Semi-arid Watershed Research Articles

Analysis of Low Flows in Semi-Arid Watersheds: Söğütözü and Terme Creek Cases

This study aims to analyze the physical, chemical, and hydrological characteristics of low flows in the Söğütözü and Terme Creek Watersheds of Çankırı province during the dry season, and to ascertain the relationship between water quality and flow in these watersheds. The goal of the study is to clarify the causes and effects of low river flows by examining watershed characteristics, assessing water quality, and analyzing trends. Low river flows during the dry season are vital for resource utilization and ecosystem health. To achieve this, initial low flow calculations were performed using daily average flow data collected over a 43-year period, from 1967 to 2010, in both watersheds. Water sampling was conducted weekly on the same day and at the same time during the period from April to December, coinciding with the recorded low flows in these watersheds. Furthermore, an analysis was conducted to determine if a trend existed in the annual low flow index values. During the study period, 35 water samples were analyzed both physically and chemically for 18 parameters to ascertain correlation between low streamflow and water quality. Consequently, it was determined that a significant correlation existed between streamflow and phenolphthalein alkalinity, total hardness, calcium hardness, organic matter, ammonium, turbidity, pH, electrical conductivity, and salt in the Söğütözü Creek watershed; as well as between streamflow and methylorange alkalinity, total hardness, magnesium hardness, chloride, sodium, turbidity, electrical conductivity, and salt in the Terme Creek watershed. A notable trend was observed solely for the Q95 Flow Index among the five indices utilized in low flow calculations (Q95, Q90, Q75, Q50, and 7Q1).

Ecological Network Construction Based on Ecosystem Services and Landscape Pattern Indices in the Tabu River Basin

Unsustainable human activities threaten the ecological security of arid and semi-arid watersheds. However, improving the connectivity of ecological networks can enhance ecosystem services and preserve biodiversity in these fragile environments. In this study, we aimed to identify ecological sources of the Tabu River Basin by quantifying five key ecosystem services and using a morphological spatial pattern analysis model. We further optimized resistance surfaces using landscape pattern indices, constructed an ecological network, and provided recommendations for sustainable management. The results obtained were as follows: (1) Ecosystem services in the Tabu River Basin exhibit clear spatial heterogeneity, with significant increases in water yield, food supply, and carbon sequestration over the past two decades. (2) Landscape pattern indices, such as the aggregation index, patch cohesion index, and Shannon’s diversity index, showed declines, indicating heightened landscape fragmentation and reduced connectivity. (3) Over the study period, the number of ecological sources increased from 6 to 17, although the total area of these sources decreased. The number of corridors expanded from 9 to 36, with a total length increase of 362.47 km, whereas pinch points increased from 27 to 40. (4) After optimization, one additional corridor and 24 new pinch points were identified, alongside a marked reduction in resistance in the northern and central-western regions. These results provide a robust scientific basis for optimizing land-use planning and reinforcing ecological security in the Tabu River Basin.

Hydrological modelling using SWAT in a Complex Semi-Arid Watershed

Hydrological models have emerged as essential tools for examining the hydrological processes of complex watersheds addressing various environmental and water resource issues. This study focuses on modeling the hydrology of the Merguellil watershed in central Tunisia using the Soil and Water Assessment Tool (SWAT). The SWAT model is a physical modeling tool developed to forecast hydrological processes and is well-documented as an effective tool for resource water management. The primary objective of this research is to evaluate the SWAT model's performance in simulating monthly hydrological processes within the Merguellil watershed in central semi-arid Tunisia. Calibration of the model was conducted from 2002 to 2011, followed by a validation from 2012 to 2017. Sensitivity analysis identified key parameters, including Curve Number, Slope Length, and effective hydraulic conductivity, as the most sensitive. The findings demonstrate that the model exhibits satisfactory performance according to goodness-of-fit criteria during both the calibration and validation phases. The Nash–Sutcliffe Efficiency (NSE) were 0.65 and 0.41, respectively, for calibration and validation periods. The coefficient of determination (R²) and Kling-Gupta efficiency (KGE) are both equal to 0.7, the RMSE-observations standard deviation ratio (RSR) is less than or equal to 0.6, for calibration. The Percent Bias (PBIAS) indicates that the model overestimates the discharges by +23.5% during the calibration period. In addition, the runoff in Merguellil watershed demonstrated a notable spatio-temporal variability, significantly influenced by the complexity and heterogeneity of its environment.

