Semi-arid Catchment Research Articles

Soil erosion assessment using SWAT, in relation withLand use, agricultural practices, and future climate change in a semi-arid catchment in Tunisia

Abstract Soil erosion is a severe environmental concern arising from intensive agricultural uses, land degradation, and anthropogenic activities. This problem threatens agricultural productivity and sustainable development, particularly in emerging countries. Therefore, evaluating soil erosion is essential in conservation, planning, and management on a watershed or basin scale. This study aims to assess the erosion of soil loss in the El Gouazine Watershed, central Tunisia, using the Soil and water assessment tool (SWAT). We define the impact of soil and water conservation management implementation combined with climate change scenario. We identified the spatial distribution of erosion rates based on soil properties, topography, and land use. The observed specific erosion rate of the watershed is estimated at 1.6 t.ha−1.yr−1, whereas according to the SWAT model, the average soil loss rate is 1.4 t.ha−1.yr−1. Furthermore, the obtained results highlight importance of the slope factor in affecting the severity of the soil loss rates in the El Gouazine watershed. It was also demonstrated that it’s urgent to prioritize other measures such as contour cropping or conservation agriculture, to enhance and strengthen the soils’ resistance against the detachment due to discharge water. In this context, this research found that these techniques decrease considerably the soil loss by 22% for the strip cropping, 33% for the No-tillage, and 72% for the bench terracing. Moreover, these farming techniques, contribute at the same time to the amelioration of the water balance by reducing the evapotranspiration and enhancing the soil water storage. To go further in this study a soil erosion forecast using the worst-case scenario for climate change RCP 8.5 was conducted. an overview of the future soil erosion patterns is obtained. We noticed then a decrease of the average annual soil loss rate until 2050 and then a prominent increase from 2051 to 2100.

Assessing sand dams for contributions to local water security and drought resilience in the semi-arid eastern Shashe catchment, Zimbabwe

Climate change is increasing the frequency and severity of droughts in semi-arid regions. Small-scale water storage can help build drought resilience, particularly in rural areas with no access to formal water infrastructure. Sand dams, which store water by capturing water in sand-filled ephemeral rivers during the wet season, are one promising storage option. While emerging studies indicate tentative evidence of their benefits, the focus on resilience is under-addressed. This study evaluates the impact of sand dams on resilience to climate variability and changes through a participatory case study approach in the Shashe catchment, a semi-arid catchment shared by Botswana and Zimbabwe. Participatory research was conducted via site inspections, focus group discussions, and interviews at 20 sand dams utilized by 19 villages across the Zimbabwean portion of the Shashe catchment. The results show that sand dams significantly improved local water availability, most notably with a significant increase in the number of months per year that water could be collected from the dam site (mean = 6.5 months before, to mean = 10.9 months after construction, p < 0.05). This increase is also reflected in drought years (mean = 5.8 months before, to mean = 9.6 months after construction, p < 0.05). Sand dams also contribute to the adaptive capacity of communities via key benefits such as diversification of livelihood activities, improved health and hygiene, and reduced erosion in the surrounding area due to increased vegetation. In sum, the study demonstrates clear benefits to communities facing drought, supporting calls to elevate sand dams on the development agenda.

Spatiotemporal dynamic of soil erosion and the key factors impact processes over semi-arid catchments in Southwest China

