Changes In Hydrological Variables Research Articles

On the challenges of simulating streamflow in glacierized catchments of the Himalayas using satellite and reanalysis forcing data

Abstract Hydrologic assessment of climate change impacts on complex terrains and data-sparse regions like High Mountain Asia is a major challenge. Combining hydrological models with satellite and reanalysis data for evaluating changes in hydrological variables is often the only available approach. However, uncertainties associated with forcing dataset, coupled with model parameter uncertainties, can have significant impacts on hydrologic simulations. This work aims to understand and quantify how the uncertainty in precipitation and its interaction with the model uncertainty affect streamflow estimation in glacierized catchments. Simulations for four precipitation datasets (IMERG, CHIRPS, ERA5 Land, and APHRODITE) and two glaciohydrological models (GDM and HYMOD_DS) are evaluated for the Marsyangdi and Budhigandaki river basins in Nepal. Temperature sensitivity of streamflow simulations is also investigated. Relative to APHRODITE, which compared well with ground stations, ERA5 Land overestimate the catchment average precipitation for both basins by more than 70%; IMERG and CHIRPS overestimates by ∼20%. Precipitation uncertainty propagation to streamflow exhibits strong dependencies to model structure and streamflow components (snowmelt, icemelt, rainfallrunoff), but overall uncertainty dampens through precipitation-to-streamflow transformation. Temperature exerts a significant additional source of uncertainty in hydrologic simulations of such environments. GDM was found to be more sensitive to temperature variations, with >50% increase in total flow for 20% increase in actual temperature, emphasizing that models that rely on lapse rates for the spatial distribution of temperature have much higher sensitivity. Results from this study provide critical insight into the challenges of utilizing satellite and reanalysis products for simulating streamflow in glacierized catchments.

Hydrological trends captured by assimilating GRACE total water storage data into the CLM5-BGC model

Terrestrial water storage (TWS) encompasses various components such as snow water, surface water, canopy water, soil water, and groundwater. Therefore, TWS is strongly affected by several factors including climate change, floods, drought, and anthropogenic activities. Predicting and quantifying TWS is critical in understanding terrestrial water cycles, as well as for managing water resources at the regional and global scale. TWS can be monitored with the Gravity Recovery and Climate Experiment (GRACE) satellites and estimated using land surface models (LSMs) on various spatial scales. The present study demonstrated how large-scale TWS data assimilation affects model performance by assessing the variations in TWS and related hydrological variables. Moreover, unlike traditional physical-based models, the assimilation of TWS data into an LSM enabled the assessment of anthropogenic impacts. TWS data from GRACE was assimilated into the Community Land Model version 5 (CLM5) with the CLM5-BGC biogeochemistry module covering the East Asia region. The model was forced using 40-ensemble meteorological forcing data from 2003 to 2010 and GRACE TWS data were assimilated using the ensemble adjustment Kalman filter (EAKF). Data assimilation markedly improved the performance of the model, as demonstrated by a decrease in the root mean square error from 0.71 mm/month in the free run (FR) without data assimilation to 0.48 mm/month in the data assimilation (DA) run, in addition to an increase in the correlation coefficient from 0.55 in FR to 0.69 in DA. Additionally, unlike the FR model, the DA model effectively captured the observed negative trends of the TWS, soil moisture, and evapotranspiration in Northern India and the Northern China Plain. Finally, our findings also demonstrated that the anthropogenic contribution to the negative trend of the TWS over Northern India and Northern China Plain was marginally increased in the DA model compared to the FR model. Collectively, our findings demonstrated that TWS data assimilation enables a more realistic evaluation of the long-term and large-scale changes in hydrologic variables.

Assessing the Impact of Climate and Land-Use Changes on the Hydrologic Cycle Using the SWAT Model in the Mun River Basin in Northeast Thailand

