📄 earth_science

Identification of Hydrologic Landscapes in Brazilian river basins and their application as indicators of water yield and storage

This study applies the Hydrologic Landscapes classification method to the PCJ River Basins in southeastern Brazil, demonstrating that integrating relief, soil, aquifer, and climate data via GIS effectively identifies specific regions capable of simultaneously maximizing water yield and storage to guide sustainable water management in water-scarce areas.

Original authors: Matheus Henrique Mortene, Ronalton Evandro Machado, Tarcio Rocha Lopes, Sergio Nascimento Duarte, Marco Antonio Jacomazzi

Published 2026-07-06

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CC BY 4.0

Original authors: Matheus Henrique Mortene, Ronalton Evandro Machado, Tarcio Rocha Lopes, Sergio Nascimento Duarte, Marco Antonio Jacomazzi

Original paper licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). ✨ This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer

Imagine the land not just as dirt and rocks, but as a giant, complex sponge that decides how much rain becomes a river and how much disappears into the ground to be stored for later. This is exactly what the researchers in this paper set out to map for the Piracicaba, Capivari, and Jundiaí (PCJ) river basins in Brazil.

Here is a simple breakdown of what they did and what they found, using everyday analogies.

The Big Problem: A Busy City with a Leaky Bucket

The PCJ region is like a bustling, wealthy city that is also a major farm. It has lots of people and factories needing water, but it's also running low on it. The problem is that the land there is a bit "leaky" or "hard to soak." When it rains, the water often runs off the surface too quickly (causing floods) instead of soaking deep down to refill the underground water tanks (aquifers).

The researchers wanted to find the "sweet spots" in this landscape:

Water Yield: Where does the land produce the most river water? (Good for immediate use).
Water Storage: Where does the land act like a good sponge, soaking water deep underground? (Good for saving for dry days).

The Method: The "Land Recipe"

Instead of looking at the whole river basin as one big blob, the researchers used a computer map (GIS) to chop the land into tiny 30-meter squares (like pixels on a screen). For every single pixel, they mixed together four ingredients to create a unique "Hydrologic Landscape" recipe:

Relief (The Shape): Is the land flat like a pancake, rolling like a gentle hill, or steep like a mountain?
Soil Permeability (The Surface Sponge): Is the topsoil loose and sandy (easy to soak) or hard and clay-like (water runs off)?
Aquifer Permeability (The Underground Sponge): What is the rock deep underground like? Can water move through it easily?
Climate (The Rain Bucket): Is it a very wet, humid area, or just moderately wet?

By mixing these four ingredients, they created 57 different types of landscapes (which they called Hydrologic Landscape Regions, or HLRs). Think of it like having 57 different types of sponges, each with a unique texture and shape.

The Findings: What Makes a Good Sponge?

1. The "Runoff" vs. "Soak" Balance
The researchers tested their map against real river data from the last 20 years. They found some interesting rules:

Water Yield (The River Flow): The amount of water flowing in the rivers was most strongly linked to the soil. If the topsoil was very permeable (sandy/loose), more water made it into the rivers. Surprisingly, the steepness of the land mattered less than the soil type.
Water Storage (The Underground Tank): The ability to store water deep underground was linked to flat land and permeable underground rock. If the land is flat, water has time to sink. If the underground rock is porous, the water can travel deep and get stored.

2. The Big Challenge
They discovered a tricky situation: About half of the basin has "hard" soil and "hard" underground rock. In these areas, water can't soak in well. It rushes over the surface, leading to erosion and less water stored underground. It's like pouring water onto a plastic tarp—it just slides off.

3. The Good News: The "Double-Dip" Zones
The most exciting discovery is that some areas can do both at the same time.

Scenario 1 (The Ideal): They found specific areas, particularly in the Corumbataí River sub-basin (near the Guarani Aquifer, a massive underground water reserve), that are steep enough to produce good river flow but have permeable soil and rock that also allow for excellent underground storage.
The Connection: They found a strong link between areas that produce lots of river water and areas that store lots of water. It turns out you don't always have to choose between "floods" and "droughts"; some landscapes are naturally good at doing both.

The Conclusion: A Map for Saving Water

The paper concludes that this "Hydrologic Landscape" map is a powerful, low-cost tool. It's like a treasure map for water managers.

Instead of guessing where to plant trees or build conservation projects, officials can look at this map and see exactly which "sponge types" (HLRs) are the best for:

Protecting: Keeping the "double-dip" zones safe so they keep producing and storing water.
Investing: Putting money into conservation projects where they will have the biggest impact.

The study highlights that while the PCJ region faces water scarcity, there are specific, identifiable pockets of land that are naturally equipped to handle water well. Protecting these specific areas is key to keeping the region's water supply secure.

