Attributing the hydrological impact of different land use types and their long-term dynamics through combining parsimonious hydrological modelling, alteration analysis and PLSR analysis

Simulation of long-term storage dynamics of headwater reservoirs across the globe using public cloud computing infrastructure

Reservoirs play an important role in relation to water security, flood risk, hydropower and natural flow regime. This study derives a novel dataset with a long-term daily water-balance (reservoir volume, inflow, outflow, evaporation and precipitation) of headwater reservoirs and storage dynamics across the globe. The data is generated using cloud computing infrastructure and a high resolution distributed hydrological model wflow_sbm. Model results are validated against earth observed surface water area and in-situ measured reservoir volume and show an overall good model performance. Simulated headwater reservoir storage indicate that 19.4-24.4 % of the reservoirs had a significant decrease in storage. This change is mainly driven by a decrease in reservoir inflow and increase in evaporation. Deployment on a kubernetes cloud environment and using reproducible workflows shows that these kind of simulations and analyses can be conducted in less than a day.

Assessment of land use/ land cover change derived catchment hydrologic response: An integrated parsimonious hydrological modeling and alteration analysis based approach

Land use/land cover (LULC) change, often a consequence of natural or anthropogenic drivers, plays a decisive role in governing global catchment dynamics, and subsequent impact on regional hydrology. Insight into the complex relationship between the drivers of LULC change and catchment hydrology is of utmost importance to decision makers. Contemplating the dynamic rainfall-runoff response of the Indian catchments, this study proposes an integrated modeling-based approach to identify the drivers and relative contribution to catchment hydrology. The proposed approach was evaluated in the tropical climate Nagavali River Basin (NRB) (9512 km2) of India. The Soil and Water Assessment Tool (SWAT) hydrological model, which uses daily-scale rainfall, temperature, wind speed, relative humidity, solar radiation, and streamflow information was integrated with the Indicators of Hydrologic Alteration (IHA) technique to characterize the plausible changes in the flow regime of the NRB. Subsequently, the Partial Least Squares Regression (PLSR) based modeling analysis was performed to quantify the relative contribution of individual LULC components on the catchment water balance. The outcomes of the study revealed that forest land has been significantly converted to agricultural land (45-59%) across the NRB resulting in mean annual streamflow increase of 3.57 m3/s during the monsoon season. The affinity between land use class and streamflow revealed that barren land (CN = 83-87) exhibits the maximum positive response to streamflow followed by the built-up land (CN = 89-91) and fallow land (CN = 88-93). The period 1985-1995 experienced an increased ET scenario (911-1050 mm), while the recent period (2005-2020) experienced reduced ET scenario owing to conversion of forest to agricultural land. Certainly, the study endorses adopting the developed methodology for understanding the complex land use and catchment-scale hydrologic interactions across global-scales for early watershed management planning.

Novel spatial models for analysis the long-term impact of LULC changes on hydrological components at sub-basin level

The main objective of this research is to assess the impacts land use and land cover changes (LULC) on hydrological components using novel spatial models at sub-basin scales. The Soil and Water Assessment Tool (SWAT) was employed to analyze the long-term effect of LULC on hydrological components. The results of the calibrated and validated SWAT model demonstrated that run-off and actual evapotranspiration (ET) are expected to experience the largest increase, more than 130% and 90% in autumn, whereas the largest decrease is anticipated to occur in the summer and winter for potential evapotranspiration (PET) (-59%) and ET (-80%) by the projected time. The impacts of hydrological components, elevation, LULC, and an indicator of urbanization and land-use intensity (La) on water yield (WYLD) at sub-basin levels were then considered by four novel spatial models due to the problem of multicollinearity which is prevalent in traditional models. In particular, the Moran eigenvector spatially varying coefficients (MESVC) showed that the soil class out of LULC categories and lateral flow among hydrological properties are expected to have a statistically significant effect on spatial fluctuation of WYLD at the sub-basin scale. The results of spatially filtered unconditional quantile regression (SF-UQR) confirm the findings of the MESVC model and further implied that the lateral flow remains as a statistically significant contributor to WYLD only in lower quantiles (e.g., for quantiles lower than 0.25). The impacts of LULCs on WYLD were statistically lower than the effects caused by the hydrological components.