Climate change (CC) and land-use change (LUC) will alter a basin’s hydrological processes and water balance. Quantifying their significance is imperative in formulating appropriate countermeasures and management plans. This study assesses projected changes in hydrological variables under CC and LUC scenarios to provide multi-dimensional insight into water balance relevant to an agricultural watershed in Northeast Thailand. The soil and water assessment tool (SWAT) is utilized to simulate hydrological variables (evapotranspiration, soil moisture, surface runoff, and water yield) for the baseline (1981–2010) and the near-future (2021–2050) after calibrating the model. CC projections considering the CMIP6 model ensemble for the high-emission scenario (SSP5-8.5) show the annual rainfall may not change significantly (0.5% increase compared to baseline). However, the surface runoff will likely increase by 33% due to the projected increase in rainfall intensity and extremes. The increase in urban areas due to LUC for the business as usual (BAU) scenario is also expected to boost surface runoff by 38–87%. Similarly, the LUC scenario with forest expansion will increase evapotranspiration by up to 1.3%. While CC is anticipated to raise water yield by 11%, LUC may reduce it by 6%. Under the combined scenario, the yield is expected to increase by 8%, indicating CC as the dominating driver. The results show that although CC governs the runoff from the basin, land-use change will also impact flows at a monthly scale. Importantly, soil moisture in the future will decrease (8–9%) under both stressors, which will reverberate in the basin’s agricultural livelihood and socioeconomic settings unless the appropriate adaptation measures are implemented.

Hydrological processes in a watershed on the transition from Amazon to Cerrado in Brazil

This study analyzed the spatial and temporal variability of hydrological processes of precipitation, evapotranspiration, discharge, water storage, runoff, and a dryness index in the Mearim River basin, in the Amazon-Cerrado transition in Brazil. Furthermore, we investigated whether the changes found are due to land use and land cover change (LULC) or climate and whether these changes are significant. We used the Large Basin Hydrological Model (MGB), which showed good or optimal performance for most sub-basins and periods. The study period comprised thirty-one years (1990–2020), subdivided into three analysis periods, according to the temporal dynamics of LULC in the basin. The temporal variability indicated increases of 7.3% in precipitation, 3.7% in AET, and 29.7% in mean discharge in the second period (2001–2010) compared to the first period (1990–2000), followed by a decline in the last (2011–2020) period: 6.6% for precipitation, 5.6% for AET and 12.7% for discharge. In the seasonal analysis, it was possible to identify that in the dry period, evapotranspiration and discharge are sustained by soil water storage, and it is still observed an 86.5% reduction in precipitation between the wet and dry seasons is also observed. Statistical evidence reveals that changes in hydrological variables are insignificant and that there are no trends of increase or decrease in the amount of water in the basin. Even with the strong dynamics of LULC, there are no significant effects on the discharge, which leads us to believe that the basin climate controls temporal changes in the PMIM hydrological processes.

Separating the relative contributions of climate change and ecological restoration to runoff change in a mesoscale karst basin

Ecological restoration changes land cover types and management practices. However, understanding of their hydrological response is limited, especially in karst ecosystems, which are vulnerable to environmental changes. This study aimed to identify the spatial variability of the hydrological response in a mesoscale karst basin to climate change and ecological restoration. We examined changes in hydrological variables and normalized difference vegetation index (NDVI) from 1985 to 2015 in Longjiang River Basin (LRB) and its three main sub-basins, namely Huanjiang (HJB), Zhongzhouxiaojiang (ZZB), and Dongxiaojiang (DXB) sub-basins. The proportions of karst landscapes (POK) were 81, 66, 61, and 26%, respectively. Three independent quantitative methods were employed to distinguish the relative contributions of climate change and human activity. Annual runoff showed a good linear correlation with precipitation in all the basins, with insignificantly lower average values during 2003–2015 than during 1985–2002. The relative contribution of precipitation to annual runoff change varied from 17.5 to 52.9%. At a seasonal scale, flood season precipitation and runoff decreased with similar patterns of annual variation, whereas non-flood season values increased from 1985–2007 to 2008–2015. The quantitative results showed that the relative contribution rates of human activity to non-flood season runoff change were strongly linked to POK values and related vegetation changes. Annual NDVI improved after the implementation of ecological restoration projects, with change rates negatively related to POK values. Human activity was the dominant cause of increased non-flood season runoff in the LRB and HJB (accounting for 57 and 78%, respectively), whereas increases in non-flood season runoff in the ZZB and DXB were mainly caused by precipitation changes (accounting for 84 and 172%, respectively). These findings highlighted the facts that ecological restoration had changed hydrological processes and seasonal-scale analyses might therefore facilitate a better understanding of runoff changes in karst basins.