The heterogeneity of geographic environments and vegetation types plays a crucial role in determining soil erosion patterns. Highlighting the influence of environmental factors and their gradient changes on soil erosion is key to effective control and management. In this study, conducted in the arid-hot valley area of the Jinsha River, one of the main sand-producing areas in the Yangtze River Basin, we considered the topographic factors and land cover and applied the Revised Universal Soil Loss Equation (RULSE) with geographic information system (GIS) was applied to quantify the contribution and change pattern of different factors to soil erosion in the arid-hot valleys. Then, the spatial and temporal patterns of soil erosion in 2000 and 2015 were analyzed, showing erosion amount of 1812.96 t/(km2·a) and 1965.98 t/(km2·a), respectively. The increase in erosion was attributed to the frequency of heavy rainfall events. In terms of spatial distribution, strong soil erosion has been mainly observed in arid-hot basins, such as Yuanmou and Huaping. The soil erosion modulus (SEM) was closely connected to topographical elements, increasing incrementally with slope. High soil erosion areas were predominantly distributed across slopes between 18° and 36°. In contrast, SEM decreased gradually with increasing altitude, with strong erosion occurring primarily below 2000 m. SEM for different land use types followed this order: bare land > cultivated land > grassland > forest > paddy land. Based on the relationship between vegetation cover and soil erosion, the area could be roughly divided into three types of area by vegetation coverage: erosion control areas (30% vegetation coverage), erosion-vegetation transition areas (30–70%), and vegetation control areas (>70%). These results provide an essential reference for assessing soil erosion patterns and implementation of ecological vegetation restoration measures in arid-hot valleys.

Critical View of Overland Flow Estimation in Semi-arid Catchments Under Land Subsidence with Long-term Field Measurements

Critical View of Overland Flow Estimation in Semi-arid Catchments Under Land Subsidence with Long-term Field Measurements

Estimation of soil water storage according to its multi-scale correlations with environmental factors

Soil water storage (SWS) is a crucial parameter for vegetation restoration and a variety of hydrological processes in semi-arid regions. Its dynamics reflect the integrated influence of different factors operating at various intensities and scales. In this study, we investigated the scale-specific correlations between SWS and six selected environmental factors along a 1340-m long sampling transect in a semi-arid catchment using multivariate empirical mode decomposition (MEMD). The six factors were bulk density, elevation, clay, silt, sand, and soil organic carbon content. The multivariate data series of the SWS and factors of interest were separated into seven intrinsic mode functions (IMFs) and residue. IMF1, IMF3, and IMF5 were identified as the primary scales according to the percentage of total variance (about 50%) in SWS under different soil moisture conditions. The scale-specific correlations between SWS and the corresponding factors varied with scale but were irrelevant to soil moisture condition. SWS at each IMF and residue could be estimated using the associated scale-specific environmental factors at the same IMF or residue. SWS at the measurement scale was satisfactorily estimated by summarizing all the predicted IMFs and residue, which outperformed measurement-scale SWS estimation based on multivariate stepwise linear regression between SWS and the associated factors. Soil moisture condition could affect the relative importance of the environmental factors in overall SWS estimation. In general, soil properties were the dominant predictor of SWS under different soil moisture conditions. MEMD provides a useful opportunity to deal with scale-specific correlations between various variables.

Assessment of surface water quality in a highly anthropised semi-arid catchment (Case of Wadi El Hatab Basin, Tunisia, North Africa)

Anthropogenic contamination of surface waters is a critical issue in urbanised catchments, especially in Mediterranean environments with scarce water resources and intermittent rivers. A combined hydro-geochemical and multivariate statistical methodology has been applied to evaluate the surface water quality of Wadi El Hatab catchment (Central Tunisia). More than half of the 28 samples collected in March 2021 exceeded WHO standards for sodium, chloride and sulphate contents. Most polluted sites are directly influenced by domestic and industrial effluents that contaminate the river for kilometers. Principal Component Analysis and Hierarchical Ascendant Classification distinguished four contamination sources: water-rock interaction, domestic wastewater, untreated industrial effluents mainly from paper mills and irrigation return. A Water Quality Index model indicated that 75% of the surface water was unsuitable for drinking purposes; while according to chemical indices such as saturation index, sodium absorption coefficient (SAR), magnesium ratio (MR) and percent sodium (%Na), 25 to 49% of samples were unsuitable for irrigation. Considering that water of Wadi El Hatab is used for vegetable irrigation, serious public health issues may arise. The investigation demonstrated that a single representative sampling campaign could be a useful way to identify contamination sources when interpreted with both statistical and geochemical tools.