Potential effects of Land Use Land Cover Change on streamflow over the Sokoto Rima River Basin

This research investigated the effects of Land Use Land Cover Change (LULCC) over the Sokoto Rima River Basin (SRRB) using a setup of Weather Research and Forecasting (WRF) atmospheric model to generate the parameters to force WRF hydrological (WRF-Hydro) model which comprises of a parent domain at 12km horizontal resolution with an updated MODIS Land Use (LU) data and the nested domain at 4km resolution which focuses on the SRRB. The calibration of the model was done by modifying the infiltration and the Manning's roughness parameters. WRF-Hydro model was used to run simulations with the control LU and five different LU scenarios generated for Urban (Ur), Grassland (Gr), Savanna (Sa), Forest (Fr) and Barren (Ba). For the period analysed, simulation with Gr scenario increased streamflow in all the forecast points, while the Sa decreases it. A strong correlation was noted between the input precipitation and streamflow for all LU scenarios, and a significant Specific Discharge to Rainfall (SDR) for Ur, Fr and Ba scenarios. There was an increase in streamflow in the dry period due to afforestation and a decrease due to deforestation. Areas where grasslands were converted into savanna showed a little increase in evapotranspiration ET. There was more ET for the Sa scenario than the Gr scenario in the wet period, while there was more ET in the dry period for Gr scenario than it is for the Sa scenario. The study has shown that ET is a major factor to changes in streamflow due to LU changes over the basin. The sensitivity of the model to LULCC is reasonable, but more research is recommended to compare results with different hydrological model popularly used for LULCC impact studies.

Enhancing LULC scenarios impact assessment in hydrological dynamics using participatory mapping protocols in semiarid regions

Land use and land cover (LULC) scenarios in rural catchment hydrology are crucial to describe the effects of future water dynamics. However, there is a lack of understanding of the effectiveness of including static land covers at the subbasin level to provide inter-annual stability in changing the different water balance components. We developed a step-by-step mapping protocol to extend and enrich the hydrological assessment of future LULC scenarios defined through participatory stakeholder involvement. This novelty included specific allocation of static and dynamic LULC change among the scenarios and then compared the change of water dynamics to the current situation. For this, we quantified the LULC impact on the components of the water balance from three contrasting participatory scenarios implemented with the SWAT model in a rural basin in central Spain. The Land-sharing scenario (LSH) had the highest percentage of permanent grassland and shrubs and no increase of irrigated land compared to baseline. The land-sparing scenario (LSP) intensified agricultural land use close to urban areas, and the land balance scenario (LBA) was intermediate. The LSH increased the aquifer recharge by +1.7% and streamflow by +1.5%, while evapotranspiration and soil water storage decreased by -0.2%. In contrast, the LBA decreased in the riverine flux of -0.5%, an aquifer recharge of -0.6%, a soil water storage of -3.5%, and an evapotranspiration rate of +0.3%. Thus, LSH revealed that the allocation of permanent land cover such as grassland could buffer water dynamics, suggesting that dedicated planning and allocation of permanently vegetated LULC will favour land and water conservation.

A hybrid optimization method for sample partitioning in near-infrared analysis

The division of calibration and validation is one of the essential procedures that affect the prediction result of the calibration model in quantitative analysis of near-infrared (NIR) spectroscopy. The conventional methods are Kennard-Stone (KS) and sample set partitioning based on joint x-y distances (SPXY). These algorithms use Euclidean distance to cover as many representative samples as possible. This paper proposes an Adaptive Hybrid Cuckoo-Tabu Search (AHCTS) algorithm for partitioning samples based on optimization. The algorithm combines the characteristics of cuckoo search (CS) and tabu search (TS) and fuses with an adaptive function. For comparison, using fishmeal samples as spectral analysis data, KS, SPXY, and AHCTS algorithms were used to divide the modeling samples to establish partial least squares regression (PLSR) models. The experimental results showed that the model established by the proposed algorithm performs better than KS and SPXY. It reveals that the AHCTS method may be an advantageous alternative for quantitative analysis of NIR spectroscopy.

