Human activities have resulted in rapid hydrological change around the world, in many cases producing shifts in the dominant hydrological processes, confounding predictions, and complicating effective management and planning. Identifying and characterizing such changes in hydrological processes is therefore a globally relevant problem, one that is particularly challenging in sparsely monitored environments. We develop a novel, process‐based approach for attribution of hydrological change in such scenarios and apply the approach to the TG Halli watershed outside Bangalore, India, where streamflow has declined considerably over the last 50 years. The approach consists of (a) employing a range of field instrumentation and experiments to identify contemporary streamflow generation mechanisms, (b) using these observations to constrain our understanding and generate hypotheses pertaining to historical changes, and (c) evaluating these hypotheses with a range of evidence including proxies for historical hydrological processes. The body of evidence in the TG Halli watershed indicates the historical presence and subsequent loss of a shallow groundwater table that previously discharged to the stream, meaning that groundwater depletion is the most likely driver of streamflow decline. These findings present a viable path towards improved predictions of future water resources and sustainable water management within the watershed. Our process‐based approach to attribution has the potential to improve understanding of human‐driven hydrological change in regions with poor monitoring of hydrological systems.
Climatic and anthropogenic drivers of a drying Himalayan river
Abstract. Streamflow regimes are rapidly changing in many regions of the world. Attribution of these changes to specific hydrological processes and their underlying climatic and anthropogenic drivers is essential to formulate an effective water policy. Traditional approaches to hydrologic attribution rely on the ability to infer hydrological processes through the development of catchment-scale hydrological models. However, such approaches are challenging to implement in practice due to limitations in using models to accurately associate changes in observed outcomes with corresponding drivers. Here we present an alternative approach that leverages the method of multiple hypotheses to attribute changes in streamflow in the Upper Jhelum watershed, an important tributary headwater region of the Indus basin, where a dramatic decline in streamflow since 2000 has yet to be adequately attributed to its corresponding drivers. We generate and empirically evaluate a series of alternative and complementary hypotheses concerning distinct components of the water balance. This process allows a holistic understanding of watershed-scale processes to be developed, even though the catchment-scale water balance remains open. Using remote sensing and secondary data, we explore changes in climate, surface water, and groundwater. The evidence reveals that climate, rather than land use, had a considerably stronger influence on reductions in streamflow, both through reduced precipitation and increased evapotranspiration. Baseflow analyses suggest different mechanisms affecting streamflow decline in upstream and downstream regions, respectively. These findings offer promising avenues for future research in the Upper Jhelum watershed, and an alternative approach to hydrological attribution in data-scarce regions.
more »
« less
- Award ID(s):
- 2142967
- NSF-PAR ID:
- 10378009
- Date Published:
- Journal Name:
- Hydrology and Earth System Sciences
- Volume:
- 26
- Issue:
- 2
- ISSN:
- 1607-7938
- Page Range / eLocation ID:
- 375 to 395
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
more » « less
Abstract -
Top-Down Approach for Time-Variant Anthropogenic Signature Attribution in Socio-Hydrological SystemsIn the Anthropocene, humans have altered the properties and processes of hydrological systems across scales. The extent of human intervention in the landscape limits the utility of traditional hydrological modelling schemes. Since purely hydrological conceptual models no longer fit these systems, hydrologists must integrate key human interventions into conceptual models of human-modified catchments. Despite the advances in analyzing the observed changes within the hydrological cycle using bottom-up (or reductionist) modelling approaches, the aptitude of top-down hydrologic schemes for socio-hydrological system analysis is still untested. Here we show the potential of top-down hydrological modelling human modified watersheds using anthropogenic hydrological signatures. Specifically, we assess the ability of the top-down modelling method in human-modified catchments to improve the representation hydrological signatures (e.g. mean monthly runoff, flow duration curve) while ensuring a sufficient, but not excessive, level of complexity in model formulation. First, we develop new conceptual models which include human hydrological modifications commonly identified in the literature. Then, we link these new features in the conceptual models to features in the hydrological signatures. We apply the proposed methodology to the Lake Mendocino Watershed in Northern California, US. We compare a purely hydrological model developed for this catchment based on natural watershed properties using naturalized streamflow to a hydrological model of the human-modified catchment using observed streamflow. We anticipate that the proposed approach contributes to the development of detection and attribution frameworks for key anthropogenic changes of observed hydrological variability and improved model performance in human-modified catchments.more » « less
-