Exploring riverine solute behavior, dynamics, and export into the Arabian Sea from a semi-arid catchment, Western India

Exploring riverine solute behavior, dynamics, and export into the Arabian Sea from a semi-arid catchment, Western India

Performance evaluation of different gridded precipitation and CMIP6 model products with gauge observations for assessing rainfall variability under the historical and future climate change scenario over a semi-arid catchment, India

This study evaluates performance of a series of freely available gridded precipitation datasets that have been developed from the satellite (CHIRPS and TRMM TMPA), rain-gauged interpolation (APHRODITE and IMD-gridded), and Coupled Model Intercomparison Project Phase 6 (CMIP6)-General Circulation Models (GCMs) with the gauged observations (n = 49) for assessing their ability in capturing annual and seasonal rainfall characteristics over Central Godavari River basin in India. A rank matrix was developed for the purpose of comparing the performance of gridded datasets with that of gauged observations. This matrix was generated on the basis of the goodness-of-fit (GOF) statistics. The results reveal that APHRODITE dataset (r = 0.91–0.94; NSE = 0.68 to 0.87) has performed the best across all three zones, followed by the IMD-gridded (r = 0.93–0.95; NSE = 0.57 to 0.77) and CHIRPS datasets (r = 0.89–0.92; NSE = 0.38 to 0.60). However, TRMM TMPA dataset show the poorest accuracy with the gauged observation for all three zones. Additionally, projections from the CMIP6-GCMs reveal that the western part of the basin will receive more rainfall as compared to the eastern and central parts of the basin in future (2050s). This is opposite from prevailing rainfall variability pattern in the basin. The Zone 1 and Zone 2 of the basin are found to be comparatively more susceptible to the climate change under Shared Socio-Economic Pathway (SSP)245 and SSP585 scenarios than Zone 3. These results will aid in the future development of actionable agricultural water management plans for the basin, especially for rainfed agriculture, by assisting in the selection of the most effective gridded precipitation and CMIP6-GCM datasets.

Impact of Climate Variability on Rainfall Characteristics in the Semi-Arid Shashe Catchment (Botswana) from 1981–2050

Futuristic rainfall projections are used in scale and various climate impact assessments. However, the influence of climate variability on spatial distribution patterns and characteristics of rainfall at the local level, especially in semi-arid catchments that are highly variable and are not well explored. In this study, we explore the influence of climate variability on the spatial distribution and rainfall characteristics at a local scale in the semi-arid Shashe catchment, Northeastern Botswana. The LARS-WG, Long Ashton Research Station Weather Generator downscaling method, three representative scenarios (RCP 2.6, RCP 4.5, and RCP 4.5), three trend detection methods (Mann-Kendall, Sen’s slope, and innovative trend analysis) and L-moment method were used to assess climate change impacts on rainfall. Two data sets were used; one with 40 years of observed data from 1981–2020 and the other with 70 years from 1981–2050 (40 years of observed and 30 years of projected data from 2021–2050). Generally, the study found trend inconsistencies for all the trend detection methods. In most cases, Sen’s Slope has a high estimate of observed and RCP 2.6, while ITA overestimates rainfall totals under RCP 4.5 and RCP 8.5. The trend is increasing for annual total rainfall in most gauging stations while decreasing for annual maximum rainfall. The catchment is homogeneous, and Generalized Logistic distribution is the dataset’s best-fit distribution. Spatial coverage of a 100-year rainfall between 151–180 mm will be 81% based on observed data and 87% based on projected data under RCP 2.6 scenario when it happens. A 200-year rainfall between 196–240 mm under RCP 4.5 and 8.5 has high spatial areal coverage, at least 90% of the total catchment. The outcomes of this study will provide insightful information for water resource management and flood risk assessment under climate change. There is a need, however, to assess the transferability of this approach to other catchments in the country and assess the performance of other advanced modelling systems, such as machine learning, in this region.