Using SWAT and SWAT-CUP for hydrological simulation and uncertainty analysis of the arid and semiarid watersheds (Case study: Zoshk Watershed, Shandiz, Iran)

The aims of this study are capability assessment of the SWAT model and SWAT-CUP software in hydrological simulation and evaluation of uncertainty of SWAT model in estimating runoff. In the modeling process, the basin was divided into 12 sub-basins and 294 hydrological units (HRUs). Model calibration and uncertainty analysis were performed using the sequential uncertainty fitting (SUFI2) algorithm for 2000–2006 and 2007–2010, respectively. Based on the sensitivity analysis results, the parameters are the USLE_P soil protection factor, wet soil density (SOL_BD), and CN among the most important parameters in determining the amount of output runoff. Among these factors, SCS-CN was recognized as the most sensitive parameter. Based on the results, the coefficients R2, bR2, and Nash–Sutcliffe index (NS) were 0.75, 0.59, and 0.67 for calibration period and 0.46, 0.24, and 0.42 for validation period. The results of the model showed the model performance is weak in the stage of calibration. This is due to the lack of accuracy and precision in the statistics available in the region, the lack of statistics on the amount of water collected from the upstream gardens of the area, as well as the lack of statistics on the existing springs. The model is therefore recommended for applications in arid and semiarid catchments within Iran with similar data. Due to the limited availability of hydrological data in Iran, this study has not assessed and compared the uncertainty related to the SWAT model of future runoff.

Applying Data-Driven Modeling for Streamflow Prediction in Semi-Arid Watersheds: A Comparative Evaluation of Machine Learning and Deep Learning Methodologies

Applying Data-Driven Modeling for Streamflow Prediction in Semi-Arid Watersheds: A Comparative Evaluation of Machine Learning and Deep Learning Methodologies

Spatial patterns of hydroecological health in the semi-arid yellow river basin: Revelations from machine learning models

The ecosystem of semi-arid watersheds is influenced by a combination of natural climate factors, rainfall, and habitat destruction, resulting in complex mechanisms of spatial differentiation and evolution of water ecological health. Indicator selection in mainstream water ecological health assessment methods, such as the Index of Biotic Integrity (IBI), often relies on subjective reference point choices. This approach tends to overlook the comprehensive impacts and interactions among various environmental stressors. For watersheds significantly influenced by natural climatic factors, considerable uncertainties arise, leading to a lack of scientific justification for establishing water ecological health protection goals. In this study, the nonlinear capabilities of the random forest (RF) model were applied to reduce subjectivity in traditional water ecological health assessments and to more accurately reveal the emerging spatial differentiation patterns and underlying causes of water ecological health in the Wei River Basin (WRB), the largest typical semi-arid watershed of the Yellow River in China. Our findings indicate: (1) Traditional evaluation indices indicate that the overall water ecological health of the WRB is classified as sub-healthy (60 %). The core indicators include dominant species, total algal density, and the percentage of diatom density, with no significant spatial differentiation observed. (2) An improved water ecological health assessment method for semi-arid watersheds, based on the RF model, has been developed to replace traditional subjective judgment steps. This method establishes a complex multi-input–output response relationship (R2>0.85) between environmental stress indicators and the biological integrity index for the WRB. (3) The model results identify key driving factors affecting changes in water ecological health in semi-arid watersheds, with the sensitivity of the new model increasing nearly 11-fold compared to traditional IBI methods. (4) Following improvements, the water ecological health characteristics of the WRB exhibit significant spatial heterogeneity, with a higher dispersion coefficient (1.21), and demonstrate enhanced nonlinear response trends to climatic factors. The application of machine learning models indicates that traditional methods may underestimate the extent of ecological health degradation in watersheds and tend to oversimplify spatial heterogeneity characteristics.

Performance of Loss Models for Predicting Flood Hydrographs in a Semiarid Watershed with Limited Observations Using Deterministic and Probabilistic Hydrologic Models

Performance of Loss Models for Predicting Flood Hydrographs in a Semiarid Watershed with Limited Observations Using Deterministic and Probabilistic Hydrologic Models

Monitoring annual meteorological drought in arid and semi-arid watersheds by SPI12 drought index and spatial autocorrelation pattern analysis: a case study of the Khuzestan province, Southwest Iran

Monitoring annual meteorological drought in arid and semi-arid watersheds by SPI12 drought index and spatial autocorrelation pattern analysis: a case study of the Khuzestan province, Southwest Iran