Technical Summary: Identification of Hydrologic Landscapes in Brazilian River Basins and Their Application as Indicators of Water Yield and Storage

Problem Statement
The Piracicaba, Capivari, and Jundiaí (PCJ) River Basins in southeastern Brazil represent a critical socioeconomically active region facing significant water scarcity despite high rainfall. The area is characterized by intense anthropogenic pressure from urban expansion and agriculture, alongside the degradation of the original Atlantic Forest cover. While the region possesses abundant springs, declining per capita water availability threatens its economic potential. Existing hydrological classification models often rely on long-term hydrometric series or lack the resolution to simultaneously identify areas favorable for both water yield (production) and water storage. There is a specific gap in the literature regarding the application of Hydrologic Landscapes (HL) classification to Brazilian river basins for the dual purpose of identifying water production and storage zones.

Methodology
This study applied the Hydrologic Landscapes (HL) framework, originally proposed by Winter (2001) and refined by Wolock et al. (2004), to the PCJ basins. Unlike previous large-scale applications that assigned a single HL region (HLR) value to entire sub-basins, this research adopted a high-resolution, pixel-by-pixel classification approach (30 m × 30 m) as proposed by Brown et al. (2014).

The methodology integrated four hydrologic evaluation units into a single Information Plan (IP) using Geographic Information Systems (GIS):

Relief: Derived from a 30m SRTM Digital Elevation Model, reclassified into Flat, Transitional, and Mountain classes based on slope.
Soil Permeability: Assessed via Saturated Hydraulic Conductivity ( $K$ ) using data from Oliveira (1999), reclassified into Low, Moderate, and High.
Aquifer Permeability: Characterized by specific flow ( $Q_e$ ) data from the SAP database, reclassified into Low, Moderate, and High.
Climate: Synthesized using the Feddema Moisture Index ( $I_f$ ), calculated from 20-year mean precipitation and potential evapotranspiration (Thornthwaite & Mather method), reclassified into Moist, Wet, and Very Wet.

These units were overlaid to generate 57 distinct Hydrologic Landscape Regions (HLRs). To validate the classification, the study utilized 20-year (2001–2020) streamflow data from 27 gauging stations. Two key metrics were calculated:

Water Yield Ratio ( $R/P$ ): The ratio of annual water yield to precipitation.
Fuh's Parameter ( $m$ ): A non-dimensional parameter derived from Fuh's equation, reflecting basin characteristics such as slope, infiltration, and storage capacity.

Key Results

Classification: The study identified 57 unique HLRs within the PCJ basins. A significant finding was that approximately 48.8% of the basin area combines low aquifer permeability with low soil permeability, creating unfavorable conditions for water storage and aquifer recharge.
Drivers of Water Yield: Validation revealed that water yield ( $R/P$ ) was most strongly correlated with soil saturated hydraulic conductivity ( $R^2 = 0.102$ ), followed by slope ( $R^2 = 0.015$ ) and the Feddema Moisture Index ( $R^2 = 0.006$ ). High water yield was observed in mountainous areas with permeable soils and humid climates.
Drivers of Water Storage: The $m$ parameter, indicative of storage capacity, showed a positive correlation with saturated hydraulic conductivity ( $R^2 = 0.192$ ) and an inverse correlation with slope ( $R^2 = 0.128$ ). High storage potential was associated with flat or transitional terrains and high aquifer permeability.
Dual Capacity: A statistically significant positive correlation ( $R^2 = 0.46$ ) was found between the $m$ factor and the $R/P$ ratio. This indicates that specific basins can simultaneously support high water production and high storage capacity.
Favorable Scenarios: Two scenarios were defined for each objective:
- Water Yield: Favorable areas are concentrated in mountainous regions with wet/very wet climates. Scenario 1 required high soil permeability, while Scenario 2 included low soil permeability areas.
- Water Storage: Favorable areas are located in flat/transitional terrains with high aquifer and soil permeability under wet/very wet climates.
Spatial Distribution: Favorable areas for both yield and storage were primarily located in the Corumbataí River sub-basin (downstream), overlapping with the Guarani Aquifer. Notably, these favorable zones are situated upstream of major urban centers like Piracicaba and Campinas.

Significance and Claims
The paper claims that the HL classification method is a cost-effective and transferable tool for identifying priority areas for conservation and resource allocation in water-scarce regions. By moving from micro-basin averages to pixel-level classification, the study enables the precise delineation of hydrologic conditions that would otherwise remain undetected.

The authors emphasize that the identification of areas with high $R/P$ and high $m$ values demonstrates that river basins can simultaneously support water production and storage. This dual capacity supports targeted interventions, such as reforestation and infrastructure investment, specifically in the Corumbataí sub-basin and the Guarani Aquifer recharge zones. The study concludes that while the specific interactions of parameters are unique to the PCJ basins, the methodology provides a robust framework for understanding how physiographic factors interact to influence hydrological behavior, thereby aiding in sustainable management under conditions of water scarcity. The authors caution that these specific findings should not be generalized to other basins without localized analysis.

The Big Problem: A Busy City with a Leaky Bucket

The Method: The "Land Recipe"

The Findings: What Makes a Good Sponge?

The Conclusion: A Map for Saving Water

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