Assessing the Impacts of Land Use and Climate Changes on River Discharge towards Lake Victoria

The Lake Victoria basin’s expanding population is heavily reliant on rainfall and river flow to meet their water needs, making them extremely vulnerable to changes in climate and land use. To develop adaptation and mitigation strategies to climate changes it is urgently necessary to evaluate the impacts of climate change on the quantity of water in the rivers that drain into Lake Victoria. In this study, the semi-distributed hydrological SWAT model was used to evaluate the impact of current land use and climate changes for the period of 1990–2019 and assess the probable future impacts of climate changes in the near future (2030–2060) on the Simiyu river discharge draining into Lake Victoria, Northern Tanzania. The General Circulation Model under RCPs 4.5, 6.0 and 8.5 predicted an increase in the annual average temperature of 1.4 °C in 2030 to 2 °C in 2060 and an average of 7.8% reduction in rainfall in the catchment. The simulated river discharge from the hydrological model under RCPs 4.5, 6.0 and 8.5 revealed a decreasing trend in annual average discharge by 1.6 m3/s from 5.66 m3/s in 2019 to 4.0 m3/s in 2060. The increase in evapotranspiration caused by the temperature increase is primarily responsible for the decrease in river discharge. The model also forecasts an increase in extreme discharge events, from a range between 32.1 and 232.8 m3/s in 1990–2019 to a range between 10.9 and 451.3 m3/s in the 2030–2060 period. The present combined impacts of climate and land use changes showed higher effects on peak discharge at different return periods (Q5 to Q100) with values of 213.7 m3/s (Q5), 310.2 m3/s (Q25) and 400.4 m3/s (Q100) compared to the contributions of climate-change-only scenario with peak discharges of 212.1 m3/s (Q5), 300.2 m3/s (Q25) and 390.2 m3/s (Q100), and land use change only with peak discharges of 295.5 m3/s (Q5), 207.1 m3/s Q25) and 367.3 m3/s (Q100). However, the contribution ratio of climate change was larger than for land use change. The SWAT model proved to be a useful tool for forecasting river discharge in complex semi-arid catchments draining towards Lake Victoria. These findings highlight the need for catchment-wide water management plans in the Lake Victoria Basin.

Landscape connectivity dynamics of the transboundary Mara River catchment, East Africa, and implications for river and wetland response in a globally important conservation region

Wetlands in semiarid regions are important systems that provide numerous ecosystem services in otherwise dry environments. Their functioning depends on the connected transfer of materials from source to sink which is determined by the degree of landscape connectivity. A combination of catchment characteristics, climatic variables and human activities are important factors driving landscape connectivity; however, wetland management rarely considers all these factors when conserving these systems. In this paper, we evaluate the landscape connectivity of the Mara River catchment in East Africa to understand the hydrogeomorphic processes driving material transfer to the floodplain wetlands. This semiarid catchment hosts the UNESCO World Heritage Serengeti-Mara ecosystem providing a unique opportunity to assess the influence of protected areas on wetland-catchment connectivity patterns. We use the index of connectivity and Emerging Hot Spot analysis to assess the hillslope-channel connectivity and identify specific subcatchments which have contributed positively to the wetland’s connectivity over time. The catchment-scale analysis highlights significant spatiotemporal variability in connectivity patterns and associated sediment dynamics. The results reflect that the Upper catchment is highly connected owing to the steep slopes and high rainfall and is likely responsible for the greatest sediment inputs into the fluvial network. Under greater future climatic and anthropogenic pressures, this region is expected to contribute increased sediment inputs into the river which will likely affect the geomorphic response of the downstream floodplain wetlands and may need to be prioritised for management. The protected areas are generally poorly connected due to intact vegetation and their local sediment contribution to the fluvial network is comparatively lower. Consideration of their placement in the catchment is important for managing these ecologically significant areas. Understanding the patterns and variability of hillslope-channel connectivity and the broader-scale landscape connections can help to identify and prioritise existing and emerging hotspots of change in large, semiarid catchments.