Assessment of Multiple Trace Metal Fluxes in a Semi-Arid Watershed Containing Mine Tailing, Using a Multiple Tool Approach (Zaida Mine, Upper Moulouya Watershed, Morocco)

Few studies have quantified the complex flux of trace metals from mine tailings to rivers through water erosion, especially in the semi-arid region of North Morocco (Zaida mine) where soil erosion is a severe issue. This study applies (i) methods to understand and estimate the complex flux of trace metals from mine tailings to rivers, using the RUSLE model combined with the concentration of trace metals in the soil and additionally (ii) pollution indices and statistical analyses to assess the sediment contamination by Cd, Cu, Pb, and Zn. Our study revealed that the basin has a low erosion rate, with an average of 9.1 t/ha/yr. Moreover, the soil contamination is particularly high at the north of the mine tailings, as prevailing winds disperse particles across the basin. The assessment of the sediments indicated that Pb is the main contaminant, with concentrations exceeding 200 mg/kg specifically downstream of the tailings. This study also identified high a concentration of trace elements 14 km away from the tailings alongside the Moulouya river, due to the specific hydrological transport patterns in the area. This research contributes to a better understanding of the transport and fate of the trace metals in mining areas. It proposes a replicable method that can be applied in other regions to assess the contamination flows and thereby assist water resource management.

The combined impact of climate change scenarios and land use changes on water resources in a semi-arid watershed

This study proposes a combined approach involving climate change scenarios and varied land use and land cover (LULC) projections in a semi-arid watershed (El Grou) located in Morocco. The Soil and Water Assessment Tool (SWAT) model was coupled with land use scenarios generated using the Cellular Automata-Markov model for three dates (2030, 2040, and 2050) and future climate projections from the CMIP5 model under RCP 4.5 and 8.5 scenarios. Future precipitation and temperature projections indicate a decrease in annual precipitation by 5 % to 34 % and an increase in average temperatures, particularly during the summer months. LULC changes reveal a significant decline in agricultural lands and forests, with an expansion of bare soil by 2050. These changes, analyzed through three SWAT models representing different time periods, indicate a decrease in surface runoff by 41 % to 73 %, a reduction in total water yield by 21 % to 53 %, and a decline in groundwater recharge, posing significant challenges for water resource management and ecosystem health. The study underscores the need for integrated land and water management strategies, incorporating climate change adaptation measures, to ensure sustainable water resource utilization and build resilience in the El Grou watershed and similar semi-arid regions.

River–aquifer interactions enhancing evapotranspiration in a semiarid riparian zone: A modelling study

AbstractThe hydrologic flows across the river–aquifer interface play an important role in groundwater dynamics and biogeochemical reactions within the subsurface; however, little is known about the effects of river–aquifer interactions on land surface processes. In this study, we developed a fully coupled three‐dimensional (3D) land surface and subsurface model at a high resolution (~1 km) that accounts for high‐frequency hydrologic exchange flow conditions to investigate how river–aquifer interactions modulate surface water budgets in the Upper Columbia‐Priest Rapids watershed, a typical semiarid watershed located in the northwestern United States where river stage fluctuates in response to reservoir releases changing. Our results show that the spatiotemporal dynamics of river–aquifer interactions are highly heterogeneous, driven mainly by river‐stage fluctuations. Adding 6.64 × 106 m3 year−1 of water over the watershed from the river to groundwater owing to the lateral flow, river–aquifer interactions led to an increase in soil evaporation and transpiration supplied by higher soil moisture content, particularly in deeper subsurface. In a hypothetic future scenarios where a 5‐m rise in river stage was assumed, the hydrologic flow exchange rates were intensified, resulting in higher surface water over the entire watershed. Overall, lateral flow induced by river–aquifer exchanges leads to an increase in evapotranspiration of ~75% in the historical period and of ~83% in the hypothetical future scenario. Our study demonstrates the potential of coupled model as an effective tool for understanding river–aquifer–land surface interactions, and indicates that river–aquifer interactions fundamentally alter the water balance of the riparian zone for the semiarid watershed and will likely become more frequent and intense in the future under the effects of climate change.