Climate and Land Use Effects on Hydrologic Processes in a Primarily Rain‐Fed, Agricultural Watershed

AbstractAnticipating changes in hydrologic variables is essential for making socioeconomic water resource decisions. This study aims to assess the potential impact of land use and climate change on the hydrologic processes of a primarily rain‐fed, agriculturally based watershed in Missouri. A detailed evaluation was performed using the Soil and Water Assessment Tool for the near future (2020–2039) and mid‐century (2040–2059). Land use scenarios were mapped using the Conversion of Land Use and its Effects model. Ensemble results, based on 19 climate models, indicated a temperature increase of about 1.0°C in near future and 2.0°C in mid‐century. Combined climate and land use change scenarios showed distinct annual and seasonal hydrologic variations. Annual precipitation was projected to increase from 6% to 7%, which resulted in 14% more spring days with soil water content equal to or exceeding field capacity in mid‐century. However, summer precipitation was projected to decrease, a critical factor for crop growth. Higher temperatures led to increased potential evapotranspiration during the growing season. Combined with changes in precipitation patterns, this resulted in an increased need for irrigation by 38 mm representing a 10% increase in total irrigation water use. Analysis from multiple land use scenarios indicated converting agriculture to forest land can potentially mitigate the effects of climate change on streamflow, thus ensuring future water availability.

A Bayesian Networks approach for the assessment of climate change impacts on nutrients loading

Climate change is likely to strongly affect both the qualitative and quantitative characteristics of water resources. However, while potential impacts of climate change on water availability have been widely studied in the last decades, their implication for water quality have been just poorly explored since now. Accordingly, an integrated assessment based on Bayesian Networks (BNs) was implemented in the Zero river basin (Northern Italy) to capture interdependencies between future scenarios of climate change with water quality alterations (i.e. changes in nutrients loadings). Bayesian Networks were used as integrative tool for structuring and combining the information available in existing hydrological models, climate change projections, historical observations and expert opinion producing alternative risk scenarios to communicate the probability (and uncertainty) of changes in the amount nutrients (i.e. NO3−, NH4+, PO43−) delivered from the basin under different climate change projections (i.e. RCP 4.5 and 8.5)The model predictive accuracy and uncertainty were evaluated through a cross comparison with existing observed data and hydrological models’ simulations (i.e. SWAT) available for the case study and, in addition, sensitivity analysis was performed to identify key input variables, knowledge gaps in model structurers and data. Simulated scenarios show that seasonal changes in precipitation and temperature are likely to modify both the hydrology and nutrient loadings of the Zero river with a high probability of an increase of freshwater discharge, runoff and nutrient loadings in autumn and a decrease in spring and summer with respect to the current conditions 1983–2012. Greater increase for both river flow and nutrients loadings are predicted under the medium and long term RCP8.5 scenarios. Diffuse pollution sources play a key role in determining the amount of nutrients loaded: both NH4+ and PO43− loadings are mainly influenced by changes in hydrological variables (i.e. runoff) while NO3− loadings, despite being highly dependent on flow conditions, are also influenced by agronomic practices and land use (i.e. irrigation, fertilization). Highlighting key components and processes from a multi-disciplinary perspective, BN outputs could support water managers in tracking future trends of water quality and prioritizing stressors and pollution sources thus paving the way for the identification of targeted typologies of management and adaptation strategies to maintain good water quality status under climate change conditions.

Long-Term Changes of Hydrological Variables in a Small Lowland Watershed in Central Poland

Climate-induced changes in small watersheds are still not well researched because long-term datasets are usually missing for these watersheds. Such studies can, however, improve our understanding of the watershed response to climatic changes at such a small scale being transparent. In this study, we investigate trends in temperature, precipitation and river-flow characteristics in a small watershed, typical for Central Poland, with 53 years of observations (1963–2015) using the Mann-Kendall test. Particularly, we examine whether any trends in hydro-meteorological variables can be identified, and if any associated changes in water resources in this region can already be observed. We found that this short period already allows for detecting some changes in hydro-meteorological variables. These changes could be characterized by a significant increase in the mean annual air temperature on a daily basis, and a significant decrease in the mean annual discharge on a daily basis and in the minimum annual discharge on a daily basis. Yet, no significant trend could be detected for the total annual precipitation, the maximum summer rainfall, and the maximum annual discharge on a daily basis. These findings indicate that water resources are decreasing in this region, which affects natural habitats, agriculture and local communities.