Assessment of future climate and hydrological changes in semi-arid catchment using the SWAT model and bias-corrected EURO-CORDEX ensemble: a case of the Ouergha catchment, North of Morocco

Assessment of future climate and hydrological changes in semi-arid catchment using the SWAT model and bias-corrected EURO-CORDEX ensemble: a case of the Ouergha catchment, North of Morocco

Analyzing changes in land use practices and livelihoods in the Bhungroo irrigation technology Volta basin piloted sites, West Mamprusi District, Ghana

This study focused on assessing agricultural land use practices and livelihood changes as affected by long-term erratic rainfall patterns characterized by flood and drought cycles in the Bhungroo Irrigation Technology (BIT) piloted sites, West Mamprusi upper and lower catchment area in Ghana. The study defined environmental flow restoration (EFR) factors based on irrigation water demand, natural outflows (e.g. drainage, seepage and percolation), evaporative outflows, rainfall and stored water. Agro-ecological resilience (AER) factors included flooding intensity, crop production under dry season, flood recession, and irrigation, and livestock as well as mixed farming was assessed. The framework of Evaluating Sustainable Land Management (FESLM) was utilized to assess changes in land use and trends analyzed by the Auto Regressive Integrated Moving Average (ARIMA) technique. The main observed land use practices were subsistence cultivation with limited irrigation 72.8%, subsistence cultivation without irrigation 64.5% and livestock keeping with some cultivation 87.4%. Corresponding land use mean weights impact on the EFR and AER were 9, 9, 8, and 9, 8, 7 respectively. The study noted that precipitation was of higher significance than temperature in terms of hydro-climate determinants in both upper and lower catchment areas. Mean monthly precipitation amount and intensity were highest between June–July in the upper catchment while in the lower catchment it was between July–August. These months corresponded with high risk of flooding and waterlogging in both catchments. The opposite was observed in March with access to irrigation water being the most EFR factor influencing the resilience of BIT especially at the upper catchment area. In terms of academic contribution, this paper posits in the hydro-climatic context that excess pressure exerted from a combination of micronized factors such as waterlogging of farmlands, flood recession rate, irrigation capabilities, climate preparedness, adoption and transferability of innovations and scalability of climate innovations are likely be the critical elements which accelerate the climate vulnerability of arid and semi-arid catchment areas. Based on these observations, the study carefully recommends improved BIT infrastructure, including the integration of grundfos pumping systems, solar pay-as-you-go energy, MTN mobile payment systems, participatory decision support systems (PDSS), with improved awareness on determinants of EFR including crop-livestock integration in ecological organic agriculture as resilience building options.

A multi-criteria approach for improving streamflow prediction in a rapidly urbanizing data scarce catchment

ABSTRACT This study advocates a multi-criteria approach to improve the streamflow predictions in a data-scarce catchment of Chennai metropolitan city of India using the Soil Water and Assessment Tool (SWAT). The remotely sensed evapotranspiration (ET) data, groundwater recharge estimation, and parameter regionalization were used to improve model prediction. Dynamic change of Land Use and Land Cover (LULC) was accounted for along with multi-parameter calibration for reducing the uncertainty in model parameters. The results revealed an improved streamflow prediction accuracy by 10%, especially in the prediction of medium and high flows with the Nash-Sutcliffe efficiency of 0.60. The enhanced parameters were regionalized to ungauged sub-basins and validated using a measured flow event downstream of regionalization with 15% prediction uncertainty. This semi-arid catchment is dominated by ET (58%) and runoff (27%) in the region's hydrology. The finding of this study can be applied to improve the hydrological modelling and predictions in data-scarce regions.