Enhanced groundwater availability through rainwater harvesting and managed aquifer recharge in arid regions

Climate change in desert areas and semi-arid watersheds may offer a promising solution for the water scarcity problem that Bedouins and local inhabitants face. This study investigated the integrated water resources management in arid and semi-arid regions using rainwater harvesting in combination with the managed aquifer recharge (RWH-MAR) technique. The study also used recharge wells and storage dams to achieve the sustainability of groundwater supplies in the context of climate change and management of the flow to the Gulf of Suez. Therefore, different return periods of 10, 25, 50, and 100 years were considered for the annual flood volume resulting from those watersheds. Moreover, hydrologic modeling was carried out for the El Qaa plain area, South Sinai, Egypt, using the Watershed Modeling System (WMS) and the groundwater modeling of SEAWAT code. Our findings show that for different scenarios of climate change based on return periods of 10, 25, 50, and 100 years, the aquifer potentiality reached 24.3 MCM (million cubic meters) per year, 28.8 MCM, 36.7 MCM, and 49.4 MCM compared to 21.7 MCM at 2014 with storage of groundwater ranges 11.8%, 32.1%, 69%, and 127.4%, respectively. These findings have significant implications for the system of RWH-MAR and groundwater sustainability in El Qaa Plain, South Sinai. The RWH-MAR proved to be an effective approach that can be applied in different water-stressed and arid regions to support freshwater resources for sustainable future development and food security, as well as protect communities from extreme flash flood events.

Integrated machine learning reveals aquatic biological integrity patterns in semi-arid watersheds

Semi-arid regions present unique challenges for maintaining aquatic biological integrity due to their complex evolutionary mechanisms. Uncovering the spatial patterns of aquatic biological integrity in these areas is a challenging research task, especially under the compound environmental stress. Our goal is to address this issue with a scientifically rigorous approach. This study aims to explore the spatial analysis and diagnosis method of aquatic biological based on the combination of machine learning and statistical analysis, so as to reveal the spatial differentiation patterns and causes of changes of aquatic biological integrity in semi-arid regions. To this end, we have introduced an innovative approach that combines XGBoost-SHAP and Fuzzy C-means clustering (FCM), we successfully identified and diagnosed the spatial variations of aquatic biological integrity in the Wei River Basin (WRB). The study reveals significant spatial variations in species number, diversity, and aquatic biological integrity of phytoplankton, serving as a testament to the multifaceted responses of biological communities under the intricate tapestry of environmental gradients. Delving into the depths of the XGBoost-SHAP algorithm, we discerned that Annual average Temperature (AT) stands as the pivotal driver steering the spatial divergence of the Phytoplankton Integrity Index (P-IBI), casting a positive influence on P-IBI when AT is below 11.8 °C. The intricate interactions between hydrological variables (VF and RW) and AT, as well as between water quality parameters (WT, NO3–N, TP, COD) and AT, collectively sculpt the spatial distribution of P-IBI. The fusion of XGBoost-SHAP with FCM unveils pronounced north-south gradient disparities in aquatic biological integrity across the watershed, segmenting the region into four distinct zones. This establishes scientific boundary conditions for the conservation strategies and management practices of aquatic ecosystems in the region, and its flexibility is applicable to the analysis of spatial heterogeneity in other complex environmental contexts.

Development of A Distributed Modeling Framework Considering Spatiotemporally Varying Hydrological Processes for Sub-Daily Flood Forecasting in Semi-Humid and Semi-Arid Watersheds

Development of A Distributed Modeling Framework Considering Spatiotemporally Varying Hydrological Processes for Sub-Daily Flood Forecasting in Semi-Humid and Semi-Arid Watersheds

Trade-offs between Farm Incomes and Soil Loss: An Application of Bio-economic Modelling in a Semi-Arid Watershed in South India

Soil erosion is an economic problem and the cost of soil loss and its consequences could be very harsh. The major threat facing the sustainability and productivity is erosion and the associated nutrient loss through run off. And climatic shocks like drought and floods also aggravate the soil loss Aims: The study aims to bring out the different plans and the tradeoff analysis of the conservation and degradation through a multi objective framework. Study Design: Primary data was collected from the farmers of the Padmaram watershed, in Mahbubnagaar district, Telangana state in south India. A detailed questionnaire was prepared which included the current management practices, the input costs and output prices associated. And the soil loss was estimated using the RUSLE equation. And the tradeoffs were obtained by the multiobjective linear programming (MOLP) Place and Duration of Study: The padmaram watershed was selected, from kondurg mandal of Mahabubnagar district of Telanagana. Methodology: The multi–objective linear programming MOLP, is employed to get an efficient solution where in conflicting objectives are simultaneously optimized subject to constraints. The soil loss under different climatic scenarios were modelled using the CMIP data for 2020s,2050s and 2080s. The impact of climate change on soil loss and farm incomes were also assessed. Results: The operational land holding of a small farmer was 1.32 ha, medium famer about 2.71 ha and a large farmer about 4.99 ha. Cotton and maize were two major crops grown in the watershed holding an area of 56 per cent followed by paddy which occupies about 15 per cent. The major rabi crops were maize and rabi paddy (9 %). The cropping intensity of the watershed was 116.87 per cent. The soil loss from 60.0% of the watershed area was below 3.0 t ha−1 y−1. The soil loss from 27.5% area ranged from 3.1 to 4.5 t ha−1 y−1 and remaining 12.5% area have soil loss more than 4.6 t ha−1 y−1. Soil loss and net returns for future climate scenarios were assessed. Conclusion: The analysis of trade off between production and conservation would be useful in identifying optimum crop plans with reduction in soil loss. The results stress that the interventions in agriculture have varying costs and environmental and economic impacts. Their implementation requires appropriate investment decisions.

