urban catchment using a water balance approach

Land-use changes often have significant impact on the water cycle, including changing groundwater/surface-water interactions, modifying groundwater recharge zones, and increasing risk of contamination. Surface runoff in particular is significantly impacted by land cover. As surface runoff can act as a carrier for contaminants found at the surface, it is important to characterize runoff dynamics in anthropogenic environments. In this study, the relationship between surface runoff and groundwater recharge in urban areas is explored using a top-down water balance approach. Two empirical models were used to estimate runoff: (1) an updated, advanced method based on curve number, followed by (2) bivariate hydrograph separation. Modifications were added to each method in an attempt to better capture continuous soil-moisture processes and explicitly account for runoff from impervious surfaces. Differences between the resulting runoff estimates shed light on the complexity of the rainfall–runoff relationship, and highlight the importance of understanding soil-moisture dynamics and their control on hydro(geo)logical responses. These results were then used as input in a water balance to calculate groundwater recharge. Two approaches were used to assess the accuracy of these groundwater balance estimates: (1) comparison to calculations of groundwater recharge using the calibrated conceptual HBV Light model, and (2) comparison to groundwater recharge estimates from physically similar catchments in Switzerland that are found in the literature. In all cases, recharge is estimated at approximately 40–45% of annual precipitation. These conditions were found to closely echo those results from Swiss catchments of similar characteristics.

Impacts of Climate Change on the Hydrometeorological Characteristics of the Soan River Basin, Pakistan

The global hydrological cycle is vulnerable to changing climatic conditions, especially in developing regions, which lack abundant resources and management of freshwater resources. This study evaluates the impacts of climate change on the hydrological regime of the Chirah and Dhoke Pathan sub catchments of the Soan River Basin (SRB), in Pakistan, by using the climate models included in the NEX-GDDP dataset and the hydrological model HBV-light. After proper calibration and validation, the latter is forced with NEX-GDDP inputs to simulate a historic and a future (under the RCP 4.5 and RCP 8.5 emission scenarios) streamflow. Multiple evaluation criteria were employed to find the best performing NEX-GDDP models. A different ensemble was produced for each sub catchment by including the five best performing NEX-GDDP GCMs (ACCESS1-0, CCSM4, CESM1-BGC, MIROC5, and MRI-CGCM3 for Chirah and BNU-ESM, CCSM4, GFDL-CM3. IPSL-CM5A-LR and NorESM1-M for Dhoke Pathan). Our results show that the streamflow is projected to decrease significantly for the two sub catchments, highlighting the vulnerability of the SRB to climate change.