Accounting for operational irrigation options in mesoscale hydrological modelling of dryland environments

Agricultural irrigation practices can be a substantial proportion of the water budget in dryland environments. As the pressure on freshwater resources is growing in such environments, sustainable management of these resources is vital. Key issues need to be addressed: How does withdrawal for irrigation purposes alter a river’s flow regime and water yield? How can publicly available irrigation data help to improve the performance of hydrological models? A novel irrigation module was introduced to improve the performance of a mesoscale hydrological model (WASA-SED) and was tested on the hydrological upper meso-scale, as well as on a smaller subbasin in a semi-arid catchment in the Rio São Francisco basin. We could show that including irrigation practices into the modelling process especially helps to improve the quality of the modelled low flows during the dry season, where we achieved an improvement of the RMSE of up to 19%.

Scale-specific controls of soil water storage along a transect in a semiarid catchment

Spatiotemporal variability in soil water storage (SWS) is controlled by different factors operating at various intensities and scales. The traditional Pearson’s correlation analysis can be used to identify the linear correlations at the measurement scale only. In this study, wavelet coherency analysis was used to investigate scale-specific relationships between SWS and selected controlling factors. SWS of 135 sampling locations were calculated along a 1,340-m long sampling transect established in a typical catchment on the Loess Plateau, China. The selected controlling factors were soil saturated hydraulic conductivity (Ks); clay, silt, sand, and soil organic carbon (SOC) contents; elevation; and aboveground biomass (AGB). The spatial pattern of SWS measured in growing and nongrowing seasons and at different soil depths was similar. At all the sampling points, except for some locations in the depression area, SWS in the growing season was relatively greater than that in the nongrowing season. The influence of factors on SWS varied with scale, with soil Ks, clay and sand contents significantly correlated with SWS at large scales. Season and soil depth had no significant effect on scale-specific relationships between SWS and the controlling factors. The wavelet coherency analysis identified the type of correlation at different scales and locations. The outcomes of this study offer meaningful insights into the hydrological processes that can be observed in the Loess Plateau region and could have significant implications for future hydrological systems and water resources management.

Spatiotemporal variation of rainfall and its implications on water resources management: The case of Manyame River catchment in Zimbabwe

AbstractRainfall is an important hydrometeorological variable that affects river flow regimes and the amount of water flowing into surface water bodies and underground aquifers. Changing rainfall patterns and magnitudes contribute to extreme events including floods, droughts, and landslides. Thus, understanding of rainfall distribution is vital to sound water resources planning and allocation to users. Owing to the shortage of evenly distributed of rainfall stations in the arid and semiarid catchments, satellite rainfall products are regarded as reliable alternatives. This study therefore analyzes the long‐term spatiotemporal variation of rainfall in Manyame River catchment in Zimbabwe. Monthly rainfall data spanning from 1982 to 2019 were retrieved from the Climate Hazards Group Infra‐Red Precipitation with Station (CHIRPS). In situ rainfall data obtained from the country's two Meteorological Services Department were used to determine the accuracy of satellite data. Rainfall trends were tested using the Mann–Kendall trend test. The results showed goodness of fit between in situ and remotely sensed rainfall data. There is no significant increasing and decreasing trends in quarterly, monthly, and wet season rainfall. Statistically significant decreasing trends were observed in the central parts of the catchment during the dry season. The changing rainfall trends highlight the need for integrated and adaptive water resource management strategies to avoid adverse effects of floods and droughts in the catchment.