Machine learning models for streamflow regionalization in a tropical watershed

This study aims to assess different machine learning approaches for streamflow regionalization in a tropical watershed, analyzing their advantages and limitations, and to point the benefits of using them for water resources management. The algorithms applied were: Random Forest, Earth and linear model. The response variables were the three types of minimum streamflow (Q_7.10, Q₉₅ and Q₉₀), besides the long-term average streamflow (Q_mld). The database involved 76 environmental covariates related to morphometry, topography, climate, land use and cover, and surface conditions. The elimination of covariates was performed using two processes: Pearson's correlation analysis and importance analysis by Recursive Feature Elimination (RFE). To validate the models, the following statistical metrics were used: Nash-Sutcliffe coefficient (NSE), percent bias (PBIAS), Willmott's index of agreement (d), coefficient of determination (R²), root mean square error (RMSE), mean absolute error (MAE) and relative error (RE). The linear model was unsatisfactory for all response variables. The results show that nonlinear models performed well, and their covariate of greatest predictive importance was flow equivalent to the precipitated volume, considering the subtraction of an abstraction factor of 750 mm (Peq750). Generally, the Random Forest and Earth models showed similar performances and great ability to predict the minimum streamflow and long-term average streamflow assessed, constituting powerful and promising alternatives for the streamflow regionalization in support to the management and integrated planning of water resources at the level of river basins.

Multiple drivers of hydrological alteration in the transboundary Srepok River Basin of the Lower Mekong Region

Human-induced changes in land and water resources adversely affect global hydrological regimes. Hydrological alteration of the natural flow regime is considered to have a significant damaging and widespread impact on river ecosystems and livelihoods. Therefore, understanding the hydrological alteration of rivers and the potential driving factors affecting such alterations are crucial to effective water resources management. This study analyses the impact of changes in land use, climate, and hydropower development on the hydrological regime of the Srepok River Basin in the Lower Mekong Region. The Lower Mekong Basin (LMB) in Southeast Asia is known for its agriculture, forests, fisheries, wildlife, and diverse natural ecosystems. Historical land use and climate change are quantified (utilising European Space Agency land cover and observed meteorological data) and correlated with the hydrological indicators using the Indicators of Hydrologic Alteration (IHA) software. Moreover, pre and post impacts on the hydrological regime by hydropower development are quantified using the Range of Variability Approach (RAV) in IHA software. The results reveal that land use, rainfall, and temperature affect different aspects of the hydrological regime, with corroborating evidence to support variation among the most correlated IHA and environmental flow component (EFC) parameters with the three drivers. The highest and lowest correlations among the IHA and EFC parameters under each driver are against land use (0.85, -0.83), rainfall (0.78, -0.54), and minimum and max temperatures (0.42, -0.47). Among the parameters, the fall rate has the most significant effect on hydrological alteration of all drivers. Hydropower development in the basin mostly affects the fall rate and reversal. Identifying the connection between these multiple drivers and hydrological alteration could help decision-makers to design more efficient and sustainable water management policies.

Hydrological Response of Dry Afromontane Forest to Changes in Land Use and Land Cover in Northern Ethiopia

This study analyzes the impact of land use/land cover (LULC) changes on the hydrology of the dry Afromontane forest landscape in northern Ethiopia. Landsat satellite images of thematic mapper (TM) (1986), TM (2001), and Operational Land Imager (OLI) (2018) were employed to assess LULC. All of the images were classified while using the maximum likelihood image classification technique, and the changes were assessed by post-classification comparison. Seven LULC classes were defined with an overall accuracy 83–90% and a Kappa coefficient of 0.82–0.92. The classification result for 1986 revealed dominance of shrublands (48.5%), followed by cultivated land (42%). Between 1986 and 2018, cultivated land became the dominant (39.6%) LULC type, accompanied by a decrease in shrubland to 32.2%, as well as increases in forestland (from 4.8% to 21.4%) and bare land (from 0% to 0.96%). The soil conservation systems curve number model (SCS-CN) was consequently employed to simulate forest hydrological response to climatic variations and land-cover changes during three selected years. The observed changes in direct surface runoff, the runoff coefficient, and storage capacity of the soil were partially linked to the changes in LULC that were associated with expanding bare land and built-up areas. This change in land use aggravates the runoff potential of the study area by 31.6 mm per year on average. Runoff coefficients ranged from 25.3% to 47.2% with varied storm rainfall intensities of 26.1–45.4 mm/ha. The temporal variability of climate change and potential evapotranspiration increased by 1% during 1981–2018. The observed rainfall and modelled runoff showed a strong positive correlation (R2 = 0.78; p < 0.001). Regression analysis between runoff and rainfall intensity indicates their high and significant correlation (R2 = 0.89; p < 0.0001). Changes were also common along the slope gradient and agro-ecological zones at varying proportions. The observed changes in land degradation and surface runoff are highly linked to the change in LULC. Further study is suggested on climate scenario-based modeling of hydrological processes that are related to land use changes to understand the hydrological variability of the dry Afromontane forest ecosystems.