Spatial-temporal Evolution and Driving Factors Analysis of Ecological Environment Quality in Daihai Basin based on AWRSEI

Watersheds are an important ecological security barrier and social and economic development area. In order to evaluate the ecological environment quality of arid and semi-arid watersheds more objectively and accurately, based on the remote sensing ecological index (RSEI), the salinity index was introduced to construct a remote sensing ecological index (AWRSEI) suitable for arid and semi-arid watersheds, and the Daihai watershed was taken as an example to analyze its applicability. Based on the AWRSEI model, four phases of Landsat TM/OLI composite images were used to quantitatively evaluate the ecological and environmental quality of the Daihai Basin from 2001 to 2020. The spatio-temporal evolution of the ecological and environmental quality of the Daihai Basin was analyzed using the coefficient of variation and spatial autocorrelation, and the cause analysis and driving factor explanation power analysis were carried out using geographic detectors. The results showed that:① the average correlation coefficient between AWRSEI and various ecological factors was 0.860, which was more representative than that of a single index component. The load positive and negative values and ranking of the first principal component were consistent with those of RSEI, the contribution rate of eigenvalues was 3.69% higher than those of RSEI, and the evaluation results were closer to the real surface conditions, which is suitable for the ecological environment quality assessment of arid watersheds. ② The annual average of the AWRSEI index in the Daihai Basin from 2001 to 2020 was 0.427, which indicated a poor ecological environment quality basis. During the study period, the average of AWRSEI showed a fluctuating trend and reached the highest value of 0.502 in 2020. The overall ecological environment quality improved significantly, the deterioration area decreased by 20.51%, and the improvement area increased by 12.71%. In terms of spatial distribution, the ecological environment quality of forest land in the southern and northwestern high-altitude areas of the Daihai Basin was good, whereas that in the northern and southern mid-altitude areas was poor, and that in the northern area was better than that in the southern area. ③ The average variation coefficient of AWRSEI in the Daihai Basin was 0.280, the ecological environment quality was stable, and the overall change fluctuation was small; the high fluctuation was mainly concentrated in the southern part of the lake and the residential area. There was a significant spatial autocorrelation in the ecological environment quality of the Daihai Basin, and the high-high agglomeration area was mainly distributed in the forest area at high altitude and the cultivated land area at low altitude. Low-low concentration areas were scattered in the middle altitude area. ④ The improvement of the ecological environment in Daihai Basin from 2001 to 2020 was mainly due to the increase in NDVI and the decrease in NDBSI and NDSI. NDVI and NDBSI were the combination with the strongest interaction and the strongest interpretation of the ecological environment. Land use was the dominant factor of AWRSEI and had the strongest explanatory power. The combination of land use and meteorological factors was the strongest interaction, and the relationship between each driving factor was enhanced.

Trade-off of ecosystem productivity and water use related to afforestation in southcentral USA under climate change