Projected Changes in Hydrological Variables in the Agricultural Region of Alberta, Canada

The responses of regional hydrological variables to climate change are of prime concern for agricultural water resources planning and management. Therefore, the seasonal (April–September) and annual (January–December) evolution of precipitation, temperature, evapotranspiration (ET), soil moisture (SM), deep aquifer recharge (DA), and water yield (WYLD) was investigated using established statistical techniques for the historical, near and far future (1983–2007: His, 2010–2034: NF, 2040–2064: FF) in the agricultural region of Alberta, Canada. Previously calibrated and validated agro-hydrological models (Soil and Water Assessment Tool) were used to generate these variables. Future changes were investigated under two representative concentration pathways, i.e., RCP 2.6 and RCP 8.5, projected by nine global climate models (GCM). Results revealed that Alberta had become warmer and drier during the His period. The future projection showed an increase in precipitation, SM, DA, and WYLD, in turn, indicated more water resources. Precipitation and temperature were projected to increase between 1 to 7% and 1.21 to 2.32 °C, respectively. Seasonal precipitation showed a higher trend magnitude than that of annual precipitation. The temperature generally had an increasing trend in the future with a maximum in the southern Alberta. Monthly average ET was likely to increase and decrease in the rising and falling limbs of the bell-shaped curve with the peak in July. A comparison of water demand from two land use types (dominant land use and barley) during the His period showed that water deficit existed in July and August. The results of this study could help in understanding anticipated changes in hydrological variables and decision-making regarding the regional agricultural water resources management.

Identification of dominant source of errors in developing streamflow and groundwater projections under near‐term climate change

AbstractUncertainties in projecting the changes in hydroclimatic variables (i.e., temperature and precipitation) under climate change partly arises from the inability of global circulation models (GCMs) in explaining the observed changes in hydrologic variables. Apart from the unexplained changes by GCMs, the process of customizing GCM projections to watershed scale through a model chain—spatial downscaling, temporal disaggregation, and hydrologic model—also introduces errors, thereby limiting the ability to explain the observed changes in hydrologic variability. Toward this, we first propose metrics for quantifying the errors arising from different steps in the model chain in explaining the observed changes in hydrologic variables (streamflow and groundwater). The proposed metrics are then evaluated using a detailed retrospective analyses in projecting the changes in streamflow and groundwater attributes in four target basins that span across a diverse hydroclimatic regimes over the U.S. Sunbelt. Our analyses focused on quantifying the dominant sources of errors in projecting the changes in eight hydrologic variables—mean and variability of seasonal streamflow, mean and variability of 3 day peak seasonal streamflow, mean and variability of 7 day low seasonal streamflow, and mean and standard deviation of groundwater depth—over four target basins using an Penn state Integrated Hydrologic Model (PIHM) between the period 1956–1980 and 1981–2005. Retrospective analyses show that small/humid (large/arid) basins show increased (reduced) uncertainty in projecting the changes in hydrologic attributes. Further, changes in error due to GCMs primarily account for the unexplained changes in mean and variability of seasonal streamflow. On the other hand, the changes in error due to temporal disaggregation and hydrologic model account for the inability to explain the observed changes in mean and variability of seasonal extremes. Thus, the proposed metrics provide insights on how the error in explaining the observed changes being propagated through the model under different hydroclimatic regimes.

Assessment of Climate Change Impact on Water Resources in the Upper Senegal Basin (West Africa)

This study assesses the potential impacts of climate change on water resources and the effect of statistical bias correction on the projected climate change signal in hydrological variables over the Upper Senegal Basin (West Africa). Original and bias corrected climate data from the regional climate model REMO were used as input for the Max Planck Institute for Meteorology-Hydrology Model (MPI-HM) to simulate river discharge, runoff, soil moisture and evapotranspiration. The results during the historical period (1971-2000) show that using the bias corrected input yields a better representation of the mean river flow regimes and the 10th and 90th percentiles of river flow at the outlet of the Upper Senegal Basin (USB). The Nash-Sutcliffe efficiency coefficient is 0.92 using the bias corrected input, which demonstrates the ability of the model in simulating river flow. The percent bias of 3.88% indicates a slight overestimation of the river flow by the model using the corrected input. The evaluation demonstrates the ability of the bias correction and its necessity for the simulation of historical river regimes. As for the potential changes of hydrological variables by the end of 21st century (2071-2100), a general decrease of river discharge, runoff, actual evapotranspiration, soil moisture is found under two Representative Concentration Pathways (RCP4.5 and RCP8.5) in all simulations. The decrease is higher under RCP8.5 with uncorrected data in the northern basin. However, there are some localized increases in some parts of the basin (e.g. Guinean Highlands). The projected climate change signal of these above variables has the same spatial pattern and tendency for the uncorrected and bias corrected data although the magnitude of the corrected signal is somewhat lower than that uncorrected. Furthermore, the available water resources are projected to substantially decrease by more than -50% in the majority of the basin (especially in driest and hottest northern basin with RCP8.5 scenario) for all data, except the Guinean highlands where no change is projected. The comparison of simulations driven with uncorrected and bias corrected input reveals that the bias correction does not substantially change the signal of future changes of hydrological variables for both scenarios over the USB even though there are differences in magnitude and deviations in some parts of the basin.