Enhancing streamflow simulation using hybridized machine learning models in a semi-arid basin of the Chinese loess Plateau

Accurate and efficient streamflow simulations are crucial in arid and semi-arid regions for water resources management. Process-based hydrological models generally perform inferior in arid and semi-arid catchments. Data-driven machine learning methods show very promising results in terms of prediction accuracy. In this study, we explore the synergies between process-based hydrological model and machine learning model to improve the predictive capability for semi-arid basins. We developed three hybridization approaches that combine the simulations of the Hydrologiska Byråns Vattenbalansavdelning (HBV) model with Long Short-Term Memory (LSTM) neural networks. In particular, one tight hybridization model is developed to consider the feedback between the LSTM model and the HBV model. Further, we investigated the predictive capability of both standalone HBV and LSTM models with short-length data for training, i.e., one-year data in the context of poorly gauged basins.The results show distinct improvements in the three types of hybrid models when compared with the HBV model and standalone LSTM model in terms of both NSE (12.3 % ∼ 25.6 %) and KGE (6 % ∼ 67.9 %). The model performance of the tight hybridization is the best among all the hybrid models, not only in terms of metrics but also hydrological signatures and the simulation of extreme flows. When calibrated with short-length data records, the LSTM was more robust than HBV, producing acceptable NSE and KGE values. Moreover, there is a strong correlation (0.92) between LSTM model performance and the similarity of flow duration curves (FDCs) between streamflow series in the calibration and validation periods.The results suggest that the hybridization of LSTM and HBV may provide an enhanced simulation capacity for semi-arid regions. Besides, the LSTM model can be successfully calibrated with representative short-length data and the characteristics of the representative short-length data are found. This study provides new insights into the potential use of hybridized machine learning in hydrological simulations.

Groundwater Variability in a Semi-Arid River Basin, Central India

The Betwa River basin, a semi-arid catchment that has been classified as a major hotspot of groundwater depletion in Central India. The rainfall and streamflow intermittency have affected agricultural practices due to the variability of groundwater availability for irrigation. This study evaluates the spatial and temporal variations of groundwater level (GWL) in the last 25 years (1993–2018) in the catchment. We applied a nonparametric Seasonal Trend decomposition based on the Loess (STL) method to decompose the GWL time series into the seasonal, trend, and remainder components. We observed that the GWL in the northeastern regions of the basin has depleted about 3–5 mbgl in the last two decades. During the same period, the basin has experienced a reduction in the rainfall magnitude (2.07 mm/yr). We observed that the overexploitation of groundwater for irrigation and rainfall variability have greatly impacted the GWL condition in the study area. Further, if the groundwater extraction continues at present rates, the Betwa River basin may experience severe depletion in the future.

Benefits, equity, and sustainability of community rainwater harvesting structures: An assessment based on farm scale social survey

Rainwater harvesting systems (RWHs) are implemented globally to bridge the frequent water supply-demand gaps. This study explores, through farmer household surveys (n = 492), how farmers perceived the benefits of RWHs, the equitability of benefits, and the role of contextual and psychological factors towards the behaviour of maintaining such systems. The study is carried out in a semi-arid catchment in the Indian state of Gujarat where RWHs, in the form of Check dams (CDs), have been implemented extensively. Results show that the benefits of CDs are perceived in good rainfall years through enhanced availability of water for expanding crops and irrigated areas. Farmers reported limited benefits of CDs in dry years. This is because of limited runoff and no carryover of stored groundwater, due to underlying shallow hard rock aquifer with little primary porosity, from wet years to dry years. Overall, ∼ 40%–50% of sampled farmers reported no benefits from CDs and the benefits decreased with distance. This reflects a spatially inequitable distribution of benefits skewed towards the farmers nearest to the CDs. The sustainability of CDs is a challenge with already ∼40% of CDs reportedly not working and 72.8% of farmers reported doing no maintenance activity. This is because 91.2% of farmers reported playing no role in its construction. The results show contextual (participation during construction, economic indicators) and sociopsychological factors (attention to CD condition, maintenance effort) significantly affect the behaviour towards maintaining the CDs. This highlights the need to complement RWHs with wider drought management and water demand management interventions to achieve drought resilience, and adherence to project exit protocols to secure the sustainability of investments.