The increase of tree canopy cover due to woody plant encroachment and tree plantations modifies both carbon and water dynamics. The tradeoffs between ecosystem net primary productivity (NPP) and water use with increasing tree cover in different climate conditions, particularly under future climate scenarios, are not well understood. Within the climate transition zone of the southern Great Plains, USA, we used the Soil and Water Assessment Tool+ (SWAT+) to investigate the combined impacts of increasing tree cover and climate change on carbon and water dynamics in three watersheds representing semiarid, subhumid, and humid climates. Model simulations incorporated two land use modifications (Baseline: existing tree cover; Forest +: increasing evergreen tree cover), in conjunction with two climate change projections (the RCP45 and the RCP85), spanning two time periods (historic: 1991–2020; future: 2070–2099). With climate change, the subhumid and humid watersheds exhibited a greater increase in evapotranspiration (ET) and a corresponding reduction in runoff compared to the semi-arid watershed, while the semi-arid and subhumid watersheds encountered pronounced losses in water availability for streams (>200 mm/year) due to increasing tree cover and climate change. With every 1 % increase in tree cover, both NPP and water use efficiency were projected to increase in all three watersheds under both climate change scenarios, with the subhumid watershed demonstrating the largest increases (>0.16 Mg/ha/year and 170 %, respectively). Increasing tree cover within grasslands, either through woody plant expansion or afforestation, boosts ecosystem NPP, particularly in subhumid regions. Nevertheless, this comes with a notable decrease in water resources, a concern made worse by future climate change. While afforestation offers the potential for greater NPP, it also brings heightened water scarcity concerns, highlighting the importance of tailoring carbon sequestration strategies within specific regions to mitigate unintended repercussions on water availability.

Modeling the influence of small reservoirs on hydrological drought propagation in space and time

To increase drought preparedness in semi-arid regions across the world many small and medium reservoirs have been built in recent decades. Together these reservoirs form a Dense Reservoir Network (DRN) and its presence generates numerous challenges for water management. Most of the reservoirs that constitute the network are unmonitored and unregistered, posing questions on their cumulative effects on strategic reservoirs and water distribution at watershed scale. Their influence on hydrological drought propagation is thus largely unexplored. The objective of this study is then to assess DRN effects on droughts both in time and space. A modeling-analytical framework is proposed to achieve this goal. A mesoscale semi-distributed hydrological model was utilized to simulate both a network of large strategic reservoirs and a DRN in a large-scale tropical semiarid watershed. To investigate the effects in time and space generated by the network’s presence, the differences between multiple network scenarios were analyzed. Results show that the presence of the DRN accelerates the transition from meteorological to hydrological drought phases by 20% on average and slows down the recharge in strategic reservoirs by 25%, leading to a 12% increase of periods in hydrological drought conditions in a highly strategic basin and 26% without strategic reservoirs. This is because the DRN increases the hydrological dysconnectivity at catchment scale, reducing inflow to strategic reservoirs. In space, the DRN shifts upstream the basin’s water storage capacity by 8%, but when both large and small reservoirs are present the stored volume distribution behavior is not straightforward. The findings confirm the need to consider the effect of small reservoirs when developing and implementing drought management policies and reservoir-management approaches at regional scale.

Quantifying climate change impacts on future water resources and salinity transport in a high semi-arid watershed

High salinity mobilization and movement from salt-laden deposits in semi-arid landscapes impair soils and water resources worldwide. Semi-arid regions worldwide are expected to experience rising temperatures and lower precipitation, impacting water supply and spatio-temporal patterns of salinity loads and affecting downstream water quality. This study quantifies the impact of future climate on hydrologic fluxes and salt loads in the Gunnison River Watershed (GRW) (14,608 km2), Colorado, using the APEX-MODFLOW-Salt hydro-chemical watershed model and three different CMIP5 climate models projection downscaled by Multivariate Adaptive Constructed Analogs (MACA) for the period 2020–2099. The APEX-MODFLOW-Salt model accounts for the reactive transport of major salt ions (SO42−, Cl−, CO32−, HCO3−, Ca2+, Na+, Mg2+, and K+) to streams via surface runoff, rainfall erosional runoff, soil lateral flow, quick return flow and groundwater-stream exchange. Model results are analyzed for spatial and temporal trends in water yield and salt loading pathways. Although streamflow is primarily derived from surface runoff (65%), the predominant source of salt loads is the aquifer (73%) due to elevated concentrations of groundwater salt. Annual salt loading from the watershed is 582 Mkg, approximately 10% of the salt load in the Colorado River measured at Lee's Ferry, AZ. For future climate scenarios, annual salt loads from the watershed increased between 4.1% and 9.6% from the historical period due to increased salt loading from groundwater and quick return flow. From the results, applying the APEX-MODFLOW-Salt model with downscaled future climate forcings can be a helpful modeling framework for investigating hydrology and salt mobilization, transport, and export in historical and predictive settings for salt-affected watersheds.

Analyzing the uncertainty of potential evapotranspiration models in drought projections derived for a semi-arid watershed

Analyzing the uncertainty of potential evapotranspiration models in drought projections derived for a semi-arid watershed