Abstract. Dryland regions are characterised by water scarcity and are facingmajor challenges under climate change. One difficulty is anticipating howrainfall will be partitioned into evaporative losses, groundwater, soilmoisture, and runoff (the water balance) in the future, which has importantimplications for water resources and dryland ecosystems. However, in orderto effectively estimate the water balance, hydrological models in drylandsneed to capture the key processes at the appropriate spatio-temporal scales.These include spatially restricted and temporally brief rainfall, highevaporation rates, transmission losses, and focused groundwater recharge.Lack of available input and evaluation data and the high computational costsof explicit representation of ephemeral surface–groundwater interactionsrestrict the usefulness of most hydrological models in these environments.Therefore, here we have developed a parsimonious distributed hydrologicalmodel for DRYland Partitioning (DRYP). The DRYP model incorporates the keyprocesses of water partitioning in dryland regions with limited datarequirements, and we tested it in the data-rich Walnut Gulch ExperimentalWatershed against measurements of streamflow, soil moisture, andevapotranspiration. Overall, DRYP showed skill in quantifying the maincomponents of the dryland water balance including monthly observations ofstreamflow (Nash–Sutcliffe efficiency, NSE, ∼ 0.7),evapotranspiration (NSE > 0.6), and soil moisture (NSE ∼ 0.7). The model showed that evapotranspiration consumes > 90 % of the total precipitation input to the catchment andthat < 1 % leaves the catchment as streamflow. Greater than 90 % of the overland flow generated in the catchment is lost throughephemeral channels as transmission losses. However, only ∼ 35 % of the total transmission losses percolate to the groundwater aquiferas focused groundwater recharge, whereas the rest is lost to the atmosphereas riparian evapotranspiration. Overall, DRYP is a modular, versatile, andparsimonious Python-based model which can be used to anticipate and plan forclimatic and anthropogenic changes to water fluxes and storage in drylandregions.more » « less
-
High elevation mountain watersheds are undergoing rapid warming and declining snow fractions worldwide, causing earlier and quicker snowmelt. Understanding how this hydrologic shift affects subsurface flow paths, biogeochemical reactions, and solute export has been challenging due to the entanglement of hydrological and biogeochemical processes. Coal Creek, a high-elevation catchment (2,700 3,700 m, 53 km2) in Colorado, is experiencing a higher rate of warming than surrounding low-lying areas. This warming corresponds with dynamic and increased responses from biogenic solutes and dissolved organic carbon (DOC), whereas the behavior of geogenic solutes and dissolved inorganic carbon (DIC) has remained relatively unchanged. DOC has experienced the largest concentration increase (>3x), with annual average flow weighted concentrations positively correlated to average annual temperature. This suggests temperature is the main driver of increasing DOC levels. Although DOC and DIC response to warming is influenced by many drivers, the relative contribution of each remains unknown. DOC and DIC were analyzed to incorporate both carbon component products of soil respiration (DOC and CO2) and to represent high solute concentrations transported by shallow (DOC) versus deep (DIC) subsurface flow. The contrasting behavior of these carbon solutes indicates climate change and warming are driving changes in organic matter decomposition and soil respiration. Modeling results from the process-based model HBV-BioRT show increased temperatures cause earlier snowmelt and streamflow generation and lower peak discharge. As stream flow generation occurs earlier, so do DOC flushing and DIC dilution events. Additionally, post-snowmelt periods show greater DOC production and concentrations under warming scenarios. Results indicated increased production of DOC in post-snowmelt periods. DOC is then flushed out by earlier snowmelt partitioned through the shallow soil zone. Most process-based studies lack a watershed-scale understanding of carbon transformation and flow path alterations. This work demonstrates complex hydrologic and biogeochemical coupling at the watershed scale to illustrate how water flow paths and chemistry are responding to a changing climate in highelevation mountain watersheds.more » « less
-
more » « less
Abstract By utilizing functional relationships based on observations at plot or field scales, water quality models first compute surface runoff and then use it as the primary governing variable to estimate sediment and nutrient transport. When these models are applied at watershed scales, this serial model structure, coupling a surface runoff sub‐model with a water quality sub‐model, may be inappropriate because dominant hydrological processes differ among scales. A parallel modeling approach is proposed to evaluate how best to combine dominant hydrological processes for predicting water quality at watershed scales. In the parallel scheme, dominant variables of water quality models are identified based entirely on their statistical significance using time series analysis. Four surface runoff models of different model complexity were assessed using both the serial and parallel approaches to quantify the uncertainty on forcing variables used to predict water quality. The eight alternative model structures were tested against a 25‐year high‐resolution data set of streamflow, suspended sediment discharge, and phosphorous discharge at weekly time steps. Models using the parallel approach consistently performed better than serial‐based models, by having less error in predictions of watershed scale streamflow, sediment and phosphorus, which suggests model structures of water quantity and quality models at watershed scales should be reformulated by incorporating the dominant variables. The implication is that hydrological models should be constructed in a way that avoids stacking one sub‐model with one set of scale assumptions onto the front end of another sub‐model with a different set of scale assumptions.
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.5194/hess-26-375-2022