Understanding the Changing Characteristics of Droughts in Sudan and the Corresponding Components of the Hydrologic Cycle

Abstract Droughts are becoming the most expensive natural disasters in former Sudan and have exerted serious impacts on local economic development and ecological environment. The purpose of this paper is to improve understanding of the temporal and spatial variations of droughts by using the Standard Precipitation Index (SPI) and to discuss their relevance to the changes of hydrological variables in Sudan. The analysis results show that 1) droughts start in the late 1960s in Sudan and severe droughts occur during the 1980s in different regions of Sudan—the annual precipitation and soil moisture also reveal the evidence that the droughts prevail since the late 1960s; 2) the greater negative soil moistures anomalies are found in central and southern Sudan during the rainy seasons while greater negative anomalies of precipitation occur only in central Sudan compared between 1969–2009 and 1948–68; 3) the precipitation recycling ratio averaged over 1948–2009 decreases from south to north and the percentage of local actual evapotranspiration to local precipitation in dry conditions is greater than that in wet conditions; and 4) the highest (second highest) correlations appear between soil moisture and precipitation (actual evapotranspiration) and the significant decreases in annual soil moisture are associated with the decrease of annual precipitation and the increase of annual temperature. This suggests that continuous droughts in Sudan are caused jointly by the decrease of precipitation and the increase of temperature in the region.

Comparison of Hydrological Simulations of Climate Change Using Perturbation of Observations and Distribution‐Based Scaling

Projected climate change effects on groundwater and stream discharges were investigated through simulations with a distributed, physically based, surface water–groundwater model. Input to the hydrological model includes precipitation, reference evapotranspiration, and temperature data of the HIRHAM4 regional climate model (RCM). The aim of this study was to determine whether the choice of bias‐correction method, applied to the RCM data, affected the projected hydrological changes. One method consisted of perturbation of observed data (POD) using climate change signals derived from the RCM output, while the other consisted of distribution‐based scaling (DBS) of the RCM output. Distribution‐based scaling resulted in RCM control period data closely approaching the observed climate data and thereby considerably improved the simulation of recharge and stream discharges. When comparing the simulations using both methods, only small differences between the projected changes in hydrological variables for the scenario period were found. Mean annual recharge increased by 15% for the DBS method and 12% for POD, and drain flow increased by 24 and 19%, respectively, while the increases in base flow were similar (7%). For both methods, daily stream discharges up to and including the median showed little change, while increases occurred for the higher quantile values. This study showed that the choice of bias‐correction method did not have a significant influence on the projected changes of mean hydrological responses in this catchment, although further analysis is necessary to determine whether extremes are affected. Furthermore, the characteristics of the hydrological system likely reduced the sensitivity of the projected changes to the choice of method.

Statistical Assessment of the Impact of Conservation Measures on Streamflow Responses in a Watershed of the Loess Plateau, China

The objective of this study was to examine the trends of changes in stream- flow in a watershed of the Loess Plateau, where a series of soil conservation measures were implemented since the late 1950s. Both parametric and non-parametric Mann- Kendall test were used to identify the trends in hydrologic variables over the last 50 years, and it showed significant downward trends in annual runoff, surface runoff and baseflow. The Pettitt's test was used to detect the change points of runoff, which occurred in 1973, and the whole 50-year records could be divided into contrast (from 1957 to 1973) and treated (from 1974 to 2006) periods. It was observed that the average annual runoff during treated period reduced by 60%, surface runoff and baseflow reduced by 65% and 55%, respectively in comparing with the contrast period. But the proportion of baseflow to total runoff showed a significant increasing from 0.57 to 0.63. Seasonal runoff also showed decreased trend with the highest reduction occurring in summer and lowest in winter. Annual precipitation in whole period showed no significant trend, so the changes in hydrologic variables were induced by conservation measures. Comparison of the flow duration curves for the